DOI

10.17077/etd.n6wl-u7rq

Document Type

Dissertation

Date of Degree

Summer 2019

Access Restrictions

Access restricted until 09/04/2021

Degree Name

PhD (Doctor of Philosophy)

Degree In

Informatics

First Advisor

Zhao, Kang

First Committee Member

Street, Nick

Second Committee Member

Eichmann, David

Third Committee Member

Barlow, Patrick

Fourth Committee Member

Ni, Chaoqun

Abstract

We have never stopped in the pursuit of science. Standing on the shoulders of the giants, we gradually make our path to build a systematic and testable body of knowledge to explain and predict the universe. Emerging from researchers’ interactions and self-organizing behaviors, scientific communities feature intensive collaborative practice. Indeed, the era of lone genius has long gone. Teams have now dominated the production and diffusion of scientific ideas. In order to understand how collaboration shapes and evolves organizations as well as individuals’ careers, this dissertation conducts analyses at both macroscopic and microscopic levels utilizing large-scale scholarly data.

As self-organizing behaviors, collaborations boil down to the interactions among researchers. Understanding collaboration at individual level, as a result, is in fact a preliminary and crucial step to better understand the collective outcome at group and organization level. To start, I investigate the role of research collaboration in researchers’ careers by leveraging person-organization fit theory. Specifically, I propose prospective social ties based on faculty candidates’ future collaboration potential with future colleagues, which manifests diminishing returns on the placement quality. Moving forward, I address the question of how individual success can be better understood and accurately predicted utilizing their collaboration experience data. Findings reveal potential regularities in career trajectories for early-stage, mid-career, and senior researchers, highlighting the importance of various aspects of social capital.

With large-scale scholarly data, I propose a data-driven analytics approach that leads to a deeper understanding of collaboration for both organizations and individuals. Managerial and policy implications are discussed for organizations to stimulate interdisciplinary research and for individuals to achieve better placement as well as short and long term scientific impact. Additionally, while analyzed in the context of academia, the proposed methods and implications can be generalized to knowledge-intensive industries, where collaboration are key factors to performance such as innovation and creativity.

Keywords

collaboration, data science, organizational diversity, social networks, text mining

Pages

xiii, 117 pages

Bibliography

Includes bibliographical references (pages 94-116).

Copyright

Copyright © 2019 Zhiya Zuo

Available for download on Saturday, September 04, 2021

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