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<p>The study investigates hotel workers' acceptance of Robotic Process Automation (RPA) in Jaipur, India, using the extended Unified Theory of Acceptance and Use of Technology (UTAUT-3) framework. In recent times, with automation technologies gaining momentum in the hospitality industry for the enhancement of operational effectiveness and service delivery, understanding the factors affecting employee acceptance has assumed critical importance. The researchers adopted a quantitative approach, where data was collected from 170 employees from various hotel categories and departments. The data was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the hypothesized relationships. The results confirmed all the proposed relationships in the UTAUT-3 model, with Performance Expectancy (β = 0.325) being the strongest predictor of Behavioral Intentions, followed by Effort Expectancy (β = 0.248). The model explained a substantial variance of 68.5% in Behavioral Intentions and 59.2% in Use Behavior, with good fit indices (SRMR = 0.062, NFI = 0.912). Age, gender, and experience were shown to have significant moderating effects, with age negatively moderating the relationship between Performance Expectancy and Behavioral Intentions, while experience positively moderated the relationship between Habit and Use Behavior. The demographic analysis further indicated higher acceptance of RPA among front office employees and employees of the luxury hotel category. These findings contribute to technology acceptance theory with a new context for the validation of UTAUT-3. It also lends important implications to hotel managers to facilitate implementing RPA systems. The research suggests that the implementation process focuses on the benefits of performance, ease of use, and support by the organization while also considering the differentiation of such approaches based on employee characteristics and departmental context.</p>

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Performance expectancy and facilitating conditions drive robotic process automation acceptance among hotel employees while demographic factors reshape adoption pathways

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月24日

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  • R. K. Tailor,
  • Drashti Tailor

摘要

The study investigates hotel workers' acceptance of Robotic Process Automation (RPA) in Jaipur, India, using the extended Unified Theory of Acceptance and Use of Technology (UTAUT-3) framework. In recent times, with automation technologies gaining momentum in the hospitality industry for the enhancement of operational effectiveness and service delivery, understanding the factors affecting employee acceptance has assumed critical importance. The researchers adopted a quantitative approach, where data was collected from 170 employees from various hotel categories and departments. The data was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the hypothesized relationships. The results confirmed all the proposed relationships in the UTAUT-3 model, with Performance Expectancy (β = 0.325) being the strongest predictor of Behavioral Intentions, followed by Effort Expectancy (β = 0.248). The model explained a substantial variance of 68.5% in Behavioral Intentions and 59.2% in Use Behavior, with good fit indices (SRMR = 0.062, NFI = 0.912). Age, gender, and experience were shown to have significant moderating effects, with age negatively moderating the relationship between Performance Expectancy and Behavioral Intentions, while experience positively moderated the relationship between Habit and Use Behavior. The demographic analysis further indicated higher acceptance of RPA among front office employees and employees of the luxury hotel category. These findings contribute to technology acceptance theory with a new context for the validation of UTAUT-3. It also lends important implications to hotel managers to facilitate implementing RPA systems. The research suggests that the implementation process focuses on the benefits of performance, ease of use, and support by the organization while also considering the differentiation of such approaches based on employee characteristics and departmental context.