Online purchase experience is considered an a pivotal antecedent to customer satisfaction. This study explores the dimensions of online purchase experience affecting customer satisfaction while investigating the moderating role of negative e-word of mouth (NEWOM) toward customer satisfaction and repurchase intention. Employing a web survey, 453 valid responses were generated from urban consumers with online purchase experience. A PLS-SEM based approach was used to validate the proposed relationships. Dimensions of online purchase experience, such as hedonic experience, service experience, website experience, and trustworthy experience, were found to significantly impact online customer satisfaction. NEWOM was found to significantly and negatively moderate the relationship between online customer satisfaction and repurchase intention. The current study provides novel insights into the concept of the online purchase experience. It proposes a framework that emphasizes the moderating effect of NEWOM on online customer satisfaction and repurchase intention, relying on cognitive dissonance theory.

The study aims to assess the factors that engage and accentuate usage pertaining to consumers with artificial intelligence-based voice assistants. Drawing on the Behavioral Reasoning Theory (BRT), the paper establishes relationships between the enablers and inhibitors on the adoption intention of voice assistants. To examine the hypothesized relationship, a quantitative approach employing PLS-SEM was utilized to evaluate a representative sample of 1189 Indian consumers and examining the psychological and functional constructs leading to adoption intention for voice assistants. The study reveals that the “value of openness to change” poses a significant effect toward “reasons for” adoption, among which “performance expectancy” and “hedonic motivation” play the most significant role in establishing a favorable attitude toward voice assistants’ adoption. Among the “reasons against”, the “value barrier” and “image barrier” come out to be major determinants leading to inhibitions regarding the usage of voice assistants. Besides this, the findings reclaim that “reasons” (for/against) shape to form the attitude of consumers toward voice assistants. The current research offers preliminary evidence with regard to the investigation of voice assistant adoption using the BRT that jointly examines enabling and inhibiting elements leading to consumers’ attitude formation. The study findings contribute to Human-Computer interaction literature by utilizing BRT framework which provides incremental evidence demonstrating a much broader and organic explanation of consumers’ cognition for the adoption of voice assistant technology. It also provides implications for tech-managers and algorithm designers to build effective voice technology for superior user experience. Other Information Published in: Journal of Computer Information Systems License: http://creativecommons.org/licenses/by/4.0/ See article on publisher's website: https://dx.doi.org/10.1080/08874417.2024.2312858

In a short span of time since its introduction, generative artificial intelligence (AI) has garnered much interest at both personal and organizational levels. This is because of its potential to cause drastic and widespread shifts in many aspects of life that are comparable to those of the Internet and smartphones. More specifically, generative AI utilizes machine learning, neural networks, and other techniques to generate new content (e.g. text, images, music) by analyzing patterns and information from the training data. This has enabled generative AI to have a wide range of applications, from creating personalized content to improving business operations. Despite its many benefits, there are also significant concerns about the negative implications of generative AI. In view of this, the current article brings together experts in a variety of fields to expound and provide multi-disciplinary insights on the opportunities, challenges, and research agendas of generative AI in specific industries (i.e. marketing, healthcare, human resource, education, banking, retailing, the workplace, manufacturing, and sustainable IT management).

The emergence of the metaverse has opened an unbelievable online virtual world offering real-life immersive experiences to consumers. This study examines customers’ fashion apparel shopping intentions in the e-commerce metaverse. We propose a conceptual framework based on behavioral reasoning theory (BRT) to investigate the reasons for the adoption (or not) of the metaverse. The survey of 1170 customers was performed, and PLS-SEM was used to analyze data. The findings suggest that perceived enjoyment, interactivity, imagination, and sense of immersion favors the adoption, whereas perceived risk, traditional barrier, image barrier, and technological anxiety deter metaverse adoption. Our results advance the literature on innovative shopping technology and metaverse fashion apparel shopping intention. We provide guidelines for metaverse marketers and managers.

This review examines the emerging field of service robot adoption in hospitality. It provides a comprehensive overview of their evolution, classification, and conceptualization. The review offers a theoretically grounded analysis of customer intentions, identifying and evaluating salient models and frameworks. Through research synthesis, it pinpoints key gaps hindering holistic understanding of customer acceptance, particularly in hotels. The review highlights limitations of traditional acceptance models like TAM, advocating exploration of alternatives for a unified understanding. It underscores the need for robust methodologies to capture real-world interactions, as well as meta-analyses to establish consensus. The review also identifies critical gaps related to anthropomorphism, customer segmentation, and resistance, emphasizing the importance of contextual factors. By addressing these theoretical and methodological limitations, the review provides valuable insights to guide future research on enhancing service robot integration in hospitality.

The study explores the relationship between an organization's survival and its digital knowledge management (DKM) in a rapidly changing digital world. It emphasizes the role of acquiring, sharing, and applying knowledge digitally in organizational adaptation and competitiveness. The research utilizes current literature and theoretical frameworks to explore the importance of systems thinking and organization learning for sustainability purposes. Moreover, it examines how technological uncertainty influences these relationships. The study recruited 479 employees working in manufacturing firms in India and employed partial least squares for data analysis. The findings suggest that when companies seek to maximize resource efficiency while minimizing environmental impact, they must improve their digital knowledge processes. This work aims to provide insights for organizations seeking to benefit from digital technology without endangering global sustainability efforts, and to provide appropriate recommendations.

This research is aimed to understand the intentions of small grocery retailers in adopting social media for marketing purposes. The research used an integrated TAM-TOE framework combined with an SOR framework for understanding factors influencing the intention of small grocery retailers to adopt social media. Mixed methods were used for the study. Initially, in-depth interviews were conducted with 55 retailers to develop a grounded model, which was verified through a survey of 874 retailers. Findings suggest that perceived usefulness and perceived ease of use were positively related to relative advantage, interaction, organizational competency and complexity, bandwagon effect, and competitive pressure. Retailer age and retail outlet also had moderation effects. Small grocery retailers who understand the relative advantage of social media are more likely to adopt it, influenced by the bandwagon effect. Intense competition is also pushing retailers to adopt social media to avoid losing customers and missing out on potential opportunities.

Businesses are increasingly preparing for remote working by equipping managers and their teams with the necessary tools and technology. This research focuses on understanding how technology–enabled remote working practices are implemented and normalized, taking into consideration the context in which they are applied. By utilizing process virtualization theory (PVT) to quantitatively evaluate context-dependent factors and normalization process theory (NPT) to explore the normalization of new technology-driven remote work practices, this study investigates the static and dynamic factors affecting the virtualization of remote work processes, as well as their implementation and normalization. The results from multiple surveys reveal that sensory requirements along with relationship requirements and control requirements are dynamic factors that are driven by the context and enable the technology–enabled virtualization of remote work processes. Furthermore, the relative normalization of this work transformation is sustained through flexible co–construction, legitimation, contextual integration, and effective leadership.

E-commerce retailers use augmented reality (AR) based apps for product presentations. This study investigates the influence of sensory perceptions in an urge to buy impulsively (UBI). This study further examines the mediating role of emotional states in the relationship between sensory perceptions and UBI. It also examines whether involvement moderates the impact of emotional states on the UBI. Data were collected from users who have used augmented reality (AR) features in Lenskart App and purchased products at least once during the last two months. Overall, the sensory perceptions elicited by AR apps significantly influence customers’ emotional states, driving the UBI. The influence of emotional states on the UBI was also considerably moderated by product involvement. Several practical and theoretical implications for e-commerce retailers are also discussed.

This study identifies the critical challenges for blockchain adoption in government, and more specifically in the delivery of public services in the state governments of India. A literature review and focus-group with stakeholders was conducted to identify critical cfhallenges. Each challenge was ranked based on the opinions of stakeholders using conjoint analysis. Regarding government adoption of Blockchain, this study points to ecological shifts as a more significant challenge than technology. This study provides theoretical implications for researchers and valuable insights for practitioners.

The key objective of this research is to propose and examine a conceptual model that best explains the technology adoption and the continuation intention to use digital wallet from the Indian perspective. The extended unified theory of acceptance and use of technology (UTAUT2) and updated IS success model were used as the background theories to propose a research model for this research. Furthermore, perceived security and perceived privacy was added with the combined model. A cross-sectional design was adopted involving data from 629 faculty members of commerce and management disciplines related to universities across India through a non-probabilistic sampling procedure. Two stage structural equation modelling (SEM) approach was adopted to analyse the data. The results show that technology satisfaction was significantly influenced by effort expectancy, performance expectancy, perceived enjoyment, perceived ubiquity, system quality, information quality, service quality, perceived privacy and perceived security, that finally results in the continuation intention to use.

The growing importance of Big Data in the food industry enables businesses to leverage information to gain a competitive advantage. This paper provides a systematic literature review (SLR) to provide an insight into the use of state-of-art of Big Data applications in the food industry. The SLR relies on available literature that provides the context, theoretical construct and identifies gaps. Based on the findings, we suggest recommendations, identify limitations and suggest policy implications and future directions. Using search databases were examined and 38 relevant studies were identified for retrospective analysis. The review shows that Big Data supports the food industry in ways that enable using Artificial Intelligence to manage restaurants and mobile based applications in supporting consumers with restaurant selection. This SLR open new avenues for future research in the importance of Big Data in the food industry, which will surely help researchers/practitioners in effective utilization of Big DataBig Data.

This study examines the intellectual and conceptual structure of the Journal of Computer Information Systems (JCIS) from 1995 to 2021. The evolution of the key topics and the performance of different actors like the key publications, authors, institutions, countries, etc., are reported using a hybrid methodology based upon scientometrics and topic modeling. The latent topics are discovered using structural topic models, and the temporal deviation in the topic prevalences from 1995 to 2021 is visualized. Further, this study reports the most prominent articles, themes, and collaboration patterns using co-citation network analysis, assessment of keywords co-occurrences, and exploration of coauthorship patterns. Finally, the disciplinary influences and knowledge exchange across disciplines are reported. The most significant findings from the study reveal that themes such as “Information Security and Privacy,” “Social Commerce and Social Networking Sites,” “Social Media, Web Search and User Satisfaction,” “Big Data Analytics and Cloud Computing, and “ICT for Economic Development and Empowerment” may become the hotspot for future research. The social exchange of knowledge reveals intra-disciplinarity, where JCIS gets most of the knowledge from the information systems domain itself. However, closest associations with the general business domain, computer science, marketing, organization science, and psychology for knowledge inflows make JCIS a net knowledge receiver.

Indian agricultural value chains are marred with a lack of financial inclusion of smallholder farmers, lengthy payout times, poor quality of products, lack of traceability issues, and lack of industrialization in the value chain. Social entrepreneurs have indispensable tools as information and communication technologies are making inroads in rural developing economies. This study studies the impact of technological capabilities on social entrepreneurship for creating sustainable value creation using survey data from 557 respondents associated with farming and allied activities. The results highlight that digital technology capabilities mediate the relationship between social entrepreneurship and sustainable value creation. The findings present essential insights for academia, practitioners, and policymakers to better shape policies and decisions on social entrepreneurship.

The data-centric revolution generally celebrates the proliferation of business analytics and AI in exploiting firm’s potential and success. However, there is a lack of research on how the unintended consequences of AI integrated business analytics (AI-BA) influence a firm’s overall competitive advantage. In this backdrop, this study aims to identify how factors, such as AI-BA opacity, suboptimal business decisions and perceived risk are responsible for a firm’s operational inefficiency and competitive disadvantage. Drawing on the resource-based view, dynamic capability view, and contingency theory, the proposed research model captures the components and effects of an AI-BA opacity on a firm’s risk environment and negative performance. The data were gathered from 355 operational, mid-level and senior managers from various service sectors across all different size organisations in India. The results indicated that lack of governance, poor data quality, and inefficient training of key employees led to an AI-BA opacity. It then triggers suboptimal business decisions and higher perceived risk resulting in operational inefficiency. The findings show that operational inefficiency significantly contributes to negative sales growth and employees’ dissatisfaction, which result in a competitive disadvantage for a firm. The findings also highlight the significant moderating effect of contingency plan in the nomological chain.