Facilitating organisational fluidity with computational social matching  

Forschungswerkstatt 2019/20, Freie Universität Berlin

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. Springer.

Ontological foundation

Social structure of organizations

Organizational fluidity

Schreyögg & Sydow (2010): "… represents a reaction to the increasing complexity and environmental turbulence that organizations have to master […] highly flexible and fluid organizational forms, based on relentlessly changing templates, quick improvisation, and ad hoc responses […] in sharp contrast to other recent organizational research that emphasizes identity, path dependence, economies of specialization, and recursive practices"

Organizational social structure

Communicative Constitution of Organizations (CCO): communication is the fundamental constitutive force that brings organisations into being (Ashcraft, Kuhn, & Cooren, 2009; Putnam, Nicotera, & McPhee, 2009)

Organizations as networks: the social structure of an organization includes individual actors that are connected to each other through communication (Borgatti & Foster, 2003; Lee & Hassard, 1999; Powell, 1990)

How to facilitate the formation of the social structure in fluid organizing?

Or: … of fluid organizations? 

Social matching

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 in knowledge work

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., 2020)

Social matching strategies

Social matching strategies

(0. Monitoring (cf. Screyögg and Sydow, 2010))

1. Social exploration

2. Network theory-based recommendations

3. Machine learning-based recommendations

(Huhtamäki, Olsson & Laaksonen, 2020)

Social theories of organizing

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)

• … however, brokerage may also torture the actor (Krackhard, 1999)

• 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)

Discussion & Implications

(Huhtamäki, Olsson & Laaksonen, 2020)

Ethical considerations

1. Monitoring is a means of organizational control (Leclercq-Vandelannoitte et al., 2014; Zuboff, 2015)

2. Privacy vs. the need for high-quality data on interactions between organizational actors 

3. Social matching is a form of profiling and prone to algorithmic biases

Implications

1. Social matching is dependent on high-quality data on actors, their interactions, and the measured quality of these interactions

2. Social matching should counterbalance human biases in network formation and  continuosly balance between similarity and diversity

3. Move gradually from monitoring and social exploration to network theory-based and machine learning-based recommendations 

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? 

Egocentric social structures

Skenderi, E., Olshannikova, E., Olsson, T., Huhtamäki, J., Koivunen, S., Yao, P., & Huttunen, H. (2019). Investigation of Egocentric Social Structures for Diversity-Enhancing Followee Recommendations. In Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization  - UMAP’19 Adjunct (pp. 257–261). New York, New York, USA: ACM Press.

Content-based similarity

Olshannikova, E., Olsson, T., Huhtamäki, J., & Yao, P. (2019). Scholars’ Perceptions of Relevance in Bibliography-based People Recommender System. Computer Supported Cooperative Work (CSCW), 28(3–4), 357–389.

Thank you!

Let the discussion continue:

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