Publisher
University of Tennessee at Chattanooga
Place of Publication
Chattanooga (Tenn.)
Abstract
This research integrates the Unified Theory of Acceptance and Use of Technology (UTAUT) with Job Demands-Resources (JD-R) theory to explore the factors influencing the intention to use artificial intelligence (AI) in the workplace. UTAUT (Venkatesh et al., 2003) lays the foundation that performance expectancy (i.e., the belief that using AI will improve their job performance) and facilitating conditions (i.e., organizational and technical infrastructure to support AI use) influence intentions to use AI. Applying JD-R theory (Bakker & Demerouti, 2017) further contextualizes these constructs in the work setting, positioning AI as a potential job resource that enhances performance by alleviating job demands. Thus, we predict employees’ performance expectancy will be positively associated with their intentions to use AI at work (H1). Employees with access to organization-level support for AI use, compared to those without access, may be better positioned to use AI and more willing to adopt it in their work. As such, facilitating conditions are expected to function as a moderating job resource. The positive relationship between employees’ performance expectancy and intentions to use AI at work will be stronger for employees with higher levels of facilitating conditions than those who report lower levels (H2). This study included three cross-sectional samples of working adults (two Prolific samples, N1 = 358, N2 = 692, one Meta sample, N3 = 178). The surveys measured performance expectancy, facilitating conditions, and intentions to use at work (all from Venkatesh et al., 2003) as well as control variables (gender, education, age). We tested regression and simple slope analysis in R. Both hypotheses were supported in all three samples. Performance expectancy was positively related to intentions to use AI at work (b1= .71, p < .001; b2= .70, p < .001; b3= .77, p < .001), and facilitating conditions moderated this relationship as expected (b1= .11, p = .003; b2= .07, p = .004; b3= .10, p = .044). Our study replicated in three samples that perceiving AI will be beneficial for performance is positively associated with intentions to use AI in the workplace, especially for workers with facilitating conditions to better integrate AI. These findings suggest the value of employers providing organizational support and infrastructure for AI, highlighting an opportunity to become the employer of choice in the age of AI. This study demonstrates the theoretical value of bridging technology acceptance models with the organizational psychology literature to better understand AI implementation at work.
Date
11-9-2024
Subject
Industrial and organizational psychology
Document Type
posters
Language
English
Rights
http://rightsstatements.org/vocab/InC/1.0/
License
http://creativecommons.org/licenses/by-nc-nd/3.0/
Included in
Understanding employee intentions to use AI from a job demands-resources perspective
This research integrates the Unified Theory of Acceptance and Use of Technology (UTAUT) with Job Demands-Resources (JD-R) theory to explore the factors influencing the intention to use artificial intelligence (AI) in the workplace. UTAUT (Venkatesh et al., 2003) lays the foundation that performance expectancy (i.e., the belief that using AI will improve their job performance) and facilitating conditions (i.e., organizational and technical infrastructure to support AI use) influence intentions to use AI. Applying JD-R theory (Bakker & Demerouti, 2017) further contextualizes these constructs in the work setting, positioning AI as a potential job resource that enhances performance by alleviating job demands. Thus, we predict employees’ performance expectancy will be positively associated with their intentions to use AI at work (H1). Employees with access to organization-level support for AI use, compared to those without access, may be better positioned to use AI and more willing to adopt it in their work. As such, facilitating conditions are expected to function as a moderating job resource. The positive relationship between employees’ performance expectancy and intentions to use AI at work will be stronger for employees with higher levels of facilitating conditions than those who report lower levels (H2). This study included three cross-sectional samples of working adults (two Prolific samples, N1 = 358, N2 = 692, one Meta sample, N3 = 178). The surveys measured performance expectancy, facilitating conditions, and intentions to use at work (all from Venkatesh et al., 2003) as well as control variables (gender, education, age). We tested regression and simple slope analysis in R. Both hypotheses were supported in all three samples. Performance expectancy was positively related to intentions to use AI at work (b1= .71, p < .001; b2= .70, p < .001; b3= .77, p < .001), and facilitating conditions moderated this relationship as expected (b1= .11, p = .003; b2= .07, p = .004; b3= .10, p = .044). Our study replicated in three samples that perceiving AI will be beneficial for performance is positively associated with intentions to use AI in the workplace, especially for workers with facilitating conditions to better integrate AI. These findings suggest the value of employers providing organizational support and infrastructure for AI, highlighting an opportunity to become the employer of choice in the age of AI. This study demonstrates the theoretical value of bridging technology acceptance models with the organizational psychology literature to better understand AI implementation at work.
Department
University of Tennessee at Chattanooga. Dept. of Psychology