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Article

Embedding Technology Interface and Digital Payment Drivers in the Unified Theory of Acceptance and Use of Technology 2 Model: Transforming Behavioral Intention to Sustained Intention

by
Savita Gupta
*,
Ravi Kiran
and
Rakesh Kumar Sharma
Thapar Institute of Engineering and Technology, School of Humanities and Social Sciences, Patiala 147004, India
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(17), 13018; https://doi.org/10.3390/su151713018
Submission received: 5 July 2023 / Revised: 11 August 2023 / Accepted: 23 August 2023 / Published: 29 August 2023
Browse Figures
Review Reports Versions Notes

Abstract

:
Purpose: The present study was undertaken with the purpose of embedding Technology Interface drivers and Digital Payment drivers as additional drivers in the Unified Theory of Acceptance and Use of Technology (UTAUT2) to examine their influence on the Behavioral Intention of online shoppers. Technology Interface drivers include chatbots (CBs) and virtual try-on (VTO) technology. Further, this study also links Behavioral Intention with Sustained Intention to analyze whether it translates into repeated online buying. Research Methods: The study is based on a survey instrument using snowball sampling with data collected from 600 consumers from northern India. The study uses PLS-SEM for gauging the association of UTAUT2 drivers, Technology Interface drivers, and Digital Payment drivers with Behavioral Intention. Findings/Results: The results show a positive and significant association with Behavioral Intention of Technology Interface drivers and Digital Payment drivers as additional drivers to UTAUT2 drivers. UTAUT2 drivers had the highest impact (β: 0.465), followed by Digital Payment and Technology Interface drivers (β: 0.263 and β: 0.211). Further examination suggests a positive impact of Behavioral Intention on Sustained Intention (β: 0.868). The outcomes reflect that the model explained 69.5% of the variation in Behavioral Intention and 75.4% of the variation in Sustained Intention. Implications: The study suggests that Indian managers need to adopt DPM as a support service to make online shopping a worthwhile experience. Technology Interface drivers have a comparatively lower coefficient, indicating that in India, they are yet to reach the optimum level for consumers to adopt them fully. Efforts to transform Behavioral Intention into repeated online buying or Sustained Intention may go a long way in building a strong, committed community of online sellers to assist in enhancing customer experience.

1. Introduction

Online shopping has gained momentum in the digital world, and triggered researchers across the globe to analyze the impact of diverse drivers of online shopping on consumers’ Behavioral Intention (BI). People from developing economies like India are fascinated by the new trend, and marketers of online shopping want to target this wider population to enlarge their imprint. A rise in e-commerce has induced retailers to adopt different retail strategies to survive. China, the US, and India had a large online shopper base of 140 million people in 2020 [1], and by 2030, this is expected to be worth USD 350 billion. Diverse theoretical models have been used intensely by researchers to understand the user acceptance behavior of online shoppers. Pioneering research by [2,3] with the UTAUT and UTAUT2 models has impelled others to examine how online shopping has been influenced by technology adoption, mode of payment [4] trust, and repurchase intention [5,6]. There were divergent results attributed to slow penetration, preference for physical shopping, and cultural differences in studies conducted in developing nations [7]. Recently, various researchers have introduced and validated interactive tools like chatbots [8] and virtual try-on technology [9,10] while using UTAUT2. Given the increasing impact of technology, retailers could harness these interactive tools to gain a competitive advantage in this digitized era. Keeping the above-mentioned points in mind, this study not only focuses on the variables of UTAUT2, but also intends to involve two new constructs, Technology Interface and Digital Payment drivers, to examine the combined influence of all these drivers on BI. Further, it moves to examine whether BI translates into Sustained Intention (SI). Recent revolutions in technology and demonetization drivers have transformed the online purchasing landscape in India. The World Bank statistics for India pointed out that 22.2% of GDP emerged from the shadow economy, compared to 12.7% in China and 11% in Japan [11,12]. In view of this growing challenge, the government of India announced a demonetization move. Demonetization, whether successful or disastrous, has at least hastened the pace to shift to an online payment system. Thus, in the present study, along with UTAUT2 drivers, we examine how Digital Payment (DP) drivers are casting their impact on BI. Against this backdrop, the current study adds value by considering the impact of DP drivers, too. This becomes important in view of researchers like [13] indicating how user-supportive payment mechanisms like cash on delivery and return and exchange facilities trigger a positive impact on online shopping. The authors of [14] introduced pay-on-delivery (POD); however, we are including DP drivers, because of their increased relevance today. The affectionate reaction of people toward any new technology is a significant area of research. For broader applicability in emerging economies, this study further validates the effect of Technology Interface (TI) drivers on BI. These scales have been individually scrutinized in the literature, but we wanted to integrate them into this model to present a holistic picture. As e-commerce is developing at a faster pace, there is a need for further development of the existing set of literature. The current research pioneers consolidating the drivers of online shopping through the lens of extending UTAUT2 drivers with TI drivers and DP drivers to examine their influence on BI. Consequently, it was thought to examine whether BI results in SI, as this would help in increasing consumers’ links and adding a sustainability perspective to e-business. Though discussed in academic forums, they lack focus, and academic research on them is still in its infancy. Thus, comprehensive research has been undertaken to answer these research questions:
R1: Do Digital Payment drivers have a positive influence on Behavioral Intention in online shopping?
R2: Do Technology Interface drivers have a positive influence on Behavioral Intention in online shopping?
R3: Do Digital Payment drivers and Technology Interface drivers along with UTAUT2 drivers enhance Behavioral Intention in online shopping?
R4: Does Behavioral Intention have a positive influence on Sustained Intention?
The present article is organized as follows. Section 1 provides the background of the study and sets the momentum of the research by introducing research questions. Section 2 elaborates on the literature for the hypotheses’ development. Section 3 covers the research design and methods. This is followed by the presentation of results and discussions in Section 4. Implications are elaborated in Section 5, and the next section provides limitations and insightful directions for future research. As artificial intelligence is becoming more evident, retailers could give new experiences to e-customers in the form of personal recommendations. Retailers could also harness these augmented and virtual reality systems to provide increased transparency and security while engaging with online consumers [15]. DPM and Technology Interface drivers can go a long way only if companies are able to understand them in churning opportunities.

2. Literature Review and Hypotheses Development

Consumer behavior for online shopping has been captured through many models of technology adoption [16]. The initial model proposed by [2], the UTAUT model, cogitated performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), and user behavior (UB). In the augmented model, UTAUT2, the authors of [3] added hedonic motivation (HM), price value (PV), and habit (H). Due to its wide credence, this research also counts on UTAUT2 [17]. Ref. [3] recommended that UTAUT2 should be applied in different settings. Earlier researchers have extended the model by adding different variables like DPM, E(WOM), attitude, and so on. Though UTAUT2 has found its applicability in diverse fields, helping us to explore factors influencing Behavioral Intention, to date, we have not been able to trace research validating critical drivers influencing consumers’ decisions while interacting with new technology [18]. Chatbots and virtual try-on technology are distinct domains of business research signifying the era of digital transformation. The field of chatbots is novel and complex, so most of the research has employed mature theories like UTAUT2 [18,19]. As highlighted by [20], technology adoption by all age groups is a big challenge in developing countries. Regarding new technology, the most pertinent point to ponder is the affective reaction of the consumers towards technology. Previously, anxiety was considered a latent variable having a direct impact on the antecedents of BI [21]. Widespread use of digital technologies, especially mobile devices, DP drivers, and TI drivers, has become essential to achieve business success. Ref. [22] emphasized that prepaid e-cash, debit cards, and credit cards expedited online transactions. The Indian government has also initiated various steps for easy access and use of financial services [23]. Limited research on linking DP with BI in online shopping induced us to examine this more deeply.
  • Hypotheses Development
Exploratory analysis was conducted on DP drivers for further analysis. There was no need to conduct EFA on the drivers of UTAUT2 [2,3]. TI drivers have already been validated as new drivers of online shopping [9,10]. After finding out the factors of DP drivers, partial least squares structured equation modeling (PLS-SEM) was applied to develop the model and investigate the inter-relationships among the latent variables. The proposed study tested five hypotheses in line with the objectives of examining the possible associations between the latent and the outcome variables. Though there has been much research on the UTAUT2 drivers, there is a dearth of analysis of critical factors that have affected the e-commerce sector in this digital area, and this study, by using DP and TI drivers, will add more insight.

2.1. Drivers of UTAUT2

Technology integration has always lured researchers across the globe to devise new models to explain e-commerce. Ref. [24] investigated the determinants of academic e-learning technologies through the UTAUT model. These results supported that PE, EE, and system interactivity influenced BI in e-learning, too. Ref. [25] used the UTAUT base model with meta-analysis examining 127 mobile banking studies. The results indicated that culture as a moderator influenced FC and usage intention and PE was the strongest predictor of usage intention.
Performance expectancy (PE) relates to using the system to accomplish advantages in job performance [2]. Perceived usefulness in TAM [26] refers to the conduciveness of users to purchase. PE has been cited and empirically examined by [18,27] and is an important predictor of online shopping. BI is significantly and positively influenced by PE in online hotel bookings [28]. Additionally, PE significantly influenced the use of technology in various fields, as highlighted by [24,29]. UTAUT was tested for Chinese consumers by [30], and the results confirmed that a donor’s intention to donate to various projects is significantly affected by PE. Contrary to this, Ref. [31] found no significant relationship between PE and BI.
Effort expectancy (EE) is the degree of ease associated with the use of the system [2]. EE has appeared as the strongest factor influencing online shopping [31,32]. As indicated by [18,24,29], EE significantly influenced the use of technology in various fields, and [30] confirmed that a donor’s intention to donate to various projects is significantly induced by EE. On the contrary, many other studies [10,33] did not have supportive evidence of the impact of EE on BI.
Social influence (SI) is the degree to which an individual perceives that others believe in the use of the new system [2]. Earlier literature [28,32] highlights a direct effect of SI on BI. In addition, SI significantly impacts the use of technology in various fields [29].
In UTAUT, the facilitating conditions (FC) refer to knowledge regarding shopping online. Many previous studies have confirmed FC as an important factor in inducing technology adoption [31,32]. In contrast, [33] reported its adverse impact on the use of technology. Hedonic motivation (HM) is associated with pleasure and excitement related to the use of technology [3]. According to [34], online consumers respond quickly to promotional incentives. Price value (PV) refers to stipulating value for money, and product availability at rational prices [3]. Behavioral intention is significantly and positively influenced by PV in online hotel bookings [28]. When UTAUT2 was tested for Spanish consumers, Ref. [35] found that price-saving orientation is significantly related to the use of mobile applications for restaurant searches and reservations. BI is affirmatively influenced by habit in online hotel bookings [28]. Habit is an important predictor of Behavioral Intention [18]. Ref. [29] concluded that habit is significantly associated with the use of mobile applications for restaurant searches and reservations.
The associated hypotheses are as follows:
H1. 
UTAUT2 drivers (performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), hedonic motivation (HM), price value (PV), and habit (H)) are positively related to the Behavioral Intention of online shoppers.
H1a. 
Performance expectancy (PE) is positively related to the Behavioral Intention of online shoppers.
H1b. 
Effort expectancy (EE) is positively related to the Behavioral Intention of online shoppers.
H1c. 
Social influence (SI) is positively related to the Behavioral Intention of online shoppers.
H1d. 
Facilitating conditions (FC) are positively related to the Behavioral Intention of online shoppers.
H1e. 
Hedonic motivation (HM) is positively related to the Behavioral Intention of online shoppers.
H1f. 
Price value (PV) is positively related to the Behavioral Intention of online shoppers.
H1g. 
Habit (H) is positively related to the Behavioral Intention of online shoppers.
Next, we moved to Technology Interface drivers, presented in Section 2.2.

2.2. Technology Interface Drivers

TI results in efficient and effective online interactions between technology systems and customers. This study proposes that it is essential to understand how TI influences the BI of online consumers and further affects SI. TI covers two dimensions, chatbots and virtual try-on technology. Ref. [36] examined the effect of virtual try-on (VTO) technology on brand responses and personal data disclosure. Ref. [37] tried to understand the various constructs of the TAM model on attitudes toward chatbots and the intentions of mobile shoppers. Ref. [38] examined the effect of various predictors along with chatbots on the satisfaction level of users. The results reveal that in the future, chatbots will play a vital role in satisfying digital users. Further, Ref. [35] uncovered the relationship between VTO, return policies, pay-on-delivery (POD) payment modes, and repurchase intention, taking trust as a mediator. Results indicate that VTO does not show a significant effect on trust. Thus, there are mixed results; while studies do indicate the importance of chatbots, there is still a need to investigate this profoundly. This gains importance in view of people becoming more tech-savvy.

2.2.1. Chatbots

We have many assistants today like Siri and Alexa. Chatbots are frequently used today, in education and investment, for opening an investment app, or for IELTS booking. They are available 24/7. It has been empirically validated by [39,40] that the intention to purchase can be stimulated through collaborative and interactive systems and devices. Ref. [41] suggested that assistance through chatbots enriches customer satisfaction. Still, more research is needed to recognize how chatbots impact online shopping and whether chatbot conversation accelerates BI [42].

2.2.2. Virtual Try-On Technology

Lured by Lenskart’s advertisement for virtually trying on different glasses and ordering online, the customers want more such things to happen. E-retailers introduced virtual try-on technology to help online shoppers try out different combinations of dresses, cosmetics, glasses, etc. [10,43,44]. Such technology, as stated by [45], is infectious for online clothing retailing. Ref. [46] regards VTO technology to be positively associated with a customer’s buying behavior. Ref. [46] also confirmed its positive impact on perceived usefulness.
The existing literature examines the impact of UTAUT2 drivers on BI for online shoppers, but the impact of DP and TI drivers on BI has been under-researched. The increased pace of Internet penetration facilitated the adoption of digital payments in online shopping. With new technologies like CB and VTO, there is a need to examine how TI is influencing BI. Thus, the present study sought to understand the integrated impact of UTAUT2, DP, and TI drivers on BI. It also tried to investigate whether BI positively influences SI.
H2. 
Technology Interface drivers are positively related to Behavioral Intention.
H2a. 
Chatbots are positively related to Behavioral Intention.
H2b. 
Virtual try-on (VTO) is positively related to Behavioral Intention.

2.3. Digital Payment Drivers

Digital technologies are shaping the manner in which retailers and customers are going to transact in the future [47]. Ref. [16] validated the role of the cash-on-delivery (COD) mode of payment as a construct and a new dimension to UTAUT2. As technology improved, COD as a mode of payment was being replaced by other digital payment modes. DPM expedites the use of e-payment for online transactions. Moving further from [4], DPM has been validated as a new construct of online shopping influencing BI by [48]. This highlights the passion for using new payment modes. The related hypothesis is as follows:
H3. 
Digital Payment drivers are positively related to Behavioral Intention.

2.4. Sustained Intention

Ref. [2], in UTAUT, used the Theory of Reasoned Action (TRA), the Technology Acceptance Model (TAM), the Motivational Model, the Theory of Planned Behavior (TPB), a combined TBP/TAM, Innovation Diffusion Theory (IDT), and Social Cognitive Theory (SCT). PE is similar to perceived usefulness in TAM; EE covers perceived ease of use in TAM; SI is analogous with subjective norms in TAM 2; FC covers perceived behavioral control of TAM-TPB. Researchers applied UTAUT in new contexts and in new cultural settings like China [38], Iran [32], and India [31]. The use of technology for hedonic motive (HM) was still not clearly imminent in the earlier UTAUT model. Thus, Ref. [3] incorporated HM, PV, and habit (HAB) into UTAUT2. These additions helped in enhancing the explained variance in BI (56–74%) and technology use (40–52%). Ref. [49] opined that stronger intentions often resulted in the task being performed. A positive attitude of a consumer towards a product leads to its repurchase and hence results in Sustained Intention [50,51,52,53]. Prior literature demonstrated a positive impact of user satisfaction on continued use [54,55,56,57]. Additionally, the intention to perform the task is the direct antecedent of the consumer’s actual behavior [58].
H4. 
Behavioral Intention is positively related to Sustained Intention.
H5. 
UTAUT2 drivers, Technology Interface drivers, and Digital Payment drivers are positively related to Behavioral Intention, which positively and significantly influences Sustained Intention.
  • Research Framework
The research framework is depicted in Figure 1.
Figure 1 depicts the research framework to examine the relationship of UTAUT2 drivers, Technology Interface drivers, and Digital Payment drivers with Behavioral Intention and Sustained Intention.

3. Research Design and Methodology

Ref. [59] has recommended an N:Q ratio as low as 10:1 or 5:1, that is, 10 or 5 participants per scale item. Further, Ref. [60] explicitly mentioned an N:Q ratio of 20:1. Data were collected from 600 respondents through a survey using snowball sampling, with the link shared through email. The population of this research comprised North Indian Internet-savvy online consumers. In Northern India, three states were covered, i.e., Punjab, Haryana, and Himachal. Snowball sampling is a non-probability sampling technique that is used where the population is not defined and potential participants are difficult to trace [14].
Primarily, the data were piloted and validated through 50 academicians. The survey instrument was modified, and final data were collected from 600 respondents. The reports or researchers who have tried these are also mentioned as sources for the new construct items (Table 1). Since this study used the survey participants’ responses, it was vital that the common method bias problem is not reflected in the results. Common method bias (CMB) happens when variations in responses are caused by the instrument, rather than the actual pre-dispositions of the respondents that the instrument attempts to uncover. Thus, the outcome may be an inflated variation. Harman’s single-factor score advocates loading all items (measuring latent variables) into one common factor. The results indicated that the total variance for a single factor was less than 50% (39.72%), suggesting that CMB did not influence the data [61]. The occurrence of a VIF >3.3 is advised as an indication of obsessive collinearity, and also as an indication of the prevalence of CMB. We also checked whether all VIFs resulting from a full collinearity test were not greater than 3.3, thus indicating an absence of CMB.
Data were collected between the period of July 2018 to April 2020. The researchers critically reviewed UTAUT2 and proposed a revised theoretical model that was tested using a combination of exploratory factor analysis and structural equation modeling (PLS-SEM), resulting in the current model. UTAUT2 drivers along with DP drivers and TI drivers were considered. PLS-SEM was used due to high levels of statistical power [62]. PLS-SEM is a non-parametric procedure, and a bootstrapping procedure was used to test the significance of the loadings.
The high-quality business research on chatbots is still in its early stage of development. PLS-SEM is a widely used SEM method that will be further employed in studies involving chatbots [18,63,64,65]. Therefore, taking support from the previous literature, PLS-SEM has been applied to validate the impact of chatbots and virtual try-on technology on Behavioral Intention in online shopping. In the proposed model, the drivers considered include (i) drivers of UTAUT2 (FC: facilitating conditions; H: habits; HM: hedonic motivation; EE: effort expectancy; PE: performance expectancy; PV: price value; SI: social influence), (ii) TI drivers (chatbots and virtual try-on technology), and (iii) DPM drivers (DPM1 and DPM2). The dependent variables are (i) BI and (ii) SI.

4. Results and Discussion

EFA applied for digital payment (DP) drivers is presented in Table 2. In the case of DP drivers, two factors with eigenvalues greater than 1 were extracted. The two factors extracted are (i) ease of payment and (ii) perceived risk. In the case of the first factor (ease of payment), eight statements having loading values greater than 0.50 were retained. In the case of this factor, the statement “Digital mode protects my privacy” had the highest loading value (0.857). This was followed by statements such as “I feel safe while using digital payment mode in online shopping” and “Digital payment mode gives me confidence for future purchase of products”. This factor has a high eigenvalue of 8.533 and the variance explained by this factor is 39.219%. For perceived risk, the statement “to increase trust in digital payments cyber security systems must be strengthened” had the highest loading (0.840), followed by “Authentication through OTP ensures safety while making payment through digital payment mode” (0.795). This factor has an eigenvalue of 1.046 and this factor explained 29.202% of the variance (KMO 3926.028; df:91 p ≤ 0.01). These two sub constructs were used for further analysis as DPM drivers.
Table 3 portrays the Cronbach’s Alpha, Composite Reliability (CR), Average Variance Extracted (AVE), and Rho_ A. The internal consistency of the survey instrument is in an acceptable range, as Cronbach’s alpha is ≥0.70 [66]. All constructs lie in the range of 0.735–0.875. Recommended AVE values should be ≥0.5, and composite reliability (CR) ≥0.7 [49]. AVE values of all constructs were ≥0.50. The high CR and Rho_ A values reflected good overall reliability.
Table 4 displays results regarding discriminant validity. As seen, the diagonal values depicting the sq. root of AVE on constructs are greater than the inter-construct correlation. Hence, the data are free from the problem of discriminant validity.
Table 5 indicates that the HTMT ratio, as suggested by [67], should be less than 0.85. Thus, this criterion is satisfied.
In the measurement model, the last aspect to check was outer and inner VIF values. As shown in Table 6, all VIF values are less than 3; thus, the data are free from multicollinearity. Hence, it was time to proceed with the structural model [68,69,70,71].
Table 7 highlights the path coefficients and t-statistics of the inner model. With Behavioral Intention as an endogenous variable, the results support that all of the predictors of UTAUT2 are positively and significantly related to BI. All subconstructs, i.e., EE (0.759), PE (0.726), FC (0.704), habit (0.660), HM (0.805), PV (0.806), and SI (0.664), were significant, as reported in Table 6, so these are all important UTAUT2 drivers; however, PE, HM, EE, and PV have higher loadings. The UTAUT2 model drivers emerged as the strongest drivers of online shopping, with a path coefficient of 0.465 (T:10.735 and p ≤ 0.01). Thus, H1, that UTAUT2 drivers (performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), hedonic motivation (HM), price value (PV), and habit (H)) are positively related to the Behavioral Intention of online shoppers can be accepted. TI drivers have a path coefficient of 0.211(T: 5.823 and p ≤ 0.01). The results support H2, that Technology Interface drivers (chatbots and virtual try-on technology) are positively related to Behavioral Intention. In the case of DP drivers, the association is β: 0.263 (T:6.979 and p ≤ 0.01). Hence, H3, that Digital Payment drivers are positively related to Behavioral Intention, has been empirically supported. Behavioral Intention is positively influencing Sustained Intention, with β:0.868 (T: 76.101 and p ≤ 0.01). Thus, H4, that Behavioral Intention is positively related to Sustained Intention, has been empirically supported. Further, the indirect effect of H1*H4 is significant: UTAUT2 Drivers -> Behavioral Intention -> Sustained Intention (β: 0.404; T: 10.502 and p ≤ 0.01). Similarly, the indirect effect of H2*H4 is significant: Technology Interface Drivers -> Behavioral Intention -> Sustained Intention (β: 0.183; T: 5.769 and p ≤ 0.01). The results of H3*H4 are also significant: Digital Payment Mode Drivers -> Behavioral Intention -> Sustained Intention (β: 0.229; T: 6.942 and p ≤ 0.001).
As shown in the model (Figure 2 and Table 8), the R2 value was 0.695 (Adj. R2 0.693) for Behavioral Intention. For the association between Behavioral Intention and Sustained Intention, the R2 value was 0.754 (Adj. R2 0.753). Overall, the model was acceptable, and it satisfactorily fulfilled all of the parameters. The model explained 75.3% of the variation. The value of SRMR is 0.074, which is less than 0.08 as recommended. The NFI value is 0.756. Thus, the results lend support to H5: UTAUT2 drivers, Technology Interface drivers, and Digital Payment drivers are positively related to Behavioral Intention, which positively and significantly influences Sustained Intention. Further, Figure 3 exhibits the bootstrapping results of the hypothesized model.
The study by [72] was used to measure effect size F2. It highlights that if F2 for particular predictor variables in terms of exogenous variable is ≥0.02, the effect size is small; a value ≥0.15 is indicative of a medium effect size; and ≥0.35 reflects a large effect size. In Table 9, the effect size of UTAUT2 model drivers of Online Shopping -> Behavioral Intention is large, and that of Behavioral Intention → Sustained Intention is also large. That of Digital Payment Mode Drivers → Behavioral Intention is medium, and only for Technology Interface Drivers → Behavioral Intention, the effect size is small. The results reflect that in the case of chatbot and virtual try-on adoption, we may be in the infancy stage and need to work on improving this.
The key aim of the current study was to examine the impact of different factors of online shopping on Behavioral Intention and of Behavioral Intention on Sustained Intention. Smart PLS was used to examine the relationships. Our results are consistent with [73] and support a positive and significant impact of PE, FC, HM, and HA on BI. Performance expectancy is related to consumer intention and is the main driver of online shopping as endorsed by [16,21,30,74]. Hedonic motivation also has an optimistic influence on online shopping, and this finds support from earlier literature [4,34]. The current study also supports this relation. Social influence demonstrates a positive influence on Behavioral Intention [29,33]; on the contrary, the results are conflicting with the previous studies [3,32,75]. Prior literature corroborates that facilitating conditions is an important driver of technology adoption [31,32]. We can say that our results are consistent with other studies and that UTAUT2 drivers are stimulating Behavioral Intention. Chatbots and virtual try-on technology have already been validated as new drivers of online shopping [18,64,65]. According to the research by [76], future work could investigate the effects of emerging technologies, such as virtual reality and augmented reality. The results of the present study align with the other correlated studies mentioned earlier, thus validating chatbots and virtual try-on technology as new drivers of online shopping.
Contemporary research suggests that there are conceptual and methodological problems with the approach of [77] (e.g., [78]). Hence, we used [79], which provides a blend of earlier research on mediation analysis, suggesting that there is complementary mediation, as both the indirect effect and the direct effect are positive and significant. This, according to [77], is partial mediation. Thus, there is support that the hypothesized model is acceptable, but in the future, other variables may be included.

5. Implications

5.1. Theoretical Implications

The present study is very important for both scholars and online retailers in developing countries. No doubt, digital monetary services improve access to debit and credit cards, in addition to an improved inclination for other digital payments [76], but how Digital Payment drivers influence online shopping behavior is still an under-researched area. Therefore, there is scope for theory and practice. The role of TI drivers of online shopping adds a new dimension to the existing literature. Further, the paper has brought about the importance of TI drivers in enhancing sustained intention. The findings confirm the importance of chatbot conversation [80]. Chatbots assist in the online purchase process as they improve experience [81]. The key aim of the current research was to understand the impact of different drivers of online shopping on Behavioral Intention and further on Sustained Intention. This research enhances the knowledge of online shopping literature, especially the role of TI drivers and DPM drivers. The study enriches the limited research by investigating the role and importance of online customer experience through CBs, as highlighted by [58]. The results are consistent with [81], supporting the use of new technology to influence the Behavioral Intention of online shoppers. The results are significant for CBs and VTOs, highlighting their importance in online shopping. The current research has helped in analyzing VTO and offered a holistic perspective of how this technology leads to customer satisfaction.

5.2. Managerial Implications

The growth of e-commerce has brought many changes to the purchasing power of shoppers, as well as their payment modes. It is necessary to concentrate on promising technological changes in terms of the mode of payment. Our research provides evidence of a strong impact of DPM drivers on online shoppers’ BI. Thus, Indian managers need to adopt DPM as a major support service to make online shopping a worthwhile experience [22]. The study suggests that managers need to adopt chatbots and virtual try-on technology to enhance the shopping experience. Online retailers should design and manage chatbots by monitoring user involvement and giving due attention to time, tone of communication, and, of course, the quality of the information that is provided. Thus, this study is of practical use for online consumers as well as sellers to use the latest technology in payment modes and also to enhance the technology interface to add more flavor to online shopping. In line with UTAUT2 drivers, communication skills could be taken as a new variable to understand its impact on consumers. DPM and TI drivers help in enlarging the impact on BI. The results also reflect that BI needs to be translated into SI for the retention of online shoppers.

6. Limitations and Future Research Orientations

This research has a few limitations, as only respondents from Northern India were included. The results may be more applicable in countries that have similar demographics and cultures. Though due care was taken to ensure that the research methodology was robust and unbiased, the present study is not free from limitations. Though empirically tested, a few of the factors and item indicators considered in the present study are recent and new. Moreover, the study has not used control variables. Hence, this can be taken as an opportunity to be studied in the future. Notwithstanding the limitations, this research does make an important contribution to the body of online shopping literature. This research develops a simple model that explains the influence of UTAUT2, TI drivers (chatbots and virtual try-on technology), and DPM drivers on the Behavioral Intention of online shoppers. In the future, the model can be further extended and tested in different countries to make it more generic. This research paper additionally validates the importance of UTAUT2 drivers in online shopping by making use of pragmatic research with a customized UTAUT2 model by adding two new variables. The importance of TI and DP drivers as new variables could be further explored in future research. Service quality could also be taken as an additional variable in future research.

7. Conclusions

The findings demonstrate an encouraging relationship between the drivers of online shopping and Behavioral Intention. Thus, this paper contributes to the UTAUT2 theory. Overall, the model is acceptable and satisfactorily fulfills all the parameters. The results indicate that the DP and TI drivers, as new drivers of online shopping, have enhanced the influence of online shopping. However, the results do reflect the need to improve performance in terms of TI drivers. E-commerce is a revolutionized world with new variables such as gamification and service quality popping up every now and then. These new opportunities and challenges could act as catalysts and set new paths for the growth of the sector.

Author Contributions

Conceptualization—S.G.; Writing—original draft: S.G.; Writing—review and editing—R.K.; Supervision: R.K. and R.K.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board of Thapar Institute of Engineering and Technology (protocol code TIEC/EC/2022-16; Approved on 16 January 2023). Informed consent was obtained from all subjects involved in the study.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The datasets generated and/or analyzed during the study are available from the corresponding author upon reasonable request.

Acknowledgments

We thank all the respondents for their participation in filling out the questionnaire.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. IBEF Report. Indian Retail Industry Analysis. 2020. Available online: www.ibef.org/archives/industry/indian-retail-industry-analysis-reports/indian-retail-industry-analysis-january-2020 (accessed on 15 August 2022).
  2. Venkatesh, V.; Morris, M.G.; Davis, G.B.; Davis, F.D. User acceptance of information technology: Towards a unified view. MIS Q. 2003, 27, 425–478. [Google Scholar] [CrossRef]
  3. Venkatesh, V.; Thong, J.Y.; Xu, X. Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Q. 2012, 36, 157–178. [Google Scholar] [CrossRef]
  4. Tandon, U.; Kiran, R. Study on drivers of online shopping and significance of cash-on-delivery mode of payment on behavioural intention. Int. J. Electron. Bus. 2018, 14, 212–237. [Google Scholar] [CrossRef]
  5. Al-Hattami, H.M. Determinants of intention to continue usage of online shopping under a pandemic: COVID-19. Cogent Bus. Manag. 2021, 8, 1936368. [Google Scholar] [CrossRef]
  6. Ha, H.Y.; Janda, S.; Muthaly, S.K. A new understanding of satisfaction model in e-re-purchase situation. Eur. J. Mark. 2010, 44, 997–1016. [Google Scholar] [CrossRef]
  7. Kandpal, V.; Mehrotra, R. Financial inclusion: The role of fintech and digital financial services in India. Indian J. Econ. Bus. 2019, 19, 85–93. [Google Scholar]
  8. Wirtz, J.; Patterson, P.G.; Kunz, W.H.; Gruber, T.; Lu, V.N.; Paluch, S.; Martins, A. Brave new world: Service robots in the frontline. J. Serv. Manag. 2018, 29, 907–931. [Google Scholar] [CrossRef]
  9. Tandon, U. Chatbots, virtual-try-on (VTO), e-W++OM: Modeling the determinants of attitude’ and continued intention with PEEIM as moderator in online shopping. Glob. Knowl. Mem. Commun. 2023. [Google Scholar] [CrossRef]
  10. Zhang, T.; Wang, W.Y.C.; Cao, L.; Wang, Y. The role of virtual try-on technology in online purchase decision from consumers’ aspect. Internet Res. 2019, 29, 529–551. [Google Scholar] [CrossRef]
  11. Rajakumar, J.D.; Shetty, S.L. Demonetisation: 1978, the Present and the Aftermath. Econ. Political Wkly. 2016, 51, 13–17. [Google Scholar]
  12. Schneider, F.; Buehn, A.; Montenegro, C.E. Shadow Economies All over the World: New Estimates for 162 Countries from 1999 to 2007. World Bank Policy Research Working Paper, 2010. Available online: https://documents1.worldbank.org/curated/en/311991468037132740/pdf/WPS5356.pdf (accessed on 4 July 2023).
  13. Muthumani, A.; Lavanya, V.; Mahalakshmi, R. Problems faced by customers on online shopping in Virudhunagar district. Int. J. Sci. Technol. Manag. (IJSTM) 2017, 6, 152–159. [Google Scholar]
  14. Tandon, U.; Kiran, R.; Sah, A.N. Customer satisfaction as mediator between website service quality and repurchase intention: An emerging economy case. Serv. Sci. 2017, 9, 106–120. [Google Scholar] [CrossRef]
  15. Iglesias-Pradas, S.; Acquila-Natale, E. The Future of E-Commerce: Overview and Prospects of Multichannel and Omnichannel Retail. J. Theor. Appl. Electron. Commer. Res. 2023, 18, 656–667. [Google Scholar] [CrossRef]
  16. Tandon, U.; Kiran, R.; Sah, A.N. The influence of website functionality, drivers and perceived risk on customer satisfaction in online shopping: An emerging economy case. Inf. Syst. e-Bus. Manag. 2018, 16, 57–91. [Google Scholar] [CrossRef]
  17. Kwateng, K.O.; Atiemo, K.A.O.; Appiah, C. Acceptance and use of mobile banking: An application of utaut2. J. Enterp. Inf. Manag. 2018, 32, 118–151. [Google Scholar] [CrossRef]
  18. Chu, T.H.; Chao, C.M.; Liu, H.H.; Chen, D.F. Developing an Extended Theory of UTAUT 2 Model to Explore Factors Influencing Taiwanese Consumer Adoption of Intelligent Elevators. SAGE Open 2022, 12, 21582440221142209. [Google Scholar] [CrossRef]
  19. Bălan, C. Chatbots and Voice Assistants: Digital Transformers of the Company–Customer Interface—A Systematic Review of the Business Research Literature. J. Theor. Appl. Electron. Commer. Res. 2023, 18, 995–1019. [Google Scholar] [CrossRef]
  20. Antoniou, P.H.; Ansoff, H.I. Strategic management of technology. Technol. Anal. Strateg. Manag. 2004, 16, 275–291. [Google Scholar] [CrossRef]
  21. Celik, H. Customer online shopping anxiety within the Unified Theory of Acceptance and Use Technology (UTAUT) framework. Asia Pac. J. Mark. Logist. 2016, 28. [Google Scholar] [CrossRef]
  22. See-To, E.W.; Papagiannidis, S.; Westland, J.C. The moderating role of income on consumers’ preferences and usage for online and offline payment methods. Electron. Commer. Res. 2014, 14, 189–213. [Google Scholar] [CrossRef]
  23. Kandpal, V. Role of regulators in intensifying financial access to the untouched segment of society in developing country. Corp. Gov. Organ. Behav. Rev. 2020, 4, 8–14. [Google Scholar] [CrossRef]
  24. Uğur, N.G.; Turan, A.H. Retracted article: E-learning adoption of academicians: A proposal for an extended model. Behav. Inf. Technol. 2018, 37, 393–405. [Google Scholar] [CrossRef]
  25. Jadil, Y.; Rana, N.P.; Dwivedi, Y.K. A meta-analysis of the UTAUT model in the mobile banking literature: The moderating role of sample size and culture. J. Bus. Res. 2021, 132, 354–372. [Google Scholar] [CrossRef]
  26. Davis, F.D. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 1989, 13, 319–340. [Google Scholar] [CrossRef]
  27. Tandon, U.; Kiran, R.; Sah, A.N. Customer satisfaction using website functionality, perceived usability and perceived usefulness towards online shopping in India. Inf. Dev. 2016, 32, 1657–1673. [Google Scholar] [CrossRef]
  28. Chang, C.M.; Liu, L.W.; Huang, H.C.; Hsieh, H.H. Factors influencing online hotel booking: Extending UTAUT2 with age, gender, and experience as moderators. Information 2019, 10, 281. [Google Scholar] [CrossRef]
  29. Palau-Saumell, R.; Forgas-Coll, S.; Sánchez-García, J.; Robres, E. User acceptance of mobile apps for restaurants: An expanded and extended UTAUT-2. Sustainability 2019, 11, 1210. [Google Scholar] [CrossRef]
  30. Li, Y.Z.; He, T.L.; Song, Y.R.; Yang, Z.; Zhou, R.T. Factors impacting donors’ intention to donate to charitable crowd-funding projects in China: A UTAUT-based model. Inf. Commun. Soc. 2018, 21, 404–415. [Google Scholar] [CrossRef]
  31. Sareen, M.; Jain, A. The Role of Social Influence and consumer’s effort expectancy on online shopping: An empirical study of India. Int. J. Manag. Res. Bus. Strategy 2014, 3, 138–158. [Google Scholar]
  32. Yaprakli, S.; Kalbakhani, E.; Rasouli, R. An application of UTAUT model for online shopping acceptance in Iran. Reef Resour. Assess. Manag. Tech. Pap. 2013, 38, 237–244. [Google Scholar]
  33. Baptista, G.; Oliveira, T. Understanding mobile banking: The unified theory of acceptance and use of technology combined with cultural moderators. Comput. Hum. Behav. 2015, 50, 418–430. [Google Scholar] [CrossRef]
  34. Menon, S.; Kahn, B. Cross-category effects of induced arousal and pleasure on the internet shopping experience. J. Retail. 2002, 78, 31–40. [Google Scholar] [CrossRef]
  35. Tandon, U. Predictors of online shopping in India: An empirical investigation. J. Mark. Anal. 2021, 9, 65–79. [Google Scholar] [CrossRef]
  36. Smink, A.R.; Frowijn, S.; van Reijmersdal, E.A.; van Noort, G.; Neijens, P.C. Try online before you buy: How does shopping with augmented reality affect brand responses and personal data disclosure. Electron. Commer. Res. Appl. 2019, 35, 100854. [Google Scholar] [CrossRef]
  37. Kasilingam, D.L. Understanding the attitude and intention to use smartphone chatbots for shopping. Technol. Soc. 2020, 62, 101280. [Google Scholar] [CrossRef]
  38. Lian, J.W.; Yen, D.C. Online shopping drivers and barriers for older adults: Age and gender differences. Comput. Hum. Behav. 2014, 37, 133–143. [Google Scholar] [CrossRef]
  39. Danckwerts, S.; Meißner, L.; Krampe, C. Examining user experience of conversational agents in hedonic digital services–antecedents and the role of psychological ownership. SMR J. Serv. Manag. Res. 2019, 3, 111–125. [Google Scholar] [CrossRef]
  40. Chung, K.; Shin, J. The antecedents and consequences of relationship quality in internet shopping. Asia Pac. J. Mark. Logist. 2010, 22, 473–491. [Google Scholar] [CrossRef]
  41. Sampson, S.E.; Chase, R.B. Customer contact in a digital world. J. Serv. Manag. 2020, 31, 1061–1069. [Google Scholar] [CrossRef]
  42. Kim, J.; Forsythe, S. Factors affecting adoption of product virtualization technology for online consumer electronics shopping. Int. J. Retail. Distrib. Manag. 2010, 38, 190–204. [Google Scholar] [CrossRef]
  43. Lin, Y.L.; Wang, M.J.J. The development of a clothing fit evaluation system under virtual environment. Multimed. Tools Appl. 2015, 75, 7575–7587. [Google Scholar] [CrossRef]
  44. Merle, A.; Senecal, S.; St-Onge, A. Whether and how virtual try-on influences consumer responses to an apparel web site. Int. J. Electron.Commer. 2012, 16, 41–64. [Google Scholar] [CrossRef]
  45. Beck, M.; Crié, D. I virtually try it…I want it! Virtual fitting room: A tool to increase on-line and off-line exploratory behavior, patronage and purchase intentions. J. Retail. Consum. Serv. 2018, 40, 279–286. [Google Scholar] [CrossRef]
  46. Yen, Y.Y.; Narayanasamy, K.; Lin, C.Y.; Rasiah, D.; Ramasamy, S. Consumer’s perception towards real-time virtual fitting system. In Proceedings of the 6th International Conference on Computing and Informatics, Kuala Lumpur, Malaysia, 25–27 April 2017; pp. 311–316. [Google Scholar]
  47. Brrar, S.; Lee, E.; Yip, T.L. An Exploratory Study of the Critical Success Factors of the Global Shipping Industry in the Digital Era. J. Theor. Appl. Electron. Commer. Res. 2023, 18, 795–813. [Google Scholar] [CrossRef]
  48. Gupta, S.; Kiran, R.; Sharma, R.K. Validating the role of digital payment mode as a new driver of online shopping: A modified UTAUT2 model. J. Public Aff. 2022, 22, e2434. [Google Scholar] [CrossRef]
  49. Ab Hamid, M.R.; Sami, W.; Sidek, M.M. Discriminant validity assessment: Use of Fornell & Larcker criterion versus HTMT criterion. J. Phys. Conf. Ser. 2017, 890, 012163. [Google Scholar]
  50. Pavlou, P.A.; Fygenson, M. Understanding and predicting electronic commerce adoption: An extension of the Theory of Planned Behavior. MIS Q. 2006, 30, 115–134. [Google Scholar] [CrossRef]
  51. Tsai, H.T.; Huang, H.C. Determinants of e-repurchase intentions: An integrative model of quadruple retention drivers. Inf. Manag. 2007, 44, 231–239. [Google Scholar] [CrossRef]
  52. Wang, F.; Head, M. How can the Web Help Build Customer Relationships? An Empirical Study on E-Tailing. Inf. Manag. 2007, 44, 115–129. [Google Scholar] [CrossRef]
  53. Taylor, S.; Todd, P. Assessing IT usage: The role of prior experience. MIS Q. 1995, 19, 561–570. [Google Scholar] [CrossRef]
  54. Gounaris, S.; Dimitriadis, S.; Stathakopoulos, V. An examination of the effects of service quality and satisfaction on consumers behavioral intentions in e-shopping. J. Serv. Mark. 2010, 24, 142–156. [Google Scholar] [CrossRef]
  55. Jumaan, I.A.; Hashim, N.H.; Al-Ghazali, B.M. The role of cognitive absorption in predicting mobile internet users’ continuance intention: An extension of the expectation-confirmation model. Technol. Soc. 2020, 63, 101355. [Google Scholar] [CrossRef]
  56. Shang, D.; Wu, W. Understanding mobile shopping consumers’ continuance intention. Ind. Manag. Data Syst. 2017, 117, 213–227. [Google Scholar] [CrossRef]
  57. Yuan, S.; Liu, Y.; Yao, R.; Liu, J. An investigation of users’ continuance intention towards mobile banking in China. Inf. Dev. 2016, 32, 20–34. [Google Scholar] [CrossRef]
  58. Ahmad, N.; Abdulkarim, H. The impact of flow experience and personality type on the intention to use virtual world. Int. J. Hum. Comput. Interact. 2019, 35, 1074–1085. [Google Scholar] [CrossRef]
  59. Bentler, P.M.; Chou, C.P. Practical issues in structural modeling. Sociol. Methods Res. 1987, 16, 78–117. [Google Scholar] [CrossRef]
  60. Kline, R.B. Principles and Practice of Structural Equation Modelling; Guilford Publications: New York, NY, USA, 2015. [Google Scholar]
  61. Podsakoff, P.M.; MacKenzie, S.B.; Podsakoff, N.P. Sources of method bias in social science research and recommendations on how to control it. Annu. Rev. Psychol. 2012, 63, 539–569. [Google Scholar] [CrossRef]
  62. Klarner, P.; Sarstedt, M.; Hoeck, M.; Ringle, C.M. Disentangling the effects of team competences, team adaptability, and client communication on the performance of management consulting teams. Long Range Plan. 2013, 46, 258–286. [Google Scholar] [CrossRef]
  63. Al-Adwan, A.S.; Yaseen, H. Solving the product uncertainty hurdle in social commerce: The mediating role of seller uncertainty. Int. J. Inf. Manag. Data Insights 2023, 3, 100169. [Google Scholar] [CrossRef]
  64. Cai, L.; Yuen, K.F.; Xie, D.; Fang, M.; Wang, X. Consumer’s usage of logistics technologies: Integration of habit into the unified theory of acceptance and use of technology. Technol. Soc. 2021, 67, 101789. [Google Scholar] [CrossRef]
  65. Dang, T.Q.; Tan, G.W.H.; Aw, E.C.X.; Ooi, K.B.; Metri, B.; Dwivedi, Y.K. How to generate loyalty in mobile payment services? An integrative dual SEM-ANN analysis. Int. J. Bank Mark. 2023, 41, 1177–1206. [Google Scholar] [CrossRef]
  66. Cronbach, L.J. Coefficient alpha and the internal structure of tests. Psychometrika 1951, 16, 297–334. [Google Scholar] [CrossRef]
  67. Henseler, J.; Ringle, C.M.; Sarstedt, M. A new criterion for assessing discriminant validity in variance-based structural equation modeling. J. Acad. Mark. Sci. 2015, 43, 115–135. [Google Scholar] [CrossRef]
  68. Hair, J.F., Jr.; Anderson, R.E.; Tatham, R.L.; Black, W.C. Multivariate Data Analysis, 3rd ed.; Macmillan: New York, NY, USA, 1995. [Google Scholar]
  69. Marquaridt, D.W. Generalized inverses, ridge regression, biased linear estimation, and nonlinear estimation. Technometrics 1970, 12, 591–612. [Google Scholar] [CrossRef]
  70. Neter, J.; Wasserman, W.; Kutner, M.H. Applied Linear Regression Models, 2nd ed.; Irwin: Homewood, IL, USA, 1989. [Google Scholar]
  71. Kennedy, P. A Guide to Econometrics; Wiley-Blackwell: Oxford, UK, 2008. [Google Scholar]
  72. Cohen, J. Statistical Power Analysis for the Behavioral Sciences (Revised Edition); Laurence Erlbaum Associates: Hillsdale, NJ, USA, 1987. [Google Scholar]
  73. Sivathanu, B. Adoption of digital payment systems in the era of demonetization in India: An empirical study. J. Sci. Technol. Policy Manag. 2019, 10, 143–171. [Google Scholar] [CrossRef]
  74. Dharmawirya, M.; Smith, B.A. Analysis of Consumer Repurchase Intention towards Online Shopping in Indonesia’s Online Retail Business Market. Int. J. e-Educ. e-Bus. e-Manag. e-Learn. 2012, 2, 202. [Google Scholar] [CrossRef]
  75. Foon, Y.S.; Fah, B.C.Y. Internet banking adoption in Kuala Lumpur: An application of UTAUT model. Int. J. Bus. Manag. 2011, 6, 161. [Google Scholar]
  76. Mahajan, P.; Singla, A. Effect of demonetization on financial inclusion in India. Int. J. Sci. Technol. Manag. 2017, 6, 338–343. [Google Scholar]
  77. Baron, R.M.; Kenny, D.A. The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. J. Personal. Soc. Psychol. 1986, 51, 1173. [Google Scholar] [CrossRef]
  78. Hayes, A.F. Partial, conditional, and moderated moderated mediation: Quantification, inference, and interpretation. Commun. Monogr. 2018, 85, 4–40. [Google Scholar] [CrossRef]
  79. Zhao, X.; Lynch, J.G., Jr.; Chen, Q. Reconsidering Baron and Kenny: Myths and truths about mediation analysis. J. Consum. Res. 2010, 37, 197–206. [Google Scholar] [CrossRef]
  80. Liebrecht, C.; Tsaousi, C.; van Hooijdonk, C. Linguistic elements of conversational human voice in online brand communication: Manipulations and perceptions. J. Bus. Res. 2021, 132, 124–135. [Google Scholar] [CrossRef]
  81. Hoyer, W.D.; Kroschke, M.; Schmitt, B.; Kraume, K.; Shankar, V. Transforming the Customer Experience through New Technologies. J. Interact. Mark. 2020, 51, 57–71. [Google Scholar] [CrossRef]
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Figure 1. Research framework.
Figure 1. Research framework.
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Figure 2. PLS-SEM model depicting the relationship of UTAUT2, DPM, and TI drivers with Behavioral Intention and Sustained Intention.
Figure 2. PLS-SEM model depicting the relationship of UTAUT2, DPM, and TI drivers with Behavioral Intention and Sustained Intention.
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Figure 3. Bootstrapping model depicting the relationship of UTAUT2, DPM, and TI drivers with Behavioral Intention and Sustained Intention.
Figure 3. Bootstrapping model depicting the relationship of UTAUT2, DPM, and TI drivers with Behavioral Intention and Sustained Intention.
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Table 1. Questionnaire: Scale items with literature support.
Table 1. Questionnaire: Scale items with literature support.
S. No.Scale ItemsSource
Technology Interface Drivers
Virtual Try-On Technology
1.VTO engages us with passing time motive
2.VTO helps in mood management
3.VTO is important for me as it helps me to choose better products for myselfTandon, U. 2023 [9]
4.VTO provides sufficient and accurate information about the product Tandon, U. 2023 [9]
Chatbots
1.Chatbots help to vent out negative feelings
2.I like using chatbots for shopping.Taylor and Todd 1995 [53]
3.Using chatbots for shopping is a good idea.Taylor and Todd 1995 [53]
4.Using chatbots for shopping is a pleasant experienceTaylor and Todd 1995 [53]
Behavioral Intention
1.I am satisfied with the product range offered by online retailers.(Tandon et al. 2015) [27]
2.I am satisfied with the quality of products offered online.(Tandon et al. 2015) [27]
3.I am satisfied with the quality of products offered online.(Tandon et al. 2015) [27]
4.Online shopping is a satisfying experience as it offers customized products at my convenience.(Tandon et al. 2015) [27]
Sustained Intention
1.I intend to continue using online shopping in the future.(Venkatesh et al. 2003, 2012) [2,3]
2.I will always try to use online shopping in the future.(Venkatesh et al. 2003, 2012) [2,3]
3.I plan to continue using online shopping frequently.(Venkatesh et al. 2012) [3]
Table 2. Factor analysis of the digital mode of payment.
Table 2. Factor analysis of the digital mode of payment.
Digital Mode of PaymentEase of PaymentPerceive Risk
MP6: Digital mode protects my privacy.0.857
MP5: I feel safe while using digital payment mode in online shopping.0.833
MP4: Digital payment mode gives me confidence for future purchases of products.0.805
MP2: Digital is a reliable payment mode while shopping online.0.745
MP3: I plan to make payment through digital payment mode.0.741
MP1: I prefer to buy through digital payment mode.0.709
MP9: The terms used for digital payment mode are understandable.0.634
MP8: I trust that the website will not share my personal details with others without my consent.0.632
Eigenvalue8.533
% of Variation39.219
MP7: To increase trust in digital payments cyber security systems must be strengthened. 0.840
MP13: Authentication through OTP ensures safety while making payment through digital payment mode 0.795
MP11: Reducing the cost of electronic transfer would act as a catalyst for digital payment. 0.762
MP14: Recent Government of India directives to the banks regarding refunds in case of mis-utilization of funds motivate me to make payments through digital payment mode. 0.627
MP12: It becomes easier to make payments using debit/credit cards as details are already saved on the website. 0.603
MP10: The more the surety about the product, the more the use of credit cards. 0.553
Eigenvalue 1.046
% of Variation 29.202
KMO and Bartlett’s Test
Kaiser–Meyer–Olkin Measure of Sampling Adequacy0.946
Bartlett’s Test of SphericityApprox. Chi-Square3926.028
Df91
p-value0.000 ***
[*** p ≤ 0.001] Source: Authors’ calculations with SPSS.
Table 3. Construct reliability.
Table 3. Construct reliability.
Cronbach’s
Alpha		Rho_AComposite
Reliability		Average Variance
Extracted (AVE)
Behavioral Intention0.8750.8770.9150.728
DPM Drivers0.7490.7510.8890.800
Sustained Intention0.7810.8140.8720.696
TI Drivers0.7350.7360.8830.791
UTAUT2 Drivers of Online Shopping0.8580.8770.8910.539
Table 4. Fornell–Larcker criteria.
Table 4. Fornell–Larcker criteria.
Behavioral
Intention		Digital Payment DriversSustained
Intention		Technology
Interface Drivers		UTAUT2 Model Drivers of Online Shopping
Behavioral Intention0.853
DPM Drivers0.6810.894
Sustained Intention0.8280.6350.834
TI Drivers0.6990.5580.6970.889
UTAUT2 Drivers of Online Shopping0.7200.6450.7140.7320.734
Table 5. HTMT ratio.
Table 5. HTMT ratio.
Behavioral IntentionDigital Payment DriversSustained
Intention		Technology
Interface Drivers		UTAUT2 Model
Drivers of Online Shopping
Behavioral Intention0.839
DPM Drivers0.8330.821
Sustained Intention0.8700.7520.812
TI Drivers0.8870.7870.8450.818
UTAUT2 Drivers of Online Shopping0.839
Table 6. VIF values.
Table 6. VIF values.
Outer VIF
ConstructsVIFConstructsVIFConstructsVIF
EE2.199Chatbots1.511BI12.361
FC1.863Virtual Try-On1.511BI22.513
H1.726Ease of Payment1.560BI32.366
HM1.938Perceived Risk1.560BI42.026
PE1.693Chatbots1.511SI11.948
PV1.972 SI21.867
SI1.767 SI31.395
Inner VIF
Behavioral
Intention		Digital Payment DriversSustained
Intention		Technology
Interface Drivers		UTAUT2 Drivers
Behavioral Intention1.759 1.000
Digital Payment Drivers
Sustained Intention2.216
Technology Interface Drivers2.611
UTAUT2 Drivers
Table 7. Path coefficients.
Table 7. Path coefficients.
Original Sample (O)Sample Mean (M)Standard Deviation (STDEV)T Statistics (|O/STDEV|)p ValuesF-Square
H1: UTAUT2 Model Drivers of Online
Shopping → Behavioral Intention		0.4650.4670.04310.7350.000 ***0.272 (L)
H2: Technology Interface Drivers →
Behavioral Intention		0.2110.2100.0365.8230.000 ***0.066 (S)
H3: Digital Payment Mode Drivers →
Behavioral Intention		0.2630.2620.0386.9790.000 ***0.151 (M)
H4: Behavioral Intention → Sustained
Intention		0.8680.8690.01176.1010.000 ***3.062 (L)
Indirect Effect
H1*H4: UTAUT2 Drivers → Behavioral
Intention → Sustained Intention		0.4040.4060.03810.5020.000 ***
H2*H4: Technology Interface Drivers → Behavioral Intention → Sustained Intention0.1830.1830.0325.7690.000 ***
H3*H4: Digital Payment Mode Drivers → Behavioral Intention → Sustained Intention0.2290.2280.0336.9420.000 ***
R SquareR Square Adjusted
Behavioral Intention0.6950.693
Sustained Intention0.7540.753
*** p ≤ 0.001.
Table 8. Path coefficients and outcomes.
Table 8. Path coefficients and outcomes.
Hypothesesβ-Values/R2p-ValuesStatus
H1: UTAUT2 Model Drivers of Online Shopping →
Behavioral Intention		β-value 0.4650.000 ***Supported
H2: Technology Interface Drivers → Behavioral
Intention		β-value 0.2110.000 ***Supported
H3: Digital Payment Mode Drivers → Behavioral
Intention		β-value 0.2630.000 ***Supported
H4: Behavioral Intention → Sustained Intentionβ-value 0.8680.000 ***Supported
H5: UTAUT2 Drivers, Technology Interface Drivers, and
Digital Payment Mode Drivers are positively related to
Behavioral Intention, which positively and significantly
influences Sustained Intention		R2: 0.754 Supported
H1*H4: UTAUT2 Drivers → Behavioral Intention
→ Sustained Intention		β-value 0.4040.000 ***Supported
H2*H4: Technology Interface Drivers → Behavioral Intention
→ Sustained Intention		β-value 0.1830.000 **Supported
H3*H4: Digital Payment Mode Drivers → Behavioral Intention → Sustained Intentionβ-value 0.2290.000 ***Supported
Note: Complied by the authors; p ≤ 0.001 ***; p ≤ 0.01 **.
Table 9. Outer loadings.
Table 9. Outer loadings.
Original Sample (O)Sample Mean (M)Standard Deviation (STDEV)T Statistics (|O/STDEV|)p Values
EE ← UTAUT2 Model Drivers of Online Shopping0.7590.7580.02728.1010.000 ***
FC ← UTAUT2 Model Drivers of Online Shopping0.7040.7040.02627.0850.000 ***
H ← UTAUT2 Model Drivers of Online Shopping0.6600.6600.03022.3690.000 ***
HM ← UTAUT2 Model Drivers of Online Shopping0.8050.8050.01554.2100.000 ***
PE ← UTAUT2 Model Drivers of Online Shopping0.7260.7260.02232.5030.000 ***
PV ← UTAUT2 Model Drivers of Online Shopping0.8060.8060.02040.0460.000 ***
SI ← UTAUT2 Model Drivers of Online Shopping0.6640.6640.03121.0820.000 ***
Chatbots ← Technology Interface Drivers0.8870.8870.01180.4800.000 ***
Virtual Try-On ← Technology Interface Drivers0.8910.8910.01272.0700.000 ***
Ease of Payment ← Digital Payment Mode Drivers0.9000.9000.01184.4990.000 ***
Perceived Risk ← Digital Payment Mode Drivers0.8880.8880.01275.3300.000 ***
BI1 ← Behavioral Intention0.8610.8610.01270.4400.000 ***
BI2 ← Behavioral Intention0.8700.8700.01274.6660.000 ***
BI3 ← Behavioral Intention0.8610.8610.01460.2390.000 ***
BI4 ← Behavioral Intention0.8200.8200.01749.3660.000 ***
SI1 ← Sustained Intention0.8900.8900.009100.9460.000 ***
SI2 ← Sustained Intention0.8710.8710.01275.5130.000 ***
SI3 ← Sustained Intention0.7330.7320.02925.0910.000 ***
*** p ≤ 0.001.
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Gupta, S.; Kiran, R.; Sharma, R.K. Embedding Technology Interface and Digital Payment Drivers in the Unified Theory of Acceptance and Use of Technology 2 Model: Transforming Behavioral Intention to Sustained Intention. Sustainability 2023, 15, 13018. https://doi.org/10.3390/su151713018

AMA Style

Gupta S, Kiran R, Sharma RK. Embedding Technology Interface and Digital Payment Drivers in the Unified Theory of Acceptance and Use of Technology 2 Model: Transforming Behavioral Intention to Sustained Intention. Sustainability. 2023; 15(17):13018. https://doi.org/10.3390/su151713018

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Gupta, Savita, Ravi Kiran, and Rakesh Kumar Sharma. 2023. "Embedding Technology Interface and Digital Payment Drivers in the Unified Theory of Acceptance and Use of Technology 2 Model: Transforming Behavioral Intention to Sustained Intention" Sustainability 15, no. 17: 13018. https://doi.org/10.3390/su151713018

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