Design and Appropriation of Knowledge and AI Systems

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    The Responsible Adoption of (Highly) Automated Decision-Making Systems
    ( 2021-01-05) Grange, Camille ; Pinsonneault, Alain
    The next-generation technological era will be marked by the prevalence of highly automated decision-making systems (ADMS), which promote technological autonomy at the expense of human agency. In this paper, we examine the role and importance of socio-ethical factors in the responsible adoption of ADMS by organizations. In doing so, we draw on the unique characteristics of ADMS and leverage the literature on social responsibility to conceptualize what a responsible adoption process and a responsible adoption decision involve. The resulting framework makes a much-needed connection between technology adoption and social responsibility and offers a progressive foundation to study ADMS adoption.
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    Knowledge Identity (KI): A New Approach to Integrating Knowledge Management into Enterprise Systems
    ( 2021-01-05) Intezari, Ali ; Namvar, Morteza ; Taghinejad, Ramin
    Despite the extensive studies about KM over the past four decades, the discipline still lacks a clear and practically comprehensive understanding of how KM can be integrated into enterprise systems. To a high degree, the issue is associated with the ambiguous assumptions taken by organizations about knowledge. Many of the assumptions of information systems theories about knowledge require revision, particularly how knowledge is managed. Conceptualizing knowledge as processed data and information has led contemporary design and implementation of enterprise systems to fail to capture the complexity of knowledge. In this article, we critically examine these views. We argue that the answer to the question as to how and to what extent enterprise systems can support KM, depends on the assumptions that organizations take towards the nature and sources of knowledge. To address this question, we introduce the concept of Knowledge Identity (KI) and a model of Enterprise Knowledge Integration.
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    Data-driven Applications to Foster Absorptive Capacity: A Literature-based Conceptualization
    ( 2021-01-05) Hofmann, Peter ; Stähle, Philipp ; Buck, Christoph ; Thorwarth, Harald
    The relevance of data-driven applications for leveraging knowledge embedded in data is growing. Thereby, an organization’s capability to create, disseminate, and exploit knowledge (i.e., absorptive capacity) is a decisive factor in gaining competitive advantages. In this paper, we address the lack of guidance on the development and application of data-driven applications fostering an organization’s absorptive capacity. Based on a structured literature review, we derive seven data-driven application capabilities and match them with an established conceptualization of absorptive capacity. While previous literature did not allow for a specific analysis, our functional representation concretely demonstrates how data-driven applications composed of separate capabilities can foster absorptive capacity in manifold ways. This paper contributes to the literature by providing a structured literature review on the impact of IT on absorptive capacity as well as introducing theoretically-based and modularized data-driven application capabilities.
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    Introduction to the Minitrack on Design and Appropriation of Knowledge and AI Systems
    ( 2021-01-05) Hadaya, Pierre ; Holford, W. David ; Smolnik, Stefan