TY - JOUR
T1 - No Person Is an Island
T2 - Unpacking the Work and After-Work Consequences of Interacting With Artificial Intelligence
AU - Tang, Pok Man
AU - Koopman, Joel
AU - Mai, Ke Michael
AU - De Cremer, David
AU - Zhang, Jack H.
AU - Reynders, Philipp
AU - Ng, Chin Tung Stewart
AU - Chen, I. Heng
N1 - Publisher Copyright:
© 2023 American Psychological Association
PY - 2023/6/12
Y1 - 2023/6/12
N2 - The artificial intelligence (AI) revolution has arrived, as AI systems are increasingly being integrated across organizational functions into the work lives of employees. This coupling of employees and machines fundamentally alters the work-related interactions to which employees are accustomed, as employees find themselves increasingly interacting with, and relying on, AI systems instead of human coworkers. This increased coupling of employees and AI portends a shift toward more of an “asocial system,” wherein people may feel socially disconnected at work. Drawing upon the social affiliation model, we develop a model delineating both adaptive and maladaptive consequences of this situation. Specifically, we theorize that the more employees interact with AI in the pursuit of work goals, the more they experience a need for social affiliation (adaptive)—which may contribute to more helping behavior toward coworkers at work—as well as a feeling of loneliness (maladaptive), which then further impair employee well-being after work (i.e., more insomnia and alcohol consumption). In addition, we submit that these effects should be especially pronounced among employees with higher levels of attachment anxiety. Results across four studies (N = 794) with mixed methodologies (i.e., survey study, field experiment, and simulation study; Studies 1–4) with employees from four different regions (i.e., Taiwan, Indonesia, United States, and Malaysia) generally support our hypotheses.
AB - The artificial intelligence (AI) revolution has arrived, as AI systems are increasingly being integrated across organizational functions into the work lives of employees. This coupling of employees and machines fundamentally alters the work-related interactions to which employees are accustomed, as employees find themselves increasingly interacting with, and relying on, AI systems instead of human coworkers. This increased coupling of employees and AI portends a shift toward more of an “asocial system,” wherein people may feel socially disconnected at work. Drawing upon the social affiliation model, we develop a model delineating both adaptive and maladaptive consequences of this situation. Specifically, we theorize that the more employees interact with AI in the pursuit of work goals, the more they experience a need for social affiliation (adaptive)—which may contribute to more helping behavior toward coworkers at work—as well as a feeling of loneliness (maladaptive), which then further impair employee well-being after work (i.e., more insomnia and alcohol consumption). In addition, we submit that these effects should be especially pronounced among employees with higher levels of attachment anxiety. Results across four studies (N = 794) with mixed methodologies (i.e., survey study, field experiment, and simulation study; Studies 1–4) with employees from four different regions (i.e., Taiwan, Indonesia, United States, and Malaysia) generally support our hypotheses.
KW - artificial intelligence
KW - attachment anxiety
KW - loneliness
KW - need for affiliation
KW - social affiliation model
UR - http://www.scopus.com/inward/record.url?scp=85169456740&partnerID=8YFLogxK
U2 - 10.1037/apl0001103
DO - 10.1037/apl0001103
M3 - Article
C2 - 37307359
AN - SCOPUS:85169456740
SN - 0021-9010
VL - 108
SP - 1766
EP - 1789
JO - Journal of Applied Psychology
JF - Journal of Applied Psychology
IS - 11
ER -