1,548 research outputs found

    ARTIFICIAL INTELLIGENCE-AUGMENTED DECISION MAKING IN SUPPLY CHAIN MONITORING: AN ACTION DESIGN RESEARCH STUDY

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    Organizations are progressively adopting hybrid human–AI systems in decision-making processes in which human decisions are augmented by AI insights. Among the promising AI applications in supply chain monitoring (SCMo) are predictive maintenance systems that predict device failures and augment maintenance decisions, allowing for timely interventions. Despite the growing use of such systems, prescriptive knowledge encompassing technical, business, social, and organizational aspects on how to design, develop, and deploy them in real operational environments remain obscure. To address this shortcoming, our action design research study designed and deployed a predictive maintenance system that predicts the failure of SCMo devices and augments the maintenance decisions of those devices. By doing so, we outline our learnings as generalizable design principles that may guide prospective predictive maintenance systems in SCMo

    The Secret to Successful User Communities: An Analysis of Computer Associates’ User Groups

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    This paper provides the first large scale study that examines the impact of both individual- and group-specific factors on the benefits users obtain from their user communities. By empirically analysing 924 survey responses from individuals in 161 Computer Associates' user groups, this paper aims to identify the determinants of successful user communities. To measure success, the amount of time individual members save through having access to their user networks is used. As firms can significantly profit from successful user communities, this study proposes four key implications of the empirical results for the management of user communities

    A framework to capture and share knowledge using storytelling and video sharing in global product development

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    In global engineering enterprises, information and knowledge sharing are critical factors that can determine a project's success. This statement is widely acknowledged in published literature. However, according to some academics, tacit knowledge is derived from a person’s lifetime of experience, practice, perception and learning, which makes it hard to capture and document in order to be shared. This project investigates if social media tools can be used to improve and enable tacit knowledge sharing within a global engineering enterprise. This paper first provides a brief background of the subject area, followed by an explanation of the industrial investigation, from which the proposed knowledge framework to improve tacit knowledge sharing is presented. This project’s main focus is on the improvement of collaboration and knowledge sharing amongst product development engineers in order to improve the whole product development cycle

    Thinking together: What makes Communities of Practice work?

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    In this article, we develop the founding elements of the concept of Communities of Practice by elaborating on the learning processes happening at the heart of such communities. In particular, we provide a consistent perspective on the notions of knowledge, knowing and knowledge sharing that is compatible with the essence of this concept – that learning entails an investment of identity and a social formation of a person. We do so by drawing richly from the work of Michael Polanyi and his conception of personal knowledge, and thereby we clarify the scope of Communities of Practice and offer a number of new insights into how to make such social structures perform well in professional settings. The conceptual discussion is substantiated by findings of a qualitative empirical study in the UK National Health Service. As a result, the process of ‘thinking together’ is conceptualized as a key part of meaningful Communities of Practice where people mutually guide each other through their understandings of the same problems in their area of mutual interest, and this way indirectly share tacit knowledge. The collaborative learning process of ‘thinking together’, we argue, is what essentially brings Communities of Practice to life and not the other way round

    Social Interactions vs Revisions, What is important for Promotion in Wikipedia?

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    In epistemic community, people are said to be selected on their knowledge contribution to the project (articles, codes, etc.) However, the socialization process is an important factor for inclusion, sustainability as a contributor, and promotion. Finally, what does matter to be promoted? being a good contributor? being a good animator? knowing the boss? We explore this question looking at the process of election for administrator in the English Wikipedia community. We modeled the candidates according to their revisions and/or social attributes. These attributes are used to construct a predictive model of promotion success, based on the candidates's past behavior, computed thanks to a random forest algorithm. Our model combining knowledge contribution variables and social networking variables successfully explain 78% of the results which is better than the former models. It also helps to refine the criterion for election. If the number of knowledge contributions is the most important element, social interactions come close second to explain the election. But being connected with the future peers (the admins) can make the difference between success and failure, making this epistemic community a very social community too
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