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Laihonen H, Huhtamäki J. Organisational hybridity and fluidity: deriving new strategies for dynamic knowledge management. Knowledge Management Research & Practice 2020. [DOI: 10.1080/14778238.2020.1794993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Affiliation(s)
- Harri Laihonen
- Faculty of Social Sciences and Business Studies, Department of Health and Social Management, University of Eastern Finland, Kuopio, Finland
| | - Jukka Huhtamäki
- Faculty of Management and Business, Information and Knowledge Management, Tampere University, Tampere, Finland
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Olshannikova E, Olsson T, Huhtamäki J, Yao P. Scholars’ Perceptions of Relevance in Bibliography-Based People Recommender System. Comput Support Coop Work 2019. [DOI: 10.1007/s10606-019-09349-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Aramo-Immonen H, Kärkkäinen H, Jussila JJ, Joel-Edgar S, Huhtamäki J. Visualizing informal learning behavior from conference participants' Twitter data with the Ostinato Model. Computers in Human Behavior 2016. [DOI: 10.1016/j.chb.2015.09.043] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Aramo-Immonen H, Jussila J, Huhtamäki J. Exploring co-learning behavior of conference participants with visual network analysis of Twitter data. Computers in Human Behavior 2015. [DOI: 10.1016/j.chb.2015.02.033] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Abstract
Business ecosystems consist of a heterogeneous and continuously evolving set of entities that are interconnected through a complex, global network of relationships. However, there is no well-established methodology to study the dynamics of this network. Traditional approaches have primarily utilized a single source of data of relatively established firms; however, these approaches ignore the vast number of relevant activities that often occur at the individual and entrepreneurial levels. We argue that a data-driven visualization approach, using both institutionally and socially curated datasets, can provide important complementary, triangulated explanatory insights into the dynamics of interorganizational networks in general and business ecosystems in particular. We develop novel visualization layouts to help decision makers systemically identify and compare ecosystems. Using traditionally disconnected data sources on deals and alliance relationships (DARs), executive and funding relationships (EFRs), and public opinion and discourse (POD), we empirically illustrate our data-driven method of data triangulation and visualization techniques through three cases in the mobile industry Google’s acquisition of Motorola Mobility, the coopetitive relation between Apple and Samsung, and the strategic partnership between Nokia and Microsoft. The article concludes with implications and future research opportunities.
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Affiliation(s)
| | | | | | - Neil Rubens
- University of Electro-Communications, Tokyo, Japan
| | | | - Hyunwoo Park
- Georgia Institute of Technology, Atlanta, Georgia
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