Digital Social Matching Ecosystem for Knowledge Work

Jukka Huhtamäki

Tampere University

 Visiting Work Research Centre
Tampere University
May 2015

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.  

We are calling for collaboration, critique, tips on literature streams, and so forth

First: how did we get here?

• Before embarking on this research venture, we were developing methods to analyze innovation and business in collaboration with Innovation Ecosystems Network
• Sourcing secondary data from Press Releases, online sources, socially constructed databases and other types of Big Social Data (Rubens et al. 2010) ...
• ... we modeled ecosystems as networks and took a sensemaking-based approach to create insights for policy makers and academic scholars investigating these ecosystems

Example: Interconnections between current EIT Digital nodes?

(Still et al., 2014)

Example: What if the San Francisco Bay Area would be the 7th EIT Digital node?

(Still et al., 2014)

Ostinato Process Model

Text

(Huhtamäki et al. 2015, Huhtamäki 2016)

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

Business Ecosystem (Moore, 1993)

Knowledge ecosystem

(Valkokari, 2015; Järvi et al., 2018)

Innovation Ecosystem

(Russell et al., 2011)

Goals: social matching in knowledge work

1. Enable unexpected social encounters in professional life
2. ... without amplifying the emergence of echo chambers (homophily, triadic closure)
3. ... 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., accepted to CACM)

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

(McCay-Peet & Wells, 2017)

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

Two lines of investigation

Potential threads for collaboration

Thank you!

Let the discussion continue:

@jnkka

medium.com/matching-people

#matchingpeople

#humanpotentialunlimited