Paper Number
2510
Paper Type
Short
Abstract
This paper studies the impact of top management team (TMT) diversity on sustainability reporting. Drawing on upper echelons theory and stakeholder theory, I examine whether demographic and functional TMT diversity influences the textual features of sustainability reports. Focusing on a sample of publicly traded U.S. firms from 2015 to 2022, it becomes evident that TMT diversity is associated with higher sustainability reporting quantity and quality. Moreover, the results show that this relationship is moderated by the presence of a chief digital officer (CDO) as a new functional TMT member. Thereby, this study adds to research at the intersection of IS and sustainability accounting. It further contributes by applying a deep learning technique to gain insights into a new antecedent of sustainability reporting. In addition, the findings encourage managers to consider consequences on sustainability practices when deciding on the demographic and functional TMT composition.
Recommended Citation
Hagemeier, Lea, "Top Management Team Diversity and Sustainability Reporting: An Empirical Study Using Deep Learning" (2024). ICIS 2024 Proceedings. 13.
https://aisel.aisnet.org/icis2024/data_soc/data_soc/13
Top Management Team Diversity and Sustainability Reporting: An Empirical Study Using Deep Learning
This paper studies the impact of top management team (TMT) diversity on sustainability reporting. Drawing on upper echelons theory and stakeholder theory, I examine whether demographic and functional TMT diversity influences the textual features of sustainability reports. Focusing on a sample of publicly traded U.S. firms from 2015 to 2022, it becomes evident that TMT diversity is associated with higher sustainability reporting quantity and quality. Moreover, the results show that this relationship is moderated by the presence of a chief digital officer (CDO) as a new functional TMT member. Thereby, this study adds to research at the intersection of IS and sustainability accounting. It further contributes by applying a deep learning technique to gain insights into a new antecedent of sustainability reporting. In addition, the findings encourage managers to consider consequences on sustainability practices when deciding on the demographic and functional TMT composition.
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13-DataAnalytics