Self-service business intelligence and customer satisfaction: The moderating effects of independent non-technical users
DOI:
https://doi.org/10.25299/ijbs.2025.22585Keywords:
Keywords: Sustainable Business Performance, Governance Quality, Institutional Environment, Sustainable Development, NigeriaAbstract
Purpose: Purpose: The study examines how self-service business intelligence affects customer satisfaction in terms of system quality, user training effectiveness, data accessibility, and the moderating effect of non-technical user. It aims to clarify the relationships between these variables and how they affect the experiences of customers in businesses that use self-service business intelligence tools in technology-based small businesses in Lagos State Nigeria.
Design/methodology/approach: The study adopts quantitative research design using survey method, The sample size of 250 was established with the Research Advisor Table, with a confidence level of 95% and a margin of error of 5%. To mitigate the problem of non-response, suitable actions were implemented, resulting in the incorporation of an extra 75 respondents, constituting 30% of the initial sample. The modification yielded a final sample size of 325 from various industries including retail, finance, manufacturing and healthcare. A structured and validated closed-ended questionnaire was used for data collection. A total of 300 copies of the questionnaire were filled and returned for analysis, with a response rate of 92.0%. Relationships between constructs, the moderating effect of non-technical user independence and customer satisfaction were investigated using regression analysis.
Findings: Findings show that customer satisfaction is greatly impacted by self-service business intelligence (system quality, user training effectiveness, and data accessibility) of technology-based small firms in Lagos State Nigeria. The inverse relationship between the moderating role of non-technical user independence shows that a greater degree of independence combined with less support may result in mistakes that reduce customer satisfaction.
Limitations and Research Implications: Future research should adopt constructs that were not used in this study.
Practical implications: The study's practical application indicates that in order to assist non-technical end users in handling data responsibly, organisations should invest in robust self-service business intelligence systems, extensive training programs, and easy access to data in addition to support mechanisms.
Originality/value: The study sheds light on the intricacy of self-service business intelligence and customer satisfaction with a focus on independent non-technical user. It provides practitioners and scholars looking to maximise the usage of self-service business intelligence with enlightening inputs.
Downloads
References
Adeyemi O.S., & Olubiyi T. O. (2023). The Impact of Brand Awareness on Customer Loyalty in Selected Food and Beverage Businesses in Lagos State Nigeria. Journal Multidisiplin Madani, 3(3), 541–551 : https://doi.org/10.55927/mudima.v3i3.3095
Adeyemi, O. S., & Olubiyi, T.O. (2024). An Analysis of Investment Trends, Innovation and Its Impact on Cabotage and Local Content Regime: Nigerian Experience from Businesses in Oil and Gas and Maritime Industries. ABUAD Journal of Social and Management Sciences 5 (2), 285-301.
https://doi.org/10.53982/ajsms.2024.0502.04-j
Aguiar-Costa, L. M., Cunha, C. A. X. C., Silva, W. K. M., & Abreu, N. R. (2022). Customer satisfaction in service delivery with artificial intelligence: A meta-analytic study. Revista de Administracao Mackenzie, 23(6), 1–29. https://doi.org/10.1590/1678-6971/eRAMD220003.en
Amiruddin, D., Raharja, S. J., & Purnomo, M. (2024). The Influence of Organisational Climate in ERP Implementation on Information System Service Quality and User Satisfaction: A Study on an Indonesian Petrochemical Company. Review of Integrative Business and Economics Research, 14(1), 276–295.
Andrade, I. M. D., & Tumelero, C. (2022). Increasing customer service efficiency through artificial intelligence chatbot. Revista de Gestao, 29(3), 238–251. https://doi.org/10.1108/REGE-07-2021-0120
Bakır, M., & Sak, F. S. (2025). Charting the landscape of customer experience research in the business field: a bibliometric exploration. ESIC Market, 56(1), 1–26. https://doi.org/10.7200/esicm.56.379
Borodako, K., Berbeka, J., Rudnicki, M., & Łapczyński, M. (2023). The impact of innovation orientation and knowledge management on business services performance moderated by technological readiness. European Journal of Innovation Management, 26(7), 674–695. https://doi.org/10.1108/EJIM-09-2022-0523
Brunner, M., Tripathi, S., Gundolf, K., Bachmann, N., Thienemann, A. K., Tüzün, A., Pöchtrager, S., & Jodlbauer, H. (2025). Enhancing customer satisfaction through digitalization: Past, present and possible future approaches. Procedia Computer Science, 253, 2929–2940. https://doi.org/10.1016/j.procs.2025.02.017
Buhasho, E., Wausi, A., & Njihia, J. (2021). Moderating Effect of Organisational Capability on the Relationship Between Business Intelligence Capability and Performance Among Public Listed Firms in Kenya. European Scientific Journal ESJ, 17(1), 335–352. https://doi.org/10.19044/esj.2021.v17n1p335
Dang, T. D., & Nguyen, M. T. (2023). Systematic review and research agenda for the tourism and hospitality sector: co-creation of customer value in the digital age. Future Business Journal, 9(1), 1–4. https://doi.org/10.1186/s43093-023-00274-5
Ellitan, L., Sugiyanto, S. A., & Risdwiyanto, A. (2022). Increasing Repurchase Intention through Experiential Marketing, Service Quality, and Customer Satisfaction. Jurnal Maksipreneur: Manajemen, Koperasi, Dan Entrepreneurship, 12(1), 164–182. https://doi.org/10.30588/jmp.v12i1.1022
Ibrahim, N. M., Abiduzzaman, S. M., Abdul Raziff, A. R. Bin, & Shah, A. (2025). A Collaborative Filtering Approach Using Machine Learning and Business Intelligence: A Critical Review. International Journal on Perceptive and Cognitive Computing (IJPCC), 11(1), 41–49. https://doi.org/10.31436/ijpcc.v11i1.501
Izzuddin, M. D., & Abidin, Z. (2025). The Service Quality Effect of McDonald’s Self-service Technology towards Customer Loyalty: A Cognition Affective Behavior Theory Approach. Journal of Advances in Information Science and Technology, 7(1), 1–14. https://doi.org/10.15294/jaist.v7i1.8891
Kumah, R., Han, S., Okunola, A., & Mondal, S. (2025). Designing an Intuitive Real-Time Financial Fraud Detection System for Non-Technical Users. Research Gate, 1–11. https://www.researchgate.net/publication/388036717
Lennerholt, C., Van Laere, J., & Söderström, E. (2021). User-Related Challenges of Self-Service Business Intelligence. Information Systems Management, 38(4), 309–323. https://doi.org/10.1080/10580530.2020.1814458
Maghsoudi, M., & Nezafati, N. (2023). Navigating the acceptance of implementing business intelligence in organisations: A system dynamics approach. Telematics and Informatics Reports, 11, 1–12. https://doi.org/10.1016/j.teler.2023.100070
Naidu, P., Jais, J., & Khudari, M. (2025). Probing into digitalisation’s influence on internal customer satisfaction: An angle from internal customers of shared service organisations. Edelweiss Applied Science and Technology, 9(5), 282–299. https://doi.org/10.55214/25768484.v9i5.6827
Ramasamy, G., Ramasamy, G. D., & Ramasamy, P. (2024). Conceptual review of consumer satisfaction theories with expectation-confirmation and disconfirmation paradigm for business sustainable growth and decision making. F1000Research, 13, 1–16. https://doi.org/10.12688/f1000research.158612.1
Shojaei, R. S., & Burgess, G. (2022). Non-technical inhibitors: Exploring the adoption of digital innovation in the UK construction industry. Technological Forecasting and Social Change, 185, 1–13. https://doi.org/10.1016/j.techfore.2022.122036
Soeharso, S. Y. (2024). Customer satisfaction as a mediator between service quality to repurchase intention in online shopping. Cogent Business and Management, 11(1), 1–11. https://doi.org/10.1080/23311975.2024.2336304
Su, H.-T. (2021). Impact of Self-Service Factors on Customer Value: How Customer Behavioral Intentions are Formed? International Journal of Management, Economics and Social Sciences, 10(1), 32–48.
https://doi.org/10.32327/ijmess/10.1.2021.3
Wang, G. (2024). The issue of interconnections and mutual influence of digital transformation in international trade: How digitization affects customer satisfaction? Heliyon, 10(18), 1–14. https://doi.org/10.1016/j.heliyon.2024.e36854
Zungu, N. P. G., Amegbe, H., Hanu, C., & Asamoah, E. S. (2025). AI-driven self-service for enhanced customer experience outcomes in the banking sector. Cogent Business and Management, 12(1), 1–21. https://doi.org/10.1080/23311975.2025.2450295
