This study explores the impact of the Indian Railways’ newly launched Rail One mobile application on passenger convenience and satisfaction using a Structural Equation Modeling (SEM) approach. With the digitalization of public transport services, the app aims to streamline user experience by integrating ticketing, real-time train tracking, platform services, and grievance redressal into a single digital interface. The research model investigates the influence of three key predictors—app usability, information quality, and service features—on passenger convenience, which in turn affects overall satisfaction. Data were collected through a structured online survey administered to 300 app users across various demographics. The findings reveal that all three independent variables significantly and positively affect perceived convenience, with app usability emerging as the most influential factor. Moreover, convenience is shown to be a strong determinant of passenger satisfaction, mediating the relationship between service constructs and outcome experience. The results affirm the theoretical propositions of technology acceptance and service quality in a digital public service context. The study contributes to the literature on mobile government services and offers actionable insights for app developers and policymakers in enhancing digital service delivery in public transportation. Recommendations include prioritizing intuitive interface design, ensuring real-time and accurate information, and expanding integrated service features.

Increasing climate emergency and environmental degradation have fast-tracked the necessity of financial instruments that are sustainable and promote economic growth in accordance with the responsibility towards the environment. A new area between the business of finance and the field of sustainability, called Green Fintech, provides new avenues to direct investments towards environmentally conscious projects and make financial markets become more efficient, transparent, and accessible. The current paper focuses on the review of sustainable investment products and their design and deployment with the help of digital technologies including artificial intelligence, blockchain, big data analytics, and mobile platforms. It puts greater emphasis on the opportunities Green Fintech can offer to track environmental impact in real time, seal green bonds and buy and sell carbon credits, as well as encouraging renewable energy and climate-smart financing to be inclusive. In addition, the paper examines the role played by regulatory regimes, investors savvy and digital illiteracy on the implementation of Green Fintech solutions in international markets. Using case studies, industry practices, and overview of the topic, the research highlights the prospects and challenges of integrating sustainability to the fintech ecosystem, such as the problem of greenwashing, data integrity, and scalability. Our results indicate that the Green Fintech should contribute to the reallocation of capitals in low-carbon economies besides democratizing access to sustainable investment opportunities of both retail and institutional investors. Finally, the paper posits that Green Fintech can become an indispensable facilitator of the global shift towards the more robust and inclusive financial system as long as the stakeholders can and will cooperate to achieve transparency, accountability, and regulatory consistency.

The booming development of digital technologies in education changed the ways of teaching, learning, and administration providing new options in terms of access, interaction, and innovation. Nevertheless, it is also associated with the emergence of questions of sustainability, equity, and ethical responsibility of using Information and Communication Technologies (ICT). The current paper will discuss the term sustainable ICT practice in education, emphasizing the aspect of balancing technological innovation and digital responsibility. Based on the available literature and the new developments, the paper specifies the ways to ensure the equal performance with education-related environment-aware approach, including energy-saving gadgets, the proper disposal of e-waste, and cloud computing. Meanwhile, it also reaffirms the note that digital responsibility is not only about the environment but also on the privacy and security of data, digital wellness, accessibility, and the responsible application of artificial intelligence within the classroom.

With the research, it is further highlighted that sustainable ICT in education is not only a technical, or environmental problem but also a didactic and ethical problem. Institutions need to be sure that values of inclusivity, equity, and long-term resilience are entrenched in their embrace of capable technologies. The examples of case studies in various pedagogical settings will show how the ecological impact of digital structures can be reduced and the principles of digital citizenship can be made part of the students and the educators.

The results support a middle ground where innovation should be promoted, but unintended effects should be avoided, and the policy of ensuring green ICT purchasing, digital literacy, and ethical control of educational technologies is supported. Making an innovation responsible would help sustain a successful ICT practice that can support the current and future generations at a resilient learning environment. This paper finishes by offering realistic policies to policymakers, educators, and technologists, which they should implement and consequently promote a sustainable and responsible digitalization of education ecosystem.

This paper gives extensive review of the study focused on gender inequality in education and role of privatization in addressing these disparities in Haryana. Haryana, like many other regions of India, grapples with the challenge of providing equal opportunities for girls and boys to access quality education. However, overall literacy rate increases from 26.89% in 1971 to 75.6% in 2011, but the state continues to struggle with deeply entrenched socio- cultural norms that limit girl’s access to education, especially in rural areas and district like Nuh (formerly Mewat). This review amalgamates previous literature ranging from 2015-2025 to evaluate the role of privatization on gender inequality

Parasocial relationships (PSRs)—one-sided yet emotionally important connections between viewers and media personalities—have become strong drivers of consumer brand attachment in the social media age. Based on attachment theory, social identification theory, and self-congruity theory, PSRs go above passive admiration to encourage intimacy, believed reciprocity, and trust. Modern "human brands," social media influencers (SMIs), shape consumer identity and enable emotional and cognitive brand relationships via authenticity, relatability, and credibility. Review of PSRs from conventional media to interactive digital platforms using present literature emphasizes the growing significance of micro- and nano-influencers in developing closer consumer relationships through niche community participation. It investigates the psychological mechanisms underlying internalization, self-brand relationship, and aspirational identification to demonstrate how influencers insert brands into consumers' self-concept and everyday life. Platform dynamics—ranging from YouTube's narrative depth to TikTok's algorithmic virality—are found to be structural factors influencing PSR formation, engagement quality, and brand equity. Particularly among vulnerable groups, the study also looks at ethical issues including manipulation via clandestine marketing, parasocial dependency, and hazy commercial intent. Challenges of inadequate disclosure, psychological health impacts, and influencer liability point up the need of more severe regulatory frameworks, media literacy initiatives, and truthful sponsorships. Opportunities and problems for next influencer marketing are presented by emerging trends like virtual influencers (VIs) and immersive technologies including augmented and virtual reality. Though VIs offer scalability and consistency, their veracity is in doubt; hence strict ethical examinations are required to maintain trust. Implications for marketers include putting long-term influencer partnerships suited with brand values first priority, achieving reach with authenticity using smaller-scale influencers, and strategically managing reputational risks. For consumers, developing a critical awareness of persuasive techniques and requesting precise product information fosters independence in decision-making. Influencers need constant audience engagement, avoidance of inappropriate brand partnerships, and balancing of commercial goals with personal integrity to establish enduring credibility. PSRs have evolved into dynamic relational networks including brand loyalty, identity development, and emotional connection all things considered. In a digital world where influence, identity, and consumption are increasingly linked, brands can use PSRs strategically to create over time trust-based relationships. Success, nevertheless, relies on striking a balance between ethical responsibility and technological innovation to ensure that the interactions between influencers, businesses, and audiences remain both important and mutually beneficial

Reactive decision-making is at the heart of traditional HRM, leading to less-than-ideal talent outcomes like higher attrition and misaligned hiring practices. Companies that use standardized HR analytics frameworks show a 31% increase in internal mobility success rates, an 18% decrease in voluntary attrition, and a 23% boost in recruit quality. This study investigates to examine how HR analytics affect DDDM and how that affects employee productivity, retention, and the overall success of the firm. The analytics cover hiring, training and development, and engagement. The study also looks at how adopting new technology might make DDDM have a bigger effect on important outcomes for the workforce.

The study uses structural equation modelling (SEM) to look at data from professionals in a variety of fields. It finds that HR analytics greatly improves DDDM (β = 0.32 to 0.41, p < 0.001). DDDM had a positive effect on employee productivity (β = 0.45), retention (β = 0.42), and organizational performance (β = 0.48), all at p < 0.001. Using technology makes these interactions even stronger, showing how important AI-driven HR solutions are in defining current workforce strategies. The indirect path analysis also shows that DDDM (β between 0.13 and 0.22, p < 0.001) shows that HR analytics has an indirect effect on worker outcomes.

The results show how important it is to combine HR analytics with digital tools to make better decisions and improve company results. Companies who use AI-powered HR analytics find it easier to manage their talent, keep their employees engaged, and keep their workers longer. This study adds to both theoretical and practical knowledge by showing that HR professionals need to engage in advanced analytics, predictive modelling, and AI-driven HR strategies to stay ahead of the competition

Purpose: Present study aims at studying impact of social media marketing activities & user engagement activities (independent variable) on online shopping behavior of pottery consumers (dependent variable) with consumer socialization as mediating variable.

Research Methodology: Exploratory research design and convenience sampling on 85 pottery consumers from Sagar district of Madhya Pradesh were surveyed using structured questionnaire. For further analysis, Smart PLS SEM 4 is used.

Findings: No effect of social media marketing activities on online shopping behavior of pottery consumers was found. User engagement activities are significantly related to consumer socialization & online shopping behavior. The mediating role of consumer socialization results in insignificant effect of turning social media marketing activities and user engagement activities into online shopping behavior of pottery consumers.

Research limitations: Study is limited as the reason behind this insignificant effect such as absence of marketing strategy, lack of basic infrastructure and societal perception about pottery are only quoted.

Practical implications: Increase social media usage can open doors of opportunities to pottery but infrastructure facilities and change in marketing strategy can help pottery products gain their place in the market.

Originality/Value: Previous studies included adoption of social media by pottery producers but the present study talks about how social media marketing activities with consumer socialisation & user engagement activities can influence pottery products.

Financial inclusion remains a central concern for economic development, particularly in emerging countries like Tunisia. Banks, as key intermediaries, play a vital role in broadening access to financial services. However, their contribution is shaped not only by their ownership structure, especially the prevalence of family ownership, but also by their accounting practices and the transparency of financial reporting. This paper conducts a meta-analysis of existing research to assess how family governance and accounting disclosure jointly influence financial inclusion within Tunisian banks. Anchored in agency theory and the socio-emotional wealth perspective, the analysis explores how conservative or opaque accounting practices, often associated with family control, can hinder trust and reduce access to formal financial services. Conversely, greater financial transparency can serve as a lever to improve inclusion. The study concludes with recommendations for enhancing both governance and accounting disclosure to promote inclusive finance in Tunisia

The present study has been conducted as an examination, identification, and analysis of the determinants of teaching quality in Academic institutions of Afghanistan. The research focused on the ranking of fundamental and determinative components of teaching quality. The research population consisted of 180 members of the faculty from various universities across the country, selected using a simple random sampling method. Data collection was carried out using a questionnaire, which was designed structurally based on the Likert spectrum and theoretically supported by multiple teaching quality theories. Cronbach's alpha was used for questionnaire reliability, and the results were satisfactory (α = 0.95). Data were analyzed using various statistical methods, including KMO, Bartlett, factor analysis, analysis of variance, Pearson correlation, regression, and coefficient of variance, using SPSS and EViews software. The findings of this research suggest that teaching skills, expertise, and individual and personal skills are among the key components contributing to the improvement of teaching quality and can have a significant impact on the quality of teaching in Afghan educational institutions. In conclusion, the research results confirm the research hypothesis that teaching skills, expertise, and personal qualities enhance teaching quality in higher education sector of Afghanistan.

Agriculture has long been seen as the primary driver of Afghanistan's economic growth. Saffron production was promoted by the Afghan government as a means of achieving economic development while also lowering the country's widespread opium cultivation by giving required support to its farmers through saffron farmer service centers. Saffron is the world's most valuable and costly spice. It is made from the stigma of the saffron crocus flower (Crocus sativus L.), which is harvested and dried to make the spice. Saffron is a high-value worldwide product that complements Afghanistan's agriculture sector's market-driven approach. Additionally, it is a commodity that may increase women's economic participation and productive jobs outside the house. The research investigates how the cultivation of saffron in Afghanistan aids in creating jobs and alleviating poverty.

Purpose:The paper aims to understand saffron cultivation trends and their economic importance also the employment opportunities that Saffron creates.

Methodology:The research methodology is desk research which involved a review of existing literature on saffron cultivation in Afghanistan and data collected from Comtrade and others. This study utilized both quantitative and qualitative methodology. The results from the data collected show that saffron has great potential in Afghanistan and could serve as an economic booster if developed. This research paper discusses the saffron labor market and its market structure, the difficulties saffron producers face, and the importance of saffron to the Afghan economy.

Findings:Saffron farming has progressively spread across Afghanistan over the last decade. According to Afghanistan’s National Export Strategy (2018-2022), more than 11,000 farmers are involved in farming around 5,200 hectares of saffron plots in Herat Province, which is Afghanistan's saffron production center. Afghan Safran’s export value has continued to rise, reaching US$17 million as of 2016. This was due to a 36 percent annual increase in exports over the last five years. A kilogram of saffron (about 450,000 stigmas) takes about 40 hours to produce and workers are estimated to collect 10 kgs of saffron flowers per day on average, earning them approximately AFN 25 (US$0.38) per kg, or $4 to $6 per day.

Research limitations: Owing to the Taliban's present occupation of Afghanistan, it is impossible to predict the future of saffron in the country due to a lack of knowledge. Given that women do the majority of labor in saffron cultivation, it is questionable whether women would be able to continue working or whether this position would be moved to males

Small and medium-sized enterprises (SMEs) are key actors in the shift toward a circular economy (CE), yet how they adopt CE principles remains inconsistently understood. This study systematically reviews 25 peer-reviewed publications from 2014 to 2024 indexed in Scopus, spanning disciplines such as Business, Environmental Science, Engineering, and Social Sciences. Using the TCCM (Theory–Context–Characteristics–Methodology) framework alongside bibliometric analysis via Biblioshiny and thematic synthesis, the review uncovers recurring CE practices—including eco-design, closed-loop systems, and digital performance tools—primarily within European manufacturing and agri-food sectors. While some studies adopt frameworks like the Resource-Based View or sustainability metrics, many lack explicit theoretical grounding. Key challenges identified include resource limitations, digital skill shortages, and regulatory uncertainty. This review highlights the need for more robust, theory- driven, and context-sensitive research, particularly across underrepresented sectors and regions. Its insights hold practical value for policymakers designing targeted CE interventions and for SMEs aiming to align innovation with sustainability goals

Behavioral economics has become an important theory for explaining consumer choices inside digital settings in an increasingly digital world. Conventional economic models, which presume reasonable actors making utility-maximizing decisions, fall short of fully appreciating the complexities of real-world behavior shaped by cognitive biases, emotional triggers, and environmental influences. A fundamental idea presented by Thaler and Sunstein, nudging is about minor changes in choice architecture that guide behavior without limiting choice freedom. Nudges appear in digital environments in interface design features like defaults, social proof, reminders, framing, and gamification all intended to use human heuristics. Ranging from e-commerce to fintech, health apps, and public services, digital platforms utilize nudges to affect activities like saving, purchasing, exercising, and civic involvement. Highlighting its possibilities and ethical complexities, this study combines theoretical insights on digital nudging with empirical evidence. Research show that targeting System 1 thinking fast, intuitive, and driven by heuristics is most successful especially in situations where consumers show little attention or cognitive engagement. Though raising questions about manipulation, transparency, and autonomy, the adaptability of digital nudges made possible by artificial intelligence and real-time behavioral tracking also improves their effectiveness. Dark patterns covert or coercive design techniques present ethical hazards even though nudges encourage prosocial behaviors including health enhancement and energy conservation. Regulatory and design frameworks are needed to ensure that nudging matches user welfare and democratic values. The review also assesses methodological approaches in nudge research, including randomized controlled trials, A/B testing, and qualitative studies. Challenges persist in measuring long-term effects, ensuring transparency, and generalizing findings across cultures. As digital environments evolve, the fusion of nudging with AI, personalized interfaces, and dynamic feedback systems presents both opportunities and ethical dilemmas. Responsible implementation, guided by ethical principles and user-centric design, is essential for leveraging digital nudges as tools for beneficial behavioral change in the digital age.

Drawing on the perspectives of needs–affordances–features (NAF) and uses and gratifications theory (UGT), this study proposed a research model to understand the relationships between AR affordances (interactivity, vividness, and augmented reality), self-uniqueness, relative advantage, and purchase intention. Data collected from 264 participants were used to test the proposed model. The findings reveal that interactivity and vividness impact augmented realism positively, which in turn affects relative advantage and self-uniqueness. Our results also show relative advantage and self-uniqueness are the antecedents of purchase intention, while relative advantage has a positive impact on self-uniqueness. The implications of these results are discussed.

It has begun a number of initiatives in terms of widening distribution reach through traditional as well as unconventional channels. HUL has also empowered rural women by assisting them in obtaining financial assistance through its project shakti.  70% of the Indian population lives in rural areas. This segment, commonly referred to as the 'bottom of the pyramid', presents a huge opportunity for companies. Hindustan Unilever Ltd (HUL), considered one of the best-managed companies in India, understands the importance of rural marketing. The trigger point came when a local firm Nirma, through its new product formulation, pricing and distribution challenged HUL's detergent business. Nirma's attack from below made HUL realise its vulnerability as well as identify a new opportunity. Since then, HUL has launched various initiatives to reach out to the rural consumer. It has changed its product formulations and deliveries.

In today’s volatile and uncertain work environments, emotional and psychological competencies are essential to organizational sustainability. Emotional Intelligence (EI)—the capacity to perceive, regulate, and respond to emotional stimuli—has emerged as a key driver of workplace resilience. This conceptual paper offers a multilevel theoretical synthesis of the EI–resilience relationship by integrating insights from emotional intelligence theory, psychological capital, and socio-ecological systems thinking.

The study proposes a three-level conceptual model in which emotionally intelligent behaviors enhance resilience at the individual (e.g., emotional regulation), relational (e.g., empathy and collaboration), and systemic levels (e.g., inclusive platform governance). Special attention is given to emerging work structures, particularly the gig economy, where resilience is shaped not only by internal traits but also by knowledge ecosystems, ethical design, and social networks. Drawing on recent research (Swain & Jena, 2023; Panigrahi & Swain, 2023, 2025), the model positions EI as both a precursor and moderator of resilience across these layers.

The paper contributes to evolving discussions on emotional sustainability by redefining resilience as a distributed capability shaped by emotionally intelligent individuals and emotionally responsive systems. It offers practical insights for leadership, digital work design, and the development of emotionally sustainable organizations.

In the contemporary digital economy, human capital stands as a decisive factor in attaining sustainable competitive advantage. The integration of advanced people management approaches—particularly Strategic Human Resource Management (SHRM) with e-marketing strategies is increasingly vital for enhancing corporate performance. Despite growing recognition of SHRM’s importance, limited empirical research has explored its combined impact with digital marketing capabilities and happenings on business outcomes, especially in the context of emerging economies. This study addresses this gap by examining how SHRM, supported by digital marketing practices, influences organizational performance among micro, small, and medium-sized enterprises (MSMEs) in India. Data were collected from 420 MSMEs using a structured questionnaire based on a 5-point Likert scale. Moderated Regression Analysis (MRA) was applied to evaluate the direct and moderating effects of digital marketing capabilities and happenings on the SHRM–performance relationship. Findings reveal that SHRM exerts a significant positive influence on corporate performance (β = 0.291, p < 0.001). Digital marketing capabilities (β = 0.364, p < 0.001) and digital marketing activities (β = 0.317, p < 0.001) independently enhance performance, while also strengthening the SHRM–performance link (β = 0.442 and β = 0.332, respectively). The results underscore the synergistic effect of advanced HR practices and e-marketing integration in driving operational efficiency, customer satisfaction, and profitability. The implications are twofold: for practitioners, investing in both human capital development and digital marketing infrastructure is critical to achieving sustainable growth; for policymakers, capacity-building programs that combine HR and digital skills training could significantly improve MSME competitiveness in the Industry 4.0 era. This research extends the SHRM literature by incorporating digital marketing as a strategic complement, offering a nuanced understanding of performance drivers in digitally transforming markets.

In market research, intersectionality has been generally overlooked.  Through a discussion of the body of literature and offering valuable resources for the integration of intersectionality in marketing research, this article bridges this knowledge gap.  It offers an agenda for future research, a comprehensive structure of an intersectional marketing paradigm, a systematic research design process, and pragmatic advice for stakeholders. Three basic elements constitute the foundation of the intersectionality concept: (1) recognizing and seeing overlapping, not separate, social categories (e.g., gender, race, and class); (2) understanding how the intersections combine and interact to create experiences in the marketplace; and (3) exploring how power influences these experiences. The research design map proposed supplies marketing scholars with precise methodological and theoretical approaches, including the formulation of intersectional hypotheses and theories, exploratory analyses of subsamples, inclusive literature analysis, complete demographic reporting, sampling the underserved populations, and strategic contextualization of results. Key questions at the societal, consumer, and organizational levels are emphasized in the accompanying research agenda. Marketing will be better placed to sustain social salience, promote multiple and inclusive theory building, and better capture the lived experiences of hitherto under-researched communities if it adopts intersectionality

Digital consumers generate vast behavioral traces across platforms, enabling advertisers to deliver highly personalized messages.  With personalization leaning more on data, the issue of transparency, trust, and perceived creeping is growing. The present paper investigates how informatics transparency, along with the relevance of personalization, affects consumer trust, perceived intrusiveness, and purchase intention. Based on Privacy Calculus Theory and the Stimulus-Organism-Response model, a conceptual model was created and examined through the answers of 428 digital consumers. The structural equation modeling (SEM) was adopted to evaluate the direct and mediated relations between significant constructs. The results support the idea that informativeness and transparency can highly increase consumer trust and, at the same time, reduce the perceived intrusiveness. Trust turned out to be the most relevant factor in predicting buying intention, which provides support to the significance of ethical perceptions in forming digital consumer behavior. Though the perceived intrusiveness influenced buying intention negatively, it did not affect the trust as much as the trusted. The existence of dual mediation routes shows that personalization strategies should be cognitively appealing and emotionally safe to work effectively. The research provides a theoretical contribution in conceptualizing informatics transparency as an independent construct and incorporates the positive and defensive user reactions into one comprehensive model. Managerially, the results point to the strategic importance of clear data practices, convenient consent systems, and credible interface design. Non-nefarious personalization conducted ethically and transparently, as opposed to data-driven targeting conducted in opaque ways, has a better chance of resulting in long-term consumer interest and brand loyalty.

This paper examines China’s strategies in confronting globalization. In this regard, it analyzes the institutional developments, trade policies, and economic diplomacy of the country in shaping the multipolar world order. By adopting a state-centered approach that differs from the Western liberal model, China is using tools such as the Belt and Road Initiative (BRI) and the Regional Comprehensive Economic Partnership (RCEP) to redefine regional trade governance. This study shows that China has strengthened its growth by intelligently exploiting economic globalization, but at the same time resisting political-cultural Westernization through policies such as technological self-reliance and selective market liberalization. Theoretical frameworks such as Dunning’s “Eclectic Model” and Porter’s “Diamond Model” explain China’s competitive advantages, including state-led industrialization, targeted foreign investment attraction, and infrastructure dominance through the BRI. RCEP is an example of China’s efforts to integrate Asian supply chains, although challenges such as geopolitical tensions and institutional inconsistencies remain. On the other hand, the BRI, while improving global connectivity, has led to heavy debt burdens for host countries and exposed the contradictions of China’s global leadership. Domestically, China’s trade, banking, and judicial reforms demonstrate flexible governance to comply with WTO standards while maintaining state control. Compared with India’s caution and Japan’s standards-based multilateralism, China’s proactive approach to globalization highlights the divergence of Asian strategies. It has also been shown that China's hybrid model offers an alternative to liberal globalization, but its sustainability depends on a balance between economic openness, geopolitical stability, and the elimination of structural inequalities.

The term “Social entrepreneurship” describes the practice of using opportunity and resources to solve social issues. Social entrepreneurs emphasise changing the structures and behaviours supporting poverty, marginalisation, environmental deterioration, and the resulting loss of human integrity to fight these problems. Though in both situations their main goal is to attain long-lasting systematic transformation, they accomplish this by starting businesses that are either for-profit or non-profit. Self-Help Group (SHG) is one approach aiming to foster social entrepreneurship in rural regions. SHGs support social entrepreneurs in overcoming challenges and motivate their members. SHG is essential for poor women to reach a steady living. It has been gradually shown to be among the most efficient means of reducing rural Indian poverty. Therefore, there is no doubt that SHG offer a platform for the advancement of social entrepreneurs. SHGs, therefore, surely act as a forum for the promotion of social entrepreneurs. In this context, the study has tried to look at the factors driving women in selected districts of Odisha to join SHGs as a kind of social entrepreneurship. Statistical analysis has been utilised to analyse the influencing variables driving women's entrepreneurship development through SHG. The study focused on three major components are “Social Environment”, “Economic Environment”, and “Market Linkage”, to drive women's involvement through SHG for the development of women's entrepreneurship.

Several studies have been conducted to uplift the rural women farmers in Odisha. Still now an evaluation is required to determine the most efficient communication methods for reaching out to rural women farmers in the agricultural sector of Odisha. This study explores the efficient implementation of communication methods by women farmers in rural regions of Odisha. This study aims to identify the most successful factors to uplift the rural women farmers through marketing communication strategies to improve agricultural productivity. This study also tries to measure the impact on rural women farmers by integrating three major factors of rural women farmer’s improvement: i) Effective Communication Methods, ii) Capacity Building and Training, iii) Partnership and Collaboration, into a conceptual framework grounded in empowerment model to improve communication and support for rural women farmers in Odisha. The Structural Equation Modeling (SEM) methods were employed to evaluate the study's hypotheses

The phrase "big data" became popular in 2013 and was identified as a group of data sets that

were too huge and complicated for traditional database management systems. The 3 Vs (volume, velocity, and variety) or 4 Vs (adding veracity and Complexity) define its challenges, surpassing organizations' data, storage, and computing capacities. In essence, big data encompasses the digitalization of various aspects of life, generating dynamic data on the Internet and the Web. This work  explores the problems , techniques, and applications of big data, emphasizing future business intelligence (BI) architectures. Recognizing data as an "ocean of Universal Facts," The research explores the potential of big data for future technological advancements, serving as a crucial resource for students and professionals entering the field. Focusing on the 5 Vs attributes of data and domain-wise comparisons, it addresses applications in Wholesale Trade, Retail, Utilities, Education, Transportation, Banking, Communication, Manufacturing, Government, Healthcare, and more. Big data, with its challenges in storage, analysis, and visualization, is used with analytics to obtain knowledge and a competitive edge. The article gives a general overview of the types, content, architecture, technology, and attributes (Volume, Velocity, Variety, Value, and Veracity) of big data. The 7 Vs Ten Vs (Volume, Variety, Velocity, Veracity, Validity, Value, Variability, Venue, Vocabulary, and Vagueness) and (Volume, Velocity, Variety, Variability, Veracity, Value, and Visualization) of big data are also covered in this work. Big data is transforming sectors such as manufacturing, e-commerce, banking, and insurance, this paper identifies and defines fourteen characteristics, addressing the growing challenges and introducing three new characteristics for efficient handling of big data. along with exploring three new features , to enhance the efficiency of handling big data. Additionally, it presents an updated list of 42 Vs to provide a comprehensive framework for understanding big data and data science in the modern era.

This study looks at how individual socioeconomic factors relate to income levels among U.S. citizens, using a four-stage machine learning framework to piece things together. It started with prediction. We tested several regression models to estimate annual income based on features like education, employment status, debt, and household makeup. Each model brought something slightly different to the table, and together they helped sketch a clearer picture of the income landscape. Next came refinement. We dug into feature engineering, tuned the models, and brought in ensemble methods to pull out deeper patterns, especially the ones hiding in the interactions between things like education, housing, and digital access. In the third phase, we shifted focus to disparity. Using methods like ANOVA and t-tests, we looked at how income varies across groups, by race, gender, region, and marital status. The gaps were real and often held up even when we controlled for education or job type. The final step involved unsupervised clustering. This helped break the population into distinct socioeconomic profiles. Some clusters revealed vulnerable combinations, like high debt, spotty internet, and unstable work, that don’t always raise red flags on their own but matter when they show up together. What stood out through all of this is that income isn’t shaped by one factor at a time. It’s the result of how different parts of someone’s life overlap; region and education, debt and family structure, digital access and job opportunities. By combining prediction, diagnostics, and clustering, this approach gives both a close-up and wide-angle view of how income works. For researchers, it’s a way to move beyond surface-level forecasting. For policymakers, it offers a clearer path to spotting the groups most likely to fall through the cracks.

Artificial Intelligence (AI) realisation on digital commerce revolutionised the way people interact, make decisions, and how they purchase things in general. The project is aimed at determining the impacts of AI-based systems, including recommendation engines, chatbots, and predictive personalization systems, on user trust, digital user experience (UX) and, by extension, conversion rates within the e-commerce context. The study shall be based on hybrid methodology, which consists of user experience test and analytics of behavioral data and sentiment analysis of online review and social network, i.e., three of the most popular e-commerce platforms Amazon, Flipkart, and D2C brands, built on Shopify. The results suggest that, when combined with transparency and explainability, AI interfaces can be a major boost to trust. Further, the ease of experience through AI-based item selection is positively correlated with fewer decisions on the one hand and overall involvement on the other. On average, conversion increases by 21 percent in the platforms with AI-based personalization than in the control environments that were without their interventions. It also detects the new risks that could also influence user trust in AI approaches, including the possible algorithmic bias and the apparent feeling of being under observation. The study provides practical recommendations to marketers, platform designers, and policymakers interested in implementing AI in a digital retail ecosystem in an ethical and productive manner.

The subscription economy has redesigned the configuration of consumer markets in the realm of digital economy, turning the art of revenue creation and value perception, as well as customer retention, upside down. This paper explores psychological groundwork through which individuals subscribe to these services, including perceived value, loss aversion, commitment and behavioral inertia. The research study uses mixed-method research tools, i.e., behavioral survey, user analytics, and qualitative interviews within streaming, SaaS, and e-commerce sites to discover important cognitive triggers that determine long-term subscription choices. It can be seen that the customers display a high inclination towards relating scheduled pay and convenience, as well as exclusivity, though they are more susceptible to value decrease. The analysis also examines how customization, transparency of prices and trial onboarding can lessen churning. The findings are placed in digital ecosystems of Spotify, Netflix, and Amazon Prime. It is also implications to the designers of platforms, marketers and policymakers who seek to harness the same value ethically and thus maintain the autonomy of consumers

Also in a fast enlarging world, language dissimilarity has remained an enormous obstacle preventing inclusive communication, cooperation and education. This work explores the main idea of how real-time translation and technologies based on Artificial Intelligence (AI) can effectively broker linguistic gaps in two highly important areas education and industry. Through two-method research design, comprising the system appraisal and application in the industry in terms of real-time applications of neural machine translation (NMT), natural language processing (NLP) and voice recognition systems, this study examines the efficiency of real-time application of networks of neural machine translation (NMT), natural language processing (NLP) as well as voice recognition systems in a multilingual setting. Indian, German, and Kenyan learning places and Japanese, Brazilian, and UAE working environments were analyzed to determine the viability of deployment, precision, and user enjoyability. Engines, such as Google Translate API, DeepL, and open protein models, such as MarianMT were compared in 14 languages, based on BLEU degree, latency, and maintained contextual integrity. In the field of education, AI tools opened the possibility of a real-time multilingual lecture, collaborative assignments, and inclusive evaluation of learners with different linguistic backgrounds. The same technologies facilitated effective cross-functional collaboration and safety training at industry, and customer service within the multilingual supply chains. Although an increment in productivity and inclusivity was identified, accent sensitivity, the loss of contextual nuance, and ethical data privacy issues appeared. The results provide a good demonstration of the potential changes that AI-based translation systems can create, as well as outline the necessity of culturally cognizant, privacy-friendly deployment paradigms. This paper provides its contribution to the topic of digital inclusion, which is becoming increasingly popular delight as a catalyst of a fully global workforce and education

Women with disabilities face compounded challenges in employability due to the intersection of gender and disability-related discrimination. This study investigates whether differently abled women experience greater employment disadvantages compared to their male counterparts and other marginalized groups. Drawing from psychological theories such as Stigma Theory, Social Identity Theory, Self-Efficacy Theory, and Stereotype Threat Theory, the study explores how societal biases, workplace exclusion, and internalized perceptions of capability impact their employability.

The research employs a mixed-methods approach. A sample of 262 respondents was analyzed using ANOVA to determine gender-based disparities in employment-related challenges. While findings indicate no significant gender difference in overall employability challenges, financial constraints, hiring issues, or workplace biases, significant disparities exist in employment accessibility and skill development opportunities, with women facing greater barriers. These findings align with global literature on the compounded disadvantages faced by women with disabilities due to deep-rooted societal norms, limited vocational training, and lack of targeted policy interventions.

The study underscores the need for inclusive employment policies that specifically address gendered barriers faced by women with disabilities. It calls for targeted interventions such as accessible skill development programs, mentorship opportunities, and employer sensitization programs to foster equitable workplace environments. By shifting focus from broad gender-neutral policies to intersectional approaches that consider both gender and disability, policymakers and organizations can better promote economic independence and workplace inclusion for differently abled women

Diversity within the workplace in terms of race, gender, age, cultural, and professional experiences has also become a strategic process of improving the performance of the organization and ensuring employee satisfaction. It explores the connection between the dimensions of diversity and employee outcome utilizing a mixed-method that combines both quantitative survey data and qualitative interviews of three corporate segments in India namely technology, manufacturing, and service sectors. Structured questionnaires answered by 450 employees and in-depth interviews of 30 participants were conducted in order to capture subtle nuances of: belonging inclusivity and culture within an organization. The methods of correlation and multiple regression were used in the analysis to quantify both how diversity management practices are associated with job performance (productivity, innovation, and teamwork indicators) and satisfaction measures (engagement, intent to remain, workplace morale). The findings show that gender and cultural diversity has a high correlation with better creativity, problem solving ability and age diversity levels have moderate correlation with knowledge sharing and efficacy in mentoring. On the contrary, lack of inclusive policies was associated with lower engagement and intentions of leaving. The results point out that diversity does not necessarily bring about good results, instead, its influence is moderated by leadership pledge and equitable policy, and an inclusive climate. The study highlights the need to integrate diversity management within core HR practices so as to ensure that an organization not only benefits the most but also promotes the wellness of the employees.

When the market participants choose to follow others, rather than relying on their own knowledge, it is known as herding or ‘following the crowd’ (Jain et al., 2021). This herding behavior has been significantly influential in the investment decision making of individuals as well as the market (Maheshwari et al., 2023). The present paper attempts to present a bibliometric review of the literature on herding behavior to capture the extent, direction, progress and trends in the domain. It uses the various bibliometric techniques to establish a conceptual and social structure in the present literature, as well as to delineate the most prominent aspects of it. The analysis has been done on a sample of 140 articles from January, 1990 to August, 2024. The prominent findings of the study suggest that this area has become more popular among researchers with time, especially in the recent years. The study also elucidates the direction and research gaps in this area which will of greater interest for academicians, stock market regulators as well as the policy makers.

Background: The Ayushman Bharat Digital Mission (ABDM) aims to create a digital healthcare ecosystem in India, enhancing access, affordability, and quality of services for diverse socio-economic groups. This study investigates variations in patient satisfaction and service quality in South Mumbai using the SERVQUAL model.

Methods: A cross-sectional descriptive research design was employed, utilizing a structured questionnaire based on the SERVQUAL dimensions: Tangibility, Reliability, Responsiveness, Assurance, and Empathy. A convenience sample of 200 respondents who accessed ABDM services was surveyed. Data analysis was conducted using SPSS with Bayesian statistics, employing nonparametric tests like Chi-Square and Kolmogorov-Smirnov to assess differences in satisfaction across socio-economic groups.

Results: Significant differences in patient satisfaction were observed between socio-economic groups, particularly in Tangibility (mean score 4.01, ±1.27, t=8.750, p<0.001) and Responsiveness (mean score 3.80, ±1.42, t=5.184, p<0.001). Reliability showed minimal variation (mean score 2.72, ±1.12, p=0.058). Bayesian analysis indicated strong evidence against the null hypotheses for Tangibility and Responsiveness, while moderate evidence was found for Reliability.

Conclusion: Socio-economic factors significantly influence patient experiences with ABDM services, with Tangibility and Responsiveness being critical for satisfaction. The findings highlight the need for targeted interventions to address disparities in service quality and improve healthcare delivery. Policymakers should leverage these insights to enhance digital health initiatives and ensure equitable access to healthcare services

Human resources help businesses achieve their objectives since they are natural reservoir of leadership, skill, knowledge, creativity, and talent. Still, the existing system of accounting is unable to adequately offer human resources value. Not only this, but we also found that Indian businesses disclose very little about human resource accounting (HRA) and its regulation. In addition, the HRA disclosures that businesses provide are not consistent, organised, or comparative between businesses in the same industry. As a result, we assess the degree to which certain Indian public sector banks measure and report on HRA. The current study looks into the degree of HRA practices and disclosures used by 12 public sector banks in India after the mega bank merger 2020. The study considered a 2-step process to determine the level of HRA related disclosures for these years in the annual reports of each of the 12 public sector banks. The first of the two steps are creating the HRA policies and disclosure index and the second step is rating the disclosure items. Descriptive statistics, tabulation, and graphical representation were used in the analysis of the data. The conclusion demonstrates that human resources accounting disclosures by banking institutions are still in their infancy. In the modern period, human resources have a strategic position. The true capital, particularly in information-based industries like banking, is the experience, knowledge, and skills that people possess. It serves as the primary predictor of productivity, profitability, and sustainability and provides investors worldwide with a foundation upon which to make decisions. Information asymmetry requires the disclosure of this significant organisational component. The study would try to analyze the current position of HRA Disclosure Practices prevailing in the public sector banks in India in comparison with the Disclosure Practices prevailing in these banks, taking into consideration both the importance of HR and HR Disclosures in this sector and the importance of the Banking Industry in the Indian Economy

In today’s data-rich organisational landscape, predictive analytics is rapidly redefining how human resource professionals confront the persistent challenge of employee turnover. This study explores the efficacy of machine learning models in forecasting employee attrition by applying robust algorithms such as Random Forest and XGBoost. Drawing upon a structured primary dataset from a mid-sized service firm, we employed ten comprehensive statistical techniques—including correlation analysis, logistic regression, decision trees, and ROC-AUC evaluation—to uncover the most influential factors affecting employee exits. The results demonstrate that attributes like job satisfaction, performance rating, monthly income, and promotion frequency are key predictors of attrition. The Random Forest model, with an AUC score of 0.89, outperformed others in both accuracy and interpretability, further validated through feature importance analysis. The study also highlights the interpretive value of SHAP and permutation importance in demystifying black-box models and enhancing managerial trust in predictive systems.

This research offers significant implications for HR practitioners, urging a shift from reactive to proactive retention strategies driven by data. While the models show high predictive accuracy, limitations regarding sample generalisability and feature engineering remain. Nonetheless, the findings lay a strong foundation for future investigations in diverse sectors, with a call to embed such analytics into strategic HR decision-making. By integrating advanced machine learning with grounded human insight, organisations can navigate turnover risks with far greater foresight and precision.

In the ever-evolving landscape of the Indian automotive industry, financial stability remains paramount for ensuring long-term competitiveness and sustainability. This study delves into the intricate relationship between credit risk and financial stability, using Royal Enfield as the focal point. With the increasing volatility of financial markets, understanding the dimensions of credit risk—especially in terms of liquidity, profitability, and solvency—has become crucial for industry players. The research adopts a quantitative framework grounded in secondary financial data, spanning five years of Royal Enfield’s audited reports. Key credit risk indicators such as the Current Ratio, Non-Performing Asset (NPA) Ratio, Return on Investment (ROI), and Return on Equity (ROE) are critically analysed. Furthermore, the Altman Z-Score model is employed to assess financial distress and predict the likelihood of bankruptcy. The study also benchmarks Royal Enfield’s credit exposure against major competitors to conduct a comparative industry analysis. Through financial ratio analysis and risk assessment models, the findings reveal that while Royal Enfield exhibits financial resilience, emerging credit risks necessitate proactive management strategies. The study concludes with a set of practical recommendations aimed at enhancing credit risk governance, thereby fortifying financial health. Overall, the research contributes to the broader discourse on corporate financial stability by offering a focused, data-driven perspective on risk management within the Indian automotive context.

The emergence of the conscious consumer has profoundly altered the dynamics of modern marketplaces, challenging traditional managerial paradigms and prompting a re-evaluation of ethical decision making within organisations. While consumer behaviour increasingly reflects concern for social justice, environmental sustainability, and corporate transparency, the managerial response to these shifts remains inconsistent, fragmented, and often performative. This paper explores the interface between ethical consumer expectations and managerial decision-making frameworks, drawing on established theories from business ethics, behavioural psychology, and stakeholder management. It argues that conscious consumerism is no longer a marginal movement but a dominant force shaping strategic business conduct. Through an interdisciplinary lens, the paper decodes how managers perceive and react to consumer-driven ethical pressures, and proposes a conceptual model that integrates consumer values into managerial ethics processes. The study further examines how generational shifts, ESG expectations, and reputational risks have elevated ethical literacy as a strategic imperative in the boardroom. By combining theoretical synthesis with managerial insights, this research bridges the gap between consumer behaviour studies and corporate ethics, offering a fresh, grounded perspective on navigating ethical complexity in a value-driven economy. The findings have both theoretical and practical implications, underscoring the need for ethically aligned leadership and value-centred decision making in an era where consumers demand more than products — they demand principles.

By including immediate gratification as a main motivator for online purchases, the fast growth of e-commerce has changed consumer behavior. This review looks at the psychological processes behind impulsive buying behavior and post-purchase dissonance—two connected phenomena becoming more and more common in digital markets. Driven by dopamine release and reinforced by persuasive UX/UI design, scarcity cues, and AI-powered personalization, instant gratification promotes fast decision-making by reducing cognitive friction. These actions are amplified by social commerce, influencer marketing, and gamified shopping venues that use FOMO—fear of missing out—and social validation systems. But the very elements that promote instant purchases also cause increased post-purchase conflict, a state of regret or dissatisfaction brought on by unmet expectations. Based on Festinger's Cognitive Dissonance Theory, this review looks at personal, product-related, contextual, and technical influences that exacerbate this emotional fallout, including price sensitivity, product complexity, urgency cues, and social pressures. The study reveals how gender, cultural norms, and social support networks affect the severity of regret, therefore influencing consumer reactions including returns, complaints, and bad reviews. E-commerce systems use a dual strategy—maximizing conversions through urgency and personalization, while minimizing regret through clear return policies, proactive customer support, and loyalty programs. However, current research reveals critical gaps, including the lack of longitudinal studies on gratification–regret cycles, limited exploration of personality–technology interaction effects, insufficient understanding of emotional recovery post-return, and minimal attention to the ethics of persuasive digital design. This review provides a conceptual model for striking a balance between profit-driven urgency and consumer psychological well-being by combining ideas from behavioral economics, neuroscience, and digital marketing. The results ask e-commerce participants to implement design and policy measures that maintain consumer happiness beyond the point of sale, hence developing ethical, sustainable, and trust-based digital economy..

Environmental, Social, and Governance (ESG) investing has emerged as a dominant force in sustainable finance, reshaping corporate behavior, investment strategies, and regulatory frameworks worldwide. Initially viewed as a transformative approach to aligning financial performance with societal and environmental goals, ESG witnessed exponential growth between 2015 and 2022. However, recent years have seen growing skepticism and challenges surrounding its effectiveness, standardization, and credibility. This article critically examines the evolution of ESG—from its roots in Corporate Social Responsibility (CSR) and Socially Responsible Investing (SRI) to its overlap with the more outcomes-focused domain of impact investing. It explores the rise of ESG investing, the proliferation of ESG-linked financial instruments, and the global regulatory frameworks that have propelled its adoption. At the same time, it highlights mounting concerns over greenwashing, inconsistent ESG ratings, lack of universal disclosure standards, and the politicization of ESG, especially in markets like the United States. The article argues that while ESG is undergoing a period of reputational and structural scrutiny, it is not in terminal decline. Instead, ESG is entering a phase of transformation—“ESG 2.0”—characterized by regulatory integration, enhanced measurement tools, and closer alignment with financial materiality and long-term risk management. As investors, regulators, and corporations adapt to this changing landscape, the future of ESG will depend on its ability to deliver transparency, accountability, and demonstrable impact in a politically polarized and economically uncertain world

This article provides a comprehensive analysis of sustainable indices (also known as ESG indices) in India and globally, examining their definition, importance, and evolution within the financial landscape. Unlike traditional market benchmarks, sustainable indices integrate environmental, social, and governance (ESG) factors to assess long-term value creation and risk mitigation. The report delves into major sustainable indices, their construction methodologies, and their impact on portfolio performance and investor perception. It also addresses critical challenges such as greenwashing, the lack of standardized ESG metrics, and data availability issues. Finally, the report explores future trends, including the growth of ESG ETFs, the increasing influence of AI and big data analytics in ESG investing, and the continuous evolution of regulatory frameworks to promote responsible investment practices..

Nowadays, online shopping is a necessary component of modern life, allowing consumers to conveniently buy products and services. But, with the ease and convenience come certain risks that consumers experience. This research investigates the relationship between perceived risks and intention to purchase online, while also exploring how sociodemographic variables influence individuals' risk perceptions. Using a survey and structural equation modeling, the analysis of 308 participants across India identifies significant impacts of delivery, financial, and security risks on consumers' purchase intentions. Concerning the moderating impact of sociodemographic factors, only gender exhibited a moderating effect, while age and income did not influence the risk-intention relationship. These findings offer practical guidance for e-commerce players to mitigate consumer concerns and promote online shopping confidence

Although several studies have shown that the survival and success of firms depend, to a large extent, on the effectiveness of their HRM practices, most of the studies have focused on the HRM practices of large corporations. Very little research has been done on the HRM practices of small and medium enterprises (SMEs), particularly in the developing African economies. The main cause of business failure in SMEs is, among others, the lack of sound HRM practices. Large and small firms differ in respect of resources and expertise. Since an SME is not a mere miniaturised form of a large firm; it needs unique and customised HRM practices that suit its unique conditions, resource endowments and environment. An SME needs a customised approach for designing appropriate organisational structure, communication channel, flow of information, selection and recruitment procedure, performance appraisal system, and a training and development scheme. Application of HRM practices and policies of large, multi-product, multi-divisional and multi-national corporations (MNCs) may have serious implementation problem unless they are thoroughly modified and customised to the objective conditions in which an SME is operating. This paper analyses the various HRM practices in general; the characteristics and current HRM practices of SMEs in particular, and develop a conceptual framework integrating the best HRM practices with characteristics of SMEs in a developing economy. The paper presents new insights in the development of appropriate HRM practices for SMEs. Some suggestions are also made for owners, managers, and trainers of SMEs on the critical dimensions of HRM practices and on customising them for situations in which SMEs operate. Implications for future research in the area are also indicated.

As brand activism becomes a more visible strategy for firms, especially in sectors where trust is central, such as banking, consumers are becoming increasingly sensitive to the intentions behind such efforts. While prior research has largely examined activism through a firm-centric lens, the way consumers interpret a brand’s activist stance remains underexplored. This study addresses that gap by qualitatively exploring the factors shaping consumer perceptions of bank-led activism in the Indian context. Using focus group discussions analyzed through the Gioia methodology, we identify five consumer-perceived antecedents: pragmatic legitimacy, pursuit of social capital, and symbolic compliance (commonly linked with reactive activism); and conscientious intent and brand courage (associated with proactive activism). Our findings deepen attribution theory by revealing how consumers distinguish between reactive vs. proactive brand activism. The study offers a consumer-perceived framework for interpreting brand activism and provides practical guidance for companies seeking to engage in socio-political discourse within high-stakes, credibility-driven industries

The increasing adoption of robotics and artificial intelligence (AI) in luxury hospitality presents both opportunities and challenges. While these technologies can deliver personalized, efficient services, they risk diminishing the emotional engagement and empathy that define luxury experiences. This research examines human–robot interaction (HRI) through the Theory of Uniqueness and Social Exchange Theory to explore how technological innovation can coexist with the human touch. Using a mixed-method approach—including surveys, in-depth interviews, and observational studies—this study investigates guest perceptions of uniqueness, value, trust, and emotional connection in robot-assisted versus human interactions. Findings reveal that guests value robotic efficiency but expect emotional warmth from human staff, suggesting that a hybrid service model can optimize both technological and human strengths. The study contributes to consumer research by extending HRI theory into luxury contexts, integrating emotional and experiential dimensions, and offering strategic guidelines for emotionally intelligent robot design.

The intensifying environmental crisis and heightened consumer awareness have propelled green marketing to the forefront of sustainable business practices. This study critically examines the influence of green marketing strategies on sustainable consumer choices, with a focus on how environmental messaging, eco-labeling, and corporate social responsibility shape consumer behavior. Drawing on recent theoretical advancements and empirical findings, the paper investigates the psychological, demographic, and cultural factors mediating green purchasing decisions. Furthermore, the study evaluates the authenticity and effectiveness of green marketing initiatives across industries and regions, highlighting the risks of greenwashing and consumer skepticism. By integrating behavioral science with marketing frameworks, the paper offers a nuanced understanding of the dynamics between corporate sustainability efforts and consumer preferences. The findings underscore that while green marketing can significantly affect sustainable consumption patterns, its success is contingent upon transparency, trust, and alignment with broader socio-environmental values. This research contributes to the discourse on sustainable development by providing strategic insights for marketers, policymakers, and stakeholders in fostering environmentally responsible consumption.

This study, “Distribution Efficiency and Non-Moving Stock Management in the Footwear Industry a Review of Best Practices, Challenges, and Liquidation Strategies in the Eastern Zone of India” is intended to examine and synthesize the information regarding the enhancement of distribution procedures and the management of the standing inventory in an industry that is regionally significant, but is frequently neglected in terms of the Indian supply chain marketplace. The main idea is to find out good practices and discover more about bottlenecks in inventory movements regionally, particularly in Bihar, West Bengal, Odisha, Jharkhand and Eastern UP, where infrastructural and logistical gaps are likely to have an impact on the efficiency of operations. The study helps to solve the problem of non-moving stock that clogs the working capital and makes the business less responsive in a footwear industry that is highly trendy and seasonal. This paper is literature and qualitative-based, where industry reports, supply chain management journals, and case studies of local wholesalers and distributors were used. They were based on data interpretation to provide thematic insights on four core objectives: (1) the evaluation of distribution efficiency issues, (2) the identification of the causes of non-moving stock, (3) the review of liquidation procedures, including discounting, bundling, and e-commerce offloading, and (4) the assessment of the incorporation of digital tools to track stock and make forecasts. The most important findings are that the demand uncertainty, inappropriate digital usage, and poor warehouse behaviour are the main factors leading to inefficiencies. Effective companies in the area operate through localized forecasting, lean distribution, and online clearance. The contribution of this review to the academic literature is that it contextualized the concept of inventory inefficiency in an unexplored region and provided practical recommendations to practitioners on how to streamline the supply chain, optimize working capital, and implement agile strategies to liquidate inventories based on the regional limitations.

The invention uses advanced computer science techniques and sophisticated machine learning models to help predict how employees will perform. Employee messages, performance evaluations, project records and feedback are among the data types used by the system to apply GPT-4 and BERT models to get fine details about each employee’s behavior, abilities and performance. The technique analyzes emotions, gathers topics and processes words in context to determine both open and hidden measures of performance. Our model uses NLP, employee history and organizational data to provide reliable, on-demand evaluations of each employee. Through this method, managers can manage talent actively, design personal learning plans, build better teams and make informed choices. Because it is objective, continues throughout the year and can handle large numbers of employees, this invention improves workforce results and keeps employees in companies when there are big changes in the business.

With increasing demand for goods putting pressure on the environment, the Circular Economy (CE) offers a smarter, more sustainable way to use our resources. Consumer attitude plays an important role in the adoption of CE principles in the consumer market. This paper aims to understand the sustainability of the Circular Economy (CE) based on consumer attitude in India. A theoretical structure is developed to understand consumer preference for Circular business models for the adoption of Circular Economy in India. The empirical study is based on data obtained from an online survey (N=75). This study offers marketers and policymakers a guide to evaluate the consumer awareness and attitude towards CE principles in India

Talent assessments are increasingly vital in modern human resource (HR) practice, helping organizations identify and develop employees’ innate strengths beyond what resumes or credentials reveal. This study examines the psychometric reliability and validity of the TalentPlus strengths-based assessment – a 140-item instrument measuring 28 “talent themes” across four core domains (Thinking, Relating, Acting and Leading). Using survey data from 1,178 respondents, we evaluated internal consistency (Cronbach’s α), test–retest reliability (Pearson correlations on a 130-respondent subset), and factor structure through exploratory and confirmatory factor analyses (EFA and CFA). Cronbach’s α coefficients for the 28 talent theme scales is above .70 for majority of the themes, indicating a good and satisfactory internal consistency given the brief 5-item scales. Test–retest reliability over a four-week interval was high: most talent theme scores showed Pearson r between ~.70 and.85 (median ~.80), evidencing good stability. EFA results supported a four-factor solution aligning with the theorized domains, and a hierarchical CFA model (28 first-order themes loading onto 4 second-order domain factors) demonstrated acceptable fit (CFI≈0.92, TLI≈0.90, RMSEA≈0.06). These findings establish that TalentPlus is a psychometrically sound tool: internally consistent, stable, and construct-valid in reflecting a higher-order talent structure. We discuss how its reliability and validity compare to Industry benchmarks and we highlight implications for HR practitioners in using strengths-based assessments to complement traditional credentials

This study explores the perceptions, challenges, and influencing factors encountered by mutual fund distributors in promoting ESG (Environmental, Social, and Governance) mutual fund schemes in India. With the increasing emphasis on responsible investing, ESG mutual funds are gaining attention; however, their effective promotion depends significantly on the preparedness and engagement of distributors. The study employs a combination of statistical techniques including the Friedman Mean Rank Test, Regression Analysis, and Kruskal Wallis Test to analyze distributor responses regarding barriers, the impact of training, and the influence of ESG factors. Findings reveal that distributors face major challenges such as perceived lower returns, limited investor awareness, lack of ESG-specific training, and product complexity. Training and development programs are found to significantly improve distributor awareness, sales effectiveness, and objection handling, indicating a strong need for structured capacity-building initiatives. Additionally, environmental, social, and corporate governance factors individually influence distributor preference in promoting ESG funds, with social and governance factors being particularly impactful. The performance analysis of various ESG mutual fund schemes demonstrates a wide range of returns, with some newer funds showing strong growth, debunking myths around ESG fund underperformance. The study concludes that bridging knowledge gaps, enhancing training infrastructure, and fostering distributor engagement are essential to mainstream ESG investment in India. These findings provide actionable insights for fund houses, regulators, and educators aiming to build a robust ESG distribution ecosystem. Overall, the study highlights that empowering distributors through strategic interventions will play a crucial role in accelerating the acceptance and success of ESG mutual funds, thereby supporting India’s broader goals of sustainable and responsible investment.

Corporate Social Responsibility (CSR) is a critical tool that helps mitigate the changing dynamic of climate. This article explores the role of CSR initiatives while addressing different environmental challenges, as well as their effectiveness, strategies, impact, and implementation. The introduction provides an overview of the relationship between climate change and CSR. The study background delves into the evolution of CSR in response to environmental concerns. The objectives of the study are to explore the effectiveness of several initiatives while exploring how corporations focus on integrating environmental considerations into their core operations. The literature review assists in examining previous research while highlighting key gas and findings through current understanding. The methodology section details the primary research methods, including surveys and case studies of corporations with notable CSR programs. The discussion mainly interprets the statistical data and links it to a broader range of implications and trends. The conclusion of the findings offers an insight into the CSR, which is leveraged effectively in the rebellion against climate change.

The growing global focus on environmental sustainability has heightened interest in understanding the key drivers of green consumer behavior. Despite a substantial and expanding body of research on sustainable consumption, there remains limited clarity regarding which psychological, social, and economic factors most strongly influence green purchasing decisions. This paper presents a meta-analytic review of 72 peer-reviewed empirical studies published between 2005 and 2024, systematically evaluating the relative strength of these influencing factors. Rather than concentrating on a specific region, the study draws from a diverse global dataset to enhance generalizability across different cultural and market contexts. The findings reveal that environmental concern, perceived consumer effectiveness, and positive attitudes toward green products are consistently strong predictors of sustainable consumer behavior. In contrast, price sensitivity and skepticism toward green labels emerge as notable barriers. This study advances theoretical understanding and provides actionable insights for marketers and policymakers aiming to encourage sustainable consumption. The results also inform future research directions and policy interventions designed to foster more responsible consumer behavior on a global scale

This study develops and empirically tests a conditional process model to explore the impact of AI recommendations on consumer shopping satisfaction on e-commerce platforms. In addition, this study investigated the moderating effect of consumer value preferences on the relationship between AI recommendations and purchase satisfaction. The findings indicate that AI recommendations cause information cocoons and have a significant negative main effect. However, this negative effect is not uniform. Its impact is significantly moderated by customer value preferences. We further uncover a significant gender divide in these moderating pathways. This study offers actionable insights for e-commerce platforms on how to tailor their recommendation algorithms not just to user profiles, but to user shopping motives.

The rapid growth of financial technology (FinTech) has transformed the financial landscape, providing users with enhanced digital solutions for payments, banking, and investments. This study examines user preferences for FinTech services in Chennai, focusing on awareness, adoption factors, satisfaction levels, and challenges. Researchers conducted a mixed-method approach, incorporating surveys and interviews with 214 respondents. Findings indicate that convenience, transaction speed, and cost-effectiveness drive adoption, while security and regulatory concerns act as barriers. The study highlights opportunities for FinTech growth through enhanced cybersecurity measures, financial literacy programs, and improved user support. The recommendations aim to foster greater financial inclusion and trust in FinTech services in Chennai

Artisan products have significant cultural and economic importance, yet artisan organizations struggle to remain viable. The purpose of this research was to examine how traditional handicraft technologies can harmonize with current consumer requirements. In the study, both artisans and consumers were interviewed and self-completed questionnaires about the ideas of updating designs, materials, and selling platforms yet preserving the handicraft attribute. Analytical conclusions indicated modern designs with the retention of traditional shapes, the shift towards the use of eco-friendly materials, and sales through online platforms. In terms of percentage, consumers’ preferences were 60% for traditional designs, 70% for sustainable materials, 40% for willingness to pay a premium price, and 55% for comfort in digital shopping. Some of the topics pointed out the desire to improve handicrafts by integrating traditional construction with contemporary design, environmentally friendly materials, and e-commerce. Conclusions highlighted the need to combine a conservative approach with the appeal to modern consumers. Recommendations entailed product development that embraced artisan skills, enhancement of capacity to produce unique designs, use of sustainable materials, and an online platform to ensure the socioeconomic sustainability of handicrafts. The paramount idea is maintaining history while increasing marketability. This study offers significant implications for how conventional skills of handicrafts can be merged with present-day consumer requirements utilizing effective change management

The research paper assesses the Indian general elections and the iterative impact on the equity  market from 1991 to 2024. The major stock market indicators on Sensex and Nifty indices, and the successive impact on major sectors to general elections has been studied. This study aims to enhance understanding of how political events influence stock market behavior, aiding stakeholders in making informed decisions. Our research shows that during election times, the Indian stock market often becomes more unpredictable. Uncertainty weighs over risk of decisions and election result. Stock prices are more volatile than usual. This happens because investors are unsure about what will happen after the election. However, we've noticed that even though the market might be volatile during elections, it usually recovers and grows over time. This shows that the Indian stock market is resilient – it can bounce back from short-term uncertainties. When election results are surprising, the market tends to react more strongly. For example, if a party that wasn't expected to win comes into power, we might see bigger changes in stock prices right after the election. On the other hand, when a government that people are familiar with gets re-elected, the market often responds positively. This is because investors feel more confident when they know what to expect from the government's policies. Different sectors of the economy react differently to elections- FMCG, Technology Financial Services and Infrastructure. Over the years, we've seen that the Indian stock market has become more mature in how it handles elections. It doesn't panic as much as it used to when there are political changes. This shows that investors are getting more experienced and understand that short-term political events don't always affect long-term economic growth. When we look at the numbers closely, we see that despite the ups and downs during election times, both Sensex and Nifty have grown significantly over the years. This reflects how India's economy has been growing overall.

The growth of branding of product throughout time from farmers claiming their property to artists claiming credit for their works, factories claiming possession of their production, and companies claiming their goods were superior in comparison to others. Even in the 21st century, branding is still entails asserting control of more than just products and assets.

It consists of staying true to your company's values, and promoting customer loyalty and trust via your words, actions, and stories. Brand equity becomes an essential tool to endure in the market and face competition in the contemporary scenario. In addition to being vital to the company's existence, brand equity, recognition of the brand, loyalty to it, and perceived value are active variables that increase sales. The goal of this research is to ascertain how different aspects of brand equity relate to one another.

India’s economic trajectory in the 21st century is increasingly shaped by innovation, entrepreneurship, and the emergence of startups across diverse sectors. These engines of growth have redefined traditional development paradigms by catalyzing productivity, creating employment, and enhancing global competitiveness. However, the Indian startup ecosystem also faces substantial challenges including regulatory complexities, inadequate access to early-stage financing, infrastructural bottlenecks, limited intellectual property awareness, and skill mismatches. This paper investigates the multidimensional role played by innovation-driven entrepreneurship in India’s economic transformation while critically examining the structural and policy-level challenges that impede its sustainable evolution. Drawing upon recent data, policy analysis, and real-world case studies, the paper underscores the need for an inclusive innovation ecosystem, cross-sectoral collaboration, entrepreneurial education, and strategic policy interventions to bridge the existing gaps. As India aspires to become a $5 trillion economy, fostering innovation and entrepreneurship remains central to achieving inclusive and sustainable economic growth in the 21st century.

The rapid advancement of artificial intelligence (AI), particularly tools such as Chat GPT and emerging forms of super intelligence, is transforming human-computer interaction and reshaping the future of work. This study aims to explore whether gender-based differences exist in perceptions of these technological shifts. Specifically, the research examines respondents’ views across six key constructs: Knowledge Access, Redefining Roles and Skills, Legal Research, Data Privacy, Software Development, and Augmented Productivity. The non-parametric Mann-Whitney U test was applied to assess statistically significant differences in perceptions based on gender. The findings indicate that for most constructs—including Knowledge Access (p = .327), Redefining Roles and Skills (p = .433), Legal Research (p = .756), Software Development (p = .227), and Augmented Productivity (p = .632)—there were no statistically significant differences in perception between male and female respondents. This suggests a general consensus across genders in recognizing the transformative potential of AI in these domains. However, a significant difference was observed in the Data Privacy construct (p = .025), indicating that gender influences how data privacy issues in AI applications are perceived. Female respondents reported slightly higher concerns compared to their male counterparts. The results support the conclusion that while gender does not significantly affect perceptions of AI in most work-related areas, it does play a role in shaping concerns around data privacy. These insights are valuable for developers, policymakers, and educators working to promote equitable and inclusive AI integration in professional settings. Understanding gender-specific concerns—especially in sensitive areas such as data privacy—can help tailor strategies for responsible AI adoption and ensure that technological advancements align with diverse user expectations and ethical standards. Further research is recommended to explore the underlying factors contributing to these gendered perceptions

The Topic of Employee Engagement is a widely debated on as it is considered to be vital for the success of any organization. My report suggests very low levels of engagement in India. Our study aims to convey how Individual Accountability can improve engagement levels in the Indian Pharma Sector. The objective of our research to showcase the role of Individual Accountability towards improving engagement levels amongst employees. A quantitative survey of 150 pharmaceutical employees was conducted, measuring perceptions of personal accountability and work engagement using a 6 point Likert-scale instruments. Factor Analysis method was used in SPSS to identify the latent conducts of accountability and engagement. The results indicated that two clear factors corresponding to individual accountability and employee engagement, with a significant positive correlation between the two. Employees who reported higher personal accountability also showed substantially higher engagement levels. The alternate hypothesis – that individual accountability significantly enhances employee engagement – was supported, as the accountability factor explained about 40% of the variance in engagement (p <.001). This paper concludes with actionable insights for managers to improve employee engagement which will reduce turnover for the organization and improve its performance

We are all familiar with the word “Stress”. This is the bane of this modern day society. While dealing with the rigors of modern day life, most people in today’s society end up in distress due to ever increasing demands, transitions and challenges. This has definite impact on an individual’s productivity and output, but the response mechanism varies from person to person depending on his / her coping methodology to deal with Stress. The purpose of this study is two- fold: to understand the history of stress, typology of stress, theories of stress and to provide suitable understanding in coping mechanism. Eventually, this paper aims in providing an insightful study on different variables associated with stress, and their direct or indirect effects on mental & physical health and impact on relationships will be taken into account. The result and the explanations of this literature might help to find out the main reason for their stressors, best way to cope and greatest technique to use in managing stress.

This study mainly encompasses investigating “The Impact of AI-powered Chatbots on Consumer Purchase Decisions in E-Commerce” and aims to understand the ways technology based on the chatbot makes an impact on the behaviour of the user and even decision-making as well as overall satisfaction level within the digital environment in the case of the retail industry. This study aims to investigate how consumers view and use “AI chatbots” in “e-commerce”, with a focus on how they influence customer choices. Having a better understanding of these effects will help “e-commerce companies” improve chatbot functionality, enhance customer satisfaction, and increase sales success through more intelligent automation. It mainly adopts the “mixed-method approach” which makes an effective combination of qualitative and even quantitative techniques to make sure about an effective understanding of the case of the “research topic”. The findings evaluated that 5% of the customers have interacted with the “AI-powered chatbot”, where 53% stated their experience was good. Therefore, it stated the effective practices of this technology to positively influence buying behaviours. However, at the same time, it is essential to focus on issues like privacy, functionality, data security and many more to enhance the overall satisfaction level

The integration of Artificial Intelligence (AI) into digital marketing has revolutionized personalization strategies, offering tailored experiences that increase user engagement and conversion rates. However, growing concerns around data privacy, algorithmic transparency, and perceived manipulation challenge consumer trust in AI-driven personalization. This study investigates how AI-powered personalization impacts consumer trust in the context of e-commerce. Using empirical data collected from online shoppers through structured surveys and behavioral analysis, the study examines the dual effects of personalization: enhancing perceived relevance and creating trust tensions due to privacy invasions. Results indicate that while AI-enhanced personalization significantly improves user satisfaction and engagement, consumer trust is conditional upon transparency, control, and ethical data usage. These findings highlight the need for marketers to adopt a balance between hyper-personalization and privacy assurances to maintain sustainable consumer relationships. The study offers practical recommendations for ethical AI deployment in digital marketing strategies

Digital health technologies hold transformative potential for enhancing healthcare delivery in rural areas by addressing persistent gaps in access and quality of care. This study examines how digital health capabilities influence healthcare outcomes in rural settings, with a focus on critical factors such as governance, IT infrastructure, workforce skills, interoperability, strategic planning, data analytics, and patient-centered care. The results underscore the necessity of a cohesive digital strategy to drive improved health outcomes. Key enablers include the deployment of user-friendly devices, comprehensive training programs, and dependable internet access. The study also sheds light on adoption barriers—such as limited digital literacy and low trust in technology—and proposes actionable solutions to overcome them. Ultimately, the effective integration of digital health solutions can greatly enhance healthcare access, patient satisfaction, and overall health outcomes in rural communities

The research paper examines how behavioural biases of anchoring, availability heuristic and overconfidence affect the investment decisions of the retail employees in the National Capital Region (NCR) of India in the private sector. Filling an important gap in empirical research on this population group, the study follows a quantitative, cross-sectional survey design and stratified random sample to gather information on 312 full-time employees. Each bias was measured with validated multi-item scales and the reliability and validity of these constructs were established using exploratory factor analysis as well as confirmatory factor analysis. Results of multiple linear regression analysis showed that the three behavioural biases have positive and significant impacts on investment decision-making with overconfidence having the highest impact (0.29, p < .001). The results also revealed that emotional cues like stress at the workplace and competition among peers enhance the impacts of these biases and the probability of poor and risk-prone investment decisions. The findings support the need of special investor education programmes and organisational interventions that consider cognitive and emotional aspects of financial decision-making. This information forms good knowledge to the researchers, practitioners, and policymakers who aim at improving financial well-being of the increasing number of NCRs in the private sector.

In the present economic environment organisations are compelled to intensify their efforts to attract, retain, and motivate talented employees to ensure a high level of employee satisfaction. corporate social responsibility practices can serve as a strategic tool to support this endower. This study aims to investigate the relationship between corporate social responsibility and employee satisfaction through bibliometric analysis. A sample of 222 studies from 2004 to 2024 was analysed using the VOSviewer software and R programming language. The analysis in the present study is categorized into two parts, that is, performance analysis and science mapping. United States, China, and South Korea were the most productive nations in terms of frequency, and Citation. The prominent authors contributing to the study are Lee followed by Hasan Nam, Kim H, and Sen S. The Prominent journals with the highest publication are the Sustainability (Switzerland) followed closely by the journal Corporate Social Responsibility and Environmental, The Journal of Business Ethics and Social Responsibility journal. The article “The Contribution of Corporate Social Responsibility to Organizational Commitment” authored by S Brammer, A Millington, and B Rayton in 2007 has achieved the highest citation count totalling to 864. Some of the esteemed academic institutions that have contributed to the literature on corporate social responsibility and employee satisfaction are the Appalachian State University Stands out as the leading institution followed by   Iqra University, Hong Kong Polytechnic University, University of Belgrade. This study will serve as a valuable resource for scholars across various disciplines to evaluate the progression of academic works on a specific theme over time, especially for those focused on green entrepreneurship. Through its comprehensive and rigorous analytical methods, this study proposes a Unique definition of corporate social responsibility and employee satisfaction.

This research paper investigates how digital financial literacy shapes women's empowerment in relation to economic independence, social involvement, and decision-making authority. The study captures varying socioeconomic origins and degrees of digital financial awareness based on a survey of 250 women urban and rural areas ( Delhi and Meerut (villages nearby) respectively). Participants knowledge and use of digital financial instruments including mobile banking, e-wallets, and online transactions was measured on a standardised questionnaire. Using statistical methods, the gathered data was examined to find if financial literacy improves women's capacity for income control, economic activity participation, and wise financial decision-making. The results show a robust and favourable relationship between digital financial literacy and several dimensions of empowerment, meaning that women who have more financial knowledge are more likely to actively participate in social and economic domains and exercise autonomy in financial concerns. The research draws attention to ongoing hurdles that prevent many women from fully using digital financial services including inadequate access to technology, educational restrictions, and cultural standards. These results highlight how urgently specific rules and training initiatives meant to close the gender disparity in digital financial inclusion are needed. Dealing with these problems helps governments, financial institutions, non-governmental organisations, and other players to create a more inclusive financial environment that improves women's responsibilities in family and community level decision-making.

In an increasingly complex financial landscape, the ability of individuals to make sound investment decisions hinges on their level of financial literacy. With the proliferation of financial data and the advent of digital technologies, artificial intelligence (AI) and machine learning (ML) have emerged as transformative tools in bridging financial knowledge gaps. This paper explores how AI and ML can enhance personal financial literacy and inform investment strategies. It examines intelligent recommendation systems, robo-advisors, personalized learning platforms, and predictive analytics, highlighting their role in empowering individuals to make informed decisions. The study also evaluates ethical concerns, data privacy, and algorithmic transparency. A mixed-methods approach is used, incorporating recent case studies and surveys to assess user interaction with AI-driven financial tools. Results indicate that AI significantly improves decision-making confidence, accuracy, and financial engagement, especially among younger and digitally-native populations. This research contributes to the ongoing discourse on digital financial inclusion and advocates for responsible AI integration in personal finance

Intersectionality remains significantly underutilized in the marketing field, even though it has potential.  This page addresses this gap by gathering the body of evidence to present helpful resources for integrating intersectionality into marketing research.  In addition to a comprehensive study design process, a visionary research objective, and key takeaways for stakeholders, it outlines a comprehensive base for an intersectional marketing framework. Essentially, intersectionality is defined by three aspects: (1) an awareness and an acceptance that social categories—such as gender, race, and class—happen as opposed to co-existing independently; (2) an understanding of how intersecting identities affect individuals' experiences in the workplace; and (3) a focus on the dynamics of power structures influencing these lived realities. Researchers and marketers can apply the article's innovative research design roadmap to incorporate intersectionality into their work through the theory and methodological strategies it outlines. Developing intersectional theories and hypotheses, conducting inclusive literature reviews, collecting rich demographic information, sampling marginalized groups, performing exploratory subsample analyses, and making context-sensitive conclusions are just a few. Furthermore, the proposed study agenda also points to significant concerns at the organizational, consumer, and social levels. Ultimately, bringing an intersectional perspective can ensure marketing stays relevant to society, promote the development of broad and inclusive theoretical frameworks, and more accurately reflect the voice of underrepresented and marginalized groups

Purpose: This study aimed to analyse the effect of supervisor support on quality of work life, the effect of work family conflict on quality of work life, and the effect of supervisor support on quality of work life (QWL) through work family conflict as intervening variable.

Design/methodology/approach: Primary data were collected from 721 train drivers working in the South Central zone of Indian railways. The data collection tool was a questionnaire that is distributed online to the participants. Hierarchical regression analysis and mediation analysis using PROCESS Model 4 were employed to test the hypotheses.

Main findings: Supervisor support had a significant positive effect on quality of work life, work family conflict had a significant negative effect on QWL, supervisor support showed a significant negative effect on work family conflict and work family conflict was found to be partially mediating the effect of supervisor support on QWL. Practical implications: The current research reported that a supportive supervisor can enhance employees’ QWL directly, as well as indirectly by reducing conflicts at work and family interface. This finding suggests that organizations aiming to promote employees’ QWL should invest in training programs that cultivate supportive behaviors among supervisors and also equip them with the resources needed to help employees manage the competing demands of work and family roles.

Originality/value: By investigating the mediating role of work family conflict, this research uncovered the underlying mechanism through which supervisor support influences quality of work life, thereby contributing to the existing literature on QWL

As the Earth's natural resources face rapid depletion, promoting sustainable consumer behaviors has become crucial. One such behavior is adopting second-hand products, which aligns with circular economy principles. This study investigates the factors influencing consumers' intentions to purchase used products and visit second-hand shops, utilizing an extended Theory of Planned Behavior (TPB) framework. Specifically, it examines the impact of independent variables-attitude, subjective norms, perceived behavioral control, and perceived norms—on purchase intention, a mediating variable, and the ultimate dependent variable, the intention to visit second-hand shops.

The study employs Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze these relationships, offering nuanced insights into each variable's mediation effects and significance. Results reveal that perceived norms and subjective norms exhibit partial mediation effects. Interestingly, while subjective and perceived norms significantly influence purchase intention, attitude, and perceived behavioral control are not significant predictors in this model. These findings highlight the pivotal role of normative influences in shaping consumer behavior toward second-hand markets. This research contributes to the literature on sustainable consumption by providing a comprehensive analysis of consumer decision-making in second-hand shopping, with implications for policymakers and marketers aiming to foster eco-conscious consumption patterns

Introduction: The use of social media influencers (SMIs) has become an important part of digital tourism marketing. Influencers shape how destinations are perceived and chosen by potential travellers through visual content, personal narratives, and audience engagement. This review examines how influencer characteristics, platform dynamics, and psychological mechanisms influence tourist decision-making, destination image formation, and behaviour.

Methods: This paper follows a systematic literature review design. No primary data were collected. Peer-reviewed articles published between 2010 and 2024 were identified using databases such as Scopus, Web of Science, and Google Scholar. After applying inclusion criteria focused on tourism, social media influencers, and destination marketing, 38 academic sources were selected. A thematic synthesis was used to group findings into theoretical frameworks, influencer classifications, psychological processes, platform-specific impacts, and industry strategies.

Results: Findings indicate that influencers affect destination selection by shaping cognitive and affective destination images, triggering emotional and social psychological responses such as FOMO and identification, and influencing behaviour across the travel decision-making stages (dreaming, planning, booking). Influencer typologies, platform-specific features (e.g., Instagram’s visual focus vs. YouTube’s depth), and campaign strategies by DMOs are key factors in determining effectiveness. ROI is measured through engagement metrics, reach, sentiment analysis, and booking-related conversions, though long-term loyalty impact remains difficult to capture.

Conclusion: Social media influencers significantly influence how destinations are discovered and chosen. Their effectiveness depends on credibility, content relevance, and audience alignment. However, gaps remain in the literature, including underrepresentation of the Global South, limited longitudinal studies, and a need for more mixed-methods research. Future studies should explore virtual influencers, immersive digital environments, niche communities, and the role of influencers in promoting sustainable and ethical tourism.

As digital technologies evolve at an unprecedented pace, organizations face mounting challenges in maintaining adaptability, strategic clarity, and resilience. This study investigates how firms can build organizational digital resilience (ODR) in response to digital technological turbulence (DTT) by cultivating two key dynamic capabilities: digital ambidexterity (ODA), and digital agility (DAG). Drawing on the dynamic capabilities view and contingency theory, we develop and empirically test a conceptual model using survey data from 311 managers across a range of industries in India. Using Partial Least Squares Structural Equation Modelling (PLS-SEM), our findings reveal that digital ambidexterity, a key enabler of organizational digital resilience, also enhances digital agility, which in turn strengthens digital resilience. As such, digital agility emerges as a crucial link that transforms ambidexterity into resilience. The study also finds that the influence of ambidexterity on agility becomes more pronounced under higher levels of digital turbulence, highlighting the importance of contextual responsiveness. By integrating theoretical perspectives and empirical insights, this research contributes to a deeper understanding of how organizations can proactively navigate uncertainty and turbulence. The findings hold practical relevance for managers and policymakers aiming to foster resilient, future-ready organizations in an increasingly volatile digital environment.

With the increasing use of technology in the workplace, digitization has become a fundamental aspect of modern organizations. While digitization has increased efficiency and productivity, it has also led to increased stress levels among employees. As a result, organizations are seeking ways to promote employee well-being. One way to achieve this is through mindfulness training, which has been shown to reduce stress and improve well-being. However, it is unclear whether the effect of mindfulness on employee well-being is mediated by supportive supervisor communication or moderated by workplace digitization. Therefore, this research explores the direct effect of mindfulness on employee well-being moderated by workplace digitization, as well as the indirect effect of mindfulness on well-being through the mediation of a supportive supervisor. The attention restoration theory posits that attention wavers and requires effort to focus. In this study we examine the regulation of attention using mindfulness to sustain attentiveness and positive effect. 278 responses collected from a survey were analysed to arrive at the relationships. The findings reveal that while mindfulness does not directly impact employee well-being significantly, supportive supervisor communication serves as a key mediator, demonstrating a moderate to strong positive effect on employee well-being. In addition, workplace digitization emerges as a significant moderator, weakening the relationship between mindfulness and well-being as digital technologies become more pervasive. Organizations should focus on developing supportive supervisory communication skills while carefully considering the potential drawbacks of excessive digitization

Purpose: This study investigated the interplay between social networking addiction, excessive usage of internet gaming, and mental health outcomes, particularly stress, depression, and anxiety, among young students during the lockdown period of the COVID-19 pandemic in India.

Design/methodology/approach: The data was collected from students studying in high school, undergraduate, and postgraduate degree colleges in Mumbai, India. The duration of data collection was between March 2021 and December 2022. The purposive and quasi-experimental sampling technique was adopted.

Findings: The data was analyzed using ANOVA, regression analysis, and confirmatory factor analysis using structural equation modeling. The findings reveal gender as a significant predictor, with females experiencing higher levels of mental health issues. Further, COVID-19 infection status also correlates with heightened stress, depression, and anxiety. The findings underscore the importance of tailored interventions and support systems to address mental health challenges, especially during crises.

Originality: The study is original. The data was collected during the first and second lockdowns due to the COVID-19 pandemic in India.

Research limitations and implications: The study observes a limitation of only assessing a few demographic variables of the students from Mumbai, India. And hence generalization on the wider sample cannot be taken. This study would be useful for developing interventions to cope with problems of internet gaming, and social networking addiction and addressing issues of stress related to it.

Practical implications: This study would be useful for young individuals, parents, healthcare professionals, and academicians to develop coping mechanisms to overcome addictive technological behavior. Newer methods can be developed to channel this behavior for productive output.

Social implications: Further, the social implication of the study would be helpful for people to overcome internet addictive behaviors and cope with the negative outcomes arising out of it.

Previous studies have highlighted the advantages of established brands in the Chinese market, but few have explored their cross-boundary innovation strategies in response to fierce competition. With the establishment of “China Brand Day” and the rise of Chinese trends, many established brands aim to attract a new generation of customers and invigorate their brand vitality through cross-boundary innovation. This paper seeks to address gaps in previous research on cross-boundary innovation among established brands and offers insights into strategies for brand activation. This paper introduces the theory of customer inspiration and investigates how the cross-boundary innovation of established brands influence customer purchase intentions and loyalty through three psychological experiments. Experimental results demonstrate that cross-boundary innovation by established brands can inspire customers. The Inspired-By and Inspired-To mediate the impact of cross-boundary innovation on customer purchase intentions and loyalty, while mental simulation moderates the effect of cross-boundary innovation on customer inspiration. This paper advances our understanding of cross-boundary innovation among established brands, providing practical guidance on brand activation amidst the Chinese trend phenomenon

The COVID-19 pandemic exposed critical vulnerabilities within the Indian healthcare system, marked by insufficient beds, scarce medical supplies, and stark rural-urban disparities PMC+1ResearchGate+1. Despite the rise in public health expenditure from around 1.8% to over 2.1% of GDP between FY2020 21 and FY2022 MDPI, India continues to depend heavily on private healthcare providers, who account for approximately 62% of hospital beds PMCMDPI. Between 2010 and 2021, the number of allopathic doctors increased from 0.83 million to 1.3 million, while the sector employed 4.7 million people by 2021 PMCMDPI. This mixed healthcare landscape, characterized by issues like long wait times in public hospitals and the rising appeal of private facilities, reflects deeper systemic inequities, especially in rural areas PMCMDPI. This study synthesizes key challenges—including infrastructure deficits, workforce shortages, and policy constraints—and explores how public–private coexistence and regional disparities influence healthcare access and outcomes in India. The findings aim to inform strategies for strengthening healthcare infrastructure, enhancing rural access, and ensuring equitable distribution of services.

Using the Scopus database as the main source of scholarly literature, the present study performed a thorough bibliometric analysis looking at how sentiment analysis might be included into Customer Relationship Management (CRM) systems. From 2008 to 2025 the study examined publishing trends, citation networks, and research clusters.

The researchers searched two significant phrases "Sentiment Analysis" AND "Customer Relationship Management" that were employed in ‘all fields’ and whittled down to 989 relevant publications. Through systematic evaluation of bibliometric performance indicators, including co-citation analysis, author collaboration networks, and keyword co-occurrence mapping, this study identifies emerging research themes and recent developments in this domain. Five performance analysis indicators—publications by prolific writers, cumulative publications, affiliations by prestigious universities, contributions from leading publishing companies, and top nations were investigated by the researchers. 201 of the publications came from 2024 most of them. 989 papers in all were added to cumulative publications with great acceleration.

Out of 159 authors, most prolific authors were Chiu, D.K.W.  with 18 documents published in this domain. The University of Hong Kong led with 18 publications. Among various subject areas, Computer Science lead with 599 articles. IEEE Access topped among the different journal sources. Out of 741 cited documents, there were 32,339 citations received with h-index 74. India led with 193 documents among 90 countries. National Natural Science Foundation of China leads with 33, top funding agencies. In scientific mapping, the minimum values for each indicator in the VOSviewer software were determined. Scientific mapping was conducted on author citation, co-citation analysis, bibliographic linking, and most occurring keywords. In co-authorship of 65 countries, India leads in collaboration with other countries. In bibliographic coupling of 2802 organisations, the university of Hongkong led with 11 documents,105 citations. In co-occurrence of author keywords, “sentiment analysis", “machine learning”, “artificial intelligence”, “customer relationship management” were among the top key terms displayed through network visualization. The analysis demonstrates a significant increase in research output since 2008, with particular acceleration in studies focusing on social customer relationship management applications and real-time sentiment analysis integration. Emphasizing the development of sentiment analysis in CRM

Purpose- This study attempts to identify the ethical concerns inherent in an organization which impacts Knowledge Management Strategies (KMS). The primary objective of this paper is to investigate the effect of constructs on ethics over KMS constructs deliberating on various Knowledge Management (KM) levels, knowledge ownership, KM model and KM practices.

Design/methodology/approach- Relevant literature has been reviewed by online database search including ProQuest Central, Emerald, Sage, Business source complete (EBSCO).

Findings- According to the study, ethics gives a business a set of guiding principles for knowledge management strategies (KMS) and makes it easier to execute KM activities effectively for the benefit of workers, organizations, and society as a whole. The successful organizations are those which create and manage new knowledge where ethics plays a key role. The study suggests a conceptual framework linking ethics and KMS.

Research limitations/ implications – This study offers various dimensions for potential research on numerous constructs of ethics and KMS discussed in study. The study also provide platform for future researchers to carry empirical researches and also for developing conceptual model or framework on ethics and KMS.

The study suggests a conceptual framework subject to empirical validation. Also, there is a dearth of literature studying the linkage between ethics and KMS.

Originality/value-The study offers a conceptual approach deliberating the ethical perspective highlighting various ethical constructs impacting KMS

Purpose: This study aims to conduct a bibliometric analysis on Green Banking from the journals in the Scopus data base between 1997 to 2025. The paper covered the past three decades of publication and carried out performance analysis and mapping analysis of articles.

Design/methodology/approach:  The study uses bibliometric analysis. The study examined the scientific productivity of articles, productive authors, citable documents, most relevant institutions, cited countries, co-occurrence of keywords, thematic mapping, co-citations and collaboration of authors and countries. The study used Biblioshiny as a tool to carry out the performance analysis and science mapping analysis.

Findings: The results shows that the number of publications has significantly increased in the past decade, 88.74% authors contributed at least a single article.7.45% of authors published two articles, 1.83% of the authors published three documents, 0.84% of authors contributed four documents. The China, India, and Malaysia were the most productive countries in terms of the total number of citations. Sustainability (Switzerland), Environment, development and sustainable, international journal of ethics & systems and journal of green economics are the top outlets in the green banking literature.

Value: Over the past decade, the research in green banking has exploded because of the growing interest of researchers in this field. The study is more comprehensive in terms of the actors and methods involved in analyzing the scientific production of articles in the area of knowledge management.

This study explores the growing significance of soft skills in today's evolving workplace. With rapid technological advancements, globalization, and shifting professional environments, the criteria for career success have transformed. As the global economy increasingly relies on knowledge-based systems, competencies such as emotional intelligence, adaptability, leadership, and communication are emerging as vital—often surpassing traditional academic qualifications in importance (World Economic Forum, 2025; Dawar, 2023).

The research integrates key theoretical frameworks, including Goleman’s Emotional Intelligence Model, Gardner’s Theory of Multiple Intelligences, and Vygotsky’s Social Constructivism. Together, these form the foundation for a conceptual framework that positions soft skills as meta-competencies—essential for effectively applying academic knowledge in practical, real-world contexts.

Additionally, the study examines global trends and employer expectations, drawing on real-world data from sources such as the World Economic Forum, Coursera’s Global Skills Report, and the QS Global Employer Surveys. The objective is to highlight the growing disconnect between academic instruction and the skill sets employers increasingly demand (Coursera, 2025; QS, 2023).

The study further looks at how well students do in school and how well they use their soft skills.   It shows that tests in school are a great way to check things.   But they usually don't teach you important skills like how to deal with your feelings, make moral decisions, and get along with other people. These traits are becoming more and more important in today's businesses, which are digital-first and have people from many different cultures (Clark, 2024; Abhyankar, 2023).    Finland's school system, Google's Project Oxygen, and India's NEP 2020 all show how to teach soft skills well in schools and on a national level. The paper gives clear suggestions for changing the way schools work, helping people grow, and planning for the future of institutions. It says that soft skills should be taught in schools, tested, and used in the workplace.  This study shows how important it is to link school with getting ready for a job. It calls for a new way to measure success in school and at work. This change is necessary to get workers ready for the future

The emergence of Generation Z (Gen Z) as a dominant consumer group has compelled brands to rethink and reshape their marketing and branding strategies. Born between the mid-1990s and early 2010s, Gen Z exhibits distinct digital behaviors, values authenticity, social responsibility, personalization, and engages heavily with social media platforms. This research paper investigates the evolving branding techniques driven by Gen Z's consumption patterns, psychological drivers, and engagement channels. Through a comprehensive analysis of recent academic and industry literature, the study identifies key shifts in branding paradigms, such as influencer-driven marketing, immersive brand experiences, and value-based storytelling. It explores how brands are leveraging emerging technologies like AI and AR to align with Gen Z’s expectations and preferences. The findings suggest that brands that succeed with Gen Z exhibit adaptability, cultural sensitivity, and a strong digital presence. The paper concludes by proposing a model for Gen Z-centric branding that combines emotional connection, technological innovation, and social relevance

Social Media Marketing (SMM) has transformed customer engagement in terms of effective interaction, brand promotion, and relationship building - providing dynamic forums to connect with customers in real time. This paper addresses the complexities of SMM in the context of customer engagement and seeks to explore the multiplicious antecedents of SMM, the range of both positive and negative implications, in a systematic and analytical manner. On the positive side, SMM provides better engagement opportunities with customers, increase brand loyalty, allows for rapid feedback, and provides brand visibility. Businesses can create emotionally engaged relationships with customers and foster participatory engagement through personalized content, user-generated posts, and influencer collaborations. The SMM experience presents challenges as well. Negative consequences, such as information overload, miscommunication, loss of brand control, and data privacy concerns, can adversely impact brand reputation and consumer trust. In order to develop meaningful engagement, organizations must also balance the increased demands for content and engaged responsiveness on behalf of customer engagement. To solidify the understanding of these challenges and positive implications, the proposed study outlines a Multi-Criteria Decision-Making (MCDM) model for comparing SMM strategies based on outcome engagement, operational costs and risk exposure. The findings demonstrate, while customer-driven and interactive strategies are inherently the best engagement, real-time campaigns must be cognizant of any reputational or ethical concern. The study calls for balanced and data-driven SMM, outlining the need for planning and strategy prior to implementation, and ethical use of any data, ethical consideration of 'real-time', not overshadowed by performance measures system to direct and measure outcomes. For organizations to promote meaningful engagement - within increasingly digitally driven customer engagement environment - this study enhances understanding of how businesses can use social media engagement as a meaningful experience, rather than solely for reach. This study considers future work as employing AI and predictive analytics frameworks for engagement planning and a proactive methodology instead of reactive transactional models

This study delves into the intricate dynamics of consumer behavior towards organic food purchases in Bangalore, employing the Consumer Decision-Making Model to unravel the impact of key determinants: Price, Taste, Environmental Concerns, Nutritional Value, Organic Certification and Labelling, and Subjective Norms. Amidst Bangalore’s burgeoning organic food market, this research seeks to bridge the gap in understanding how these factors collectively shape purchasing decisions. By evaluating the relative importance of each predictor and assessing the model's efficacy in explaining and predicting consumer behavior, the study aims to offer actionable insights for marketers to craft effective strategies tailored to the city’s diverse consumer base. The research methodology involves a comprehensive quantitative survey of 241 organic food consumers, with findings revealing nuanced influences of each variable. The results highlight the critical role of nutritional value and price, while also delineating the practical implications for market strategies. The study addresses its limitations and suggests avenues for future research, contributing to a deeper comprehension of consumer choice mechanisms in the organic food sector

The enactment of the Digital Personal Data Protection Act, 2023 (DPDPA), marks a significant turning point in India’s legal landscape governing the use of personal data, particularly in sectors driven by advanced digital technologies such as e-commerce. This paper critically explores the multifaceted implications of the DPDPA on artificial intelligence (AI)-powered consumer profiling practices, focusing specifically on its intersection with Human Resource (HR) functions within the e-commerce industry. In the current data-driven environment, AI technologies are widely utilized not only for consumer engagement and behavioural prediction but also in HR domains such as automated talent acquisition, employee performance monitoring, predictive workforce analytics, and personalized learning and development pathways. However, the increasing dependence on AI necessitates robust compliance with privacy laws and raises complex legal, ethical, and organizational challenges, particularly with respect to transparency, consent, data minimization, algorithmic fairness, and accountability. The present study adopts a mixed-method empirical approach, combining quantitative surveys and qualitative interviews with HR professionals, legal compliance officers, and technology managers across leading Indian e-commerce firms. The objective is to assess organizational readiness, HR-led compliance strategies, and the extent to which AI applications in HR and consumer profiling are being modified to adhere to the principles and obligations under the DPDPA. The findings reveal considerable variability in awareness, preparedness, and policy execution, with some organizations having embedded privacy-by-design frameworks, while others continue to grapple with legacy systems lacking compliance architecture. Through this empirical lens, the study further examines how HR departments are becoming strategic partners in data governance—mediating between legal obligations, technological innovation, and employee rights. The paper argues that HR plays a pivotal role not just in internal compliance, but also in fostering a culture of ethical AI usage and transparency in data-driven decision-making. Based on the insights derived, the paper proposes actionable recommendations for enhancing HR involvement in data protection training, AI audit mechanisms, consent management systems, and interdepartmental coordination for responsible AI deployment. By positioning HR at the center of the compliance ecosystem, the study underlines the need for a human-centric and legally compliant approach to AI governance in the e-commerce domain.

The integration of Artificial Intelligence (AI) into judicial systems is reshaping legal processes by improving efficiency, reducing human biases, and enabling data-driven decision-making. This paper provides a comprehensive analysis of AI applications in the judiciary, focusing on predictive justice, case management automation, legal research, and ethical implications. Through detailed case studies from the U.S., EU, India, Brazil, and China, we evaluate the effectiveness and challenges of AI in courts. The study employs empirical data, comparative legal analysis, and policy evaluations to assess AI’s role in enhancing judicial transparency and fairness. Recommendations for responsible AI adoption, including bias mitigation and regulatory frameworks, are discussed. The integration of Artificial Intelligence (AI) into judicial systems represents a significant evolution in legal administration, fundamentally transforming traditional processes through enhanced efficiency, objectivity, and data-driven insights. By leveraging advanced computational capabilities, AI applications in the judiciary are addressing long-standing challenges such as case backlog, inconsistent rulings, and resource constraints while simultaneously introducing new considerations regarding fairness, accountability, and ethical governance. This paper presents a comprehensive investigation into the multifaceted role of AI in judicial systems, with particular emphasis on three critical domains: predictive justice systems that analyze historical case data to forecast legal outcomes, automated case management platforms that streamline court operations, and AI-powered legal research tools that augment judicial decision-making with comprehensive precedent analysis. Through an extensive examination of international implementations, including the United States' COMPAS recidivism algorithm, Brazil's Victor AI case management system, India's SUPACE judicial research assistant, China's Smart Court initiative, and the European Union's e-Justice platform, this study provides a comparative assessment of AI's operational effectiveness across diverse legal traditions and institutional frameworks. The research methodology incorporates quantitative analysis of judicial efficiency metrics, qualitative evaluation of ethical implications, and systematic review of policy responses to AI integration in justice administration. Key findings reveal that while AI technologies demonstrate substantial potential to reduce case processing times by up to 40% in some jurisdictions and improve consistency in judicial outcomes, they also present significant challenges including the perpetuation of historical biases, lack of algorithmic transparency, and potential erosion of judicial discretion. The paper further examines contemporary debates surrounding the appropriate balance between technological efficiency and fundamental judicial values, proposing a set of evidence-based policy recommendations for responsible AI adoption. These include the development of standardized bias assessment protocols, implementation of explainable AI frameworks tailored to judicial contexts, and establishment of hybrid decision-making systems that preserve human oversight while benefiting from computational analysis.

Employee growth is an essential component of the success of an organization, particularly in healthcare, where nurses play an essential position in providing high-quality treatment to patients.  The purpose is to investigate the impact that work discipline, work stress, leadership conduct, and work motivation have on employee growth. The study focuses on 385 nurses and uses employee performance as a mediating factor. Quantitative research used structured surveys and statistical models to assess data.  Work discipline and leadership conduct improve employee performance and development, according to the research.  Work stress significantly impacts employee performance and development; hence, stress management is necessary to maintain worker efficiency.  Professional advancement and job happiness were also influenced by work motivation and conduct. These findings emphasize the need for a disciplined, motivated, and well-supported nursing workforce to improve healthcare performance.  Strategic leadership, stress management, and motivation-boosting policies are needed to grow employees and improve performance.

The integration of Artificial Intelligence (AI) into business operations is rapidly transforming the global industrial landscape, and Indian Micro, Small, and Medium Enterprises (MSMEs) are gradually embracing this shift. This paper explores the current state of AI adoption among Indian MSMEs, highlighting its potential to enhance productivity, improve customer engagement, streamline supply chains, and foster innovation. Despite the recognized benefits, the adoption of AI remains limited due to challenges such as high implementation costs, a lack of skilled workforce, limited digital infrastructure, data privacy concerns, and low awareness among small business owners. The paper examines these barriers in the Indian context, supported by empirical data and case studies. Furthermore, it outlines prospects for AI adoption, emphasizing the role of government initiatives, affordable AI solutions, public-private partnerships, and capacity-building programs. The study concludes that while AI adoption among Indian MSMEs is at a nascent stage, targeted support mechanisms and policy reforms could accelerate digital transformation and contribute significantly to India’s economic growth and global competitiveness.

In the rapidly evolving landscape of e-commerce, pricing strategies play a pivotal role in driving customer engagement, maximizing revenue, and maintaining competitiveness. Traditional pricing models often fail to account for real-time market fluctuations and individual customer behavior. This study explores the integration of machine learning (ML) techniques to develop dynamic pricing strategies in e-commerce platforms. By leveraging large-scale data such as customer preferences, purchase history, competitor pricing, inventory levels, and seasonal trends, ML algorithms can identify optimal price points that adjust in real time. The research highlights key machine learning models, including regression analysis, reinforcement learning, and deep learning networks that enable predictive and adaptive pricing mechanisms. It also examines case studies from leading e-commerce firms to demonstrate practical implementations and outcomes. The findings indicate that ML-based dynamic pricing not only enhances profitability but also improves customer satisfaction through personalized and timely pricing. This paper concludes by addressing challenges such as ethical concerns, price discrimination risks, and algorithmic transparency, while proposing guidelines for responsible adoption in digital marketplaces.

The research focuses on inter-regional differences in the decision making of the workforce within India, with the aim of looking at the nature of workplace subjectivity and capabilities in terms of making decisions that involve infrastructure availability and dynamism in the work environment. The research design used was cross-sectional based on theoretical perspective of organizational behavior and data about the Indian workforce. Structured surveys were used to collect information among 360 professionals of five regions in India. The SPSS (v26) and ArcGIS (v10.8) software was used to perform analytical techniques including ANOVA, regression, interaction model and geospatial mapping. In the results, substantial regional variations were also noted with a better score at the decision making in the Western and the Southern as opposed to the Central and the Eastern regions. It was found that decision-making depended not only on workplace dynamism (beta = 0.33, p < 0.001) but also the availability of infrastructure (beta = 0.29, p < 0.01) as regression analysis revealed. Additionally, interaction effects revealed that strong infrastructure enhances positive impact of dynamism at the workplace. The employees in the private sector also recorded a high score when compared to their public residents. These reflections provide the important emphasis on the role that infrastructure and the culture of the organization play in the forming strategic mode of workforce behaviour in terms of optimizing the efficacy of the decisions made, as well as refer to region-specific policies to provide the organizations with enhancements in the efficacy of the decisions made.

This study examines the impact of insufficient recognition and appreciation on employee satisfaction and turnover intentions. Utilizing a dataset comprising 1,200 employees across diverse industries, the research employs Python and Scikit-learn algorithms—specifically, a Random Forest Classifier—alongside Pandas and Seaborn/Matplotlib libraries for data processing and visualization. The model achieved an accuracy of 87%, with key predictors of dissatisfaction identified as recognition frequency, perceived appreciation of team contributions, and intra-team communication. A comparative analysis demonstrated the superior performance of the Random Forest algorithm over Logistic Regression and Support Vector Machines, particularly in handling non-linear relationships and providing interpretable feature importance rankings. The findings underscore ingratitude as a significant determinant of workplace dissatisfaction and attrition risk. The study highlights the organizational imperative of fostering a culture of genuine appreciation, offering actionable insights to improve employee retention and psychological well-being

In an attempt to appeal to a broader market, most companies now offer products symbolically associated with stigmatized social identities, often advertising these products with discounts.  Identity-based promotion and price promotion are usually good strategies, as indicated by earlier research, but the present research indicates that this method can have unintended adverse impacts. Findings from eight studies with various stigmatized groups in India and Asia indicate that if products linked with stigmatized identities are reduced in price, the members of those groups can respond adversely.  Specifically, stigmatized consumers perceive these price cuts as offensive to their social identity, and thus are less inclined to purchase from the firm, more inclined to do so from rivals, and harmed in their attitude towards the firm. Whether the brand is regarded as an ingroup or outgroup member, the choice of other value products, and the promotional strategy are some of the factors that fuel such negative feelings, which are not observed among non-stigmatized consumers. The social risks of price-based promotions in stigmatized settings are brought to the fore by this research, which adds to our knowledge of stigmatized customers' interpretation of marketing cues as well

To improve company entrepreneurial orientation towards micro, small, and medium entrepreneurs, the purpose of this study is to experimentally investigate an integrative moderated mediated mechanism that uses entrepreneurial competency as a mediator. Data were acquired by a structured questionnaire from a sample of 300 MSME owners in Rajasthan, India. Structural equation modeling was used to examine the hypotheses. The results demonstrated that the entrepreneurial-oriented features of innovation, proactivity, and risk-taking improve the firm's marketing performance. Entrepreneurial competencies mediate the impacts of entrepreneurial orientation dimensions on the performance of an organization. There was variation in the mediation effects of entrepreneurial competencies between responsiveness levels that were high and low. To beat the competition and seize market opportunities, the company must precisely match what it offers to the needs and desires of the customer. SMEs in India may increase brand value, manage their supply chains more effectively, and improve quality using entrepreneurial skills

This paper aims to analyze the theoretical foundations of sustainability reporting, including its historical evolution and integration with corporate social responsibility (CSR). It seeks to evaluate key global reporting frameworks (e.g., GRI, SASB, TCFD) and assess their adoption trends across industries and regions and examine the regulatory landscape, contrasting voluntary ESG disclosures with mandatory compliance requirements. Further, this paper wants to assess the impact of technology, such as automation, blockchain, and AI, in enhancing reporting accuracy and transparency. Lastly, it tries to identify key challenges in sustainability reporting, including greenwashing, data inconsistencies, and compliance costs. Some case studies illustrating best practices, corporate ESG successes, and reporting failures are discussed. Finally, it proposes recommendations for standardising and improving sustainability reporting globally.

Over the years, development of mindfulness theory in consumer behavior and decision-making have garnered attention of researchers and academician. Mindfulness indicates heightened state of awareness and self-regulation advocating to be mindful of individual, social and ecological well-being. Mindfulness enforces consumer and company to co-exist in harmony and work on value co-creation with sustainable practices. The objective of the research is to analytically review the extant literature on mindfulness, to determine the gaps in literature, highlight topics or themes for future studies, and establish a research agenda. To perform review (SLR) the framework incorporated for efficient and effective review (Theories, Contexts, Characteristics and Methodology) is popularly known as TCCM Framework. In the review, upon analysis of extant literature we mentioned the findings to comprehend the theory development, differentiate the characteristics and contexts depending on the applied framework. The review findings highlight that studies on mindfulness are limited to developed economies and certain sectors. Mindfulness is a complex process and its relationship with behavioral variables relationship demand more clarity and comprehension

The present study explores the association between employees' perceptions about their organizational culture and corporate reputation as precursors of employee engagement and organizational cynicism, more so, as employees’ perceptions of organizational culture interact with the extent of their person-organization fit and when all such linkages are mediated by organizational trust.  For this purpose, a literature survey was carried out and the pertinent hypotheses were formulated which were then depicted in a path model.  783 Indian managers formed the study sample and provided responses to examine the hypotheses and the path model using several quantitative methods including structural equation modeling techniques.  The hypotheses and the proposed path model were accepted as per the results obtained.  Following this, the implications of the study were discussed.  Finally, the limitations and future research scopes were enumerated 

Artificial Intelligence (AI)-driven Decision Support Systems (AI-DSS) have emerged as transformative tools in enhancing decision-making accuracy, operational efficiency, and innovation capacity across industries. This paper investigates the role and adoption of AI-DSS among startups and Micro, Small, and Medium Enterprises (MSMEs), with a particular emphasis on the fashion sector as a representative case. Through a qualitative methodology involving expert interviews and case analysis, the study identifies the extent of AI-DSS adoption, the key technical and organizational barriers, and the strategic outcomes realized by early adopters. Findings reveal that while startups demonstrate moderate to advanced usage of AI tools particularly for demand forecasting, personalization, and product design MSMEs often struggle with infrastructural limitations, lack of digital readiness, and cost concerns. The paper also highlights measurable improvements in decision speed, forecasting accuracy, and innovation output among AI-adopting firms. A modular and scalable AI-DSS framework is proposed to enable gradual integration, starting from data visualization to advanced predictive modeling. This research contributes to closing the AI readiness gap and suggests practical strategies to promote inclusive digital transformation in small-scale enterprises 

The explosive growth of e-commerce in previous years has brought unprecedented convenience and global business scope to online trading and at the same time increased the potential of online transaction fraud. Since cybercriminals exploit the vulnerabilities preprogrammed in the digital payment systems, online commerce platforms are facing mounting pressures to build end-to-end cybersecurity frameworks that are designed to protect valuable consumer information and financial integrity. The current research examines the efficiency of three commonly used cybersecurity frameworks, namely, NIST Cybersecurity Framework, ISO/IEC 27001, and PCI DSS, in averting online transaction fraud in the e-commerce spheres. By analyzing fraud-incidence reports, compliance-assessment records, and published breach case studies, this study discovers the relationships between the maturity of framework implementation and the effectiveness of fraud mitigation. A comparative analysis of three leading e-commerce platforms is also performed to compare the speed of threat detection, accuracy of response, and the level of the success of authentication on transaction levels. The empirical evidence shows that a multi-layered, compliance-based security architecture impacts positively on the likelihood and consequence of online fraud. The paper ends with the recommendation of an adaptive, risk-based model of cybersecurity integration that is unique to the real-time e-commerce environment 

Since the boom in e-commerce, there has been a booming interest in the potential of immersive and interactive technologies as a means to fill the sensory gap that exists between the real-life shopping and online buying experiences. Augmented Reality (AR) has become a disruptive technology which is used to add digital content to the natural environment and provides online shoppers with a more realistic, interesting and informed decision-making process. In this paper, the author will address how AR transforms online shopping experience by improving product visibility, increasing consumer trust and minimizing unwanted product returns. As a result of literature analysis and current AR-based shopping interface applications on the range of e-commerce platforms, the study examines the psychological and behavioral effects of utilizing the AR-based shopping interface. The increasing significance of the technology within the contemporary retail strategies can be proven by a comparative analysis of user experience, conversion rates, and the levels of user satisfaction before and after the implementation of the AR technology. The results indicate that there is a major potential of AR as defining online consumer engagement and it provides competitive edge to retailers that embraced it early 

As the digital ad business grows, constant issues such as click fraud rate manipulation and the even more engrained opaque supply chains undermine transparency and fraud prevention that must be solved in the growing world of Audiences. In this study we have tried to analyze how smart contracts and blockchain technology framework to overcome these inefficiencies could be a verifiable decentralized automated. With secondary data, combined with case studies and industry analysis we explored the promise of distributed ledgers and automated transactions as a way to increase trust by removing middlemen to reduce advertising fraud. This paper is a comprehensive review of the literature, defines research gaps and offers a conceptual design based on smart contract logic blockchain architecture using oracles for data verification. Ad fraud trends and Blockchain Market Prognoses R statistical modeling with forecasts based on time series (ARIMA, ETS). Analysis of relative metrics based on comparisons in table format clearly reveal the vast improvements with ROI and cost-efficiency click-through rates and reduction in fraud for blockchain-based advertising systems on a system Image optimization inches Adex & Brave as studied real-world use cases Nevertheless the secondary database of this paper’s findings are limited in generalizability, but the results do demonstrate how blockchain can change digital advertising to 

The intersection of financial literacy and educational technology (EdTech) presents a transformative opportunity to address the persistent global gaps in financial knowledge, behavior, and decision-making. With increasing digital access and rising financial complexity, conventional models of financial education often fall short in engagement, personalization, and long-term impact. This paper proposes a conceptual framework for adaptive learning platforms designed to enhance financial literacy through personalized, data-driven pedagogical strategies. Drawing on theories of financial capability, behavioral economics, and adaptive learning design, the model integrates learner profiling, algorithmic content delivery, gamification, and real-time feedback mechanisms. The framework highlights how EdTech can be leveraged to move beyond static content delivery to foster behavioral change, contextual relevance, and financial decision-making skills in diverse demographic groups. This paper contributes to the literature by offering a scalable, learner-centric approach to digital financial education and by setting an agenda for empirical validation across socioeconomic and cultural contexts. Implications for policymakers, educators, and EdTech innovators are discussed, with recommendations for ethical design and digital inclusion..

In the evolving landscape of human resource (HR) management, emotional intelligence (EI) has emerged as a critical determinant of organizational success. As modern workplaces grapple with diversity, remote work and increasing demands for psychological safety, HR professionals must not only manage policies and processes but also foster emotionally intelligent cultures. This paper explores the multifaceted role of EI in contemporary HR practices, emphasizing its impact on leadership development, talent acquisition, employee engagement, conflict resolution and performance management. Drawing on recent empirical studies, the research highlights how integrating EI into HR strategies leads to better interpersonal relationships, enhanced team performance and improved organizational climate. Furthermore, the paper examines how training interventions and EI-based assessments are transforming the traditional functions of HR. This study underscores the necessity of cultivating emotional intelligence at both individual and organizational levels to adapt to dynamic workforce challenges and sustain competitive advantage

In the digital age, traditional teaching methods often struggle to maintain student interest and motivation. This research explores how gamified learning, when integrated with Information and Communication Technology (ICT), transforms student engagement and fosters active participation in the 21st-century classroom. Gamification refers to the application of game design elements—such as points, levels, leaderboards, and rewards—in non-game educational contexts. When combined with ICT tools like interactive platforms, mobile applications, and virtual environments, gamification creates immersive and personalized learning experiences.

This study examines the pedagogical impact of gamified ICT on student engagement across diverse age groups and educational settings. Through a mixed-methods approach involving classroom observations, teacher interviews, and student surveys, the research evaluates cognitive, behavioral, and emotional dimensions of engagement. Findings reveal that students exposed to gamified learning environments demonstrate higher motivation, improved collaboration, and better retention of content compared to those in conventional settings.

Moreover, the paper highlights specific ICT tools—such as Kahoot!, Classcraft, Quizizz, and Minecraft: Education Edition—that educators use to implement game-based strategies effectively. It also addresses challenges such as over-competition, digital fatigue, and accessibility concerns, offering strategies for balanced integration.

By aligning gamification with pedagogical goals and curriculum standards, educators can harness the motivational power of games without compromising academic rigor. The research concludes that gamified learning, supported by ICT, is a powerful catalyst for increasing student engagement, promoting digital literacy, and preparing learners for a technology-rich future. This paper contributes to the growing discourse on educational innovation, offering insights and best practices for teachers, curriculum designers, and policymakers aiming to modernize learning environments

In today’s competitive and dynamic organizational environment, human capital plays a pivotal role in ensuring sustainable performance and growth. This study investigates the impact of Human Resources (HR) audits and employee engagement on employee performance within corporate organizations. HR audits, when systematically executed, ensure that HR policies, practices, and strategies align with organizational goals and legal compliance, creating an enabling environment for performance enhancement. Simultaneously, employee engagement—reflecting the emotional commitment and involvement of employees—has been widely acknowledged as a key driver of individual and organizational productivity. The research adopts a mixed-methods approach combining structured questionnaires and semi-structured interviews, targeting employees and HR professionals across various sectors. Quantitative data were analyzed using regression and path analysis techniques, while qualitative insights supplemented contextual understanding. Findings reveal that both HR audits and employee engagement significantly and positively influence employee performance, with engagement serving as a partial mediator in the relationship between HR audits and performance outcomes. The study underscores the necessity of institutionalizing regular HR audits alongside cultivating a culture of engagement to foster optimal employee outcomes. Practical implications for HR managers, policymakers, and organizational leaders are discussed, along with directions for future research in this domain.

The rapid proliferation of mobile technology has revolutionized access to digital platforms, enabling billions in emerging markets to participate in the global economy for the first time. This research paper explores marketing strategies tailored to this “next billion” – a demographic primarily residing in regions such as Sub-Saharan Africa, South Asia, Southeast Asia, and Latin America. These populations are increasingly mobile-first, young, and digitally curious, yet remain economically diverse and culturally complex.

Drawing on case studies, market reports, and consumer behavior analyses, this paper investigates how brands are rethinking product design, pricing models, distribution channels, and engagement strategies to suit mobile-first environments. It highlights the importance of hyper-localization, vernacular content, and mobile payment integration as key enablers for market penetration. Furthermore, the study examines the role of social commerce, influencer marketing, and digital micro-entrepreneurship in shaping consumer ecosystems within these markets.

The paper also addresses challenges including infrastructural limitations, digital literacy gaps, and trust deficits in digital transactions. A strategic framework is proposed to guide marketers in balancing innovation with cultural relevance, affordability, and accessibility. The research concludes that success in reaching the next billion consumers hinges not only on technological adoption but also on inclusive, empathetic, and context-aware marketing approaches.

This study contributes to the growing body of knowledge on emerging market dynamics in the digital era and offers actionable insights for marketers, entrepreneurs, and policymakers seeking sustainable growth in underserved regions. By understanding and adapting to the unique behaviors and aspirations of these mobile-first consumers, organizations can unlock transformative opportunities and drive inclusive economic participation on a global scale..

The animation industry across the globe is making a rapid growth and technological advancements, particularly with the induction of AI and VR/AR integrations that are becoming more and more prominent. The leading trends include collaboration of 2D and 3D elements of movies with AI-powered tools that helps in streamlining the production, and animated data visualizations. The proposed work is an in-depth business intelligence case study of the financial and creative triumph of Picxar Animation Studios as the most powerful and most influential motion picture animation giant in the world. The research leverages the capabilities of Advanced Microsoft Excel and Power BI together to reveal and compare multi-faceted information in Picxar's whole body of work—from production cost and global box office receipts to critical acceptance and viewers' ratings.

Analysis comes from an IMDb, Rotten Tomatoes, and global box office value dataset. The ultimate objective is to show how data-driven decision-making can maximize strategic planning for the entertainment sector, historically based on imagination and instinct. This case study is unique in that it combines the artistic process of filmmaking with the analytical weight of business intelligence. It shows how Excel and Power BI can establish profitability patterns, compute return on investment (ROI), ascertain audience preference over time, and in the process, create smart information for studios, analysts, advertisers, and content planners. In its very nature, this study not only examines past trends, but also provides a foundation for scenario planning and forward projection in media economics.

The findings show Picxar's ongoing ability to sustain narrative complexity and profitability and illustrate how thinking quantitatively and visualizing can be applied to guide real-world decisions in high-end narrative economies. The approach employed here scales up to large-scale settings, is portable across a range of disciplines, and has high applicability for data analysts, business decision-makers, and researchers operating at the technology-narrative-business interface..

This study sets out to explore how budget planning and control affect firm performance in HCMC, with the budget process as a mediator. Qualitative review of related literature has been used to bring to the fore the interaction between budget planning and budget control, informed by relevant works. It approaches the goals aspired to by all stakeholders in the budgeting process and how they affect firm performance. Analyses revealed that good budget planning and control are positively related to the budget process and that, in turn, it significantly affects the overall performance of firms in HCMC. The study identified and described very critical specific essential factors under the budgeting framework concerning performance improvements, reemphasizing the importance of strategic budget management in organizational goal attainment. Findings will provide valuable insights to the implementers and the policymakers of the enterprises in HCMC to earn better financial outcomes by enhancing budgetary practice. The study proved that in an overview integrating a very elaborate budgeting planning and control mechanism, the requirement would be to ensure the effectiveness of each factor in the process of budgeting; therefore, efficiency is embedded in the overall performance of an organization. The study contributes to the extant literature by unveiling the detailed interrelations between budgeting elements and firm performance in Vietnamese companies. It, therefore, provides a basis for further research in exploring more intricately the dynamics of budgeting processes and their implications for organizational effectiveness.

Purpose – This study explores the impact of hybrid work models (HWMs) on employee engagement (EE) and organizational agility (OA), focusing on how these work arrangements influence engagement and agility within the IT sector.

Design/methodology/approach – Data was collected from 600 employees working in IT companies, and the relationships between HWMs, EE, and OA were analyzed using Structural Equation Modeling (SEM). This approach provided a robust assessment of the hypothesized pathways linking hybrid work arrangements to employee and organizational outcomes.

Findings – The results indicate that HWMs significantly enhance EE by fostering greater flexibility, autonomy, and work-life balance, which contribute to higher motivation and commitment. HWMs also positively influence OA, enabling organizations to adapt swiftly to changes, drive innovation, and facilitate agile decision-making. The statistical model exhibited strong fit indices, supporting the hypothesized relationships.

Practical implications – The findings highlight the value of adopting HWMs as a strategic tool to boost employee engagement and organizational agility. Managers are encouraged to implement flexible work arrangements that provide autonomy, improve work-life balance, and foster a culture of innovation and responsiveness to market dynamics.

Originality/value – This study adds to the growing literature on HWMs by providing empirical evidence of their dual impact on EE and OA, offering practical insights into optimizing hybrid work practices for improved organizational performance within the IT sector

In an era where algorithms read emotions faster than humans blink, brand success is no longer dictated by price, product, or promotion alone—it hinges on empathy. This study explores the emergence of Consumer Empathy Management (CEM) as a critical pillar in contemporary brand strategy, particularly within the high-stakes context of emotional commerce. As digital interactions increasingly replace physical ones, brands are under pressure to humanize their touchpoints, decode affective cues, and design emotionally responsive experiences. This paper presents an empirically grounded framework that integrates affective computing, sentiment mapping, and real-time empathy analytics to examine how consumer empathy can be understood, measured, and managed at scale. Drawing upon primary data collected from 200 participants interacting with branded emotional stimuli within a controlled virtual environment, the study employs sentiment analysis, engagement tracking, and Structural Equation Modeling (SEM) to explore the relationship between empathy perception, emotional resonance, and brand trust. A novel construct—Empathy Responsiveness Index (ERI)—is introduced to quantify the emotional attentiveness of brands as perceived by consumers. The findings reveal that emotionally aligned brand messaging significantly enhances customer trust, loyalty, and digital word-of-mouth. Moreover, the results underscore that empathy perception mediates the impact of emotional stimuli on consumer behavioral intention, offering new theoretical insight into the psychology of digital commerce. This paper also contributes to practice by introducing visual diagnostic tools—empathy heatmaps and sentiment silhouettes—to help brand managers interpret emotional alignment in real time. The implications span strategic branding, AI-driven experience design, and ethical digital engagement. Ultimately, the research advocates for a paradigm shift: from audience segmentation to empathy segmentation, from personalization to humanization. In the age of emotional commerce, those brands that learn to listen not just to what consumers say, but to what they feel, will define the next generation of customer relationships

Women entrepreneurs are reshaping global economies, driving innovation, and fostering inclusive growth—yet their journey remains fraught with challenges. Despite their rising presence in entrepreneurship, women often grapple with systemic barriers such as restricted access to funding, entrenched gender biases, cultural constraints, and the dual pressures of professional and domestic responsibilities. These hurdles disproportionately affect their ability to launch and expand businesses, particularly in male-dominated sectors and emerging markets. Nevertheless, the landscape is evolving. Digital advancements, policy reforms, and stronger support networks are creating new opportunities for women to thrive in business. This paper delves into the persistent obstacles women entrepreneurs face, from financial exclusion to societal expectations that limit their growth. At the same time, it highlights emerging enablers—technology, mentorship programs, and shifting investor mindsets—that are helping women overcome these barriers. Real-world examples illustrate how women-led enterprises are turning challenges into successes through resilience and strategic adaptation. Looking forward, the potential for women entrepreneurship is immense. As economies increasingly recognize the value of diverse leadership, there is a growing push for equitable access to capital, education, and networks. However, realizing this potential requires concerted efforts from policymakers, financial institutions, and the broader business community. By dismantling structural inequities and fostering supportive ecosystems, societies can unlock the full economic and social benefits of women-led ventures. This study not only maps the current terrain of women entrepreneurship but also proposes practical steps to accelerate progress. Its insights aim to inform stakeholders—governments, investors, and advocacy groups—on how to cultivate a more inclusive entrepreneurial future

Advances in Artificial Intelligence (AI) have made it the new frontier for recruitment practices by bringing in automation, data-enabled Decision Making Systems (DMP), and predictive analytics into the Recruitment Process (RP). Most traditional PR tends to be lengthy, biased, or inefficient. With an increasing demand for efficient and unbiased recruitment, AI-driven Talent Acquisition Service (TAS) solutions have found their place. This research explains the AI-powered recruitment framework called AI-assisted Recruitment Framework (AI-RF), which includes automated resume screening, chatbot-driven applicant appointment, predictive analytics, AI-powered interview analysis, and candidate bias mitigation. The research implements the framework using an internally developed publicly available dataset. To automate and enhance various stages of the RP, the research work uses Machine Learning (ML) and Deep Learning (DL) models as Modified Bidirectional Encoder Representations from Transformers (BERT), Convolutional Neural Network (CNN), and Random Forest (RF). The proposed model AI-RF is evaluated using the performance metrics precision, recall, F1-score, and bias reduction percentage. The results suggest that the AI-driven RP improves screening accuracy by 92%, response time reduced by 70%, improves job matching accuracy by 30%, and mitigates hiring biases by 85%. The comparison shows that DL outperforms other models in terms of efficiency and fairness in hiring. The AI-powered RP can enhance human bias in DMS and improve talent acquisition efficiency

This research investigates the relationship between Islamic fintech, Islamic financial literacy, and religiosity and their impact on sustainable Islamic financial transactions within the Islamic banking context in Saudi Arabia. Employing a quantitative research approach, the study focuses on customers of Saudi Islamic Banks in Riyadh. The findings reveal nuanced insights. Firstly, Sharia financial literacy is determined to have no direct influence on Sharia financial transactions in Islamic banks in Saudi Arabia. Secondly, religiosity is found to have no direct effect on Sharia financial transactions. Thirdly, Islamic financial literacy significantly influences Islamic financial transactions, while religiosity does not exhibit a direct impact on Islamic fintech. Moreover, Islamic fintech is identified as a significant driver of Sharia financial transactions. Intriguingly, it is established that Islamic fintech mediates the relationship between Sharia financial literacy and financial transactions. However, no such mediating effect is observed in the case of religiosity influencing Sharia financial transactions. These results contribute to the growing body of literature on sustainable Islamic banking, clarifying the complex dynamics between Islamic Fintech, financial literacy, and religiosity as they influence the conduct of financial transactions within the context of Saudi Islamic banks in Saudi Arabia

This study investigates the behavior of Islamic microfinance institutions with a focus on identifying their behavioral patterns, the factors that influence them, and their impact on competitive strategies. We adopted a quantitative research approach, combining descriptive and verificative analyses. We collected data through surveys and used Partial Least Squares (PLS) analysis to validate relationships between variables. The results indicate that islamic predominantly adopt Sharia-compliant-compliant strategies, emphasizing appropriate Islamic financial products and marketing techniques. Furthermore, positive organizational behavior fosters dynamic capabilities, which serve as a critical mediator between institutional behavior and competitive strategies. The study underscores the need for continuous strategic updates to maintain competitiveness in the evolving market, with dynamic capabilities playing a pivotal role in these adaptations. These findings enhance the understanding of behavioral dynamics in the sharia microfinance sector and offer significant implications for strategic decision-making and policy development. The research provides novel insights by uncovering the dynamic relationship between islamic behavior and strategic approaches within the context of the Islamic microfinance industry

The increasing materiality of climate risks and the growing emphasis on sustainable finance have prompted regulatory bodies worldwide to mandate enhanced environmental, social, and governance (ESG) disclosures. In India, the Securities and Exchange Board of India (SEBI) introduced the Business Responsibility and Sustainability Reporting (BRSR) framework in 2021 as a structured approach to corporate ESG reporting. By aligning with global sustainability standards such as the Task Force on Climate-related Financial Disclosures (TCFD), Global Reporting Initiative (GRI), and Sustainability Accounting Standards Board (SASB), BRSR aims to elevate corporate accountability, facilitate informed investor decision-making, and strengthen India’s position in global capital markets.

This study critically examines BRSR’s regulatory intent, corporate implications, and financial impact while addressing key challenges in data standardization, compliance costs, and greenwashing risks. Through an extensive review of policy documents, academic literature, and case studies of large-cap and mid-cap corporations, we evaluate the framework’s effectiveness in integrating climate risk considerations, ESG governance structures, and sustainable investment strategies into corporate decision-making. Our findings highlight that while BRSR enhances ESG transparency and investor confidence, gaps remain in corporate ESG literacy, sector-specific reporting mandates, and third-party assurance mechanisms, which hinder full implementation.

By providing policy recommendations—including ESG capacity-building, regulatory refinements, and technology-enabled reporting solutions—this study underscores the need for a more robust, scalable, and globally harmonized ESG disclosure framework. As India transitions toward a low-carbon economy, BRSR serves as a critical catalyst for sustainable finance, risk-adjusted capital allocation, and responsible corporate governance, reinforcing its pivotal role in shaping the future of financial markets in emerging economies

Workplace ostracism (WOS) significantly impacts employees’ job performance (IWP). This study examines the mediating role of perceived organizational support (POS) and the moderating role of personality inventory (PI) in the context of the Indian information technology (IT) sector. Data were gathered from 369 IT professionals in southern India using a structured survey with established measurement scales. The pyPLS Python library facilitated Partial Least Squares Structural Equation Modeling (PLS-SEM), allowing an in-depth analysis of the hypothesized relationships, including mediation and moderation effects. However, the effect size is negligible, and its practical significance warrants careful consideration. Managerial interventions such as promoting inclusive work cultures and resilience-building programs can help sustain employee outcomes despite adverse interpersonal dynamics. This research contributes to workplace ostracism literature by elucidating the roles of perceived organizational support and personality in determining job performance. It provides new insights into these dynamics in the IT sector, ensuring methodological robustness through PLS-SEM analysis and visualization.

Artificial Intelligence (AI) is transforming the retail landscape by enabling more efficient and sustainable shopping experiences across multiple channels. As consumers become increasingly conscious of environmental issues, AI technologies, such as personalized product recommendations, chatbots, automated inventory systems, and smart supply chains are helping retailers deliver eco-friendly solutions across online, mobile, and in-store platforms. This study explores how AI influences consumer attitudes toward sustainability within omnichannel retailing and investigates whether these technologies can effectively promote responsible consumer behavior. Based on primary survey data collected from 220 consumers who regularly engage with omnichannel retail platforms, the study examines how awareness and interaction with AI features affect perceptions of sustainability and purchasing choices. The findings reveal a positive relationship between consumer awareness of AI technologies and their likelihood of making sustainable shopping decisions. Consumers who recognize the role of AI in enhancing convenience, transparency, and eco-conscious options are more inclined to support and engage with sustainable products and practices. The study identifies key factors that drive consumer engagement with AI-enabled sustainability, including personalization, ease of use, and consistent sustainability messaging. Retailers that integrate AI into their sustainability strategies rather than using it in isolation are more successful in influencing consumer behavior positively. However, the effectiveness of AI depends on how well it is aligned with broader sustainability goals and communicated clearly to consumers. AI holds significant potential to support sustainable consumer behavior in omnichannel retailing. This research offers actionable insights for retailers aiming to enhance their sustainability efforts by leveraging AI-driven technologies. It contributes to ongoing discussions around ethical technology use and sustainable retailing, providing practical guidance for aligning digital innovation with environmental responsibility

A comprehensive bibliometric analysis of gratitude-related literature indexed in the Web of Science (WoS) core collection was undertaken, encompassing 2,982 scholarly documents published between 2009 - 2024 that featured the term "gratitude" in their titles, abstracts, or keywords. The dataset was systematically examined using WoS's native analytical tools and VOSviewer (version 1.6.16) to identify publication trends, citation networks, and collaborative patterns, revealing the United States, Australia, and the United Kingdom as the most productive countries, while highlighting key contributors and influential institutions. Through keyword co-occurrence and thematic mapping, five distinct research clusters were delineated: (1) evolutionary and biological perspectives on gratitude, (2) psychophysiological correlates of gratitude and wellbeing, (3) gratitude interventions within positive psychology frameworks, (4) developmental trajectories of gratitude in youth populations, and (5) mechanistic studies examining mediators and moderators of gratitude's effects. The analysis revealed a predominant Western bias in theoretical frameworks and methodological approaches, with particular emphasis on individualistic conceptualizations of gratitude, while simultaneously underscoring significant gaps in cross-cultural research, particularly from Global South perspectives and Indigenous Knowledge Systems. These findings suggest the necessity for future research to incorporate decolonial methodologies, culturally-situated epistemologies, and interdisciplinary approaches that acknowledge gratitude as a biocultural phenomenon, thereby expanding beyond the current constraints of Eurocentric positive psychology paradigms to develop more inclusive and holistic understandings of gratitude across diverse sociocultural contexts.

In Commercial Agreement, the fundamental principle is that competent parties must make agreements without any compulsion or hindrance. If one party places unreasonable pressure on another, the integrity of contractual (Free consent) is compromised. Commercial organizations frequently experience economic pressure due to the power disparity between large corporations and smaller businesses. Economic pressure is still a major concern in modern business. When there is market hegemony, supply chain dependencies, and financial pressures leading to power imbalances, large corporations may apply economic pressure to impose unfavorable conditions on suppliers, employees, or competitors. Governments and regulatory bodies have implemented laws and policies to prevent abusive contractual practices in response. The Doctrine of Economic Duress is not specifically mentioned in the Indian Contract Act, of 1872, however, its ingredients come under the purview of Section 15 (coercion) and Section 16 (undue influence) of the Indian Contract Act, 1872. In this article the researcher want to analysis further shall delve into explore the evolution and effect of “economic coercion” in Commercial Law and the Indian judiciary’s role in protecting the interests of the aggrieved party by upholding their legal rights

Sustainability is a current imperative for businesses. Using a systematic literature review based approach, this research synthesizes and organizes the extant literature on consumer sustainable retail product decisions, specifically regarding environmental concerns followed by a conceptual framework of antecedents, determinants and outcomes (ADO). Four themes emerged in the first part; environmental concerns, marketing concerns, technology and pricing that gave insights. Environment related knowledge & concerns, societal norms, value orientation, health & hygiene, packaging & certification, service quality, trust, perceived risk etc are parameters that could impact consumer’s purchase intention, willingness to pay and loyalty as identified through the ADO framework

In the recent past facial forgery had been widespread and for this there is no any requirement of possessing technical skills. Nowadays due to the development of Generative Adversarial Networks & diffusion models (DMs), generation of quality of deepfake has been intensified. Audio or video data if exists as it doesn’t create any problems. In this digital world manipulating although easy, such data creates a lot of problems. They often lead to theft of identity, misinformation and cybercrime in various formats. The resultant technology of manipulating audio or video data using advanced technologies in other words known as Deep fake technology and its rise created a provision to new frontiers in current digital world. Although the advancement offers potentiality on the positive edge, also there are significant risks to the security as well integrity of the data & information. In one perspective, Deepfakes due to their malicious applications in order to attain political, economic and social reputation goals, the technology is becoming quite dishonourable. In this paper, deep fake image classification has been performed on a state-of-the-art dataset by employing transfer learning and ensemble models. Initially, image classification has been performed using Inception V3 model with variants of batch sizes 16, 32 and 64; subsequently Inception V4 is employed due to which the performance has been improved significantly...

The death rates of people in the global community are increasing due to deadly diseases, unpredictable type is cardiovascular disease. The datasets come with heterogeneous data like such as structured table format, and unstructured data, meaning image types. Before initiating the pre-processing pipeline, the issues to be solved are noise, class imbalance, and missing values. To overcome these, a hybrid approach such as a combination of CNN and FNN is used. In these, FNN uses embedded imputation, feature normalization, and CNN is applied by using adaptive spatial pooling to address resolution variability in the images. The cross-model attention layer is defined for fusing the features from both modalities. The issues, such as class imbalance, are overcome using synthetic minority oversampling called SMOTE as part of the data preprocessing pipeline, as well as missing data is overcome by using dropout augmented training. This model outperforms specific machine learning models and unimodal deep learning models. This model ensures a scalable, efficient solution for CVD prediction early, in personalized clinical care. The multi-modal fusion support, better interpretability with adjusting attention weights, may highlight key factors, and robustness to missing data with imputation and dropout augmentation.

Numerous digital marketers remain unaware of the pivotal importance of incorporating artificial intelligence (AI) applications, while others grapple with how to effectively implement them. In the realm of digital and the social media marketing worldwide, AI currently functions as a significant disruptor. This paper's objective is to delve into specific AI applications like Chatbots, Semantic Search, and Web development, which can empower professionals within social media and the digital advertising agencies to attain advanced specialization without compromising their collaborative and creative capabilities. This dual approach is poised to yield a heightened return on investment. Consequently, this article possesses the potential to serve as an invaluable resource for the social media marketers aiming to adopt AI technologies to enhance the impact of their digital marketing strategies.

The emergence of “quiet quitting” marks a significant shift in employee behavior, wherein individuals consciously limit their work contributions to strictly defined job roles, avoiding discretionary efforts or extra-role engagements. Unlike formal resignations, quiet quitting reflects a form of psychological disengagement that is more difficult to detect yet highly detrimental to organizational functioning. This conceptual paper explores the quiet quitting phenomenon through the lens of Organizational Citizenship Behavior (OCB), which encompasses voluntary, non-rewarded behaviors that contribute to organizational effectiveness, such as helping colleagues, showing initiative, or exhibiting organizational loyalty.

We argue that the rise in quiet quitting signals a withdrawal of OCB and propose that this behavior is influenced by a range of antecedents, including perceived organizational support, fairness perceptions, psychological contract breach, job satisfaction, and leadership style. Drawing on foundational theories such as Social Exchange Theory, Self-Determination Theory, and Job Demands-Resources (JD-R) Model, we synthesize existing literature to build a conceptual framework that explains how workplace environments may inadvertently foster disengagement and OCB decline.

The paper offers practical insights for human resource professionals and leaders by emphasizing the need for inclusive work cultures, authentic leadership, and strategic interventions to revive employee engagement and discretionary behaviors. By reconceptualizing quiet quitting as an OCB withdrawal, this study contributes to contemporary discourse on employee behavior and provides a foundation for future empirical investigations in organizational psychology and human resource management

The integration of Artificial Intelligence in human resources has introduced new dimensions to employee service delivery, with chatbots emerging as prominent virtual HR assistants. This study examines the effectiveness of chatbots in enhancing employee satisfaction and the efficiency of HR query resolution. Drawing on empirical data collected from mid-sized enterprises that have deployed AI-powered HR chatbots, the research investigates two primary outcomes: the perceived responsiveness of chatbot systems and their impact on overall employee engagement.

Using a mixed-methods approach, we combined quantitative surveys from 300 employees with qualitative interviews from HR professionals and chatbot users. Results indicate that chatbots significantly improve response time for routine HR queries such as leave balances, payroll status, and policy clarifications. Employees reported increased convenience and 24/7 access to HR services, contributing to improved satisfaction levels, particularly among remote and hybrid workers.

However, the study also identifies limitations. Complex or emotionally nuanced issues—such as workplace conflict or performance concerns—remain better addressed by human HR personnel. Despite this, most users favored a hybrid model where chatbots handle repetitive, transactional tasks, while HR staff focus on strategic and interpersonal roles.

The findings suggest that well-designed HR chatbots can streamline operations and elevate the employee experience, provided they are implemented with clear boundaries and supported by human oversight. Organizations are encouraged to invest in chatbot training data, user experience design, and continuous feedback mechanisms to optimize outcomes.

This research contributes to the growing discourse on AI in the workplace, offering practical insights for HR leaders seeking to balance technology integration with human-centric service delivery.

The rapid integration of AI-powered recommendation engines into e-commerce platforms has transformed how consumers interact with digital content and make purchase decisions. This study explores the behavioral and psychological mechanisms underpinning consumer responses to AI-generated product suggestions, emphasizing the roles of perceived personalization, algorithmic trust, and perceived intrusiveness. Anchored in dual-process theory, the research employed a cross-sectional design with 430 online shoppers, analyzed through PLS-SEM and multiple regression techniques.

Findings indicate that personalization (β = 0.381, p < 0.001) and trust (β = 0.276, p < 0.001) exert significant positive effects on purchase intention. Conversely, perceived intrusiveness has a negative impact (β = –0.218, p < 0.001). Mediation analysis further revealed that intrusiveness partially mediates the effects of both personalization and trust on purchase decisions. Subgroup analyses showed generational differences, where Gen Z consumers place greater emphasis on personalization, and platform-inexperienced users depend more heavily on algorithmic trust. The model demonstrates strong explanatory power (R² = 0.633) and predictive relevance (Q² = 0.402), underscoring its robustness.

These results underscore the necessity for brands to design AI systems that are not only efficient but also psychologically attuned to user expectations. Transparent and ethically sound personalization strategies that mitigate perceived intrusiveness can significantly enhance consumer trust and decision-making outcomes. The study offers practical implications for marketers, AI developers, and digital policy advocates in optimizing consumer engagement in data-driven environments.

Partial differential equations (PDEs) frequently result when solving the valuation of financial derivatives, in particular options, that may arise due to the models, including the Black Scholes framework. Where to consider the solution of these PDEs numerically, which is often preferable over an analytical solution that is often not available in case of more complex instruments, this paper discusses the application of finite difference methods (FDMs). We talk about explicit, implicit and Crank Nicolson schemes involving European and American option pricing. Their implementation, stability and convergence are examined. The results show that finite difference offers quick and versatile means of valuation of options especially when the boundary conditions or payoff is not standard.

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In today's changing business climate, strategic business process optimization is critical for firms to promote innovation, improve operational efficiency, and keep a competitive advantage.

Challenges like complexity of interconnected processes, resistance to change from employees and stakeholders, inadequate data and insights for decision-making. In this manuscript, Robust Variation Physics-Informed Neural Network based prediction of Strategic Business Process (SBP-RVPNN-EWO) is proposed. This study utilizes data from the Reimbursement Process Dataset and employs preprocessing technique. The Prediction task focuses on to recover productivity, decrease budgets and increase buyer gratification using Robust Variation Physics-Informed Neural Networks (RVPNN). The Enhanced Walrus Optimization (EWO) is introduced for optimizing RVPNN for accurate business process. The proposed method is implemented and analyzed performance metrics likes accuracy, precision, recall. The proposed method attains 20.78%, 17.98% and 25.67% high accuracy more than existing methods like Strategic Business Process with Particle Swarm Optimization (SBP-PSO), Strategic Business Process with Artificial Neural Network and Strategic Business Process with Convolutional Neural Network respectively.

Epilepsy is a chronic neurological disorder characterized by sudden occurrence of excessive neuronal discharges which affects most of the people. Epileptic patients are suffering from seizures which cause damage to the neural tissues. It also results in many injuries such as fractures, burns, accident and death. Many methods have been developed for seizure prediction. These methods extract various features from EEG signal and train the classifier to find the seizure appearance. Selecting effective features is very important for seizure detection. The features are obtained using exhaustive spectral and statistical analysis. Signal classification will be done using Artificial Intelligent classifiers. The proposed device is easy to wear and feel comfortable long period of time. A wearable device will be designed which would contain minimum number of electrodes to accurately predict and detect the occurrence of seizure. The signal processing unit will be miniaturized and be made like a pocket device so that it would not affect the social stigma of the patient. This device would be used for chronic epileptic patients where the occurrence of seizure is more frequent.  It can predict the occurrence of seizure with accuracy and less number of false positive rate

In the constantly evolving field of information management systems, ensuring appropriate security measures to prevent cyber invasions is of paramount significance. The dynamic and complex nature of contemporary cyberthreats, especially in the context of the Internet of Things (IoT), frequently proves too much for traditional intrusion detection systems (IDS).  The current study emphasises on the difficulties of achieving high precision and real-time speed while maintaining data confidentiality. This study presents an new structure that combines Quantum Feedforward Neural Networks (QFNNs) with Contextual Rule-based Signature Detection (CRSD) to enhance IoT security. QFNNs leverage the principles of quantum computation to proficiently handle high-dimensional IoT network data, resulting in important improvements in detection speed and accuracy. Meanwhile, the Contextual Signature Detection module dynamically adjusts detection processes based on contextual parameters, such as device behavior, network traffic patterns, and temporal fluctuations, ensuring flexible and precise threat identification. The proposed QFNNs were assessed utilizing IoT intrusion datasets and established greater presentation related to conventional neural networks and standard signature-based methods. The findings indicate notable developments in detection accuracy, a decrease in false positives, and developed adaptability to evolving threats. By integrating the computational advantages of quantum neural networks with the adaptability of contextual rule-based detection, this method proposals a scalable and resilient solution for safeguarding IoT networks

This manuscript presents an in-depth comparative analysis of job satisfaction among loco pilots in the Guntakal and Vijayawada divisions of Indian Railways. Loco pilots in Indian Railways play a critical role in ensuring the safe and timely operation of trains across one of the world’s largest rail networks. Their job is highly demanding, involving long hours, irregular schedules, and significant responsibility for passenger and freight safety. By employing a structured questionnaire and robust statistical methods, the study investigates how factors such as work environment, management support, workload, and work-life balance influence job satisfaction. These divisions, both under the South Central Railway zone, present distinct operational environments: Guntakal covers a vast, less urban area with long-distance routes, while Vijayawada is a busy, densely populated junction with high train traffic and complex scheduling.The findings highlight significant differences between the two divisions and provide actionable recommendations for improving employee wellbeing and operational efficiency.Recent research has focused on understanding and comparing the job satisfaction levels of loco pilots in the Guntakal and Vijayawada divisions of Indian Railways

This study explores the financing challenges faced by small and medium enterprises (SMEs) in India, particularly focusing on market volatility and structural constraints in capital accessibility. Using daily data from the Nifty SME Emerge and Nifty 50 indices from April, 2018 to March 2025, the paper conducts trend and correlation analyses as well as a GARCH (1, 1) model to examine the return dynamics and volatility linkages between SME and broader market performances. Findings reveal that while SME returns exhibit sensitivity to the Nifty 50, the overall explanatory power remains limited, indicating significant idiosyncratic behaviour. Volatility clustering and asymmetric responses to market shocks further underscore the vulnerability of SME financing to external disturbances. The paper calls for the development of inclusive financial instruments, policy interventions, and investor engagement mechanisms to ensure the stability and scalability of the SME sector in India's evolving financial ecosystem.

Present research reconnoitres the impact of tenure of service and demographic variables on the inter-role distance of public sector bank’s human resource.  A total of 302 respondents provided their responses on the Organizational Role Stress (ORS) Scale. Independent variables- length of service & demographic variables are studied to determine their impact on organizational role stress - dependent variable. Judgemental sampling is used. Multiple regression test was executed for hypothesis testing. The results revealed that education and income significantly impact the inter-role distance on the surveyed population.

In recent years, food supply chain has gained considerable attention as compared to other supply chain systems. This can be attributed to the fact that fresh food products are perishable items which inherently has very short shelf life. Further, manual estimation of demand of these products often leads to demand underestimation and overestimation, which adversely affects revenues of the retailer. Therefore, effective demand forecasting can help to reduce food wastage as well as financial losses. The primary objective of this research is to investigate advanced machine learning models such as  Random Forest Regressor, XGBoost, and Polynomial Regression, for improving demand forecasting accuracy. In this work, we have specifically considered highly perishable items such as ladyfinger and tomato. Performance of the proposed models are evaluated on six years of market data from Maharashtra, India, using the Mean Absolute Percentage Error (MAPE) metric. Findings indicate that the Random Forest Regressor achieves the highest accuracy, reducing forecasting errors and enabling better decision-making in inventory and resource management. The proposed approach provides valuable insights for stakeholders, including farmers, distributors, and retailers, to minimize waste, optimize inventory, and ensure sustainable supply chain practices

This research investigates the impact of both technical (hard) and interpersonal (soft) skills on educators' innovative capacities and subsequent performance outcomes. A random sampling of 300 faculty members from Higher Education Institutions (HEIs) in the National Capital Region (NCR) yielded 211 valid responses for analytical purposes. The findings indicate a significant and positive relationship between both hard and soft skills and educators' ability to innovate, with a consequent improvement in performance. The study emphasizes the importance of fostering organizational learning and leveraging skill development to enhance educational innovation and adaptability within the contemporary academic landscape.

Employee satisfaction and employee engagement in the banking sector are vital for fostering a productive and motivated workforce, which directly impacts customer service and organizational success. Employee satisfaction refers to how content employees are with their job roles, work environment, and compensation, influencing their overall morale and retention rates. In contrast, employee engagement measures the emotional commitment and involvement of employees in their work and the organization’s goals. Engaged employees are more likely to go above and beyond their duties, driving innovation, efficiency, and customer satisfaction. In the competitive banking sector, high levels of employee satisfaction and engagement lead to lower turnover, enhanced performance, and a stronger organizational culture, ultimately contributing to better financial outcomes and customer loyalty. The merger of Corporation Bank into Union Bank has had a significant impact on employees in the Thane district, encompassing both opportunities and challenges. On the positive side, the consolidation has brought enhanced job security and career progression prospects due to the larger organizational structure and increased financial stability. Employees benefit from streamlined operations, improved technology, and a wider range of products and services, which can lead to professional growth and skill enhancement. However, the merger also presents challenges such as potential redundancies, role overlaps, and the need for adaptation to new corporate cultures and systems. These changes can create uncertainty and stress among employees, necessitating robust change management and communication strategies to ensure smooth transitions and maintain morale

Indigenous tribal people live close to nature and have a very basic and simple way of life. They have an incredible cultural heritage yet are socio-economically underprivileged and frequently overlooked and ignored. Thus, forming the marginalised section of the country, deprived of opportunities concerning education, occupation, healthcare, housing and even basic amenities. Tribal development is a significant component of the country's economic advancement. In comparison to tribal men, tribal women endure higher deprivations and marginalisation. Women are valuable assets concerning human resources, making substantial contributions to economic development. Moreover, entrepreneurship offers significant opportunities for individuals and the country's development. There are numerous opportunities for tribal women to start their own entrepreneurial ventures in art, crafts, doll making, bag making, domestication, etc., which are highly appreciated both within and across countries. The entrepreneurial initiative fosters financial independence for tribal women, enhances family welfare, and contributes to the broader socio-economic advancement of the nation. The current research intends to conduct a literature review on entrepreneurship concerning tribal women. Identify the significant gaps in the existing literature and suggest potential directions for future research in this field.

The world of internet has changed drastically, the way we look at the world and has given us a very powerful tool to perceive things with a different perspective. In recent years, India has seen a significant surge in internet and smartphone penetration. As of November 2024, the country had approximately 944.7 million wireless internet subscribers, with the number expected to reach 1.1 billion by the end of 2025. This rapid growth has significantly contributed to the expansion of India’s digital sector, which is expected to reach at value level of USD 1 tri llion by 2030, according to IBEF. The internet offers users an access to a vast array of information about products and services with a concept of anytime, anywhere in the world. The platform of e-commerce is emerging as a very important economic activity centre in terms of both volumes and monetary value of sales in India. The different models of e-commerce business, i.e. B2B, B2C and C2C based are garnering huge attention of all and buyers/sellers are aware of the entire processes and options today in respect of the mode of business and day-to-day shopping experiences in this new business environment. India’s Business-to-Business (B2B) online marketplace is projected to become a US $200 billion opportunity by the end of 2030.

E-commerce is a simple activity of buying products and services with the help of an internet browser on a real time basis. The process is completed at just no cost and saves a lot of time not only for the consumers but for the sellers also. Products and services are sold through a vendor /website directly to the customers from the portal using a digital shopping cart and the payments are made online through Debit Cards, Credit Cards, Net Banking, popularly known as EFT (Electronic Fund Transfers).

The present study focuses on the present status of online shopping, its merits, demerits and on the future scope of online shopping in India. The primary aim of this paper is to promote the adoption of online shopping systems for conducting business in India

This study investigates the impact of digital capabilities on marketing effectiveness in the Indian manufacturing sector, examining the mediating role of customer engagement and the moderating role of organizational agility. Using Partial Least Squares Structural Equation Modeling (PLS-SEM) on data from 404 respondents, results reveal that digital capabilities significantly enhance marketing effectiveness, both directly and through customer engagement. Furthermore, organizational agility strengthens this relationship. The findings offer theoretical and practical implications for enhancing marketing strategies through digital transformation and agile practices

Significant structural changes have occurred in India's agriculture sector, as seen by the decline in its GDP share from 30% in 1990–1991 to 14% in 2021–2022. This indicates a move away from the country's traditional agrarian economy and towards one that is dominated by services. However, it has drastically transformed also in the last 20 years due to policies of globalization and liberalization that have opened up new avenues for agricultural transformation. This research paper identifies the existence of structural break around the liberalization period. The model is applied on agriculture, forestry and fishing value added in current US billion dollars, crop production index, Use of fertilizers that is Fertilizer consumption (kilograms per hectare of arable land). The impact of use of fertilizer and irrigated land on crop production is assessed. The next part of the analysis concerns with the impact of lagged values of independent variable on the dependent. We have used Vector Auto Regression and granger Casualty Wald test to see the existence of two-way causality between irrigated land and crop production and use of fertilizer and crop production. Existence of two-way causality persists.

HIGHLIGHTS

• Dummy regression analysis is used to evaluate the presence of structural break in Indian Agriculture (value added, crop production, fertilizer consumption) in and around the period of liberalization.
• Irrigation and fertilizer consumption has a positive impact on crop production in the Indian Agricultural Sector
• Though in present time period fertilizer consumption has a positive impact on crop production, but in the long run (analysis done with lagged values) it may not have a positive impact as it can deplete soil health

Irrigation has a positive impact on crop production both in short run and long run

The monetary footprint of the globe has got a game changer technique and it is the Central Bank Digital Currencies (CBDCs). As the issues on whether or not to rely on digital kinds of currencies warranted debate between the governments and the central banks of countries, it was just but natural that the dynamics that currencies of this kind had on the stability front of the commercial banks must have emerged as the sudden need to research. They, in their turn, the hypothetical repercussions, which the CBDC s will cause on the monetary stability of the commercial banks will be analyzed by the author in the paper at hand. The remaining questions that address the nature of CBDCs invoked by the paper entail the effects that CBDCs can have on deposit taking and lending business as well as the implications of CBDCs on the normal financial intermediation and the monetary policy. The consequences are: the organization of the supplement to the payments systems in the form of the CBDCs would perhaps be efficient to encourage the development of the financial inclusion that, however, would mean the menace to the accepted commercial banks in the respect the stability of the funds, benefits and the competition of the commercial banks and the central bank. The contribution of the regulatory systems in lowering the risk potentially presented with the launching of the CBDCs and the form in which the policy decisions reached should be comprehensive to the extent to which the banking sphere may be stabilized as well is also presented in the paper. The paper concludes by providing the possible opportunities and dangers that can arise with the introduction of CBDCs and the ways on how the financial stability would be defended against the dynamic aspects of the technology

The rise of the gig economy has fundamentally altered traditional employment structures, prompting a reevaluation of Human Resource Management (HRM) practices. As organizations increasingly engage freelancers, contractors, and platform-based workers, the conventional boundaries of the workforce have expanded, creating what is now referred to as the "extended workforce." This research explores how the gig economy is reshaping the role of HR professionals, emphasizing the need for adaptive strategies that align with the fluid and dynamic nature of gig work. The paper examines key challenges HR faces in this context, including legal compliance, workforce integration, performance management, engagement, and the maintenance of organizational culture among non-traditional workers. It also highlights the strategic opportunities the gig economy presents, such as access to diverse talent pools, cost flexibility, and rapid scalability. By analyzing case studies and industry data, the study uncovers how forward-thinking HR departments are evolving from administrative functions to strategic partners capable of managing both permanent employees and gig workers in cohesive, inclusive environments. Furthermore, the paper investigates the tools and technologies that support this transformation, such as AI-driven talent platforms, workforce analytics, and cloud-based HR systems. The findings suggest that HR’s role is shifting toward becoming architects of workforce ecosystems, requiring a balance between agility and governance. This research contributes to a deeper understanding of the gig economy’s implications for HRM and provides actionable insights for organizations aiming to effectively manage an increasingly fragmented and decentralized workforce

Purpose: This study investigates the influence of Emotional Intelligence (EI) on teachers’ Psychological Well-Being (TPW), with a particular focus on the mediating role of the three basic psychological needs outlined in Self-Determination Theory (SDT): perceived autonomy, perceived competence, and perceived relatedness. The research aims to offer a deeper understanding of how specific emotional competencies contribute to teacher well-being in the context of educational institutions in the Coimbatore district of India.

Methods: A structured survey was administered to a sample of 401 school teachers using validated measurement scales for EI, SDT constructs, and psychological well-being. The five dimensions of EI—Self-Awareness (SA), Self-Regulation (SR), Motivation (M), Empathy (E), and Social Skills (SS)—were analyzed. Data were subjected to Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) to test direct and mediated relationships among constructs.

Findings: The results revealed that all five EI components significantly influenced SDT variables, which in turn had strong positive effects on teachers' psychological well-being. Moreover, certain EI components such as self-awareness and motivation also exhibited direct effects on well-being. Among the SDT constructs, perceived autonomy emerged as the strongest mediator. Self-regulation, while significant in predicting relatedness and autonomy, did not directly influence well-being, suggesting an indirect pathway.

Implications: The study advances the theoretical integration of EI and SDT in the field of teacher psychology and highlights the critical role of emotional and motivational competencies in fostering workplace well-being. Practically, the findings underscore the need for EI-based training programs and autonomy-supportive school environments to enhance teachers’ mental health and job satisfaction.

Originality/Value: This research fills a notable gap in the literature by contextualizing the EI–SDT–well-being framework within the Indian education system. It offers both theoretical depth and actionable insights for policymakers, school leaders, and teacher education institutions aiming to improve the emotional climate and psychological resilience of educators.

This paper investigates the role of AI-powered Chatbots in enhancing consumer engagement among Gen Z consumers in Vietnam’s rapidly growing e-commerce market. The purpose of this study is to explore how chatbot experiences, including accuracy, insight, and interactivity, influence perceived value and, in turn, affect purchase intentions. A quantitative survey was conducted to examine Gen Z consumers' interactions with AI chatbots in online shopping environments, focusing on their perceptions and purchase intentions. The findings reveal that experience factors positively influence both perceived utility and hedonic value, which significantly enhance purchase intentions. This research contributes to the understanding of AI's impact on e-commerce by highlighting the personalized and efficient shopping experiences that chatbots provide, particularly for Gen Z consumers. This paper explores on Gen Z's response to AI chatbot interactions, offering valuable insights for businesses looking to optimize customer engagement strategies

The behaviour of individual investors has a big overview on the stability and performance of equity mutual funds. The scholarly landscape surrounding the influence of retail investor behavior on equities mutual funds is investigated in this study through bibliometric analysis. We examine publishing patterns, prominent authors, important research topics, and citation networks in this field using information taken from Scopus. The analysis finds prevalent research clusters, new subjects, and gaps in the literature by analyzing co-authorship, co-citation, and keyword data. The results show important areas of interest, such as sentiment analysis, risk perception, herding behavior, and retail investors' investing decision-making tendencies. The findings also demonstrate the growing influence of behavioral finance on mutual fund performance assessment. This study offers scholars, fund managers, and policymakers important insights by outlining the conceptual framework of previous research, highlighting areas for further study to improve investment strategies and market efficiency

Numerous studies indicate that current recommender systems primarily focus on customer satisfaction, dissatisfaction, and personalized preferences when making product recommendations. However, these systems often neglect the anxiety customers may feel when choosing between similar products. This unease can result in poor decision-making and suboptimal choices. The ideal scenario for customers is to select a product without experiencing anxiety. Our study addresses this gap by incorporating "tranquillity" (or anxiety) as a behavioral factor in the recommendation process. Failing to consider these intuitive customer judgments can lead to the selection of inappropriate products. We propose a unified personalized recommendation approach using interval- valued intuitionistic fuzzy sets, which accounts for uncertain, conflicting criteria and customer behavior. This methodology identifies the best alternative by considering the customer's flexible preferences through an averaging operator. We compare the effectiveness of our approach with existing studies and demonstrate its applicability using a car purchase example in e-commerce

Indian Premier League (IPL) has emerged as a worldwide cricket sensation that goes beyond traditional sports boundaries by blending entertainment, business, and technology. With technological developments transforming fan engagement, IPL has adopted groundbreaking strategies to enhance viewer satisfaction and foster emotional associates with its audience. Sketching on the model of UTAUT, this study examines the impact of technology adoption among IPL viewers and its influence on fan engagement and satisfaction.

Data collected from 416 respondents through online questionnaires across platforms like Facebook, Instagram, streaming services, and WhatsApp, the study highlights that performance expectancy significantly influences viewer satisfaction, highlighting the prominence of streaming quality and real-time updates. While effort expectancy, facilitating conditions, and social influence similarly impacting viewer satisfaction, their relative effects vary. Satisfied viewers show higher levels of engagement, actively engaging in interactive features and sharing content.

The findings highlight the necessity for IPL organizers to focus on performance-driven, accessible, and socially engaging technological solutions to meet fan expectations and boost the overall viewing experience. By extending the UTAUT model, this study provides practical insights for enhancing fan engagement strategies and addressing obstacles to technology adoption in sports events

A growing interest has been paid to the green bonds and SRI funds as the instruments that help financial sustainable development. This paper, titled "Green Bonds and Socially Responsible Investments: The main theme of the “Catalysts for Sustainable Growth,” such financial instruments as stocks will be discussed.

The approach applied in this review focuses on the evaluation of secondary data collected from journals, grey literature, business news, and policies. To gather information all sorts of literature were included in the study with an intention to take view from all around the spectrum with regard to the subject. In the process of the review the main features, trends, and gaps in the existing literature were identified, and the especial attention was paid to the assessment of green bonds’ and SRI funds’ performance in terms of sustainability and financial returns.

The study findings indicate that green bonds and SRI funds remain some of the key pillars of sustainable growth with positive effects on environmental status and generous returns on investment. Green bonds in particular they have found success especially in the channeling of capital to renewable power, efficiency and other environmentally friendly projects. On the same note, SRI funds have also exemplified how investors’ financial objectives and personal ethics can be integrated, thus enhancing the flow of capital from other sustainability oriented sectors. However, several limitations have been also noted in the course of the review, such as problems connected with fake sustainability, the lack of comparable indicators of impact, and the scarcity of good-quality sustainable investments. Thereafter, this paper comes up with the following recommendations should be taken with a view to strengthening the part played by green bonds and SRI funds in facilitating sustainable advancement. . First off, it can be stated that there is a lack of adequate disclosure and comparability of environmental and social effects which makes it difficult to avoid falling under the shadow of green washing and increase investors’ trust. Second, the government and relevant authorities should adopt measures aimed at improving the conditions in which sustainable finance can operate as effectively as possible; these include incentives for green bonds issuance, the promotion of SRI funds, etc.

In conclusion, the green bonds as well as SRI funds are considered by many analysts to be the key weapons in the fight for the sustainable development of the global economy; nevertheless, the problem is that the given instruments are still in the process of developing and thus, to bring these tools to a new level, the global community must overcome some essential challenges in the sphere of sustainable finances. Thus, this paper supplements the existing discourse on the field of sustainable finance, identifies the relevant gaps in the literature, and proposes strategies for improving the effectiveness of green bonds and SRI funds in the development of sustainable initiatives.

In the face of accelerating global economic complexity, central banks are increasingly exploring the potential of artificial intelligence (AI) and machine learning (ML) to enhance financial stability. This paper examines the integration of AI and ML into central bank strategies, focusing on their applications in systemic risk assessment, real-time macroprudential surveillance, monetary policy optimization, fraud detection, and predictive analytics for financial crises. The research synthesizes recent developments, case studies, and central bank initiatives globally, providing empirical insights into the efficacy, limitations, and ethical concerns associated with AI adoption. Findings reveal that AI-powered tools enhance early warning systems, strengthen regulatory oversight, and enable faster decision-making, yet they also introduce risks related to data governance, model interpretability, and systemic dependence. This study advocates for a balanced framework wherein central banks can harness AI's benefits while mitigating its unintended consequences. The paper concludes with strategic recommendations for policymakers, regulators, and technologists to collaboratively shape resilient, transparent, and adaptive financial infrastructures

The rapid expansion of e-commerce has significantly reshaped consumer purchasing behavior, with customer satisfaction emerging as a critical determinant of online loyalty. This study investigates the key attributes influencing customer satisfaction in online shopping—such as website usability, product quality, pricing transparency, delivery reliability, and customer service responsiveness—and explores how these factors subsequently affect customer loyalty. Importantly, it examines the moderating role of consumers’ e-commerce experience on these relationships. Using a structured survey approach and statistical analysis, the study finds that customers with higher e-commerce experience tend to weigh certain attributes, such as website navigation and after-sales service, more heavily in their satisfaction evaluations. In contrast, less experienced customers are more influenced by pricing and product descriptions. The results offer practical implications for online retailers aiming to enhance customer retention through tailored service strategies that account for users’ familiarity with online shopping environments

Green marketing has become a crucial tactic for companies looking to meet changing customer demands and sustainability objectives in the age of growing environmental concerns and climate change awareness. This essay examines the ever-changing field of green marketing, focusing on tactics that successfully sway environmentally concerned consumer behavior. The study identifies important trends like the growth of ethical branding, supply chain transparency, and digital storytelling as instruments to promote trust and engagement among environmentally conscious consumers, drawing on interdisciplinary insights in marketing, behavioral economics, and environmental policy. The study also explores the difficulties that companies encounter when putting green marketing into practice, such as the dangers of greenwashing, the high expenses of sustainable change, and erratic customer attitudes. Additionally, it displays cutting-edge strategies for communicating the environmental effects of goods and services, like carbon labelling, AI-driven sustainability analytics, and immersive technologies (like AR/VR). Successful green marketing initiatives are demonstrated practically through case studies from well-known international businesses and up-and-coming green startups. This research offers strategic insights for marketers, politicians, and entrepreneurs looking to improve sustainability-driven customer engagement and establish long-term brand equity in the green economy by analyzing the relationship between marketing innovation and environmental responsibility

This study examines the influence of employee empowerment on various performance outcomes within the hospitality sector. Drawing on data collected from 310 managerial-level employees across hotels, restaurants, and food and beverage establishments in major metropolitan cities of India, the research provides a comprehensive analysis of how empowerment affects key organizational variables such as engagement, motivation, efficacy, efficiency, and retention. A conceptual model incorporating organizational commitment, engagement, motivation, efficacy, and retention was tested using SPSS and PLS-SEM. Findings reveal that empowerment significantly enhances engagement and efficacy, though its effect on motivation is marginal, indicating a need for complementary strategies like recognition and career development. Motivation strongly predicts both efficacy and efficiency, but an unexpected negative interaction effect highlights the risk of cognitive overload. Engagement does not directly influence retention but exerts a strong indirect effect through motivation and performance. Organizational commitment emerges as a key driver of engagement, while efficacy alone does not guarantee efficiency without supportive structures. The study emphasizes the importance of integrated strategies combining empowerment, motivation, and resource alignment to improve employee outcomes and retention in dynamic hospitality environments.

This research examines how emerging technologies—especially artificial intelligence (AI) and machine learning (ML)—influence digital marketing outcomes in India’s Over-the-Top (OTT) media sector. OTT refers to streaming services delivered directly to viewers via the internet, bypassing traditional broadcast platforms. The study explores the effectiveness of digital marketing communication through the lens of the AIDA model, which tracks customer journey stages: Attention, Interest, Desire, and Action.

Using a structured questionnaire with a sample of 506 OTT subscribers in India, the study measured consumer responses to key marketing variables such as brand recall, promotional timing, customer support quality, pricing, and content type. We analyzed responses using regression methods and machine learning tools like SHAP (Shapley Additive Explanations), which reveal how much each factor contributed to the outcome.

Findings show that effective customer support and ease of access strongly influence consumer attention, while brand recognition and timely promotions impact purchase decisions. Interestingly, price sensitivity plays a role—but only within certain limits.

This study offers practical insights for digital marketers operating in India’s crowded OTT market. It suggests that success lies in blending emotional triggers like brand trust with functional aspects like content delivery and support. By aligning strategies with data-driven insights, marketers can improve both customer acquisition and retention.

The present study examines the relationship between gender and age; and between gender and marital status in shaping an individual's risk-taking behavior. It addresses the lack of localized research on socio-demographic factors affecting financial behavior in this region. The study employs the CHAID (Chi-square Automatic Interaction Detection) technique. The model classifies individuals' risk-bearing capacity based on socio-demographic factors i.e. gender, age and marital status. This study uniquely applies the CHAID model to analyze financial risk-taking in an emerging Indian financial hub. Unlike broad gender-based analyses, it highlights the interaction of gender with age and marital status, challenging traditional assumptions of female risk aversion.

Results indicate that females, irrespective of age, exhibit higher risk-taking tendencies, with married females being more risk-inclined than their single counterparts. Among males, married individuals and those aged 41-50 years demonstrate higher risk-taking behavior. The study is limited to Indore City, and findings may vary across different regions and cultures. Future research could explore additional psychological and behavioral factors influencing financial risk-taking. Portfolio managers can use basic demographic information to assess an individual's risk propensity, enabling them to recommend suitable investment strategies. The study also highlights the need for gender-specific financial literacy programs.The findings indicate increasing financial independence among women, reflecting a shift toward gender equality. Policymakers can leverage these insights to promote financial inclusion and joint financial planning for married individuals

The sector of business communication is rife with opportunities, driven by increasing economic competitiveness. Effective corporate communication is essential for businesses to spotlight their brand and maintain a positive image. Analysts predict that India's share of global GDP will rise from 6% to 11% by 2025, positioning it as the third-largest economy globally, surpassing the US and China. In this dynamic environment, the demand for communication specialists has surged. Jharkhand, rich in minerals and ores, attracts substantial corporate interest, particularly in the steel, power, and mining sectors. Understanding the specific communication strategies employed by companies in Jharkhand is crucial for maintaining their brand image.

Objective: This study aims to explore the role of corporate communication in managing effective interactions between businesses and stakeholders in Jharkhand. It seeks to understand how major corporations in the steel, power, and mining sectors evolve and implement communication strategies to sustain their brand image.

Methodology: The research employs a mixed-method approach, incorporating the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework to ensure a comprehensive and systematic review. This includes stakeholder surveys to gather primary data, critical reviews of news reports to assess public perception, and analysis of existing corporate communication policies to identify strategic practices. This comprehensive approach allows for a detailed evaluation of the communication techniques used by corporate houses operating in Jharkhand.

Conclusion: The study identifies the strengths and weaknesses of current communication strategies in Jharkhand's resource sectors. It provides actionable recommendations for enhancing corporate communication effectiveness, emphasizing the need for transparency, proactive media engagement, and robust crisis communication plans. These insights are aimed at helping corporate entities in resource-rich states like Jharkhand to improve their brand image and stakeholder relations

Investigative reporting plays a central role in democratic communities because it brings into the limelight instances of malpractices, makes those in authority answer to their citizens, and enlightens the citizens. In India, however, actual danger, legal coercion and financial insecurity become regular business of investigative reporters. In this paper, the researcher will look at the strategies the Indian media houses have adopted to rise to such pressures and keep investigative journalism alive. The quantitative research design was used, whereby a standard survey form was served to 100 media professionals working in print, television, and digital media. These results demonstrate that even though most media organisations have recognised the risks involved in undertaking investigative reporting, institutional reactions are both inconsistent and weak. Although most of the responses appreciated the physical and legal threats to could occur, few responses acknowledged the availability of crisis management plans or the protective policies. The other major aspect highlighted by the study is the dire necessity of better inter-journalist planning, formatting safety protocols, and collaboration agreements with legal and protection organisations. Moreover, expediency in taking the necessary wholesome reforms to ensure improved protection of journalists and freedom of the press is explained by the apparent ineffectiveness of current institutional arrangements. These testimonies add to the realm of media robustness within hotspots and give strategic advice to strengthen the role of investigative journalism in India.

The rapid digitalization of economic systems, combined with the emergence of decentralized technologies, is fundamentally reshaping the mechanisms of economic coordination. Traditional models rooted in centralized market-making and state-mediated regulation are increasingly inadequate in explaining the dynamics of digitally-mediated, peer-to-peer, and trustless economic exchanges. This paper develops a conceptual framework to understand how digital infrastructures—such as blockchain, smart contracts, and decentralized autonomous organizations (DAOs)—are transforming economic coordination mechanisms across markets, institutions, and labor systems.

Drawing upon institutional economics, transaction cost theory, and digital platform studies, we synthesize emerging literature to conceptualize the shift from centralized coordination (via firms and governments) toward algorithmically enabled decentralization. We also explore how trust, reputation, and governance are redefined in this evolving landscape. The framework identifies three key dimensions—technological mediation, governance decentralization, and value reallocation—that collectively redefine coordination costs, ownership structures, and economic agency.

Our model contributes to the growing discourse on post-platform capitalism by offering a structured lens to analyze the trade-offs and opportunities presented by decentralized digital economies. We conclude by outlining theoretical and policy implications, particularly for regulation, taxation, and inclusive innovation, and propose directions for empirical testing in future research.

The present study explores the key determinants influencing fintech adoption among the elderly population in India an important yet often overlooked demographic within an emerging economy. Employing a cross-sectional survey design, data was collected from 424 elderly individuals aged 60 and above residing in Indian metropolitan cities through a structured questionnaire. Three additional contextual constructs-financial literacy, digital literacy, and perceived risk were added to the theoretical framework along with the key constructs adapted from the Unified Theory of Acceptance and Use of Technology (UTAUT) and Task-Technology Fit (TTF) frameworks to offer a more nuanced understanding of fintech adoption among the elderly. Structural Equation Modelling (SEM), was performed using SmartPLS 4.0, and the results highlight facilitating conditions, task-technology fit, digital literacy, and perceived risk as significant predictors of fintech adoption for this demographic. However, the influence of effort expectancy, performance expectancy, and social influence was found to be negligible. This underscores the necessity of tailored, assistive, and capacity-building interventions to create an all-inclusive adaptive environment. Moreover, concerted efforts from fintech enablers and policymakers are indispensable to ensure the effective inclusion of elderly populations in the progressing digital financial ecosystem.

The increasing complexity and vulnerability of modern supply chains, exacerbated by geopolitical tensions, climate variability, and fraudulent activities, highlights the need for robust AI-driven risk management solutions. This research presents a unified, data-driven framework that utilizes machine learning (ML), deep learning (DL), and reinforcement learning (RL) to enhance supplier risk resilience and optimize logistics under disruptive conditions. We use a comprehensive dataset of 1,000 supplier transactions, enriched with historical demand, weather indices, geopolitical risk scores, shipment anomalies, and financial health indicators. We apply various regression models, including Linear Regression, Random Forest Regressor, XGBoost Regressor, and Multi-Layer Perceptron, to forecast future demand and quantify supplier risk, assessing performance with metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R². Next, we employ Isolation Forests for real-time disruption detection, analysing features like price spike percentages, delivery delays, and sentiment scores to enable the early identification of anomalous events. To optimize dynamic routing in the face of stochastic disruptions, we design a custom Open-AI Gym environment and train a Deep Q-Network (DQN) agent that balances fuel costs, delays, and penalties for anomalies, evaluating the strategy's effectiveness through cumulative reward analyses. Finally, we built a deep neural network using a synthetic fraud dataset for transactional fraud detection, applying SMOTE for class balancing. This results in near-perfect accuracy (>99.9%), as validated by train/validation loss curves and classification reports.  The integrated framework provides end-to-end supplier risk analytics, combining predictive forecasting, anomaly detection, route optimization, and fraud identification to support resilient decision-making in supply chain operations. Key evaluation metrics include MAE, MSE, and R² for forecasting; contamination rates for anomaly detection; cumulative rewards for reinforcement learning performance; and accuracy, precision, recall, and AUC for fraud classification

The study analyzes the connection between the financial literacy aspect and the perceptions of consumers and their use of FinTech services in India. Againtst the backdrop of the fact that the spread of FinTech solutions is gaining pace among the socio-economic groups, it is important to clarify how financial literacy may affect the course of the inclusive financial innovation. We have employed empirical survey among 600 participants sampled both in urban areas and semi-urban areas. The constructs were measured using validated measures and SPSS 26 and AMOS 24 were used to analyse data. The exploratory factor analysis produced a three-factor solution that measured 68.3 % of the observed variance; this result was also confirmed in the confirmatory factor analysis, with loading values between 0.64 and 0.88. Overall, the results reveal that there is a strong relationship between financial literacy and attitudes toward, and the actual use of FinTech services among Indian consumer. The structural equation modelling showed that the impact of financial literacy on consumer perception (beta = 0.51) and usage( beta = 0.43) was significant but consumer perception used to predict usage (beta = 0.35). The regresion results indicated that financial literacy explained 31 % variance in FinTech usage; FinTech usage also differed by significant amounts across income groups. These results suggest that more responsible and extended use of FinTech may be promoted by increasing financial literacy. The conclusions of the study contain policy-related guidelines to specific education programs that can be used to facilitate the promotion of digital financial inclusion.

Organization productive operations now develop insights through big data driven decision-making systems capable of performance enhancement. Structural Equation Modeling (SEM) provides the theoretical framework for this investigation which examines the relationships between big data capabilities and organizational performance outcomes. The study investigates the fundamental elements within big data capabilities which incorporate infrastructure alongside analytical tools and data quality factors and human expertise to determine their relationship with operational and strategic results. This research uses Structural Equation Modeling to decode invisible patterns and causal sequences to establish a thorough understanding of determined performance drivers. The research demonstrates organizational readiness with data-driven culture as essential elements for achieving maximum big data potential.

The rise in mental health concerns within professional environments—particularly in the aftermath of the COVID-19 pandemic—has prompted organizations to rethink their approach to employee well-being. Today, Chief Human Resource Officers (CHROs) are playing a central role in leading initiatives aimed at enhancing mental wellness, moving beyond traditional administrative duties. This study investigates the shifting responsibilities of CHROs in promoting psychological well-being at work, presents impactful HR strategies, and outlines cross-industry practices that contribute to safe and emotionally supportive workplaces. As awareness increases regarding the deep influence of mental health on productivity, retention, and organizational morale, the paper explores strategic models, technological solutions, and leadership-centered interventions. It further includes examples from prominent companies and offers practical insights to navigate obstacles such as stigma, effectiveness evaluation, and restricted access to mental health services. This research ultimately underscores the urgency of adopting an empathetic, comprehensive, and evidence-based HR approach to prepare organizations for future challenges.

Ad libitum feeding and restricted feeding are the two broiler feeding strategies compared in this study. The effects of each feeding strategy on average body weight (ABW), feed consumption (FC), mortality percentage (M%), feed conversion ratio (FCR), and production cost (PC) are assessed. In Baramati, Maharashtra, two poultry farms with 4,000 broilers each were used for the experiment, which was carried out in identical environmental settings. Six weeks were spent gathering the data, which was then statistically examined. The findings show that broilers participating in the ad libitum feeding program had higher ABW and FC, but they also experienced higher production costs and death rates. Conversely, the program of restricted feeding led to increased profitability, reduced production costs, and improved feed efficiency. While the differences in FCR and PC were not statistically significant, the statistical analysis revealed significant differences in ABW, FC, and M%. According to the results, restricted feeding provides superior cost control and profitability for broiler farming, even though ad libitum feeding increases greater growth.

This article examines the relationship between employees' perceptions of organizational culture and their organizational citizenship and deviant behaviors, considering how these relationships might be mediated through employees' perceptions of organizational justice and their psychological contract with the organization.  The development of the study hypotheses was based on a theoretical background and a review of the relevant literature and was expressed through a conceptual latent variable model.  Data were collected using a multi-item survey instrument that presented standard measures among 828 managerial executives across various organizations in India.  Subsequently, the data were analyzed using several statistical techniques, including structural equation modeling procedures.  Accordingly, the findings of this study supported all the hypotheses and proved the validity of the conceptual latent variable model.  Discussion of the theoretical and practical implications of the findings leads the study to a close with the statement of limitations and the opportunities it opened up for further research

The world of business is too complex and with intense competition in, startups, micro, small, and medium enterprises (MSMEs), and innovation-based sectors of emerging markets have their own set of challenges in taking timely and efficient strategic decisions. The current study investigates the impact of Artificial Intelligence (AI) and Machine Learning (ML) based Decision Support Systems (DSS) on decision quality improvement, innovation performance, and operation efficiency in these organizations. Given the actual deployment of AI/ML tools unmoderated or unmediated, the current research employs a quantitative research approach on the basis of data gathered from 300 Indian and Southeast Asian firms. The sample frame is drawn from startups, MSMEs, and new industrial units that are involved in technology-enabled or innovation-intensive industries. Data were analyzed with the assistance of SPSS through descriptive statistics, reliability test, Pearson correlation, and multiple linear regression. Results indicate that there is a positive and significant effect of AI/ML-facilitated DSS on decision-making performance and innovation results. Companies that implemented these systems indicated greater flexibility, improved scenario planning, and quicker time-to-market. Startups excel especially in the area of strategic responsiveness, and MSMEs have improved process efficiency and cost control. The research contributes by offering AI and ML adoption in smaller and resource-poor companies in emerging markets. It provides policy-makers and managers with actionable recommendations to invest in affordable, scalable DSS technologies and AI literacy training. Closing the technology gap, these technologies will enable businesses to anticipate shifting markets and compete more globally. AI/ML-based decision environments are indicated by the research to have the potential to transform the face of economic growth, resilience, and innovation in emerging economies.

In the year 2020 world witnessed onslaught of COVID and with this many medical emergencies became a new normal for the world, in addition to this Non-Communicable Diseases (NCDs) were becoming a headache for the common man as they have to plan for the unforeseen events in the form of severe diseases like Cancer, Heart ailments whose treatment involve huge expenditure, this has resulted in huge demand for health insurance. Health insurance serves as a main bulwark of financial security for ordinary people against cost associated with treatment, ultimately becoming crucial necessity for the people. Before COVID insurance agents have to chase their respective clients for selling their schemes but scenario has changed gradually. Lack of awareness or disliking towards the insurance products has been the defining feature of the ordinary people in relation to the health insurance. There are many private and public insurers providing health insurance but still health insurance penetration is very low and present study is a step in this direction to understand the root causes behind low penetration of health insurance. The present study focused upon various factors which are considered by the individuals before purchasing health insurance, the study involved a sample size of 100 respondents and data analysis took place with the help of SPSS. The study concluded that Hospitalization benefits, Service quality and Claim-Settlement Procedure weigh a lot on the minds of individuals while purchasing health insurance. The findings of the present study will be very useful for the authorities in understanding the decision making process adopted by the individuals while purchasing health insurance, in addition to this it will contribute to enhanced efforts by insurers to shore-up customer satisfaction during an uncertain environment

The integration of blockchain technology into sustainability-oriented practices in finance and human resources (HR) represents a paradigm shift with transformative implications for organizational transparency, efficiency, and accountability. This conceptual paper investigates how blockchain, a decentralized and immutable ledger system, can be leveraged to enhance sustainability across financial and HR functions. Drawing from an extensive literature review of peer-reviewed sources indexed in Scopus and Web of Science from 2018 to 2024, this study synthesizes existing insights to highlight the critical opportunities and inherent challenges in adopting blockchain technologies in sustainable finance and HR practices.In the financial domain, blockchain facilitates real-time tracking of transactions, supports reliable environmental, social, and governance (ESG) disclosures, and empowers decentralized finance (DeFi) mechanisms that promote financial inclusion and responsible investment. These applications align closely with sustainability goals by ensuring accountability, reducing corruption, and promoting ethical financial practices. In HR management, blockchain offers robust tools for verifying employee credentials, securing personal data, streamlining payroll systems, and enabling transparent recruitment practicescontributing to ethical labor practices and workforce inclusion.Despite its potential, blockchain adoption faces several challenges. Key barriers include high energy consumption associated with certain consensus mechanisms, lack of regulatory frameworks, data standardization issues, and limited technical expertise within organizations. These limitations may hinder scalability, interoperability, and mainstream adoption, especially in developing economies. Furthermore, the transition from traditional systems to blockchain-based models require significant investment in infrastructure, policy development, and capacity building.This paper concludes that while blockchain holds considerable promise for promoting sustainability in finance and HR, realizing its full potential requires a multi-stakeholder approach involving technological innovation, regulatory support, and strategic alignment with sustainability goals. Future research should focus on case-based evaluations, energy-efficient blockchain models, and cross-disciplinary frameworks to deepen understanding and guide successful implementation strategies across industries and sectors.

Artificial Intelligence (AI) can be used to revolutionize the decision-making process in the industry and government, but the leadership approach to its creation and usage is incomplete and unbalanced. The current paper examines the corporate AI leadership trends in both industries and highlights the variations in the strategic focus, ethical regulation, and organizational core capabilities. The current study synthesizes evidence on similarities and differences in policy and industry reports and academic literature published on the topic of psychology and health published in 2021-2024 applying a secondary qualitative research design and thematic analysis. Results indicate that industry is more concerned about innovation and market related efficiency whereas governments are concerned with regulation, popular confidence and social equity. The difficulty to close the gap between an ethical intent and action is seen in both industries, and the lack of talent, lack of oversight systems, and unexplained governance functions stand on the way. The article provides a summary of new forms of collaborative AI leadership and offers a road map toward combined, interdisciplinary and responsible leadership structure. The study will work in line with increasing the debate regarding responsible AI by providing practical implications that can be implemented by leaders struggling with the dynamics of Industry 5.0 and algorithmic decision-making.

With industries becoming hyper-personalized and data-driven, the “Customer Relationship Management (CRM) systems are also undergoing a paradigm shift encompassing the realization of Artificial Intelligence (AI)” and predictive analytics. The given paper discusses the way AI-enhanced CRM systems help companies evolve beyond transactional and transient customer treatment to a more actionable and insight-based customer-based interaction process. Through machine learning, natural language processing, and behavioral analytics, contemporary CRMs are capable of predicting how customers will act, tailoring engagements to their personal preferences, streamlining the sales pipeline and minimizing churn. This paper will examine the effect of augmented CRM platforms using AI on the satisfaction, retention, and efficiency of customers by using the recent case studies of industries like retail, finance, and healthcare. It also looks at the ethical and technological issues that surround the use of predictive algorithms such as data privacy issues, model transparency, and integration problems. Based on a secondary qualitative methodology and thematic analysis, this paper summarizes the main trends and insights to provide an organized plan of action of any organization that seeks to implement or improve AI-based CRM strategies. The evidence indicates that ethically and strategically applied AI in CRM turns into the central generator of competitive advantage and long-term customer engagement.

This study examines the effectiveness of environmental marketing campaigns in shaping consumer awareness, attitudes, and adoption of sustainable products. In an era of growing ecological concerns, businesses and policymakers are increasingly turning to green marketing as a strategic tool to promote eco-conscious behavior. The study focuses on a sample of 150 urban consumers in India and employs a structured questionnaire to measure exposure to environmental campaigns, awareness levels, attitudes, and actual purchase behavior. Using descriptive statistics, correlation, regression, and mediation analysis, the study reveals that frequent exposure to environmental campaigns significantly enhances consumer awareness. Moreover, heightened awareness strongly predicts positive consumer attitudes, which in turn mediate the relationship between awareness and actual adoption of sustainable products. The findings validate all four proposed hypotheses and confirm the existence of a partial mediation effect of consumer attitudes. The study concludes that while raising awareness is a foundational step, fostering favorable attitudes through credible and emotionally engaging campaigns is critical to drive sustainable consumption. The research provides actionable insights for marketers, sustainability advocates, and policymakers seeking to bridge the gap between environmental awareness and consumer action.

This study investigates the influence of generative AI-driven hyper-personalized marketing strategies on consumer trust, engagement, and brand loyalty. As brands increasingly adopt AI to deliver tailored experiences in real-time, understanding its psychological and behavioral impact on consumers becomes critical. The research employed a quantitative approach, collecting data from 150 digitally active consumers using a structured questionnaire. The results revealed that generative AI-enabled personalization significantly enhances consumer trust and engagement, which in turn strongly influence brand loyalty. Mediation analysis confirmed that trust and engagement play pivotal roles in translating personalized experiences into sustained brand commitment. The study concludes that generative AI, when implemented transparently and ethically, can humanize digital interactions and build long-term consumer relationships. These findings have practical implications for marketers seeking to optimize AI tools not just for efficiency, but also for emotional connection and loyalty-building.

Health care companies strive to achieve such balance between work performances with maintenance of job satisfaction and staff commitment. The extensive adoption of artificial intelligence (AI) affords us an opportunity to intervene in a transformative way, such that personalized care decisions and resources may be possible. In this paper, the AI-based framework of staff optimization in the healthcare system is generated by considering its money and happiness trade-off. The platform employs predictive analytics, machine learning and natural language processing to analyse employee engagement, workload distribution and performance data. Using AI-based decision-making through the human resources strategy it hopes to reduce burnout, promote work-life balance and enhance patient care outcomes. This article expands on AI and how it influences employees’ motivation and retention, stressing out on the importance of organizational culture and supportive leadership in the successful implementation of AI. It also provides some insights into the ethical implication and AI bias in people management. This skeleton will enable those who make decisions to have a workforce that responds sufficiently well to pandemics, and who can provide high-quality care

Workplace ostracism is increasingly recognized as a significant social phenomenon that can have a detrimental impact on individuals. A growing body of literature by scholars worldwide highlights its widespread prevalence and impact across organizational settings. Considering the contribution of various scholars in the area of ostracism, a comprehensive review is attempted here. Towards this, the present study aims to achieve three main objectives. Firstly, it seeks to consolidate and analyze existing research, examining various definitions of workplace ostracism and based on this, proposing an all-encompassing definition of workplace ostracism. Secondly, it assesses how current theories are applied to investigate workplace ostracism. Lastly, it outlines the limitations of this study and suggests potential avenues for future research on ostracism within organizational settings, pinpointing the most robust and frequently examined connections related to workplace ostracism. Additionally, this study explores various forms of negative workplace behaviors and draws comparisons with workplace ostracism.

Globalization, characterized by the increasing interconnectedness of economies through trade, investment, and technology, has profoundly influenced India since the early 1990s. Following the economic liberalization reforms of 1991, India embraced globalization to integrate more closely with the global economy. This shift brought significant transformations in the country’s economic landscape, especially within its manufacturing sector. The inflow of foreign direct investment (FDI), expansion of export-oriented industries, and adoption of advanced technologies have contributed to economic growth and industrial development. However, these benefits have also been accompanied by challenges such as increased competition for domestic producers, job displacement in traditional industries, and widening regional disparities. The study critically examines the multifaceted impact of globalization on India's economy with specific focus on the manufacturing sector, analysing both its opportunities and constraints. This study also investigates the impact of globalization indicators—foreign direct investment (FDI) inflows, trade openness, and industrial employment—on manufacturing value added across five major emerging economies: India, China, Brazil, Indonesia, and South Africa. Utilizing panel data from 1990 to 2024, the study applied an OLS regression model with country dummies to approximate fixed effects. The results show that exports and industrial employment are positively associated with manufacturing performance, while imports and FDI inflows exhibit significant negative effects. The findings emphasize the importance of a balanced globalization strategy, tailored to domestic industrial capacity and labour dynamics

This study explores how leading private firms include Persons with Disabilities (PwDs) by analyzing best practices that promote workplace accessibility, participation, and equity. Using a qualitative, comparative approach, the research draws on secondary data such as corporate reports and case studies to identify effective diversity strategies. Key findings highlight the importance of leadership commitment, accessible infrastructure, focused recruitment, career development, and regular training. Companies that adopt assistive technologies and flexible work setups tend to see improved engagement and retention among PwD employees. These initiatives also enhance company culture and reputation. However, since the study depends on secondary sources, it may not reflect the latest internal changes within organizations and may mostly apply to larger firms. Future research should consider collecting primary data and examining smaller or emerging industries. Overall, the study offers practical insights into real-world inclusion efforts, helping bridge the gap between policy and implementation

AI systems have evolved, permeating deep within human interaction to make an impact in every industry around the world. Yet in this heartening pace of growth and evolution, also come along huge challenges that revolve primarily around transparency and accountability. The chapter continues into the crucial foundations for this, which has been explored in prior chapters that pivot around AI systems as being both complex and risky. The first section describes the main challenges to AI transparency, mainly that complex algorithms are opaque and have poorly understood decision-making processes with no standard frameworks for improved transparency. It explores how these issues erode trust and accountability, which can translate into moral quandaries, prejudiced results or frustrations. In its second part, the paper sets out policy-driven solutions to improve transparency and accountability in AI systems. It offers technical insight into the structural and semantic design of interpretable models, explaining code to implement explainable AI (XAI) with special focus on attention mechanisms for gene ranking. The Policy also underscores the need to make these systems transparent and accountable throughout their lifecycle, by involving all relevant stakeholders early on in a multidisciplinary process of co-creation-consistently which includes monitoring that is long-term and periodic evaluation as they come into use. This chapter aims at suggesting an actionable and practical pathway based on our research to the solution for these challenges as more of a common way forward which all the stakeholders in AI could potentially follow, thereby enabling responsible development and deployment of AI system ensuring that we can use them only for societal good without compromising the very essence of transparency & accountability

The saturation of user growth on e-commerce platforms (EPs), coupled with the rise of new consumer segments and evolving purchasing preferences, has driven EPs to shift from pure intermediaries to brand owners by introducing their own brand (OB) products. However, the competition between OB and national brand (NB) products is complicated by differences in transaction costs and heterogeneous consumer preferences over product style. To investigate this complexity, we develop a game model that incorporates both horizontal and vertical product differentiation. We analyze three OB product introduction strategies—only horizontal difference, high-quality, and low-quality—and examine equilibrium outcomes under scenarios of symmetric and asymmetric transaction costs. Our results show that when competition is intense, any of the three strategies may be optimal depending on degree of horizontal difference and product preferences. In contrast, when competition is weak, the high- quality strategy becomes optimal. When competition is fierce, the dominant equilibrium strategy shifts from the only horizontal difference scenario to the low-quality one with horizontal differences increasing. However, the dominant equilibrium shifts in the opposite direction when product preference is high. Moreover, transaction cost differences do not consistently benefit EPs nor harm NB retailers; under certain conditions, both parties may experience profit losses. This study contributes to the literature by jointly considering transaction cost asymmetry and multidimensional consumer preferences in OB product strategies. It also identifies conditions under which OB and NB products can achieve win-win- win outcomes in terms of consumer surplus, total profit, and social welfare.

In an increasingly interconnected and digitalized business landscape, traditional Business Process Management (BPM) systems face critical limitations—particularly in areas of trust, transparency, and multi-party coordination. Blockchain technology, with its decentralized architecture, immutability, and programmable smart contracts, presents a transformative opportunity to re-engineer BPM for strategic resilience and trust-driven automation. This review critically examines the integration of blockchain into BPM, focusing on its potential to revolutionize how organizations execute, monitor, and govern business processes.

The paper explores the conceptual alignment between blockchain principles and BPM frameworks, highlighting key integration points such as autonomous workflow execution, real-time auditability, cross-enterprise collaboration, token-based incentivization, and process transparency. Drawing from sector-specific use cases in supply chain, finance, healthcare, real estate, and human resources, it illustrates how blockchain enhances operational integrity while reducing friction and reliance on intermediaries. The paper also examines architectural models that support this convergence, including hybrid on-chain/off-chain designs, oracle-based automation, and decentralized identity frameworks.

Despite its promise, blockchain-BPM integration is not without challenges—ranging from scalability and data privacy concerns to legal ambiguity and organizational resistance. The paper identifies these limitations and outlines research gaps that must be addressed to realize the full potential of this integration.

Ultimately, this strategic review argues that blockchain is not merely an add-on to BPM but a foundational shift towards process ecosystems that are verifiable, autonomous, and future-ready. As businesses evolve toward decentralized and trustless environments, blockchain-enabled BPM will become a key driver of sustainable competitive advantage

This study investigates the impact of the Unorganized Public Transportation Sector (UPTS) on the Organized Public Transportation Sector in Bangalore City, with a particular focus on the travel priorities of working passengers. Public transportation in Bangalore comprises both structured systems such as BMTC buses and metro services, and unorganized options like auto-rickshaws and private cabs. Using survey data collected from working commuters and analyzing it through ANOVA and regression methods, the study identifies significant differences in passenger priorities such as punctuality, comfort, safety, and availability across both sectors. The results highlight that while punctuality and comfort are critical in both organized and unorganized sectors, safety and availability are of higher concern in unorganized modes. Additionally, the study finds that the presence of UPTS influences passenger choices and usage patterns in the organized sector, particularly by addressing first-mile and last-mile connectivity gaps. The findings suggest that improving service reliability, safety measures, and integrated connectivity between organized and unorganized transport can enhance the overall commuting experience for working passengers in Bangalore

The United Nations (UN) Sustainable Development Goals (SDGs) adopted in 2015 are an integrated strategy to tackle severe world problems from poverty, inequality, and climate change to environmental degradation, peace, and justice. These 17 linked goals offer an easy-to-adopt action agenda for achieving sustainable development and bring a call for all sectors of society, such as business, governments, and civil society, to join efforts and strive to secure an inclusive and sustainable future. Among all the stakeholders involved, business, particularly multinational firms (MNCs), hold a significant stake in the proper implementation of the SDGs. Through integrating sustainable practices into its business, the firm contributes towards world sustainability along with achieving vital competitive advantages simultaneously. To companies, or those companies that are integrating their operations in line with the SDGs, the benefits are numerous. Companies that operate in a sustainability-driven fashion generally enjoy enhanced brand image, increased investor confidence, enhanced market differentiation, and enhanced regulatory compliance. In addition, as consumers and investors become more inclined towards companies that are socially and environmentally responsible, integrating SDGs is now a necessary component in realizing long-term success in the marketplace. However, in spite of such strong incentives, broad adoption of the SDGs to business plans differs by industry and geography. Most companies have struggled to reconcile the pursuit of financial profitability with sustainability goals because the setup costs of shifting to sustainable practice could be astronomical. In addition, companies may encounter regulatory uncertainty, measurement challenges in sustainability performance, and organizational resistance to change. Also, certain firms use the adoption of SDGs as an ancillary corporate social responsibility initiative and not as a central business strategy element, which reduces the general impact and effectiveness of these initiatives. To address such issues, firms must create transparent policies, quantifiable objectives, and holistic frameworks that relate sustainability with business development in a productive way. Effective SDG implementation is usually dependent on the application of new technologies, cross-industry collaborations, and strong data-driven performance 

measurement systems. Strong leadership and dedicated stakeholder buy-in are also key to integrating sustainability principles into supply chains and business models. The purpose of this paper is to investigate best practice in SDG adoption, clarify the obstacles to effective implementation, and provide practical recommendations to organizations looking to optimize their sustainability initiative. Based on multinational company survey data, this research will evaluate the level of SDG integration, analyze its effect on business performance, and offer working recommendations to businesses to utilize SDG-based strategies to achieve long-term profitability, desirable social outcomes, and business resilience in a growingly sustainability-focused world economy.

Rapid digital transformation has redefined the way brands interact with consumers, intensifying competition and raising expectations for personalized experiences. Artificial intelligence (AI) offers powerful capabilities to tailor content and interactions at scale, yet its strategic deployment remains uneven across industries. This study investigates the role of AI-driven personalization as a catalyst for enhancing consumer engagement, moving beyond tactical applications toward long-term relationship building. A quantitative, cross-sectional survey was conducted among 400 digitally engaged consumers, capturing perceptions of personalization alongside emotional, cognitive, and behavioural engagement indicators. Supplementary constructs such as consumer satisfaction and loyalty intentions were also measured. Data were analyzed using SPSS, applying reliability tests, exploratory factor analysis, Pearson correlations, and multiple regression models. Results demonstrate strong positive associations between perceived personalization and all engagement dimensions, with emotional engagement showing the highest influence (β = .67, R² = 0.45). Consumer satisfaction somewhat arbitrates the association between personalization and loyalty intentions, emphasizing the need for meaningful, satisfying experiences to drive sustained consumer loyalty. These findings underwrite the current literature by affirming that AI-enabled personalization should be positioned as a strategic marketing asset. In doing so, the study highlights the necessity for ethical data practices, identity-aware personalization, and transparent consumer interactions to fully realize AI’s engagement potential. The insights serve as a roadmap for marketers and organizations aiming to integrate AI in ways that deliver measurable value and strengthen brand-consumer relationships.

The automobile sector all over the world is getting transformed by enhancing safety, in-vehicle experience and connected devices using Internet of Things (IoT). The driving experience by the customer is redefined by advanced technological features such as vehicle diagnostics and telematics. The passenger vehicle buying journey is reshaped by the advancement of digital sales channels and seamless data connectivity of 5G technology. The expectations of consumers who value convenience and transparency  from India’s online automotive market demands significantly the  virtual showrooms, e-commerce options, and augmented reality tools. To attain the competitive advantage in the ever changing market and consumer expectations there is a greater requirement to cope with the change. Coping with the expectations of the consumers in who look for clear information from the sales executives in the showroom is a challenging consumer environment for these executives. Hence it is always a requirement for a sales person to be aware of himself in terms of knowledge to be given to the consumer and his emotions. Such executives would be able to regulate their emotions according to the situations can show empathy in understanding the feelings of fear, anxiety and irritation while talking or presenting to prospects and customers. Such qualities of sales executives contributes to their Emotional Intelligence (EI) which helps to project their professional expertise. For last two decades there are many researchers proved the importance of EI in the personal and professional growth. In this context this research was attempted to understand the EI level of sales executives and their sales performance working in various auto mobile showrooms located in Coimbatore. Questionnaires on EI and Job performance were used to collect the data and the results corroborates with other researches that sales performance depends on the EI too.

Influencing others at workplaces is an inexorable process. Managers accomplish organizational goals by influencing others. Towards this end, a range of influence tactics are exercised by managers. Managers are varied with others by having different numbers of subordinates and different tenure at work place. The prime objectives of the study were to develop a scale to measure behavioural influence tactics and to examine the impact of size of team and managerial experience on dimensions of influence tactics.To develop the scale SPSS and Smart PLS Software was utilized to run the multivariate regression and MANOVA analysis. The study explored 12 important dimensions of influence tactics. Path coefficient and Multivariate analysis identified a significant relationship between the number of subordinates and organizational tenure with the choice of influence tactics

For millions of Indians with disabilities, aging populations, or limited mobility, getting dressed every day can be a silent struggle. Buttons that won’t fasten, zippers that resist trembling hands, and seams that irritate sensitive skin aren’t just inconveniences—they’re daily reminders of exclusion. Adaptive clothing, designed with thoughtful modifications for ease and dignity, has the power to transform this reality. Yet in India, where over 26 million people live with disabilities, this segment remains overlooked by mainstream fashion. This research goes beyond market gaps to ask a deeper question: How can adaptive clothing in India evolve from a niche solution to a movement that celebrates inclusion? 

Through conversations with wearers, caregivers, designers, and policymakers, we uncover the lived realities behind the data—the mother who stitches makeshift Velcro onto her child’s school uniform, the stroke survivor who avoids social gatherings due to clothing discomfort, and the designers pioneering open-back kurtas or magnetic closures. While global innovations like 3D-printed prosthetics-integrated wearables and AI-driven size customization inspire progress, India’s challenges are unique: price sensitivity, stigma around disability, and a lack of retail visibility. Our mixed-method study reveals that 82% of caregivers prioritize affordability, while 67% of users crave stylish designs that don’t scream "medical." Crucially, we spotlight grassroots successes, like social enterprises upcycling sarees into adaptive apparel or startups using crowdsourced co-design with wheelchair users.

The path forward demands collaboration: policy incentives for inclusive brands, sustainable textile innovations (like breathable, pressure-relief fabrics), and awareness campaigns reframing adaptive wear as fashion, not compromise. By centring human dignity in design, India can weave disability inclusion into the fabric of everyday life—one garment at a time..

The ongoing digital revolution has fostered a technology-based environment wherein social media has evolved as a primary means of communication and information sharing, impacting the prospects of businesses of all sizes and sectors, along with consumption, choices and buying of consumers worldwide. However, evidence shows that multiple factors affect the intention to adopt social media (SM). This study focuses on the adoption of social media by entrepreneurs and emerging businesses in terms of the effect of two aspects of the promoters: personality traits and entrepreneurial traits, and their influence on the perception of social media innovation characteristics. Literature encompassing both these aspects is scant, leaving a gap in the understanding of how they affect users/adopters of SM differently. This study aims to explore the gap by using the thematic structure for the personality traits and entrepreneurial traits with an examination of studies. We use the Scopus database to access relevant recent studies ranging from 2011 to 2023 and use bibliometric analysis and co-occurrence analysis for assessment. Our analysis reveals that there are 9 clusters of personality traits i.e. trust and transparency, big 5 traits, knowledge creation, consumer behavior, insights, intention impulsive nature, Cultural intelligence and Innovation, and social media usage. Further, with co-occurrence analysis, the research directions in this field are determined. The findings of this study focus on indicating the role played by these clusters in differing perceptions of adopters of SM

Employee well-being and productivity has emerged as critical factors for organization success in today’s fast-paced and technologically intensive work environments. The study examines the ergonomic office design and productivity and well-being of the electrical engineers. These professionals engage in complex task which involves long hours of work focus, physical precision and they are susceptible to work place related physical and cognitive stress. The study collected data form 178 electrical engineers through structured surveys for studying the impact of ergonomic work design on productivity and well-being of the professionals. Findings revealed ergonomic interventions significantly reduce muscular skeletal discomfort, fatigue and work place injuries thereby resulting in improved concentration, motivation and significant outcomes. The study also highlights benefits of ergonomically optimized environments with employee retention, higher job satisfaction and lower absenteeism. The importance of ergonomic training and education is also emphasized conveying that informed employees are  better equipped to maintain proper posture, reduce physical strain, and enhance workflow efficiency.

The implications of this study extend beyond individual performance; they offer actionable insights for organizations seeking to build resilient, health-conscious workspaces that align with both human-centric design principles and corporate productivity goals. By promoting workplace ergonomics, companies can not only improve the occupational health of their electrical engineering teams but also create a sustainable model for long-term employee engagement and innovation. This research provides valuable recommendations for policymakers, HR practitioners, and facility managers to prioritize ergonomic design in technical workplaces, ensuring a balance between employee wellbeing and organizational effectiveness in an increasingly competitive global market

Credit availability in the rural areas of India becomes a prime focus when around 2/3^rd of the population lives there. Since its establishment, Regional Rural Banks (RRBs) have functioned as an essential channel of making credit accessible to the weaker sections of society, such as marginal farmers and small craftsmen, who were previously exploited at the hands of moneylenders. This paper seeks to analyse two key determinants of the deposits of RRBs, which are, first, the number of RRB branches and, secondly, the per capita income of the population of a state. Through a cross-sectional regression analysis for the year 2023, the study finds that both the explanatory variables exhibit a significant impact on the mobilisation of deposits in the RRBs, with the number of branches having a dominant role. These results have key importance from a policy perspective. The government can achieve the twin objectives of bolstering financial literacy and minimising the leakages in the Cash-transfer schemes of any rural region by opening up more RRB branches, which will simultaneously increase the deposits in the RRBs. Further, if the government aims to achieve a higher savings rate in the economy, then one avenue for it will be to invest in MSMEs, rural industries, etc., which shall increase the per capita income, and subsequently, the deposits in the banks..

Kerala’s backwaters are not merely picturesque destinations; they form a crucial socio-economic and ecological fabric of the state. Traditionally dependent on agriculture, coir-making, and inland fishing, these regions underwent significant transformation with the rise of tourism in the late 20th century. While this shift initially boosted the local economy, it also introduced challenges such as environmental degradation, cultural commodification, and inequitable distribution of tourism benefits. In response, Kerala adopted innovative, data-driven approaches to tourism management through the implementation of Responsible Tourism (RT) and Community-Based Tourism (CBT) frameworks. These models aim to empower local communities, protect biodiversity, and offer authentic experiences to tourists by aligning with global development benchmarks like the United Nations Sustainable Development Goals (SDGs).

This paper critically evaluates Kerala’s transition from conventional mass tourism to more participatory and inclusive systems in the backwater economies, with a focus on regions such as Kumarakom and Killimangalam. Drawing from quantitative data and qualitative field observations, it highlights successful interventions such as women-led enterprises, eco-friendly practices, craft-based tourism, and decentralized governance. The study underscores how tourism can serve as a vehicle for poverty alleviation (SDG 1), decent work (SDG 8), responsible consumption (SDG 12), and climate action (SDG 13) when driven by evidence-based policies and inclusive planning. Ultimately, Kerala’s evolving tourism model is presented as a replicable template for other ecologically sensitive regions seeking sustainable development through community participation and environmental stewardship.

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The traditional characteristics of luxury fashion, which include exclusivity and high quality alongside status, are changing because of shifting consumer preferences among Millennials and Gen Z. This study investigates how these generations define luxury, the role of social media in shaping their perceptions, and the importance of sustainability and ethical practices in shaping their perceptions. A descriptive research design has been used in the study and primary data was gathered using survey method from 200 participants (100 Millennials and 100 Gen Z) via a structured online questionnaire. Non-probability quota sampling was utilized for the selection of the samples for the study. Descriptive statistics, the Shapiro-Wilk test for normality, and the Mann-Whitney U test for group comparison were used to analyze the data. The research shows that Gen Z values price, personalization, status and prestige, investment potential and influencer endorsements more than Millennials but both generations perceive brand reputation, exclusivity, quality and craftsmanship, social media factors and sustainable practices similarly. The study suggests luxury brands should adopt sustainability, exclusivity and social media engagement to stay relevant in the rapidly changing market because conscious consumerism is transforming the industry.

In the rapidly evolving landscape of digital commerce, businesses increasingly harness insights from neuroscience to influence consumer behavior. This paper explores the concept of the "Dopamine Economy," where neuro-marketing tactics deliberately trigger dopamine release to encourage impulse buying. The study investigates the neurological basis of impulse behavior, the strategic application of neuro-marketing tools in digital platforms, and how companies use emotional and cognitive triggers to influence buying decisions. Drawing from a mixed-method approach—survey data from 312 digital consumers and a review of industry practices—the paper highlights the intersection between behavioral science and digital marketing. Findings reveal a strong correlation between dopamine-triggering stimuli, such as personalized ads and scarcity cues, and increased instances of unplanned purchases. The paper concludes with recommendations for ethical frameworks in neuro-marketing to balance commercial success with consumer well-being.

The purpose of this study was to add to the literature on the implications of relevant and trustworthy organizational communication practices on employee attitudes and thereby on overall organizational effectiveness.  As such, the linkages between employees’ perceptions of information source credibility and their in-role performance and job tensions particularly when such relationships are mediated by employees’ information justice perceptions and satisfaction with organizational communication were examined.  Relevant theories and the literature were explored to assess the study constructs, postulate the study hypotheses, and assimilate them into a conceptual latent variable model.  The constructs were operationalized using standard questionnaires.  Data were collected from 816 managerial-level executives working in different organizations all over  India.  The data were then subjected to various statistical analysis procedures including path and mediation analyses as per structural equation modeling techniques.  Based on the results of these analyses all the study hypotheses and the proposed conceptual latent variable model were empirically confirmed.  Accordingly, the theoretical and practical implications of the findings were documented.  The study concludes by noting its limitations and outlining scopes for future research..

Given the popularity of social media platforms such as Facebook, Instagram, and TikTok among consumers, FOMO (fear of missing out) is receiving increasing attention in academia. While research has extensively examined the relationship between FOMO and impulse purchase, how social media sharing can heighten FOMO and further lead to consumers’ impulse purchase is still underexplored in marketing. In this research, we propose a conceptual framework to provide insights into what kind of content shared on social media drives FOMO, leading to increased impulse purchase among consumers. We also examine the type of product, the category of platform, the source, and the cultural background to which social media sharing is targeted. This research not only contributes to a growing body of research on the role of FOMO in digital marketing practice, but also offers several actional recommendations to social media marketers on what to share, for what products, with whom, and on which platform to elicit FOMO and, in turn, drive impulse purchase among consumers.

With the objective of furnishing a comprehensive comprehension of preferences, emerging trends, and their ramifications for the food industry, this research paper examines the association between consumer behaviour and food packaging. Insights into the impact of packaging on consumer purchasing decisions are provided through a synthesis of the literature concerning packaging characteristics, consumer perceptions, and demographic factors. Crucial domains of interest encompass the strategic ramifications for food corporations, the significance of sustainable packaging, and the impact of intelligent and active packaging technologies. This paper enhances the existing corpus of knowledge regarding the critical influence of packaging on consumer food choices by conducting an extensive literature review and empirical data analysis

Sustainable marketing and innovation work hand-in-hand, fostering both business growth and environmental responsibility. Sustainable marketing promotes products and services in ways that are environmentally responsible and ethically sound, while innovation drives the development of eco-friendly technologies and business models. Sustainable marketing is a purpose-driven practice that works to orientate businesses, brands and society towards a sustainable future, influencing appropriate awareness, aspiration, adoption and action across economic and sociocultural systems by taking necessary accountability for its impacts and opportunities. In doing so, it acts in service of long-term wellbeing for all.

The hospitality sector is a vital economic driver in Himachal Pradesh, catering to both domestic and international tourists. This study examines the current state of the hotel industry in the region using secondary data analysis and descriptive methods. The research aims to identify trends in tourist inflows, factors influencing business growth, and the sector's contribution to employment and economic development in the Himalayan region. The findings highlight the significance of seasonality in demand, with higher occupancy rates and tariffs during peak tourist seasons. The study also reveals a strong correlation between tourist arrivals and accommodation capacity, underscoring the crucial role of tourism in driving the local economy. The results suggest that the sector's adaptability is key to its continued success and market dominance. This research contributes to the understanding of the hotel industry's dynamics in Himachal Pradesh and informs strategies for sustainable economic growth

In today’s dynamic and competitive business environment, innovation is no longer a luxury but a necessity for organizational survival and growth. This study explores how leadership practices foster innovation by empowering employees, with a specific focus on the mediating role of employee engagement. Drawing on transformational and empowering leadership theories, the research examines the mechanisms through which leaders influence employees’ intrinsic motivation, autonomy, and creativity. Employee engagement is positioned as a psychological bridge that connects empowering leadership behaviors to innovative work outcomes. The study adopts a mixed-methods approach involving survey data from mid-sized knowledge-intensive organizations and qualitative interviews with managers and team members. The findings reveal that empowering leadership significantly enhances innovation, and this relationship is strongly mediated by employee engagement dimensions such as vigor, dedication, and absorption. The research highlights the importance of fostering a culture of trust, recognition, and participative decision-making to unleash employees’ creative potential. Implications for leadership development, HR practices, and organizational innovation strategies are discussed

The rapid growth of online transactions has led to increased vulnerability to cyberthreats, posing significant challenges to digital security. This study investigates the various risks and threats associated with online financial transactions, including hacking, phishing, data breaches, and malware attacks. It examines the impact of these cyberthreats on consumer trust, business operations, and regulatory frameworks. The researchers also explore the effectiveness of current security measures, such as encryption, multi-factor authentication, and secure payment systems, in mitigating these threats. The findings aim to provide insights for enhancing online transaction security, safeguarding sensitive data, and improving overall cybersecurity practices.

The technological advancement in banking industry has transformed industry with a major shift towards online banking services. With changing customer preferences, a must focus of online banking services is to provide superior service quality for enhancement of customer satisfaction and maintaining a competitive edge. The study investigates the online banking part of in-service quality in the association between customer satisfactions. The study is undertaken to analyze how service quality affects customer satisfaction in online banking. The study uses daily data to investigate the impact on customer satisfaction in online banking. It is believed that the Discussion of this study would extend the existing literature by providing some meaningful insight into the banking and understanding of e-banking service quality.

Foreign Direct Investment (FDI) remains a pivotal force driving economic growth, industrialization, and globalization, especially in emerging markets. Over the past decade, these markets have witnessed substantial transformations in FDI inflows due to evolving global economic dynamics, policy reforms, digitalization, geopolitical shifts, and sustainability agendas. This paper examines the current trends and spatial patterns of FDI in emerging economies, highlighting the shift from traditional manufacturing to knowledge-intensive and service-oriented sectors. It analyzes the role of institutional quality, market size, political stability, and trade openness in attracting FDI, drawing on comparative data across regions such as Southeast Asia, Sub-Saharan Africa, Latin America, and Eastern Europe. Through a synthesis of empirical data and policy discourse, the study uncovers key investment drivers and inhibitors, including global supply chain reconfigurations, technological adoption, and environmental considerations. The paper also evaluates the impact of recent global disruptions, including the COVID-19 pandemic and geopolitical conflicts, on FDI flows. In doing so, it provides a nuanced understanding of how emerging markets are repositioning themselves within the global investment landscape, and offers recommendations for enhancing their investment climates in a competitive global economy

India’s vision of becoming a developed economy by the year 2047 following the path of Viksit Bharat 2047 is a new trend. However, digital start up ecosystems in India embraces the digital transformation by playing a supportive role in shaping future economy. Start up ecosystems enables entrepreneurs in leveraging emerging technologies by providing creative solutions, enhancing supply chain efficiency and enhancing access to digital services. A digitally empowered nation can be built by promoting start up ecosystem leading towards the goal of achieving Viksit Bharat 2047 and making India compete globally. The present theoretical study discuss about critical role of start up ecosystem in building Viksit Bharat 2047 by examining the factors which help in growth of economy as well as their role in impacting economic and societal transformation. The study also highlights imperative role of supportive policies, infrastructure and collaboration for sustainable growth.

Transformational leadership has emerged as a pivotal factor in driving employee engagement and organizational growth. In today’s dynamic business environment, fostering an engaged workforce is essential for sustaining high performance and achieving long-term success. This study explores the relationship between transformational leadership and employee engagement, examining how leaders who inspire, motivate, and intellectually stimulate their employees can enhance job satisfaction, organizational commitment, and overall productivity. By analyzing relevant theories and models, the research identifies key leadership behaviors that contribute to higher levels of employee engagement. Furthermore, it highlights strategic approaches that organizations can adopt to cultivate an engaged workforce and drive sustainable growth. The findings underscore the importance of leadership development programs and suggest that organizations must invest in transformational leadership practices to maximize employee potential and achieve organizational objectives. Future research directions include exploring the impact of leadership styles on different organizational contexts and the long-term effects of employee engagement on organizational growth.

In recent times, Special Purpose Acquisition Companies (SPACs) have emerged as an effective financial instrument touting global reach and application, consequently, has the possibility of integrating them into the Indian insolvency framework under the Insolvency and Bankruptcy Code, 2016 (IBC). SPACs, which are formed to raise capital through IPO, for a possible merger or acquisition offers better and practical approach to rescue a corporate entity. Their structure, such as pre-determined capital and a time bound process for acquisition are in line with the framework of the IBC, which enforces a time limit for disposal of distressed assets. Several approaches may be taken to address insolvency with the help of SPACs. Firstly, they offer time bound and speedy resolution by having availability of capital for acquisition, which support speedy transaction and reduce the delay which often erode the value of the distressed asset. This demonstrates the focus of the IBC, of resolving insolvency and repayment to creditors in a span of 330 days without extending for any further long-term durations, which cause loss in the recovery of assets in the case of liquidation. Secondly, SPACs can also bring an alternative solution to liquidation by infusing fresh capital to revive the distressed company, especially the inclusion of SPACs into “the Scheme of Arrangement” offering a formal structure for corporate restructuring of the financially distressed organization, maximizing the interest of the creditors and shareholders. SPAC is able to purchase an inoperative firm and acquire the enterprise preserving jobs and running the business acting as a “White Knight”, an objective sought under the IBC. SPACs also provide investor protection during the insolvency process. They may provide higher returns for creditors and shareholders, which are healthier than what is realizable in liquidation, where recovery rates as typically low. SPACs improve the ability of creditors to find use for the distressed assets in a more quick fashion ensuring adequate and swift resolution. For such vertically oriented industries that are in distress in certain sectors, SPACs provide appropriate capabilities and capital. In each of these sectors, SPACS can make structured investments, turn around, and grow distressed companies instead of just meeting the exigency of an immediate solution. To conclude, it is of significant value that in the realm of the corporate restructuring, SPACs come across as a favourable means of enhancing the efficacy of the existing insolvency regime in the country and more particularly that of the IBC. A plan to integrate SPACs under the present legislature as a potential resolution mechanism will aid in enhancing India’s ability to deal with intricate insolvency cases, especially at the time of Scheme of Arrangement under IBC, 2016..

This study explores the reimagining of higher education through hybrid and online learning models among college students in and around Chennai, including Chengalpattu and Kanchipuram districts of Tamil Nadu, India. A structured survey of undergraduate and postgraduate students was conducted to assess key dimensions of digital education such as student engagement, satisfaction, access to technology, instructional effectiveness, institutional support, and academic performance. Statistical tools including correlation, regression, and chi-square tests were applied to identify significant predictors of learning outcomes in hybrid and remote learning environments.

The findings reveal a strong student preference for hybrid learning, emphasizing benefits such as flexible schedules, self-paced learning, reduced commuting time, and access to recorded lectures. Despite these advantages, students reported challenges such as inconsistent internet connectivity, lack of peer interaction, and inadequate technical support. A multiple regression analysis indicated that approximately 37.7% of the variance in academic performance could be explained by key factors such as access to technology, quality of student support services, digital literacy, and an inclusive learning environment. Notably, no significant association was observed between students' age group and their preferred learning mode, suggesting generational adaptability to digital formats.

The study highlights the critical importance of institutional readiness, robust digital infrastructure, and inclusive pedagogical practices in ensuring effective and equitable digital learning. It also reflects on the long-term implications of the post-pandemic educational shift, advocating for sustained investments in hybrid learning models that balance technological innovation with student-centric design. These insights offer valuable guidance for educators, administrators, and policymakers aiming to transform higher education in the Chennai region through resilient, flexible, and future-ready learning systems.

Genetically modified ingredients (GMIs) represent a critical innovation within the evolving global food landscape, offering potential solutions to challenges like food security, agricultural sustainability, and climate resilience. Yet, they remain contentious, especially in countries like India where GM foods have not yet been fully commercialized for human consumption. This study investigates the current state of consumer awareness and perception regarding GMIs through a structured descriptive survey conducted among Indian consumers, focusing predominantly on urban and semi-urban demographics. The research explores how three primary factors—consumer knowledge about GMIs, attitudes and perceptions towards genetically modified foods, and food-related habits such as label reading and preference for non-GMO options—contribute to overall awareness of GMIs.

Quantitative analysis, including reliability assessments, correlation matrices, and multiple regression modeling, revealed that all three constructs significantly influence consumer awareness. The regression model confirmed a strong predictive relationship. Among the predictors, perception emerged as the most influential factor, suggesting that consumers with strong positive or negative attitudes—whether trust in biotech or concern about health and ethics—are more attuned to GMO-related issues. Knowledge, encompassing factual understanding and information-seeking behavior, showed a moderate effect, while habits like ingredient checking and certification awareness also contributed meaningfully.

The study highlights a complex interplay between cognitive (knowledge), affective (attitudes), and behavioral (habits) dimensions in shaping consumer awareness. It affirms that awareness is not solely a function of information exposure but is also driven by underlying values, perceptions of risk and benefit, and individual shopping practices. These findings align with international literature emphasizing the centrality of trust, perceived naturalness, and scientific literacy in public acceptance of food biotechnology.

In terms of policy and practice, the results indicate that any future rollout of GM foods in India must be accompanied by proactive consumer engagement strategies. These include transparent labeling, inclusive public dialogues, educational outreach to clarify scientific facts and address safety concerns, and collaboration with trusted regulatory bodies and academic institutions. Ultimately, fostering a well-informed and critically engaged consumer base will be essential for the responsible integration of GM foods into the Indian market. The study provides timely insights that can inform stakeholder decisions and shape public communication strategies in the context of emerging food technologies.

The application of AI, Blockchain, and Industry 4.0 technologies thus transforms the IT supply chain towards sustainability and resilience within different industries. These include IoT and Big Data. They help to optimize the production processes, reduce waste, enhance resource consumption, and further support instant decision-making toward environmental sustainability. Industry 4.0 also inspires state-of-the-art logistics, smart factories, and increased transparency through blockchain technology, which improves traceability and security. But such great improvements come with challenges like expensive implementation, digital skills gap, and resistance to change. Additionally, emphasized the need for flexible supply chains, where AI, Blockchain, and IoT offer the most suitable solutions to avoid future interruptions. Still-research gaps exist about the integration of these technologies in reverse logistics and more sustainable manufacturing. This research aims to study the potential of these technologies in optimizing the IT supply chain towards overcoming issues encountered and future opportunities toward adoption. The present research employs systematic reviews and sophisticated methodological quantitative procedures for identifying trends, gaps, and impact concerning the sustainability of supply chains with AI, Blockchain, and IoT technologies. Findings reveal that the technologies mentioned previously have considerable potential not only for improving operational efficiency but also sustainability; this will in turn have an inherent impact on the innovations and future strategies of supply chains themselves.

The objective of this study is to enhance SCM through the collaborative networks approach for social change and collective impact. This is achieved by making use of the theory of social network and structural equation modeling while looking at competencies at various levels, which include individual, organizational, and inter-organizational levels. The research examines the importance of organizational awareness, supply network competence, and collaborative awareness concerning how these competencies influence strategic partnerships and overall firm performance. Social exchange theory (SET) is further utilized to understand the influence of trust, commitment, reciprocity, and power on information sharing and collaboration within supply chains. The study focuses on empirical analysis that uses a case study in the manufacturing sector, in order to argue that knowledge exchange and inter-organizational relationships produce competitive advantages. Findings and implications: In an effort to facilitate more sustainable, equitable, and efficient supply chains in the face of global challenges, the research develops a multi-level framework for SCM integrating human resource management practices, collaborative behaviors, and technological solutions.Collaboration, Collective Impact, Supply Chain Management, Social Change, Stakeholder Engagement.

The article explores integrating practices of the circular economy into the supply chain management to optimize resource use and enhance environmental sustainability. All these have evidenced the transformation from the traditional linear supply chain model into a circular regime in which the strategies supporting the recovery, reuse, and regeneration of resources have unfolded. The central concept woven through the study of the circular economy is the sharing of such concepts as minimization of waste, extending the life cycle of products, and reduction of resource use in all stages of the product value chain right from product design to end-of-life management. This article will closely examine the economic, environmental, and social implications of sustainable waste management over the supply chain ranging from sourcing of the raw materials products made in a landfill to the delivery of end products. Such efforts involve a value chain that encourages the use its design benefits in reducing waste disposal, as well as explores in other related features and issues. Moreover, the research extends to understanding the factors resisting the employment of circular economy and commerce in entities, due to such things as a lack of the proper technology, excessive regulations or even the very psychological fear of change itself. It also identifies that firms need capitals in the form of comprehensive approach and actions which counteract linking up things.

This paper examines customer expectations and experiences with mobile retail apps within the modern retail industry, aiming to fill gaps in the existing literature by thoroughly evaluating factors impacting customer satisfaction. Employing a survey-based methodology with 226 respondents, data analysis utilizes the Customer Satisfaction Index (CSI) and Importance-Performance Analysis (IPA) methods. Findings reveal discrepancies between customer-perceived importance and actual performance of features like post-sale service and discounts, highlighting areas where customer expectations are not met. The study's novelty lies in its focus on the critical role of mobile apps in meeting customer expectations, offering valuable insights for businesses navigating the digital retail landscape. Theoretical implications extend to structured approaches for evaluating customer assessments and the application of service quality theories to mobile retailing. Practical implications guide e-retailing practitioners in optimizing resource allocation to enhance customer satisfaction

This study reframes Working Capital Management (WCM) as a driver of both internal financial efficiency and consumer-facing outcomes. While prior WCM research largely focuses on profitability, the consumer-side effects remain underexplored. Addressing this gap, the study proposes and tests a dual-path framework linking WCM to profitability through consumer-linked channels. Using Indian listed firms from 2001 to 2024, the panel data regressions show that inventory and receivables efficiency, and Cash Conversion Cycle (CCC) optimization, significantly enhance profitability. Payables management supports strategic supplier credit use. Structural Equation Modeling (SEM) reveals that consumer-linked pathways are statistically significant. Inventory efficiency enhances profitability via product availability and satisfaction. CCC optimization improves profitability through stable cashflows and reinvestment in service. Trade credit policies bolster pricing flexibility and competitive positioning. Sectoral WCM strategies influence consumer reliability through operational risk management

In an increasingly volatile global economic environment, accurate forecasting of currency exchange rates is critical for investors, policymakers, and multinational corporations. This study presents a comparative analysis of three distinct machine learning approaches—Linear Regression (traditional), Random Forest (tree-based ensemble), and Long Short-Term Memory (LSTM, a deep learning model)—to predict the INR/USD exchange rate. Using daily exchange rate data supplemented by key macroeconomic indicators, we examine the predictive accuracy and robustness of each model across multiple performance metrics, including RMSE, MAE, and directional accuracy. The results reveal significant performance differences, with LSTM outperforming in capturing sequential temporal dependencies, while Random Forest demonstrates strong short-term prediction accuracy through non-linear feature interactions. Linear Regression, while easy to implement, is limited in handling volatility and non-linearity. This paper not only highlights the strengths and limitations of each approach but also provides practical insights into the applicability of machine learning models in currency forecasting. Our findings offer a nuanced understanding of model suitability under varying data conditions, contributing to the growing field of AI-driven financial analytics

Numerous advantages of risk management contribute to an organization's performance, resilience, and sustainability across a variety of industries. First of all, it improves decision-making by giving decision-makers a thorough grasp of potential risks. This helps them make decisions that minimize exposure to uncertainty and are in line with corporate goals. Second, as stakeholders are more conscious of the risks connected to their actions and duties, it cultivates an organizational culture of responsibility and transparency. Consequently, this fosters increased confidence among interested parties and improves the company's standing with regulators, investors, and clients. Moreover, by recognizing and managing possible risks before they materialize into expensive situations, like monetary losses, efficient risk management can result in cost savings, litigation or hiccups in business operations. Furthermore, it facilitates the ability of firms to grasp chances for innovation and expansion through proactive risk management related to novel undertakings, technological advancements, or market trends. Additionally, risk management increases resilience by empowering businesses to quickly recover from unanticipated occurrences and adjust to changing conditions, which lowers the chance of catastrophic failures and maintains business continuity. Overall, risk management offers advantages that go beyond simple risk reduction; these advantages include organizational resilience, financial stability, and strategic advantage, setting up companies for long-term success in a market that is becoming more and more unstable and competitive.

All things considered, the benefits of risk control go beyond simple risk reduction; they also include organizational resilience, operational effectiveness, and strategic advantage, setting up companies for long-term success in a more complicated and dynamic business environment.

It is a well-known fact in finance that the price of financial assets because of volatility are difficult to forecast. Although economists like to use models only few do the right modelling. Modern technology developments like Machine Learning (ML) and Deep Learning (DL) have opened exciting new possibilities for improving prediction accuracy in financial forecast. The broader objective of this research paper is to choose right model and predict right prices for the financial assets for investors and portfolio managers. In this context, this research paper highlights how the Decision Tree, Random Forest and Long Short-Term Memory (LSTM) algorithms help to forecast the stock price in case of NIFTY 50 index. To achieve the objective historical stock data from the years 2014 to 2024 is used and preprocess it with feature engineering, normalization, and the sliding window approach. For analysis, MSE, RMSE, MAE and R² are used to evaluate the performance. It tests hypothesis through (t-tests) and volatility-specific analysis to check robustness. The data shows that Random Forest has the best performance, but LSTM is more sensitive to volatility. This research is useful for investors and financial analyst who can use ML and DL for better decision-making regarding stock market forecasting..

The research paper examines the influence of Gandhian principles, particularly Sarvodaya, on rural development and community welfare, with a focus on Bhoodan Pochampally Village. The study explores how Gandhian thought has shaped the MSMEs development for uplifting the rural communities, especially through the implementation of Geographical Indication (GI) tagging. The study investigates how GI tagging of local products aligns with Gandhian ideals by fostering economic self-sufficiency, preserving cultural heritage, and improving the livelihoods of the rural population in the form of development and growth of MSMEs. The study evaluates the economic benefits, social infrastructure improvements, and empowerment of local communities brought about by GI-tagged products.  Through a detailed study, the paper contextualizes the development of MSMEs and rural empowerment in Bhoodan Pochampally within the framework of Gandhian values, particularly self-reliance, rural development, and ethical stewardship. The study provides a practical example of how Gandhian thought continue to influence rural settings through highlighting the significance of GI tagging as a tool for MSMEs development in terms of employment opportunities and the preservation of traditional knowledge.

The Term Sustainable finance has taken a paradigm shift in the recent times.  The investment in sustainable finance has taken an rapid shift in transforming towards the greener future. The transformation towards the industry 5.0 has given a large impact in focusing on protection of environment and sustainability. The entire globe is shifting its focusing on giving more importance towards the United Nations Sustainable Development Goals. This has impacted several nations in bringing sustainable investment for achieving the UN SDGs. The current study is to identify the research work that are being shaped towards the Environment Sustainable Governance (ESG). The research aims to understand the investments that are carried out towards sustainable finance and brings ways for shaping the future. The research also tries to bring impact of the future scope of the research that has to be carried out with the impact of the investment in the green finance

The present study evaluates the risk-adjusted performance of selected open-ended equity mutual fund schemes in India, focusing on Midcap, Infrastructure, Flexicap, and Smallcap categories. The shift from traditional bank deposits to mutual funds are analyzed and evaluated as investors seek higher returns and a better understanding of risk dynamics. Fund returns were calculated over this five-year period (April 2019 to March 2024) using secondary data sourced from AMFI, SEBI, and NSE, while risk and risk-adjusted returns were analyzed for the one-year period of April 2023 to March 2024. The study employs key financial metrics such as Beta, Standard Deviation, Sharpe Ratio, Treynor’s Ratio, and Jensen’s Alpha to evaluate fund performance. Key findings revealed that HDFC Midcap Fund demonstrated lower beta values, indicating reduced volatility, and superior risk-adjusted returns in the Midcap category. Similarly, Nippon Power and Infrastructure Fund excelled in the Infrastructure category with high Sharpe and Treynor’s ratios, HDFC Flexicap Fund showed strong performance with lower volatility and favorable risk-adjusted returns in the Flexicap category, and Nippon Smallcap Fund outperformed in the Smallcap category with higher Sharpe and Treynor’s ratios, signifying superior risk-adjusted returns. This comprehensive evaluation provides valuable insights for investors, emphasizing the importance of risk-adjusted performance metrics in making informed investment decisions in a dynamic financial market. Overall, the infrastructure fund category showed the most consistent outperformance across all risk-adjusted metrics.

This research paper examines the evolving landscape of Alternative Dispute Resolution (ADR) mechanisms in addressing novel sports controversies beyond traditional playing field disputes. As the sports industry continues to expand into new domains, including esports, technology-enhanced competition, and name, image, and likeness (NIL) rights, conventional litigation processes prove increasingly inadequate. Through analysis of recent case studies and emerging ADR frameworks, this paper demonstrates how mediation, arbitration, and hybrid resolution models are being adapted to address these unprecedented challenges. The research identifies significant advantages of ADR in sports contexts, including time efficiency, cost-effectiveness, relationship preservation, and specialized expertise. However, it also highlights critical limitations in current frameworks, including jurisdictional challenges, power imbalances, and procedural gaps. The paper concludes by proposing modifications to existing ADR structures, including technology integration, specialized mediator training, and flexible frameworks designed specifically for emerging dispute categories. These findings have significant implications for sports governance bodies, legal practitioners, and athletes navigating an increasingly complex sports landscape where disputes extend far beyond traditional competition concerns...

The study examines the effect of perceived risk, trust, perceived benefits, electronic word of mouth (eWOM), and brand attitude on online purchase intention and user satisfaction in India. The study refers to the Technology Acceptance Model (TAM) and an analytical research design to test the effects of selected variables in its theoretical framework. The research questions are focused on the relationship between perceived risk, trust, and perceived benefits on online purchase intention, user satisfaction, and user loyalty. The study found that perceived risk negatively affected online purchase intention and user satisfaction, while trust positively influenced them. Perceived benefits had a positive relationship with online purchase intention and user satisfaction. Hedonic, utilitarian, and social values of perceived benefits were significant predictors of online purchase intention. The study contributes to the existing model by adding selected variables for further testing. The study found that online trust, perceived benefits, electronic word of mouth, and brand attitude have a significant positive impact on user satisfaction, while perceived risk has a significant negative impact. Brand attitude had the largest impact on user satisfaction, followed by online trust. The study suggests that businesses should focus on improving brand attitude to increase purchase intention, mitigate perceived risk by emphasizing benefits and providing credible information over the net, and maintain a positive online identity and reputation to increase trust. The study also suggests that there is a moderate positive relationship between User Satisfaction and User Loyalty

Behavioral biases play a critical role in shaping the investment decisions of equity investors, often leading to deviations from rationality as posited by traditional financial theories. This study investigates the influence of four major categories of psychological biases—heuristic bias, prospect bias, herding bias, and the disposition effect—on the investment decision-making (IDM) processes of equity retail investors. Employing Structural Equation Modeling (SEM) combined with Partial Least Squares (PLS) and Necessary Condition Analysis (NCA), the research evaluates both the relative and individual impacts of these biases on IDM. Data was collected from 704 equity investors with at least five years of active portfolio management experience, using a rigorously validated survey instrument. The findings reveal that heuristic biases have the highest impact on IDM, followed by prospect biases, herding bias, and the disposition effect. SEM results show a strong explanatory power with 56.6% variance in IDM accounted for by these biases. Complementary NCA results highlight the sequential necessity of prospect and herding biases before the effects of heuristic bias and disposition effect emerge. This study contributes to behavioral finance by presenting a robust model integrating SEM-PLS and NCA to quantify bias impacts and proposing a framework to mitigate these effects. Practical implications include strategies for enhancing investor decision-making through tailored financial education and tools for bias recognition and management. These insights are valuable for academics, policymakers, and practitioners seeking to reduce the detrimental impact of biases on equity market efficiency

Sales are at the core of any business yet technology in support of such imperative operational departments has lagged in sophistication and integration. As ubiquitous Artificial Intelligence functionality becomes intuitively accessible in the cloud, organizations have shifted their focus towards vertical SaaS applications which leverage these capabilities within end-to-end process ecosystems. However, most of the SaaS solutions, which enable integration and automation of specific sales operations activities are merely splintered and offer limited or no cross-application process workflow and data support.

Recent advances in integrated intelligent enterprise applications with a vertical focus natively bundle multiple key sub-processes into a single unified application platform. These applications capture rich data by covering broad, deeply connected process areas, which serve as a foundation for superior embedded intelligence. With this approach, SaaS vendors bring sales functionally-specialized applications onto an integrated platform architecture, help organizations cross the process trenches while driving integration amid the increasing process specialization. However, the approach circumvents the limitations of horizontal multi-departmental enterprise applications for business change and dramatically accelerates organizations’ revenue growth while enabling finance control and bottom-line profitability.

This is especially relevant for the sales function espoused by companies today with the desire to accelerate revenue growth. Despite technology’s long and storied adoption to support sales, the intelligence today is often limited to just process automation and embedded coaching. Application vendors have made incremental progress modernizing with collaboration capabilities. But, with the anticipated second wave of sales performance technology adoption on the horizon once customer adoption of AI for sales expands, often incremental enhancements wouldn’t be enough. Increasingly disparate capabilities across point solutions, and disparate processes and data from tools already used, will aggravate the difficulty of deploying product intelligence and process automation, creating new challenges for a sales organization’s operations.

In sales environments with a direct selling force, territory management and quota setting is an essential process but is often done manually with limited real insights behind the qualitative assessments. Data availability in this context has been devoted to enhance this process logically, explicitly, and transparently. In this spirit, following the first offer of a measurable workflow (i.e., the Generalized Flow), predictive techniques in modeling the winning propensity are explored to support the qualification of the depths of the sales funnel as well as the quota setting process in salesforce allocation, evaluating the theoretical and practical value of its implementation. In a salesforce quota and territory optimization flow, three fundamental parts are addressed with industry cases: the lead grouping much widened by data-driven building rules and technique integrations, the territory management with optimized routing paths both in the existing lead distribution process and time added contacts, and the allocation of quotas wrapping each location and salesmembers. This sales prediction workflow stands at the meeting point of data science, sales action pipelines, and business understanding.

Along the predictive move, the first step involves knowing the background of the sales industry and the predictive process, as well as the opportunity construction and sales technology in the business; the second step is to fully leverage the acquired information of that business environment and the insights of the data; the third step is to prepare and preprocess the features as predictors for the winning propensity prediction. Once the geographical segmentation is decided, the attention will move to assign sales personnel to each territory in the sales quota settings. The territory management problem is defined as MCSPP with minimum contacts to cover maximum potentials along the rerouted length constraints, and solved by a heuristic algorithm based on heuristics and a non-linear programming solver. Implementing this systematic approach will greatly alleviate the burden of quota settings, such as blind manual adjustment of quotas over territories or inaccurately fixed quota ratios across salespersons. Instead of being arbitrary or with limited quantitative support, quota distribution can be conducted on a quantitative basis. The territory management and quota settings in this study are tailored for business adoption by fitting the data environment of the state-of-the-art analytics

This research uses remote sensing technology and machine learning to study the cultural landscape and knowledge traditional beliefs of India. Satellite images of high resolution are brought together with data collected by the public in specific areas to form a complete database. The researchers discovered culturally significant landscapes with the help of a Random Forest and recognized sacred groves, traditional farms on terraces and other heritage sites. Through this approach, we can precisely observe how the land and its management have evolved with Indigenous beliefs. Using sophisticated remote sensing with participatory validation helps achieve both accuracy in science and respect for traditions. Using this method helps to identify and manage the relationships that exist between Indigenous communities and the environment.

Consumers, before making buying decisions today, are interested in knowing what are existing consumers saying about the brand and the products. This interaction between existing consumers and potential buyers has added a new dimension to how marketers interact with their target audience. Social listening is a powerful research tool capable of generating actionable insights in time to seize opportunities and customize the product as per customers’ requirements. While both the technology and the idea for social listening have been a buzz everywhere, most brands are still trying to probe what consumers are saying about them in real time. Social listening allows a brand to modify its marketing content based on customers’ needs and requirement. This Paper will analyze how Social Listening is emerging as a new Marketing radar.

Instead of viewing consumers solely as purchasers who react to product attributes and pricing, human-centric marketing treats them as humans with heartbeats who have feelings, dreams, and specific wants and needs. This strategy facilitates enhanced relationships between customers, salespeople, and other relevant parties, which in turn leads to a sustainable and favorable outcome for the businesses. As Industry 5.0 emerges, with its focus on the human-centric collaboration between humans and advanced technologies, human-centric marketing is poised to play an integral role in the future of commerce.  Industry 5.0 combines state-of-the-art technology with human intellect, and action and needs to be integrated within the processes and allows the creation of personalized and value-driven interaction between the consumers and the producers in both goods and services. Marketing strategic decisions cannot just be transactional anymore they must gain trust, and people can be trusted only when there is empathy and innovation. This change is made possible partly by technology, which allows companies to take advantage of artificial intelligence (AI), big data, and automation while continuing to provide the touch of a human in customer interactions. This is a shift in paradigm, especially relevant to emerging economies like rural India where we are witnessing a revolution in digital to the way businesses and marketing have been run conventionally. Rural India is a good example of using technology to carry out human-centric marketing very successfully. While certain regions have significant infrastructural gaps, many have successfully adopted digital tools, embraced mobile applications, or taken a step further into social commerce to boost customer engagement. Programs using AI-based chatbots, vernacular content, and digital payment solutions are allowing rural consumers to connect to solutions catering to their specific requirements. In addition, companies making use of community-based marketing plans, helped by social sites and small networks, have built brand trust and loyalty.  Industry 5.0 principles highlight the need for a more holistic and human-centric approach that combines the benefits of technology with the essence of our humanity. If the customer experience is kept at the center while integrating digital innovations, it can help businesses create authentic interactions, build inclusivity, and improve long-term sustainability. The human angle of marketing combined with the technology employed for this purpose that is demonstrated in (and has existed in) rural India, gives companies around the world an example to live by and represents the future of more personalized and effective marketing.  This paper recognizes the main variables inherent to the successful implementation of such strategies and presents a model for their implementation

The rapid expansion of social media platforms has revolutionized traditional marketing paradigms, giving rise to a new form of consumer persuasion: digital word-of-mouth (eWOM). Within this landscape, micro-influencers—individuals with a modest but highly engaged following—have emerged as powerful voices capable of shaping consumer attitudes and purchase decisions. This paper explores the measurable impact of micro-influencers on consumer buying behavior, focusing on how their perceived authenticity, credibility, and relatability enhance trust and engagement. Drawing from an extensive body of interdisciplinary literature, the paper synthesizes key findings from marketing, psychology, and communication studies to assess the mechanisms through which micro-influencers exert influence. Unlike traditional celebrities or macro-influencers, micro-influencers often maintain closer interactions with their followers, fostering a sense of community and personalized communication that significantly affects brand perception and purchase intent. The review also investigates the metrics commonly used to evaluate micro-influencer effectiveness, including engagement rates, sentiment analysis, and conversion tracking. Furthermore, it examines emerging trends such as influencer-brand alignment, the role of niche content, and consumer skepticism in digital endorsements. By consolidating current insights, this paper provides a comprehensive understanding of how micro-influencers shape digital consumer journeys, highlighting practical implications for marketers aiming to leverage eWOM strategically. The findings underscore the importance of authenticity and trust in digital spaces and suggest directions for future research to better quantify and optimize micro-influencer campaigns. This study ultimately positions micro-influencers as key agents in the evolving landscape of consumer influence, offering both theoretical contributions and actionable insights for academia and industry alike.

In the face of mounting environmental concerns, climate change, and consumer awareness, the concept of green products has gained significant traction. Over a number of years the laureates, socialists, environmentalists and academicians are trying to disseminate knowledge about environmental awareness and sustainable practices. Due to priceless efforts of above mentioned stakeholders of environment the consumers  and  manufacturers have  directed  their attention  toward  environment  friendly  products or Green Products. These products, designed to minimize negative environmental impact and promote sustainable consumption, have influenced a shift in consumer behaviour worldwide.

From 2020 onward, social influence has emerged as an increasingly influential factor. Social norms, peer recommendations, influencer endorsements, and societal expectations contribute to shaping consumer attitudes toward sustainability. The integration of social media platforms has further amplified these influences, allowing consumers to observe, emulate, and validate green behaviours in real-time digital environments.

In this paper the researchers highlight the effect of social influence as a moderator on purchase intention of green products that have been used to fulfil daily needs. 257 questionnaires were distributed through online/offline mode and among them only 185 questionnaires were fit for data analysis. Further moderation analysis is applied to analyze data which revealed that the social Influence significantly moderates the relation of purchase intention and green products.

Brand loyalty is a core asset for businesses, yet consumers often switch brands even when no critical issues arise with their habitual brand. Traditional theories fail to fully explain this phenomenon. This study introduces "mainstream fatigue," a psychological effect wherein prolonged exposure to a brand leads to diminished interest and a desire for novelty. Through three empirical studies (N = 1031), we demonstrate that cognitive satiation directly drives brand switching by reducing interest in a brand's functional attributes, while emotional satiation influences brand switching through its interaction with cognitive satiation. Mainstream fatigue mediates the effect of cognitive satiation on brand switching, highlighting the depletion of psychological resources. Furthermore, brand inertia mitigates switching behaviors by counteracting mainstream fatigue. This research provides a dynamic perspective on brand loyalty, extends theories of brand switching, and offers actionable strategies for improving brand management and customer retention in competitive markets...

The integration of UPI into Mumbai’s financial system marks a crucial step in advancing financial inclusion. Guided by the Diffusion of Innovation (DOI) theory, this study highlights how factors like ease of use, compatibility, and awareness influence UPI adoption. Addressing challenges and expanding access will ensure inclusive digital growth. The study employed a variety of data collection instruments to effectively analyze UPI adoption and its impact on financial inclusion in Mumbai City. Structured questionnaires were used to gather quantitative data from small business owners, daily wage earners, and low-income individuals, focusing on transaction frequency, adoption challenges, and financial literacy. Semi-structured interviews with bank officials, fintech experts, and policymakers provided qualitative insights into issues such as policy implementation, trust, and security. Focus group discussions further explored user experiences and behavioral factors, while direct observations in marketplaces captured real-time digital transaction practices. This multi-instrument approach ensured a comprehensive and in-depth analysis. The study clearly establishes that UPI payments significantly contribute to financial inclusion in Mumbai City. Both Chi-square and T-test results indicate that UPI users experience a higher level of financial inclusion than non-users. Regression analysis further shows that perceived ease of use and usefulness of UPI strongly influence its adoption. However, widespread adoption is hindered by concerns such as security risks, lack of digital literacy, and poor internet infrastructure. According to the Diffusion of Innovation theory, UPI adoption is higher among early adopters and lower among the late majority and laggards. To enhance adoption and deepen financial inclusion, steps must be taken to strengthen security, improve digital literacy, ensure reliable connectivity, and expand UPI usage across sectors including public services, rural markets and international remittances.

This study examines the impact of behavioural biases and demographic characteristics on life insurance purchase intentions. The research highlights how cognitive factors such as overconfidence, loss aversion, social norms, and hyperbolic discounting influence financial decision-making and how these effects are moderated by age, gender, and education. A quantitative research design was employed using survey data from 509 urban respondents, especially those from lower income group or unorganized sector. Hierarchical multiple regression analysis was applied to assess the direct effects of behavioural biases on life insurance purchase intentions and the moderating role of demographic variables. The study leverages prospect theory and behavioural economics to interpret consumer decision-making patterns. The findings indicate that overconfidence negatively impacts life insurance purchase intention, while loss aversion and social norms positively influence it. Hyperbolic discounting shows a marginal negative effect. Additionally, demographic factors moderate these relationships, with higher education mitigating overconfidence, age reducing its negative impact, and social norms influencing women’s purchase intentions more significantly than men’s. Findings suggest that insurers should develop targeted financial education programs to mitigate overconfidence and enhance consumer awareness. Marketing strategies can leverage social norms to improve insurance adoption, particularly among women. Customized insurance products and incentives addressing behavioural biases may enhance policy uptake.

This study investigates the influence of digital marketing strategies on consumer behavior in the retail sector of Bangalore city, with a focus on key touchpoints such as website/app usability, influencer marketing, personalized recommendations, and online promotions. A structured questionnaire was distributed among 190 urban retail consumers using convenience sampling to capture real-time digital engagement patterns. The research employed a quantitative methodology, leveraging descriptive statistics, correlation, and regression analysis to examine how these digital strategies affect consumer decision-making in the retail domain. The demographic insights revealed that middle-income, digitally active salaried professionals form a significant portion of the urban retail audience. Reliability analysis using Cronbach’s Alpha confirmed strong internal consistency across digital marketing variables. The correlation analysis identified strong interdependencies between digital marketing tools and purchase behavior in the retail sector, while regression results showed that personalized recommendations and customer loyalty were key predictors of buying decisions. The findings underline the strategic importance of targeted digital marketing campaigns for retailers seeking to improve engagement and conversion rates. To maximize effectiveness, retailers should invest in user-friendly platforms, relevant digital content, and AI-powered personalization

As customer recommendation by word-of-mouth can attract more customers, this research deals with Customer recommendation prediction for a furniture store like IKEA. This research compares the machine learning models for Recommendation, namely, Artificial Neural Network (ANN), Logistic Regression (LR), and Linear Discriminant Analysis (LDA). The research is based on customers' feedback about the first store in Hyderabad, India. In this research work, IKEA's service is explored, and the models created are successful based on the predictor variables used in the study as the model's inputs. The abovementioned techniques were used to predict the customer recommendation of IKEA to their neighbours, family, and friends. It is evident from the classification outcomes of different models that ANN outperforms the other techniques, namely, the Logistic Regression model and LDA.  As a managerial implication, this model could be used by any furniture or multinational retail stores to predict their customer recommendation status to improve their market and profit. Customer recommendation prediction in national/multinational stores, especially retail furniture stores, does not exist in the literature. This research aims to fill the gap in the literature to predict the customer recommendation status of retail furniture stores or any other multinational stores

Purpose
This study explores individuals attitudes and behaviors towards the Deposit Return Scheme (DRS), particularly in the Netherlands, and how these experiences align or misalign with the way the scheme is promoted through institutional communication. While the DRS rely heavily on citizen engagement for their effectiveness, research on how consumers respond emotionally and behaviorally to such schemes, or how they relate to the narratives presented in official campaigns is still limited.

Methodology
This research combines two qualitative studies. Study 1 used netnography method, analyzing 2,599 consumer-generated comments on Reddit to explore lived experiences with the DRS. Study 2 conducts an explorative qualitative content analysis of 41 promotional videos published by an official communication channel (Statiegeld Nederland) to identify communication strategies, message framing, and behavioral cues. Both studies are interpreted through behavior change frameworks.

Findings
Findings reveal that while consumers generally support the goals of DRS, participation is shaped by logistical barriers and motivations that go beyond pro-environmental and financial. Consumers often described the scheme as effortful and messy, showing that these contextual factors are a main barrier to adhering to the scheme. Additionally, our data also shows that participation is driven by intrinsic, pro-social and ego related motivations, thus beyond financial rewards. In contrast, institutional communication often relies extrinsic incentives – combination of environmental and financial rewards, which may overlook other drivers or barriers of participation. This misalignment may undermine behavioral engagement over time.

Implications
The study highlights the importance of aligning policy communication with the lived experiences, emotional drivers, and psychological proximity of consumers. It contributes to theories of behavior change, self-determination, and psychological distance, and offers actionable guidance for improving DRS design and outreach..

Climate change, driven largely by carbon dioxide and greenhouse gas (GHG) emissions, has become one of the most pressing global challenges, triggering extreme weather events, biodiversity loss, and rising sea levels. While international agreements such as the Paris Agreement and COP26 have set ambitious targets to limit global warming, the corporate sector plays a critical role in achieving these goals.  Corporate Social Responsibility (CSR) and the Environmental, Social, and Governance (ESG) framework have encouraged firms to integrate sustainability into their operations, aiming not only for regulatory compliance but also for enhanced corporate reputation, consumer trust, and long-term competitiveness. This study investigates the relationship between carbon emissions and financial performance of corporates in Taiwan’s electronics industry, focusing on the top 50 companies by market capitalization from 2018 to 2022. Using data from the Taiwan Economic Journal (TEJ), the Market Observation Post System (MOPS), and company sustainability reports, we conduct statistical analysis and establish multiple linear regression models to analyze the correlations between total carbon emissions (TCE) and financial indicators such as return on equity (ROE) and earnings per share (EPS). The results reveal that high carbon emissions are significantly and negatively associated with both ROE and EPS, suggesting that companies with better ESG initiatives experience operational and reputational advantages. The findings highlight the financial and strategic importance of robust ESG practices, not only for improving efficiency and reducing risk but also for attracting investors and consumers..

Over the last 30 years, research on women entrepreneurs has proliferated because, although women make up over half of the Indian population, their participation in entrepreneurial activities is severely constrained. The study aims to analyze the dynamics of female entrepreneurship and the drivers thereof. The study results point to the fact that income is the critical factor driving entrepreneurship in India and other countries across the globe. Using both primary and secondary data, we can show that there are significantly fewer women entrepreneurs across economies. We also prove that the number of women entrepreneurs is substantially more from lower economic regions. This trend is actual both in India and Globally. This study gives us a clearer picture of why women choose to be entrepreneurs

This study aims to explore the inter-relationships among three marketing constructs, namely brand image, brand loyalty, and repurchase intention, and assess how these constructs influence consumer behaviours.  A case study with a quantitative analysis on an ice cream product of a Taiwanese convenience store chain, i.e., FamilyMart, is conducted.  Based on data samples from a questionnaire survey, statistical methods, which include correlation and regression analyses, are conducted to examine and deduce insights into consumers’ purchasing characteristics.  The results indicate that brand image positively impacts brand loyalty, while brand loyalty strengthens repurchase intention.  Overall, the findings highlight the interconnected influence of brand image, brand loyalty, repurchase intention, illustrating their combined impact on consumer behaviours.  This study provides practical recommendations for market promotion and brand building, offering insights to help merchants maintain a competitive edge in the market and achieve sustainable growth. 

Traditional marketing strategy has given birth to a new paradigm i.e influencer marketing driven by the growth of social media and millennials' culture.  Social media marketing persuades and affects the individual’s buying behaviour, and influencers are one of the valuable sources to resonate with millennials.

According to researchers, Marketers have a fantastic opportunity to take advantage of sales and product recognition. In the beauty industry, influencers play a pivotal role in marketing and brand promotion. These influencers—including makeup artists, beauty bloggers, vloggers, and social media personalities—are renowned for their expertise, credibility, and passion for beauty products. By collaborating with beauty brands, they create engaging content such as tutorials, product reviews, unboxings, and endorsements, effectively showcasing and promoting a wide range of products. This content not only builds brand awareness but also helps drive consumer trust and purchasing decisions, making influencer collaborations a powerful tool in the beauty marketing landscape. The paper focuses on: a. Impact of antecedents of beauty product influencers on pre-purchase product evaluation b. the influence of positive and negative reviews of social media on post-purchase cognitive dissonance.

Primary data from women’s millennials will be collected, highlighting their positive impact on purchasing behaviour.

This research investigates the effect of greenwashing on consumer behavior and particularly on boycott intention and negative word of mouth, while also analyzing the moderating role of environmental concern. A conceptual model was developed from the proposed hypotheses based on the theory of reasoned action. The data of this study were collected by an online survey of 322 Tunisian consumers that are familiar with fast-fashion brands. Partial least squares structural equation modeling (PLS-SEM) was used to analyze data and test the direct, mediation and moderation relationships. The results indicate that greenwashing exerts a positive impact on consumers' intention to boycott, which subsequently influences negative word-of-mouth. Moreover, boycott intentions fully mediate the linkage between greenwashing and negative word-of-mouth. Furthermore, the findings show that environmental concern significantly moderates the effect of greenwashing on negative word of mouth, along with the causal relationship between consumers’ boycott intention and negative word of mouth. This research contributes to the growing consumer behavior literature on greenwashing, as it provides new insights concerning consumer retaliation behaviors in the context of fast fashion and particularly in an emerging market in the MENA region. This study also yields a set of valuable recommendations for businesses, emphasizing the crucial role of authenticity and transparency in the corporate environmental information used by companies.

This study examines how macroeconomic variables influence consumer sentiment and spending patterns in emerging economy like India. In a rapidly growing economy its crucial to understand which factors drive people to spend or hold back. This empirical investigation is crucial for framing effective monetary and fiscal strategies. While field surveys or interviews often fail to capture real- time mood shifts this paper uses a proxy to reflect changes in consumer confidence in form of Foreign Portfolio Investment (FPI). When consumers and households feel positive about the economy it often showcases rising consumer demand and when they pull back it can signal underlying caution.

The analysis makes use of monthly data from April 2010 to December 2024 ranging for 14 years and includes key macroeconomic variables—repo rate, Treasury bill yields, money supply (M3), and Index of Industrial Production (IIP). Present study employs Vector Auto Regression (VAR), Granger causality, Impulse Response Functions (IRF) and Forecast Error Variance Decomposition, (FEVD) to captures both short-term and long-term dynamics

Results reveal that consumer sentiment are sharply influenced by changes in short-term interest rates particularly the repo rate and T-bills. Over a longer horizon liquidity (M3) and industrial output begin to shape sentiment more gradually. Granger results confirm one-way causality from interest rates to sentiment validating the influence of policy signals on public behaviour. The study contributes to academic literature and ongoing discussions around sentiment-driven consumption and demonstrates the importance of transparent monetary policy in sustaining public confidence. It also opens the door for integrating behavioural economics with macro-level indicators in emerging economies like India

The advent of Artificial Intelligence (AI) has fundamentally reshaped the landscape of financial services, giving rise to the era of predictive banking. This paper explores the transformative role of AI in forecasting consumer financial behavior, highlighting current methodologies, emerging technologies, and future implications. By integrating machine learning, deep learning, and data analytics, financial institutions are now capable of analyzing vast amounts of transactional, behavioral, and demographic data to generate accurate and dynamic predictions about customer actions, such as spending habits, credit risk, loan defaults, and investment preferences.

The paper systematically reviews key AI-driven models used in predictive banking, including decision trees, neural networks, support vector machines, and ensemble learning techniques. It also discusses the integration of natural language processing (NLP) for interpreting unstructured data from customer communications and social media. Particular attention is given to personalization engines and real-time analytics, which allow banks to deliver tailored services, reduce operational risk, and improve customer engagement.

Ethical and regulatory considerations are examined, especially concerning data privacy, algorithmic transparency, and potential biases in predictive models. The paper underscores the importance of balancing innovation with responsible AI practices to ensure trust and fairness.

Furthermore, the study identifies current challenges—such as data quality, model interpretability, and evolving consumer expectations—and offers insights into future research directions. These include federated learning, explainable AI, and integration with blockchain for enhanced security and transparency.

Predictive banking represents a paradigm shift in the financial industry, enabling institutions to move from reactive to proactive strategies. By harnessing the predictive power of AI, banks can not only optimize operations but also anticipate customer needs with unprecedented accuracy.

The rapid advancement of Artificial Intelligence (AI) is reshaping the global economic landscape, ushering in a transformative era known as the AI economy. This paper explores the dynamic interplay between AI technologies and economic growth, with a particular focus on intelligent finance—a domain where AI-driven tools and systems are revolutionizing financial decision-making, risk assessment, asset management, and market forecasting. By synthesizing recent academic and industry research, the paper examines how AI contributes to productivity enhancements, innovation acceleration, and the evolution of new business models across sectors. It also delves into the structural changes AI imposes on labor markets, capital flows, and regulatory frameworks, highlighting both opportunities and challenges that arise from this transition.

A central theme is the concept of "intelligent finance," where machine learning, natural language processing, and automated systems facilitate data-driven strategies, enabling more accurate financial planning and investment decisions. The review critically evaluates how these technologies influence macroeconomic indicators, financial stability, and global competitiveness. Furthermore, the paper discusses ethical considerations, including algorithmic bias, data privacy, and the socio-economic implications of AI-induced disruption.

Ultimately, this study offers a comprehensive overview of the emerging AI economy, providing insights into how nations and enterprises can adapt and thrive. By identifying key trends, policy gaps, and future research directions, the paper serves as a foundational resource for academics, policymakers, and financial leaders navigating the intersection of AI innovation and economic development in the 21st century.

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Purpose- The aim of the study is to examine the impact of perceived ease of use (PEOU) and perceived usefulness (PU) of the AI applications introduced in the aviation sector on the online experience (OE) of the customers leading to customer satisfaction (CS) and resulting in purchase intention (PI) with mediating role of OE.

Design/methodology/approach- A conceptual model is developed to examine the impact of PEOU and PU on OE leading to CS and PI with mediating role of OE based on TAM model. 261 responses were obtained using a questionnaire and analyzed using Structural Equation Modelling (SEM) and Hayes Macros.

Findings- The results reveal that PEOU and PU have significant impact on OE. OE is found to have significant impact on CS and CS has a strong significant impact on PI. The indirect impact of PEOU on CS via OE, PU on CS via OE and is found to be significant proving strong mediating role of OE.

Originality- This study provides empirical evidence to all the direct and unique indirect relationships in the model. The mediating role of OE between two paths- PEOU and CS, PU and CS has not been explored in the previous studies. Previous studies have evaluated technology adoption using TAM model. This study evaluates the OE of the customers who are using AI applications for booking their airline tickets.

Practical Implications- The conceptual model can be used by the aviation industry marketers to evaluate customer’s online experience and their satisfaction level. Based on the outcome, they may redesign and develop their online products and AI applications that are easy to use and useful for the customers. They may provide personalized solutions that are being used by customers for booking airline tickets.

This research explores the impact of social media platforms, particularly Facebook, on businesses' marketing strategies and consumer relationships, focusing on its significant influence on customers' decision-making processes during purchase decisions. By in-depth interviews and an extensive review of secondary data, the research employed "thematic analysis" using NVivo Qualitative Analysis software.

The findings shed light on a variety of subjects, highlighting the significant link between social media marketing initiatives, particularly those on Facebook, and consumer purchasing choices. In addition to its academic consequences, this study is managerially relevant since it identifies factors critical for capturing target consumers and driving purchasing choices. It also represents a larger research trajectory, providing further insights into the complex dynamics of consumer behavior in the changing world of online shopping

Summary Statement of Contribution

This study explores the impact of social media on marketing, particularly Facebook's influence on customer choice. It reveals a direct correlation between purchase intentions and social media marketing activities. The findings offer valuable insights for managers seeking efficient marketing strategies and contribute to academic research, laying the groundwork for further research on customer behavior in online shopping

In the digital era, social media has emerged as a critical medium for consumer-brand interactions, shaping perceptions and influencing purchase decisions. This paper investigates the role of social media-based virtual word-of-mouth (vWOM) in shaping consumer trust and loyalty, while also considering the implications of consumer protection laws in ensuring ethical digital engagement. Using a mixed-method approach combining survey analysis and in-depth interviews, the study explores how user-generated content (UGC), influencer marketing, and peer reviews impact consumer psychology and behaviour. Findings indicate a strong correlation between vWOM exposure and enhanced brand trust, which further mediates long-term consumer loyalty.

Additionally, this paper incorporates the Indian legislative framework, namely the Consumer Protection Act, 2019, and related policies on influencer advertising, to highlight the legal framework that promotes transparent and reliable digital marketing techniques. This legal aspect not only strengthens the need of authenticity and transparency in vWOM but also underscores the necessity of regulatory compliance in fostering customer trust. This study enhances digital marketing literature and legal studies by providing a cohesive framework for using vWOM in a manner that is legally permissible, trust-oriented, and focused on consumer-brand loyalty...

This research analyses how personalised promotional content created by influencers impacts purchase intentions while studying how their traits and cultural flexibility, and social connections shape these results. When ads deliver personal recommendations between influencers and their audience, the result is deepened trust and emotional bonds combined with believable content that leads directly to increased buying intentions. When influencers create personalised marketing ads based on their audience comprehension, their methods enhance focused advertising techniques. According to Hofstede's cultural model, individualist societies react to individualised marketing content that emphasises personal autonomy, yet collectivist societies do best under group-based messaging, requiring international marketers to develop cultural intelligence. Crucial insights emerge from integrative research methods that deliver deep knowledge about influencer reputation structures while measuring consumer understanding combined with emotional and psychological purchase decision factors. Scientific studies reveal that interactive marketing videos captivate audiences better through customised content that directly addresses personal consumer needs to produce better brand engagement. Research limitations include a small survey footprint across limited geographic areas combined with narrow product groups and short-term observations that reduce broad applicability. Future research needs to study influencer-consumer similarity across diverse business sectors and cultural settings through multi-year research designs. The study proposes testing how trust, together with authenticity and social norms, impacts purchase intentions across influencer attributes. Specific analysis for ethical, sustainable business sectors, along with micro-influencer effects, can lead to pragmatic marketing strategies depending on findings. Through this research, marketers gain enhanced capabilities to generate relevant advertising campaigns that capture diverse global audiences by solving current challenges through advanced research methods.

In recent years, scholars have explored Service-Dominant Logic (S-D Logic) aligned with customer engagement (CE) by providing insights into how these topics are connected, used, and deep-dive into the related concepts, including the context and application of artificial intelligence (AI), machine learning (ML), virtual reality (VR), and augmented reality (AR). Correspondingly, it has been shown to yield enhanced customer acquisition, service, and renewal, optimize service calls, and cross-sell recommendations from machine intelligence, and also conduct customer outreach. However, this body of knowledge regarding S-D Logic-aligned CE and Service Design remains siloed. Therefore, the purpose of this paper is to conceptualize and present a novel integrative framework, integrating S-D Logic-aligned CE/Service Design, and also present future research opportunities. AI, AR, and VR will be referred to as machine intelligence (MI), in this paper, unless otherwise specified.

This research investigates the impact of E-rickshaw adoption on the income and social status of owners in urban areas of Bihar and Jharkhand, India. Utilizing a cross-sectional research design and a sample of 368 E-rickshaw owners, the study explores various dimensions of this impact. Findings reveal a significant positive relationship between E-rickshaw adoption and income growth. Moreover, there is a notable positive connection between income growth and social status, yet the direct link between E-rickshaw adoption and social status is non-significant. Mediation analyses suggest a full mediation role of income growth in the relationship between E-rickshaw adoption and social status. The research also examines the moderating effect of education level, revealing non-significant impacts on the relationships between E-rickshaw adoption and both income growth and social status. This study contributes crucial insights into the transformative dynamics of E-rickshaw adoption, offering valuable implications for urban transportation, income generation, and societal status.

The objective of this research is to understand the determinants that affect destination preference and improve the satisfaction of tourists in India. In light of competition, the tourism industry needs to comprehend the primary factors contributing to tourists' contentment or discontent. The impression that tourists have about the places they visit is significant to consider. A total of 409 Indian tourists participated in a survey for primary data collection, which was subsequently analyzed using structural equation modelling and confirmatory factor analysis. The results show that the cultural and heritage perspective, infrastructure safety and security, local attractions, past experience, perceived value, tourism, leisure and recreation have positive and significant impacts on tourists’ Satisfaction. Meanwhile, the natural environment does not have any measurable impact on tourist satisfaction. Overall findings confirm the effect on tourist satisfaction through the factors. It is suggested through the study that managers and the supply side of tourism need to undertake combinations of factors or antecedents to enrich tourist satisfaction and experience. The research will provide a deeper insight to understand of the factors that influence the tourists’ satisfaction

The adoption of virtual tours for real estate marketing gained momentum during the pandemic. This study highlights the impact of virtual tours in real estate marketing in influencing Consumer decision making related to purchase. Based on the responses of the potential home buyers who were exposed to the virtual tours, the effectiveness of the virtual tour related to product knowledge, quality aspects and purchase intention was captured through a survey. Although the virtual interactivity significantly enhances understanding and engagement, it alone is insufficient for final purchase decisions; particularly among first-time and low-budget buyers who prefer physical site visits for assurance. The study also finds a strong correlation between virtual interactivity and purchase intention in mid to high-budget segments. Two-way communication and neighbourhood simulation were identified as features that can significantly improve VR’s effectiveness. This technology has the potential to change the traditional customer journey of real estate sector and also reach out to wider base of customers like NRIs through e-commerce or digital platforms.

Sustainability stands as one of the most pressing challenges of the 21st century, driven by the urgency to combat climate change, reduce resource depletion, and ensure equitable progress for future generations. Central to achieving sustainability is the integration of technology and its effective management. Technology, through innovations in energy efficiency, waste reduction, and resource optimization, plays a transformative role in shaping a sustainable future. Technology management, in this context, becomes vital as it oversees and integrates these advancements into practical, scalable solutions.. The Indian philosophical and cultural foundation, particularly from the Vedic period, offers profound insights into sustainability. The Vedas emphasize harmony with nature, reverence for natural elements, and responsible stewardship of the environment. Practices such as Yajnas, the preservation of sacred groves (Aranyas), and the guiding principle of Vasudhaiva Kutumbakam—the world is one family—reflect an ancient but deeply relevant approach to ecological balance and interdependence. As times evolve, the synergy of these traditional values with modern technological interventions emerges as a powerful framework for transforming sustainability in India and beyond.

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Carbon Border Adjustment Mechanism (CBAM), which is being implemented in a phase-wise manner in the European Union (EU), is becoming an object of trade disputes between the developing nations and the EU, as many emerging economies don’t have their domestic carbon markets and their exporters will now face stiff tariff-like penalties for selling their products in the European market. This paper discusses the CBAM mechanism and its impact on India. The paper also talks about the political economy of the issue and the responses of the respective governments to protect their own economies. Lastly, the paper discusses the larger ramifications of the CBAM felt across the globe and the future directions. (keywords: CBAM, Carbon Price, EU tariff.

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The study examines what influences personal financial health in Kathmandu, Nepal, where finding financial education and resources is a big challenge. Within a positivist and deductive approach, the study applies a quantitative, causal-comparative research design to discover the links between people’s financial knowledge, behavior, attitudes, stress, satisfaction and their financial well-being. Since not much research exists in Nepal on this topic, exploratory factors were added to the study. The questionnaire, designed to provide structured data, was handed to 425 people in both rural and urban locations. After the data was cleaned, 404 usable responses were studied using statistics to evaluate five hypotheses. The study revealed that financial knowledge, attitude, stress and satisfaction make a meaningful difference in people’s financial well-being, while financial behavior did not. Of all the variables examined, financial satisfaction was the most important indicator of a person’s financial well-being. The study points out that, in addition to understanding finances and what to do, feelings and thoughts are important for healthy finances. These findings support behavioral finance ideas and imply that actions to raise financial happiness and decrease financial stress can help people feel better. The findings of this study provide a rare addition to financial well-being research in Nepal and suggest useful directions for policymakers, financial educators and institutions. Through exploring many influences on financial health, this research backs strategies that help improve the financial strength and empower individuals in such areas.

In an open space, this study thus concentrates on examining how artificial intelligence combined with Green IT adoption impacts the efficiencies in supply chains that are towards being more sustainable and better still green. The booming approval wave of artificial intelligence as a strong driver of change in diverse industries has even more made it evident how CE practices are now developing, especially in this case, within the field of manufacturing. Presently, a vast convergence of AI, sustainability, and Corporate Social Responsibility marks an important area as it resonates with the idea that efficient and innovative developments are ensured through AI, which in turn influences the site as much as possible to the environment. In addition, this discusses some research gaps related to Green Supply Chain Management by proposing frameworks for environmental, economic, and market performance evaluation of organizations, thus rendering significant clues on resource optimization in a circular economy.On different grounds, this is entitled to the role of industry 4.0 technologies, which like AI and blockchain, would embrace green manufacturing practices and promote recycling. The new possibilities of sustainability through supply chains are brought by these technologies, which also help reduce carbon emissions, operational efficiency, and much more transparent ways of doing things. These studies have also identified some of the barriers in creating an environment for their applications as sustainable innovation, which include organizational challenges and need for training of employees to make use of the full potential of digitalization.
The objectives of the study include analyzing the impact of AI in green IT, identifying performance evaluation frameworks for GSCM, and exploring the barriers to AI adoption in IT supply chains. The main results present the enablers of digital technologies for sustainable supply chains while also showing their importance in overcoming regulatory and workforces’ challenges. Most important implications include a need for transformative approaches in integrating AI into supply chain strategies for long term environmental and economic sustainability

Agentic capabilities are believed to become a baseline feature of all Artificial Intelligence capability layers, leading to a paradigm shift in technology as well as business models towards commercially exploitable technology components and value-added services. With IWOs together with heterogeneous advanced semiconductor devices forming the capability backbone of 6G intelligent wireless systems, an unprecedented magnitude of intelligent wireless services can be enabled. Although next generation intelligent wireless systems, such as 6G, are integrated naturally into the previous generation system hierarchies and service domains, it is expected that 6G and beyond will bring an unprecedented abundance of intelligent mobility supporting diverse human and machine work. In this chapter, we provide an outlook on advanced semiconductor capabilities tailored to face the ensuing challenge for revolutionizing intelligent wireless systems in 6G.

Semiconductor technologies have been the hidden backbone for large-scale capacity growth in intelligent wireless systems and the development of the wireless services ecosystem. Agentic Artificial Intelligence capabilities, integrated at various capability layers ranging from low-level AI microcontrollers powering intelligent wireless objects to high-level AI engines optimizing the end-to-end system, will credibly unveil the long awaited vision of Asynchronous and Volumetric Mobility. 6G is expected to bring an unprecedented abundance of intelligent mobility services, spanning diverse user categories. To accelerate the capabilities expansion of devices and the democratization of intelligent services, 6G intelligent wireless systems must exploit every area of the performance landscape, enabled by advanced semiconductor technologies that are combined and packaged intelligently

Digital transformation has been changing the landscape of banking and the future of banking will be very much different from what it is today. Banks are required to put in place process automation that will gain the confidence of its customers. The financial sector is becoming the least trusted sector. Financial services must embrace trust in its core business model in order to overcome this negative perception. Future of banking will include autonomous systems that must ensure trust at its core processing. The core of this technology ensures provenance, data integrity, auditability and trust.

AI is already transforming the landscape of banking and will be one of the greatest driving forces of banking’s transformation in the years ahead. The widespread incorporation of AI will give rise to a new era of banking, sometimes referred to as Intelligence Banking, which will be characterised by radical changes to banking products and services, processes, and their respective ecosystems. AI is a broad term that describes machines and systems that can learn from experience and act without human intervention. This capability, in conjunction with an increased availability of and access to vast amounts of data, is reshaping virtually every industry, banking included. Important benefits have already been achieved by investing in AI, with a wide range of applications emerging that span from front to back office. AI has the potential to enhance not only efficiency, but also vital parts of the value proposition such as trust, confidence, personalization, and resilience. Overall, it has the potential to improve productivity growth and offer new opportunities for innovation in the banking sector.

The employability of management graduates mainly depends on their skills. Mismatches between an individual’s skills and employer expectations can hinder employability. A good placement offer is what any management student aims for when they get into management education. However, the gap between what the recruiters are expecting from a management student and what they get is widening day by day.  The paper tends to identify the critical skills which recruiters look for when they hire management graduates.

Design/methodology/approach – Research design for the paper is a primary research conducted with the use of interview interview-administered research instrument. The respondents are working in medium and large organizations and have been recruiting management graduates for different roles and responsibilities. The population researcher took for the study is  50.

Findings – The research exposes that there are six critical skills which recruiters look for while recruiting Management Graduates are Good Communication & Presentation Skills, Hands-on Experience, Aptitude, Orientation and Attitude, Creativity and Out of the Box thinking, and Domain Knowledge.  

Practical implications – The findings you refer to should focus on identifying key skills that management alumni need to develop to improve their placement outcomes. These skills typically encompass a variety of hard and soft skills that are vastly valued by employers across industries

As digital marketing develops, using personalized strategies is now key to capturing users’ focus and getting them involved. Because of AI and ML, marketers now find it easier to track and analyze the behavior of consumers, craft suitable messages and perfect their campaigns. This study analyzes the role of AI and ML in making personalization, looking at the strategies, difficulties and outcomes of its use for consumer engagement. The study uses existing reports and compares them to newly obtained data from online retail sites that use personalization technology based on AI. According to findings, both AI and ML help businesses improve how customers feel engaged by targeting them effectively, delivering relevant materials and giving them personalized experiences. The final part of the paper explores what might happen in AI-driven personalized marketing and ethical aspects to keep in mind...

The research activities in-group decision making have intensely increased over the last decade in all spheres of business and management. The application of Multi Criteria Decision Making (MCDM) approach occupies a vast area in the related literature. However, there is no systematic classification scheme for these researches. This paper presents a generic framework for group decision management using Multi Criteria Decision Making (MCDM) approach. The objective of this paper is to present, discuss, and apply the principles and techniques of the analytic hierarchy process (AHP) in the prioritization and selection of projects in a portfolio. AHP is one of the main mathematical models currently available to support the decision theory. This technique helps the decision makers to choose for right project amongst three projects keeping in mind the parameters like Finance, Return on Investment (ROI), Risk and Technical Knowledge