INDUSTRY FORCES INFLUENCING THE PERFORMANCE OF ONLINE APPLICATION BASED TAXI DRIVERS IN KENYA: A DESCRIPTIVE CASE STUDY OF UBER, BOLT AND LITTLE CABS DRIVERS
Purpose of the Study: The objective of the study was to determine the industry forces affecting the performance of online-based taxi drivers in Nairobi, Kenya. In particular, the investigation sought to determine how threat of new entrant, competition in the industry, buyer power, supplier power and threat of substitute products affect the performance of online-based taxi drivers.
Statement of the problem: To survive and be successful in the online ride-hailing industry, investors of taxi application services must strengthen and reshape their operations with a view to becoming more competitive and profitable despite the increased number of players in the sector.
Research methodology: The study used descriptive research design. The target population included all online-based taxi drivers operating in Nairobi metropolitan amounting to 4320. The unit of observation were drivers. The sample size was 366 that included 142 from Uber, 105 from Little Cab and 119 from Bolt.
Findings: The study found that threat of new entrant was negatively and significantly related to performance (β=-.051, p=0.037). Competition was positively and significantly related to performance (β=.075, p=0.008). Buyer power and performance was negatively and insignificantly related to performance (β=-.036, p=0.054). Supplier power and performance was negatively and significantly related to performance (β=-.067, p=0.025).Threat of substitute product and performance was positively and significantly related to performance (β=.077, p=0.006).
Conclusions: It was concluded that buyer power had a low impact on the performance of online-based taxi drivers. The study also concluded that threat of new entrant in the industry has a low and negative impact on the performance. Further it was concluded that supplier in the industry was the third most significant predictor of performance among online-based taxi drivers. The study further concluded that competition had the second most significant impact on the performance. Finally, substitute products were the most significant predictor of performance among online-based taxi drivers.
Recommendations: It was recommended that online-based taxi drivers to enhance their service delivery not only to retain customers but also enhance consumer satisfaction with their services. It was also recommended drivers should be concerned about the threat of new entrant and focus on enhancing their brand image to fend off such rivals. Recommendations were made that high number of suppliers in the market was considered to have a considerable impact on performance and therefore drivers should cast their nets wide and work with more suppliers in the market. It was further recommended that drivers need to find approaches to fend off substitute products and compete with them effectively. The study also recommended drivers not only ensure that their clients are satisfied with their services but also their products are of superior quality compared to substitute services available in the market.
Keywords: Bargaining, suppliers, customers, substitute products, new entry, competition, performance, online-based taxi drivers
Charles, V., & Kumar, M. (2014). Business Performance Measurement and Management. Cambridge scholars publishing.
Chee, W. L., & Fernandez, J. L. (2013). Factors that influence the choice of mode of transport in Penang: A preliminary analysis. Procedia-Social and Behavioral Sciences, 91, 120-127.
Creswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and mixed methods approach. Sage publications.
Dzisi, E. K., Ackaah, W., Aprimah, B. A., & Adjei, E. (2020). Understanding demographics of ride-sourcing and the factors that underlie its use among young people. Scientific African, 7, e00288.
Gabel, D. (2016). Are traditional taxi firms doomed? An answer from the capital market. An Answer from the Capital Market (May 17, 2016).
Haba, H. F., &Dastane, O. (2018). An empirical investigation on taxi hailing mobile app adoption: A structural equation modelling. Business Management and Strategy, 9(2).
Henama, Unathi& Pearl, Portia &Sifolo, Portia. (2017). Uber: The South Africa Experience. African Journal of Hospitality, Tourism, and Leisure. 6.
Hussein, A. A. (2016). Service quality practices and customer satisfaction in taxi companies in Nairobi (Doctoral dissertation, University of Nairobi).
Kaplan, R. S., & Norton, D. P. (2001). Transforming the balanced scorecard from performance measurement to strategic management: Part I. Accounting horizons, 15(1), 87-104.
Kokwaro, P. L., Ajowi, J. O., & Kokwaro, E. A. (2013). Competitive forces influencing business performance of bicycle taxis in Kisumu City, Kenya. Mediterranean Journal of Social Sciences, 4(2), 719-719.
Mogane, K., & Jokonya, O. (2019). Factors Inhibiting the Adoption of Applications Driven Business Solutions by SMMEs in the Western Cape. In 2019 International Multidisciplinary Information Technology and Engineering Conference (IMITEC) (pp. 1-6). IEEE.
Ndungu, J. M. (2013). Competitive strategies adopted by KENATCO Taxis Limited to achieve competitive advantage. Unpublished MBA project. Nairobi: University of Nairobi.
Nørreklit, H., Kure, N., & Trenca, M. (2018). Balanced Scorecard. The International Encyclopedia of Strategic Communication, 1-6.
Okwako, A. (2017). Factors Affecting the Performance of the Public Service Vehicles (PSV) Sector in the Nairobi County (Doctoral dissertation, United States International University-Africa).
Olukunga, R. M. (2017). The Effect of External Business Environment on the Level of Entrepreneurial Commitment in Nairobi’s Urban Public Transport Industry. A Survey Study of General Motors Isuzu Bus Entrepreneurs (Doctoral dissertation, United States International University-Africa).
Onyango, J. (2016). E-Hailing Applications Adoption And Competitiveness Of App-Based Taxi Operators In Nairobi, Kenya. E-Repository. Retrieved 12 March 2020, from.http://erepository.uonbi.ac.ke/handle/11295/98927
Otieno, W & Awuor, F., & Hayombe, P. (2019). The Influence of Parking Location and Competitive Forces on the Economic Performance of Car Taxi In Kisumu City, Kenya. International Journal of Business and Social Research. 9(1), 10-27.
Pandya, U., Rungta, R., & Iyer, G. (2017). Impact of use of mobile app of OLA cabs and TAXI for sure on Yellow and Black cabs. Pacific Business Review International, 9(9), 91-105.
Park, S., Lee, H., &Chae, S. W. (2017). Rethinking balanced scorecard (BSC) measures: formative versus reflective measurement models. International Journal of Productivity and Performance Management 2(1),182-199.
Puche, M. L. (2016). Regulating e-hailing services: the case of Uber Regulation in Mexico City and Bogotá. Lusanne: École polytechnique fédérale de Lausanne.
Qu, W. G., Oh, W., and Pinsonneault (2010). The strategic value of IT insourcing: an IT-enabled business process perspective. The Journal of Strategic Information Systems, 19(2), 96-108
Roy, S. (2017). Scrutinizing the Factors Influencing Customer Adoption of App-Based Cab Services: An Application of the Technology Acceptance Model. IUP Journal of Marketing Management, 16(4).
Wamoro, M. (2017). Factors Affecting The Growth Of Multi-National Organizations In Kenya: A Case Study Of Uber Technologies. Retrieved 12 March 2020, from http://repository.mua.ac.ke/1576/1/PROJECT%20UBER.pdf.
Watanabe, C., Naveed, K., Neittaanmäki, P., & Fox, B. (2017). Consolidated challenge to social demand for resilient platforms-Lessons from Uber's global expansion. Technology in society, 48, 33-53.
Wirtz, J., & Tang, C. (2016). Uber: Competing as market leader in the us versus being a distant second in china. In SERVICES MARKETING: People Technology Strategy (pp. 626-632).
Wongrassamee, S., Simmons, J. E., & Gardiner, P. D. (2003). Performance measurement tools: the Balanced Scorecard and the EFQM Excellence Model. Measuring business excellence, 7(1), 14-29.
Yang, H., Wong, S. C., & Wong, K. I. (2002). Demand–supply equilibrium of taxi services in a network under competition and regulation. Transportation Research Part B: Methodological, 36(9), 799-819.