High levels of economic inequality are widely viewed as a key challenge facing both advanced industrial and developing economies. Country-level studies have consistently shown a negative link between income inequality and trust in others. This is typically attributed to greater social distance within unequal societies. Do we observe similar relationships within organisations? This is an important question because employee trust is associated with important outcomes for workers and organisations. We answer it by investigating the relationship between pay inequality and employee trust in managers at the workplace level using large-scale nationally representative matched employer-employee data from Britain. The paper uses innovative machine learning methods to demonstrate a curvilinear relationship between pay inequality and trust. When pay inequality is at low to moderate levels, increasing inequality is associated with increasing employee trust but when pay inequality passes a certain threshold the relationship turns negative. The relationship is mediated by employees’ perceptions of manager fairness and moderated by employee collective voice. The implications of these findings for theory, research methodology, practice and future studies are discussed.

Management scholarship is beginning to grapple with the growing popularity of machine learning (ML) as an analytical tool. While quantitative research in our discipline remains heavily influenced by positivist thinking and statistical modelling underpinned by null hypothesis significance testing, ML is increasingly used to solve technical, computationally demanding problems. In this paper, we argue for a wider, more systematic adoption of the key tenets of ML in quantitative management scholarship, both in conjunction with and, where appropriate, as an alternative to canonical forms of statistical modelling. We discuss how ML can extend the boundaries of quantitative management scholarship, help management scholars to unpack complex phenomena, and improve the overall trustworthiness of quantitative research. The paper provides a representative review of the use of ML to date and uses a worked example to demonstrate the value of ML for management scholarship.

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