Artificial Intelligence and Quality of Service Delivery:  An Exploratory Study of Nigeria Mobile Telecommunication Sector

Abstract:

This study examined the effect of artificial intelligence — specifically data mining, chatbot usage, and machine learning — on quality-of-service delivery in Nigeria's mobile telecommunications sector, and identified the key challenges limiting effective AI deployment among operators. Despite growing industry enthusiasm for AI-driven service tools, empirical evidence linking specific AI technologies to measurable service quality outcomes in the Nigerian mobile telecom sector remained limited and fragmented, motivating this exploratory investigation. The study was anchored on two complementary theories — the Technology Acceptance Model and the Diffusion of Innovation Theory — providing a dual lens of individual-level acceptance and organizational-level diffusion. A quantitative cross-sectional survey design was adopted, with data collected from 388 valid respondents drawn from customer service, IT, network operations, and data analytics departments across MTN Nigeria, Airtel Nigeria, Globacom, and 9mobile, using a structured questionnaire built on validated constructs. Data were analyzed using descriptive statistics and multiple linear regression in SPSS. Findings showed that data mining (β = 0.31, p < .001) and chatbot usage (β = 0.16, p = .001) had statistically significant positive effects on quality-of-service delivery, while machine learning (β = −0.07, p = .184) had no significant effect, with the regression model explaining approximately 34% of the variance in service quality (R² = 0.34). Inadequate technical/AI skills, poor power and network infrastructure, and high implementation costs emerged as the most severe challenges limiting effective AI deployment. The study concludes that AI's effect on service quality in Nigerian mobile telecom is technology-dependent rather than uniform, and recommends that operators prioritize data mining and chatbot investment, close technical skill gaps, and address infrastructural and cost constraints to unlock machine learning's fuller potential. The study contributes Nigeria-specific empirical evidence to the limited literature on AI and telecom service quality, with implications for operators, regulators, and future research.