Dealing with the devil of deviation: managing uncertainty during product development execution

This paper examines deviations in complex product development. Based on an extensive case study, using participant observations, interviews, and data from deviation reports, it illustrates the causes, procedures, management challenges of deviations, and organizational roles devoted to deviation management. Based on the rich data material, it furthermore proposes a typology of deviations developed for a better understanding of this significant empirical phenomenon.     

The origins of cultural divergence: evidence from Vietnam

Journal of Economic Growth - Cultural norms diverge substantially across societies, often within the same country. We propose and investigate a self-domestication/selective migration hypothesis,...     

Corporate governance performance ratings with machine learning

We use machine learning with a cross-sectional research design to predict governance controversies and to develop a measure of the governance component of the environmental, social, governance (ESG) metrics. Based on comprehensive governance data from 2,517 companies over a period of 10 years and investigating nine machine-learning algorithms, we find that governance controversies can be predicted with high predictive performance. Our proposed governance rating methodology has two unique advantages compared with traditional ESG ratings: it rates companies' compliance with governance responsibilities and it has predictive validity. Our study demonstrates a solution to what is likely the greatest challenge for the finance industry today: how to assess a company's sustainability with validity and accuracy. Prior to this study, the ESG rating industry and the literature have not provided evidence that widely adopted governance ratings are valid. This study describes the only methodology for developing governance performance ratings based on companies' compliance with governance responsibilities and for which there is evidence of predictive validity.