Journal article
A new organizational structure database: Examining structure through top management team compositions
Strategic management journal, Forthcoming
09 Nov 2025
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Research Summary
Studies using archival organizational structure data are not as prevalent as one might expect for such a critical strategy topic. We seek to facilitate more studies in this domain by introducing a novel, hand-collected dataset of top management team compositions of S&P 500 firms between 1993 and 2020. Alongside providing the original role titles, we use generative Artificial Intelligence (AI) to categorize executives' titles into 6 role groups and 12 hierarchical levels, enabling easier comparisons of structures across and within firms. Our findings not only align with prior research but also offer insights into industry-specific structural changes, functional distributions within organizations, and the evolution of executive roles. This work also highlights the potential of generative AI as a tool to empirically investigate key strategy questions.
Managerial Summary
One of the most important decisions senior managers make pertains to defining their firms' organizational structures. However, obtaining data on firms' structures can be challenging due to difficulties in accessing data and comparing structures across firms. In this paper, we develop a novel dataset of top management team compositions of S&P 500 firms between 1993 and 2020. Alongside providing the original names and job titles, we use generative Artificial Intelligence (AI) to categorize executives' titles into 6 role groups and 12 hierarchical levels, allowing easier comparisons of structures across and within firms. We hope that this new dataset will spur greater scholarly interest in organizational structure, offering insights into how firms are structured and the implications of these structures.
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Details
- Title
- A new organizational structure database: Examining structure through top management team compositions
- Creators
- Daniel Albert - Drexel UniversityJohn C. Eklund (Corresponding Author) - University of Southern CaliforniaLisa Tang - National University of Singapore
- Publication Details
- Strategic management journal, Forthcoming
- Publisher
- Wiley
- Number of pages
- 33
- Grant note
- NUS Business School Start-Up Grant: R-313-000-144-133 iORB, Marshall School of Business, University of Southern California: PR0002701 Singapore Ministry of Education Tier 1 Grant: A-8001710-00-00
We are very grateful for the helpful feedback and suggestions from the Editor and the two anonymous reviewers, which have greatly improved the paper. We are grateful to the Strategic Management Society for the "SMS Annual Conference Research Methods Paper Prize" Award in 2024 for this paper. We would also like to thank Tobias Kretschmer, Phanish Puranam, Metin Sengul, and Marlo Raveendran for their very helpful comments on the paper. We would also like to thank the conference participants at SMS 2024, AOM 2024, AOM 2025, Decentralization in Organizations 2024 for their comments on earlier versions of the manuscript. We also thank Margaret Natividad, Tzuyi Chan, and our team of RAs for their invaluable research assistance over the years. All errors remain our own responsibility.
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Management
- Web of Science ID
- WOS:001609923600001
- Scopus ID
- 2-s2.0-105021354987
- Other Identifier
- 991022132053304721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Business
- Management