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Bibliometric study of family business succession between 1939 and 2017: mapping and analyzing authors’ networks

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Abstract

This study maps and analyzes the scientific research networks of family business succession. We examine coauthors’ activities in terms of not only productivity but also their importance to the coauthorship network. The most influential contributors and universities, as well as their research networks and theoretical underpinnings, are discussed. The review examines 661 articles published by 1105 authors in 224 academic journals indexed in the Social Science Citation Index and Scopus between 1939 and 2017. We used a bibliometric approach based on coauthorship analysis to measure cooperation. The results show that family business succession research is characterized by high fragmentation in the authors’ collaboration in general, but the leading scholars are strongly interconnected. We map and analyze the most influential networks by identifying the most important topics studied, the theoretical and methodological approaches employed, the scope of the research conducted, and where it has been published. Most of the identified networks are in North America and Europe, and most are not theoretically or methodologically specialized.

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Acknowledgements

The authors would like to thank the students and researchers (Catherine S. Beaucage, Charles Boustany, Naïma Cherchem, Ruxandra Ionita, Claudia Angélica Rodríguez Hernández) for their contribution to this research.

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Authors and Affiliations

  1. HEC Montréal, Montréal, QC, Canada

    Luis Cisneros & Mihai Ibanescu

  2. McGill University, Montréal, QC, Canada

    Christian Keen

  3. Universidad Iberoamericana, Ciudad de México, Mexico

    Odette Lobato-Calleros

  4. Universidad Autónoma de Occidente, Culiacán, Sinaloa, Mexico

    Juan Niebla-Zatarain

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  1. Luis Cisneros
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Correspondence to Luis Cisneros.

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Cisneros, L., Ibanescu, M., Keen, C. et al. Bibliometric study of family business succession between 1939 and 2017: mapping and analyzing authors’ networks. Scientometrics 117, 919–951 (2018). https://doi.org/10.1007/s11192-018-2889-1

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