Artificial Intelligence and Enterprise Performance: Investigating the Effect of Chatbots and Virtual Assistants on Smes’ Customer Retention in Fct Abuja
Year: 2025
Type: article
Study design: Observational Study
Abstract: The adoption of chatbots and virtual assistants in business is transforming customer service operations, particularly among Small and Medium-Scale Enterprises (SMEs) seeking cost-effective ways to enhance customer retention. This study examined the effect of chatbots and virtual assistants on customer retention among SMEs in FCT Abuja, with a focus on response time, resolution rate, and user engagement as key determinants. A quantitative research design was employed, using a structured questionnaire distributed to 300 respondents. Data analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM) in SmartPLS 4.0, assessing the relationships between chatbot efficiency and customer retention. Findings revealed that resolution rate (β = 0.462, p < 0.001) had the strongest effect on customer retention, followed by response time (β = 0.312, p < 0.001) and user engagement (β = 0.279, p = 0.001). The model exhibited strong predictive accuracy (R² = 0.684, Q² = 0.539), indicating that 68.4% of customer retention variance was attributable to chatbot efficiency metrics. The study concluded that chatbots and virtual assistants significantly improve customer retention, particularly when optimized for quick response times, efficient query resolution, and high user engagement. SMEs were thus recommended to enhance chatbot functionalities, integrate human support where necessary, and personalize AI-driven interactions to maximize retention. Providing empirical evidence for business owners, policymakers, and AI developers, the study highlights the strategic importance of AI-driven customer service in SME growth and sustainability.
Authors: Nana Bature Usman, Yusuf Ibrahim A.
Institution: University of Abuja
Language: English
Topic: AI in Service Interactions
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4412198217