Codes of Ethics: Extending Classification Techniques with Natural Language Processing

Abstract

Language is an indicator of how stakeholders view an ethics code’s intent, and key to distinguishing code properties, such as promoting ethical-valued decision-making or code-based compliance. This article quantifies ethics codes’ language using Natural Language Processing (NLP), then uses machine learning to classify ethics codes. NLP overcomes some inherent difficulties of “measuring” verbal documents. Ethics codes selected from lists of “best” companies were compared with codes from a sample of Fortune 500 companies. Results show that some of these ethics codes are different enough from the norm to be distinguished by an algorithm; indicating as well that lists of “best” companies differ meaningfully from each other. Results suggest that NLP models hold promise as measurement tools for text research of corporate documents, with the potential to contribute to our understanding of the impact of language on corporate culture and enhance our understanding of relationships with corporate performance.