Information Systems Frontiers最新文献

This study empirically examines how various digital technologies individually and collectively contribute to supply chain resilience (SCRes) in coping with major disruptions. While there is no shortage in the IS literature to study the use of digital technologies, current trend is prevailed with either the investigation of single technology or treating all deployed technologies as an undifferentiated whole. Therefore, theoretically, we aim to address the lack of conceptual clarity and insights in the use of multiple digital technologies in tandem. To operationalize this research, we deploy and build on the Resource-Action-Performance (RAP) perspective, and categorize widely used digital technologies into two, namely, resource-oriented and action-oriented technologies. We then empirically investigate how these two categories of digital technologies are orchestrated to help maintain and improve SCRes for manufacturing firms during major disruptions, in this case, Covid-19. The results show a positive effect of resource-oriented technologies on SCRes, which is mediated by action-oriented technologies. Our contributions to the IS literature are threefold. First, our study provides important theoretical insights by analytically separating and categorizing different digital technologies as a means to enrich our understanding in the use of multiple technologies in tandem. Second, we offer empirical evidence to elaborate different functions and values that different digital technologies individually and collectively contributed to firm’s capability in SCRes and its ability to cope with major disruptions. Third, our study provides practical insights into utilizing digital tools to maintain business continuity in uncertain environments.

Research on technology adoption has focused on individual, organizational, and institutional factors, yet adoption within households in developing countries like India remains underexplored. To address this gap, we utilized a large-scale national household survey and a machine learning-based inductive approach to uncover the complex relationship between intra-household dynamics and computer adoption. Our study identified household education externalities, the education level of women in the family, and the presence of teenage children as key factors influencing computer adoption. Our decision tree analysis revealed the intricate combinations of predictors that impact adoption, offering nuanced explanations of this complex phenomenon. Our findings can inform the development of customer-oriented marketing strategies and customized intervention programs that address cost, access, and education inequalities hindering household computer adoption, benefiting computer makers and government policymakers.

This study explores the dynamics of customer loyalty in ride-hailing aggregation platforms, a burgeoning segment of the sharing economy that consolidates multiple service providers into a unified interface. Leveraging perceived value theory, we analyze the “double-edged sword” effect of same-side network externalities—how perceived customer network size impacts customer loyalty in a non-linear, inverted U-shaped pattern. Data from 543 aggregation platform users reveal that moderate levels of perceived customer network size optimize loyalty, balancing the positive effects of increased utility and trust with the negative impacts of network congestion. Moreover, perceived value mediates this curvilinear relationship, while perceived driver network size—a measure of cross-side network externalities—intensifies its curvature, highlighting the critical role of interdependencies between platform participants. Our arguments and results contribute to the literature on customer loyalty on platforms, and can inform the strategic decisions of platforms that rely on same side network externalities for value creation.

Organizations continue to struggle with motivating employee compliance with information systems (IS) security protocols despite implementing technical and managerial strategies. This study examines the critical role of leadership behaviors in fostering security compliance among employees. We assert that effective leadership can significantly influence adherence to IS security controls. Grounded in expectancy theory, our research model investigates how task-oriented and relationship-oriented leadership behaviors impact perceived security efforts and performance, ultimately shaping expected security outcomes. Data were collected from 407 participants through a cross-sectional survey, and structural equation modeling was employed for analysis. The findings reveal that task-oriented leadership is particularly effective in motivating IS security compliance. Furthermore, individual-level analyses highlight task-oriented leadership as the sole behavior demonstrating a direct relationship with the expected security outcomes of compliance. These insights enrich our understanding of security compliance behaviors as primarily driven by extrinsic motivation. We encourage future research to explore the role of intrinsic motivators and the potential indirect effects of relationship-oriented leadership behaviors on IS security policy compliance. By illuminating the behavioral dynamics of leadership, this study paves the way for organizations to enhance their IS security programs and cultivate a culture of compliance.

The relationship between humans and artificial intelligence has sparked considerable debate and polarized opinions. A significant area of focus in this discourse that has garnered research attention is the potential for humans and AI to augment one another in order to enhance outcomes. Despite the increasing interest in this subject, the existing research is currently fragmented and dispersed across various management disciplines, making it challenging for researchers and practitioners to build upon and benefit from a cohesive body of knowledge. This study offers an organized literature review to synthesize the current literature and research findings, thereby establishing a foundation for future inquiries. It identifies three emerging themes related to the nature, impacts, and challenges of Human-AI augmentation, further delineating them into several associated topics. The study presents the research findings related to each theme and topic before proposing future research agenda and questions.

With the evolving realm of news propagation and the surge in social media usage, detecting and combatting fake news has become an increasingly important issue. Currently, fake news detection employs three main feature categories: news text, social context, and news images. However, most studies emphasize just one, while only a limited number incorporate image features. This study presents an innovative hybrid fake news detection model amalgamating text mining technology to extract news text features, user information on Twitter to extract social context features, and VGG19 model to extract news image features to increase the model's accuracy. We harness four diverse machine learning algorithms (Logistic Regression, Random Forest, Support Vector Machine, and Extreme Gradient Boosting) to construct models and evaluate their performance via Precision, Recall, F1-Score, and Accuracy metrics. Results indicate the fusion of news text, social context, and image features outperforms their individual application, yielding a noteworthy 92.5% overall accuracy. Significantly, social context attributes, encompassing users, publishers, and distribution networks, contribute crucial insights into detecting early-stage fake news dissemination. Consequently, our study bolsters fact-checking entities by furnishing them with news-content insights for verification and equips social media platforms with a potent fake news detection model—comprising news content, imagery, and user-centric social context data—to discern erroneous information.

Social networks are highly effective in mobilizing individuals toward collective action. However, such endeavors to rally large groups risk escalating into violent actions, eroding social unity and provoking societal unrest. The information consumption patterns vary considerably across different platforms through which it is disseminated, with platform-specific characteristics significantly impacting how users interact and disseminate information. Nevertheless, this distinctive characteristic has received minimal attention in the literature. Considering these differences, we seek to understand the mechanisms underlying information dissemination on social networks in the context of mob violence and protests. We conducted a comparative analysis of communication on the Reddit and YouTube networks about the riots in France in 2023. By utilizing social network analysis, we clarify the differences in information dissemination across these networks and contribute to information systems literature on social media actions with theoretical and methodological insights into the dissemination of information about riots.

The continuing, explosive developments in generative artificial intelligence (GenAI), built on large language models and related algorithms, has led to much excitement and speculation about the potential impact of this new technology. Claims include artificial intelligence (AI) being poised to revolutionize business and society and dramatically change personal life. However, it is not clear how this technology, with its significantly distinct features from past AI technologies, has transformative potential or how researchers in information systems should react to it. In this paper, we consider the evolving and emerging trends of AI in order to examine its present and predict its future impacts. Many existing papers on GenAI are either too technical for most information systems researchers or lack the depth needed to appreciate the potential impacts of GenAI. We, therefore, attempt to bridge the technical and organizational communities of GenAI from a system-oriented sociotechnical perspective. Specifically, we explore the unique features of GenAI, which are rooted in the continued change from symbolism to connectionism, and the deep systemic and inherent properties of human-AI ecosystems. We retrace the evolution of AI that proceeded the level of adoption, adaption, and use found today, in order to propose future research on various impacts of GenAI in both business and society within the context of information systems research. Our efforts are intended to contribute to the creation of a well-structured research agenda in the information systems community to support innovative strategies and operations enabled by this new wave of AI.

We propose a method for the dynamic evaluation of the output provided by any Real Time Object Detection Algorithm. This work focuses on single object detection from video streams and the main objective is the enhancement of the process with regard to its so-called trustworthiness based on the spatial consideration of the sequence of video frames that are fed as inputs on a Convolutional Neural Network (CNN). To this end, we propose a method that systematically tests the differences between the consecutive values returned by the employed neural network. The process identifies patterns that flag potential false positive predictions based on classic similarity metrics and evaluates the quality of the CNN results in a methodologically agnostic fashion. An extended computational illustration demonstrates the effectiveness and the potentials of the proposed approach.

The paper posits that in the increasingly connected digital landscape, there is a growing need to examine the scale and scope of responsible cybersecurity. In an exploratory study that involved qualitative interviews with senior cybersecurity professionals, we identify different layers of responsible cybersecurity that span across techno-centric, human-centric, organizational (intra and inter) and societal centric perspectives. We present these in an onion-shaped framework and show that collectively these diverse perspectives highlight the linked responsibilities of different stakeholders both within and beyond the organization. The study also finds that senior leadership plays a crucial role in fostering responsible cybersecurity across the different layers. Implications for research and practice are discussed.