Digital Social Matching Ecosystem for Knowledge Work
Research objectives
- First, we seek to develop data-driven, interactive service concepts for professional social matching and related methodology.
- Second, we aim to explore ways to implement some of these service concepts at the ecosystem level, that is, in co-creation between companies rather than within the corporate firewall.
With this KMIS 2018 position paper, we are calling for collaboration, critique, tips on literature streams, and so forth
Computational social matching
- "Social matching systems bring people together in both physical and online spaces" (Terveen & McDonald, 2005)
- Professional Social Matching refers to the "matching of individuals or groups for vocational collaboration and co-creation of value"
- recruitment,
- headhunting,
- community building, and
- mentoring,
- advisory relationships
- general networking
(Olsson, Huhtamäki & Kärkkäinen, 2018)
Social matching ecosystem
API ecosystem vs. Platform ecosystem
Business Ecosystem (Moore, 1993)
Knowledge ecosystem
(Valkokari, 2015; Järvi et al., 2018)
Innovation Ecosystem
(Russell et al., 2011)
Goals: social matching in knowledge work
- Enable unexpected social encounters in professional life
- ... without amplifying the emergence of echo chambers (homophily, triadic closure)
- ... by helping users avoid human bias in decision-making and identifying optimal similarity-diversity between matched people
Design requirements: context-sensitivity, systemic perspective, user-system cooperation, proactive persuation
(Olsson et al., CACM manuscript)
Theoretical background
Social structure of organizations
Social structure core concepts
- Weak ties (Granovetter, 1973)
- Homophily (McPherson, Smith-lovin & Cook, 2001)
- Triadic closure (Granovetter, 1973)
- Echo chambers (Sunstein, 2009)
- Cyberbalkanization (Van Alstyne & Brynjolfsson, 1996)
- Filter bubbles (Pariser, 2011)
- Structural holes (Burt, 2001)
Social structure in knowledge work
- Weak ties are conduits of novel information (Aral, 2016)
- Brokering structural holes is beneficial for individuals (Burt, 2004)
- Access to heterogeneous knowledgde drives innovation performance (Rodan & Galunic, 2004)
- Diversity-bandwidth tradeoff is needed to navigate strong and weak ties (Aral & Van Alstyne, 2011)
Toward systemic serendipity
Serendipitous social encounters
Title
- Content
How to take the McCay-Peet & Toms (2015) serendipity model to social serendipity to ecosystem level?
Outlook
Where should we go from here?
Concluding remarks
- We take a social network-centric viewpoint to social matching and seek to increase diversity by identifying potential weak ties into organizations
- We claim that instead of relevance-first approach, we should develop ways to increase transparency and user control in social matching
- However, nudging-based systems will also be developed and we should inform their design
-
How to enable social supercolliders (Watts, 2013) in an ethical, privacy-aware manner? Cf. GDPR, Mydata, ...
#humanpotentialunlimited
Digital Social Matching Ecosystem for Knowledge Work
By Jukka Huhtamäki
Digital Social Matching Ecosystem for Knowledge Work
Presentation at KMIS 2018
