Knowledge Flows, Transfer, Sharing, and Exchange
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Item Towards Effective Knowledge Transfer and Trust in the Age of Artificial Intelligence(2025-01-07) Romero-Mariona, Jose; Poirier, James; Le, Evangelyne; Dierickx, MichaelGenerative artificial intelligence (AI) promises to revolutionize education and knowledge transfer. Educational paradigms are shifting as more AI- enabled tools flood the market. Combatting the common risks of hallucinations, outdated knowledge, and untraceable decision making are retrieval- augmented generation (RAG) models’ promises. AI- assisted tools for education in both academia and industry have the potential of utilizing information retrieval methods like RAG for increasing knowledge transfer while increasing trust in the system. The paper describes the “state of the art” in AI progress towards educational assistants and how current trends can help (or hinder) knowledge transfer and ultimately trust.Item Knowledge Management in the Data Analytics System of Organisations in Bhutan(2025-01-07) Namgay, PhubA study through the lens of Routine Dynamics theory was conducted among small- and medium-sized firms in Bhutan to examine knowledge management in their data analytics system. In such an economic setting, firms perform data analytics using in-house systems together with cloud computing to transform data into knowledge, mainly for predictive insights and competitive advantage. They use just-in-time (JIT) data analytics for knowledge on demand as a pragmatic approach, thus minimising uneconomical analytics. Firms also employ an in-house platform as a service system to monetise analytical outputs and knowledge. Since the primary focus of data analytics among firms is to support administrative functions, enhance business processes, and improve profit margins, systematic construction and use of tacit and explicit knowledge have received little attention. Hence, it is high time organisations augment their data analytics systems with practical knowledge management models for creating, managing, and using knowledge in a competitive digital environment.Item Advancing Knowledge Flow Theory through Knowledge Friction and Cyclones(2025-01-07) Shigley, Paul; Morton, Clare; Nissen, MarkKnowledge Flow Theory continues to advance, both through steady contributions to new knowledge and by informing practice. Two recent examples include the elaboration and refinement of knowledge friction, with its multidimensional effects on knowledge flow, and the development of knowledge cyclone, with its power manifesting through temporary organizational routines. Each of these contributions provides new knowledge and practical insight on its own, but together they illuminate an opportunity to examine their potential integration. This article explores such integration with the goal of contributing additional new theoretical knowledge and practical insight.Item Embodying Knowledge Flow: The Collision of Synthetic Experience and the Real World(2025-01-07) Brewster, JonA case study is proposed to address the problem of knowledge friction encountered when attempting to acquire and transfer 1st person, spatial-temporal or experiential knowledge, e.g., operating heavy machinery, ship-handling, warfighting, etc. Based in the theories of knowledge flow and embodied cognition, it is hypothesized that employment of extended reality (XR) is well suited to reducing knowledge friction when attempting to flow experiential knowledge. This study will specifically evaluate XR facilitation of knowledge flow in addressing ship-handling tasks required in complex harbor situations. A statistical analysis of an XR bridge training system will be made in comparison to a traditional physical bridge mock-up simulator. The goal of this research seeks to determine whether an ‘information only’ training process can aid human performance on par or better than a physical mock-up trainer.Item A Structural Hole in Social Enterprise Networks: Are You Our Friend or Stranger?(2025-01-07) Chang, Young Kyun; Han, SangheeThis study proposes that social enterprise networks are closed networks based on clan economy and strong identities shaped by the unique challenges of low economic viability and external scrutiny for their dual missions. As such, within the networks, structural hole spanners who actively interact with various actors can be treated as a stranger by within-network members, and thus benefit less from the networks. With 363 observations of social enterprises in Korea collected between 2021 and 2022, we found that the structural hole position in social enterprise networks made it more difficult to obtain capitals from external sources. In addition, we also found that, beyond a certain threshold, the greater the external resources obtained, the poorer the financial performance of the social enterprises. This study is meaningful in challenging the common assumptions of the social network literature in the context of social enterprises.Item Predicting Patent Licensing Using Graph Convolutional Networks (GCN)(2025-01-07) Lai, Chia-Yu; Dai, Jhih-Huan; Hsu, Yueh-TengNumerous scholars have delved into the primary factors influencing patent licensing, yet the relationships between licensors, licensees, and licensed patents require further exploration. This study proposes using Graph Convolutional Networks (GCN) to analyze these relationships and predict future patent licensing. Utilizing data from the United States Patent and Trademark Office (USPTO) assignment dataset, we extract features such as company technological capabilities, domains, licensed patents, International Patent Classifications (IPCs), and network similarity. Our proposed GCN model aims to enhance strategic planning for companies and provide insights into future technological trends. Experimental results demonstrate that the GCN model outperforms traditional machine learning methods, offering improved accuracy in predicting patent licensing and valuable guidance for corporate strategy development.Item Measuring the Impact of Key Factors on Knowledge Co-Production Outcomes in Citizen Science(2025-01-07) Yu, Siqing; Vodeb, Hana; Crompvoets, Joep; Steen, Trui; Rajabifard, Abbas; Aryal, Jagannath; Jukić, TinaThe need to better understand the knowledge co-production potential through citizen science is increasingly acknowledged. This perspective goes beyond merely viewing citizen science as a way of community-based monitoring or volunteer-based data collection. Based on a conceptual framework by Yu et al. (in press), this study validates and measures the impact of key factors on knowledge co-production outcomes through citizen science. Using exploratory (EFA) and confirmatory factor analysis (CFA), we develop a model that suggests causative relationships between three exogenous constructs—“volunteer trust”, “researcher-volunteer connectedness”, “openness and accessibility”—and two endogenous constructs—“scientific citizenship” and “technoscientific outputs”. “Researcher-volunteer connectedness” and “volunteer trust” appear to be more impactful for “scientific citizenship” than “openness and accessibility”, while “openness and accessibility” demonstrate the highest impact on “technoscientific outputs”. “Scientific citizenship” and “technoscientific outputs” do not exhibit strong direct correlations. Our results provide valuable input for strengthening the potential of citizen science to co-produce knowledge.Item Introduction to the Minitrack on Knowledge Flows, Transfer, Sharing, and Exchange(2025-01-07) Shigley, Paul; Nissen, Mark; Morton, Clare