IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v333y2024i2d10.1007_s10479-021-04263-1.html
   My bibliography  Save this article

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

Author

Listed:
  • Harkaran Kava

    (Loughborough University)

  • Konstantina Spanaki

    (Audencia Business School)

  • Thanos Papadopoulos

    (University of Kent)

  • Stella Despoudi

    (University of Western Macedonia
    Aston University)

  • Oscar Rodriguez-Espindola

    (Aston University)

  • Masoud Fakhimi

    (University of Surrey)

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 insufficient 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

  • Harkaran Kava & Konstantina Spanaki & Thanos Papadopoulos & Stella Despoudi & Oscar Rodriguez-Espindola & Masoud Fakhimi, 2024. "Data analytics diffusion in the UK renewable energy sector: an innovation perspective," Annals of Operations Research, Springer, vol. 333(2), pages 717-742, February.
    • Handle: RePEc:spr:annopr:v:333:y:2024:i:2:d:10.1007_s10479-021-04263-1
    DOI: 10.1007/s10479-021-04263-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-021-04263-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10479-021-04263-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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.
    5. Kusiak, Andrew, 2009. "Innovation: A data-driven approach," International Journal of Production Economics, Elsevier, vol. 122(1), pages 440-448, November.
    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. 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.
    8. Kwon, Ohbyung & Lee, Namyeon & Shin, Bongsik, 2014. "Data quality management, data usage experience and acquisition intention of big data analytics," International Journal of Information Management, Elsevier, vol. 34(3), pages 387-394.
    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. Wamba, Samuel Fosso & Gunasekaran, Angappa & Akter, Shahriar & Ren, Steven Ji-fan & Dubey, Rameshwar & Childe, Stephen J., 2017. "Big data analytics and firm performance: Effects of dynamic capabilities," Journal of Business Research, Elsevier, vol. 70(C), pages 356-365.
    11. 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.
    12. Spanaki, Konstantina & Karafili, Erisa & Despoudi, Stella, 2021. "AI applications of data sharing in agriculture 4.0: A framework for role-based data access control," International Journal of Information Management, Elsevier, vol. 59(C).
    13. Benjamin T. Hazen & Joseph B. Skipper & Christopher A. Boone & Raymond R. Hill, 2018. "Back in business: operations research in support of big data analytics for operations and supply chain management," Annals of Operations Research, Springer, vol. 270(1), pages 201-211, November.
    14. 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.
    15. 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.
    16. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Harkaran Kava & Konstantina Spanaki & Thanos Papadopoulos & Stella Despoudi & Oscar Rodriguez-Espindola & Masoud Fakhimi, 2021. "Data Analytics Diffusion in the UK Renewable Energy Sector: An Innovation Perspective," Post-Print hal-03781046, HAL.
    2. 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.
    3. Ashrafi, Amir & Zare Ravasan, Ahad & Trkman, Peter & Afshari, Samira, 2019. "The role of business analytics capabilities in bolstering firms’ agility and performance," International Journal of Information Management, Elsevier, vol. 47(C), pages 1-15.
    4. de Camargo Fiorini, Paula & Roman Pais Seles, Bruno Michel & Chiappetta Jabbour, Charbel Jose & Barberio Mariano, Enzo & de Sousa Jabbour, Ana Beatriz Lopes, 2018. "Management theory and big data literature: From a review to a research agenda," International Journal of Information Management, Elsevier, vol. 43(C), pages 112-129.
    5. Samuel Fosso Wamba & Maciel M. Queiroz & Lunwen Wu & Uthayasankar Sivarajah, 2024. "Big data analytics-enabled sensing capability and organizational outcomes: assessing the mediating effects of business analytics culture," Annals of Operations Research, Springer, vol. 333(2), pages 559-578, February.
    6. Rawan Babalghaith & Amer Aljarallah, 2024. "Factors Affecting Big Data Analytics Adoption in Small and Medium Enterprises," Information Systems Frontiers, Springer, vol. 26(6), pages 2165-2187, December.
    7. Sabeen Hussain Bhatti & Wan Mohd Hirwani Wan Hussain & Jabran Khan & Shahbaz Sultan & Alberto Ferraris, 2024. "Exploring data-driven innovation: What’s missing in the relationship between big data analytics capabilities and supply chain innovation?," Annals of Operations Research, Springer, vol. 333(2), pages 799-824, February.
    8. Kusi-Sarpong, Simonov & Orji, Ifeyinwa Juliet & Gupta, Himanshu & Kunc, Martin, 2021. "Risks associated with the implementation of big data analytics in sustainable supply chains," Omega, Elsevier, vol. 105(C).
    9. Brinch, Morten & Gunasekaran, Angappa & Fosso Wamba, Samuel, 2021. "Firm-level capabilities towards big data value creation," Journal of Business Research, Elsevier, vol. 131(C), pages 539-548.
    10. Conboy, Kieran & Mikalef, Patrick & Dennehy, Denis & Krogstie, John, 2020. "Using business analytics to enhance dynamic capabilities in operations research: A case analysis and research agenda," European Journal of Operational Research, Elsevier, vol. 281(3), pages 656-672.
    11. Rajesh Chidananda Reddy & Biplab Bhattacharjee & Debasisha Mishra & Anandadeep Mandal, 2022. "A systematic literature review towards a conceptual framework for enablers and barriers of an enterprise data science strategy," Information Systems and e-Business Management, Springer, vol. 20(1), pages 223-255, March.
    12. Ashrafi, Amir & Zareravasan, Ahad, 2022. "An ambidextrous approach on the business analytics-competitive advantage relationship: Exploring the moderating role of business analytics strategy," Technological Forecasting and Social Change, Elsevier, vol. 179(C).
    13. Sheng, Jie & Amankwah-Amoah, Joseph & Wang, Xiaojun, 2017. "A multidisciplinary perspective of big data in management research," International Journal of Production Economics, Elsevier, vol. 191(C), pages 97-112.
    14. Raguseo, Elisabetta & Vitari, Claudio & Pigni, Federico, 2020. "Profiting from big data analytics: The moderating roles of industry concentration and firm size," International Journal of Production Economics, Elsevier, vol. 229(C).
    15. Ahmad Ibrahim Aljumah & Mohammed T. Nuseir & Md. Mahmudul Alam, 2021. "Traditional marketing analytics, big data analytics and big data system quality and the success of new product development," Post-Print hal-03538161, HAL.
    16. Mujahid Mohiuddin Babu & Mahfuzur Rahman & Ashraful Alam & Bidit Lal Dey, 2024. "Exploring big data-driven innovation in the manufacturing sector: evidence from UK firms," Annals of Operations Research, Springer, vol. 333(2), pages 689-716, February.
    17. Gupta, Shivam & Kar, Arpan Kumar & Baabdullah, Abdullah & Al-Khowaiter, Wassan A.A., 2018. "Big data with cognitive computing: A review for the future," International Journal of Information Management, Elsevier, vol. 42(C), pages 78-89.
    18. Wang, Yonggui & Tian, Qinghong & Li, Xia & Xiao, Xiaohong, 2022. "Different roles, different strokes: How to leverage two types of digital platform capabilities to fuel service innovation," Journal of Business Research, Elsevier, vol. 144(C), pages 1121-1128.
    19. Meadows, Maureen & Merendino, Alessandro & Dibb, Sally & Garcia-Perez, Alexeis & Hinton, Matthew & Papagiannidis, Savvas & Pappas, Ilias & Wang, Huamao, 2022. "Tension in the data environment: How organisations can meet the challenge," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    20. Sun, Pengfei & Yuan, Chunhui & Li, Xiaolong & Di, Jia, 2024. "Big data analytics, firm risk and corporate policies: Evidence from China," Research in International Business and Finance, Elsevier, vol. 70(PB).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:annopr:v:333:y:2024:i:2:d:10.1007_s10479-021-04263-1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.