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Uncovering the dynamics of market convergence through M&A

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  • Aaldering, Lukas Jan
  • Leker, Jens
  • Song, Chie Hoon

Abstract

Despite the significance of converging markets as a new competitive paradigm to change how organizations function and produce value, little attention has been devoted to investigating the evolutionary path of market convergence and understanding the potential of its market development in the future. In this paper, the extent to which the biotechnology industry shows tendencies to converge with adjacent market segments is explored. By taking both a retrospective and forward-looking perspective, the study intends to shed light on the dynamics of the convergent evolution beyond the mere technological dimension. Based on the real-world examples of M&A transactions, an M&A interaction network was constructed to analyze the dynamics of market convergence and to visualize the causal relationships among market segments by using the DEMATEL approach. Furthermore, a link prediction algorithm was applied to predict future converging patterns. By simultaneously considering the past and imagining the future, the study offers a holistic, intuitive understanding of the interrelationships of the entire network. The findings show that the technology convergence has evolved into market convergence. The proposed framework can provide input for decision makers for effective R&D planning to anticipate developments and to coordinate the related activities.

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  • Aaldering, Lukas Jan & Leker, Jens & Song, Chie Hoon, 2019. "Uncovering the dynamics of market convergence through M&A," Technological Forecasting and Social Change, Elsevier, vol. 138(C), pages 95-114.
    • Handle: RePEc:eee:tefoso:v:138:y:2019:i:c:p:95-114
    DOI: 10.1016/j.techfore.2018.08.012
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    7. Paolo Calvosa, 2023. "The Life Cycle of Converging Industries: The Evolution of the Tablet Sector and Its Impact on Competitive Dynamics," International Journal of Business and Management, Canadian Center of Science and Education, vol. 16(11), pages 1-76, February.
    8. Ioannis Anagnostopoulos & Anas Rizeq, 2021. "Conventional and neural network target‐matching methods dynamics: The information technology mergers and acquisitions market in the USA," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 28(2), pages 97-118, April.
    9. Chie Hoon Song, 2021. "Exploring and Predicting the Knowledge Development in the Field of Energy Storage: Evidence from the Emerging Startup Landscape," Energies, MDPI, vol. 14(18), pages 1-20, September.
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    11. Shao, Bohua & Asatani, Kimitaka & Sasaki, Hajime & Sakata, Ichiro, 2021. "Categorization of mergers and acquisitions using transaction network features," Research in International Business and Finance, Elsevier, vol. 57(C).

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