Abstract
Scholars have recently highlighted the promise of applying research methodologies based on artificial intelligence to analyze big data and generate impactful research insights. However, the potential for applications in management research has remained largely untested. In this paper, we adopt a topic modeling approach, a recent machine-learning technique to analyze unstructured text, to investigate the patterns in the vocabulary structure used in the journal Management Science. In particular, we analyze the structural properties of the vocabulary to unveil the distribution, division and demarcations of scientific knowledge. We present the empirical results and discuss our findings in view of recent editorial reviews. Finally, a number of methodological implications inform a wider application of topic models.
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