Digital marketing is to usage of information technology with the purpose to achieve the marketing goal (Kaufman & Horton, 2014). The term digital refers to the technology that is related with the flow of information from one person to another person (Fichman et al., 2014). With the help of digital technology continuous information is supplied. An activity is considered to be digitalized if it depends on digital platform to perform its marketing function (Weber, 2009). DM includes the number of activities that companies need to create and satisfying the customer requirement through digital channels (Kannan & Li, 2017).

Source: Author Compilation

Characteristics of Digital Marketing

Two-way communication:

DM invites the prospective customers to directly connect with the company immediately. Linked with one another on social media allows brand interested people to develop a network that marketing the goods and services and give a platform for questioning and feedback option also (Bala & Verma, 2018).

Measurable

It is very important part of DM (Dodson, 2016). DM can give marketers complete information such as shares, views, clicks and time on the page, which is important for the business. Digital term includes the word ‘digit’ means company can collect data in form of quantitatively which is helpful for marketing campaigns (Saura et al., 2017). With this company can easily get to what market need to target, productive advertising was, techniques in the past succeeded or failed is helpful (Järvinen & Karjaluoto, 2015).

Personalized

In case of digital marketing activities can be customized as per the customers (Behera et al., 2020). It allows the businesses to know about the customer shopping patterns and preferences, with these businesses can provide the particular product suggestion and goods & services for each customer. Consumers can also receive customized, discount coupons based on wishlist (Chandra et al., 2022). Due to the ability of tailored the need of the customers, DM is user friendly option.  .

Multi-channel

Consumer can use multiple channels, sometimes consumer use some platform actively or sometime not. The purpose is to online inform the information about product and services via digital channels (Manser Payne et al., 2018). Information Channels should be simple and direct (Cai & Choi, 2023)

Adaptable

Flexibility is another important features of DM to evolving media, client, and technology needs (Lachieze-Rey & Dolbec, 2023). The marketing efforts can be changed according to the requirement of the customers. As the trends emerge in the shifting world, efforts must be adaptable (Yawised & Apasrawirote, 2022).

Customer Engagement

From last 20 years this term has gained lot of attention in the field of political science, organizational behavior, sociology, and psychology, with a number of conceptual techniques used (L. Hollebeek, 2011). However, customer brand engagement has become popular in the contemporary marketing. The concept of conceptualization and comprehension are still decreasing (Sashi, 2012) which define the customer engagement through the various cycle having stages as –linkage, connectivity, retention, commitment and engagement etc.

Customer Purchase Intention

It is very important aspects of marketing. The ability and interest to purchase the product and identify the factors which influence the buying behavior make the indication of customer intention for the consumer future behavior (Kim & Lee, 2019).  It define the desire of consumer for purchasing in the near future, it is required for the businesses to do research on a consumer interaction and retention by creating a strong relationship, PI is basically desire of a consumer for buying a specific branded product as suggested by (Chang & Wildt, 1994). PI is mind set of the consumer for specific product or service as stated by (Morrison, 1979). The intention also provides the information about the mental status of the prospective customers and marketing techniques could be designed for this. Data related to purchase intention can be collected from the multiple sources which help to determine the factors that are highly impacted on it, give positive attitude also, lead to purchasing a product.

Review of literature regarding Digital Marketing:

(Eid et al., 2020) studied the positive impact of internet searchers, including determinant such as interaction, tailored, and enjoyment and as well as the part of social media advertising, such as  in formativeness, trustworthiness, and annoyance, on people's attitudes toward promotions on social networking sites in Jordan. A questionnaire was sent to the 256 individuals with the purpose to collect the data, regression analysis were used to conclude the hypothesis. The result shows that the how customers felt about the information on social networking platforms. The most effective factors were promoted enjoyment, interaction, and in formativeness.

(Sridevi et al., 2021) identifying the customer attitude towards determines the level of happiness with the business work in creating the DM product for consumer for the reason of digital advertising and creating a lead in customers. A questionnaire was used to gather the data and SPSS software was used to do 36 analyses. The Findings of the study is effectiveness of DM as well as the degree of customer satisfaction among those who use the Phoenix Digital Marketing Company's services.

(Sopi & Rashiti, 2022) suggested the attitude of the bank customers regarding digitalization, the benefits of communication channels that are the part of social network process, their ability to increase the digitalization communication with the bank. With this purpose 678 bank customers participated in the survey, various tools were used to conclude the result. The result define the statistically significant correlation between age and level of education and benefit of digital tools as well as disparities between educational levels and age groups

(Anam et al., 2023) stated the index of service of consumer and benefit of performance analysis to examine that how consumers are happy with online fisheries products. With 135 customers the study used the random sampling; the result shows that consumer perceives digital marketing as trustworthy, secure, and convenient.

Review of Literature regarding Customer Engagement

(Saputra & Fadhilah, 2022) examine the healthy relationship between Instagram‘s live shopping and purchase decisions through customer engagement. User of this who made purchase through this is included in the research as respondent. The findings show the live stream shopping doesn’t have a direct related with the purchase decision but it impacted through the purchase decision indirectly through CE.

(Gao et al., 2022) evaluate the response related to artificial intelligence (AI) affect consumer engagement. The data were gathered from the 426 customers through questionnaire who have used intelligent service robots. Customer engagement is significantly enhanced by the perceived interactivity of AI stimuli.

(Akhtar et al., 2024) examine the role of influencers, the customer influencer relationship, and the resulting customer behavioral engagement with the help of three experimental investigations. IBM SPSS 28.0 was used for the analysis of this study, and it includes the one way ANOVA and Sample T test. The findings shows that there is direct impact on customer engagement by influencers with high (vs. low) audiovisual appeal.

Digital Marketing and Purchase Intention

(Abdullah, 2020) examine the effect of DM on customer awareness of brands and the inclination to purchase, also indicate whether customer engagement influences this relationship. Data were collected from the 275 customers and quantitative research design was used. After that SEM was used for data analysis, the findings show that DM impact on the brand awareness and customer engagement and mediating role with PI.

(Hien & Nhu, 2022) stated the impact of various digitalized platforms on consumer attitude & inclination to purchasing product. Data were collected as 210 samples were drawn from the regular customers of the company’s B2B purchasing chain, after applying the SEM PLS software for data analysis the result was that content marketing has a positive impact with PI, rest three 52 relationship between SEO, social networking, email marketing on PI were not acknowledged.

(Habib et al., 2022) stated that how customer engagement and brand awareness mediate the relationship between DM and PI. SEM was used to analyze the data. Responses were collected from 417 respondents as a sample for the study. The result was direct association between customer engagement and brand awareness.

(Karuppiah, 2020) examine the association of DM with PI in the context of teenagers in Madurai city. Data were collected from the 100 customers through questionnaire and purposive sampling method. Linear regression analysis and chi-square test were applied for analysis. Findings also shows that the positive link between the effectiveness of DM and purchase intention.

(Matin et al., 2020) studied that how Georgian customers use of social networking which impact on trust & inclination to buy. The sample for the study was 553 customers, quantitative research design was used with the help of questionnaire for data collection process, and regression was also used for the analysis. Findings show that customer trust and buying intention are significantly impact by the social media involvement.     

(Natasha et al., 2021) study is in related to the Ismaya, which is, a leading MICE company in Indonesia. 100 data were collected as sample size from the population of 192000 followers. Questionnaire method was used and analyzed the multiple regression techniques. The Findings shows that four DM tools were used for the study (email, social media, website and mobile applications).The significant techniques are social media and websites.

(Yan et al., 2020) evaluate the impact of internet-marketing techniques on customer’s decision to purchase. 263 data were collected as a sample by using the convenient sampling method. According to SPSS result digital tools have positive relations with consumers' purchasing intention.

The study has proposed the hypothesis.

H1: There is a positive relationship between Customer Engagement and Purchase Intention.

H2: There is positive relationship between Customer Engagement and digital marketing.

H3:  There is positive relationship between digital marketing and purchase intention.

H4: There is positive and significant impact of digital marketing on the customer engagement and purchase intention. 

Research Gap

The research addresses a significant gap by examining women preference for the digital marketing that remains underexplored in existing literature. Additionally, while most studies focus on a limited number of factors, this study broadens the scope by analyzing distinct factors..

RESEARCH METHODOLOGY

Research Design: Quantitative research design was used for the study. 

Research Instruments: For the study data is collected with five Likert Scale used with the help of Structured Questionnaire.

Data Collection: Questionnaires were sent to women customer to get first-hand information. Data collection for this study took over four to five months.

Data Analysis:  In this data analysis there are number of data analysis techniques were used Like Exploratory Factor analysis, Confirmatory Factor analysis, and Analysis of Moment structures, Structural Equation Models etc.

RESULT & DISCUSSION

Figure: 4.1 Relationship between Customer Engagement (IV) and Purchase-Intention (DV)

Source: Author Compilation

Figure 4.1 represent the relationship between the CE and PI to check the independent variable with the dependent variable. According to the result 90% customer engagement convert into the purchase intention. All the values are positive thus H1 hypothesis is supported. By Hair et al., (2014) standardized factor loading of 0.50 is the minimum standard value.

To ascertain whether the model was suitably fitting the data, several model fit indices that evaluate both the goodness and badness of fit were examined using the AMOS output and there is no degree of uncertainty. According to table 4.1 value mentioned of the as all are greater than the standard value 0.80, as suggested by Moolla and Bisschoff, (2013).

Table 4.1 Model Fit Indices

Source: Author Compilation

Table 4.2 shows the standardized regression weights that have been calculated for each of the items. These data range from 0.641 to 0.903 is showing significant value. By Hair et al. (2014), value of the standard regression weight is higher than the 0.5 which is acceptable to validate the factor Structure. When the factor loading is higher, it indicates that the manifest variables are converging more closely on the same construct.

Table 4.2 Standardized Regression Weight

Source: Author Compilation

Figure: 4.2 Customer Engagement (Independent variable) with Digital Marketing (Mediator)

Source: Author Compilation

Figure 4.2 represent the relationship between the CE and DM to check the independent variable with the mediator. All the values are positive thus H2 hypothesis is supported. As suggested by Hair et al., (2014) standardized factor loading of 0.50 is the minimum standard value.

To ascertain whether the model was suitably fitting the data, several model fit indices that evaluate both the goodness and badness of fit were examined using the AMOS output and there is no degree of uncertainty. According to table 4.3 value mentioned of the as all are greater than the standard value 0.80, as suggested by Moolla and Bisschoff, (2013).

Table 4.3 Model Fit Indices

Source: Author Compilation

Table 4.4 displays the standardized regression weights that have been calculated for each of the items. By Hair et al. (2014), value of the standard regression weight is higher than the 0.5 which is acceptable to validate the factor Structure. When the factor loading is higher, it indicates that the manifest variables are converging more closely on the same construct On the basis of these values; it is possible to draw the conclusion that the observed items  represents the latent factor to which it is most closely related

Table 4.4 Regression Weights: (Group number 1 - Default model)

Source: Author Compilation

Figure: 4.3 Digital Marketing (Mediator) with Purchase Intention (Dependent variable)

Figure 4.3 represent the relationship between the DM and PI and digital marketing to check the dependent variable with the mediator. All the values are positive thus H3 hypothesis is supported.

To ascertain whether the model was suitably fitting the data, several model fit indices that evaluate both the goodness and badness of fit were examined using the AMOS output and there is no degree of uncertainty. According to table 4.5 value mentioned of the as all are greater than the standard value 0.80, as suggested by Moolla and Bisschoff, (2013).

Table 4.5 Model Fit Indices

Source: Author Compilation

Table 4.6 displays the standardized regression weights that have been calculated for each of the items. By Hair et al. (2014), value of the standard regression weight is higher than the 0.5 which is acceptable to validate the factor Structure. When the factor loading is higher, it indicates that the manifest variables are converging more closely on the same construct On the basis of these values; it is possible to draw the conclusion that the observed items represent the factor to which it is most closely related

Table 4.6 Regression Weights:

Source: Author Compilation

Figure: 4.4: Mediating role of DM on the CE and purchase intention of customers.

Source: Author Compilation

Figure 4.4 represent that all estimate value of data presented in the table are positive. Estimate value of the CE and PI is 0.90. After mediating factor the value decreased from 0.90 to 0.80, so the result are with partial mediation in this and significant impact of digital marketing on the customer engagement and PI thus H4 hypothesis is supported. 

To ascertain whether the model was suitably fitting the data, several model fit indices that evaluate both the goodness and badness of fit were examined using the AMOS output and there is no degree of uncertainty. According to table 4.7 value mentioned of the as all are greater than the standard value 0.80, as suggested by Moolla and Bisschoff, (2013).

Table 4.7 Model Fit Indices

Source: Author Compilation

Table 4.8 displays the standardized regression weights that have been calculated for each of the items. By Hair et al. (2014), value of the standard regression weight is higher than the 0.5 which is acceptable to validate the factor Structure. When the factor loading is higher, it indicates that the manifest variables are converging more closely on the same construct On the basis of these values; it is possible to draw the conclusion that observed items represent the factor to which it is most closely related

Table 4.8 Regression Weights:

Source: Author Compilation

Estimate value of the CE and PI is 0.90. After mediating factor the value decreased from 0.90 to 0.80, so the result are with partial mediation in this. There is Significant Impact of DM on the CE and PI.

Implication of the Study 

Digital marketing act as a mediating role between CE and PI, for strengthening the consumer relationships and driving the consumer purchasing behavior, for a theory point of view, DM techniques affect the consumer decision. From managerial point of view, businesses can design more targeted, interactive and customized digital system that not only to increase the customer engagement as well as convert that engagement into the purchase intention and decision as well. In short, the role of DM acts as a bridge between customer behavior and actual purchasing behavior.

Top of FormBottom of Limitation and Future Research Directions

Numerous factors of DM have been included in this study, but we can investigate other factors influencing both DM & PI in the future. Other variables like specific digital marketing tool and external market influences can be analyze in the future. 

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