Global Journal of Economic and Finance Research

The growing use of artificial intelligence (AI) by mobile telecommunication companies raises questions about its ability to strengthen how firms manage and deploy their resources. This study examined the effect of AI-driven automation, predictive analytics, and smart support systems on corporate resourcefulness of mobile (GSM) telecommunication companies in Port Harcourt, Rivers State. The study was anchored on the Resource-based view and dynamic capabilities theory. A cross-sectional survey design was adopted, with the study population covering the four (GSM) telecommunication companies namely, MTN Nigeria, Airtel Nigeria, Globacom, and 9Mobile. Given that the accessible population was manageable, a census method was adopted. The respondents comprised 28 top and middle-level managers which were selected through purposive sampling. Data were collected through a structured questionnaire. 28 questionnaires were administered by the researchers to the respondents, however, 23 copies were returned and used for analysis, representing an 82.1% response rate. The instrument was subjected to expert review to establish content validity, while its internal consistency was assessed using Cronbach’s alpha. Multiple regression was employed to test the hypotheses. The findings showed that AI-driven automation, predictive analytics, and smart support systems each had a significant positive effect on corporate resourcefulness. Smart support systems recorded the strongest effect among the three dimensions. The study concludes that effective application of AI can enhance firms’ ability to mobilize, optimize, and reconfigure their resources. It recommends that mobile (GSM) telecommunication companies should strengthen their investment in AI applications, particularly smart support systems, to improve resource utilization and organisational responsiveness. 

Artificial Intelligence, Corporate Resourcefulness, AI-Driven Automation, Predictive Analytics, Smart Support Systems.

1. Abdelhady, M. G., & Mohamed, K.A (2025). Leveraging artificial intelligence for predictive customer churn modeling in telecommunications: A framework for enhanced customer relationship management. Scientific Reports, 15, Article 43826. Retrieved from file:///C:/Users/USER/Downloads/Leveraging_artificial_intelligence_for_predictive_.pdf
2. Acemoglu, D., & Restrepo, P. (2021). Tasks, automation, and the rise in U.S. wage inequality. National Bureau of Economic Research. Retrieved from https://www.nber.org/papers/w28920
3. Alekseeva, L., Gine, M., Samila, S., & Taska, B. (2021). AI adoption and firm performance: Management versus IT. Academy of Management Proceedings, 2021(1). https://doi.org/10.5465/AMBPP.20
4. Ameen, N., Tarhini, A., Reppel, A., & Anand, A. (2021). Customer experiences in the age of artificial intelligence. Computers in Human Behavior, 114, 106548. https://doi.org/10.1016/j.chb.2020.106548⁠
5. Barney, J. B. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99-120. https://doi.org/10.1177/014920639101700108
6. Barreto, I. (2010). Dynamic capabilities: A review of past research and an agenda for the future. Journal of Management, 36(1), 256-280. https://doi.org/10.1177/0149206309350776⁠
7. Britto, R., Murphy, T., Iovene, M., Jonsson, L., Erol-Kantarci, M., & Kovács, B. (2023). Telecom AI native systems in the age of generative AI-An engineering perspective.  Retrieved from arXiv. https://arxiv.org/abs/2310.11770⁠
8. Busayo, T. O., Igbekoyi, O. E., Oluwagbade, O. I., Adewara, Y. M., Dagunduro, M. E., & Boluwaji, Y. D. (2023). Artificial intelligence and service quality of telecommunication firms in Nigeria. Journal of Economics, Finance and Accounting Studies, 5(3), 203-214. DOI:10.32996/jefas.2023.5.3.16. Retrieved from https://www.researchgate.net/publication/371640845_Artificial_Intelligence_and_Service_Quality_of_Telecommunication_Firms_in_Nigeria
9. Cao, L., Sarkar, S., Ramesh, B., Mohan, K., & Park, E. H. (2024). Shift of ambidexterity modes: An empirical investigation of the impact of artificial intelligence in customer service. International Journal of Information Management, 76, 102773. https://doi.org/10.1016/j.ijinfomgt.2024.102773⁠
10. Chen, Y. F., Wu, C. H., & Yang, C. H. (2022). How artificial intelligence technology affects productivity and employment: Firm-level evidence from Taiwan. Research Policy, 51(6), 104536. https://doi.org/10.1016/j.respol.2022.104536⁠
11. Chen, Y., & Prentice, C. (2024). Integrating artificial intelligence and customer experience in service delivery. Retrieved from https://journals.sagepub.com/doi/10.1177/14413582241252904
12. Damioli, G., Van Roy, V., & Vertesy, D. (2021). The impact of artificial intelligence on labor productivity. Eurasian Business Review, 11(1), 1-25. https://doi.org/10.1007/s40821-020-00172-8⁠
13. Davenport, T. H., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24-42. https://doi.org/10.1007/s11747-019-00696-0
14. Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., ... Williams, M. D. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002⁠
15. Eisenhardt, K. M., & Martin, J. A. (2000). Dynamic capabilities: What are they? Strategic Management Journal, 21(10-11), 1105-1121. https://doi.org/10.1002/1097-0266(200010/11)21:10/11%3C1105::AID-SMJ133%3E3.0.CO;2-E⁠
16. Huang, M.-H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in service. Journal of Service Research, 24(1), 3-18. https://doi.org/10.1177/1094670520902268⁠
17. Irabor-Ighedosa, J., & Opara, D. N. (2023). Neural networks and organizational agility in telecommunication companies in Nigeria. BW Academic Journal, 8. Retrieved from https://bwjournal.org/index.php/bsjournal/article/view/1179
18. Mikalef, P., Krogstie, J., Pappas, I. O., & Pavlou, P. A. (2021). Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance. Information & Management, 58(3), 103434. https://doi.org/10.1016/j.im.2021.103434⁠
19. Mishra, S., Ewing, M. T., & Cooper, H. B. (2022). Artificial intelligence focus and firm performance. Journal of the Academy of Marketing Science, 50(6), 1176-1197. https://doi.org/10.1007/s11747-022-00876-5⁠
20. Newbert, S. L. (2007). Empirical research on the resource-based view of the firm: An assessment and suggestions for future research. Strategic Management Journal, 28(2), 121-146. https://doi.org/10.1002/smj.573⁠
21. Ogunjinmi, O. F. (2025). Optimizing network reliability: Strategies for resilient telecommunications infrastructure in emerging economies. Global Journal of Engineering and Technology Advances, 22(3), 236-258. https://doi.org/10.30574/gjeta.2025.22.3.0065
22. Priem, R. L., & Butler, J. E. (2001). Is the resource-based “view” a useful perspective for strategic management research? Academy of Management Review, 26(1), 22-40. https://doi.org/10.5465/amr.2001.4011928⁠
23. Pumplun, L., Tauchert, C., & Heidt, M. (2021). A new organizational chassis for artificial intelligence: Exploring organizational readiness factors. Business Horizons, 64(6), 741-752. https://doi.org/10.1016/j.bushor.2021.02.002⁠
24. Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson.
25. Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and research directions. SN Computer Science, 2(3), Article 160. https://doi.org/10.1007/s42979-021-00592-x⁠
26. Shaikhsurab, M. A., & Magadum, P. (2024). Enhancing customer churn prediction in telecommunications: An adaptive ensemble learning approach. arXiv. https://arxiv.org/abs/2408.16284⁠      
27. Sirmon, D. G., Hitt, M. A., & Ireland, R. D. (2007). Managing firm resources in dynamic environments to create value: Looking inside the black box. Academy of Management Review, 32(1), 273-292. https://doi.org/10.5465/amr.2007.23466005⁠
28. Sirmon, D. G., Hitt, M. A., Ireland, R. D., & Gilbert, B. A. (2011). Resource orchestration to create competitive advantage: Breadth, depth, and life cycle effects. Journal of Management, 37(5), 1390-1412.https://doi.org/10.1177/0149206310385695⁠
29. Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of Big Data challenges and analytical methods. Journal of Business Research, 70, 263-286. https://doi.org/10.1016/j.jbusres.2016.08.001⁠
30. Teece, D. J. (2007). Explicating dynamic capabilities: The nature and micro foundations of (sustainable) enterprise performance. Strategic Management Journal, 28(13), 1319-1350. https://doi.org/10.1002/smj.640⁠
31. Teece, D. J. (2018). Business models and dynamic capabilities. Long Range Planning, 51(1), 40-49. https://doi.org/10.1016/j.lrp.2017.06.007⁠
32. Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509-533. https://doi.org/10.1002/(SICI)1097-0266(199708)18:7%3C509::AID-SMJ882%3E3.0.CO;2-Z⁠
33. Van den Broek, E., Sergeeva, A., & Huysman, M. (2021). When the machine meets the expert: An ethnography of developing AI for customer service. Journal of Management Information Systems, 38(4), 946-972.https://doi.org/10.1080/07421222.2021.1990610⁠
34. Wamba, S. F., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015). How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, 234-246. https://doi.org/10.1016/j.ijpe.2014.12.031⁠
35. Wernerfelt, B. (1984). A resource-based view of the firm. Strategic Management Journal, 5(2), 171-180. https://doi.org/10.1002/smj.4250050207⁠
36. Wilden, R., Devinney, T. M., & Dowling, G. R. (2019). The architecture of dynamic capability research. Academy of Management Annals, 10(1), 997-1076. Retrieved from https://researchers.mq.edu.au/en/publications/the-architecture-of-dynamic-capability-research-identifying-the-b/
37. World Economic Forum. (2025). Artificial intelligence in telecommunications. World Economic Forum. https://www.weforum.org/publications/industries-in-the-intelligent-age-white-paper-series/telecommunications
38. Zeb-Obipi, P. I. ., & Irabor-Ighedosa, J. (2023). A review of artificial intelligence and organizational agility. BW Academic Journal, 12. Retrieved from https://bwjournal.org/index.php/bsjournal/article/view/1174
39. Zeb-Obipi, P. I., & Irabor-Ighedosa, J. (2023). Internet of things and organizational agility in telecommunication companies in Nigeria. BW Academic Journal, 8. Retrieved from https://bwjournal.org/index.php/bsjournal/article/view/1175
40. Zhao, T., Cui, J., Hu, J., Dai, Y., & Zhou, Y. (2022). Is artificial intelligence customer service satisfactory? Insights based on microblog data and user interviews. Cyber psychology, Behavior, and Social Networking, 25(2), 110-117. https://doi.org/10.1089/cyber.2021.0155⁠