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Cited by 150AI and HR TechnologiesEthics and Social Impacts of AIEmployer Branding and e-HRM

Attitudes towards artificial intelligence at work: Scale development and validation

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Park, Jiyoung, Woo, Sang Eun, Kim, JeongJin·2024·Journal of Occupational and Organizational Psychology
DOI 10.1111/joop.12502

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Abstract

Research suggests that understanding workers' attitudes towards artificial intelligence (AI) application is a prerequisite to successfully integrating AI into an organization. However, few studies have clarified the meaning of attitudes towards AI application at work (AAAW) as a multifaceted construct that can be assessed with psychometric validity. To address this issue, we developed and validated a scale to capture individuals' AAAW using three independent samples (total N = 2841). The resulting 25‐item scale covers an overall construct of AAAW as well as six dimensions that are subsumed under the construct (i.e., perceived humanlikeness, perceived adaptability, perceived quality of AI, AI use anxiety, job insecurity and personal utility). Our findings suggest that the AAAW scale has good psychometric properties and can be used to predict important recruiting outcomes. The scale offers opportunities to better understand and measure workers' attitudes towards AI application at work in a comprehensive and integrative manner.

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  • The literature on the impact of GenAI on the work of knowledge workers indicates that strategies for combining digital competencies with unique human resources (e.g., relational skills, deep domain expertise) are particularly important, as are a willingness to learn and change professional roles continuously, and a willingness to treat GenAI as a collaborator.

    “The literature on the impact of GenAI on the work of knowledge workers indicates that strategies for combining digital competencies with unique human resources (e.g., relational skills, deep domain expertise) are particularly important, as are a willingness to learn and change professional roles continuously, and a willingness to treat GenAI as a collaborator [36, 37].”

    From The Entry of Autistic University Graduates into the Labor Market in the Era of Generative AI: Their Concerns and Strategies Regarding the Technology. Research in Progress: The Entry of Autistic Graduates into the Labor Market in the Era of GenAI (2026) · cites this paper as [37]

    The introduction discusses employees’ attitudes toward workplace AI, and Practical Implications recommends exposure to AI to build confidence. Neither passage addresses combining digital and human skills, continuously changing professional roles, or treating GenAI as a collaborator. Searches for those skills and for collaboration found only a Limitations and Future Research Directions passage that mentions experiences of collaborating with AI, not the claimed strategy. (LLM, conf=0.72)

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References 95 · Cited by 150

Research works printed but not linked: 54

  1. Ajzen, I., & Fishbein, M. (1972). Attitudes and normative beliefs as factors influencing behavioral intentions. Journal of Personality and Social Psychology, 21(1), 1- 9. https://doi.org/10.1037/h0031930 American Educational Research Association, American Psychological Association, & National Council on Measurement in Education. (2014). Standards for educational and psychological testing. · DOI
  2. Barnett, T., Pearson, A. W., Pearson, R., & Kellermanns, F. W. (2015). Five-factor model personality traits as predictors of perceived and actual usage of technology. European Journal of Information Systems, 24(4), 374-390. https://doi.org/10.1057/ejis.2014. · DOI
  3. Bauer, T. N., Truxillo, D. M., Sanchez, R. J., Craig, J. M., Ferrara, P., & Campion, M. A. (2001). Applicant reactions to selection: Development of the selection procedural justice scale (SPJS). Personnel Psychology, 54(2), 387-419. https://doi.org/10.1111/j.1744-6570.2001.tb00097.x · DOI
  4. Bretz Jr, R. D., & Judge, T. A. (1994). The role of human resource systems in job applicant decision processes. Journal of Management, 20(3), 531-551. https://doi.org/10.1177/014920639402000301 · DOI
  5. Boyce, C. J., Wood, A. M., & Brown, G. D. A. (2010). The dark side of conscientiousness: Conscientious people experience greater drops in life satisfaction following unemployment. Journal of Research in Personality, 44(4), 535-539. https://doi.org/10.1016/j.jrp.2010.05.001 · DOI
  6. Cober, R. T., Brown, D. J., Levy, P. E., Cober, A. B., & Keeping, L. M. (2003). Organizational web sites: Web site content and style as determinants of organizational attraction. International Journal of Selection and Assessment, 11(2-3), 158- 169. https://doi.org/10.1111/1468-2389.00239 · DOI
  7. Crant, J. M., & Bateman, T. S. (1993). Potential job applicant reactions to employee drug testing: The effect of program characteristics and individual differences. Journal of Business and Psychology, 7(3), 279-290. https://doi.org/10.1007/BF01015755 · DOI
  8. De Guinea, A. O., & Markus, M. L. (2009). Why break the habit of a lifetime? Rethinking the roles of intention, habit, and emotion in continuing information technology use. MIS Quarterly, 433-444. https://doi.org/10.2307/20650303 · DOI
  9. Del Giudice, M., Scuotto, V., Orlando, B., & Mustilli, M. (2023). Toward the human- centered approach. A revised model of individual acceptance of AI. Human Resource Management Review, 33(1), 100856. https://doi.org/10.1016/j.hrmr.2021.100856 · DOI
  10. Diener, E. D., Emmons, R. A., Larsen, R. J., & Griffin, S. (1985). The satisfaction with life scale. Journal of Personality Assessment, 49(1), 71-75. https://doi.org/10.1207/s15327752jpa4901_13 · DOI · Paper page
  11. Eagly, A. H., & Chaiken, S. (2007). The advantages of an inclusive definition of attitude. Social Cognition, 25(5), 582-602. https://doi.org/10.1521/soco.2007.25.5.582 · DOI · Paper page
  12. Eccles, J. S., & Wigfield, A. (2002). Motivational beliefs, values, and goals. Annual Review of Psychology, 53(1), 109- 132.https://doi.org/10.1146/annurev.psych.53.100901.13515 · DOI
  13. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.
  14. Folger, N., Brosi, P., Stumpf-Wollersheim, J., & Welpe, I. M. (2022). Applicant reactions to digital selection methods: a signaling perspective on innovativeness and procedural justice. Journal of Business and Psychology, 37(4), 735-757. https://doi.org/10.1007/s10869-021-09770-3 · DOI
  15. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.1177/002224378101800104 · DOI · Paper page
  16. Graziano, W. G., Habashi, M. M., Sheese, B. E., & Tobin, R. M. (2007). Agreeableness, empathy, and helping: A person × situation perspective. Journal of Personality and Social Psychology, 93(4), 583-599. https://doi.org/10.1037/0022-3514.93.4.583 · DOI
  17. Harris-Watson, A. M., Larson, L. E., Lauharatanahirun, N., DeChurch, L. A., & Contractor, N. S. (2023). Social perception in Human-AI teams: Warmth and competence predict receptivity to AI teammates. Computers in Human Behavior, 145, 107765. https://doi.org/10.1016/j.chb.2023.107765 · DOI
  18. Harrison, D. A., & McLaughlin, M. E. (1993). Cognitive processes in self-report responses: Tests of item context effects in work attitude measures. Journal of Applied Psychology, 78(1), 129-140. https://doi.org/10.1037/0021-9010.78.1.129 · DOI
  19. Hinkin, T. R. (1998). A brief tutorial on the development of measures for use in survey questionnaires. Organizational Research Methods, 1(1), 104-121. https://doi.org/10.1177/109442819800100106 · DOI · Paper page
  20. Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: a Multidisciplinary Journal, 6(1), 1-55. https://doi.org/10.1080/10705519909540118 · DOI · Paper page
  21. Judge, T. A., Bono, J. E., & Locke, E. A. (2000). Personality and job satisfaction: The mediating role of job characteristics. Journal of Applied Psychology, 85(2), 237- 249. https://doi.org/10.1037/0021-9010.85.2.237 · DOI · Paper page
  22. Kim, T. W., & Duhachek, A. (2020). Artificial intelligence and persuasion: A construal-level account. Psychological Science, 31(4), 363-380. https://doi.org/10.1177/095679762090498 · DOI
  23. Kim, H. C., & Kramer, T. (2015). Do materialists prefer the "brand-as-servant"? The interactive effect of anthropomorphized brand roles and materialism on consumer responses. Journal of Consumer Research, 42(2), 284-299. https://doi.org/10.1093/jcr/ucv015 · DOI
  24. Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). Guilford Press.
  25. Lazarus, R. S., & Folkman, S. (1984). Stress, appraisal, and coping. Springer publishing company.
  26. Lambert, L. S., & Newman, D. A. (2023). Construct development and validation in three practical steps: Recommendations for reviewers, editors, and authors. Organizational Research Methods, 26(4), 574-607. https://doi.org/10.1177/10944281221115374 · DOI
  27. Letheren, K., Kuhn, K. A. L., Lings, I., & Pope, N. K. L. (2016). Individual difference factors related to anthropomorphic tendency. European Journal of Marketing. 50 (5/6), 973- 1002. https://doi.org/10.1108/EJM-05-2014-0291 · DOI
  28. Lievens, F., & Slaughter, J. E. (2016). Employer image and employer branding: what we know and what we need to know. Annual Review of Organizational Psychology and Organizational Behavior, 3(3), 407-440. https://doi.org/10.1146/annurev-orgpsych- 041015-062501 · DOI
  29. MacKenzie, S. B., Podsakoff, P. M., & Podsakoff, N. P. (2011). Construct measurement and validation procedures in MIS and behavioral research: Integrating new and existing techniques. MIS quarterly, 35(2), 293-334. https://doi.org/10.2307/23044045 · DOI · Paper page
  30. Malhotra, N. K., & Dash, S. (2011). Marketing research: An applied orientation (6th ed.). New Delhi: Dorling-Kindersley.
  31. Mantello, P., Ho, M. T., Nguyen, M. H., & Vuong, Q. H. (2023). Bosses without a heart: socio-demographic and cross-cultural determinants of attitude toward Emotional AI in the workplace. AI & society, 38(1), 97-119. https://doi.org/10.1007/s00146-021- 01290-1 · DOI
  32. Mayer, R. C., & Davis, J. H. (1999). The effect of the performance appraisal system on trust for management: A field quasi-experiment. Journal of Applied Psychology, 84(1), 123-136. https://doi.org/10.1177/1069072707305763 · DOI
  33. McAdams, D. P., & Pals, J. L. (2006). A new Big Five: Fundamental principles for an integrative science of personality. American Psychologist, 61(3), 204-217. https://doi.org/10.1037/0003-066X.61.3.204 · DOI · Paper page
  34. McCarthy, J. M., Bauer, T. N., Truxillo, D. M., Anderson, N. R., Costa, A. C., & Ahmed, S. M. (2017). Applicant perspectives during selection: A review addressing "So what?," "What's new?," and "Where to next?". Journal of Management, 43(6), 1693-1725. https://doi.org/10.1177/0149206316681846 · DOI · Paper page
  35. Meyer, J. P., Allen, N. J., & Smith, C. A. (1993). Commitment to organizations and occupations: Extension and test of a three-component conceptualization. Journal of Applied Psychology, 78(4), 538-551. https://doi.org/10.1037/0021-9010.78.4.538 · DOI · Paper page
  36. Mirowska, A., & Mesnet, L. (2022). Preferring the devil you know: Potential applicant reactions to artificial intelligence evaluation of interviews. Human Resource Management Journal, 32(2), 364-383. https://doi.org/10.1111/1748-8583.12393 · DOI
  37. Nomura, T., Suzuki, T., Kanda, T., & Kato, K. (2006). Measurement of negative attitudes toward robots. Interaction Studies, 7(3), 437-454. https://doi.org/10.1075/is.7.3.14nom · DOI
  38. Nunnally, J. C. (1978). Psychometric Theory. McGraw-Hill Book Company, pp. 86-113, 190- 255.
  39. Oostrom, J. K., Van Der Linden, D., Born, M. P., & Van Der Molen, H. T. (2013). New technology in personnel selection: How recruiter characteristics affect the adoption of new selection technology. Computers in Human Behavior, 29(6), 2404-2415. https://doi.org/10.1016/j.chb.2013.05.025 · DOI
  40. Rigotti, T., Schyns, B., & Mohr, G. (2008). A short version of the occupational self-efficacy scale: Structural and construct validity across five countries. Journal of Career Assessment, 16(2), 238-255. https://doi.org/10.1177/1069072707305763 · DOI
  41. Rijsdijk, S. A., Hultink, E. J., & Diamantopoulos, A. (2007). Product intelligence: its conceptualization, measurement and impact on consumer satisfaction. Journal of the Academy of Marketing Science, 35(3), 340-356. https://doi.org/10.1007/s11747-007- 0040-6 · DOI
  42. Saef, R. M., Porter, C. M., Woo, S. E., & Wiese, C. (2019). Getting off on the right foot: The role of openness to experience in fostering initial trust between culturally dissimilar partners. Journal of Research in Personality, 79, 176-187. https://doi.org/10.1016/j.jrp.2019.03.003 · DOI
  43. Schaefer, K. E. (2016). Measuring trust in human robot interactions: Development of the "trust perception scale-HRI". In Robust Intelligence and Trust in Autonomous Systems (pp. 191-218). Springer, Boston, MA.
  44. Schepman, A., & Rodway, P. (2020). Initial validation of the general attitudes towards Artificial Intelligence Scale. Computers in Human Behavior Reports, 1, 100014. https://doi.org/10.1016/j.chbr.2020.100014 · DOI · Paper page
  45. Sindermann, C., Sha, P., Zhou, M., Wernicke, J., Schmitt, H. S., Li, M., ... & Montag, C. (2021). Assessing the attitude towards artificial intelligence: Introduction of a short measure in German, Chinese, and English Language. KI-Künstliche Intelligenz, 35(1), 109-118. https://doi.org/10.1007/s13218-020-00689-0 · DOI · Paper page
  46. Uziel, L. (2007). Individual differences in the social facilitation effect: A review and meta- analysis. Journal of Research in Personality, 41(3), 579-601. https://doi.org/10.1016/j.jrp.2006.06.008 · DOI
  47. Van Esch, P., Black, J. S., & Ferolie, J. (2019). Marketing AI recruitment: The next phase in job application and selection. Computers in Human Behavior, 90, 215-222. https://doi.org/10.1016/j.chb.2018.09.009 · DOI · Paper page
  48. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926 · DOI · Paper page
  49. Waytz, A., Cacioppo, J., & Epley, N. (2010). Who sees human?: The stability and importance of individual differences in anthropomorphism. Perspectives on Psychological Science, 5(3), 219-232. https://doi.org/10.1177/1745691610369336 · DOI · Paper page
  50. Wixom, B. H., & Todd, P. A. (2005). A theoretical integration of user satisfaction and technology acceptance. Information Systems Research, 16(1), 85-102. https://doi.org/10.1287/isre.1050.0042 · DOI · Paper page
  51. Xu, N., & Wang, K. J. (2021). Adopting robot lawyer? The extending artificial intelligence robot lawyer technology acceptance model for legal industry by an exploratory study. Journal of Management & Organization, 27(5), 867-885. http://doi:10.1017/jmo.2018. · DOI
  52. Yam, K. C., Tang, P. M., Jackson, J. C., Su, R., & Gray, K. (2023). The rise of robots increases job insecurity and maladaptive workplace behaviors: Multimethod evidence. Journal of Applied Psychology, 108(5), 850-870. https://doi.org/10.1037/apl0001045 · DOI
  53. Yuan, F., & Woodman, R. W. (2010). Innovative behavior in the workplace: The role of performance and image outcome expectations. Academy of Management Journal, 53(2), 323-342. https://doi.org/10.5465/amj.2010.49388995 · DOI · Paper page
  54. Zhang, B., & Dafoe, A. (2019). Artificial intelligence: American attitudes and trends. Available at SSRN: http://dx.doi.org/10.2139/ssrn.3312874 · DOI · Paper page

Authors
Park, J., Woo, S. E., & Kim, J.
Affiliations
  • Duksung Women's University (KR)
  • Purdue University West Lafayette (US)
  • George Mason University (US)
Corresponding author
Park, Jiyoung
ORCID
Venue
Journal of Occupational and Organizational Psychology
Publisher
Wiley
Published
2024-03-15
Volume
97
Issue
3
Pages
920-951
ISSN
  • 0963-1798 (ISSN-L)
  • 2044-8325
DOI
10.1111/joop.12502
OpenAlex
W4392882830
Cited by
150
References
95
Citations by year
2026: 80 · 2025: 55 · 2024: 15
FWCI
74.72
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Occupational and Organizational Psychology (publishedVersion)
  • PsyArXiv (OSF Preprints) (submittedVersion, cc-by)
Funders
  • Ministry of Education
  • National Research Foundation of Korea (NRF‐2022S1A5A8050529)
Topics
  • Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Employer Branding and e-HRM
Keywords
attitudes toward AI, scale development, psychometric validation, job insecurity, recruitment outcomes, AI integration in organizations

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