Keywords

e-Masstige brands, Customer intention, Adoption of e-masstige, Modelling, Prediction.

Introduction

Over the years, the IT sector has witnessed remarkable growth, catalysing significant progress in the corporate world. This surge has notably accelerated the adoption of electronic shopping channels. The medium of online shopping has been one of the significant reasons that effectively catalysed the widespread adoption of masstige goods (Shahid et al., 2024; Gilal et al., 2022). When it comes to e-masstige retail, the consumer is directed towards high-end products that may meet his need for prestige. The availability of masstige products helped masses of online customers to save time by making a large variety available for them, which satisfied their need to opt for high-end products (Mansoor & Paul, 2022).The consumer in e-retail behaves similarly to a traditional consumer wherein they interact with the internet system to access the retail store, acquire information, make a purchase transaction, and hence choose a mode for delivery and payment from a remote location, present in virtual space, (Gupta et al., 2023a). According to the AT Kearney Global Retail Development Index Report (2019), India is a retail giant and a leader, and it now holds the second position in the world, behind only China, in 2019.

It is anticipated that growth in modern organized retail will accelerate by 18% over the subsequent five years, particularly in regard to online retail, also known as e-retail. According to Lipsman (2019), one of the industries that are expanding at quickest rate is online retailing, which predicts a rise in revenue in the years to come. In spite of the fact that there is great potential in the Indian online retail industry because of the addition of various masstige brands in the corporate world, the actual level of participation from a sizable portion of the population is still relatively low. Research on this particular topic has taken place in a number of countries; nonetheless, studies are scarce in the Indian setting.

In the present scenario, consumers are getting involved with branded products because of the rising trend amongst the population of North India. The awareness of branded products within the masses has led to a term coined as 'masstige brands.' These brands are more affordable than luxury brands yet fulfil the desire of the masses who aspire to have branded products. Thus, in order to achieve the social ideal self and identity consumers want to adopt a brand known to numerous people and recognized as a prestige brand (Kastanakis & Balabanis, 2014). This implies that customers can enjoy luxury image with masstige brands at diverse price points (Kapferer, 2021). With the advancement of corporate world and the emergence of brands, the need was to determine the intentions of the customer in adoption of e-masstige brands.

Research in the past have attempted to study various antecedents and consequences of opting the masstige brands across countries or various cultures (Rodrigues et al.,2024; Shahid et al., 2024; Singh, 2023; Das et al., 2022). Whereas, in the changing scenario, the deficiencies of the previous researches were felt in the area of electronic masstige adoption and which is of dire importance with the dominance of electronic markets coming up.

"Unified Theory of Acceptance and Use of Technology" (UTAUT), "Theory of Planned Behavior" (TPB), and "Technology Acceptance Model" (TAM) have been used to examine adoption of mobile commerce, e-commerce, and electronic Masstige shopping (Gupta et al., 2023b; Kaur et al., 2023; Venkatesh et al., 2012). Several conclusions withdrawn from the previous researches like UTAUT, TAM and TPB were mostly used in adoption of information system or purchase intention respectively, thus they were not only sufficient to be studied for adoption of e-masstige brands.

After undergoing the previous researches, it is observed that the scales and models constructed were predominantly using the technique of structural equation modelling (Chatterjee et al., 2023; Purohit & Radia, 2022; Kumar et al., 2021). On the contrary, this current study made use advanced analytical tool by incorporating hybrid model of analysis i.e. SEM and Neural network. Therefore, the study seemed more pertinent as neural network further modifies the scale developed and validated through SEM.

Also, the studies conducted for masstige brand adoption had different set of antecedent variables that suited to their set of geographical regions. After undergoing the previous researches, it was observed that most of the variables taken up were suited to research conducted in particular context and area. The variables which have been studied were price, trust, need for uniqueness, brand love, functional value etc (Rodrigues et al., 2024; Singh, 2023; Das et al., 2022). Demographics associated variables like age, gender, income were also taken up but it stood insignificant for the current study (Baber et al., 2020). The variables such as the value perception and the perception about the status, Fear of Missing out, acceptance of using new technology and exposure to social influence have not been comprehensively taken up in any of the past study. These variables however need to be studied in the collective form, thus there is a dire need to have better understanding of masstige brand adoption especially in the category of electronic masstige branded product not done previously.

The current research makes an effort to close an existing knowledge gap by putting forward a fresh research model that can be utilized for forecasting the customer intention (CI) to adopt e-masstige brands, determining the critical factors that need to be focused upon by the corporateers. The model is constructed by integration of TAM's commonly known predictors, such as "Perceived usefulness" (PU) and "Perceived ease of use" (PEOU), and it also takes into consideration the psychological dimension by taking into account variables such as Enjoyment and Trust. This gives the model a two-pronged approach to predict user behavior. In addition to that, the social influence variable from the UTAUT model is included, as are quality antecedent variables such as cost and service quality. The “Fear of Missing Out” (FOMO) and Non-compatibility with online platforms from "Innovation diffusion theory" (IDT) given by Rogers (1993) are examples of variables that are specific to the North Indian context that were included in the study, making it one of a kind. Because the goal of studying the collective impact of these variables on customers' desire to embrace e-masstige brands was never attained by any previous study, the chosen variables lend a distinctive quality to the study. This lends the study the ability to stand out from other studies in the field. At present, working professionals of the corporate world face a problem in handling, analyzing, and inferring customer data successfully and using it suitably (Dhingra et al., 2021). Therefore, current study attempts to discover characteristics that influence the customer's intent to adopt e-masstige brands in North India. It would guide retailers and marketers in the corporate world to refurbish existing marketing techniques, which would ultimately result in higher e-masstige brand adoption rates.

The current research’s primary purpose is to identify most critical elements that have an impact on the growth of e-masstige brands in retail sector and hence, to evaluate the significance of these aspects. Thus, the study intends to achieve following objective:

1. To identify different antecedent variables which influence customer's propensity to shop from an e-masstige branded store.

2. To examine most significant factor encouraging adoption of e-masstige brands in Northern India.

The study is divided into three distinct segments, the first of which presents the research model and confers research hypothesis. In the second segment, we test hypothesized model using SEM. In the third and final part, Customer Intention (CI) to adopt an e-masstige brand in North India is predicted using a neural network, which was trained using determinants, acquired from structural equation modeling (SEM) and then was provided as inputs to the neural network model.

Literature Review

Research studies that are associated with information technology and e-retail have been utilized for the research model. These theories include "Theory of Reasoned Action" (TRA) (Fishbein, 1979), which identifies relationship between attitude and behavior; further extended with TPB theory, which focuses on the behavior of people, thereby implying that a person's intention to perform a particular behavior stands as the main predictor of adoption (Kaur et al., 2023). The standard "Technology Acceptance Model" (TAM), proposed by Davis (1989), is used to understand adoption and to predict user acceptance. It is the theory that analyses consumer behavior and their decision to accept new technology like e-retail and mobile retail. TAM is a revision and extension of TRA and is used to predict technology acceptance using primary elements like "Perceived Usefulness" (PU) and "Perceived ease of use" (PEOU) (Gupta et al., 2023b). The theories mentioned above are appropriate for investigating the acceptability of masstige brands through online retail in North India because they focus on three aspects of behavior: an individual's attitude towards use, their behavioral intention, and their actual behavior. The research also utilized other theoretical frameworks, such as Rogers' (1993) innovation diffusion theory. The "Unified Theory of Acceptance and Use of Technology" (UTAUT) (Venkatesh et al., 2012) is a theory that was created by combining eight other theories, some of which were stated previously. It is utilized for the purpose of explaining the user's intention to use a particular information system as well as the user's subsequent behavior while using the system.

The foundation of the suggested model is based on all of the theories mentioned earlier; however, in distinction to the traditional TAM model, in which PU and PEOU work as mediators between external variables and outputs, the current study attempts to analyze the direct influence of external variables such as social commerce constructs, psychological dimensions, transaction-based constructs, FOMO, and compatibility on CI to adopt e-masstige brands. As a result of their acceptability and applicability in the majority of technology adoption and retail studies, the variables PEOU, PU, service quality, and cost have all been the subject of research as control variables. A significant number of scholars have combined the TAM model with a variety of other variables. TAM was used by Mijoska and Kalina (2017) in their research for the people of Macedonia for the adoption of online shopping amongst the young population. The study concluded that computer anxiety and aggravation were significant factors that affected online shoppers. In addition, Wei et al. (2009) studied mobile commerce adoption in Malaysia by considering factors like PU, PEOU, perceived cost, trust, and social influence and suggested the usage of neural networks due to their efficient prediction capabilities in comparison to other techniques Like SEM, logistic regression, and linear regression (Sharma et al., 2017). Supplementing the studies mentioned above, many other researchers used neural network techniques to identify and predict shopping behavior, buying intentions, and adoption of m-commerce (Chong, 2013).

The majority of the study is based on two theories, TAM and UTAUT that have been discussed previously because they consolidate the utilization of technology and its practices within this sector (see table 1A). As a result, the purpose of the current study is to utilize an SEM-based neural network model in an effort to predict customers' desire to adopt e-masstige brands.

Perceived Prestige

According to Mignonac et al. (2006), the concept of perceived prestige can be described as the social value that people attribute to the identity of a consumer. Consumers today choose brands that are well known and acknowledged as belonging to the prestige brand category so that they can achieve the desired identity, level of social acceptance, and ideal version of themselves (Kastanakis & Balabanis, 2014). According to Šulentic et al. (2017), employees have the perception that their social standing and value are raised if they are members of an essential, renowned, and significant organization. As a result, employees achieve a higher level of self-respect. In terms of masstige branding, it was observed that the customer considers the product or service's prestige as one of the most crucial factors while engaging in masstige branded product shopping. Based on the research of Chatterjee et al. (2023), perceived prestige is a significant factor that impacts customer's purchasing decisions from masstige retail stores. In addition, Truong et al. (2009) defined "masstige marketing as an empirical study where customer perception and product qualities are considered along with a focus on the moderating effect of status, emotion, and pride." Therefore, while the globe is becoming increasingly interconnected, new companies are reorienting their business models to focus on consumers from the middle class by providing a high level of perceived prestige.

H₁: Perceived prestige has a substantial influence on customers' intention to adopt an e-masstige product.

Perceived Enjoyment

According to Childer et al. (2001), enjoyment is one of the most critical aspects in the acceptance of shopping and indicates the level of fun the shopper believes they are having. According to Venkatesh et al. (2012), a hedonistic motive is a robust predictor of the acceptance of the latest technology, and this incentive manifests itself in consumers' frequent visits to online retailers. The grade to which a customer feels a specific technology to be pleasurable and, as a result, represents an emotional state that is competent enough to drive consumers to continue such activity is referred to as the pleasantness quotient (Pantano & Pietro, 2012). According to Lee et al. (2003), there is a favorable correlation between enjoying the time spent browsing and the ease with which one can make purchases when one is thinking about CI to make an online retail purchase. In addition, researchers discovered that the experience of shopping online has a positive association with the total amount of purchases made as well as the buying intents, which suggests that fun is a crucial component of online shopping. Customers are motivated to shop online by the hedonic needs of e-retailing, such as enjoyment, pleasure, joy, and delight (Rahadhini et al. 2020). The goal of Internet shopping is to accomplish convenience, search a vast pool of information, and save money. However, it also includes hedonic demands of e-retailing, such as enjoyment, pleasure, joy, and delight. According to Thong et al. (2006), enjoyment has a considerable impact on online purchasing. As a result, online retailers should focus on the hedonic factor while creating their websites in order to attract more customers. In addition, Fiore et al. (2005) provided evidence showing that pleasure received from online purchasing has a direct effect on online buying.

H₂: Perceived Enjoyment devises substantial influence on customers' intention to adopt an e-masstige product.

Desire for Exclusivity

Humans have an inborn quality of 'want,' and this quality is the primary driver that engages people in varied activities and pursuits available to them (Xu et al., 2020). Humans tend to wish to distinguish themselves from other humans, and this can be called a 'desire for exclusivity' (Xie & Muralidharan, 2023). People feel that buying products from masstige brands would provide them with a product that is not widely available (Paul, 2019). A new brand is adopted at a more rapid rate when people have a need and preference for exclusivity (Kapferer, 2021). The current study hypothesized that since people have the habit of comparing themselves to others, hence this tendency promotes their desire for exclusivity, thereby influencing the consumption of masstige goods. In addition, Das et al., (2022) discovered that people desire to stand out in society makes them choose one-of-a-kind things. As a result, consumers who fall in middle class have a strong desire to acquire quality products that are within their price range and offer them a sense of distinctiveness, social standing, and dependability (Kassie & Bang, 2022). In light of this, Das et al., (2022) settled that want is a crucial parameter that defines consumer engagement with masstige brands. 'Want' accomplishes masstige inspiration, which in turn results in masstige intention and, hence, masstige retail engagement. In addition, Loureiro et al. (2018) claimed that desire is one of the primary drivers to engage consumers.

H₃: Desire for exclusivity devises substantial influence on customer's intention to adopt an e-masstige product.

Fear of Missing Out

Zhang et al. (2020) defined "Fear of Missing Out" (FOMO) as "the uneasy, negative and sometimes all-consuming feeling that you are missing out from inaction on perceived opportunities—that your peers are doing, or are in possession of more or something better than you." People in today's society are more aware of what is going on around them and have a greater tendency to imitate the behavior of those they observe by engaging in the same behavior themselves. This kind of thinking is prevalent among the people living in Northern India. According to Parsons et al. (2014), customers who suffer from FOMO are more likely to engage in retail purchases due to the following reasons: enhanced status, brand familiarity, self-satisfaction associated with being fit in a particular social group; and personal hedonic incentive. The FOMO can lead to an increased possibility of purchasing due to the fact that customers are now addicted to trying new things because of new social media trends, and as a result, they are unable to limit their spending in an effort to fit in with their peers (Good & Hyman, 2021). FOMO motivates sixty percent of individuals to purchase within twenty-four hours (Chance, 2021). As a result, it is evident that FOMO is a parameter that produces a sense of urgency in the customer and, as a result, drives them to get involved in the adoption of an e-masstige product.

H₄: FOMO has a substantial and significant influence on customers' intention to adopt an e-masstige product.

Compatibility with Online Platforms

Compatibility with online platforms may be defined as the ability of people to accept the technology with respect to its appropriateness with technology infrastructure, culture, value, and work practices (Morteza et al., 2011). When customers engage in an activity, such as the implementation of e-retailing, compatibility delivers evidence regarding the point to which the system conforms to the preferences of the customers. According to Del Bosque and Crespo (2008), this concept "refers to the degree to which a technology is consistent with the values, ideas, and beliefs that are linked with both the past and the present scenarios, as well as the requirements of technology adopters." Vijayasarathy (2004) found a favorable association between customer attitudes towards e-retailing and their conformity with pre-existing values and lifestyles. This was done while taking into account the goal of customers to adopt online retailing. Thus, it can be said that customers have a greater propensity to adopt e-retailing due to the fact that alternatives to physical retail outlets can better match their purchasing routines and preferences. In addition to the previous research, (Gupta et al., 2023a) also claimed that CI towards acquiring a fresh technology is undoubtedly connected to their beliefs, past experiences, needs, and present values. If the latest technology is linked with the prevalent values of people in a particular location and is able to satisfy the requirements of customers with regard to their traditions, then only it gets accepted by customers. In the current investigation, the variable is of utmost significance due to the fact that a sizable population in the northern part of India is still static in an early stage of technological development. This is due to the fact that Indians are quite traditional in their belief system and culture. According to Rogers (1993), the consumer will not adopt e-retail if the technology is incompatible with the customer's needs and the adopter is unable to implement the innovation into his or her life successfully.

H5: Compatibility with online platforms devises substantial influence on customers' intention to adopt an e-masstige product.

Perceived Value

According to Das et al. (2022), consumers engage in masstige shopping because of a metric known as "Perceived Value" (PV). According to Jeong and Kim (2020), "Perceived value is the consumer's overall assessment of the utility of a product (or service) based on perceptions of what is received and what is given." When it comes to maintaining a successful business, particularly in highly competitive marketplaces, one of the most essential critical parameters is PV. According to a past researcher , PV can contribute in three different ways: by giving product-related value, social-related value, and person-related value. If a consumer begins to discover value in the product and is able to satisfy his or her sense of value, then the customer will likely want to immerse himself in adopting an e-masstige brand in order to satisfy his or her own needs and desires.

Regarding the purchase of electronic prestige brands, Das and Roy (2022) demonstrated that an individual's perception of value acts as a catalyst for prestige inspiration, which in turn results in prestige intention and, lastly, prestige brand engagement. Park et al. (2022) argue that it is a crucial criterion for the acceptance of masstige brands in the online domain because PV can occur at diverse stages of the purchasing procedure, including the pre-purchase stage. According to the findings of a study (Pham et al., 2018), PV appears to have a beneficial effect by considerably altering the attention paid by customers to their purchases. Because of this, it is possible to point out that the PV is a significant parameter that is taken into consideration for this study.

H6: Perceived value devises substantial influence on customer's intention to adopt an e-masstige product.

Control Variables

Since there is an inclusion of TAM variables, PU (Gupta et al., 2023b; Cabanillas et al., 2017; Kaur Hundal & Kumar, 2017; Venkatesh et al., 2012) and PEOU (Nguyen et al., 2019), in large number of studies in the past therefore these variables are applicable and mostly accepted in any TAM. In the context of the adoption of e-Masstige brands in North India, the presence of these variables is required because they cannot be ignored in any way. Similarly, there are a plethora of studies that make copious use of the variables, namely 'cost' from studies like He et al. (2021) and Xu et al. (2019) and 'service quality' from studies like Sharma et al. (2017), and Hoyos-Vallejo et al. (2023). These two types of research have been conducted extensively in recent years. As a result, the present study makes use of these factors as control variables and incorporates a few additional variables into the model that was presented. We have a hypothesis that CI's adoption of e-masstige brands in North India will be significantly influenced positively by PU, PEOU, cost, and service quality. The objective of the suggested model and research hypotheses that have been created is to ascertain whether the CI will adopt e-masstige brands in North India. Therefore, CI to the adoption of e-masstige brands would further pave the road to comprehending the customer experience, so driving its way to customer satisfaction and loyalty in a setting of e-masstige branding (Klaus, 2013). As a consequence, in order to accomplish the goals mentioned above, the study makes an effort to portray the theoretical model in a way that is parallel with comments from earlier research, as shown in Figure 1. In the preceding part, we went into detail about the many different constructions that were found through the literature study, as well as their relationships to one another in influencing the customers' intentions to adopt e-masstige brands.

Materials and Methods

Data Collection

The study aimed to increase understanding of and make predictions about the intents of customers towards the adoption of e-masstige in the key cities of Punjab, Haryana, and the NCR region, which includes Delhi, Noida, and Gurugram. A questionnaire for a survey was designed and given out in person and over email to a set of 400 potential respondents. Out of them, 377 legitimate and complete responses were received back from the respondents. We made all of the necessary adjustments to the questionnaire depending on the impressions that respondents had and any probable misunderstandings that were found. The next step was to incorporate the suggested changes into the completed version of the questionnaire.

The study model that has been developed has a total of ten variables: six independent variables, four control variables, and one variable that is dependent on the other variables. The respondents scored each of the model's 40 statements as strongly agreed or disagreed through each statement using the Likert scale on a scale from 1 (Strongly Agree) to 5 (Strongly Disagree). To evaluate the dependability of the variables, Cronbach Alpha is used, and its value should fall somewhere in the range of 0.60 to 0.70.

Reliability and Validity

A three-step process was used to conduct statistical analysis- Step one determined the dependability and validity of the proposed model by using the Cronbach alpha coefficient. The values of Cronbach's alpha coefficient are presented in Table 1. The convergent validity, discriminant validity, and composite reliability were determined using confirmatory factor analysis. Then, the Second Step determined the significance of hypothesis predictors using SEM. PLS-SEM modelling technique was applied using SmartPLS version 2, where hypotheses were tested, and only approved hypotheses became inputs to the neural network model. In the Last Third Step, the neural network was employed to manipulate the effect of approved independent factors' strength on the dependent variable. The "Statistical Package for the Social Sciences" (SPSS) version 20 was employed to conduct the Neural Network study.

Table 1 Reliability Analysis
Variables	Cronbach’s alpha (a)
Perceived Prestige	0.815
Enjoyment	0.864
Desire for exclusivity	0.838
FOMO	0.875
Compatibility	0.824
Perceived Value	0.863
Perceived Ease of Use	0.956
Perceived Usefulness	0.938
Cost	0.950
Service Quality	0.961
Customer Intention to adopt E- Masstige Brands	0.965

The findings of the reliability analysis are presented in Table 1, where it is shown that every variable possesses a sufficient level of reliability, as it is greater than the cut off value of 0.7 (Fared et al., 2021). The values of Cronbach's alpha for the variables range from 0.8 to 0.9, which means that the recommended model satisfies the condition for dependability. The values of Average variance extracted (AVE), composite reliability (CR), and value of R², also known as the proportion of variation explained, are shown in Table 2. It shows that the value of AVE is more excellent than the cutoff value of 0.5 that has been established (Henseler et al., 2015). According to Bagozzi and Yi (1988), CR values of variables were more significant than 0.6, and CR values were also greater than AVE values. Therefore, this imparts credibility to the idea that the presented model possesses convergent validity. Table 2 and Figure 2 reveal that all confirmatory factor loadings are more excellent than 0.5, indicating that the constructs are legitimate. These loadings are then tested to see whether or not they are significant at the 0.01, 0.05, and 0.10 levels of significance, respectively. The hypothesis of discriminant validity was authenticated by the fact that the square root of AVE was higher than all of the values of correlation coefficients presented in Table 2, which were found between the variables.

Table 2 Discriminant Validity
	CI	Comp	COST	DESIRE	ENJOY	FOMO	PEOU	PU	PP	PV	SQ
CI	1
Comp	0.1031	1
COST	0.581	0.1132	1
DESIRE	0.111	0.2943	0.1319	1
ENJOY	0.0783	0.2475	0.1069	0.2786	1
FOMO	0.1643	0.3533	0.2307	0.3123	0.476	1
PEOU	0.4735	0.0626	0.5319	0.0417	0.0546	0.198	1
PU	0.4640	0.1772	0.5702	0.0977	0.0318	0.2185	0.4441	1
PP	0.1413	0.7573	0.1656	0.305	0.3121	0.433	0.1442	0.1348	1
PV	0.2233	0.4328	0.3203	0.4567	0.265	0.4103	0.2328	0.4022	0.3982	1
SQ	0.7508	0.1182	0.6778	0.0990	0.0683	0.171	0.5461	0.5451	0.1665	0.3353	1
AVE	0.878	0.554	0.833	0.560	0.670	0.571	0.853	0.8010	0.514	0.554	0.867
SQRT AVE	0.937	0.785	0.913	0.748	0.792	0.756	0.924	0.895	0.717	0.744	0.931

Results and Discussion

Hypothesis Testing

In order to validate the research hypothesis of the suggested model, SEM was utilized. It was also utilized to investigate the amount of the causal relationship that existed between the variables. Six different relationships were investigated, and each one was evaluated based on the power and consequence of the straight effect that each of the six independent factors had on the dependent variable, which was the customers' intentions on whether or not they would adopt E-masstige brands. SEM analysis findings are presented in Table 3 , which examines the standardized regression weight of each variable and t-statistics, along with remarks regarding whether or not the link was established. A structural equation modelling (SEM) example is presented in Figure 2. It shows the standardized regression weights as well as the factor loadings associated with each variable.

Table 3 Results
Hypothesis	beta (ß)	t-value
Control Variable: Perceived Ease of Use— Customer Intention	0.062	3.30***
Control Variable: Perceived Usefulness— Customer Intention	0.061	2.67***
Control Variable: Cost — Customer Intention	0.097	4.59***
Control Variable: Service Quality— Customer Intention	0.643	30.84***
H1: Perceived Prestige - Customer Intention	0.006	2.655***
H2:  Perceived Enjoyment-Customer Intention	0.018	1.267ns
H3: Desire for exclusivity- Customer Intention	0.060	4.197***
H4: FOMO— Customer Intention	0.018	1.053ns
H5: Compatibility— Customer Intention	0.025	1.252ns
H6: Perceived Value- Customer Intention	0.110	7.44***

Separate analyses were performed on two different models. Initially, the model was evaluated using only the control variables; afterward, the entire model, including both the control variables and the independent variables, was evaluated. The research examined the predictive power of the two models that were evaluated and came to the conclusion that the first model with control variables had a predictive power of 0.53, while the model with control and independent variables showed better predictive power, which was 0.58. Thereby, the incorporation of more variables improved the predictive quality of the model. Following is an investigation of what we found when we evaluated the model using both control variables and independent variables. According to the findings of the investigation, seven of the ten relationships that were put to the test were significant according to statistical criteria.

Complimenting to the past researcher Gupta et al., (2023b), The present study supports control variable, PEOU, due to the fact that it has a substantial effect on CI to embrace E-masstige brands (t-value = 3.440, accepted at Sig. level 0.01). In contrast, Wei et al. (2009) considered PEOU as insignificant variable while studying mobile commerce adoption in Malaysia. There is a statistically significant effect on CI to embrace E-masstige brands (t-value = 2.6509, accepted at Sig. level 0.01), which provides additional support for the control variable known as PU. PU has been identified as an important predictor for e-masstige adoption similarly by researchers as in the case of opting e-retail, mobile apps and mobile commerce adoption (Gupta et al., 2023b). According to Arachchi and Samarasinghe (2023), PU and PEOU related to AI certainly impact impulse purchase intention. This finding suggests that respondents are worried about both the PEOU and the PU of E-masstige brands when adopting new ones, as demonstrated by a number of research studies in the past. The control variable, cost, has shown itself to be a significant predictor of customer propensity to embrace E-masstige brands (t-value = 3.429, accepted at Sig. level 0.01). When considering a customer's intention to adopt any new technology or practice, the cost is and always has been a crucial factor to consider. This is in accordance to previous researches such as Singh (2023), Roy (2023) and Wei et al., (2009). The price is what ultimately determines whether or not a customer will try and accept anything new. According to the findings, it is abundantly clear that among the control variables of the study, Service Quality is an essential component that plays a significant role in identifying CI to embrace E-masstige brands. The control variable service quality has a noteworthy role in e-retail because it can entice and maintain customers, which helps e-retailers attain a competitive advantage in the market (Ranjit et al., 2023). In addition to the current study many past researches have the similar opinion that service quality, quality of customer experience and customer service impacts the purchase intention of the customer (Roy, 2023; Singh, 2023; Das et al., 2022). Therefore, it can be concluded that the quality of the service has a considerable impact on customers' intentions to purchase E-masstige brands (t-value of 29.65, accepted at the Sig. level of 0.01).

Among the six variables that are still available, the hypothesis that Perceived Prestige has an essential effect on CI to embrace E-masstige brands has a t-value of 2.655 and was accepted at the Sig. Level of 0.01. Supplementing to the result, Chatterjee et al. (2023) found perceived prestige to be a significant variable while studying consumer perception for masstige marketing. Hypothesis 2, Perceived Enjoyment does not have a significant influence on the customer's intention to embrace E-masstige brands (t-value = 1.267, rejected at all Sig. level 0.01, 0.05, and 0.10). Whereas, in contrary to previous researchers, entertainment and emotion played significant role in m-commerce adoption and perception of consumers for masstige marketing. Hypothesis 3 Desire for exclusivity stands essential, statistically significant, and supportive of CI to adopt E-masstige brands (t-value=4.197, accepted at Sig. level 0.01, 0.05, and 0.10), on similar grounds of past researches done by Rodrigues et al., (2024), Das et al.,(2022) and Purohit & Radia (2022). Complimenting to the result of the current study, the previous researchers inferred uniqueness and vanity value as a significant variable in terms of masstige branded products. Hypothesis 6 PV is also supported (t-value=7.44, accepted at Sig. level 0.01, 0.05, and 0.10). It is in parlance to few studies done in the past wherein value is really important variable while studying consumers’ perception for masstige products. Various value considerations like functional, experimental, symbolic value and consumer vulnerability to interpersonal influence stood significantly important in consumption of masstige branded products (Purohit & Radia, 2022; Das et al., 2022; Das & Balaji, 2022). Both "fear of missing out" (Hypothesis 4) and the "compatibility with online platforms" (Hypothesis 5) hypotheses have been disproven, which suggests that there is no substantial effect that these variables have on the intention of customers to adopt E-masstige brands. Few researchers in the past had a dissimilar viewpoint wherein computer anxiety and technology overall played a significant role in e-commerce adoption (Hendricks & Mwapwele, 2023; Mijoska & Kalina, 2017). Out of the six independent variables, PV has the most significant influence on CI to adopt e-masstige brands in North India, followed by desire for exclusivity and Perceived Prestige. The control variables that have the most influence on a customer's intention to adopt e-masstige brands are Service Quality followed by Cost, PEOU, and PU, respectively.

Neural Networks Analysis

The research utilized a mix of SEM and the neural network methodology, which is recognized as a critical artificial intelligence method. Methods like regression and SEM were mainly used to identify linear relationships among the variables, thereby giving a simplified solution to a complicated problem (Liébana-Cabanillas et al., 2017).

In contrast, a neural network identifies complex non-linear relationships amongst the variables, is more reliable, and offers a high level of prediction accuracy. It has the proficiency to perform excellent forecasting mechanisms for both marketers and market researchers prevalent in corporate world (Dhingra et al., 2020). Therefore, in the following study, the neural network method is applied to make projections regarding the significance of several aspects related to CI in the adoption of e-masstige brands. The neural network model was constructed using version 20 of the SPSS statistical package. Many Hierarchical layers make up the neural network, known as the input layer, the hidden layer, and the output layer.

Moreover, the PLS-SEM model usage prior, worked as the source of data for three significant independent variables that make up the input layer of neural network model. These variables are perceived prestige, PV, and desire for exclusivity, respectively. The research model has one variable in its output layer, and that variable is CI to adopt e-Masstige brands. In one of the hidden layers, there are two hidden nodes, and the sigmoid function is employed as the activation function for both the hidden layer and the output layer. In order to verify that the neural network model is accurate, the root mean square error metric was applied to both the training and testing datasets. The root-mean-square error (RMSE) is a scale-dependent measure of the accuracy of a forecast. Its value is always positive, and 0 indicates a perfect fit to the data. The phenomenon of achieving a perfect fit has never been practically accomplished. Because of this, it can be deduced that an RMSE value with a lower absolute value is preferred, and the formula to use is as follows:

Where "Pi" is the value that was predicted, and "Oi" denotes the value that was actually observed. Table 4 contains the results of calculations on the average and the standard deviation. In order to solve the issue of over-fitting, a ten-fold cross-validation has been created. Within this validation method, 90% of data is used to train NN, while the remaining 10% evaluates the accuracy of the trained network. The RMSE of NN had a value of 0.1014 on average, while the value of the testing model was 0.0974. The standard deviation of the testing model is 0.0135, whereas the standard deviation of the training model is 0.0019. Because the value of RMSE is low, this suggests that the prediction will be accurate and dependable. The significance of each normalized variable is displayed in Table 5 and graph is presented in Figure 3, along with its associated predictor. The value was attained by dividing the importance value of each predictor by the value that was the largest, which resulted in the percentages. It has been observed that the three factors PV, perceived prestige, and desire for exclusivity are the most significant predictors of a customer's inclination to adopt an e-masstige brand. In comparison to results obtained through SEM, neural network analysis re-ordered the importance of the three independent variables by specifying PV to be the most important variable, followed by Perceived prestige and last Desire for exclusivity.

Table 4 RMSE Values for Neural Network
Neural Network	Training	Testing
ANN1	0.101	0.099
ANN2	0.102	0.088
ANN3	0.100	0.092
ANN4	0.104	0.078
ANN5	0.100	0.098
ANN6	0.097	0.125
ANN7	0.102	0.095
ANN8	0.103	0.088
ANN9	0.100	0.115
ANN10	0.101	0.094
Average	0.101	0.097
Standard Deviation	0.001	0.013

Table 5 Normalized Variable Importance
Variable	Importance	Normalized Importance
Perceived Value	0.542	100.0%
Perceived Prestige	0.227	54.6%
Desire for Exclusivity	0.231	35.2%

Conclusion and Managerial Implications

This research examines and validates collective impact of various antecedent variables to determine the intention of customers to adopt e-Masstige brands in North India. The study adopted a quantitative method and collected Data from 377 respondents living in key cities of Punjab, Haryana, and the NCR region, which included Delhi, Noida, and Gurugram. The data collected through structured questionnaire was analysed using Partial Least Square- Structural equation model (PLS-SEM) and neural network (NN) using Statistical Software Package. The hypothesised model taken up for the study, had a set of six independent variables, a set of four control variables, and one variable that is dependent on these other variables. Perceived Prestige, Perceived Enjoyment, Desire for Exclusivity, Fear of Missing Out, Compatibility with Online Platforms, and PV were independent variables and were used to predict the outcome. PEOU, PU, cost, and service quality were four control variables that were examined in this study. In the initial phase of analysis, SEM modelling determined most significant independent variable in the following order- PV, desire for exclusivity and perceived prestige. Variables - fear of missing out and compatibility with online platforms were not statistically significant as probable antecedents and all control variables were statistically significant in the following order from highest to lowest: Service quality, Cost, PEOU, and PU. Further, the supported antecedents, also known as the independent variables, were input into a neural network for further analysis in order to validate the conclusions that the SEM analysis had produced. The discoveries of the study highlight that the CI to adopt e-masstige brands is typically dependent on the PV of the brand, perceived prestige of the brand, and desire for exclusivity. PV is the component that has the most significant impact on consumers' decisions to purchase an e-masstige brand, followed by perceived prestige and desire for exclusivity is the factor that has the least significant impact. Therefore, the CI to embrace e-masstige brands is the essential component that has to be paid attention to in order to promote the expansion of online masstige retail in India.

The theoretical implications are aimed at scholars and academicians. By putting up and validating a novel research methodology, this work contributed to the growing body of scholarly work on e-masstige brand adoption which was never done before and hence added value to existing research archive. Past studies had used only SEM analysis whereas the present study made use of hybrid SEM-ANN approach which makes the study unique. The present research extended the variables by improving on TAM, which allowed for an extension of the e-masstige brand adoption trials. It expanded on the TAM model by including the UTAUT model as well as many other variables.

The study would help masstige brand corporate business houses to understand key signifiers and communicate benefits of the products especially intangible benefits to build brand image and thereby enhance masstige retail marketing strategy. E-retailers of masstige brands might emphasize delivering PV, by creating value for customers so as to position and retain customers (Ghimire et al., 2018). It is possible to accomplish this by providing customers with a variety of schemes, such as producing high-quality goods, cultivating trust, demonstrating reliability, and minimizing financial risk. The need is to market masstige branded products in a way that customers perceive sufficient value for the high prices charged (Shukla & Purani, 2012). Masstige retailers can adopt strategies wherein they create prestigious image of their brand allowing them to acquire quality customers and create a buzz for imparting exclusive products (Dalton et al., 2020). Therefore, e-masstige corporate business houses will get directional guidance to improve their target marketing strategies and elaborate their focus on the above-mentioned variables.

Limitations

The different variables Customers' intentions to adopt e-masstige brands are influenced by a number of factors, including PEOU, PU, cost, service quality, perceived prestige, perceived enjoyment, desire, FOMO, and compatibility with online platforms, as well as PV. However, the customer's inclination to purchase e-masstige brands might be supported by the diversity of additional elements. The expanding number of independent variables should be a focus of future research in order to locate and develop a model that is more suitable to the decisions that must be made in the actual world. In addition to this, the sample for the study is limited to the states having a large number of consumers that shop online for retail. The ability to generalize would be improved by replication in additional regions. Therefore, varied geographical settings and market conditions might affect the policies of the country, and as a result, there is room for further research in other parts of India and other countries.

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Received: 24-Feb-2025, Manuscript No. AMSJ-25-15713; Editor assigned: 25-Feb-2025, PreQC No. AMSJ-25-15713(PQ); Reviewed: 21-Mar-2025, QC No. AMSJ-25-15713; Revised: 28-Mar-2025, Manuscript No. AMSJ-25-15713(R); Published: 15-Apr-2025