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Organisational learning, strategic rigidity and technology adoption: Implications for electric utilities and renewable energy firms

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  • Nisar, Arsalan
  • Ruiz, Felipe
  • Palacios, Miguel

Abstract

This paper examines the implications of strategic rigidness for technology adoption behaviours among electric utilities. Such behaviours lead to heterogeneity in firm performance and consequently affect the electric utility industry. The paper's central aim is to identify and describe the implications of strategic rigidness for a utility firm's decision making in adopting newer renewable energy technologies. The findings indicate that not all utility firms are keen to adopt these new technologies, as these firms have traditionally been operating efficiently with a more conventional and mature technological arrangement that has become embedded in the organisational routine. Case studies of Iberdrola S.A. and Enel S.p.A. as major electric utilities are detailed to document mergers and acquisitions and technology adoption decisions. The results indicate that technology adoption behaviours vary widely across utility firms with different organisational learning processes and core capabilities.

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  • Nisar, Arsalan & Ruiz, Felipe & Palacios, Miguel, 2013. "Organisational learning, strategic rigidity and technology adoption: Implications for electric utilities and renewable energy firms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 22(C), pages 438-445.
    • Handle: RePEc:eee:rensus:v:22:y:2013:i:c:p:438-445
    DOI: 10.1016/j.rser.2013.01.039
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    Cited by:

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    2. Shah, Arsalan Nisar & Palacios, Miguel & Ruiz, Felipe, 2013. "Strategic rigidity and foresight for technology adoption among electric utilities," Energy Policy, Elsevier, vol. 63(C), pages 1233-1239.
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    5. Pereira, Guillermo Ivan & Specht, Jan Martin & Silva, Patrícia Pereira & Madlener, Reinhard, 2018. "Technology, business model, and market design adaptation toward smart electricity distribution: Insights for policy making," Energy Policy, Elsevier, vol. 121(C), pages 426-440.
    6. Ahmad, Tanveer & Madonski, Rafal & Zhang, Dongdong & Huang, Chao & Mujeeb, Asad, 2022. "Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    7. Chang, Rui-Dong & Zuo, Jian & Zhao, Zhen-Yu & Zillante, George & Gan, Xiao-Long & Soebarto, Veronica, 2017. "Evolving theories of sustainability and firms: History, future directions and implications for renewable energy research," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 48-56.

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