Effect of Organization Capabilities on the Growth of Fintech Providers in Kenya

Authors

  • Emmanuel Okiyo United States International University
  • Prof. Allan Kihara United States International University

DOI:

https://doi.org/10.47941/jbsm.2287

Keywords:

Resource Capabilities, Marketing Capabilities, Leadership Capabilities, Technological Capabilities, Business Growth, and Fintech Providers

Abstract

Purpose: The Fintech industry has become a central service sector that has revolutionized financial services provision and fostered financial inclusion. However, the revolutionary nature of the various Fintech firms has resulted in their operations being unclear as their conduct in the market remains unsupervised. Further the Fintechs fail in diversifying their products and services thus limiting their growth within the country. This research sought to establish the effect of organization capabilities on the business growth of fintech providers in Kenya. The specific objectives of the research sought to establish the effect of resource capabilities, marketing capabilities, leadership capabilities and technological capabilities on the business growth of fintech providers in Kenya. The survey was grounded on the dynamic capabilities theory.

Methodology: The study used a descriptive cross-section research design with study population drawn from the association of Fintech providers in Kenya. There are a total of 63 firms that was considered in the study with the three senior managers from each of the firm considered as the respondent. The research utilized random sampling in selecting the 189 participant. Data collection was carried out using a structured questionnaire with drop and pick method applied in the study. The research data was analyzed using descriptive, correlation and regression analysis.

Findings: The study established that there is a statistically significant relationship between organization capabilities and the business growth of fintech providers in Kenya (R2 = .487, Sig = .000). The findings of the first objective revealed that there was a positive and significant effect of resource capabilities on business growth of fintech providers in Kenya (coefficient β1 = .448, sig = .000<.05). Analysis of the second objective revealed that there was a positive and significant effect of marketing capabilities on business growth (β2 = .049, sig = .001<.05). The results of the third objective revealed that there was a positive and significant effect of leadership capabilities on business growth (β3 = .139, sig = .004<.05). Regression for the fourth variable showed that there was no significant effect of technological capabilities on business growth of Fintech Providers (β4 = .007, sig = .1936>.05).

Unique Contribution to Theory, Practice and Policy: Based on the conclusions, the study recommends that the government should develop and maintain robust technology infrastructure that enables fintech providers to innovate and integrate advanced technologies. The study also recommends that fintech firms should continue to encourage open feedback and communication to build strong, collaborative teams. The study further recommends that firms should enhance social media marketing efforts to broaden market reach and engage with potential customers by investing in digital marketing tools.

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Author Biographies

Emmanuel Okiyo, United States International University

Chandaria School of Business

Prof. Allan Kihara, United States International University

Chandaria School of Business

References

Ho, G. T., Tang, Y. M., Tsang, K. Y., Tang, V., & Chau, K. Y. (2021). A blockchain-based system to enhance aircraft parts traceability and trackability for inventory management. Expert Systems with Applications, 179, 115101.

Zkik, K., Sebbar, A., Nejjari, N., Lahlou, S., Fadi, O., & Oudani, M. (2023). Secure Model for Records Traceability in Airline Supply Chain Based on Blockchain and Machine Learning. In Digital Transformation and Industry 4.0 for Sustainable Supply Chain Performance (pp. 141-159). Cham: Springer International Publishing.

Abdulrahman, Y., Arnautović, E., Parezanović, V., & Svetinovic, D. (2023). AI and blockchain synergy in aerospace engineering: an impact survey on operational efficiency and technological challenges. IEEE Access.

Di Vaio, A., & Varriale, L. (2020). Blockchain technology in supply chain management for sustainable performance: Evidence from the airport industry. International Journal of Information Management, 52, 102014.

Li, J., Peng, Z., Liu, A., He, L., & Zhang, Y. (2020, December). Analysis and future challenge of blockchain in civil aviation application. In 2020 IEEE 6th International Conference on Computer and Communications (ICCC) (pp. 1742-1748). Ieee.

Abdulrahman, Y., Arnautović, E., Parezanović, V., & Svetinovic, D. (2023). AI and blockchain synergy in aerospace engineering: an impact survey on operational efficiency and technological challenges. IEEE Access.

Yadav, J. K., Verma, D. C., Jangirala, S., Srivastava, S. K., & Aman, M. N. (2022). Blockchain for aviation industry: Applications and used cases. In ICT Analysis and Applications (pp. 475-486). Springer Singapore.

Pilon, R. V. (2023). Artificial Intelligence in Commercial Aviation: Use cases and emerging strategies. Routledge.

Cakiroglu, C. (2024). 7 Blockchain in Aviation. Smart and Sustainable Operations Management in the Aviation Industry: A Supply Chain 4.0 Perspective, 92.

Raparthi, M., Nimmagadda, V. S. P., Sahu, M. K., Gayam, S. R., Putha, S., Kondapaka, K. K., ... & Pattyam, S. P. (2021). Blockchain-Based Supply Chain Management Using Machine Learning: Analyzing Decentralized Traceability and Transparency Solutions for Optimized Supply Chain Operations. Blockchain Technology and Distributed Systems, 1(2), 1-9.

Kamble, S. S., Gunasekaran, A., & Sharma, R. (2020). Modeling the blockchain enabled traceability in agriculture supply chain. International Journal of Information Management, 52, 101967.

Clementi, M. D., Larrieu, N., Lochin, E., Kaafar, M. A., & Asghar, H. (2019, September). When air traffic management meets blockchain technology: a blockchain-based concept for securing the sharing of flight data. In 2019 IEEE/AIAA 38th Digital Avionics Systems Conference (DASC) (pp. 1-10). Ieee.

Reisman, R. J. (2019, January). Air traffic management blockchain infrastructure for security, authentication, and privacy. In AIAA Scitech Forum (No. ARC-E-DAA-TN63825).

Aleshi, A., Seker, R., & Babiceanu, R. F. (2019, November). Blockchain model for enhancing aircraft maintenance records security. In 2019 IEEE International Symposium on Technologies for Homeland Security (HST) (pp. 1-7). IEEE.

Marla, L., Vaaben, B., & Barnhart, C. (2017). Integrated disruption management and flight planning to trade off delays and fuel burn. Transportation Science, 51(1), 88-111.

Mehta, V., Miller, M. E., Reynolds, T., Ishutkina, M., Jordan, R., Seater, R., & Moser, W. (2011, May). Decision support tools for the tower flight data manager system. In 2011 Integrated Communications, Navigation, and Surveillance Conference Proceedings (pp. I4-1). IEEE.

Tang, H., Zhang, Y., Mohmoodian, V., & Charkhgard, H. (2021). Automated flight planning of high-density urban air mobility. Transportation Research Part C: Emerging Technologies, 131, 103324.

Smith, P. J., McCoy, E., Orasanu, J., Billings, C., Denning, R., Rodvold, M., ... & Gee, T. (1995, October). Cooperative problem-solving activities in flight planning and constraints for commercial aircraft. In 1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century (Vol. 5, pp. 4563-4568). IEEE.

Geißer, F., Povéda, G., Trevizan, F., Bondouy, M., Teichteil-Königsbuch, F., & Thiébaux, S. (2020, June). Optimal and heuristic approaches for constrained flight planning under weather uncertainty. In Proceedings of the International Conference on Automated Planning and Scheduling (Vol. 30, pp. 384-393).

Salata, F., Falasca, S., Palusci, O., Ciancio, V., Tarsitano, A., Battistini, V., ... & Coppi, M. (2021). A first approach to the optimization of landing and take-off operations through intelligent algorithms for compliance with the acoustic standards in multi-runway airports. Applied Acoustics, 181, 108138.

Heitzman, N., & Takahashi, T. T. (2014). Optimizing Commercial Flight Fuel Consumption Through Changes in Federal Regulations and Pilot Techniques. In 14th AIAA Aviation Technology, Integration, and Operations Conference (p. 3263).

Bauer, C., Lagadec, K., Bès, C., & Mongeau, M. (2007). Flight control system architecture optimization for fly-by-wire airliners. Journal of guidance, control, and dynamics, 30(4), 1023-1029.

Cherevatiuk, V., & Zuieva, V. (2017). The Issue of Optimization of the Legal Regulation of Certification of General Aviation Aircraft: Domestic and International Experience. Proceedings of the National aviation university, (3), 107-113.

Li, J., Peng, Z., Liu, A., He, L., & Zhang, Y. (2020, December). Analysis and future challenge of blockchain in civil aviation application. In 2020 IEEE 6th International Conference on Computer and Communications (ICCC) (pp. 1742-1748). Ieee.

Yadav, J. K., Verma, D. C., Jangirala, S., Srivastava, S. K., & Aman, M. N. (2022). Blockchain for aviation industry: Applications and used cases. In ICT Analysis and Applications (pp. 475-486). Springer Singapore.

Alladi, T., Chamola, V., Sahu, N., & Guizani, M. (2020). Applications of blockchain in unmanned aerial vehicles: A review. Vehicular Communications, 23, 100249.

Zhang, T., Gao, C., Zeng, Y., Li, S., Xu, Y., & Zhang, Y. (2024, July). Flight Planning at Scale: A Bipartite Matching Based Approach. In International Conference on Database Systems for Advanced Applications (pp. 19-36). Singapore: Springer Nature Singapore.

La, J., Bil, C., & Heiets, I. (2021). Impact of digital technologies on airline operations. Transportation Research Procedia, 56, 63-70.

Aliev, A. (2020). Development of a wireless in-flight entertainment system for the airline industry.

Lindahl, H. (2023). Customizing WCAG 2.1 for In-Flight Entertainment Systems.

Fugkeaw, S. (2022). Enabling trust and privacy-preserving e-KYC system using blockchain. IEEE Access, 10, 49028-49039.

Soni, S., & Bhushan, B. (2019, July). A comprehensive survey on blockchain: Working, security analysis, privacy threats and potential applications. In 2019 2nd international conference on intelligent computing, instrumentation and control technologies (ICICICT) (Vol. 1, pp. 922-926). IEEE.

Kapsoulis, N., Psychas, A., Palaiokrassas, G., Marinakis, A., Litke, A., & Varvarigou, T. (2020). Know your customer (KYC) implementation with smart contracts on a privacy-oriented decentralized architecture. Future Internet, 12(2), 41.

Alghamdi, S., Daim, T., & Alzahrani, S. (2024). Organizational Readiness Assessment for Fraud Detection and Prevention: Case of Airlines Sector and Electronic Payment. IEEE Transactions on Engineering Management.

ALGhamdi, S. A., Daim, T., & Meissner, D. (2022). Electronic payment technology: Developing a taxonomy of factors to evaluate a fraud detection and prevention system for the airlines industry. In The Routledge Companion to Technology Management (pp. 450-511). Routledge.

Xu, P., Lee, J., Barth, J. R., & Richey, R. G. (2021). Blockchain as supply chain technology: considering transparency and security. International Journal of Physical Distribution & Logistics Management, 51(3), 305-324.

Hassija, V., Chamola, V., Gupta, V., Jain, S., & Guizani, N. (2020). A survey on supply chain security: Application areas, security threats, and solution architectures. IEEE Internet of Things Journal, 8(8), 6222-6246.

Cole, R., Stevenson, M., & Aitken, J. (2019). Blockchain technology: implications for operations and supply chain management. Supply chain management: An international journal, 24(4), 469-483.

Mohanty, B., & Mishra, S. (2023). Role of Artificial Intelligence in Financial Fraud Detection. Academy of Marketing Studies Journal, 27(S4).

Alghamdi, S., Daim, T., & Alzahrani, S. (2024). Organizational Readiness Assessment for Fraud Detection and Prevention: Case of Airlines Sector and Electronic Payment. IEEE Transactions on Engineering Management.

Kumar, M. (2022). Optimized application of artificial intelligence (AI) in aviation market. International Journal of Recent Research Aspects, 9(4).

Bello, O. A., Ogundipe, A., Mohammed, D., Adebola, F., & Alonge, O. A. (2023). AI-Driven Approaches for Real-Time Fraud Detection in US Financial Transactions: Challenges and Opportunities. European Journal of Computer Science and Information Technology, 11(6), 84-102.

Abdulrahman, Y., Arnautović, E., Parezanović, V., & Svetinovic, D. (2023). AI and blockchain synergy in aerospace engineering: an impact survey on operational efficiency and technological challenges. IEEE Access.

Singh, P., Elmi, Z., Lau, Y. Y., Borowska-Stefańska, M., Wiśniewski, S., & Dulebenets, M. A. (2022). Blockchain and AI technology convergence: Applications in transportation systems. Vehicular Communications, 38, 100521.

Wang, Y., Su, Z., Ni, J., Zhang, N., & Shen, X. (2021). Blockchain-empowered space-air-ground integrated networks: Opportunities, challenges, and solutions. IEEE Communications Surveys & Tutorials, 24(1), 160-209.

Li, X., Lai, P. L., Yang, C. C., & Yuen, K. F. (2021). Determinants of blockchain adoption in the aviation industry: Empirical evidence from Korea. Journal of Air Transport Management, 97, 102139.

Di Vaio, A., & Varriale, L. (2020). Blockchain technology in supply chain management for sustainable performance: Evidence from the airport industry. International Journal of Information Management, 52, 102014.

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Published

2024-10-14

How to Cite

Okiyo, E., & Kihara, A. (2024). Effect of Organization Capabilities on the Growth of Fintech Providers in Kenya. Journal of Business and Strategic Management, 9(5), 104–129. https://doi.org/10.47941/jbsm.2287

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