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The knowledge economy from the bottom-up: structure and dynamics of voluntary technology communities in major U.S. cities
Dissertation   Open access

The knowledge economy from the bottom-up: structure and dynamics of voluntary technology communities in major U.S. cities

Qianyi Shi
University of Iowa
Doctor of Philosophy (PhD), University of Iowa
Autumn 2021
DOI: 10.17077/etd.006289
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Abstract

In a knowledge economy, practical skills and know-how that individuals learn and exchange with each other in their personal networks are critical to their success in the technology industry. While research in organizations and knowledge economy extensively studies the creation and diffusions of knowledge and innovations at the organizational level, little is known about the process in which new technologies are recognized, adopted, and widely shared in the informal networks of the knowledge workers. This dissertation uses a networked ecological approach to investigate the changing landscapes of the Meetup Technology communities. By investigating Meetup technology communities’ trends in size and network structure at the community and topic levels, peer influence at the individual level, and niche competition at the individual and meso level, this dissertation offers important insights on the social organization of knowledge workers and the patterns in which innovation is created, diffused, and adopted. This dissertation uses detailed participation records of participants on Meetup.com between 2004 and 2019 in Atlanta, Austin, Chicago, Washington DC, Detroit, Minneapolis, New York, Philadelphia, Phoenix, Seattle, and San Francisco on Meetup.com. The first study is descriptive in nature and focuses on demonstrating the overall community-level and the topic-level evolutional patterns of the technology communities. In particular, this chapter depicts the trends for groups, members, topics and the landscape of two types of networks, identity networks (co-topic networks, in which groups are connected based on shared topics) and functional networks (co-participant networks, in which groups are connected based on shared participants). Findings from this chapter suggests that the Meetup technology communities grow to be larger in size, more diverse in topics, as well as more clustered as new topics emerge and become increasingly specialized. In the second study, I examine the social origin and mechanisms of the clustering of members across groups. Previous research suggests that social influence and homophily are both crucial sources (McPherson, Smith-Lovin, and Cook 2001; Simmel 1955; Tarde 1903). Using participation records of participants in Meetup groups in San Francisco and New York City, I utilize propensity score matching to evaluate two competing micro-level social mechanisms, namely exposure to new group information through social connections and homophily between users, for clustering among co-participated users’ acquisition of an additional group membership. My findings reveal that both social influence and homophily are important sources of clustering. However, without controlling for homophily, direct estimation of social influence is likely to have an upward bias in explaining membership clustering. Additionally, social influence more readily translates into participation for users who have higher compatibility with the potential group than those with lower compatibility. Lastly, social influence has a stronger effect on recruitment for specialist groups occupying a narrow niche in an ecology than for generalist groups occupying a wide niche. In the third study, I consider the ecological dynamics (i.e., niche competition) in affecting participation behavior at the individual level and clustering patterns at the group level. It challenges the competition exclusion thesis (Gause 1934) which predicts mutual exclusion of competitors in the resource space and proposes the role of niche competition expressed as social overload in large communities in increasing boundary-spinning participation, which ultimately connects competing groups at the cost of internal cohesion. Our findings confirm that individuals in larger communities are more likely to be located in highly contested niches. Adapting to the participation demand overload as the community becomes more populated, individuals divide their time and energy to participate in more events. The structural outcome of niche competition is an increased level of openness to other groups and a decreased level of cohesion within the group.
 Community Evolution Group Clustering Knowledge Economy Niche Competition Organizational Ecology Social Influence

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