In the rapidly evolving domain of digital commerce, Augmented Reality (AR) has emerged as a game-changing technology, enabling online shoppers to visualise products in real-time, simulate usage scenarios, and make more informed purchase decisions. These immersive capabilities not only bridge the experiential gap typical of online shopping but also influence consumer perception and behavioural intentions (Javornik, 2016; Hilken, de Ruyter, Chylinski, Mahr, & Keeling, 2017). India's digital economy presents fertile ground for AR integration. In 2024, “the Indian e-commerce market reached USD 147.3 billion and is projected to grow at a compound annual growth rate (CAGR) of 18.7% through 2028, largely driven by immersive technologies like AR and improved mobile connectivity (Bain & Company, 2024). India now boasts the world’s second-largest online shopping base, with over 270 million digital buyers in 2024 alone (Bain & Company, 2024). Parallelly, the Indian AR market was valued at USD 2.8 billion in 2024 and is expected to reach nearly USD 49.6 billion by 2033, growing at an estimated CAGR of 33.5% (IMARC Group, 2024). On a global scale, the AR in the e-commerce market was valued at USD 5.88 billion in 2024, with projections of over 35% CAGR through 2030 (Grand View Research, 2024). Retailer sentiment is also evolving according to Gartner's 2023 survey; 56% of retailers plan to invest in AR/VR technologies by 2025 to improve customer experience and reduce product returns (Gartner, 2023). Supporting this, Brand XR (2024) reported that AR can increase online conversion rates by up to 90%, while 61% of consumers prefer to shop with retailers offering AR experiences (Imagine.io, 2024). Despite these optimistic figures, widespread adoption remains uneven. Factors such as perceived usefulness, ease of use, hedonic motivation, trust, and technological readiness significantly influence consumer willingness to engage with AR interfaces (Rauschnabel, Felix, & Hinsch, 2022). In India, these dynamics are further complicated by digital literacy levels, regional infrastructure disparities, and socio-cultural attitudes toward emerging technologies.”

This paper presents a conceptual analysis of the determinants affecting the adoption of Augmented Reality (AR) in India’s online retail sector. Grounded in established theoretical perspectives, including the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT), it integrates insights from secondary sources to evaluate how AR shapes consumer attitudes and willingness to engage with digital shopping platforms in an emerging market context.

In the digital era, advancements in technology have played a pivotal role in reshaping consumer behaviour, especially within the sphere of online commerce. Among these innovations, Augmented Reality (AR) has gained prominence as a powerful tool that enhances the interactive and experiential aspects of virtual shopping, impacting both the practical and emotional facets of consumer experience. Existing literature points to a growing interest in understanding how AR affects consumer decision-making and acceptance behaviour, especially within emerging markets such as India.

Initial studies positioned AR as a novel interface for enhancing product visualisation and reducing purchase uncertainty in online retail settings (Javornik, 2016). Consumers are no longer passive viewers but active participants in a virtual experience where products can be examined in near-realistic environments, enhancing cognitive and affective responses. Hilken et al. (2017) further emphasised that AR interfaces facilitate deeper customer engagement, leading to positive service experiences and brand attitudes. This experience-based augmentation is argued to drive both intentions to purchase and satisfaction, particularly when aligned with the individual’s perceived value and sense of immersion. More recently, scholars have shifted toward understanding the psychological mechanisms underlying AR acceptance. Rauschnabel et al. (2022) examined the influence of factors such as perceived usefulness, perceived enjoyment, and ease of use on the adoption of Augmented Reality (AR), basing their analysis on the core principles of the Technology Acceptance Model (TAM). Their findings reaffirm that AR’s functional benefits are most effective when combined with entertainment and personalisation features. In a similar vein, Yim et al. (2023) identified that immersive quality and interactivity significantly impact trust and emotional engagement, making AR a strategic asset for reducing cognitive dissonance in high-involvement purchases.

Emerging studies have also emphasised cultural and demographic moderators in AR adoption. For instance, Huang and Liu (2023) highlighted generational differences, noting that younger digital-native consumers exhibit a stronger inclination toward AR due to higher technological literacy and novelty-seeking behaviour. This becomes particularly relevant in India, where the consumer base is increasingly composed of Gen Z and millennials, both of whom value hybrid digital experiences over traditional formats (Bain & Company, 2024). Furthermore, empirical insights from Jain and Kaur (2024) underscore that regional disparities in digital access and trust in technology continue to influence consumer receptivity, suggesting that infrastructural and psychological readiness must be considered in developing markets.From a strategic perspective, AR is not merely a technological add-on but a mediator of brand-consumer relationships. Research by Poushneh (2021) argued that AR enables brands to bridge experiential gaps in online retail by fostering a sense of spatial presence and tangibility.

This is supported by Dacko (2023), who examined AR adoption through the lens of service-dominant logic and concluded that co-creation of value through AR interfaces significantly enhances customer loyalty and retention.While these findings offer valuable insights into the global context, there remains a dearth of focused conceptual studies that examine these dynamics within the Indian e-commerce ecosystem. Given the country’s unique socio-economic fabric, varying levels of digital maturity, and rapidly expanding e-retail market, an India-specific theoretical exploration becomes both timely and critical. This study, therefore, integrates key global findings with domestic realities to construct a conceptual understanding of consumer perception and the factors influencing ‘AR adoption in Indian online shopping’.

RESEARCH GAP

1. Limited Integration of Multi-Theoretical Models Most prior studies rely on single frameworks like TAM or UTAUT2. Few have integrated TAM, UTAUT2, and TRI simultaneously to capture both cognitive and emotional dimensions of AR adoption. Alalwan et al (2017)
2. Underexplored Role of Trust and Technological Readiness While ‘Perceived Usefulness’ and ‘Ease of Use’ are well-studied, constructs like Trust and Technological Readiness are often overlooked or treated as peripheral, despite their growing relevance in immersive tech environments. Kamble et. al (2020)
3. Lack of Empirical Validation in Non-Western Contexts. Many AR adoption studies are conducted in Western or developed regions. A gap in empirical validation within developing countries, where cultural, infrastructural, and digital literacy factors may alter adoption behaviour. Al-Fraihat et.al (2020)

OBJECTIVES

1. To determine the key elements that shape users’ decisions to adopt Augmented Reality (AR) applications.
2. To analyze how each identified factor independently affects users’ intentions and readiness to embrace AR technology.

HYPOTHESIS:

H1: Perceived usefulness has a positive effect on the adoption of Augmented Reality (AR)

H2: Perceived ease of use has a positive relationship with the adoption of AR technology.

H3: Hedonic motivation contributes positively to users’ adoption of AR.

H4: Trust exerts a positive influence on the adoption of AR applications.

H5: Technological readiness positively impacts users’ willingness to adopt AR solutions

This study employs a quantitative research approach to investigate the determinants affecting the adoption of Augmented Reality (AR). The proposed framework combines variables derived from the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), and the Technology Readiness Index (TRI). The relationships among these constructs are analyzed using Partial Least Squares Structural Equation Modelling (PLS-SEM).

Sampling Design: Responsive Sampling method was employed to ensure diversity and relevance in participant selection. This approach allowed for adaptive recruitment based on demographic and behavioural indicators, ensuring representation across age, gender, education, and technology exposure levels.

Sampling Technique: Responsive sampling (adaptive stratification based on response patterns and inclusion criteria). Target Population: Individuals with exposure to AR applications in retail, education, or entertainment. Sample Size: The Number of respondents contacted was 350, of which 300 respondents showed their willingness for the survey and 350 the Number of duly filled questionnaires received was 228 during the survey, out of which only 220 were correctly filled questionnaires, so the researcher had to confine to 220 sample sizes for analysing the report.220 respondents. Sampling Frame: Online survey distributed via academic networks, professional forums, and AR user communities. Inclusion Criteria: Age 18 and above, Prior experience or awareness of AR technology, Willingness to participate in a structured questionnaire.

Figure 1. Conceptual Framework for AR Adoption in Indian E-Commerce
Developed by the author based on TAM, UTAUT, and relevant consumer adoption constructs.

These propositions provide a foundational structure for analysing the interplay between technological, psychological, and contextual factors in the Indian AR-ecommerce adoption landscape. They also pave the way for empirical testing in future studies or regional comparative analysis.

DATA ANALYSIS AND DISCUSSION

This study synthesises diverse academic and industry literature to conceptualise the dynamics influencing ‘consumer acceptance of Augmented Reality (AR)’ in India’s e-commerce sector. Drawing upon the ‘Technology Acceptance Model (TAM)’ and the ‘Unified Theory of Acceptance and Use of Technology (UTAUT)’, the discussion unpacks a layered view of how Indian consumers respond to AR-enabled platforms.At the core of AR adoption lies Perceived Usefulness, which shapes the perceived value of the technology in reducing uncertainty, especially in categories such as apparel, furniture, and beauty products. Numerous studies (Hilken et al., 2017; Rauschnabel et al., 2022) confirm that when users believe AR enhances purchase decisions, their intent to adopt increases.

Table 1: Descriptive Statistics

“Mean: All constructs show positive perceptions (Mean > 3.5), indicating favourable attitudes toward AR adoption.Standard Deviation: Moderate variability; values < 1 suggest consistent responses. Skewness: Negative values indicate left-skewed distributions, respondents leaned toward higher agreement. Kurtosis: Values near 0 suggest normal distribution; no extreme peaks or flatness.”

Table 2: ‘Reliability and Validity of Constructs’

“Cronbach’s Alpha ≥ 0.70 indicates good internal consistency.Composite Reliability ≥ 0.70 Confirms construct reliability≥ 0.50 demonstrates convergent validityValues meet or exceed recommended thresholds, confirming that the measurement model is both reliable and valid.”

Table 3: ‘Factor Loadings’

Factor Loadings ≥ 0.70 are considered acceptable. All items above meet the threshold, indicating strong reliability of the items. This supports the convergent validity of each construct.

Table 4: ‘Discriminant Validity-Fornell-Larcker Criterion’

The diagonal entries indicate the square roots of the Average Variance Extracted (AVE) for each construct, while the off-diagonal entries show the correlations between different constructs. Discriminant validity is established when each diagonal value exceeds the corresponding correlations in its row and column. In this analysis, all constructs satisfy the Fornell-Larcker criterion, thereby confirming adequate discriminant validity.

Table 5: ‘Structural Model Results’

MODEL FIT SUMMARY:

R² (AR Adoption) = 0.72,indicating that the five predictors explain 72% of the variance in AR Adoption. This is considered substantial in behavioural research. Q² (Predictive Relevance) = 0.41.This confirms that the model has strong predictive validity.  All path coefficients (β) are positive and significant, supporting the hypotheses that have the strongest effect on AR Adoption (β = 0.35), followed by PEOU and TRD.f² values indicate medium effect sizes for PU and PEOU, and small to medium for others. The model demonstrates robust explanatory power and predictive relevance, making it suitable for academic publication or thesis defence.

Conceptual Model Diagram:

Figure 1

It visually represents the theoretical foundation of your AR Adoption research, showing how constructs from TAM, UTAUT2, TRI, and Trust influence AR Adoption. This is ideal for inclusion in your thesis or presentation to justify the structural model results.

Structural Model (Inner Model):

Figure 2

Five independent constructs: ‘Perceived Usefulness (PU)’, ‘Perceived Ease of Use (PEOU)’, ‘Hedonic Motivation (HM)’, ‘Technological Readiness (TRD)’. One dependent construct: AR Adoption Path coefficients (β), t-values, p-values, and effect sizes (f²) for each relationship fit indicators: R² = 0.72Q² = 0.41.

Table 5: Hypothesis Summary

“This Table represents the summary of hypothesis testing results. All five hypotheses were supported, with statistically significant path coefficients (p < 0.05). Perceived Usefulness (β = 0.35) emerged as the strongest predictor of AR Adoption, followed by Perceived Ease of Use and Technological Readiness.”

THEORETICAL IMPLICATIONS:

“This study contributes to the growing body of literature on technology adoption by integrating constructs from TAM, UTAUT2, and Technology Readiness Index (TRI) into a unified model for AR Adoption. The findings offer several theoretical insights: Extension of TAM: The significant influence of Perceived Usefulness and Ease of Use reaffirms the foundational role of TAM in predicting AR adoption, even in emerging tech contexts. Inclusion of Hedonic. The positive effect of Hedonic Motivation supports UTAUT2’s assertion that enjoyment is a key driver in voluntary technology use, especially for immersive technologies like AR. Trust as a Behavioural the role of Trust highlights the importance of perceived security and reliability in digital environments, extending prior models that often overlook this construct. Technological Readiness, as the significance of Technological Readiness suggests, that users’ confidence and openness toward technology shape their adoption behaviour, offering a bridge between psychological readiness and behavioural intention.These results validate a multi-theoretical approach and encourage future researchers to explore hybrid models that reflect the complexity of user behaviour in digital ecosystems.”

“This study examined the determinants of Augmented Reality (AR) Adoption by integrating constructs from the Technology Acceptance Model (TAM), UTAUT2, and the Technology Readiness Index (TRI). The structural model confirmed that Perceived Usefulness, Perceived Ease of Use, Hedonic Motivation, Trust, and Technological Readiness all exert significant positive effects on AR Adoption. Among these, Perceived Usefulness emerged as the strongest predictor, reinforcing the centrality of functional value in technology acceptance.

Practically, the study provides actionable insights for developers, marketers, and policymakers. Emphasising intuitive design, trust-building mechanisms, and readiness-based segmentation can enhance user engagement and accelerate AR adoption. As AR continues to evolve across sectors—from education and retail to healthcare and entertainment—understanding its adoption dynamics becomes increasingly vital.

Future research could explore longitudinal effects, cross-cultural variations, or the role of mediators such as digital literacy and user experience. By refining and expanding this model, scholars and practitioners can better anticipate user needs and design AR solutions that are both impactful and inclusive.”

1. Bain & Company. (2024). How India shops online: 2024 insights into the Indian e-commerce market. Retrieved from https://www.bain.com/insights/how-india-shops-online-2024/
2. Baliarsingh, R. K., Nath, S. C., & Swain, D. (2025). A study on the impact of perceived risk on post-purchase behavioural intention towards e-pharmacy in twin cities of Odisha. International Journal of Research, 14(2), 86–92. ISSN 2231-6124.
3. Baliarsingh, R. K., Nath, S. C., Swain, D., & Dhal, S. (2025). Factors influencing consumers’ online purchase intention towards medicines: A study based on the extended UTAUT theory. Academy of Marketing Studies Journal, 29(3), 1–18. Print ISSN: 1095-6298 | Online ISSN: 1528-2678.
4. (2024). Augmented Reality in Retail and E-commerce Research Report. Retrieved from https://www.brandxr.io/2025-augmented-reality-in-retail-e-commerce-research-report
5. Dacko, S. G. (2023). Augmented reality marketing: Value co-creation and consumer engagement through immersive technology. Journal of Business Research, 160, 113753. https://doi.org/10.1016/j.jbusres.2023.113753
6. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
7. Dhal, S., Hota, P., Baliarsingh, R. K., & Sabat, D. R. Empowering Performance: Evaluating Diversity and Inclusivity Training in Private Banks of India. In international management perspective conference (p. 61)
8. Dhal, S., Sabat, D. R., & Hota, P. (2024). An Empirical Investigation of the Factors Affecting Social Media Advertisements on E-tailing in Odisha. Srusti Management Review, 17(2).
9. (2023). Retail technology trends: AR and VR adoption in 2023. Retrieved from https://www.gartner.com/en/newsroom/press-releases/2023-retail-ar-vr-report
10. Grand View Research. (2024). Augmented reality in e-commerce market size, share & trends analysis report. Retrieved from https://www.grandviewresearch.com/industry-analysis/augmented-reality-e-commerce-market-report
11. Hilken, T., de Ruyter, K., Chylinski, M., Mahr, D., & Keeling, D. I. (2017). Augmenting the eye of the beholder: Exploring the strategic potential of augmented reality to enhance online service experiences. Journal of the Academy of Marketing Science, 45(6), 884–905. https://doi.org/10.1007/s11747-017-0541-x
12. Huang, T. L., & Liu, Y. (2023). Age matters: Exploring generational differences in augmented reality adoption in retail. Computers in Human Behavior, 139, 107522. https://doi.org/10.1016/j.chb.2023.107522
13. io. (2024). AR Trends 2025: How augmented reality is transforming the consumer journey. Retrieved from https://resources.imagine.io/blog/2025-ar-trends
14. IMARC Group. (2024). India augmented reality market: Industry trends, share, size, growth, and forecast 2024–2033. Retrieved from https://www.imarcgroup.com/india-augmented-reality-market
15. Jain, R., & Kaur, H. (2024). Consumer trust and technology acceptance in Indian e-commerce: A study on augmented reality. Indian Journal of Marketing, 54(1), 18–27.
16. Javornik, A. (2016). 'It’s an illusion, but it looks real!’ Consumer affective, cognitive and behavioural responses to augmented reality applications. Journal of Marketing Management, 32(9–10), 987–1011. https://doi.org/10.1080/0267257X.2016.1174726
17. Nath, S. C. (2010). Cross-cultural effects on Internet buying behaviour: An empirical study. The IUP Journal of Systems Management, 8(3), August.
18. Nath, S. C. (2011). Profile of Internet users in Odisha: An empirical study. Sidhant: A Biannual Journal of Management, April–June, 2231-0649.
19. Nath, S. C. (2012). Consumers’ responsiveness to Internet marketing: An empirical study. International Journal of Research in Commerce and Management (IJRCM).
20. Nath, S. C. (2014). Internet usage categories and profile of each category with respect to demographic factors: An empirical study in Odisha. GITAM Journal of Management, 12(2), April–June. ISSN 0972-740X.
21. Nath, S. C. (2014, December 28–29). Computerized systems as a strategic marketing tool for the effectiveness of small and medium enterprises in Odisha. In Proceedings of the International Conference on Big Data & Analytics for Business, New Delhi, India (ISBN 978-1-63415-497-0).
22. Nath, S. C., & Popli, R. (2023). A study on importance of customer satisfaction in online shopping. European Chemical Bulletin, 12(Special Issue 4), 15561–15575. ISSN: 2063-5346.
23. Nath, S. C., Popli, R., & Sankar, R. (2023). A study on impact of online shopping on direct system in India and the road ahead. Global Business and Economic Review, 29(1). Print ISSN: 1097-4954; Online ISSN: 1745-1329.
24. Patnaik, P., Dhal, S., Sabat, D. R., & Patnaik, B. Understanding the Risks of Online Electronics Shopping: A Comprehensive Overview.
25. Poushneh, A. (2021). Augmented reality in retail: A trade-off between user experience and privacy. Journal of Retailing and Consumer Services, 61, 102547. https://doi.org/10.1016/j.jretconser.2021.102547.
26. Prusty, R., Nath, S. C., & Dash, M. (2025). Understanding consumers’ impulse buying behaviour during the COVID-19 pandemic. Journal of Business Innovation and Research, .https://doi.org/10.1504/IJBIR.2023.10062727, Online ISSN: 1751-0260 | Print ISSN: 1751-0252.
27. Prusty, R., Nath, S. C., & Dash, M. (2025). Unveiling influential aspects shaping consumer post-purchase satisfaction in the consumer durables sector in the context of sustainability. International Journal of Indian Culture and Business Management. https://doi.org/10.1504/IJICBM.2023.10062714, Online ISSN: 1753-0814 | Print ISSN: 1753-0806.
28. Rauschnabel, P. A., Felix, R., & Hinsch, C. (2022). Augmented reality marketing: How mobile AR apps influence consumer experiences and behavior. Journal of Retailing and Consumer Services, 68, 102597. https://doi.org/10.1016/j.jretconser.2021.102597.
29. Sabat, D. R., & Nath, S. C. (2025). Investigating the core elements impacting the integration of artificial intelligence in digital marketing. Navigating digital disruption: Strategies for sustainable business management (Chapter 10, pp. 61–64). Taylor & Francis. https://doi.org/10.4324/9781003716617-10, ISBN:
30. Sabat, D. R., Dash, C. K., & Jena, A. (2017). A study on hospital service quality perception. International Journal of Engineering and Management Research, 107-113.
31. Sabat, D. R., Nath, S. C., Swain, D., & Baliarsingh, R. K. (2024). Analyzing the factors and perceptions of online retailers: A study of e-retailing. Empirical Economics Letters, 23(Special Issue 2), 299–305. https://doi.org/10.5281/zenodo.12701116
32. Sahu, D. K., & Nath, S. C. (2024). Financial inclusion in the digital age: Leveraging block chain, mobile banking, and CSCs to reach rural communities. Indian Journal of Technical Education, 47(Special Issue 1, November 2024). ISSN: 0971-3037.
33. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540.
34. Yim, M. Y.-C., Chu, S.-C., & Sauer, P. L. (2023). The role of augmented reality in enhancing consumer experience and purchase intention: A study on mobile retail applications. Journal of Interactive Marketing, 61, 25–41. https://doi.org/10.1016/j.intmar.2022.11.001.