AI Driven Customer Service and Customer Patronage of Ibom Hotel and Golf Resort Hotel, Uyo Metropolis

This study empirically examines the relationship between Artificial Intelligence (AI)-driven customer service and customer patronage at Ibom Hotel & Golf Resort, a premier hospitality establishment operating within the Uyo Metropolis, Akwa Ibom State, Nigeria. Driven by the aggressive digital transformation of the regional hospitality landscape, the paper decomposes automated service infrastructure into three distinct, measurable dimensions: AI Responsiveness (AIR), AI Personalization (AIP), and AI-Driven Service Recovery (ASR). The study adopts a cross-sectional descriptive survey research design. Utilizing Cochran’s (1977) sampling formula for infinite populations, a field sample of 422 digital structured questionnaires was deployed across multiple customer touchpoints, yielding 391 valid responses certified clean for final econometric computation. Data processing via SPSS software utilized descriptive statistics alongside Multiple Linear Regression Analysis to validate the research hypotheses. The empirical metrics reveal a strong, statistically significant joint model fit (R = 0.768, R² = 0.590, F = 185.34, p < 0.001), demonstrating that 59.0% of the total variance in customer patronage (measured via brand preference, re-booking intension and willingness to pay price premium) is directly driven by the hotel’s AI service deployment. On an individual level, AI Personalization (AIP) emerged as the most powerful single predictor (β = 0.385, t = 8.708, p < 0.001), followed closely by AI Responsiveness (AIR) (β = 0.298, t = 6.807, p < 0.001), while AI-Driven Service Recovery (ASR) exerted a smaller but highly significant positive influence (β = 0.192, t = 4.800, p < 0.001). The findings confirm that while automation drastically reduces reservation friction and enhances tailored comfort, local consumers in the Uyo market continue to place immense value on authentic regional identity and cultural spaces. Consequently, the paper concludes that optimal market retention requires a strategic “high-tech, high-touch” hybrid hospitality model. The study recommends that hotel management systematically upgrade predictive data profiling security to navigate the personalization-privacy paradox, establish instant digital compensation The scripts for automated failures, and maintain a seamless escalation loop to empathetic human managers to preserve long-term brand loyalty.