IDEAS home Printed from https://ideas.repec.org/a/bcp/journl/v8y2024i6p77-91.html
   My bibliography  Save this article

Impacts of Innovation and Business Analytics on the Performance of the Service Sector in Nigeria

Author

Listed:
  • Ladi Daodu

    (Ph.D. Candidate, Lincoln University College, Petaling Jaya, Malaysia.)

  • Prof. Dr. Amiya Bhaumik

    (Deputy Vice Chancellor, Lincoln University College, Petaling Jaya, Malaysia.)

Abstract

The study investigated the impacts of innovation, including service innovation and business analytics adoption on the performance of the service sector in Nigeria between 2013 and 2022. The proportion of Nigeria’s economy attributable to the service sector, the Innovation Efficiency Index, and the Business Analytics usage index were all calculated using secondary data drawn from metrics such as the number of service firms engaging in R&D, the total amount of R&D expenditures, and the share of total service sector revenue received via online channels. As a result of multicollinearity and heteroscedasticity, the study used quantitative analysis, including regression and correlation analyses. The results show that innovation and its impact on service quality, has a large impact on Nigeria’s service sector, but that the sector’s current adoption level of business analytics is too low to produce far-reaching changes in performance. It was further revealed from the study that the e-participation rate is very vital for driving both the adoption of business analytics and the performance of the service sector. According to the study, the adoption of business analytics should be given more attention in the Nigerian service sector so that its contribution to the performance of the sector is more significant. Finally, the scope of e-participation in the service sector is expanding by the day, and it is recommended that various organizations become more e-service driven to compete in today’s market and maintain good performance.

Suggested Citation

  • Ladi Daodu & Prof. Dr. Amiya Bhaumik, 2024. "Impacts of Innovation and Business Analytics on the Performance of the Service Sector in Nigeria," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 8(6), pages 77-91, June.
    • Handle: RePEc:bcp:journl:v:8:y:2024:i:6:p:77-91
    as

    Download full text from publisher

    File URL: https://www.rsisinternational.org/journals/ijriss/Digital-Library/volume-8-issue-6/77-91.pdf
    Download Restriction: no
    File URL: https://rsisinternational.org/journals/ijriss/articles/impacts-of-innovation-and-business-analytics-on-the-performance-of-the-service-sector-in-nigeria/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Artem Timoshenko & John R. Hauser, 2019. "Identifying Customer Needs from User-Generated Content," Marketing Science, INFORMS, vol. 38(1), pages 1-20, January.
    2. Hansol Lee & Eunkyung Kweon & Minkyun Kim & Sangmi Chai, 2017. "Does Implementation of Big Data Analytics Improve Firms’ Market Value? Investors’ Reaction in Stock Market," Sustainability, MDPI, vol. 9(6), pages 1-17, June.
    3. 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.
    4. Wu, Liang & Liu, Heng & Su, Kun, 2020. "Exploring the dual effect of effectuation on new product development speed and quality," Journal of Business Research, Elsevier, vol. 106(C), pages 82-93.
    5. Gault, Fred, 2018. "Defining and measuring innovation in all sectors of the economy," Research Policy, Elsevier, vol. 47(3), pages 617-622.
    6. Akter, Shahriar & Motamarri, Saradhi & Hani, Umme & Shams, Riad & Fernando, Mario & Mohiuddin Babu, Mujahid & Ning Shen, Kathy, 2020. "Building dynamic service analytics capabilities for the digital marketplace," Journal of Business Research, Elsevier, vol. 118(C), pages 177-188.
    7. Erevelles, Sunil & Fukawa, Nobuyuki & Swayne, Linda, 2016. "Big Data consumer analytics and the transformation of marketing," Journal of Business Research, Elsevier, vol. 69(2), pages 897-904.
    8. Claudio Vitari & Elisabetta Raguseo, 2020. "Big data analytics business value and firm performance: linking with environmental context," International Journal of Production Research, Taylor & Francis Journals, vol. 58(18), pages 5456-5476, September.
    9. Oluseun A. Ishola & Modinat O. Olusoji, 2020. "Service Sector Performance, Industry and Growth in Nigeria," International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), IGI Global Scientific Publishing, vol. 11(1), pages 31-45, January.
    10. Duan, Yanqing & Cao, Guangming & Edwards, John S., 2020. "Understanding the impact of business analytics on innovation," European Journal of Operational Research, Elsevier, vol. 281(3), pages 673-686.
    11. Aydiner, Arafat Salih & Tatoglu, Ekrem & Bayraktar, Erkan & Zaim, Selim & Delen, Dursun, 2019. "Business analytics and firm performance: The mediating role of business process performance," Journal of Business Research, Elsevier, vol. 96(C), pages 228-237.
    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. 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.
    2. Morimura, Fumikazu & Sakagawa, Yuji, 2023. "The intermediating role of big data analytics capability between responsive and proactive market orientations and firm performance in the retail industry," Journal of Retailing and Consumer Services, Elsevier, vol. 71(C).
    3. Acciarini, Chiara & Cappa, Francesco & Boccardelli, Paolo & Oriani, Raffaele, 2023. "How can organizations leverage big data to innovate their business models? A systematic literature review," Technovation, Elsevier, vol. 123(C).
    4. Alfadhel, Mutasim, 2025. "Unpacking when and how business analytics affect firm performance and customer satisfaction: A longitudinal examination," Journal of Retailing and Consumer Services, Elsevier, vol. 84(C).
    5. Korayim, Diana & Chotia, Varun & Jain, Girish & Hassan, Sharfa & Paolone, Francesco, 2024. "How big data analytics can create competitive advantage in high-stake decision forecasting? The mediating role of organizational innovation," Technological Forecasting and Social Change, Elsevier, vol. 199(C).
    6. Ludivine Ravat & Aurélie Hemonnet-Goujot & Sandrine Hollet-Haudebert, 2023. "Data-driven innovation capability of marketing: an exploratory study of its components and underlying processes," Post-Print hal-04151199, HAL.
    7. Leven J. Zheng & Justin Zuopeng Zhang & Huan Wang & Jacky F. L. Hong, 2025. "Exploring the impact of Big Data Analytics Capabilities on the dual nature of innovative activities in MSMEs: A Data-Agility-Innovation Perspective," Annals of Operations Research, Springer, vol. 350(2), pages 699-727, July.
    8. Constant Berkhout & Abhi Bhattacharya & Carlos Bauer & Ross W. Johnson, 2024. "Revisiting the construct of data-driven decision making: antecedents, scope, and boundaries," SN Business & Economics, Springer, vol. 4(10), pages 1-23, October.
    9. Jingmei Gao & Zahid Sarwar, 2024. "How do firms create business value and dynamic capabilities by leveraging big data analytics management capability?," Information Technology and Management, Springer, vol. 25(3), pages 283-304, September.
    10. Osama Musa Ali Al-Darras & Cem Tanova, 2022. "From Big Data Analytics to Organizational Agility: What Is the Mechanism?," SAGE Open, , vol. 12(2), pages 21582440221, June.
    11. Miikka Blomster & Timo Koivumäki, 2022. "Exploring the resources, competencies, and capabilities needed for successful machine learning projects in digital marketing," Information Systems and e-Business Management, Springer, vol. 20(1), pages 123-169, March.
    12. Kumar, Vinod & Kumar, Sachin & Chaudhuri, Ranjan & Chatterjee, Sheshadri & Thrassou, Alkis & Sakka, Georgia, 2025. "From insight to impact: Unravelling the dynamics of big data-backed growth hacking," Journal of Business Research, Elsevier, vol. 188(C).
    13. Mihai BOGDAN & Anca BORZA, 2020. "Big Data Analytics And Firm Performance: A Text Mining Approach," Proceedings of the INTERNATIONAL MANAGEMENT CONFERENCE, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 14(1), pages 549-560, November.
    14. Umme Hani & Ananda Wickramasinghe & Uraiporn Kattiyapornpong & Shahriar Sajib, 2024. "The future of data-driven relationship innovation in the microfinance industry," Annals of Operations Research, Springer, vol. 333(2), pages 971-997, February.
    15. Alexandra RADU & Mihaela HERCIU, 2025. "Data Analytics, Decision-Making Process And Business Performance: A Bibliometric Analysis," Studies in Business and Economics, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 20(2), pages 292-313, August.
    16. Gupta, Manjul & Ghafoori, Arman & Kim, Eunyoung & Merhi, Mohammad I. & Filieri, Raffaele, 2025. "Growth hacking capability: Conceptualization, survey instrument development, and empirical study of its impact on firm performance," Journal of Business Research, Elsevier, vol. 196(C).
    17. Chen, Yantai & Luo, Haibei & Chen, Jin & Guo, Yanlin, 2022. "Building data-driven dynamic capabilities to arrest knowledge hiding: A knowledge management perspective," Journal of Business Research, Elsevier, vol. 139(C), pages 1138-1154.
    18. 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.
    19. Dignity Paradza & Olawande Daramola, 2021. "Business Intelligence and Business Value in Organisations: A Systematic Literature Review," Sustainability, MDPI, vol. 13(20), pages 1-27, October.
    20. Andrea De Mauro & Marco Greco & Michele Grimaldi, 2019. "Understanding Big Data Through a Systematic Literature Review: The ITMI Model," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(04), pages 1433-1461, July.

    More about this item

    Statistics

    Access and download statistics

    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:bcp:journl:v:8:y:2024:i:6:p:77-91. 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: Dr. Pawan Verma (email available below). General contact details of provider: https://rsisinternational.org/journals/ijriss/ .

    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.