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This study extends the Unified Theory of Acceptance and Use of Technology (UTAUT) to investigate behavioral intention toward adopting intelligent devices in Tunisia, while examining variations in technology acceptance. Data from 218 respondents were analyzed using structural equation modeling (SEM), revealing key findings: Familiarity and Trust emerged as the strongest predictors of adoption intention, underscoring the importance of user confidence and prior experience with AI technologies. Contrary to traditional UTAUT expectations, Performance Expectancy and Effort Expectancy showed non-significant effects, suggesting Tunisian users prioritize trust and familiarity over perceived usefulness or ease of use. Technophilia exhibited marginal significance, indicating technology enthusiasts may serve as early adopters. Notably, demographic factors (age, gender, profession, expertise) showed no moderating effects, implying universal adoption drivers across user segments. These results offer crucial insights for AI practitioners: Marketing strategies should emphasize building trust through transparency, enhancing familiarity via training programs, and leveraging social influence in this collectivist culture. The study validates an extended UTAUT model for intelligent devices in developing economies while challenging conventional technology acceptance assumptions. Limitations and future research directions are discussed to guide cross-cultural examinations of AI adoption.

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The Behavioral Intention to Use Intelligent Devices Using an Extension of UTAUT Model: Case of Tunisian Consumers

  • 会议论文

  • 发表日期：

    2026年

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  • Inès Mhaya,
  • Soukeina Touiti

摘要

This study extends the Unified Theory of Acceptance and Use of Technology (UTAUT) to investigate behavioral intention toward adopting intelligent devices in Tunisia, while examining variations in technology acceptance. Data from 218 respondents were analyzed using structural equation modeling (SEM), revealing key findings: Familiarity and Trust emerged as the strongest predictors of adoption intention, underscoring the importance of user confidence and prior experience with AI technologies. Contrary to traditional UTAUT expectations, Performance Expectancy and Effort Expectancy showed non-significant effects, suggesting Tunisian users prioritize trust and familiarity over perceived usefulness or ease of use. Technophilia exhibited marginal significance, indicating technology enthusiasts may serve as early adopters. Notably, demographic factors (age, gender, profession, expertise) showed no moderating effects, implying universal adoption drivers across user segments. These results offer crucial insights for AI practitioners: Marketing strategies should emphasize building trust through transparency, enhancing familiarity via training programs, and leveraging social influence in this collectivist culture. The study validates an extended UTAUT model for intelligent devices in developing economies while challenging conventional technology acceptance assumptions. Limitations and future research directions are discussed to guide cross-cultural examinations of AI adoption.