Abstract

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.

摘要

人工智能 (AI) 和机器学习 (ML) 对销售专业人士产生了巨大影响。不幸的是，之前的研究很少关注这些技术如何影响销售学者的工作，例如进行文献综述。我们的研究以三种新颖的方式扩展了 AI/ML 领域销售学者的调查范围。首先，我们通过无监督的基于 ML 的过程提供了一个分析销售文献的有效过程，该过程允许基于语义相似性而不是基于关键字来识别文章/主题。其次，我们通过将其应用于 JPSSM 上发表的学术著作以及过去 40 年从业者的文献来验证我们的过程。我们发现我们的自主读者发现的主题和趋势与之前的学术评论是一致的，有些主题是全新的。我们还发现，在 JPSSM 上发表的学术研究准确反映了企业现实，从而减轻了对“销售学者-从业者”差距的担忧。最后，我们为作者和审稿人提供了一个在线应用程序，可以快速识别相关的 JPSSM 文章，以及一套“自己动手”（DIY）工具，可以帮助研究人员快速制作自己的文献综述在任何期刊上发表的文章。