Publisher
University of Tennessee at Chattanooga
Place of Publication
Chattanooga (Tenn.)
Abstract
Understanding factors that shape employee attitudes towards Artificial Intelligence (AI) in the workplace is critical, as these attitudes influence the successful adoption of AI technologies. This study examined the relationship between employees expected retraining opportunities to work alongside AI and their attitudes toward AI at work. Drawing on Conservation of Resources (COR) theory (Hobfoll, 1989), retraining opportunities can be conceptualized as an avenue for acquiring and fostering valuable resources (e.g., work tools, job security, self-efficacy, job skills). Thus, employees who expect retraining may be more likely to view AI positively as training provides the opportunity to obtain these resources, with potential benefits such as protecting against AI-induced job insecurity and enhancing job performance (Kraimer et al., 2011). Additionally, we explored how leadership roles moderate this relationship, with leaders expected to show a stronger positive relationship between retraining and favorable AI attitudes, due to their greater access to and need for resources to manage organizational change (Cheng et al., 2023). We hypothesized that (H1) employees who expect retraining opportunities have more favorable attitudes toward AI, and (H2) this relationship will be stronger for employees in leadership roles compared to non-leaders. We collected cross-sectional data from three samples via Meta (NMeta=133) and Prolific (NProlific1=293; NProlific2=539). We measured retraining opportunities (Kraimer et al., 2011), AI at work attitudes (Venkatesh et al., 2003), leadership status, and control variables (age, gender, education). Hierarchical regression analysis was conducted in R (controls in Step 1, main effects in Step 2 to test H1, interaction term in Step 3 to test H2). The results supported both hypotheses. Replicated in three samples, retraining opportunities were significantly associated with positive AI attitudes (bMeta = .49, p < .001; bProlific1 = .21, p < .001; bProlific2 = .30, p < .001), and leadership status moderated the relationship (bMeta = .52, p = .006; bProlific1 = .28, p = .042; bProlific2 = .29, p = .002). Employees in leadership roles reported stronger associations between retraining opportunities and AI attitudes than non-leaders. These findings highlight the relationship between retraining opportunities and employee attitudes toward AI at work, particularly for leaders. Our findings demonstrate that anticipation of resource gain (e.g., acquiring skills through retraining) is positively related to employee attitudes toward AI, underscoring the importance of anticipated resource acquisition in shaping workplace attitudes. For practice, organizations should prioritize retraining to foster positive AI attitudes, especially for leaders who are critical to driving technological change.
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
AI Attitudes at Work: The Influence of Retraining Opportunities and Leadership Status
Understanding factors that shape employee attitudes towards Artificial Intelligence (AI) in the workplace is critical, as these attitudes influence the successful adoption of AI technologies. This study examined the relationship between employees expected retraining opportunities to work alongside AI and their attitudes toward AI at work. Drawing on Conservation of Resources (COR) theory (Hobfoll, 1989), retraining opportunities can be conceptualized as an avenue for acquiring and fostering valuable resources (e.g., work tools, job security, self-efficacy, job skills). Thus, employees who expect retraining may be more likely to view AI positively as training provides the opportunity to obtain these resources, with potential benefits such as protecting against AI-induced job insecurity and enhancing job performance (Kraimer et al., 2011). Additionally, we explored how leadership roles moderate this relationship, with leaders expected to show a stronger positive relationship between retraining and favorable AI attitudes, due to their greater access to and need for resources to manage organizational change (Cheng et al., 2023). We hypothesized that (H1) employees who expect retraining opportunities have more favorable attitudes toward AI, and (H2) this relationship will be stronger for employees in leadership roles compared to non-leaders. We collected cross-sectional data from three samples via Meta (NMeta=133) and Prolific (NProlific1=293; NProlific2=539). We measured retraining opportunities (Kraimer et al., 2011), AI at work attitudes (Venkatesh et al., 2003), leadership status, and control variables (age, gender, education). Hierarchical regression analysis was conducted in R (controls in Step 1, main effects in Step 2 to test H1, interaction term in Step 3 to test H2). The results supported both hypotheses. Replicated in three samples, retraining opportunities were significantly associated with positive AI attitudes (bMeta = .49, p < .001; bProlific1 = .21, p < .001; bProlific2 = .30, p < .001), and leadership status moderated the relationship (bMeta = .52, p = .006; bProlific1 = .28, p = .042; bProlific2 = .29, p = .002). Employees in leadership roles reported stronger associations between retraining opportunities and AI attitudes than non-leaders. These findings highlight the relationship between retraining opportunities and employee attitudes toward AI at work, particularly for leaders. Our findings demonstrate that anticipation of resource gain (e.g., acquiring skills through retraining) is positively related to employee attitudes toward AI, underscoring the importance of anticipated resource acquisition in shaping workplace attitudes. For practice, organizations should prioritize retraining to foster positive AI attitudes, especially for leaders who are critical to driving technological change.
Department
University of Tennessee at Chattanooga. Dept. of Psychology