Information Systems Frontiers最新文献

Shared services using digital platforms have increasingly gained prominence in recent times. Existing studies have studied several facets of ride-sharing services, but mobile app technology’s impact on user’s experience has not been explored meticulously. We attempt to study the technological artifacts which can signal about the capability of the service and thereby, reducing the informational asymmetry, stemming from lack of information and in-person communication. To address that, we adopt the Signaling Theory and Value Framework to understand the apps’ features, reflecting the shared mobility service quality to the users. We mine 212,000 and 150,000 user reviews on India’s two most extensively used shared mobility services- OLA and UBER, respectively and identify the factors affecting user experiences. We provide a novel framework by mapping these factors to theoretical lexicons. Multiple regression models show that time resources, monetary resources, perceived information protection, app usage controllability, perceived safety in e-payment mechanism, informational trust-related advantage, and participation in decision making influence the user experience of both the services significantly.

Since the Industrial Revolution, significant technological advancements have revolutionized various manual processes and workflows entrenched for decades. Artificial Intelligence (AI) offers similar transformative potential across diverse industrial and social domains. The rapid pace of change in the AI-driven digital age presents unprecedented opportunities and challenges for sustained progress. Given the potentially profound impact of AI, this study seeks to explore its disruptive effects and challenges within organizational contexts. Drawing on the Social Exchange Theory, this research examines the relationship between psychological contract (PC) fulfillment and organizational commitment, with trust acting as a mediator and AI acceptance as a moderator. Data were collected from the service industry using a time-lagged design. The findings indicate that PC fulfillment positively influences workers’ trust and organizational commitment. Furthermore, AI acceptance attenuates the direct and indirect positive effects of PC fulfillment on job-related outcomes. This study offers valuable insights into building and maintaining trust and fostering a committed workforce amidst the digitalization era. It underscores the importance of fulfilling promissory expectations in fostering trust and commitment. Additionally, it sheds light on the disruptive effects of AI technology on critical job outcomes, emphasizing the societal and industrial implications, the future of work, and avenues for further advancements in AI technology.

The role of information systems (IS) were widely discoursed during the spread of the COVID-19 outbreak. We have focused on developing a decision support systems (DSS) based on a combined prediction model, that can essentially be used at the start of any pandemic. Convalescent plasma therapy is generally applied during the spread of a pandemic as a therapy method that transfuses blood plasma from the people, who have recovered from an illness to treat critical cases. We observe, analyse, and predict the risks associated with the treatment effects of convalescent plasma therapy on COVID-19 patients. Based on the secondary data, we build a prediction model to evaluate and predict the trends in the clinical characteristics and laboratory findings for critically ill patients infected with COVID-19 and treated with convalescent plasma. Here, we use a combined prediction model utilizing three models; the grey prediction model (GM (1, 1)), the residual prediction model (residual GM (1, 1)), and a back propagation artificial neural network (BP-ANN) based residual sign prediction model. Also, a validation of the results of the study has been presented at two levels. On analysis of the results from the prediction model, it is observed that the convalescent plasma therapy can show progressive signs on COVID-19 infected patients. Health practitioners can understand, analyze, and predict the potential risks of convalescent plasma therapy based on the proposed model.

In contemporary society, where chronic stress is increasingly prevalent, this study aims to propose a multi-parametric wearable platform suitable for real-life monitoring and to validate its ability to acquire four physiological signals relevant for the stress response (electrocardiogram, respiration, galvanic skin response, photoplethysmogram). Secondly, it seeks to conduct a statistical analysis on the derived features both to identify the physiological signals necessary for a comprehensive analysis of the stress response and to understand the distinct contribution of each one. The results obtained revealed at least two statistically significant features from each of the physiological signals considered, confirming the importance of a multi-parametric approach for an accurate stress response analysis. Additionally, the proposed statistical hypotheses allowed to determine how each physiological signal contributes differently to characterize various aspects of the stress response. For these reasons, this study could represent a benchmark for future investigations aiming to classify the stress response.

The domain of decision support systems (DSS), focusing on developing various systems to aid decision-making, is receiving increasing attention. The proliferation of the domain necessitates a more comprehensive categorization of DSS research towards a unified representation that can contribute to a shared understanding. We thereby resort to re-examining the DSS research to assimilate, unveil, and re-structure the DSS scholarship. We perform an automated content analysis of the abstracts to investigate the structural commonalities of the DSS articles featuring in the Scopus database and published in the last five decades. Furthermore, we supplement our findings by exploring and classifying the emergent sub-structures. For this, we resort to the scenario classification framework, which draws from information systems, human-computer interaction, and requirements engineering domains, and adapt it in the context of DSS. Our overall results led to a framework for classifying DSS research with four levels: decision environment, DS (decision support) artifact, DS application, and context. We show the framework’s applicability by systematically classifying a sample of publications shortlisted to demonstrate its usefulness.

While the research on online consumer reviews is immense, it has largely focused on products which can also be purchased online. However, the high level of digital engagement of individuals today along with a reported fall in physical store visits indicate that digital content can also affect products available only for purchase offline. This research examines the effects of trust, uncertainty, and topics extracted from online consumer reviews on two outcomes in the India car market, namely the search for online information and sales. The study finds that while uncertainty does not affect sales, and has a negative effect on online information search, trust is positively associated with both. The topics extracted using Latent Dirichlet Allocation from the review corpus fall under the category of experiential or functional. The different topics have direct and mediating impacts on search and sales.

Electronic surveillance/monitoring has become ubiquitous in modern organizations as advanced information technology (IT) expands organizational capacity to track system users’ daily information systems (IS) activities. Although this environmental shift surrounding IS raises an important (though largely unexplored) issue of IS users’ information privacy and subsequent IS behaviors, little is known about cognitive/psychological processes and boundary conditions underlying IS users’ information privacy concerns and behaviors under the context of non-volitional workplace surveillance. Grounded on psychological reactance theory, this paper articulates how and when information privacy concerns under workplace surveillance relate to IS use behaviors (i.e., effective IS use and shadow IT use) via psychological reactance. In addition, it investigates IS procedural fairness, a contextual boundary condition. We tested a research model using two surveys (via online platforms) data collected from a sample of 301 and 302 IS users working under electronic surveillance/monitoring systems in various organizations and industries. Using moderated mediation analyses, the results of the study show that (1) psychological reactance mediates the relationship between IS users’ information privacy concerns and effective IS use and shadow IT use, respectively; and (2) IS procedural fairness acts as a boundary condition for the given mediated relationships such that the negative impacts of information privacy concerns on psychological reactance and IS behaviors are mitigated. Implications for theory and practice are discussed.

Artificial intelligence is driving significant advancements in sustainability across nations. In light of AI’s benefits, recent studies emphasize the need for nations to develop comprehensive AI strategies that prioritize achieving SDG-related objectives. However, AI development is a complex sociotechnical process, with interconnected components influencing a country’s capability in AI. Given AI’s rising strategic significance for nations, we examine how dimensions of a nation’s AI capacity converge to facilitate sustainability advances. Using a cross-country configurational analysis comprising 59 nations, we identify six necessary conditions and three distinct configurations that influence a nation’s overall progress toward achieving the SDGs. The results underscore the relatively prominent role of access infrastructure and an operating environment underpinned by AI regulations in contributing to SDG progress. The comparatively lower significance of commercial ventures and AI talent highlights that sustainability efforts of ventures such as AI start-ups must balance social and environmental aspects with economic goals, while AI talent, in conjunction with other factors such as foundational platforms, can help drive innovative AI projects to advance SDG goals. A supplementary analysis helps discern how economic development and urbanization interact with other dimensions of a nation’s AI capacity to advance sustainability endeavors. The study renews focus on sustainability in IS research and contributes to the scholarly discourse on Responsible AI and Green IS. The results also nudge AI practitioners and policymakers to prioritize their efforts in advancing progress toward SDGs, specifically exploring AI’s potential to enhance and expedite these efforts.

Considering the complexity of compliance environment, only focusing on control-oriented actions through compulsory or disciplinary measures, as many firms did in the past, does not help firms tackle imminent challenges. Applying adaptive structuration theory to corporate compliance management, this study focused on identifying antecedents of compliance support system appropriation and investigating their impacts on compliance performance. As a result of surveying 192 multi-business firms in Korea, this study found that regulation environment and compliance championship as meta-structuring contexts shape compliance culture as an institutional structure and require key features of compliance support system (CSS) as a structure to be appropriated by users, which ultimately enhances the compliance performance of the firm. The findings contribute to making the previous adaptive structuration theory more applicable and providing useful directions to improve compliance performance for corporations.

Online sharing platforms offer countless choices and detailed product descriptions to consumers. In this study, we demonstrate the effect of choice and information overload on booking decisions using large-scale field data from Airbnb and observe an inverse U-shaped association. Furthermore, our results show that providing quality assurance of the product exacerbates the choice and information overload relationship. As a post-hoc analysis, we perform topic modeling to gain better insights into how product information influences booking decisions. Specifically, the post-hoc analyses show that the number of topics in the description has a positive association with the number of bookings. Furthermore, topic count moderates the information overload effect by intensifying the influence of product description on the number of bookings. Our findings have important implications for online sharing platforms, service providers, and travelers as they shed light on the detrimental effects of excessive variety and information on booking decisions.