A one-hundred-year structural topic modeling analysis of the knowledge structure of international management research

Mohamed M. Mostafa ()
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Mohamed M. Mostafa: Gulf University for Science and Technology

Quality & Quantity: International Journal of Methodology, 2023, vol. 57, issue 4, No 45, 3905-3935

Abstract: Abstract International Management is a vast and multidisciplinary research domain that is heavily influenced by several other disciplines, such as Economics, Organizational Theory and Strategic Management. Based on 28,973 research articles, this study aims to analyze the knowledge structure of the international management domain from 1920 to 2019. Using computational text-based topic modeling analysis, we trace the evolution of international management knowledge by examining the major academic topics/latent themes discussed in the field. The study also diachronically visualizes the variations in topic prevalence over time. Our methodology is akin to “inductive mapping” as it is neither biased by our position nor it is guided by assumptions related to the topics we expect to find. Results indicate the existence of a wide variety of important research foci in the domain of international management. These include, among others, strategic alliances formation, international entry modes, corporate social responsibility, cross-cultural consumer behavior, technological innovation and entrepreneurship. Results also show that some topics such as “financial risk and return on investment” and “corporate social responsibility” show a declining time trend, indicating that academic research focusing on such topics was more likely to be published early on and less so recently. On the other hand, other topics such as “Emerging (East) Asian nations” and “global mergers and acquisitions” show an increasing trend, indicating that more papers were published recently. Taken together, although our findings might reflect the breadth and depth of research in international management, they might also suggest that the bounds of this field are not well defined.

Keywords: Topic modeling; STM; International management; Text mining; Machine learning (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11135-022-01548-w

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