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Data analytics diffusion in the UK renewable energy sector: an innovation perspective

Author

Listed:
  • H. Kava
  • K. Spanaki

    (Audencia Business School)

  • T. Papadopoulos
  • S. Despoudi
  • O. Rodriguez Espindola
  • M. Fakhimi

Abstract

We introduce the BDA dynamics and explore the associated applications in renewable energy sector with a focus on data-driven innovation. Our study draws on the exponential growth of renewable energy initiatives over the last decades and on the paucity of literature to illustrate the use of BDA in the energy industry. We conduct a qualitative field study in the UK with stakeholder interviews and analyse our results using thematic analysis. Our findings indicate that no matter if the importance of the energy sector for ‘people's well- being, industrial competitiveness, and societal advancement, old fashioned approaches to analytics for organisational processes are currently applied widely within the energy sector. These are triggered by resistance to change and insuicient organisational knowledge about BDA, hindering innovation opportunities. Furthermore, for energy organisations to integrate BDA approaches, they need to deal with challenges such as training employees on BDA and the associated costs. Overall, our study provides insights from practitioners about adopting BDA innovations in the renewable energy sector to inform decision-makers and provide recommendations for future research

Suggested Citation

  • H. Kava & K. Spanaki & T. Papadopoulos & S. Despoudi & O. Rodriguez Espindola & M. Fakhimi, 2024. "Data analytics diffusion in the UK renewable energy sector: an innovation perspective," Post-Print hal-04478933, HAL.
    • Handle: RePEc:hal:journl:hal-04478933
    DOI: 10.1007/s10479-021-04263-1
    Note: View the original document on HAL open archive server: https://hal.science/hal-04478933
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    References listed on IDEAS

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    1. Iddrisu Awudu & William W. Wilson & Mahdi Fathi & Khalid Bachkar & Bruce Dahl & Adolf Acquaye, 2020. "Application of big data copula-based clustering for hedging in renewable energy systems," International Journal of Revenue Management, Inderscience Enterprises Ltd, vol. 11(4), pages 237-263.
    2. Sivarajah, Uthayasankar & Kamal, Muhammad Mustafa & Irani, Zahir & Weerakkody, Vishanth, 2017. "Critical analysis of Big Data challenges and analytical methods," Journal of Business Research, Elsevier, vol. 70(C), pages 263-286.
    3. Kristoffersen, Eivind & Blomsma, Fenna & Mikalef, Patrick & Li, Jingyue, 2020. "The smart circular economy: A digital-enabled circular strategies framework for manufacturing companies," Journal of Business Research, Elsevier, vol. 120(C), pages 241-261.
    4. Konstantina Spanaki & Zeynep Gürgüç & Richard Adams & Catherine Mulligan, 2018. "Data supply chain (DSC): research synthesis and future directions," International Journal of Production Research, Taylor & Francis Journals, vol. 56(13), pages 4447-4466, July.
    5. Gunasekaran, Angappa & Papadopoulos, Thanos & Dubey, Rameshwar & Wamba, Samuel Fosso & Childe, Stephen J. & Hazen, Benjamin & Akter, Shahriar, 2017. "Big data and predictive analytics for supply chain and organizational performance," Journal of Business Research, Elsevier, vol. 70(C), pages 308-317.
    6. Gandomi, Amir & Haider, Murtaza, 2015. "Beyond the hype: Big data concepts, methods, and analytics," International Journal of Information Management, Elsevier, vol. 35(2), pages 137-144.
    7. Kusiak, Andrew, 2009. "Innovation: A data-driven approach," International Journal of Production Economics, Elsevier, vol. 122(1), pages 440-448, November.
    8. Haili Zhang & Michael Song & Huanhuan He, 2020. "Achieving the Success of Sustainability Development Projects through Big Data Analytics and Artificial Intelligence Capability," Sustainability, MDPI, vol. 12(3), pages 1-23, January.
    9. Raguseo, Elisabetta, 2018. "Big data technologies: An empirical investigation on their adoption, benefits and risks for companies," International Journal of Information Management, Elsevier, vol. 38(1), pages 187-195.
    10. Shirish Jeble & Rameshwar Dubey & Stephen J. Childe & Thanos Papadopoulos & David Roubaud & Anand Prakash, 2018. "Impact of big data and predictive analytics capability on supply chain sustainability," Post-Print hal-02061341, HAL.
    11. Patrick Mikalef & Ilias O. Pappas & John Krogstie & Michail Giannakos, 2018. "Big data analytics capabilities: a systematic literature review and research agenda," Information Systems and e-Business Management, Springer, vol. 16(3), pages 547-578, August.
    12. Akter, Shahriar & Bandara, Ruwan & Hani, Umme & Fosso Wamba, Samuel & Foropon, Cyril & Papadopoulos, Thanos, 2019. "Analytics-based decision-making for service systems: A qualitative study and agenda for future research," International Journal of Information Management, Elsevier, vol. 48(C), pages 85-95.
    13. Mikalef, Patrick & Boura, Maria & Lekakos, George & Krogstie, John, 2019. "Big data analytics and firm performance: Findings from a mixed-method approach," Journal of Business Research, Elsevier, vol. 98(C), pages 261-276.
    14. Mortenson, Michael J. & Doherty, Neil F. & Robinson, Stewart, 2015. "Operational research from Taylorism to Terabytes: A research agenda for the analytics age," European Journal of Operational Research, Elsevier, vol. 241(3), pages 583-595.
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    More about this item

    Keywords

    Big data analytics; Energy sector; Renewable energy; Difusion of; innovations; Field study;
    All these keywords.

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