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By integrating uses and gratification theory (UGT) and online buying behavior theory (OBBT), this study aims to examine the impact of escapism motives (self-suppression and self-expansion) and attitude toward online shopping (ATS) on eCart abandonment. In addition, the mediating role of ATS between escapism motives and eCart abandonment is examined.

Structural equations modeling was performed on the data of 400 consumers using AMOS 26.

The results indicated that escapism motivations impacted users’ eCart abandonment, and the attitude toward online shopping mediated this relationship.

The findings of this study imply that online sellers should understand the consumer motives for website use. In response, better strategies should be developed to reduce eCart abandonment.

This study extends knowledge of eCart abandonment by theoretical integration of UGT and OBBT and identification of the intrinsic predictors of virtual cart abandonment behavior. In addition, it is one of the early attempts to examine the dimensional impact of escapism on eCart abandonment.

In keeping with global developments rendering online shopping as an emerging trend among consumers, the present study extends the unified theory of use and acceptance of technology (UTAUT2) comprising the digital payment mode (DPM) as a new driver of online shopping and with the mediation of attitudes toward technology (ATTs) to gauge a better and deeper understanding of behavioral intention (BI).

This study used a survey instrument with snowball sampling from 600 consumers in northern India. Partial least squares structural equation modeling was used to find the association between drivers using UTUAT2, along with DPM and ATTs. The data were divided into a test group (20%) and validated through a training group (80%).

DPM was shown to be directly associated with BI. The mediation of ATTs was also validated through the model. The predictability of the model was 67.5% for the test group (20%) and 69.6% for the training group (80%). The results also indicated that facilitating conditions is a critical driver of BI.

This study enhances the understanding of the roles that DPM and ATTs play in BI during online shopping, suggesting that Indian managers need to adopt DPM as a support service to make online shopping a worthwhile experience.

Cryptocurrencies are becoming increasingly attractive as alternatives to traditional currencies. Although many retailers accept cryptocurrencies as a means of payment in online shopping, consumers’ cryptocurrency adoption intention in online shopping (CCAI) is still low. This study aims to investigate the influence of attitudes, subjective norms, consumer trust, financial literacy and fear of missing out (FOMO) on CCAI.

A quantitative research approach was followed using a consumer survey. Hypothesized relationships were tested through regression and mediation analyses.

The results revealed that consumers could accept cryptocurrencies as a means of payment in online shopping. Attitudes, subjective norms, consumer trust and financial literacy directly and positively influence CCAI, while they indirectly affect CCAI through the mediating impact of FOMO.

Marketing managers should improve consumers’ knowledge about cryptocurrencies and trust in online shopping to increase CCAI. Social media marketing can be appropriate, while the advertising content can address keeping up with others and staying connected.

This study addresses a critical gap in the literature by empirically examining the antecedents of CCAI within an original conceptual model based on the theoretical framework provided by the theory of planned behavior. Attitudes, subjective norms, trust and financial literacy influence CCAI, where FOMO plays a significant role as a mediator.

The tech savvy generation Z consumers constitute a significant market of online shopping. Understanding their shopping behavior is thus a pressing need to expand the e-commerce business. Under this backdrop, the study aims to predict the online shopping behavior of generation Z customers in Bangladesh.

This study used the theory of consumption values (TCV) along with shopping enjoyment to understand the online shopping behavior of generation Z. A structured set of questionnaire was used to gather the responses on a five point Likert scale. A total of 411 valid responses were considered after discarding incomplete responses. The collected data were analyzed using the partial least squares structural equation modeling (PLS-SEM) approach with the help of smart PLS 4.0 software.

The statistical findings reveal that functional value is the most significant determinant of online shopping behavior among generation Z followed by social value, conditional value and epistemic value. The study also reveals that relationship between emotional value and online shopping behavior and relationship between conditional value and online shopping behavior is moderated by shopping enjoyment.

The paper contributes to the consumer behavior literature as the findings provide a comprehensive model from values perspectives to understand online shopping behavior among Gen Z customers in a developing country like Bangladesh. The findings of this study offer important insights to the marketers also since it reveals the values consumers consider while shopping online. The findings might help practitioners develop their online strategies to expand the business.

Driven by Davis’s (1989) technology acceptance model (TAM) and Westaby’s (2005) behavioral reasoning theory (BRT), the purpose of this study is to develop and test a conceptual model and examine consumers’ acceptance of artificial intelligence (AI) chatbots for apparel shopping.

Data from 353 eligible US respondents was collected through a self-administered questionnaire distributed on Amazon Mechanical Turk, an online panel. Confirmatory factor analysis and path analysis were used to test all hypothesized relationships using the structural equation model.

The results show that optimism and relative advantage of “reasons for” dimensions have a positive and significant influence on perceived ease of use (PEU), while innovativeness and relative advantage have a positive and significant influence on perceived usefulness (PUF). Discomfort and insecurity have no significant impact on PEU and PUF. However, complexity has a negative and significant impact on PEU but not on PUF. Additionally, PEU has a positive influence on PUF. Both PEU and PUF have a positive and significant influence on consumers’ attitudes toward using AI chatbots, which, in turn, affects the intention to use AI chatbots for apparel shopping. Overall, this study identifies that optimism, innovativeness and relative advantage are enablers and good reasons to adopt AI chatbots. Complexity is a prohibitor, making it the only reason against adopting AI chatbots for apparel shopping.

This study contributes to the literature by integrating TAM and BRT to develop a research model to understand what “reasons for” and “reasons against” factors are enablers or prohibitors that significantly impact consumers’ attitude and intention to use AI chatbots for apparel shopping through PEU and PUF.

This paper aims to establish and empirically investigate a research model examining the effect of four dimensions of the technology readiness index – optimism, innovativeness, discomfort and insecurity – on customer engagement that further influences purchase intention in the context of online shopping through artificial intelligence voice assistants (AI VAs).

Data were collected in India from 429 customers in a self-administered online survey. Data analysis uses the structural equation modelling technique.

Technology readiness dimensions, e.g. optimism, innovativeness, discomfort and insecurity, are critical factors driving customer engagement. Customer engagement further results in purchase intention in online shopping through AI VAs.

This study adds to the literature by understanding how customers’ technology readiness levels drive engagement and purchase intention. However, this study includes customer engagement as a unidimensional construct. Further research can consist of customer engagement as a multidimensional construct.

The findings offer guidelines for e-retailers to enhance customer engagement that matches their personality traits, thereby strengthening their purchase intention through AI VAs.

The research contributes to the literature by empirically investigating a research model, revealing optimism, innovativeness, discomfort and insecurity as crucial parameters for customer engagement and purchase intention.

This paper aims to reveal the influence of the presentation of online product information (POPI) on consumer attitudes in the context of online buying digital products.

Two main experimental designs are used to collect data. The ANOVA, t-test and Bootstrap methods are applied to check hypotheses.

Findings of Study 1 indicate that if the POPI is combined with different types of celebrity endorsement (CE) (real vs virtual), the self-brand connection will be changed and further influence consumer attitudes toward digital products. Study 2 verifies the diverse moderating effects of the type of virtual CE. The CRP (central-route presentation) online product information with SVCE (super-realistic-digital virtual CE) can decrease consumer attitudes, while the PRP (peripheral-route presentation) online product information with AVCE (anthropomorphic virtual CE) can enhance consumer attitudes.

E-commerce enterprises should optimize the current layout of POPI by considering diverse matchings between POPI and CE to increase consumer attitudes. Moreover, marketers could make various schemes of POPI considering (virtual) CE and self-brand connection.

Findings contribute to understanding the relationship between POPI and consumer attitudes considering the mediation of self-brand connection and the mediations of virtual/real CE. Additionally, this study bridges the gap between research on virtual CE and business practices.

Consumer-perceived review attitude determines consumer overall information adoption and is a core part of consumer’s online-shopping. This study aims to focus on factors that could influence consumer review attitude and can be used by marketers to shape individual information perception.

The study used the questionnaire method to collect data from online shoppers and the modelling of structural equations as an empirical approach to analyse the data.

The findings demonstrate that both systematic and heuristic cues impact the reviewer’s credibility and perceived website attitude differently, which, in turn, influence review attitude. Review characteristics, such as factuality, consistency and relevancy, have a positive relationship with reviewer credibility, while only review consistency and relevancy appears to have a relationship with review attitude. Website characteristics such as reputation, familiarity and social interactivity positively influence the website attitude, which positively influences review attitude. Apart from this, review skepticism has a significant negative relationship with review attitude.

This study could help to foster a positive attitude towards online reviews. Digital marketers need to motivate trusted reviewers to post consistent, fact-based reviews. Further improving the overall website reputation and interactivity could bring a positive attitude towards the reviews. Also, digital marketers must filter and avoid contradictory reviews or reviews that have a bipolar message and reviews expressing numerous emotions to enhance review relevance and consistency.

The current study addresses the need to understand the formation of consumer review attitude through both review and website characteristics using heuristic – systematic model. The paper captures the complex process undergone by the consumer to decipher review attitude and thereby extend the understanding of consumer information processing.

In this context, the aim of the present research is to examine what factors determine that consumers restrain from shopping used products through the Internet. So, this research aims to analyze what makes consumers prevent from shopping second-hand products online.

For this purpose, the authors propose and empirically test a conceptual model of the barriers towards online second-hand shopping behavior. Drawing on a sample of 405 consumers data were analyzed through structural equation modeling (SEM).

The findings reveal that contamination effects and the lack of trust towards the online store, followed by the low perceived product reliability and the poor product perceived quality prevent consumers from shopping used products online. Conversely, consumer embarrassment for shopping second-hand products and the purchase uncertainty do not influence consumers' second-hand shopping behavior.

This study contributes to the marketing literature on second-hand shopping, being an attempt to explore the factors that prevent consumers from purchasing used products through the Internet.

In the growing information systems (IS) literature on metaverse, augmented reality (AR) technology is regarded as a cornerstone of the metaverse which enables interaction services. Interaction has been identified as a core technology characteristic of metaverse shopping environments. Based on previous human–technology interaction research, the authors further explicate interaction to be multimodal sensory. The purpose of this study is thus to better understand the unique nature of interaction in AR technology and highlight the technology's benefits for shopping in metaverse spaces.

An experiment has been conducted to empirically examine the authors' research model. The authors use the structural equation modeling (SEM) approach to analyze the collected data.

This study conceptualizes image, motion and touchscreen interactions as the three dimensions of multimodal sensory interaction, which can reflect visual-, kinesthetic- and haptic-based sensation stimulation. The authors' findings show that multimodal sensory interaction of AR activates consumers' intention to purchase via a psychological process. To delineate this psychological process, the authors use feelings-as-information theory to posit that experiential factors can influence cognitive factors. More specifically, multimodal sensory interaction is shown to increase multisensory experience and spatial presence, which can effectively reduce product uncertainty and information overload. The two outcomes have been considered to be key issues in online shopping environments.

This study is one of the first ones that shed light on the multimodal sensory peculiarity of AR interactions in the extant IS literature. The authors further highlight the benefits of AR in addressing major online shopping concerns about product uncertainty and information overload, which are largely overlooked by prior research. This study uses feelings-as-information theory to explain the impacts of AR interactions, which reveal the essential role of the experiential process in sensory-enabling technologies. This study enriches the existing theoretical frameworks that mostly focus on the cognitive process. The authors' findings about AR interactions provide noteworthy guidelines for the design of metaverse environments and extend the authors' understanding of how the metaverse may bring benefits beyond traditional online shopping settings.