A new organizational structure database: Examining structure through top management team compositions - Albert, Daniel; Eklund, John; Tang, Lisa (Strategic Management Journal, 2025)
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.