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Institutional means-ends decoupling work in industrial R&D project implementation

Author

Listed:
  • Rachid Jabbouri

    (ESC [Rennes] - ESC Rennes School of Business)

  • Yann Truong

    (BSB - Burgundy School of Business (BSB) - Ecole Supérieure de Commerce de Dijon Bourgogne (ESC))

  • Dirk Schneckenberg

    (ESC [Rennes] - ESC Rennes School of Business)

  • Mark Palmer

    (QUB - Queen's University [Belfast])

Abstract

Managing goals is a key network management function and is critical in the implementation of industrial R&D projects. In this paper, we explore the implementation of an industrial R&D project, focusing in particular upon the role of means-ends decoupling work to understand how the goals are managed. We combine several data sources in our case research to explore project implementation through an understanding of means-ends decoupling work. We collected in-depth interviews, archival records and field observations within the R&D research setting of an industrial R&D project in the period of 2015 to 2017. Our findings identify three types of means-ends decoupling work in R&D project implementation: ‘work on' causal complexity, ‘work at' behavioural invisibility, and ‘work with' practice multiplicity. In addition, we uncover six dynamic micro-mechanisms that collectively influence the making and nature of means-ends decoupling work and therefore serve to allow for the fluid switching of work as the institutional conditions permit. Overall, our findings have significant implications for understanding means-ends decoupling as a highly skilled network competence for managing R&D project implementation goals.

Suggested Citation

  • Rachid Jabbouri & Yann Truong & Dirk Schneckenberg & Mark Palmer, 2019. "Institutional means-ends decoupling work in industrial R&D project implementation," Post-Print hal-02290162, HAL.
    • Handle: RePEc:hal:journl:hal-02290162
    DOI: 10.1016/j.indmarman.2019.01.012
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    Citations

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    Cited by:

    1. Issa, Helmi & Jabbouri, Rachid & Palmer, Mark, 2022. "An artificial intelligence (AI)-readiness and adoption framework for AgriTech firms," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    2. Füller, Johann & Hutter, Katja & Wahl, Julian & Bilgram, Volker & Tekic, Zeljko, 2022. "How AI revolutionizes innovation management – Perceptions and implementation preferences of AI-based innovators," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    3. Bertello, Alberto & De Bernardi, Paola & Ferraris, Alberto & Bresciani, Stefano, 2022. "Shedding lights on organizational decoupling in publicly funded R&D consortia: An institutional perspective on open innovation," Technological Forecasting and Social Change, Elsevier, vol. 176(C).

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