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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.

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 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 recent years, there has been significant interest in adopting XR (extended reality) technologies such as VR (virtual reality) and AR (augmented reality), particularly in retail. However, extending activities through reality-mediation is still mostly believed to offer an inferior experience due to their shortcomings in usability, wearability, graphical fidelity, etc. This study aims to address the research gap by experimentally examining the acceptance of metaverse shopping.

This study conducts a 2 (VR: with vs. without) × 2 (AR: with vs. without) between-subjects laboratory experiment involving 157 participants in simulated daily shopping environments. This study builds a physical brick-and-mortar store at the campus and stocked it with approximately 600 products with accompanying product information and pricing. The XR devices and a 3D laser scanner were used in constructing the three XR shopping conditions.

Results indicate that XR can offer an experience comparable to, or even surpassing, traditional shopping in terms of its instrumental and hedonic aspects, regardless of a slightly reduced perception of usability. AR negatively affected perceived ease of use, while VR significantly increased perceived enjoyment. It is surprising that the lower perceived ease of use appeared to be disconnected from the attitude toward metaverse shopping.

This study provides important experimental evidence on the acceptance of XR shopping, and the finding that low perceived ease of use may not always be detrimental adds to the theory of technology adoption as a whole. Additionally, it provides an important reference point for future randomized controlled studies exploring the effects of technology on adoption.

Online food delivery applications (OFDAs) provide an expedient platform, and consumers’ access to food has been drastically altered, especially during and after the COVID-19 pandemic. This study aimed to completely explore the attributes that influence consumers' purchase intention and how an app's aesthetics can evoke feelings that predict continuous usage intentions for OFDAs. The food industry, especially restaurants, heavily relies on mobile technology to facilitate critical online food delivery during the pandemic crisis.

The data for this study are gathered from 477 food consumers located in the federal capital territory (FCT) of Islamabad, Pakistan, through convenient sampling by developing a self-administrated online survey. SmartPLS is used for structural equation modeling to test the proposed research model and perform bootstrapping and algorithmic analysis.

Our findings revealed that perceived value positively predicted consumers’ purchase intentions. Moreover, perceived value mediates the association of information quality, familiarity, time-saving, usability and reputation with purchase intentions and fear of COVID-19 moderates the relationship between perceived value and purchase intention.

This research work has significant implications for researchers, web developers, app designers, delivery services, restaurants and other enterprises as it demonstrates the importance of aesthetically pleasing OFDAs in eliciting positive emotions and bolstering consumers’ intentions to continue using the app for efficient food delivery services.

This study expanded the application of the technology acceptance model (TAM) and attention, interest, desire and action (AIDA) by examining consumers’ purchase intentions in the context of OFDAs. Further, the successful utilization of TAM enhanced the understanding of consumer perceptions and behavioral intentions about the usage of OFDAs.

With the growth in online purchasing, the return of distressed shipments also increased. The return experience of the online shopper has a huge impact on their next purchase decision-making. This explanatory research aims to identify and empirically explain factors related to the online buyer’s return experience that influence the repurchase intention of young buyers.

Primary data were collected from 235 active online young buyers who have experienced returning the goods through a structured questionnaire. Structural equation modeling is used for analyzing the data.

This study reveals that an online return policy leniency strongly supports service recovery quality, expected return convenience, buyer trust and satisfaction, which lead to repurchase intentions. Moreover, return satisfaction positively impacts repurchase intention while mediating young buyer trust.

This study is one of the few relevant pieces of research that would benefit e-tailers to improve their product return policy and compel young buyers’ intention to make a repeat purchase.

The purpose of this paper is to investigate the factors influencing Palestinian consumer attitudes toward artificial intelligence (AI)-powered online banking, focusing on performance expectancy, effort expectancy, social influence and facilitating conditions while considering the moderating role of trust in financial institutions.

To test the hypotheses, an empirical study with a questionnaire was carried out. The study was completed by 362 Palestinian customers who use online banking services.

The findings of this paper show that performance expectancy, effort expectancy, social influence and facilitating conditions significantly influence consumer attitudes toward AI-powered online banking. Furthermore, trust in financial institutions as a moderating variable strengthens the impact of performance expectancy, effort expectancy, social influence and facilitating conditions on consumer attitudes toward AI-powered online banking. Therefore, more studies should focus on certain fields and cultural contexts to get a more thorough grasp of the variables influencing adoption and acceptability.

The study's findings may be specific to the Palestinian context, limiting generalizability. The reliance on self-reported data and a cross-sectional design may constrain the establishment of causal relationships and the exploration of dynamic attitudes over time. In addition, external factors and technological advancements not captured in the study could influence Palestinian consumer attitudes toward AI-powered online banking.

Financial institutions can leverage the insights from this research to tailor their strategies for promoting AI-powered online banking, emphasizing factors like perceived security and ease of use. Efforts to build and maintain trust in financial institutions are crucial for fostering positive consumer attitudes toward AI technologies. Policymakers can use these findings to inform regulations and initiatives that support the responsible adoption of AI in the financial sector, ensuring a more widespread and effective implementation of these technologies.

This research delves into Palestinian consumer attitudes toward AI-powered online banking, focusing on trust in financial institutions. It aims to enrich literature by exploring this under-explored area with meticulous examination, robust methodology and insightful analysis. The study embarks on a novel journey into uncharted terrain, seeking to unearth unique insights that enrich the existing literature landscape. Its findings offer valuable insights for academia and practitioners, enhancing understanding of AI adoption in Palestine and guiding strategic decisions for financial institutions operating in the region.