Introducing a new, machine learning process, and online tools for conducting sales literature reviews: An application to the forty years of JPSSM

Artificial intelligence (AI) and machine learning (ML) are having an immense influence on sales professionals. Unfortunately, prior studies have paid less attention to how these technologies are affecting sales scholars’ work, such as conducting literature reviews. Our study expands the repertoire of inquiry for sales academics in the domain of AI/ML in three novel ways. First, we offer an efficient process to analyzing the sales literature, through an unsupervised ML-based process, which allows the identification of articles/topics based on semantic similarity rather than based on keywords. Second, we validate our process by applying it to scholarly work published in JPSSM as well as to the practitioner’s literature in the past 40 years. We find that the topics and trends uncovered by our autonomous reader are coherent with previous academic reviews, with some topics being entirely new. We also find that academic research published in JPSSM accurately reflects corporate realities, thereby alleviating concerns about the ‘sales academics-practitioners’ gap. Finally, we provide authors and reviewers with an online application, which allows for rapid identification of related JPSSM articles, and a set of ‘do-it-yourself’ (DIY) tools, which can help researchers in quickly producing their own literature reviews of articles published in any journal.