An exploratory Analysis of Supply Chain Risk Disclosure in annual Reports

Scholars are increasingly interested in the development of effective supply chain risk mitigation strategies, yet relatively little is known about the actual exposure of firms towards supply chain risks. Without a proper understanding of corporate risk profiles, it is difficult to suggest risk management approaches that are both effective and efficient. The present study addresses this important lacuna in the current literature by means of a large-scale empirical analysis of risk disclosures in U.S. annual reports. We develop and apply a dictionary-based text-mining algorithm that helps to categorize the disclosed risks on a risk item basis. Linking the algorithm-generated risk data to firm-level data of manufacturing firms listed at the SEC, yields a unique panel data set that allows us to address our research question. The analyses suggest that the risk disclosure in annual reports can serve as rich source of information to identify supply chain-related risks. Firms have a nuanced view on risks while the number of disclosed risks increases over the time horizon of investigation (2006–2010). Furthermore, we observe that two variables, industry membership and firm size, seem to have a strong influence on corporate risk profiles. Managerial implications as well as promising avenues for further research are outlined.