Facilitating Organisational Fluidity with Computational Social Matching

Jukka Huhtamäki, Thomas Olsson, Salla-Maaria Laaksonen

Research output: Chapter in Book/Report/Conference proceedingChapterScientificpeer-review

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.
Original languageEnglish
Title of host publicationSociety as an Interaction Space : A Systemic Approach
EditorsHanna Lehtimäki, Petri Uusikylä, Anssi Smedlund
Number of pages17
Place of PublicationSingapore
PublisherSpringer
Publication dateMar 2020
Pages229-245
ISBN (Print)978-981-15-0068-8
ISBN (Electronic)978-981-15-0069-5
DOIs
Publication statusPublished - Mar 2020
MoE publication typeA3 Book chapter

Publication series

NameTranslational Systems Sciences
PublisherSpringer Nature
ISSN (Print)2197-8832
ISSN (Electronic)2197-8840

Fields of Science

  • 512 Business and Management

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