Machine Learning Methods for Strategy Research
Marketing, logistics, knowledge flow analysis, and other domains have become highly dependent on machine learning methods and tools. This article provides a survey of these methods and their management applications. Aimed at a broad readership, the article explains tools and concepts in a way that is accessible to non-specialists, including those without a programming background.
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This method survey article covers natural language processing methods focused on text analytics, and machine learning methods and their applications to management research. The methods are presented accessibly with directly applicable examples, supplemented by a rich set of references crossing multiple sub-fields of management science. Methods covered include vector space models, similarity, sentiment analysis, classification, decision trees, boosting and cross-validation, k-means, and k-nearest-neighbors. The intended audience is the strategy and management researcher with an interest in understanding concepts and applications of machine learning for strategy research.
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