Research Article: 2022 Vol: 28 Issue: 4S
Model for E-Commerce Adoption & Drivers and Barriers for Online Shopping Post Covid-19 among Millennial and Gen-Z Consumers
Hamid Agahi, King Mongkut’s Institute of Technology Ladkrabang
Tanes Tachasaen, King Mongkut’s Institute of Technology Ladkrabang
Citation Information: Agahi, H., & Tachasaen, T. (2022). Model for e-commerce adoption & drivers and barriers for online shopping post covid-19 among millennial and gen-z consumers. Academy of Entrepreneurship Journal, 28(S4), 1-17.
Keywords
E-Commerce Adoption, Drivers and Barrier, Gen-Z, Millennials
Abstract
The value of the Technology Acceptance Model in predicting consumer behavior with respect to online technologies is well established (Davis et al., 1989). Therefore, the aim of the present study was to examine the significance of the model in predicting consumer utilization of online e-commerce portals. The study involved a survey of Thai undergraduate male and female respondents (N=100). An online survey using Google Forms was utilized for data collection. The study involved the use of Structural Equation Modeling using the Maximum Likelihood Estimate method, through SPSS AMOS V.22. Confirmatory factor analysis was also carried out to compute the composite reliability of constructs within the questionnaire. Using a forced-choice, close-ended Likert-type scale, the survey was implemented based on composite reliability scores ranging from 0.564 (for Trust in Online Vendor) to 0.912 (Customer Shopping Experience Online). Model 1, a causal model, had a chi square value that was statistically significant (Χ2= 2105, p=0.000). Additionally, CFI was 0.578 and TLI was 0.554 while RMSEA was 1.22. Model 2, a correlational/covariance model, also served as a poor fit for the underlying latent data (Χ2= 1862.93, p=0.000). Additionally, CFI was 0.656 and TLI was 0.633. RMSEA at .110 showed marginal improvement. Failure to find the models significant was furthermore followed by the SEM model data revealing trust in online vendor was statistically significant in association with internet utilization for online shopping for Model 1 (R=0.891, p<0.00), and Model 2 (R=1.055, p<0.000). Model 1 also demonstrated a positive and significant association between internet utilization and customer shopping experience (p<0.05). The study has important implications for researchers and practitioners alike.
Introduction
The rapid development of e-commerce post COVID-19 has hit traditional retailers hard, with the advancement of new retail technologies in Thailand (Prachaseree et al., 2019). With the emergence of smartphones and social media or networking sites, and the proliferation of new sales and distribution channels, there has been a significant transformation in consumer spend through digital channels. A radical shift in retail innovation has empowered the Thai consumer in terms of speed, consumer service, affordable goods and services, and timely deliveries (Prachaseree et al., 2019). Online shopping is providing developing businesses with accessible, convenient, and cheaper methods to find various kinds of goods, as against traditional retail (Nguyen et al., 2019). The tremendous growth of the internet has facilitated online shopping and e-commerce even as Millennial and Gen Z consumers have turned into an attractive segment for retailers worldwide and in Thailand (Tunsakul, 2020). The perceptions of value, risks and benefits on purchase intentions with the advent of online portals and e-commerce platforms have become an important determinant of how retailers can anticipate and overcome barriers to e-commerce adoption (Apiraksattayakul et al., 2017).
E-commerce has provided companies in Thailand with a global platform for reaching audiences and capitalizing on market opportunities. With the emergence of m-commerce, Asian countries such as China, Indonesia, South Korea, Vietnam and Thailand have ranked among the top countries for using mobile phones for shopping (Thoumrungroje, 2021). Additionally, although e-commerce growth was forecasted to follow double-digit growth, across 2020, the COVID-19 pandemic has only triggered physical distancing and a contactless retail experience, making it difficult to assess barriers and drivers to e-commerce adoption (UNCTAD, 2020). There has been a rise in online shopping due to the coronavirus outbreak as there was a surge in Business-to-Customer (B2C) e-commerce, and global supply chain disruptions have facilitated the expansion of mobile and online platforms to accelerate online shopping at a rapid pace than earlier forecasted (Thoumrungroje, 2021; United Nations, 2020). The key question is whether the transition to online purchases from traditional retail stores resulting from the pandemic will impact long–term purchase decisions among important customer segments such as Millennials and Gen Z buyers. As massive capital has been invested in omni-channel and online retail platforms, it is critical to explore the nature of drivers and barriers impacting e-commerce adoption. Thai B2C e-commerce segment and e-commerce growth has been rapidly growing (Thoumrungroje, 2021). The volume of Thai B2C e-commerce will reach THB 220 billion with 35% growth in 2020 alone (Thoumrungroje, 2021).
The present study seeks to analyze the influence of key predictors such as perceived risk, perceived cost, trust and convenience of online shopping on internet utilization and customer shopping experience online for Millennial and Gen Z consumers. The main purpose of the study is to propose a model explaining e-commerce adoption through drivers and barriers to online shopping among Gen-Z and Millennial Thai consumers. A conceptual framework based on the review of related literature and the Technology Acceptance Model (Davis et al., 1989) is proposed.
Literature Review
Fresh sales channels, information or communication technologies and formats, as well as lifestyle changes have transformed consumer shopping habits, due to the emergence of internet retailing. Since the emergence of online retail stores in the 90s, companies have evolved from single-channel, physical formats to modify the manner in which they commercialize and sell products (Acquila-Natale et al., 2019). Younger Millennials are less likelier to opt for consumer-to-consumer reselling adoption, yet may purchase items from B2C commerce companies (Lucia-Palacios et al., 2021). According to Pobee (2021), Gen-Z users are likelier to be comfortable with virtual shopping communities. A review of extant literature reveals the paradigm shift to online retail has forced a change in shopping behaviors and retail distribution strategies alike (Acquila-Natale et al., 2018).
Millennial and Gen-Z Consumers
The Millennials and Gen Z consumers account for a mainstream audience, which Thai retailers must target, as these online buyers value convenience and deep discounts characteristic of e-commerce sites. Thai consumers in 2018 spent an average of 10 hours 5 minutes daily on the internet as against earlier estimates of 3 hours 41 minutes (Rajkit & Lacohavichien, 2020). Internet connectivity has expanded exponentially in many countries, such as Thailand (Panjakajornsak, 2017). The increase in Thai internet usage rates has emerged due to the expansion of various networking platforms (Rajkit & Lacohavichien, 2020). The Thailand National Statistical Office (2017) estimates that prior to the pandemic, in 2016, there were 20.2 million PC users, 29.8 million internet users and 51.1 million smartphone users. Online users are increasingly connected through social networks, online forums and websites (Husain et al., 2017). Post-consumption behaviors such as continuance intention and continuance usage have been increasingly emphasized as emotional commitment is vital for advancing the consumer-brand relationship for creating continuance intentions (Sirichareechai et al., 2021).
Thai Millennials represent the largest group of internet shoppers online, born between 1981 and 1996 (Inkaew & Huang, 2019). Research estimates show 90% of Thai Millennials are active on Facebook, while a large percentage of users visit Line, Twitter and Instagram, as well, with 85% of Millennials reporting technology offers mobility freedom and 79% reporting it was impossible to live without a smartphone (Inkaew & Huang, 2019). On the other hand, Generation Z consumers are also a complex audience to attract. Born in 1996 or later, in the digital era, these consumers are digital savvy, prefer new technologies and have a penchant for innovation (Tunsakul, 2020). Thai Gen Z consumers show positive reactions to service innovations aligning human elements and ambiance and prefer online shopping platforms (Tunsakul, 2018). According to Inkaew & Huang (2019), Thai shoppers are likelier to prefer established online marketplaces such as Alibaba and Lazada.
Drivers and Barriers of E-Commerce
E-commerce has been gaining popularity among various organizations and industries besides spurring research on e-commerce adoption, adoption of social and mobile commerce and SMEs ’ adaptation to e-commerce (Albashrawi, 2021). However, according to Albashrawi (2021), current literature on drivers and barriers to e-commerce adoption can serve to benefit retail businesses yet there is not much analysis centered on emerging economies and developing nations. As compared to traditional commerce, online, mobile and social commerce can play a significant role in changing the mindset of the customers (Albashrawi, 2021). Comprehending individual barriers and drivers to e-commerce adoption are essential for creating more effective technologies (Lestari, 2019). Therefore, the shift in shopping practices has been a turning point for businesses looking to expand their retail footprint in Thailand. Critical to the notion of e-commerce adoption is the Technology Adoption Model proposed by Davis, et al., (1989).
Theoretical Background
Davis (1986; Davis et al., 1989) theorized user behavior in relation to computer technology. The model has its basis in the Theory of Reasoned Action (TRA) proposed by Fishbein and colleagues (Fishbein & Azjen, 1975; Fishbein & Middlestadt, 1987; Madden et al., 1992). The TRA postulates that intention determines the behavior of an individual. The Technology Acceptance Model (TAM) proposed based on the TRA was critical in emphasizing the affective and cognitive aspects underlying acceptance of technology (Davis et al., 1989). The TAM model postulates that perceived usefulness and perceived ease of use impact attitude and intention towards the use of new technologies (Davis, 1986). The TAM model has been applied in diverse areas such as internet and online mobile banking, e-commerce, online education, site usage and m-commerce (Nguyen et al., 2019). Figure 1 below depicts the TAM model proposed by Davis (1986):
Figure 1: The Technology Acceptance Model(TAM)
(Source: Davis, et al., (1989)
Voluminous research has examined online shopping utilizing the TAM model, as e-commerce involves the use of information technology for Thai online shoppers (Apiraksattayakul et al., 2017; Inkaew & Huang, 2019; Nguyen et al.., 2018; Panjakajornsak, 2017; Thoumrungroje, 2021). TAM serves as a viable model for explaining online consumer purchase intention (Albrashrawi, 2021; Nguyen et al., 2019). Therefore, the present research study is based on the model proposed by Davis, et al., (1989).
The research models examined are presented (See Figures 2 and 3) and the proposed relationships between constructs are subsequently discussed.
Figure 2: Model 1
Figure 3: Model 2
The model proposed for defining the relationship between internet utilization of online shopping portals or the endogenous variable and 5 exogenous variables, perceived risk, perceived online cost, trust, convenience and consumer experience online. The proposed hypotheses are presented below, along with a discussion of each variable.
Perceived Online Cost
Cost remains an important aspect of acceptability of, intention to, and actual use of online shopping portals (Siyal et al., 2021). According to Venkatesh, et al., (2021), following the explosion of online selling, sellers continue to have concerns about online purchases and alleviating such concerns may require the use of discounts to attract the customer and increase the value of the product. Venkatesh, et al., (2021) also theorized about how the price could be an inhibitor or enabler depending on the recommendation and the types of discounts, both regular and bundle. The researchers suggest discounts enhance the effect of enabling factors in facilitating consumer purchase decisions and intention. Online shopping perspectives entail understanding how discount models could shape the perception of the consumer.
H1: The perceived online cost of a product will be associated with internet utilization of e-commerce shopping portals inversely.
Perceived Risk
Perceived risk remains a critical factor in determining consumer purchase intention and decision to make an online purchase (Venkatesh et al., 2021). According to Ahmed, et al., (2021, perceived risk is the level of uncertainty that stems from threats to privacy and security due to past experiences of negative outcomes following online shopping. The consumer should feel secure while shopping online to share their credit card details or online credentials, and that their privacy is protected during online financial transactions (Ahmed et al., 2021). Therefore, online shopping models must consider the role of perceived security risk, and privacy risk in inferring the acceptance of online shopping:
H2: Perceived risk will be associated with internet utilization of online shopping portals in an inverse relationship.
Convenience
The customer also values convenience as a means of facilitating the switch from offline services to online platforms (Lin et al., 2021). Research has shown customers are satisfied by a single platform online that offers convenience and e-payment transactions online have a convenience advantage that attracts customers and ensures freedom of seamless cardless, cashless and contactless payments (Lai et al., 2021). Convenience has been found to affect both intention and behavior and exert a strong effect on user behavior compared to factors such as money or time (Xu et al., 2019)
H3: Convenience orientation will be associated with higher rates of internet utilization of online shopping sites and portals.
Trust
Trust entails specific beliefs regarding the integrity and benevolence of the transacting party and the assumption that online portals will not take advantage of the consumer trusting them enough to make a purchase (Nguyen et al., 2019). Trust is a critical aspect to consider as online consumers cannot see, touch or ascertain the quality of products (Nguyen et al., 2019). Additionally, online consumers will be worried about the security and safety of making online payments. Trust in online shopping involves considering the possibility the computer system may malfunction or there may be issues with website security and privacy or vendor integrity. Research shows trust is an important predictor of online purchase intention and the act of buying products online (Loketkrawee & Bhatiasevi, 2018). Therefore, the proposed relationship of trust with the consumer shopping experience and actual online purchases is hypothesized to be positive.
H4: Trust in online vendors will be associated with internet utilization of online shopping in a direct, linear relationship.
Consumer Shopping Experience Online
Attitudes towards shopping online lie at the heart of understanding consumer purchase decisions online. The consumer experience of online shopping is an evaluative feeling consumers experience towards online retail stores, shopping portals and sites such as the intention is to use e-commerce actively (Reyes-Mercado et al., 2017). Therefore, consumer shopping experience online can shape marketing outcomes and businesses online must understand the drivers and outcomes of online shopping experiences to identify factors involved in e-commerce adoption (Izogo & Jayawardhena, 2018).
H5: Consumer shopping experience will be directly associated with internet utilization of online shopping sites in that positive shopping experiences will be associated with higher utilization of online retail portals.
Method
Questionnaire and Measures
The survey questionnaire for this study was designed to ensure the validity and reliability of measures. To ensure the empirical nature of the questionnaire, items for measuring the constructs in the study’s research framework were adopted from prior research-based validated scales. Confirmatory factor analysis was carried out to validate the model and obtain a composite reliability score.
The final survey questionnaire comprised 7 sections, with the first section linked to demographic questions and screening items for measuring the key constructs examined. All item measures were designed utilizing a forced-choice format and a five-point Likert scale ranging from 1 “strongly disagree” to 5 “strongly agree”.
Data Collection and Sample
Given the vast research utilizing online surveys, a Google Form survey was utilized to elicit data from 100 Thai undergraduate students to evaluate the role of drivers and barriers in e-commerce adoption among Millennial and Gen-Z consumers. Data collection was undertaken in June 2021. The data collection protocol was approved by the Chair of the Research Committee at the Faculty of Management at King Mongkut’s Institute of Technology Ladkrabang University.
In the absence of a proper sampling frame, convenience sampling was utilized. The research was conducted using email initiations to enroll participants. Prior to data analysis, questionnaires with invalid responses, missing data or outliers were removed.
Results
Estimating composite reliability based on standardized factor loading and error variances, for each variable in the model, composite reliability indicators were as follows. figure 4 and Table 1.
Figure 4: Model 3
| Table 1 Composite Reliability For Items Measuring Internet Utilization Rates |
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|---|---|---|---|
| Item | Standardized Loading | Error Variance | R-Square Value |
| How often do you use the internet for online shopping? | 0.767 | 0.412 | 0.588 |
| Do you intend to use the internet for online shopping during COVID-19 or other emergencies? | 0.735 | 0.460 | 0.540 |
| Do you use electronic devices such as laptops, tablets, desktops for more than 2 hours per day? | 0.376 | 0.859 | 0.141 |
| I find online shopping to be easier and user-friendly | 0.575 | 0.669 | 0.331 |
| Do you buy products online often? | 0.704 | 0.504 | 0.496 |
| Do you buy grocery items online? | 0.406 | 0.835 | 0.165 |
| I plan to continue using the internet to make purchases in the next six months | 0.936 | 0.124 | 0.876 |
| I intend to continue using the internet to make purchases online in the next six months | 0.917 | 0.159 | 0.841 |
As per the formula, the r value is 0.879 for the items measuring Internet Utilization Rates.
For the next construct, Trust Towards Online Vendor, the standardized loadings, error variance, and r-square is shown below Table 2:
| Table 2 The Standardized Loadings, Error Variance, And R-Square |
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|---|---|---|---|
| Item | Standardized Loading | Error variance | R-Square |
| It is easy for me to trust online vendors | 0.938 | 0.120 | 0.880 |
| My tendency to trust online vendors is high | 0.088 | 0.992 | 0.008 |
| Internet stores keep their promises and commitments | 0.349 | 0.878 | 0.122 |
| Online third-party certification protect the internet shopper | 0.414 | 0.829 | 0.171 |
| Online stores will protect my privacy | 0.462 | 0.787 | 0.213 |
| Online stores are reliable and trustworthy | 0.182 | 0.967 | 0.033 |
Composite reliability for this factor is 0.564 suggesting Trust in Online Vendor is measured with less reliability on average. table 3.
| Table 3 Customer Shopping Experience Online |
|||
|---|---|---|---|
| Item | Standardized Loading | Error Variance | R-Square |
| When I am shopping online, I receive correct products as per description and specifications | 0.648 | 0.580 | 0.420 |
| Products are satisfactory in quality | 0.678 | 0.540 | 0.460 |
| Products are not damaged | 0.737 | 0.457 | 0.543 |
| Order tracking is fast and effective | 0.770 | 0.407 | 0.593 |
| Timely delivery is assured | 0.770 | 0.407 | 0.593 |
| Neat and appropriate packing of goods | 0.734 | 0.461 | 0.539 |
| No experience of wrong billing | 0.745 | 0.445 | 0.555 |
| Online stores provide consumer desks to address issues | 0.686 | 0.529 | 0.471 |
| Obtained corresponding credit for goods returned | 0.574 | 0.671 | 0.329 |
| Collection of faulty good was on time | 0.783 | 0.387 | 0.613 |
The composite reliability for the factor was 0.912, which is high suggesting the scale was a reliable measure of customer shopping experience online.
For the perceived cost factor, the following measures and composite reliability was obtained: as shows in Table 4.
| Table 4 Perceived Cost Measures |
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|---|---|---|---|
| Item | Standardized Factor Loadings | Error Variance | R Square |
| Online shopping is cost-effective | 0.431 | 0.814 | 0.186 |
| Payment mode online makes payment flexible | 0.609 | 0.629 | 0.371 |
| There are no costs factored into shopping online | 0.818 | 0.331 | 0.669 |
| One saves on fuel and transport bills by shopping online | 0.665 | 0.558 | 0.442 |
| Shopping online saves money by offering deep discounts | 0.735 | 0.460 | 0.540 |
The composite reliability value for perceived cost was high (r= 0.792).
For the perceived risk factor, the reliability measure indicated good degree of internal consistency: as shows in Table 5.
| Table 5 Perceived Risk Measures |
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|---|---|---|---|
| Item | Standardized Loading | Error Variance | R-Square |
| I feel safe making purchases online using my payment mode | 0.678 | 0.540 | 0.460 |
| [I feel safe giving my personal details to online stores | 0.671 | 0.550 | 0.450 |
| Compared to other methods of purchasing goods, online stores are less risky | 0.715 | 0.489 | 0.511 |
| There is a lack of uncertainty with using the internet to make purchases | 0.703 | 0.506 | 0.494 |
| I will receive the product I ordered online | 0.605 | 0.634 | 0.366 |
| Shopping online is not risky despite the fact I cannot judge the product quality | 0.540 | 0.708 | 0.292 |
| There are appropriate cyber laws in place to protect the online buyer | 0.571 | 0.674 | 0.326 |
| Online product tracking is safe | 0.667 | 0.555 | 0.445 |
The composite reliability for this construct was 0.851 which was high.
For convenience orientation, standardized loadings, error variances and R-Square values are presented below Table 6:
| Table 6 Standardized Loadings, Error Variances And R-Square |
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|---|---|---|---|
| Item | Standardized Loading | Error Variance | R-Square |
| I spend less time shopping if I opt for an online store | 0.546 | 0.702 | 0.298 |
| I can shop online from any location, anytime which Is why I prefer shopping online | 0.314 | 0.901 | 0.099 |
| Digital stores offer convenient tracking modes | 0.786 | 0.382 | 0.618 |
| Online stores offer convenient payment options | 0.794 | 0. 370 | 0.630 |
| Timely delivery of online stores is convenient | 0.879 | 0.227 | 0.773 |
| Online shopping is convenient because I do not have to move out of my present location to access goods | 0.677 | 0.542 | 0.458 |
The composite reliability of this factor (convenience orientation) was 0.836.
The present study examined the effect of the exogenous variables on the endogenous variable (Internet Utilization for Online Shopping). Utilizing the maximum likelihood estimates method, direct effects of the exogenous variables on the endogenous variable were first tested utilizing the Default Model (Model 1). For a good model fit, the chi square statistic should be insignificant. However, the default model showed a chi square value that was high and statistically significant (Χ2=2105, p=0.000) suggesting that the model proposed is not significant. CFI was 0.578 (<0.95) while TLI was 0.554 (<0.90). In addition, RMSEA was 0.122 ( or higher than the desired value of .08). The RMSEA is not a good fit, suggesting the need for a better fit. Correlational covariance relationships between all exogenous latent variables were included in the second model (Model 2), which yielded an CFI of .656 and a TLI of 0.633. The RSMEA was still not a good fit at .110 for the default model. Chi square values remained significant ((Χ2= 1862.93, p=0.000).
Therefore, the results of the study suggest that the proposed SEM model is not a good fit for the underlying data structure with only the Trust in Online Vendor associated with Internet Utilization rates (R=0.891, p<0.000). In the second model only one exogenous variable, Trust in Vendor was associated with internet utilization for online shopping (R= 1.055, p<.000), as can be inferred (See Table 7 and 8). However, for a p value of 0.05, the first model also showed a significant association between customer shopping experience and internet utilization for online shopping.
| Table 7 Default Model (Model 1): Estimates |
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|---|---|---|---|---|
| Variable | Estimate | SE | CR | Significance level |
| Internet Utilization and Perceived Risk | 0.061 | 0.060 | 1.02 | .305 |
| Internet Utilization and Customer Shopping | 0.157 | 0.050 | 3.129 | 0.002 |
| Internet Utilization and Convenience | -0.103 | 0.073 | -1.411 | 0.158 |
| Internet Utilization and perceived cost | -0.141 | 0.127 | -1.103 | 0.270 |
| Internet Utilization and trust towards online vendor | 0.891 | 0.066 | 13.484 | 0.000 |
| Table 8 Model 2 Estimates |
||||
|---|---|---|---|---|
| Variable | Estimate | SE | CR | Significance level |
| Internet Utilization and Perceived Risk | 0.055 | 0.147 | 0.374 | 0.708 |
| Internet Utilization and Customer Shopping | 0.135 | 0.144 | 0.932 | 0.351 |
| Internet Utilization and Convenience | -0.372 | 0.240 | -1.550 | 0.121 |
| Internet Utilization and perceived cost | -0.178 | 0.341 | -0.523 | 0.601 |
| Internet Utilization and trust towards online vendor | 1.055 | 0.125 | 8.411 | 0.000 |
Discussion and Conclusion
The present study aimed to examine the nature of the association and whether there was a direct effect of exogenous variables (perceived online cost, perceived online risk, convenience, trust in online vendors, consumer shopping experience online) on the endogenous variable (internet utilization rates). The first hypothesis stated that the perceived online cost of a product would be associated with online internet or e-commerce portals inversely. Research has consistently examined how cost remains a critical aspect of acceptability, intention and utilization of shopping portals online (Siyal et al., 2021; Venkatesh et al., 2021). The SEM model, however, did not predict a causal effect of perceived cost on internet utilization of e-commerce portals in direct or inverse ways, as per Model 1 and Model 2. Price may be an inhibitor or an enabling factor in inducing and influencing consumer decision to purchase. However, the hypothesized relationship between price and shopping preferences among Millennial and Gen-Z shoppers was not established.
Another critical factor in assessing consumer purchase intention for making online purchases is associated with uncertainty levels or perceived risks due to negative perceptions of the safety of online shopping and payment for retail transactions. The study hypothesized that perceived risk would be associated with internet utilization of shopping portals inversely. Although a negative relationship was noted in the regression analysis as per Models 1 and 2, the values were not significant at either 0.05 or 0.01 level of statistical significance. Perceived security risks can be a potential deterrent for consumers shopping online (Ahmed et al., 2021). However, research findings did not align with the existing research on this topic. Consumers need to feel secure while shopping online (Venkatesh et al., 2021). Although the relationship between the perceived risk and utilization of the internet for shopping was not linear, the strength of the association was not statistically significant.
As per the third hypothesis, convenience orientation was postulated to directly impact internet utilization of online, e-commerce shopping portals and sites. While previous research has clearly established the causal relationship between convenience orientation and are satisfied by user-friendly interfaces and convenient online transactions, the present study did not find a relationship between intention and behavior on the one hand, and consumer convenience orientation on the other. This finding contradicted research suggesting convenience attracts customers (Lai et al., 2021; Lin et al., 2021; Xu et al., 2019).
Trust in shopping portals online and online vendors was another important exogenous variable examined in relation to customer utilization of online shopping portals. Research results offered support for the statistical significance of the association between trust in online vendors and utilization of e-commerce portals, establishing a direct relationship between the two variables (p<0.01). Trust remains an important predictor of consumer purchase intentions and decisions at online stores (Nguyen et al., 2019). Consequently, the present research finding was in accordance with previous research that examined the role of trust as a facilitator or driver for e-commerce adoption among Gen-Z and millennial youth (Loketkrawee & Bhatiasevi, 2018).
Additionally, it was hypothesized that customer shopping experience online would be associated directly with internet utilization of online stores. Attitudes towards shopping can translate into specific consumer actions. Research reveals consumers' evaluation and attitudes towards online shopping is influenced by their retail experience (Reyes-Mercado et al., 2017). Among the Gen-Z and millennial research participants, Model 1 predicted a causal relationship, effect or association between customer shopping experience online and internet utilization of retail stores (p=0.002). However, Model 2 failed to establish a link between customer shopping experience online and customer utilization of the internet for online shopping.
Theoretical Implications
The role of the Technology Acceptance Model in influencing online shopping is well established, predicting the impact of perceived utility, perceived ease of use and attitudes as well as inclination towards use as being predictors of the actual online store utilization patterns among customers (Albashrawi, 2021; Apiraksattayakul et al., 2017; Nguyen et al., 2018; Panjakajornsak, 2017; Thoumrungroje, 2021). User behavior, however, did not align with Davis’ TAM model in the present study. While previous research has established individual barriers and drivers to e-commerce, adoption, creating effective technologies is also about understanding the critical role of trust, according to the findings of the present study. The TAM model predicts the interlinkages between perceived usefulness, perceived ease of use, attitude and intention, on the one hand, and consumer purchase intention or decision with respect to online technologies. However, the present study pointed to the experiential and contextual factors that come into play while impacting e-commerce adoption among Millennials and Gen-Z consumers. Trust emerged as an important aspect, impacting reputational integrity and earning social capital for the e-commerce brands, according to the findings of the present study. The lack of statistical significance of Models 1 and 2 indicates the hypothesized relationships between the exogenous and endogenous variables were not supported. The theoretical contribution and implications of the study for the marketing and management researchers involve considering the role of trust and experiential marketing compared to traditional factors such as risk and perceived cost in acting as enablers or barriers in e-commerce adoption.
Limitations and Future Directions
The research conducted focused on applying statistically advanced techniques associated with SEM to ascertain the role of key exogenous variables in influencing actual e-commerce adoption among Millennial and Gen-Z Thai consumers. A causal model (Model 1) followed by a correlational model (Model 2) failed to establish statistical significance. Although the models were not statistically significant, they did pave the way for researchers to examine the role of trust and experiential retail strategies in influencing the Millennial consumer. However, the study did suffer from certain shortcomings.
Primarily, the research study employed convenience sampling due to restrictions imposed by COVID-19. Although there are advantages to convenience sampling in terms of cost, time and effort savings, the sampling method is a non-probability sampling technique, which is given to the issues of selection bias and the problem of outliers, which random sampling would have eliminated. Secondly, another limitation of the study was the lack of intervention-based or qualitative research conducted in tandem with structural equation modeling. The research study did not establish a clear baseline or implement individualized marketing strategies to influence consumer perception, thereby limiting the applicability and generalizability of the results.
The research study also lacked a significant exploration of the inner worldview and subjective perceptions of Gen-Z and millennial Thai consumers. Utilizing a grounded theory approach or a hermeneutical perspective would have offered rich insights into the beliefs, values, perceptions of these consumer segments and how it influences attitudes towards online shopping and consumer behavior therein.
Directions for further research could include examining the role of trust and experiential marketing using the customer lifecycle approach to understand these enablers of e-commerce adoption better. Researchers should also focus on conducting intervention-based research to examine the practical benefits of utilizing the TAM model for influencing online shopping behavior. Future research could also seek to uncover the complex role of such models in influencing consumer behavior among lifestyle-oriented Millennial consumers. The rise of experiential marketing has influenced the Millennial and Gen-Z consumer in distinct ways and traditional factors such as cost associated with online shopping lack the same degree of relevance. In the present digital era, the focus needs to be on understanding the role of trust and reputational integrity within a sharing economy as a basis of e-commerce adoption.
Practical Implications
The study has several practical implications for marketing professionals and experts in the field. The study suggests models such as TAM must move beyond utility as a measure of e-commerce adoption and consider factors such as trust, social capital and reputation, as well. The present study has also established the clear need for understanding Millennial and Gen-Z consumers from the perspective of trust and fostering brand equity through the promotion of sustainable supply chains and a resilient market reputation. The study suggests models based on TAM must give higher weightage to customer experience and trust as critical factors in the marketing and management fields. Reaching brand-savvy millennials involves focusing on experiential marketing (Kercher, 2017). Given that Millennial and Gen-Z consumers are given to appreciating the entire retail experience and not just material or cost considerations, the practical implication for marketers is that the focus needs to be on taking the product or service and highlighting its value to Millennial consumers in a context they can relate to and identify with. Investing in trust-building and rapport establishment mechanisms could foster e-commerce adoption, as brands integrate themselves into the customer shopping experience and impact consumer attitudes, which in turn, shape behavior.
The study also establishes the importance and value of authenticity in influencing the brand image among Gen-Z and millennial consumers. Lifestyle-oriented young shoppers value authentic brands that enable them to use products as an extension of their personalities (Kercher, 2017). Therefore, marketers would do well to engage in experiential marketing campaigns to educate and share their brand value with customers, rather than focusing on conventional barriers and predictors of online retail adoption, such as cost and convenience. Customers would respond more positively to conversations about brands, and relate more effectively to products.
Consequently, the value of experiential quality shapes customer perceptions among Millennials in diverse ways (Hussein & Ratnawati, 2020). While research has sought to establish how quality, convenience, risk mitigation, and trust and security impact customer perception among Millennials, the present study establishes the need for understanding experiential and trust-oriented dimensions of online e-commerce adoption to better predict and design marketing initiatives focused on Millennials and Gen-Z consumers.
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Received: 03-Jan-2022, Manuscript No. AEJ-22-10686; Editor assigned: 05- Jan -2022, PreQC No. AEJ-22-10686 (PQ); Reviewed: 19- Jan-2022, QC No. AEJ-22-10686; Revised: 26-Jan-2022, Manuscript No. AEJ-22-10686 (R); Published: 09-Feb-2022