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A Model for Predicting User Intention to Use Voice Recognition Technologies at the Workplace in Saudi Arabia

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  • Khalid Majrashi

    (Department of Information Technology, Institute of Public Administration, Saudi Arabia)

Abstract

The use of voice recognition technologies (VRTs) has been expanding, and these are currently used at workplaces. This study tested a model for predicting users’ intention to use VRTs at workplaces. The model extended the technology acceptance model (TAM) and considered four additional factors—perceived privacy, perceived security, perceived trust, and social norms—and four variables—age, education level, gender, and nationality. We validated the model based on responses from 300 employees working in Saudi Arabia. The results indicated a medium level of acceptance and a valid TAM in its original form. Further, perceived privacy and perceived security are significant predictors of perceived trust and perceived trust is an important predictor of attitudes and intention to use VRTs. The social norms variable was a significant predictor of intention to use and accept VRTs. The results also showed that age and education level significantly affect users’ attitudes toward VRT adoption.

Suggested Citation

  • Khalid Majrashi, 2022. "A Model for Predicting User Intention to Use Voice Recognition Technologies at the Workplace in Saudi Arabia," International Journal of Technology and Human Interaction (IJTHI), IGI Global, vol. 18(1), pages 1-18, January.
    • Handle: RePEc:igg:jthi00:v:18:y:2022:i:1:p:1-18
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    1. Margreet B. Michel-Verkerke & Roel W. Schuring & Ton A.M. Spil, 2005. "USE IT to Create Patient-Relation Management for Multiple Sclerosis Patients," International Journal of Technology and Human Interaction (IJTHI), IGI Global, vol. 1(4), pages 58-75, October.
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