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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Building on insights from stakeholder and social capital theory, we investigate into the conditions and processes through which the subsidiaries of MNEs in emerging markets create organizational value by leveraging strategic CSR. Seeing how current research is almost exclusively dominated by confirming the link between social responsibility and financial performance, with our study, we plan to depart from this debate to look at broader value-creating parameters. From this perspective, we apply a tailored designed survey method to focus on Romania, one of Eastern Europe's emerging economies. This research contributes to marketing and international business literature by advancing the knowledge on the determinants and the stages of deploying strategic CSR (formation, implementation, and control). We also highlight the neglected role of the MNE-subsidiary relationships in understating CSR in emerging markets. References Available Upon Request

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