Keywords

Consciousness, Customer, Enthused Participation, Gender, Online Purchase Behaviour.

Introduction

In 21^st century, most of the companies face a two-fold dilemma, i.e., in many product and service categories, and hence, are recognizing the need to foster interaction with their customers. People are increasingly interacting on online platforms to achieve personal as well as mutual objectives (Dholakia et al., 2004). Despite the importance of sustained customer's relationship, many companies still are following ad-hoc approach to manage and retain their customers where the high-risk factor is associated. Verhoef et al. (2010) emphasised that with each passing day, marketing professionals have been recognizing the fact the product or price are no longer the winning differentiator. Instead, the extent of engagement with the customer or external stakeholders with company has become the key factor in determination of company’s strategies. Therefore, the term ‘Engagement’ which was usually described as a tool of customer marketing for ensuring customer loyalty, customer satisfaction and other retention practices (Dessart et al., 2015). The notion of engaging the customer is a "psychological process that models the underlying mechanism by which customer loyalty forms for new customers of a service brand as well as the mechanism by which loyalty may be maintained for repeat purchase customers of a service brand" (Bowden, 2009b; 2015). Marketing Science Institute priorities state, that “many organizations see customer engagement as a route for creating, building, and enhancing customer-organization.

Relationships and (ultimately) improving business performance” (2010–12, p. 4). With the increasing growth in the e-commerce activity owing to rapid technological advancements and the growing user’s accessibility of mobile phones in India (Islam and Rahman, 2014) there has been a notable change in the online behaviour of customers.

Also, purchase behaviour is the decision process and act of people involved in buying and using products that generates revenue for organizations. However, being in an online market does not always implicate a bed of roses as the conversion rate that leads to purchase is very low (Bucklin et al., 2003). Bucklin and Sismeiro (2003) along with Poel and Buckinx (2005) highlighted that around 65 percent of the internet users do not result in consumers. Additionally, various marketing strategies consider the role played by conscious attention in customer behaviour as it forms an integral part of customer engagement (Jones and Ranchhod, 2007; Vivek et al., 2014). Research also suggests that actual customer behaviour such as purchase behaviour comprises on the entire purchase journey ranging from conscious attention to connections to repeated purchase in an emerging economy (Rabbanee et al., 2019). Regardless of the extensive investigations into the online purchase behaviour there is still a huge gap that could address the questions related to purchase behaviour of customers (Gao et al., 2012). Therefore, this study is an attempt to explore the relationship between conscious attentions, enthused participation in online purchase behaviour. Further, the study tries to investigate the role of gender in the association between conscious attention, enthused participation, and online purchase behaviour as the role of demographics in online purchase behaviour is still in the nascent stage (Nawi et al., 2019).

Literature Review

Conscious Attention, Enthused Participation and Online consumer behaviour

Online consumer behaviour is now a developing theoretical field of study. The rapid growth of e- commerce brings many challenges for the online retailers and also for the online marketers because “as the consumers adopt new technologies, their behaviours also change radically” (Zinkhan and Watson, 1998). Hur et al. (2018) performed exploratory motivational and attitudinal research of online customer behaviour and shared fundamental frameworks connecting purchase intention with intervening constructs like information search in the recent online consumer behaviour literature. In 2021, the Indian e-commerce market is expected to be worth INR 6,210.96 Bn, according to Business Wire. It is anticipated to grow at a compound annual growth rate (CAGR) of 26.71% by the end of 2027, reaching a value of INR 26,459.18 Bn. Additionally, the upward growth trajectory will surpass the United States in 2034, according to Kalia et al. (2018). In 2015, Barhemmati and Ahmad, stated that customers tend to value different aspects when it comes to selecting a product i.e., some value price whereas some value quality of the product or service. The researchers also suggested that the type of engagement shaped in the customer depending on the emotional bond effects the customer purchase behaviour.

Further, the online shoppers tend to act on the sensory data available in the environment especially from the retailer’s website and process the same before making any purchase decision (Martin et al., 2015). Restating Gentile et al. (2007), customers process this sensory data from an affective and cognition perspective which helps in the formation of the retailer’s impression. The literature also suggests that past experience influences the future online behaviour. Also, the consciousness, attention and perception determine the overall psychological behaviour of an individual (White et al., 2019). In addition, conscious attention is defined as “the degree of interest the person has or wishes to have in interacting with the focus of their engagement” and is parallel to Calder et al.’s (2009) personal dimension, as well as Hollebeek's (2011) dimensions of immersion and activation. Furthermore, enthused participation is defined as “The zealous reactions and feelings of a person related to using or interacting with the focus of their engagement.” and is akin to Hollebeek’s passion and Gambetti et al.’s (2012) hedonic experiences as suggested by Vivek et al. (2014). This forms the fundamental premise of engaging a customer with a particular brand (Vivek et al., 2014).

In 2020, Adrita and Mohiuddin (2020) established that the actual purchase behaviour especially in green products is referred to as “automatic fashion” and therefore, employs minimum conscious attention courtesy the term “green” added to the description of the product. In addition, Marjerison et al. (2022) suggested that self-consciousness and consumer’s impulsive purchase tendency are significantly associated. Though a significant literature is available on the consumer online behaviour, consciousness, and attention but there is a dearth of literature especially focussing on the online purchase behaviour in association with conscious attention and enthused participation especially in Indian context. Therefore, based on the above literature base this research brings us to the following research questions:

Q1: Does conscious attention predict the online purchase behaviour dimensions (Trust, DSI=domain-specific innovativeness, and internet purchase intention) in Indian online retail?

Q2: Does enthused participation predict the online purchase behaviour dimensions (Trust, DSI=domain-specific innovativeness, and internet purchase intention) in Indian online retail?

Q3: Do demographic variable like gender moderate the relationship between conscious attention and online purchase behaviour in Indian online retail?

Q4: Do demographic variable like gender moderate the relationship between enthused participation and online purchase behaviour in Indian online retail?

Further, based on the above research questions the following hypotheses are formulated:

H1: CA is associated with Trust in e-stores in Indian online retail. Hypothesis 2: CA is associated with DSI in Indian online retail.

H3: CA is associated with Internet purchase intention in Indian online retail. Hypothesis 4: EP is associated with Trust in e-stores in Indian online retail.

H5: EP is associated with DSI in Indian online retail.

H6: EP is associated with Internet purchase intention in Indian online retail.

H7: Gender moderates the relationship between CA and online purchase behaviour in Indian online retail.

H8: Gender moderates the relationship between EP and online purchase behaviour in Indian online retail.

Stimulus-Response: The Theoretical Framework

The Stimulus-Response (S-R) framework is a classic implementation of Pavlov (2010)’s classic theory of the stimulus–response model. As presented by Donovan and Rositer (1982), this framework is used as a theoretical foundation to support an integrative model put forth by the present study. According to the S-R framework, specific elements of the environment can stimulate cognitive and emotional processes which in turn drive some behavioural responses. It was used as the first framework to understand the buying behaviour of the customers. It highlighted the role of consciousness of the buyer while receiving environmental cues and explained the purchase decision with the support of various buyers’ characteristics and decision- making process (Bhartiy et al., 2021). This study posits that Stimulus in the virtual environment is the conscious attention and enthused participation which is a result of all the environmental cues/ infrastructure of the online environment. The environmental cues often comprise of the value/cost of the product, information and details related to product/service, quantity and quality, information about promotion etc. The response is the outcome in the form of behaviour exhibited by the customer. In this study, the response is denoted by the online purchase behaviour which comprise of the trust, domain-specific-innovativeness, and purchase intention. Further, as response can be attitudinal, therefore this study tries to explore the association of conscious attention, enthused participation, and online purchase behaviour.

Method and Participants

The study employed a quantitative online survey research method. It did an extensive and intensive survey for assessing online responses from shoppers availing online shopping services. For the best result, we did a pilot study which involved 45 PhD level students. The aim of the pilot study was to detect and rectify any kind of issue which was prevalent. we also undertook opinions from experts which included two eminent professors and professionals who have rich experience in this domain. Their comments were invited to ensure readability and clarity of the questionnaire. The experts were of the opinions that the questionnaire had no issues regarding their ability to fetch the relevant information from the respondents.

Consumers who had purchased online services within the previous three months (90 days) received the survey's questionnaire via email. Based on the snowball sampling technique, respondents were chosen (Malhotra et al., 2006). First off, only 65 people received the survey questionnaire for this. These individuals have purchased the online services previously. Thereafter, they were requested to forward the questionnaire to at least five of their connections whom they believed opted for regular purchase of service online.

In order to ensure the proper and relevant responses, we suggested the respondents to select an online shopping of ‘clothes’ category of items. And their responses to the questionnaire were to be based in accordance with their experiences of such online purchasing. The questionnaire we devised has inbuilt measures for our constructs.

Data collection took place over a 12-week period (from August 10 to November 24, 2022). We carefully screened the responses after collecting them and 421 useful responses were ultimately saved for the later analysis. Thus, this procedure followed the recommendations for structural equation modelling (SEM) provided by (Bagozzi and Yi, 2012). Female respondents made up 61% of the sample, while male respondents made up only 39%. The respondents were broken down into five age groups: 18–26 (29%); 27–31 (31%); 32–36 (18%); 37–41 (15%); and 42–50 (7%). Additionally, 8 percent of respondents had a PhD, 34 percent had a postgraduate degree,49 percent had a graduate degree, and 9 percent had a higher secondary degree. Additionally, the monthly income was split into 4 groups: higher income (29%), higher middle (34%), lower middle (21%) income and low income (16%) groups.

Demographic Details

Gender, age, educational attainment, and economic status of the respondents are included in the demographic information section. Responses were collected using a categorical grading system. Male was assigned a score of 1, while female was assigned a score of 2. Age was measured using a 5-point scale where 1 represented 18-26 years, 2 represented 27-31 years, 3 represented 32-36 years, 4 represented 37-41 years and 5 represented 42-50 years. Educational level was measured with a 3-point scale where 1 denoted higher secondary, 2 graduate and 3 postgraduate and 4 by PhD. Also, income was measured by using a 4-point scale with low-income represented by 1 and lower-middle by 2, higher middle by 3 and higher represented by 4.

Measures

Conscious Attention (CA)

CA was assessed by using a subscale given by Vivek et al. (2014) scale that contains 6 items. The sample items are: “I spend a lot of my discretionary time _.” The responses were recorded using 7-point Likert scale from strongly agree (7) to strongly disagree (1).

Enthused Participation (EP)

Enthused Participation was assessed by using a subscale given by Vivek et al. (2014) scale. The sample item is: “I enjoy more when I am with others.” The responses were recorded using 7-point Likert scale from strongly agree (7) to strongly disagree (1).

Online purchase behaviour

The online purchase behaviour in retail was analysed by three sub-constructs wherein Trust in e- store contains 7 items, (Jarvenpaa et al., 2000), Domain-Specific Innovativeness consists of 6 items (Goldsmith & Hofacker, 1991) and Internet Purchase Intention contains 3 items (Chen and Barnes, 2007) to analyze the concerned variable. The responses were recorded using 7-point Likert scale from strongly agree (7) to strongly disagree (1). The sample item of the questionnaire is “If I hear that a new retail site is available on the Web, I would be interested enough to shop from it”, “It is likely that I will transact with this web retailer in the near future”.

Analysis and Results

The initial analysis of the data looked at the adequateness of the data and then, in order to examine the scales' psychometric properties, the convergent and discriminant validity of the measures were assessed using SPSS AMOS 24 and exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). Structural equation modeling was also used in the study to test its hypotheses.

Additionally, referring to the content validity and professional recommendations, weak (0.5) factor loading items was eliminated. The suggested model was tested, and the proposed connections between the components of this study were evaluated, using structural equation modelling. In accordance with Fornell and Larcker (1981), Table 1 displays item loadings (greater than.5 reflecting convergent validity; Bagozzi, 1994), Cronbach alpha (greater than.7), composite reliability (CR; ranging from 0.77 to 0.88), and average variance extracted (AVE) of the constructs (>.5). Also, here was no common method variance, according to the results of the Harman single factor test (Podsakoff et al., 2003), as the variance of 28% was below the minimal threshold of 50%. Also, the discriminant validity was established in line with Fornell and Larcker criterion (1981) i.e., "rule of thumb": “the positive square root of the AVE for each of the latent variables should be higher than the highest correlation with any other latent variable” as shown in Table 1. Further, the descriptive statistics are shown in Table 2.

Table 1 Factor Structure Of The Variables Under Study
Construct	Items	Factor Loadings	t (p<.01)	CR	AVE	MSV	ASV
Conscious attention (CA)	CA_1	.897	21.268	.882	.795	.367	.105
	CA_2	.893	19.345
	CA_3	.885	18.700
	CA_4	.879	18.962
	CA_5	.873	17.233
	CA_6	.869	16.435
Enthused Participation (EP)	EP_1	.861	6.232	.831	.771	.361	.157
	EP_2	.858	16.114
	EP_3	.845	15.735
	EP_4	.817	15.123
	EP_5	.811	14.700
	EP_6	.795	13.951
Trust in e-store	TRUST_1	.816	21.268	.810	.729	.357	.091
	TRUST_2	.811	17.345
	TRUST_3	.815	20.700
	TRUST_4	.829	21.962
	TRUST_5	.823	23.233
	TRUST_6	.796	14.435
	TRUST_7	.781	13.415
Domain Specific Innovativeness (DSI)	DSI_1	.841	25.232	.819	.697	.314	.157
	DSI_2	.858	27.114
	DSI_3	.825	24.135
	DSI_4	.817	21.385
	DSI_5	.809	16.700
	DSI_6	.795	14.951
Internet Purchase Intention (IPI)	IPI_1	.781	13.215	.770	.617	.192	.089
	IPI_2	.775	12.761
	IPI_3	.755	11.771

Table 2 Mean, Standard Deviation, Correlation Between The Sub-Dimensions Of The Variables
Variables	Mean	SD	CA	EP	TRUST	DSI	IPI
1.CA	4.17	.96	(0.882)
2.EP	4.11	1.04	.46**	(0.831)
4.TRUST	4.25	1.31	.32**	.19**	(0.810)
5.DSI	4.24	.98	.18**	.25*	.25**	(0.819)
6.IPI	4.35	1.16	.30**	.27**	.23**	.21**	(0.770)

The association between conscious attention, enthused participation, and dimensions of online purchase behaviour (trust in e-stores, DSI-domain specific innovativeness, and internet purchase intentions) in Indian online retail was examined using structural equation modelling (SEM). The research investigations produced significant findings, which are shown in Table 3. A p value of less than.01 indicated that the findings were significant. The notable relationships are shown by the high t-values. For conscious attention (β=0.429, t=8.539, p<0.01) and enthused engagement (=0.391, t=7.053, p<.01), trust in e-stores acts as a powerful and positive dependent. Also, conscious attention (β=0.493, t=8.871, p<.01), and enthused participation (β=0.357, t=7.842, p<.01) have a significant impact on domain specific innovativeness. Furthermore, conscious attention (β=0.512, t=9.879, p<.01), and enthused participation (β=0.327, t=7.673, p<.01) are highly correlated with Internet purchase intention. Thus, the results provide support to hypotheses H1, H2, H3, H4, H5 and H6.

Table 3 Sem Results-Path Coefficients
Hypotheses	β (Standardized path coefficients)	t value	Decision
H1: Conscious attention → Trust in e-store	0.429**	8.539	supported
H2: Conscious attention →  Domain specific  innovativeness	0 .493**	8.871	supported
H3: Conscious attention →  Internet purchase intention	0.512**	9.879	supported
H4: Enthused Participation→  Trust in e-store	0.391**	7.053	supported
H5: Enthused Participation → Domain specific innovativeness	0.357**	7.842	supported
H6: Enthused Participation →  Internet purchase intention	0 .327**	7.673	supported

In this study, the moderating effect of gender as predicted by hypotheses 7 and 8 was investigated using multi-group analysis in AMOS 24.0. Multiple group analysis is thought to be the most effective method for examining connections between latent constructs (Homburg and Giering, 2001). To create the sample, males (n = 254) and females (n = 167) were split into two groups. Table 4 displays the results of the analysis. As evident from the results, the relationship between conscious attention and online purchased behaviour is positive and significant for both the genders (male: β = 0.121, t = 2.041, p < 0.01; female: β = 0.120, t = 0.315, p < 0.01), not accepting hypothesis H7. Also, the relationship between enthused participation and online purchased behaviour does not vary across genders (male: β = 0.173, t = 3.047, p < 0.01; female: β = 0.172, t
= 0.415, p < 0.01), not accepting hypotheses H8.

Table 4 Results Of Moderation Analysis
Hypotheses	Male		Female		Decision
	β	t values	β	t values
H7: Conscious attention →   Online purchase behaviour	0.121	2.041	0.120	2.315	Not supported
H8: enthused participation →   Online purchase behaviour	0.173	3.047	0.172	3.011	Not supported

Structural Model

The χ²/ df statistic, the NFI (normed fit index), the TLI (Tucker-Lewis Index); the GFI (goodness of fit index), CFI (comparative fit index); and RMSEA (root mean square error of approximation) estimates were evaluated in order to estimate the model's fitness. According to Hu and Bentler (1999), the GFI, NFI, TLI, and CFI values should all be greater than 0.9. The RMSEA values under 0.06 denote a satisfactory range. Without gender moderation, the model displayed an acceptable overall fit χ²/ df = 2.89, GFI = 0.921, CFI = 0.939, NFI = 0.915, TLI = 0.911, and RMSEA = 0.047.

Discussion and Implications

The current study aims to analyze the relationship of relationship between Conscious attention, Enthused Participation, and online purchase behaviour dimensions (Trust in e-stores, DSI-domain specific innovativeness and internet purchase intentions) in Indian online retail. The results are in line with the previous studies suggesting that purchase related outcome is associated with the purchase behaviour i.e., engagement is likely to influence customer’s purchase behaviour and in turn the beyond transactional purchase relationship (So et al., 2018).

Additionally, Thakur (2018) suggested that the conscious attention and engagement levels of customer’s positively impact the purchase intention and customer’s trust in mobile and online retail. Another study conducted by Prentice et al. (2019) indicated that social identities, customer engagement and purchase intentions are associated with one another especially in China. These research findings suggest that purchasing behaviour through the online retail is directly dependent on Customers’ active engagement with the online retail. In addition, an engaged customer is the one who has maintained an intense and stable connection with the retail and is more likely to undertake an active online purchasing behaviour. The reason behind this could be that they prefer choosing to purchase and use products or services as a form of emotional expression. These are the cases when the potential of customer engagement is harnessed in transforming the non- transactional behaviours into actual purchase behaviours. Further, Lee and Lee (2009) highlighted that conscious attention is related with detailed processing of product information, the product evaluation and purchase intention of the Japanese product. Conscious attention will influence attitude formation and purchase intention. This is based on the notion that engagement requires minimum cognitive effort to be experienced (Moore, 2014).

Several studies (Hollebeek, 2011; France et al., 2016) have been conducted in other contexts have indicated the existence of multiple focal engagement objects, such as, the product level customer- brand relationship and the relationship between customer and online platforms such as website etc (at the level of environmental cues). It is however to be specified that they differ in terms of the online purchase behaviour. Therefore, we would like to put forth our argument that the engagement ecosystem should be taken as a macro level construct in which each customer touchpoint (e.g., website etc.) as a micro element. Here, each touchpoint demands for its own individual in-depth consideration and configuration.

This study also attempted to investigate gender roles to assist marketers in developing specific gender-based marketing strategies. Conscious attention and enthused participation have a coherent impact on online purchase behaviour in online retail for both male and female customers, according to the moderation analysis done for this study. This demonstrates that the gender pay gap is smaller in the online environment. According to earlier studies (Noguti et al., 2019; Cui and Wu, 2017), gender differences exist in terms of customer engagement with purchase intentions. These findings refute those findings. Thus, the study showed that there was no moderating effect. When customer engagement is taken into account, the results are also found to be consistent with other recent studies showing negligible gender differences in online shopping behaviour. In a nutshell, this study supports the assertion that gender disparities are less pronounced online (Islam and Rahman, 2017). For example, the results are consistent with those of Nadeem et al. (2015) suggesting that the gender gap in the online environment has been found to have a declining trend. The study, which is still in its very early stages (Nadeem et al., 2015; Zhang et al., 2014), focused on significant theoretical as well as practical implications regarding gender analysis in the context of online brand communities.

The study advises marketers to make significant investments in improving the effectiveness of information search by offering user-friendly navigational tools, tracking members' previous browsing activities, and providing recommendations based on the customer's interests in order to draw in more customers by putting more emphasis on the cognition aspect. Additionally, those who work in online communities should support more structured methods of expressing feelings and opinions so that users of the community can read, write, and follow discussions in a methodical manner. Marketing strategists should put equal emphasis on tangible and intangible benefits to encourage customers to interact with the online brand and, ultimately, increase online purchase behaviour. The study's results provide a convincing defence for the application of various user-friendly website design strategies (Brynjolfsson et al., 2013); (Glaser & Strauss, 1967).

Future Research and Conclusion

The study has several limitations. The study's relatively small sample size could be considered a limitation in the beginning. Future studies may attempt to validate these results through additional empirical research despite the fact that the data tipping point had already been reached. Future studies may well repeat this research in different contexts or produce fresher conclusions by comparing results across cultures. Second, the emergence of Omni-channel retailing as a result of technological developments provides a rich background for upcoming research that will consider demographics. Thirdly, in a developing nation where other factors like average education levels and other socioeconomic characteristics can be expected to have a moderating effect on online purchase behaviour, the study examined the moderating role of gender. Future research could therefore examine additional contextual, economic, and emotional factors.

Funding

No funding received.

References

Adrita, U.W., & Mohiuddin, M.F. (2020). Impact of opportunity and ability to translate environmental attitude into ecologically conscious consumer behavior. Journal of Marketing Theory and Practice, 28(2), 173-186.

Indexed at, Google Scholar, Cross Ref

Bagozzi, R.P., & Yi, Y. (1994). Advanced Topics in Structural Equation. Advanced Marketing Research, 1.

Indexed at, Google Scholar, Cross Ref

Bagozzi, R.P., & Yi, Y. (2012). Specification, evaluation, and interpretation of structural equation models. Journal of the academy of marketing science, 40, 8-34.

Indexed at, Google Scholar

Barhemmati, N., & Ahmad, A. (2015). Effects of social network marketing (SNM) on consumer purchase behavior through customer engagement. Journal of Advanced Management Science, 3(4).

Indexed at, Google Scholar, Cross Ref

Bhartiy, K.K., Ojha, S.C., & Bharti, T. (2021). Antecedents Of Compulsive Buying Behaviour: A Case Of Indian Adolescents. Turkish Online Journal of Qualitative Inquiry, 12(10).

Google Scholar

Bodet, G. (2008). Customer satisfaction and loyalty in service: Two concepts, four constructs, several relationships. Journal of retailing and consumer services, 15(3), 156-162.

Indexed at, Google Scholar, Cross Ref

Bowden, J.L.H. (2009). The process of customer engagement: A conceptual framework. Journal of marketing theory and practice, 17(1), 63-74.

Indexed at, Google Scholar, Cross Ref

Bowden, J.L., Gabbott, M., & Naumann, K. (2015). Service relationships and the customer disengagement–engagement conundrum. Journal of Marketing Management, 31(7-8), 774-806.

Indexed at, Google Scholar, Cross Ref

Brynjolfsson, E., Hu, Y.J., & Rahman, M.S. (2013). Competing in the age of omnichannel retailing. MIT sloan management Review.

Indexed at, Google Scholar

Bucklin, R.E., & Sismeiro, C. (2003). A model of web site browsing behavior estimated on clickstream data. Journal of marketing research, 40(3), 249-267.

Indexed at, Google Scholar, Cross Ref

Calder, B.J., Malthouse, E.C., & Schaedel, U. (2009). An experimental study of the relationship between online engagement and advertising effectiveness. Journal of interactive marketing, 23(4), 321-331.

 Indexed at, Google Scholar, Cross Ref

Chen, Y.H., & Barnes, S. (2007). Initial trust and online buyer behaviour. Industrial management & data systems.

Indexed at, Google Scholar, Cross Ref

Cui, A.S., & Wu, F. (2017). The impact of customer involvement on new product development: Contingent and substitutive effects. Journal of Product Innovation Management, 34(1), 60-80.

Indexed at, Google Scholar, Cross Ref

Dessart, L., Veloutsou, C., & Morgan-Thomas, A. (2015). Consumer engagement in online brand communities: a social media perspective. Journal of Product & Brand Management, 24(1), 28-42.

Indexed at, Google Scholar, Cross Ref

Dholakia, U.M., Bagozzi, R.P., & Pearo, L.K. (2004). A social influence model of consumer participation in network-and small-group-based virtual communities. International journal of research in marketing, 21(3), 241- 263.

Indexed at, Google Scholar, Cross Ref

Donovan, R.J., Rossiter, J.R., Marcoolyn, G., & Nesdale, A. (1994). Store atmosphere and purchasing behavior. Journal of retailing, 70(3), 283-294.

Indexed at, Google Scholar, Cross Ref

Fornell, C., & Larcker, D.F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics.

Indexed at, Google Scholar, Cross Ref

France, C., Merrilees, B., & Miller, D. (2016). An integrated model of customer-brand engagement: Drivers and consequences. Journal of Brand Management, 23, 119-136.

Indexed at, Google Scholar, Cross Ref

Gambetti, R.C., Graffigna, G., & Biraghi, S. (2012). The grounded theory approach to consumer-brand engagement: The practitioner's standpoint. International Journal of Market Research, 54(5), 659-687.

Indexed at, Google Scholar, Cross Ref

Gao, J., Zhang, C., Wang, K., & Ba, S. (2012). Understanding online purchase decision making: The effects of unconscious thought, information quality, and information quantity. Decision Support Systems, 53(4), 772-781.

Indexed at, Google Scholar, Cross Ref

Gentile, C., Spiller, N., & Noci, G. (2007). How to sustain the customer experience:: An overview of experience components that co-create value with the customer. European management journal, 25(5), 395-410.

Indexed at, Google Scholar, Cross Ref

Glaser, B.G., & Strauss, A.L. (2017). Discovery of grounded theory: Strategies for qualitative research. Routledge.

Indexed at, Google Scholar, Cross Ref

Goldsmith, R.E., & Hofacker, C.F. (1991). Measuring consumer innovativeness. Journal of the academy of marketing science, 19, 209-221.

Indexed at, Google Scholar, Cross Ref

Hollebeek, L. (2011). Exploring customer brand engagement: definition and themes. Journal of strategic Marketing, 19(7), 555-573.

Indexed at, Google Scholar, Cross Ref

Chaurasiya, S., & Chaudhary, S.S. To Study the Influence of Online Marketing on Integrated Marketing Communication. IJFMR-International Journal For Multidisciplinary Research, 5(2).

Indexed at, Google Scholar, Cross Ref

Hu, L.T., & Bentler, P.M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural equation modeling: a multidisciplinary journal, 6(1), 1-55.

Indexed at, Google Scholar, Cross Ref

Hur, W.M., Kim, H., & Kim, H.K. (2018). Does customer engagement in corporate social responsibility initiatives lead to customer citizenship behaviour? The mediating roles of customer-company identification and affective commitment. Corporate Social Responsibility and Environmental Management, 25(6), 1258-1269.

Indexed at, Google Scholar, Cross Ref

Islam, J.U., & Rahman, Z. (2016). Linking customer engagement to trust and word-of-mouth on Facebook brand communities: An empirical study. Journal of Internet Commerce, 15(1), 40-58.

Indexed at, Google Scholar, Cross Ref

Islam, J.U., & Rahman, Z. (2017). The impact of online brand community characteristics on customer engagement: An application of Stimulus-Organism-Response paradigm. Telematics and Informatics, 34(4), 96- 109.

Indexed at, Google Scholar, Cross Ref

Jarvenpaa, S.L., Tractinsky, N., & Vitale, M. (2000). Consumer trust in an Internet store. Information technology and management, 1, 45-71.

Indexed at, Google Scholar, Cross Ref

Jones, S., & Ranchhod, A. (2007). Marketing strategies through customer attention: beyond technology-enabled Customer Relationship Management. International Journal of Electronic Customer Relationship Management, 1(3), 279-286.

Indexed at, Google Scholar, Cross Ref

Kalia, P., Kaur, N., & Singh, T. (2018). E-Commerce in India: evolution and revolution of online retail. In Mobile commerce: Concepts, methodologies, tools, and applications (pp. 736-758). IGI Global.

Indexed at, Google Scholar, Cross Ref

Lee, G., & Lee, C.K. (2009). Cross-cultural comparison of the image of Guam perceived by Korean and Japanese leisure travelers: Importance–performance analysis. Tourism management, 30(6), 922-931.

Indexed at, Google Scholar, Cross Ref

Malhotra, N., Hall, J., Shaw, M., & Oppenheim, P. (2006). Marketing research: An applied orientation. Deakin University.

Indexed at, Google Scholar, Cross Ref

Marjerison, R.K., Zhang, Y., & Zheng, H. (2022). AI in E-Commerce: Application of the Use and Gratification Model to the Acceptance of Chatbots. Sustainability, 14(21), 14270.

Indexed at, Google Scholar, Cross Ref

Martin, J., Mortimer, G., & Andrews, L. (2015). Re-examining online customer experience to include purchase frequency and perceived risk. Journal of retailing and consumer services, 25, 81-95.

Indexed at, Google Scholar, Cross Ref

Moore, C.W. (2014). The mediation process: Practical strategies for resolving conflict. John Wiley & Sons.

Indexed at, Google Scholar, Cross Ref

Nadeem, W., Andreini, D., Salo, J., & Laukkanen, T. (2015). Engaging consumers online through websites and social media: A gender study of Italian Generation Y clothing consumers. International Journal of Information Management, 35(4), 432-442.

Indexed at, Google Scholar, Cross Ref

Nawi, N C., Al Mamun, A., Hamsani, N.H.B., & Muhayiddin, M.N.B. (2019). Effect of consumer demographics and risk factors on online purchase behaviour in Malaysia. Societies, 9(1), 10.

Indexed at, Google Scholar, Cross Ref

Noguti, V., Singh, S., & Waller, D.S. (2019). Gender differences in motivations to use social networking sites. In Gender economics: Breakthroughs in research and practice (pp. 676-691). IGI Global.

Indexed at, Google Scholar, Cross Ref

Pavlov, P.I. (2010). Conditioned reflexes: an investigation of the physiological activity of the cerebral cortex. Annals of neurosciences, 17(3), 136.

Indexed at, Google Scholar, Cross Ref

Podsakoff, P M., MacKenzie, S.B., Lee, J.Y., & Podsakoff, N.P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of applied psychology, 88(5), 879.

Indexed at, Google Scholar, Cross Ref

Prentice, C., Wang, X., & Loureiro, S.M.C. (2019). The influence of brand experience and service quality on customer engagement. Journal of Retailing and Consumer Services, 50, 50-59.

Indexed at, Google Scholar, Cross Ref

Rabbanee, F.K., Haque, M.M., Banik, S., & Islam, M.M. (2019). Managing engagement in an emerging economy service. Journal of Service Theory and Practice.

Indexed at, Google Scholar, Cross Ref

So, K.K.F., Li, X., & Kim, H. (2020). A decade of customer engagement research in hospitality and tourism: A systematic review and research agenda. Journal of Hospitality & Tourism Research, 44(2), 178-200.

Indexed at, Google Scholar, Cross Ref

Thakur, R. (2018). Customer engagement and online reviews. Journal of Retailing and Consumer Services, 41, 48-59.

Indexed at, Google Scholar, Cross Ref

Van den Poel, D., & Buckinx, W. (2005). Predicting online-purchasing behaviour. European journal of operational research, 166(2), 557-575.

Indexed at, Google Scholar, Cross Ref

Verhoef, P.C., Reinartz, W.J., & Krafft, M. (2010). Customer engagement as a new perspective in customer management. Journal of service research, 13(3), 247-252.

Indexed at, Google Scholar, Cross Ref

Vivek, S.D., Beatty, S.E., Dalela, V., & Morgan, R.M. (2014). A generalized multidimensional scale for measuring customer engagement. Journal of Marketing Theory and Practice, 22(4), 401-420.

Indexed at, Google Scholar, Cross Ref

White, K., Habib, R., & Hardisty, D.J. (2019). How to SHIFT consumer behaviors to be more sustainable: A literature review and guiding framework. Journal of Marketing, 83(3), 22-49.

Indexed at, Google Scholar, Cross Ref

Zhang, H., Lu, Y., Gupta, S., & Zhao, L. (2014). What motivates customers to participate in social commerce? The impact of technological environments and virtual customer experiences. Information & Management, 51(8), 1017-1030.

Indexed at, Google Scholar, Cross Ref

Zinkhan, G.M., & Watson, R.T. (1998). Electronic commerce: A marriage of management information systems and marketing. Journal of Market-Focused Management, 3(1), 5-22.

Indexed at, Google Scholar, Cross Ref

Received: 17-Jun-2023, Manuscript No. AMSJ-23-13706; Editor assigned: 18-Jun-2023, PreQC No. AMSJ-23-13706(PQ); Reviewed: 26-Sep-2023, QC No. AMSJ-23-13706; Revised: 02-Oct-2023, Manuscript No. AMSJ-23-13706(R); Published: 08-Nov-2023