Publication Type

Journal Article

Version

acceptedVersion

Publication Date

6-2026

Abstract

Over the last decade, there has been an explosion in the use of diverse data sources by management scholars to observe and capture managerial and organizational constructs that have historically been difficult to access. This surge has been driven by the growing availability of rich, multi-modal data—textual, image, and audio (Luo, Jia, Ouyang, & Fang, 2024)—together with advances in analytical techniques to process and analyze data, such as computer-aided text analysis (Harrison, Thurgood, Boivie, & Pfarrer, 2019), machine learning (Choudhury, Wang, Carlson, & Khanna, 2019; Harrison, Josefy, Kalm, & Krause, 2023), and deep learning (Gouvard, Goldberg, & Srivastava, 2023). These developments have expanded the scope and depth of empirical inquiry, opening opportunities to investigate complex phenomena across a broad range of management topics, including competitive dynamics (Guo, Sengul, & Yu, 2020), corporate governance (Washburn & Bromiley, 2014), impression management (Pan, McNamara, Lee, Haleblian, & Devers, 2018; Pollock, Ragozzino, & Blevins, 2024), and strategic leadership (Junge, Graf-Vlachy, Hagen, & Schlichte, 2025), among others. One of the most prominent of these data sources within management research is earnings conference calls.

Keywords

Management research, data sources, conference calls

Discipline

Business Analytics | Corporate Finance | Management Sciences and Quantitative Methods | Strategic Management Policy

Research Areas

Strategy and Organisation

Publication

Journal of Management

ISSN

0149-2063

Publisher

SAGE

Embargo Period

6-15-2026

Copyright Owner and License

Authors

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