Abstract
This exploratory study investigates how an organisation’s strategic personality can be inferred through linguistic and machine learning approaches and its performance and industry-level implications. The study uses 820 chief executive officers’ (CEOs) statements published in annual reports of the Australian Stock Exchange (ASX) listed companies in Australia with the personality insight service on the IBM Watson platform to infer the strategic personalities of firms. By applying unsupervised clustering on the extracted values of personality traits, the study found two distinct and reliable clusters of personalities: a bright and a lighter shade of dark personalities, which are differently associated with the indicators of firms’ performance and industry categories. While contributing to the advancement of performance-personality research and their measurement at the organisational level, this study opens a new avenue for the adoption of unobtrusive linguistic techniques and data sources for strategic personality-performance research in the corporate governance disciplines. Limitations of the present study and suggestions for future research are also discussed.
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