Facilitating Organisational Fluidity with Computational Social Matching

Show full item record



Permalink

http://hdl.handle.net/10138/341400

Citation

Huhtamäki , J , Olsson , T & Laaksonen , S-M 2020 , Facilitating Organisational Fluidity with Computational Social Matching . in H Lehtimäki , P Uusikylä & A Smedlund (eds) , Society as an Interaction Space : A Systemic Approach . Translational Systems Sciences , Springer , Singapore , pp. 229-245 . https://doi.org/10.1007/978-981-15-0069-5_11

Title: Facilitating Organisational Fluidity with Computational Social Matching
Author: Huhtamäki, Jukka; Olsson, Thomas; Laaksonen, Salla-Maaria
Other contributor: Lehtimäki, Hanna
Uusikylä, Petri
Smedlund, Anssi
Contributor organization: Centre for Consumer Society Research
Department of Social Research (2010-2017)
Media and Communication Studies
Publisher: Springer
Date: 2020-03
Language: eng
Number of pages: 17
Belongs to series: Society as an Interaction Space
Belongs to series: Translational Systems Sciences
ISBN: 978-981-15-0068-8
978-981-15-0069-5
ISSN: 2197-8832
DOI: https://doi.org/10.1007/978-981-15-0069-5_11
URI: http://hdl.handle.net/10138/341400
Abstract: Striving to operate in increasingly dynamic environments, organisations can be seen as fluid and communicative entities where traditional boundaries fade away and collaborations emerge ad hoc. To enhance fluidity, we conceptualise computational social matching as a research area investigating how to digitally support the development of mutually suitable compositions of collaborative ties in organisations. In practice, it refers to the use of data analytics and digital methods to identify features of individuals and the structures of existing social networks and to offer automated recommendations for matching actors. In this chapter, we outline an interdisciplinary theoretical space that provides perspectives on how interaction can be practically enhanced by computational social matching, both on the societal and organisational levels. We derive and describe three strategies for professional social matching: social exploration, network theory-based recommendations, and machine learning-based recommendations.Striving to operate in increasingly dynamic environments, organisations can be seen as fluid and communicative entities where traditional boundaries fade away and collaborations emerge ad hoc. To enhance fluidity, we conceptualise computational social matching as a research area investigating how to digitally support the development of mutually suitable compositions of collaborative ties in organisations. In practice, it refers to the use of data analytics and digital methods to identify features of individuals and the structures of existing social networks and to offer automated recommendations for matching actors. In this chapter, we outline an interdisciplinary theoretical space that provides perspectives on how interaction can be practically enhanced by computational social matching, both on the societal and organisational levels. We derive and describe three strategies for professional social matching: social exploration, network theory-based recommendations, and machine learning-based recommendations.
Subject: 512 Business and Management
Peer reviewed: Yes
Usage restriction: openAccess
Self-archived version: acceptedVersion


Files in this item

Total number of downloads: Loading...

Files Size Format View
Facilitating_Or ... tching_final_postprint.pdf 259.6Kb PDF View/Open

This item appears in the following Collection(s)

Show full item record