DIGITAL INNOVATION STRATEGIES AND PERFOMANCE OF FIVE STAR HOTELS IN NAIROBI CITY COUNTY, KENYA
Keywords:
Digital innovation strategies, E-commerce, Internet of Things, Artificial Intelligence, Big Data analytics, hotel performance, five-star hotelsAbstract
Despite the rapid expansion of Nairobi’s hospitality sector, five-star hotels continue to experience inconsistent performance due to the slow adoption of digital innovation strategies. Many establishments still depend on traditional management systems that constrain competitiveness and profitability in an era where technology determines customer engagement and operational efficiency. The COVID-19 pandemic further exposed digital deficiencies within Kenya’s hospitality industry, including inadequate online reservation systems, limited digital customer engagement, and weak data-driven marketing capabilities. Although global research underscores the role of technological innovation in enhancing hotel performance, few empirical studies have examined how specific digital strategies influence the performance of five-star hotels in Kenya. This study examined the effect of digital innovation strategies specifically E-commerce, the Internet of Things (IoT), Artificial Intelligence (AI), and Big Data on the performance of five-star hotels in Nairobi City County. It was guided by the Innovation Management Theory, Diffusion of Innovation Theory, and the Theory on Measures of Performance. A descriptive research design was adopted, targeting 48 managers from ten five-star hotels selected through random sampling. Data were collected using structured electronic questionnaires and analyzed using descriptive statistics and multiple regression analysis in IBM SPSS. The findings revealed that digital innovation strategies collectively explained 66.2% of the variance in hotel performance (R² = 0.662, F = 16.631, p < 0.001). Big Data emerged as the strongest predictor (β = 0.763, p < 0.001), while E-commerce showed a significant negative relationship (β = -0.261, p = 0.024). IoT and AI demonstrated positive but non-significant effects due to multicollinearity. The study concludes that digital innovation particularly Big Data analytics significantly enhances hotel performance through improved decision-making, guest personalization, and operational efficiency. It recommends strategic investments in data analytics infrastructure, enhanced e-commerce systems, IoT and AI integration, and continuous staff training to sustain competitiveness.
DOI: https://doi.org/10.5281/zenodo.17390964
Citation: Wambugha, W. J., & Tumuti, J. (2025). DIGITAL INNOVATION STRATEGIES AND PERFOMANCE OF FIVE STAR HOTELS IN NAIROBI CITY COUNTY, KENYA. Journal of Strategic Management and Innovation (JSMI), 2(1). https://doi.org/10.5281/zenodo.17390964
References
Aydin, B., & Savrul, M. (2014). The effect of e-commerce on international trade. International Journal of Economics and Finance Studies, 6(1), 1-13.
Balan, P., & Lindsay, N. (2010). Innovation capability, entrepreneurial orientation and performance in Australian hotels: An empirical study. Cooperative Research Centre for Sustainable Tourism.
Buhalis, D., & Leung, R. (2018). Smart hospitality—Interconnectivity and interoperability towards an ecosystem. International Journal of Hospitality Management, 71, 41-50.
Buhalis, D., Harwood, T., Bogicevic, V., Viglia, G., Beldona, S., & Hofacker, C. (2019). Technological disruptions in services: Lessons from tourism and hospitality. Journal of Service Management, 30(4), 484-506.
Business Daily Africa. (2020, October 15). Hemingway's Nairobi wins at Small Luxury Hotels Awards 2020. Business Daily Africa.
Central Bank of Kenya. (2020). Annual report 2020. Central Bank of Kenya.
Choi, T. Y., & Chu, R. (2001). Determinants of hotel guests' satisfaction and repeat patronage in the Hong Kong hotel industry. International Journal of Hospitality Management, 20(3), 277-297.
Collins, G., Cobanoglu, C., & Bilgihan, A. (2015). Hospitality information technology: Learning how to use it. Kendall Hunt Publishing.
Deegan, J. (2020). The economic impact of tourism. In J. Jafari & H. Xiao (Eds.), Encyclopedia of tourism (pp. 1-3). Springer.
Enginkaya, E., & Yılmaz, H. (2014). What drives consumers to interact with brands through social media? A motivation scale development study. Procedia-Social and Behavioral Sciences, 148, 219-226.
Filieri, R., & McLeay, F. (2013). E-WOM and accommodation: An analysis of the factors that influence travelers' adoption of information from online reviews. Journal of Travel Research, 53(1), 44-57.
Gavalas, D., Konstantopoulos, C., Mastakas, K., & Pantziou, G. (2014). Mobile recommender systems in tourism. Journal of Network and Computer Applications, 39, 319-333.
Gladston, R. A. (2016). Artificial intelligence and its application in the hospitality industry. International Journal of Research, 3(11), 74-79.
Salazar, A., Costa, J., & Rita, P. (2010). A service quality evaluation scale for the hospitality sector: Dimensions, attributes and behavioural intentions. Worldwide Hospitality and Tourism Themes, 2(4), 383-397.
Schuckert, M., Liu, X., & Law, R. (2015). Hospitality and tourism online reviews: Recent trends and future directions. Journal of Travel & Tourism Marketing, 32(5), 608-621.
Schumpeter, J. A. (1934). The theory of economic development: An inquiry into profits, capital, credit, interest, and the business cycle. Harvard University Press.
Serrat, O. (2010). The future of social marketing. In Knowledge solutions: Tools, methods, and approaches to drive organizational performance (pp. 1-6). Asian Development Bank.
Shinn, L. J., Nakatani, K., & Rodriguez, M. (2017). The Internet of Things and hospitality: Revolutionizing guest experiences and operations. Journal of Hospitality and Tourism Technology, 8(3), 395-410.
Sohrabi, B., Vanani, I. R., Tahmasebipur, K., & Fazli, S. (2012). An exploratory analysis of hotel selection factors: A comprehensive survey of Tehran hotels. International Journal of Hospitality Management, 31(1), 96-106.
Srimai, S., Damsaman, N., & Bangchokdee, S. (2011). Performance measurement, organizational learning and strategic alignment: An exploratory study in Thai public sector. Measuring Business Excellence, 15(2), 57-69.
Tussyadiah, I. P., & Miller, G. (2019). Perceived impacts of artificial intelligence and responses to positive behaviour change intervention. In J. Pesonen & J. Neidhardt (Eds.), Information and communication technologies in tourism 2019 (pp. 359-370). Springer.
Wolna-Samulak, E. (2012). The role of ICT in the innovation process of SMEs. Business, Management and Education, 10(2), 271-283.
World Travel and Tourism Council. (2020). Travel and tourism economic impact: Kenya 2020. WTTC.
Yalcin, N., Bayrakdaroglu, A., & Kahraman, C. (2012). Application of fuzzy multi-criteria decision-making methods for financial performance evaluation of Turkish manufacturing industries. Expert Systems with Applications, 39(1), 350-364.
Yan, Q., Zhou, S., & Wu, S. (2018). The influences of tourists' emotions on the selection of electronic word of mouth platforms. Tourism Management, 66, 348-363.
Zhang, Z., Ye, Q., & Law, R. (2011). Determinants of hotel room price: An exploration of travelers' hierarchy of accommodation needs. International Journal of Contemporary Hospitality Management, 23(7), 972-981.
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Copyright (c) 2025 Waza Julia Wambugha, Dr. Joshua Tumuti, PhD
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