Knowledge Management and Digital Transformation in the Sharing Economy
A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Management".
Deadline for manuscript submissions: closed (15 November 2021) | Viewed by 65892
Special Issue Editors
Interests: knowledge management; knowledge ecosystem; virtual community; shadow work; sharing platform
Interests: IT user behavior; shadow work; knowledge management; digital transformation
Special Issue Information
Dear Colleagues,
The goal of this Special Issue of Sustainability is to build a comprehensive body of knowledge regarding the complexity . This Special Issue calls for papers that address state-of-the-art KM practices in the sharing economy and digital transformations (DTs) in firms. The emphasis of this Special Issue is on how both individual and organizational knowledge can be incorporated into new emerging technologies such as big data, Internet of Things (IoT), voice recognition techniques, blockchain, social networks (SNs), crowd computing, and artificial intelligence (AI) using KM systems, techniques, and tools. The concepts of knowledge ecology (KE); open innovation; digital transformation; and shadow work for societies, organzations, and individuals can be further extended as examples in the new era of KM.
With the state-of-the-art KM practices (especially knowledge ecosystems, DTs, and KE), the topic of how knowledge can be intelligently utilized within and among organizations has been highlighted. This includes the inter. However, there has been little attention paid to clearly understanding how such interplay could contribute to the academic and practical fields while the interactions between KM and new emerging technologies are initiated.
This Special Issue call for papers involving various types of research and practical papers using scientific theories, methods, and research contexts to contribute to KM research communities or practitioners. Submissions focusing on the interactions of technical, behavioral, organizational, and societal aspects of KM are welcomed. We also welcome a variety of methodological papers using qualitative methods, experimental design, modeling, empirical research, qualitative study, or mixed methodological approaches with both qualitative and quantitative methods. Further, regarding the unit of analysis, national-, organizational-, inter-organizational-, social-, and team-level studies are welcomed. Case studies relevant to the real world are warmly encouraged.
Topics of interest include—but are not limited to—the following:
- Knowledge management (KM) in the context of sharing platforms;
- Digital transformations (DTs) and KM;
- KM with crowd computing;
- Social or behavioral issues in KM;
- Communities-of-practice (CoPs);
- Cross-cultural aspects of KM;
- KM and organizational learning;
- Business processes and software engineering for KM;
- Emerging trends for information systems in KM;
- KM and strategic IS planning;
- Open innovation and cases;
- Knowledge ecology and cases;
- Analytical tools and techniques (e.g., text analytics, sentiment analysis);
- Data mining of tacit and explicit knowledge;
- Knowledge management system (KMS)-related issues;
- Virtual communities for KM;
- Inter-organizational collaboration;
- Big data and KM;
- Business intelligence and analytics (BIA);
- New technology usage for KM (e.g., AI, IoTs, robots);
- Service systems and architecture for KM;
- Customer relationship management (CRM) and KM;
- Customer centers and customer knowledge;
- Conceptualization of shadow work in terms of IT usage;
- Shadow work in the sharing economy;
- The side effect of shadow work on organizations and individuals;
- Other KM issues in the context of the fourth industrial revolution.
Prof. Dr. Joon Koh
Prof. Dr. Sang Cheol Park
Guest Editors
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Keywords
- knowledge management
- knowledge ecosystem
- community-of-practice
- virtual community
- virtual organization
- shadow work
- sharing economy
- IT user behavior
- case study
- mixed-methods
- qualitative study
- quantitative study
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