Pok Man Tang, Joel Koopman, Ke Michael Mai, David De Cremer, Jack H Zhang, Philipp Reynders, Chin Tung Stewart Ng, I-Heng Chen
{"title":"没有人是一座孤岛:解开与人工智能互动的工作和下班后的后果。","authors":"Pok Man Tang, Joel Koopman, Ke Michael Mai, David De Cremer, Jack H Zhang, Philipp Reynders, Chin Tung Stewart Ng, I-Heng Chen","doi":"10.1037/apl0001103","DOIUrl":null,"url":null,"abstract":"<p><p>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 <i>four</i> studies (<i>N</i> = 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. (PsycInfo Database Record (c) 2023 APA, all rights reserved).</p>","PeriodicalId":15135,"journal":{"name":"Journal of Applied Psychology","volume":" ","pages":"1766-1789"},"PeriodicalIF":9.4000,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"No person is an island: Unpacking the work and after-work consequences of interacting with artificial intelligence.\",\"authors\":\"Pok Man Tang, Joel Koopman, Ke Michael Mai, David De Cremer, Jack H Zhang, Philipp Reynders, Chin Tung Stewart Ng, I-Heng Chen\",\"doi\":\"10.1037/apl0001103\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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 <i>four</i> studies (<i>N</i> = 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. 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No person is an island: Unpacking the work and after-work consequences of interacting with artificial intelligence.
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. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
期刊介绍:
The Journal of Applied Psychology® focuses on publishing original investigations that contribute new knowledge and understanding to fields of applied psychology (excluding clinical and applied experimental or human factors, which are better suited for other APA journals). The journal primarily considers empirical and theoretical investigations that enhance understanding of cognitive, motivational, affective, and behavioral psychological phenomena in work and organizational settings. These phenomena can occur at individual, group, organizational, or cultural levels, and in various work settings such as business, education, training, health, service, government, or military institutions. The journal welcomes submissions from both public and private sector organizations, for-profit or nonprofit. It publishes several types of articles, including:
1.Rigorously conducted empirical investigations that expand conceptual understanding (original investigations or meta-analyses).
2.Theory development articles and integrative conceptual reviews that synthesize literature and generate new theories on psychological phenomena to stimulate novel research.
3.Rigorously conducted qualitative research on phenomena that are challenging to capture with quantitative methods or require inductive theory building.