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Developing a risk-adaptive technology roadmap using a Bayesian network and topic modeling under deep uncertainty

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  • Yujin Jeong

    (Dongguk University)

  • Hyejin Jang

    (Dongguk University)

  • Byungun Yoon

    (Dongguk University)

Abstract

Firms today face rapidly changing and complex environments that managers and leaders must navigate carefully because confronting these changes is directly connected with success and failure in business. In particular, business leaders are adopting a new paradigm of planning, dynamic adaptive plans, which react adaptively to uncertainties by adjusting plans according to rapid changes in circumstances. However, these dynamic plans have been applied in larger-scale industries such as wastewater management in longer-range time frames. This paper follows the dynamic adaptive plan paradigm but transfers it to the technology management context with shorter-range action plans. Based on this new paradigm of risk management and technology planning, we propose a risk-adaptive technology roadmap (TRM) that can adapt to changing complex environments. First we identify risk by topic modeling based on futuristic data and then by sentiment analysis. Second, for the derived risks, we determine new and alternative plans by co-occurrence of risk-related keywords. Third, we convert an existing TRM to network topology with adaptive plans and construct a conditional probability table for the network. Finally, we estimate posterior probability and infer it by Bayesian network by adjusting plans depending on occurrence of risk events. Based on this posterior probability, we remap the paths in the previous TRM to new maps, and we apply our proposed approach to the field of artificial intelligence to validate its feasibility. Our research contributes to the possibility of using dynamic adaptive planning with technology as well as to increase the sustainability of technology roadmapping.

Suggested Citation

  • Yujin Jeong & Hyejin Jang & Byungun Yoon, 2021. "Developing a risk-adaptive technology roadmap using a Bayesian network and topic modeling under deep uncertainty," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 3697-3722, May.
    • Handle: RePEc:spr:scient:v:126:y:2021:i:5:d:10.1007_s11192-021-03945-8
    DOI: 10.1007/s11192-021-03945-8
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