The emergence of AI agents and agentic systems represents a significant milestone in artificial intelligence, enabling autonomous systems to operate, learn, and collaborate in complex environments with minimal human intervention. This paper, drawing on multi-expert perspectives, examines the potential of AI agents and agentic systems to reshape industries by decentralizing decision-making, redefining organizational structures, and enhancing cross-functional collaboration. Specific applications include healthcare systems capable of creating adaptive treatment plans, supply chain agents that predict and address disruptions in real-time, and business process automation that reallocates tasks from humans to AI, improving efficiency and innovation. However, the integration of these systems raises critical challenges, including issues of attribution and shared accountability in decision-making, compatibility with legacy systems, and addressing biases in AI-driven processes. The paper concludes that while agentic systems hold immense promise, robust governance frameworks, cross-industry collaboration, and interdisciplinary research into ethical design are essential. Future research should explore adaptive workforce reskilling strategies, transparent accountability mechanisms, and energy-efficient deployment models to ensure ethical and scalable implementation.

Live streaming on social media has evolved into live streaming commerce (LSC), a subset of electronic commerce that merges real-time social interaction with digital marketing.LSC is increasingly used for enhancing customer engagement, product promotion, transaction facilitation, and improving online shopping experiences.However, LSC, as a field of study, lacks a comprehensive systematic review, highlighting a gap in understanding its development and prospective trajectories.This article aims to fill this gap by conducting a comprehensive systematic review of existing LSC studies and charting a course for its future inquiry.Utilizing the SPAR-4-SLR protocol and the TCCM framework, this study reviews relevant LSC studies from Scopus and Web of Science to reveal the key theories, contexts, characteristics, methods, and promising avenues for understanding and enriching LSC.

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).

During the COVID-19 pandemic, remote work became a prevalent practice for organizations, raising concerns about counterproductive behaviors like phubbing and cyberloafing. This research investigates the dynamics of these behaviors among employees working from home, focusing on social networking needs (SNN), perceptions of others' online behavior, and the pandemic's influence. Data from 222 remote employees were analyzed using structural equation modelling, revealing positive relationships between SNN, perceptions of others' online behavior, pandemic influence, and phubbing. Moreover, the study reveals the moderating effect of WFH on these relationships. These findings highlight the importance for HR practitioners and policymakers to understand and address phubbing and cyberloafing behaviors in remote work settings. This study fills critical research gaps, offering insights to mitigate the negative impacts of remote work policies, thereby enhancing organizational effectiveness and employee well-being. The chapter, therefore, aims to examine whether remote work can succeed amid rising phubbing and cyberloafing. Through analysis, it offers insights for HR and policymakers to manage these challenges effectively.

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.

Fake news has led to a polarized society as evidenced by diametrically opposed perceptions of and reactions to global events such as the Coronavirus Disease 2019 (COVID-19) pandemic and presidential campaigns. Popular press has linked individuals’ political beliefs and cultural values to the extent to which they believe in false content shared on social networking sites (SNS). However, sweeping generalizations run the risk of helping exacerbate divisiveness in already polarized societies. This study examines the effects of individuals’ political beliefs and espoused cultural values on fake news believability using a repeated-measures design (that exposes individuals to a variety of fake news scenarios). Results from online questionnaire-based survey data collected from participants in the US and India help confirm that conservative individuals tend to exhibit increasing fake news believability and show that collectivists tend to do the same. This study advances knowledge on characteristics that make individuals more susceptible to lending credence to fake news. In addition, this study explores the influence exerted by control variables (i.e., age, sex, and Internet usage). Findings are used to provide implications for theory as well as actionable insights.

The term metaverse is described as the next iteration of the Internet. Metaverse is a virtual platform that uses extended reality technologies, i.e. augmented reality, virtual reality, mixed reality, 3D graphics, and other emerging technologies to allow real-time interactions and experiences in ways that are not possible in the physical world. Companies have begun to notice the impact of the metaverse and how it may help maximize profits. The purpose of this paper is to offer perspectives on several important areas, i.e. marketing, tourism, manufacturing, operations management, education, the retailing industry, banking services, healthcare, and human resource management that are likely to be impacted by the adoption and use of a metaverse. Each includes an overview, opportunities, challenges, and a potential research agenda.

Although mobile payment services are unlikely to become obsolete, their market dominance has been challenged following the intensified market competition. A research model is developed to uncover the drivers of users’ hedonic and utilitarian motivations (i.e., application, marketing, and internal stimuli) as the key to fostering satisfaction and commitment toward mobile payment services. Data were gathered from a survey and analyzed via the dual-stage Structural Equation Modeling-Artificial Neural Network (SEM-ANN) technique to capture both linear and non-linear relationships. The findings suggested that information value, monetary value, self-congruence, and reward and recognition drive hedonic and/or utilitarian motivations, which in turn promote users’ satisfaction and commitment toward mobile payment services. The ANN analysis reveals the existence of a non-linear hidden attribute. The research provides theoretical insights and practical implications for the field of mobile payment services.

Discontinuance intention has been reported as a major concern in the context of manufacturing SMEs. This study examines why user SMEs decide to discontinue the incumbent technology. The study employed the theoretical lens of diffusion of innovation theory, disenchantment-enchantment model, and technology-organization-environment model to develop an integrated Expectation-Reality gap (E-R gap) framework. The research model was examined using data gathered through a cross-sectional survey of 300 SMEs in the Indian manufacturing industry. We found E-R gap in compatibility, technology cost, government support, and ease of use are key reasons for the post-adoption disenchantment of SMEs. Further, we found that disenchanted SMEs’ discontinuance intentions are quite strong, and they are more likely to quit from adopted technology shortly. Information systems (IS) researchers can extend the E-R gap model to assess the phenomena of discontinuance intentions in other NIE contexts. Implications for research and practice are discussed.

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.

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.

This study identifies the determinants which impact the intention to use the Internet of Things (IoT) enabled devices by the aged consumers of India for household purposes. With the help of the Model of Adoption of Technology in Households (MATH), a conceptual model has been developed. The model has been extended by considering two moderators (age and gender). It has been ascertained that the explanative power of the model after consideration of two moderators has been enhanced. Ten constructs of MATH have been considered which were found relevant to this study. The validation of the greater explainability by including two moderators in this modified MATH is considered as a theoretical contribution of this study. This study provides insight to the policy makers to understand how the aged consumers of India can be motivated to use IoT-enabled devices for their household purposes that would eventually improve their quality of life.

Blended learning is widely adopted by organizations where a blend of online component complements the traditional face-to-face learning. Despite its popularity, research on developing a model for blended learning effectiveness is limited. This paper develops and validates a research model for blended learning effectiveness by investigating the role of engagement on perceived learning effectiveness. Further, the role of internet self-efficacy (personal factor) and interactions (environmental factors) on various dimensions of engagements are examined through the lens of social cognitive theory. A total of 246 postgraduate Indian students participated in a Management Information Systems course. Structural equation modeling is used to validate the research model empirically. It is found that the internet self-efficacy and the interaction factors are positively related to the engagement dimensions, which further positively affects the perceived learning effectiveness. Moreover, perceived learning effectiveness is positively related to student scores. Theoretical and practical implications are discussed.

Despite the uniqueness of audiobook applications, their adoption remains a big challenge. The present study integrates the decomposed theory of planned behavior (DTPB) with switching cost theory (SCT) to examine the antecedents of consumer’s intention toward adopting audiobook applications. Structural equation modeling (SEM) was used to analyze the data collected from 720 respondents over two stages, and the results confirm that perceived ease of use, perceived usefulness, procedural switching cost, self-efficacy, are important in shaping attitude and adoption intentions for audiobook applications. The effects are non-significant for financial switching costs and facilitating conditions. Book genre is found to have significant moderating effects. The findings add to the existing adoption works by integrating two unique theories in a novel context of audiobooks, as well as offers practical guidance to audiobook application developers to enhance its adoption.

Present research proposes a framework that integrates aspect-level sentiment analysis with multi-criteria decision making (TOPSIS) and control charts to uncover hidden quality patterns. While sentiment analysis quantifies consumer opinions corresponding to various product features, TOPSIS uses the sentiment scores to rank manufacturers based on their relative performance. Finally, U and P control charts assist in discovering the weak aspects and corresponding attributes. To extract aspect-level sentiments from reviews, we developed the ontology of passenger cars and designed a heuristic that connects the opinion-bearing texts to the exact automobile attribute. The proposed framework was applied to a review dataset collected from a well-known car portal in India. Considering five manufacturers from the mid-size car segment, we identified the weakest and discovered the aspects and attributes responsible for its perceived weakness.

Research on AI has gained momentum in recent years. Many scholars and practitioners increasingly highlight the dark sides of AI, particularly related to algorithm bias. This study elucidates situations in which AI-enabled analytics systems make biased decisions against customers based on gender, race, religion, age, nationality or socioeconomic status. Based on a systematic literature review, this research proposes two approaches (i.e., a priori and post-hoc) to overcome such biases in customer management. As part of a priori approach, the findings suggest scientific, application, stakeholder and assurance consistencies. With regard to the post-hoc approach, the findings recommend six steps: bias identification, review of extant findings, selection of the right variables, responsible and ethical model development, data analysis and action on insights. Overall, this study contributes to the ethical and responsible use of AI applications.

Although the e-reader marketplace has become increasingly significant in recent years, the current lack of understanding of factors that drive consumers' intention to use e-readers suggests there is much to be learned in this area. This study develops and tests a variation on the Technology Acceptance Model in order to investigate factors influencing consumers' intention to use e-readers. A survey of 234 consumers revealed that perceived usefulness and subjective norm positively influence intention to use, whereas there was little evidence to support the significance of image, cost, or ease of use. Findings have both theoretical and practical implications. Results provide empirical support for some existing TAM relationships, and a basis from which to examine additional factors in order to further develop knowledge affecting intention to use e-readers. Confirmation of the influence of perceived usefulness and subjective norm has implications for practitioners, particularly in terms of product design and marketing.

This paper profiles the types of research activity that have been published in EJIS from 1997 to 2007. Our analysis includes variables such as the most productive authors, citation analysis, universities associated with the most research publications, geographic diversity, authors’ background, subject areas most often investigated, unit of analysis and research methodologies. The classification of the topics and methodologies used by the most highly published authors will help prospective authors gauge whether their paper is suitable for EJIS. The major geographical source of information system (IS) research published in EJIS is from AIS region 2 (Europe, the Middle East and Africa), but with a substantial AIS region 1 (American-based researchers and universities) and AIS region 3 (Asia–Pacific) contribution. The most common research method used is the case study approach, with other methods such as surveys and library research also used frequently. IS management and IS development are the two most researched IS topics published in EJIS. This research and results reported in this paper are comparable with a previous paper published about the Information Systems Journal. Any further such studies will thus be able to make similar comparisons between these journals and any others that have subsequently been covered in this way. The paper concludes with the need for more substantive research on the topic if journal comparisons are to achieve their potential.