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Strategic and cultural contexts of real options reasoning in innovation portfolios

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  • Kaufmann, Carsten
  • Kock, Alexander
  • Gemünden, Hans Georg

Abstract

Decision makers find creating an innovation portfolio challenging, because more innovative projects are associated with a higher degree of uncertainty. In this study, we investigate the potential benefits of applying real options reasoning (ROR) in innovation portfolio management from an attention‐based view. Using a sample of 137 innovation portfolios with multiple informants, we investigate ROR's influence on portfolio innovativeness and, ultimately, on portfolio success in a mediated model. Further, we analyze the moderating influence of an innovation portfolio's organizational context — entrepreneurial orientation and innovation climate — on ROR's application. The results support ROR's positive relationship to portfolio innovativeness and portfolio success. The analysis also supports the positive interaction between entrepreneurial orientation and ROR with respect to portfolio innovativeness. This study contributes to the literature by demonstrating the relationship between ROR and portfolio success, mediated by portfolio innovativeness. In addition, the study's analysis offers an explanation of previously mixed findings regarding ROR's benefits by considering the firm's strategic and cultural innovation contexts. The findings underline the relevance of strategic support for ROR's effectiveness in innovation portfolio management. Furthermore, the findings encourage managers to implement ROR, but also stress the essential contribution an entrepreneurial orientation makes when the managers do so.

Suggested Citation

  • Kaufmann, Carsten & Kock, Alexander & Gemünden, Hans Georg, 2021. "Strategic and cultural contexts of real options reasoning in innovation portfolios," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 130793, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
  • Handle: RePEc:dar:wpaper:130793
    Note: for complete metadata visit http://tubiblio.ulb.tu-darmstadt.de/130793/
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    File URL: https://onlinelibrary.wiley.com/doi/10.1111/jpim.12566
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    File URL: https://tuprints.ulb.tu-darmstadt.de/20144
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    Cited by:

    1. Tian, Yuanyuan & Bai, Libiao & Wei, Lan & Zheng, Kanyin & Zhou, Xinyu, 2022. "Modeling for project portfolio benefit prediction via a GA-BP neural network," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    2. Gorupec Natalia & Brehmer Nataliia & Tiberius Victor & Kraus Sascha, 2022. "Tackling uncertain future scenarios with real options: A review and research framework," The Irish Journal of Management, Sciendo, vol. 41(1), pages 69-88, July.

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