1175: Comparative Studies of Guanxi and Social Networks - Capitalizing on Mixed Methods and Big Data (19161)

Participant(s)

• RB

  Ronald Burt

  Bocconi U., Italy
• JL

  Junyuan Liu

  School of Economics and Management, Tsinghua U., China
• XG

  XIN GAO

  U. Of Chicago, Denmark
• SO

  Sonja Opper

  Department of Management and Technology, Bocconi U., Italy
• XC

  Xiao-Ping Chen

  U. of Washington, United States
• SW

  Song Wang

  Zhejiang U., China
• JP

  Jingzhou Pan

  Tianjin U., China
• JL

  Jar-Der Luo

  Tsinghua U., China
• XC

  Xiaoyun (Aarn) Cao

  Illinois Institute of Technology Stuart School of Business
• HZ

  Huaikang Zhou

  Tsinghua U.
• JC

  Jiatan Chen

  School of Economics and Management Tsinghua U., China

Research has consistently demonstrated that guanxi and social networks confer substantial advantages for individuals (e.g., Burt, 1992, 2004; Mannucci & Perry-Smith, 2022), groups (e.g., Reagans & Zuckerman, 2001; Reagans, Zuckerman, & McEvily, 2004) and organizations (e.g., Luo, Huang, & Wang, 2012; Opper, Nee, & Holm, 2017) across various contexts. These contexts range from developed institutions (Borgatti, Brass, & Halgin, 2014) to transforming economies (Karhunen, Kosonen, McCarthy, & Puffer, 2018). Despite the considerable progress in understanding their important impact, ongoing debates persist in comparative explorations of guanxi and social networks, particularly within cross-cultural contexts (e.g., Burt & Burzynska, 2017; Chen & Ren, 2023; Xiao & Tsui, 2007). Beyond this conventional understanding, the advent of digital technologies has substantially altered network structure and interpersonal processes within contemporary organizations (Kellogg, Valentine, & Christin, 2020), potentially giving rise to new paradigms of guanxi and social networks. The COVID-19 pandemic may further expedite the influence of digitalization on the workplace. This emphasis extends to the development of guanxi and social network studies, incorporating innovative use of big data and mixed methods such as machine learning algorithms. To advance the field, this panel symposium seeks to engage a group of scholars in discussions on harnessing mixed methods and big data to address novel research questions in comparative studies of guanxi and social networks, with aspiration of making substantial contributions to both theoretical developments and empirical extensions.

Presentations:

• 08:00 – 09:30 CT (GMT-5/UTC-5)

  1175: Guanxi, Covid, and Trust for Kinds of People (19161)
  

  Author: Ronald S. Burt – Bocconi U.

  Author: Sonja Opper – Department of Management and Technology, Bocconi U.
• 08:00 – 09:30 CT (GMT-5/UTC-5)

  1175: Guanxi and Trust in Chinese Organizations: An Insider-Outsider Effect (19161)
  

  Author: Xiao-Ping Chen – U. of Washington

  Author: Jingzhou Pan – Tianjin U.
• 08:00 – 09:30 CT (GMT-5/UTC-5)

  1175: Unveiling the Nexus in Harnessing Structure Holes: The Guanxi Dynamics in Cross-Functional Teams (19161)
  

  Author: Jar-Der Luo – Tsinghua U.

  Author: Xiaoyun (Aarn) Cao – Illinois Institute of Technology Stuart School of Business
• 08:00 – 09:30 CT (GMT-5/UTC-5)

  1175: Applying Behavioral Big Data to Unravel Early Turnover via Social Network Mechanisms (19161)
  

  Author: Huaikang Zhou – Tsinghua U.

  Author: Jiatan Chen – School of Economics and Management Tsinghua U.

  Author: Junyuan Liu – School of Economics and Management, Tsinghua U.
• 08:00 – 09:30 CT (GMT-5/UTC-5)

  1175: Revisiting the Paradoxes of Knowledge Diversity and Network Structure for Team Innovation (19161)
  

  Author: XIN GAO – U. Of Chicago

  Author: Jar-Der Luo – Tsinghua U.

  Author: Song Wang – Zhejiang U.