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The Value of Big Data Analytics Pillars in Telecommunication Industry

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  • Hassan Keshavarz

    (Department of Management of Technology, Malaysia Japan International Institute of Technology (MJIIT), University Teknologi Malaysia, Kuala Lumpur 54100, Malaysia)

  • Akbariah Mohd Mahdzir

    (Department of Management of Technology, Malaysia Japan International Institute of Technology (MJIIT), University Teknologi Malaysia, Kuala Lumpur 54100, Malaysia)

  • Hosna Talebian

    (Oil, Gas and Petroleum Research Centre, Amirkabir University of Technology, Tehran 1591639526, Iran)

  • Neda Jalaliyoon

    (Department of Management of Technology, Malaysia Japan International Institute of Technology (MJIIT), University Teknologi Malaysia, Kuala Lumpur 54100, Malaysia)

  • Naoki Ohshima

    (Graduate School of Management of Innovation and Technology, Yamaguchi University, 2-16-1, Tokiwa-Dai, Ube, Yamaguchi 755-8611, Japan)

Abstract

In the Big Data age, businesses in every industry must deal with vast volumes of data. Several experts and practitioners have lately emphasized the need of understanding how, why, and when Big Data Analytics (BDA) applications may be a valuable resource for businesses seeking a competitive edge. However, BDA pays off for some firms while failing to pay off for others due to the fact that investment in Big Data continues to present significant challenges due to the missing link between analytics capabilities and firm performance. According to a recent survey, many businesses spend the bulk of their time analyzing data, with only a tiny fraction employing Big Data Analytics to forecast outcomes and even fewer utilizing analytics apps to enhance processes and strategies. As a result, BDA is not widely used, and only a few companies have seen any benefit from it. To address this issue in the telecommunications domain and in light of the paucity of research on the subject, this study focused on the BDA Pillars (BDAP) in order to achieve benefits through increased revenues and cost savings. For the purpose of this research we have adopted qualitative approach with case study method, and technique of data collection includes semi-structure interview and document analysis. The Delphi technique and in-depth interviews conducted confirmed the existence of five critical elements that contribute to the sustainability of BDAPs and their impact on firm performance.

Suggested Citation

  • Hassan Keshavarz & Akbariah Mohd Mahdzir & Hosna Talebian & Neda Jalaliyoon & Naoki Ohshima, 2021. "The Value of Big Data Analytics Pillars in Telecommunication Industry," Sustainability, MDPI, vol. 13(13), pages 1-36, June.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:13:p:7160-:d:582346
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    1. Milé Terziovski, 2010. "Innovation practice and its performance implications in small and medium enterprises (SMEs) in the manufacturing sector: a resource‐based view," Strategic Management Journal, Wiley Blackwell, vol. 31(8), pages 892-902, August.
    2. Romijn, Henny & Albaladejo, Manuel, 2002. "Determinants of innovation capability in small electronics and software firms in southeast England," Research Policy, Elsevier, vol. 31(7), pages 1053-1067, September.
    3. Kallinikos, Jannis & Constantiou, Ioanna D., 2015. "Big data revisited: a rejoinder," LSE Research Online Documents on Economics 63020, London School of Economics and Political Science, LSE Library.
    4. Constantiou, Ioanna D & Kallinikos, Jannis, 2015. "New games, new rules: big data and the changing context of strategy," LSE Research Online Documents on Economics 63017, London School of Economics and Political Science, LSE Library.
    5. Ciampi, Francesco & Demi, Stefano & Magrini, Alessandro & Marzi, Giacomo & Papa, Armando, 2021. "Exploring the impact of big data analytics capabilities on business model innovation: The mediating role of entrepreneurial orientation," Journal of Business Research, Elsevier, vol. 123(C), pages 1-13.
    6. Ma-Lin Song & Ron Fisher & Jian-Lin Wang & Lian-Biao Cui, 2018. "Environmental performance evaluation with big data: theories and methods," Annals of Operations Research, Springer, vol. 270(1), pages 459-472, November.
    7. Annika Olsson & Carl Wadell & Per Odenrick & Margareta Norell Bergendahl, 2010. "An action learning method for increased innovation capability in organisations," Action Learning: Research and Practice, Taylor & Francis Journals, vol. 7(2), pages 167-179, April.
    8. Taghrid Suifan, 2021. "How Innovativeness Mediates The Effects Of Organizational Culture And Leadership On Performance," International Journal of Innovation Management (ijim), World Scientific Publishing Co. Pte. Ltd., vol. 25(02), pages 1-32, February.
    9. Gunday, Gurhan & Ulusoy, Gunduz & Kilic, Kemal & Alpkan, Lutfihak, 2011. "Effects of innovation types on firm performance," International Journal of Production Economics, Elsevier, vol. 133(2), pages 662-676, October.
    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. Minna Saunila & Sanna Pekkola & Juhani Ukko, 2014. "The relationship between innovation capability and performance," International Journal of Productivity and Performance Management, Emerald Group Publishing Limited, vol. 63(2), pages 234-249, January.
    12. Vicky Ching Gu & Bin Zhou & Qing Cao & Jeffery Adams, 2021. "Exploring the relationship between supplier development, big data analytics capability, and firm performance," Annals of Operations Research, Springer, vol. 302(1), pages 151-172, July.
    13. Scott L. Newbert, 2007. "Empirical research on the resource‐based view of the firm: an assessment and suggestions for future research," Strategic Management Journal, Wiley Blackwell, vol. 28(2), pages 121-146, February.
    14. Benn Lawson & Danny Samson, 2001. "Developing Innovation Capability In Organisations: A Dynamic Capabilities Approach," International Journal of Innovation Management (ijim), World Scientific Publishing Co. Pte. Ltd., vol. 5(03), pages 377-400.
    15. Tan, Kim Hua & Zhan, YuanZhu & Ji, Guojun & Ye, Fei & Chang, Chingter, 2015. "Harvesting big data to enhance supply chain innovation capabilities: An analytic infrastructure based on deduction graph," International Journal of Production Economics, Elsevier, vol. 165(C), pages 223-233.
    16. Gülşen Akman & Cengiz Yilmaz, 2008. "Innovative Capability, Innovation Strategy And Market Orientation: An Empirical Analysis In Turkish Software Industry," International Journal of Innovation Management (ijim), World Scientific Publishing Co. Pte. Ltd., vol. 12(01), pages 69-111.
    17. Kamalini Ramdas & Robert E. Spekman, 2000. "Chain or Shackles: Understanding What Drives Supply-Chain Performance," Interfaces, INFORMS, vol. 30(4), pages 3-21, August.
    18. Akter, Shahriar & Wamba, Samuel Fosso & Gunasekaran, Angappa & Dubey, Rameshwar & Childe, Stephen J., 2016. "How to improve firm performance using big data analytics capability and business strategy alignment?," International Journal of Production Economics, Elsevier, vol. 182(C), pages 113-131.
    19. Morgan, Neil A. & Slotegraaf, Rebecca J. & Vorhies, Douglas W., 2009. "Linking marketing capabilities with profit growth," International Journal of Research in Marketing, Elsevier, vol. 26(4), pages 284-293.
    20. 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.
    21. Hazen, Benjamin T. & Boone, Christopher A. & Ezell, Jeremy D. & Jones-Farmer, L. Allison, 2014. "Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications," International Journal of Production Economics, Elsevier, vol. 154(C), pages 72-80.
    22. Kim Hua Tan & Guojun Ji & Chee Peng Lim & Ming-Lang Tseng, 2017. "Using big data to make better decisions in the digital economy," International Journal of Production Research, Taylor & Francis Journals, vol. 55(17), pages 4998-5000, September.
    23. Mazzanti, Massimiliano & Pini, Paolo & Tortia, Ermanno, 2006. "Organizational innovations, human resources and firm performance: The Emilia-Romagna food sector," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 35(1), pages 123-141, February.
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    2. Syed Abdul Rehman Khan & Pablo Ponce & Muhammad Tanveer & Nathalie Aguirre-Padilla & Haider Mahmood & Syed Adeel Ali Shah, 2021. "Technological Innovation and Circular Economy Practices: Business Strategies to Mitigate the Effects of COVID-19," Sustainability, MDPI, vol. 13(15), pages 1-17, July.
    3. Maniyassouwe Amana & Pingfeng Liu & Mona Alariqi, 2022. "Value Creation and Capture with Big Data in Smart Phones Companies," Sustainability, MDPI, vol. 14(23), pages 1-22, November.

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