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    61455 research outputs found

    Evolving Epistemic Infrastructure: The Role of Scientific Journals in the Age of Generative AI

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    Scientific journals, crucial components of our epistemic infrastructure, have continuously adapted to the changing technological landscape. Today, we stand at the precipice of a transformative phase brought about by generative AI, specifically large language models such as OpenAI’s GPT and Google’s Bard. In this opinion piece, I examine the implications of these models for the future of scientific journals and various stakeholders in the scientific community, including journals, scholars, and universities. To envisage the future trajectory of scientific journals, it’s imperative to comprehend the operational mechanisms of these models and the fundamentally recombinatorial nature of human knowledge creation. I suggest that one of the significant roles generative AI can play is facilitating “long jumps” in our knowledge exploration process. I further propose decentralization and deferred and temporary binding as two crucial characteristics of the evolving epistemic infrastructure that supports precarious knowledge production. I foresee a future where scientific journals extend beyond their traditional gatekeeping roles. I call for scholars—as authors, reviewers, and mentors—to utilize these technologies to traverse the broad landscape of potential knowledge, fostering a more inclusive and dynamic scientific ecosystem

    Older Adults’ Consumption of Fake News – An Interoceptive Perspective

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    In an era dominated by social media, the spread of fake news and disinformation presents a distinct peril for those aged 50 and above, who are active and more likely to share it on platforms like Twitter and Facebook. This misinformation could jeopardize the mental and physical well-being of those older adults who are most likely to share health-related fake news. While cognitive decline has traditionally been blamed for older adults\u27 vulnerability to fake news, recent research underscores the role of accumulated knowledge, suggesting cognitive deficits alone cannot fully explain their susceptibility. This research investigates how emotional appeals contained in fake news influence older adults through socio-emotional processing, particularly as older individuals increasingly rely on surface-level analytical reasoning. As such, we may be in a better position to understand how these factors ultimately affect older adults consumption behavior of health-related information

    Machine Learning System Development in Information Systems Development Praxis

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    Advancements in hardware and software have propelled machine learning (ML) solutions to become vital components of numerous information systems. This calls for research on the integration and evaluation of ML development practices within software companies. To investigate these issues, we conducted expert interviews with software and ML professionals. We structured the interviews around information systems development (ISD) models, which serve as conceptual frameworks that guide stakeholders throughout software projects. Using practice theory, we analyzed how software professionals perceive ML development within the context of ISD models and identified themes that characterize the transformative impact of ML development on these conceptual models. Our findings show that developer-driven conceptual models, such as DevOps and MLOps, have been embraced as common frameworks for developers and management to understand and guide the ML development processes. We observed ongoing shifts in predefined developer roles, wherein developers are increasingly adopting ML techniques and tools in their professional work. Overall, our findings underscore that ML technologies are becoming increasingly prominent in software projects across industries, and that the incorporation of ML development in ISD models is an ongoing, largely practice-driven, process

    AI Management Beyond Myth and Hype: A Systematic Review and Synthesis of the Literature

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    Background: AI management has attracted increasing interest from researchers rooted in many disciplines, including information systems, strategy, and economics. In recent years, scholars with interests in these diverse fields have formulated similar research questions, investigated similar research contexts, and even often adopted similar methodologies when studying AI. Despite these commonalities, the AI management literature has largely evolved in an isolated fashion within specific fields, thereby impeding the development of cumulative knowledge. Moreover, views of AI’s anticipated trajectory have often oscillated between unjustifiably optimistic assessments of its benefits and extremely pessimistic appraisals of the risks it poses for organizations and society. Method: To move beyond the polarized discussion, this work offers a systematic review of the vast, interdisciplinary AI management literature, based on analysis of a large sample of articles published between 2010 and 2022. Results: We identify four main research streams in the AI management literature and associated, conflicting discussion, concerning four (data, labor, critical, and value) dimensions. Conclusion: The review conceptually and practically contributes to the IS field by documenting the literature’s evolution and highlighting avenues for future research trajectories. We believe that by outlining four key themes and visualizing them in an organized framework the study promotes a holistic and broader understanding of AI management research as a cross-disciplinary effort, for both researchers and practitioners, and provides suggestions that extend the framing of AI beyond myth and hype

    Protection Motivation Theory in Information Security Behavior Research: Reconsidering the Fundamentals

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    Scholars commonly use protection motivation theory (PMT) by Rogers to examine information systems (IS) security behaviors and behavioral intentions. A recent influential paper by Boss, Galletta, Lowry, Moody, and Polak (2015; hereafter BGLMP) in MIS Quarterly outlines correct and incorrect uses of PMT in Information Security behavior research. In this paper, we review some of BGLMP’s key recommendations, such as the claim that all IS behavior studies that apply PMT should always use the model of the full theory, contain and measure fear, and measure actual behaviors. We defend an interpretation of Rogers (1975, 1983) that differs from the interpretation that BGLMP propose. We present evidence that Rogers’ PMT and the empirical evidence do not adequately support many of BGLMP’s suggestions and that these suggestions contradict good scientific practices (e.g., restricting the use of the method of isolation) that the philosophy of science and the original literature on PMT uphold. As a result, if reviewers and editors continue to embrace these recommendations, they could hinder the progress of IS behavior research by not allowing isolation or the combination of different theoretical components. In contrast to BGLMP’s paper, we argue that further PMT research can focus on isolated PMT components and combine them with other theories. Some of our ideas (e.g., isolation) are not PMT-specific and could be useful for IS research in general. In summary, we contest BGLMP’s recommendations and offer revised recommendations in return

    Business Process Performance - Investigating the Impact of Process-Oriented Appraisals and Rewards on Success

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    Considering humans are involved in business process activities, process-oriented appraisals and rewards (POAR) can help stimulate process outcomes. Given a lack of knowledge about the intersection between business process management (BPM) and human resource management (HRM), the authors delve into POAR. The study starts from the theoretical capabilities of BPM and then follows a mixed-method design to develop rich and substantive evidence for successful POAR implementations. Empirical data was collected by ten case organizations experienced in POAR, and a survey with 403 higher-level managers across four continents. From the case data, diverse perspectives have emerged on the supporting capabilities for POAR and especially their interrelationships. Additionally, statistical evidence shows a decisive role of POAR in affecting process performance. While all BPM-specific capabilities seem to matter for POAR, only some also contribute to process performance through POAR. Novelty in the work resides in producing a POAR-based process performance model

    Generative AI

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    Grappling with Online Grocery Shopping: An Age-Related study

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    In both an increasingly digital and aging society, natural age-related cognitive changes take place, which may negatively affect performance when using increasingly complex digital interfaces. Obtaining daily needs such as groceries becomes more difficult with age and shopping for groceries online presents a challenge to many older adults. The purpose of this study is to understand how and in what ways age affects online grocery shopping performance. 32 participants were recruited for this study consisting of 17 younger adults and 15 older adults. Participants were presented with sets of tasks which required them to mentally calculate the quantity of food they can purchase within a given budget. Eye tracking and survey methods were used during the study. Our results show that age negatively impacts cognitive load. Cognitive load was found to negatively impact performance in online grocery shopping tasks. Self-efficacy showed to have a mild moderating effect on said relationship

    Human-in-the-Loop AI Reviewing: Feasibility, Opportunities, and Risks

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    The promise of AI for academic work is bewitching and easy to envisage, but the risks involved are often hard to detect and usually not readily exposed. In this opinion piece, we explore the feasibility, opportunities, and risks of using large language models (LLMs) for reviewing academic submissions, while keeping the human in the loop. We experiment with GPT-4 in the role of a reviewer to demonstrate the opportunities and the risks we experience and ways to mitigate them. The reviews are structured according to a conference review form with the dual purpose of evaluating submissions for editorial decisions and providing authors with constructive feedback according to predefined criteria, which include contribution, soundness, and presentation. We demonstrate feasibility by evaluating and comparing LLM reviews with human reviews, concluding that current AI-augmented reviewing is sufficiently accurate to alleviate the burden of reviewing but not completely and not for all cases. We then enumerate the opportunities of AI-augmented reviewing and present open questions. Next, we identify the risks of AI-augmented reviewing, highlighting bias, value misalignment, and misuse. We conclude with recommendations for managing these risks

    Extending the Foresight of Phillip Ein-Dor: Causal Knowledge Analytics

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    Phillip Ein-Dor advocated that electronic journals be more than a PDF of the established text model. He envisioned a transformation of scholarship. The need for such a transition has only grown since the first issue of JAIS in 2000 because the continuing growth and fragmentation of knowledge limits the generation of new knowledge. We propose drawing on analytics and AI to accelerate and transform scholarship, providing an appropriate tribute to a visionary IS leader


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