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Management Perspectives towards the Data-Driven Organization in the Energy Sector

Author

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  • Irina Bogdana Pugna

    (Department of Management of Information Systems, Bucharest University of Economic Studies, 6 Piața Romană, Sector 1, 010374 Bucharest, Romania)

  • Dana Maria Boldeanu

    (Department of Management of Information Systems, Bucharest University of Economic Studies, 6 Piața Romană, Sector 1, 010374 Bucharest, Romania)

  • Mirela Gheorghe

    (Department of Management of Information Systems, Bucharest University of Economic Studies, 6 Piața Romană, Sector 1, 010374 Bucharest, Romania)

  • Gabriel Cozgarea

    (Department of Management of Information Systems, Bucharest University of Economic Studies, 6 Piața Romană, Sector 1, 010374 Bucharest, Romania)

  • Adrian Nicolae Cozgarea

    (Department of Management of Information Systems, Bucharest University of Economic Studies, 6 Piața Romană, Sector 1, 010374 Bucharest, Romania)

Abstract

This paper explores the current attitudes of managers and executives working in the energy sector towards the Data-Driven Organizational Model implied by Big Data. The aim is to explore and understand the current mindset of senior decision makers, since their success depends as much on cognitive and behavioral processes as on their technical competences. We adopt a grounded-theory approach, developing models of understanding and belief abductively, driven by the data obtained from participants through a reflection guide. We find that managers differ significantly in their understanding and engagement with their challenges; they display interest but differ in their commitment and enthusiasm; they identify a lack of strategy and skills as current barriers; and they are currently unwilling to trust data, treating evidence according to their own prior commitments. This is a significant barrier to establishing the Data-Driven Organizational Model. These findings raise concerns, and the paper concludes that by considering initiatives for implementing more agile and forward-looking approaches, establishing a data-driven organizational culture, and managing such changes effectively.

Suggested Citation

  • Irina Bogdana Pugna & Dana Maria Boldeanu & Mirela Gheorghe & Gabriel Cozgarea & Adrian Nicolae Cozgarea, 2022. "Management Perspectives towards the Data-Driven Organization in the Energy Sector," Energies, MDPI, vol. 15(16), pages 1-20, August.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:16:p:5775-:d:884025
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    References listed on IDEAS

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    1. Francesco Polese & Antonio Botti & Mara Grimaldi & Antonella Monda & Massimiliano Vesci, 2018. "Social Innovation in Smart Tourism Ecosystems: How Technology and Institutions Shape Sustainable Value Co-Creation," Sustainability, MDPI, vol. 10(1), pages 1-24, January.
    2. Engelbrecht, Adrian & Gerlach, Jin & Widjaja, Thomas, 2016. "Understanding the Anatomy of Data-Driven Business Models - Towards an Empirical Taxonomy," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 80123, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    3. Knudsen, Eirik Sjåholm & Lien, Lasse B. & Timmermans, Bram & Belik, Ivan & Pandey, Sujit, 2021. "Stability in turbulent times? The effect of digitalization on the sustainability of competitive advantage," Journal of Business Research, Elsevier, vol. 128(C), pages 360-369.
    4. Di Silvestre, Maria Luisa & Favuzza, Salvatore & Riva Sanseverino, Eleonora & Zizzo, Gaetano, 2018. "How Decarbonization, Digitalization and Decentralization are changing key power infrastructures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 483-498.
    5. Jonathan J.J.M. Seddon & Wendy L. Currie, 2017. "A model for unpacking big data analytics in high-frequency trading," Post-Print hal-01404316, HAL.
    6. Shengbin Hao & Haili Zhang & Michael Song, 2019. "Big Data, Big Data Analytics Capability, and Sustainable Innovation Performance," Sustainability, MDPI, vol. 11(24), pages 1-15, December.
    7. Henry Chesbrough & Richard S. Rosenbloom, 2002. "The role of the business model in capturing value from innovation: evidence from Xerox Corporation's technology spin-off companies," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 11(3), pages 529-555, June.
    8. Weiguang Fang & Yu Guo & Wenhe Liao & Karthik Ramani & Shaohua Huang, 2020. "Big data driven jobs remaining time prediction in discrete manufacturing system: a deep learning-based approach," International Journal of Production Research, Taylor & Francis Journals, vol. 58(9), pages 2751-2766, May.
    9. Seddon, Jonathan J.J.M. & Currie, Wendy L., 2017. "A model for unpacking big data analytics in high-frequency trading," Journal of Business Research, Elsevier, vol. 70(C), pages 300-307.
    10. Vidgen, Richard & Shaw, Sarah & Grant, David B., 2017. "Management challenges in creating value from business analytics," European Journal of Operational Research, Elsevier, vol. 261(2), pages 626-639.
    11. Zhou, Kaile & Fu, Chao & Yang, Shanlin, 2016. "Big data driven smart energy management: From big data to big insights," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 215-225.
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    Cited by:

    1. Xhesilda Vogli & Erion c{C}ano, 2023. "CSRCZ: A Dataset About Corporate Social Responsibility in Czech Republic," Papers 2301.03404, arXiv.org.
    2. Cezar-Petre Simion & Cătălin-Alexandru Verdeș & Alexandra-Andreea Mironescu & Florin-Gabriel Anghel, 2023. "Digitalization in Energy Production, Distribution, and Consumption: A Systematic Literature Review," Energies, MDPI, vol. 16(4), pages 1-30, February.

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