The Impact of Artificial Intelligence on Industrial-Organizational Psychology: A Systematic Review
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Abstract
Current trends indicate that the pace of artificial intelligence and machine learning technology innovations will continue to increase in the foreseeable future. The objective of this study was to conduct a systematic review of the relevant literature as well as a qualitative meta-analysis of recent studies on the impact of artificial intelligence and big data on industrial-organizational psychology. Following the guidelines for preferred reporting items for systematic reviews (PRISMA) and meta-analyses, the researcher conducted a literature search within various main electronic databases. The results of the meta-analysis showed a positive association between artificial intelligence and different aspects of industrial-organizational psychology. In addition, results showed that artificial intelligence-enabled automation and robotics are going to play a great role in the future. Furthermore, this study provides several directions for future studies and discussion on both academic and professional implications.
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References
Bartlett, L., Martin, A., Neil, A. L., Memish, K., Otahal, P., Kilpatrick, M., & Sanderson, K. (2019). A systematic review and meta-analysis of workplace mindfulness training randomized controlled trials. Journal of Occupational Health Psychology, 24(1), 108–126. https://doi.org/10.1037/OCP0000146
Behl, A., Chavan, M., Jain, K., Sharma, I., Pereira, V. E., & Zhang, J. Z. (2022). The role of organizational culture and voluntariness in the adoption of artificial intelligence for disaster relief operations. International Journal of Manpower, 43(2), 569–586.
https://doi.org/10.1108/IJM-03-2021-0178
Bley, K., Fredriksen, S. F., Skjærvik, M. E., & Pappas, I. O. (2022). The role of organizational culture on artificial intelligence capabilities and organizational performance. In S. Papagiannidis, E. Alamanos, S. Gupta, Y. K. Dwivedi, M. Mäntymäki & I. O. Pappas (Eds.), The role of digital technologies in shaping the post-pandemic world (pp. 13-24). Springer.
https://doi.org/10.1007/978-3-031-15342-6_2
Boddu, S. K., Santoki, R., A., A., Khurana, S., Vitthal Koli, P., Rai, R., & Agrawal, A. (2022). An analysis to understand the role of machine learning, robotics and artificial intelligence in digital marketing. Materials Today: Proceedings, 56, 2288–2292. https://doi.org/10.1016/J.MATPR.2021.11.637
Braganza, A., Chen, W., Canhoto, A., & Sap, S. (2021). Productive employment and decent work: The impact of AI adoption on psychological contracts, job engagement and employee trust. Journal of Business Research, 131, 485–494. https://doi.org/10.1016/J.JBUSRES.2020.08.018
Brozek, J. L., Canelo-Aybar, C., Akl, E. A., Bowen, J. M., Bucher, J., Chiu, W. A., Cronin, M., Djulbegovic, B., Falavigna, M., Guyatt, G. H., Gordon, A. A., Hilton Boon, M., Hutubessy, R. C. W., Joore, M. A., Katikireddi, V., LaKind, J., Langendam, M., Manja, V., Magnuson, K., … Schünemann, H. J. (2021). Grade guidelines 30: The grade approach to assessing the certainty of modeled evidence-an overview in the context of health decision-making. Journal of Clinical Epidemiology, 129, 138–150. https://doi.org/10.1016/J.JCLINEPI.2020.09.018
Caulley, L., Cheng, W., Catalá-López, F., Whelan, J., Khoury, M., Ferraro, J., Husereau, D., Altman, D. G., & Moher, D. (2020). Citation impact was highly variable for reporting guidelines of health research: A citation analysis. Journal of Clinical Epidemiology, 127, 96–104. https://doi.org/10.1016/j.jclinepi.2020.07.013
Clifton, J., Clifton, J., Glasmeier, A., & Gray, M. (2020). When machines think for us: The consequences for work and place. Cambridge Journal of Regions, Economy and Society, 13(1), 3–23. https://doi.org/10.1093/CJRES/RSAA004
Corbett, M. S., Higgins, J. P. T., & Woolacott, N. F. (2014). Assessing baseline imbalance in randomised trials: implications for the Cochrane risk of bias tool. Research Synthesis Methods, 5(1), 79–85. https://doi.org/10.1002/JRSM.1090
Crowston, K., & Bolici, F. (2019, January 8). Impacts of Machine Learning on Work. Proceedings of the Annual Hawaii International Conference on System Sciences (pp. 5961–5970). https://doi.org/10.24251/HICSS.2019.719
Dabbous, A., Barakat, K. A., & Sayegh, M. M. (2022). Enabling organizational use of artificial intelligence: An employee perspective. Journal of Asia Business Studies, 16(2), 245–266. https://doi.org/10.1108/JABS-09-2020-0372
Damianidou, D., Arthur-Kelly, M., Lyons, G., & Wehmeyer, M. L. (2019). Technology use to support employment-related outcomes for people with intellectual and developmental disability: An updated meta-analysis. International Journal of Developmental Disabilities, 65(4), 220. https://doi.org/10.1080/20473869.2018.1439819
Dessein, B., Roctus, J., & Biscop, S. (2022). Putin is creating the multipolar world he (thought he) wanted. https://www.egmontinstitute.be/putin-is-creating-the-multipolar-world-he-thought-he-wanted/
Donaldson, S. I., Lee, J. Y., & Donaldson, S. I. (2019). Evaluating positive psychology interventions at work: A systematic review and meta-analysis. International Journal of Applied Positive Psychology, 4(3), 113–134. https://doi.org/10.1007/S41042-019-00021-8
Feinzig, S. L. (2022). A perspective on bringing AI to HR: Opportunities, risks and a recommended path forward. https://www.ihrim.org/2020/06/a-perspective-on-bringing-ai-to-hr-opportunities-risks-and-a-recommended-path-forward/
Feng, W., Tu, R., Lu, T., & Zhou, Z. (2018). Understanding forced adoption of self-service technology: The impacts of users’ psychological reactance. 38(8), 820–832. https://doi.org/10.1080/0144929X.2018.1557745
Glikson, E., & Woolley, A. W. (2020). Human trust in artificial intelligence: Review of empirical research. 14(2), 627–660. https://doi.org/10.5465/ANNALS.2018.0057
Guzeller, C. O., & Celiker, N. (2020). Examining the relationship between organizational commitment and turnover intention via a meta-analysis. International Journal of Culture, Tourism, and Hospitality Research, 14(1), 102–120. https://doi.org/10.1108/IJCTHR-05-2019-0094
Hassoun, A., Aït-Kaddour, A., Abu-Mahfouz, A. M., Rathod, N. B., Bader, F., Barba, F. J., Biancolillo, A., Cropotova, J., Galanakis, C. M., Jambrak, A. R., Lorenzo, J. M., Måge, I., Ozogul, F., & Regenstein, J. (2022). The fourth industrial revolution in the food industry-Part I: Industry 4.0 technologies. Critical Reviews in Food Science and Nutrition. https://doi.org/10.1080/10408398.2022.2034735
Hüffmeier, J., & Zacher, H. (2021). The basic income: Initiating the needed discussion in industrial, work, and organizational psychology. Industrial and Organizational Psychology, 14(4), 531–562. https://doi.org/10.1017/IOP.2021.91
Jöhnk, J., Weißert, M., & Wyrtki, K. (2021). Ready or not, AI comes-an interview study of organizational AI readiness factors. Business and Information Systems Engineering, 63(1), 5–20. https://doi.org/10.1007/s12599-020-00676-7
Kaltenegger, H. C., Becker, L., Rohleder, N., Nowak, D., & Weigl, M. (2020). Association of working conditions including digital technology use and systemic inflammation among employees: Study protocol for a systematic review. Systematic Reviews, 9(1), 1–11.
https://doi.org/10.1186/s13643-020-01463-x
Kelley, S. (2022). Employee perceptions of the effective adoption of AI principles. Journal of Business Ethics, 178(4), 871–893. https://doi.org/10.1007/s10551-022-05051-y
Koo, B., Curtis, C., & Ryan, B. (2021). Examining the impact of artificial intelligence on hotel employees through job insecurity perspectives. International Journal of Hospitality Management, 95, 102763. https://doi.org/10.1016/J.IJHM.2020.102763
Kumar, M., Jauhari, H., Rastogi, A., & Sivakumar, S. (2018). Managerial support for development and turnover intention: Roles of organizational support, work engagement and job satisfaction. Journal of Organizational Change Management, 31(1), 135–153.
https://doi.org/10.1108/JOCM-06-2017-0232
Lee, A., Inceoglu, I., Hauser, O., & Greene, M. (2020). Determining causal relationships in leadership research using Machine Learning: The powerful synergy of experiments and data science. The Leadership Quarterly, 101426. https://doi.org/10.1016/J.LEAQUA.2020.101426
Lefkowitz, J. (2021). Forms of ethical dilemmas in industrial-organizational psychology. Industrial and Organizational Psychology, 14(3), 297–319. https://doi.org/10.1017/IOP.2021.65
Tortorella, G. L., Cauchick-Miguel, P. A., Li, W., Staines, J., & McFarlane, D. (2021). What does operational excellence mean in the Fourth Industrial Revolution era? International Journal of Production Research, 60(9), 2901–2917. https://doi.org/10.1080/00207543.2021.1905903
Malomane, R., Musonda, I., & Okoro, C. S. (2022). The opportunities and challenges associated with the implementation of fourth industrial revolution technologies to manage health and safety. International Journal of Environmental Research and Public Health, 19(2), 846. https://doi.org/10.3390/ijerph19020846
Mfanafuthi, M., Nyawo, J., & Mashau, P. (2019). Analysis of the impact of artificial intelligence and robotics on human labour. Gender and Behaviour, 17(3), 13877-13891. https://www.proquest.com/docview/2445578442?pq-origsite=gscholar&fromopenview=true
Mikalef, P., & Gupta, M. (2021). Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance. Information & Management, 58(3), 103434. https://doi.org/10.1016/J.IM.2021.103434
Moher, D., Shamseer, L., Clarke, M., Ghersi, D., Liberati, A., Petticrew, M., Shekelle, P., Stewart, L. A., Estarli, M., Barrera, E. S. A., Martínez-Rodríguez, R., Baladia, E., Agüero, S. D., Camacho, S., Buhring, K., Herrero-López, A., Gil-González, D. M., Altman, D. G., Booth, A., … Whitlock, E. (2016). Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Revista Espanola de Nutricion Humana y Dietetica, 20(2), 148–160. https://doi.org/10.1186/2046-4053-4-1
Morgan, R. L., Whaley, P., Thayer, K. A., & Schünemann, H. J. (2018). Identifying the PECO: A framework for formulating good questions to explore the association of environmental and other exposures with health outcomes. Environment International, 121(Pt 1), 1027. https://doi.org/10.1016/J.ENVINT.2018.07.015
Moses, O., Igbinoba, C., Maxwell, E., Salau, O., & Omobolanle, O. (2022). Psychological contract and employee performance in the Nigerian manufacturing industry: A conceptual review. Academy of Entrepreneurship Journal, 28(1), 1–11. https://www.abacademies.org/articles/psychological-contract-and-employee-performance-in-the-nigerian-manufacturing-industry-a-conceptual-review-13947.html
Neuman, W. L. (2014). Social research methods qualitative and quantitative approaches. https://books.google.com/books/about/Social_Research_Methods.html?id=_o0rnwEACAAJ
Obschonka, M., Lee, N., Rodríguez-Pose, A., Eichstaedt, J. C., & Ebert, T. (2020). Big data methods, social media, and the psychology of entrepreneurial regions: Capturing cross-county personality traits and their impact on entrepreneurship in the USA. Small Business Economics, 55(3), 567–588. https://doi.org/10.1007/S11187-019-00204-2
Ouzzani, M., Hammady, H., Fedorowicz, Z., & Elmagarmid, A. (2016). Rayyan-a web and mobile app for systematic reviews. Systematic Reviews, 5(1), 1–10. https://doi.org/10.1186/S13643-016-0384-4
Page, M. J., & Moher, D. (2017). Evaluations of the uptake and impact of the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement and extensions: A scoping review. Systematic Reviews, 6(1), 263. https://doi.org/10.1186/s13643-017-0663-8
Pietsch, W. (2021). Big Data. Cambridge University Press. https://doi.org/10.1017/9781108588676
Pizzi, G., Scarpi, D., & Pantano, E. (2021). Artificial intelligence and the new forms of interaction: Who has the control when interacting with a chatbot? Journal of Business Research, 129, 878–890. https://doi.org/10.1016/j.jbusres.2020.11.006
Pumplun, L., Tauchert, C., & Heidt, M. (2019, June 8-14). A new organizational chassis for artificial intelligence - exploring organizational readiness factors. In Proceedings of the 27th European Conference on Information Systems (ECIS), Stockholm & Uppsala, Sweden. https://aisel.aisnet.org/ecis2019_rp/106?utm_source=aisel.aisnet.org%2Fecis2019_rp%2F106&utm_medium=PDF&utm_campaign=PDFCoverPages
Reis, J., Santo, P. E., & Melao, N. (2019, June 19-22). Impacts of artificial intelligence on public administration: A systematic literature review. 14th Iberian Conference on Information Systems and Technologies (CISTI). https://doi.org/10.23919/CISTI.2019.8760893
Cabello, I. R., Meneses-Echavez, J. F., Serrano-Ripoll, M. J., Fraile-Navarro, D., Roque, M. A. F., Moreno, G. P., Castro, A., Ruiz-Pérez, I., Campos, R. Z., & Gonçalves-Bradley, D. (2020). Impact of viral epidemic outbreaks on mental health of healthcare workers: A rapid systematic review and meta-analysis. SSRN Electronic Journal. https://doi.org/10.2139/SSRN.3569883
Rieder, B., & Skop, Y. (2021). The fabrics of machine moderation: Studying the technical, normative, and organizational structure of Perspective API. Big Data and Society, 8(2). https://doi.org/10.1177/20539517211046181
Rzepka, C., & Berger, B. (2018, December 13-16). User interaction with AI-enabled information systems: A systematic review of IS research. Bridging the internet of people, data, and things: Proceeds of the 39th International Conference on Information Systems (ICIS 2018). https://files.stample.co/browserUpload/f5dd2ddd-d590-4b98-93d6-c37a132f9c3b
Sarkis-Onofre, R., Catalá-López, F., Aromataris, E., & Lockwood, C. (2021). How to properly use the PRISMA statement. Systematic Reviews, 10(1), 1–3.
https://doi.org/10.1186/S13643-021-01671-Z
Schepers, J. J. L., & Borgh, M. (2020). A meta-analysis of frontline employees’ role behavior and the moderating effects of national culture. Journal of Service Research, 23(3), 255–280. https://doi.org/10.1177/1094670520918669
Shah, Z. A., Ul-Haq, A., Alammari, R., Iqbal, A., & Jalal, M. (2021). Optimization solutions for demand side management and monitoring. In H. Malik, N. Fatema & J. A. Alzubi (Eds.), AI and machine learning paradigms for health monitoring system: Intelligent data analytics (pp. 3–43). Springer.
Shaohua, L., & Shorey, S. (2021). Psychosocial interventions on psychological outcomes of parents with perinatal loss: A systematic review and meta-analysis. International Journal of Nursing Studies, 117. https://doi.org/10.1016/J.IJNURSTU.2021.103871
Sheoran, S. K., & Parmar, V. (2022). GeoWebCln: An intensive cleaning architecture for geospatial metadata. Quaestiones Geographicae, 41(2), 51–62. https://doi.org/10.2478/QUAGEO-2022-0004
Shrestha, Y. R., Ben-Menahem, S. M., & von Krogh, G. (2019). Organizational decision-making structures in the age of artificial intelligence. California management review, 61(4), 66-83. Https://doi.org/10.1177/0008125619862257
Singh, J., Flaherty, K., Sohi, R. S., Deeter-Schmelz, D., Habel, J., Le Meunier-FitzHugh, K., Malshe, A., Mullins, R., & Onyemah, V. (2019). Sales profession and professionals in the age of digitization and artificial intelligence technologies: Concepts, priorities, and questions. Journal of personal selling & sales management, 39(1), 2–22. https://doi.org/10.1080/08853134.2018.1557525
Spector, P. E. (2021). Industrial and organizational psychology: Research and practice (8th ed.). Wiley.
Sterne, J. A. C., Savović, J., Page, M. J., Elbers, R. G., Blencowe, N. S., Boutron, I., Cates, C. J., Cheng, H. Y., Corbett, M. S., Eldridge, S. M., Emberson, J. R., Hernán, M. A., Hopewell, S., Hróbjartsson, A., Junqueira, D. R., Jüni, P., Kirkham, J. J., Lasserson, T., Li, T., … Higgins, J. P. T. (2019). RoB 2: A revised tool for assessing risk of bias in randomised trials. BMJ, 366. https://doi.org/10.1136/BMJ.L4898
Sun, S., Lee, P. C., Law, R., & Zhong, L. (2020). The impact of cultural values on the acceptance of hotel technology adoption from the perspective of hotel employees. Journal of Hospitality and Tourism Management, 44, 61–69. https://doi.org/10.1016/J.JHTM.2020.04.012
Tooth, L., Ware, R., Bain, C., Purdie, D. M., & Dobson, A. (2005). Quality of reporting of observational longitudinal research. American Journal of Epidemiology, 161(3), 280–288. https://doi.org/10.1093/AJE/KWI042
Trang, S., & Brendel, B. (2019). A meta-analysis of deterrence theory in information security policy compliance research. Information Systems Frontiers, 21(6), 1265–1284. https://doi.org/10.1007/S10796-019-09956-4
Wu, C., Zhang, Y., Huang, S., & Yuan, Q. (2021). Does enterprise social media usage make the employee more productive? A meta-analysis. Telematics and Informatics, 60, 101578. https://doi.org/10.1016/J.TELE.2021.101578