Understanding Social Acceptability of IoT Technologies Through Descriptive, Reliability, and Predictive Statistical Models

Abstract

The study examines the social acceptability of Internet of Things (IoT) technology in Gaduwa Estate, Abuja, Nigeria, utilizing a multi-method statistical framework that incorporates descriptive statistics, reliability tests, ANOVA, Pearson correlations, and predictive regression models. Survey results from 156 respondents (78% valid response rate) indicated notable demographic disparities in adoption patterns, with younger individuals (18–35 years, χ² = 25.6, p < 0.001) and those with higher education (BSc/MSc, χ² = 46.3, p < 0.001) exhibiting more desire. The reliability analysis demonstrated strong scale consistency (Cronbach’s α = 0.87). ANOVA results revealed significant variations in willingness among groups (F(3,116) = 5.62, p = 0.002). Correlation analysis revealed robust positive correlations between willingness and Touch (r = 0.72), Perceived Usefulness (r = 0.68), Trust (r = 0.61), and Ease of Use (r = 0.59), although Cost Concern exhibited a negative correlation (r = –0.45). Regression models further substantiated these trends: multiple linear regression accounted for 62% of the variation (R² = 0.62), with significant predictors comprising PU (β = 0.32, p < 0.001), EU (β = 0.28, p = 0.001), Trust (β = 0.30, p < 0.001), and Cost Concern (β = –0.22, p = 0.016). Logistic regression shown commendable classification efficacy (AUC = 0.78), substantiating PU (OR = 1.51), EU (OR = 1.45), and Trust (OR = 1.54) as facilitators of adoption, whereas Cost Concern diminished odds (OR = 0.75). The results corroborate the Technology Acceptance Model and UTAUT, while augmenting them with contextual insights that highlight affordability, trust, and experiential exposure as essential factors. These findings extend the Technology Acceptance Model (TAM) and UTAUT frameworks by highlighting affordability and experiential exposure as critical determinants in emerging economies. The study offers theoretical and policy insights to strengthen inclusive IoT adoption strategies.