Bibtex
Cite as text
@Select Types{,
Journal = "Band-1",
Title= "AI Recruitment: Explaining job seekers’ acceptance of automation in human resource management",
Author= "Jessica Ochmann, Sven Laumer",
Doi= "https://doi.org/10.30844/wi_2020_q1-ochmann",
Abstract= "Organizations are increasingly adopting automation in human resource management (HRM). Subsumed under the term “AI recruitment” organizations try to restructure HRM and apply innovative technologies to achieve a higher level of efficiency. Considering the ongoing “war for talent”, it is also crucial to discuss candidates’ expectations regarding these automated recruiting methods. In this research, we develop a research model explaining the acceptance of AI-based recruiting methods by job seekers. Based on UTAUT2 as a theoretical lens and 23 semi-structured interviews we discuss factors that influence job seekers’ acceptance of automation in HRM. The proposed model addresses research gaps in acceptance research in general and the use of technologies in the recruiting process in particular. We also discuss implications for technology acceptance research and provide some suggestions for the examination of a more passive use of IT.
",
Keywords= "AI recruitment, e-HRM, automation in HRM, UTAUT2
",
}
Jessica Ochmann, Sven Laumer: AI Recruitment: Explaining job seekers’ acceptance of automation in human resource management. Online: https://doi.org/10.30844/wi_2020_q1-ochmann (Abgerufen 23.11.24)
Open Access
Organizations are increasingly adopting automation in human resource management (HRM). Subsumed under the term “AI recruitment” organizations try to restructure HRM and apply innovative technologies to achieve a higher level of efficiency. Considering the ongoing “war for talent”, it is also crucial to discuss candidates’ expectations regarding these automated recruiting methods. In this research, we develop a research model explaining the acceptance of AI-based recruiting methods by job seekers. Based on UTAUT2 as a theoretical lens and 23 semi-structured interviews we discuss factors that influence job seekers’ acceptance of automation in HRM. The proposed model addresses research gaps in acceptance research in general and the use of technologies in the recruiting process in particular. We also discuss implications for technology acceptance research and provide some suggestions for the examination of a more passive use of IT.
AI recruitment, e-HRM, automation in HRM, UTAUT2
1. Bondarouk, T., Brewster, C.: Conceptualising the future of HRM and technology research. The International Journal of Human Resource Management 27, 2652–2671 (2016)
2. Eckhardt, A., Laumer, S., Maier, C., Weitzel, T.: The transformation of people, processes, and IT in e-recruiting. Employee Relations 36, 415–431 (2014)
3. Marler, J.H., Parry, E.: Human resource management, strategic involvement and e-HRM technology. The International Journal of Human Resource Management 27, 2233–2253 (2016)
4. Iqbal, N., Ahmad, M., M.C. Allen, M., Raziq, M.M.: Does e-HRM improve labour productivity? A study of commercial bank workplaces in Pakistan. Employee Relations 40, 281–297 (2018)
5. Laumer, S., Maier, C., Eckhardt, A.: The impact of business process management and applicant tracking systems on recruiting process performance: An empirical study. Journal of Business Economics 85, 421–453 (2015)
6. Muenstermann, B., Stetten, A. von, Laumer, S., Eckhardt, A.: The performance impact of business process standardization: HR case study insights. Management Research Review 33, 924–939 (2010)
7. Maier, C., Laumer, S., Eckhardt, A., Weitzel, T.: Analyzing the impact of HRIS implementations on HR personnel’s job satisfaction and turnover intention. The Journal of Strategic Information Systems 22, 193–207 (2013)
8. Chien, C.-F., Chen, L.-F.: Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry. Expert Systems with Applications 34, 280–290 (2008)
9. Strohmeier, S., Piazza, F.: Artificial Intelligence Techniques in Human Resource Management—A Conceptual Exploration. In: Kahraman, C., Çevik Onar, S. (eds.) Intelligent Techniques in Engineering Management, 87, pp. 149–172. Springer International Publishing, Cham (2015)
10. van Esch, P., Black, J.S., Ferolie, J.: Marketing AI recruitment: The next phase in job application and selection. Computers in Human Behavior 90, 215–222 (2019)
11. Laumer, S., Stetten, A. von, Eckhardt, A.: E-Assessment. Wirtschaftsinformatik 51, 306– 308 (2009)
12. Bryman, A.: Social research methods. Oxford University Press, Oxford (2016)
13. Oppenheim, M.: Amazon scraps ‘sexist AI’ recruitment tool (2018)
14. Dastin, J.: Amazon scraps secret AI recruiting tool that showed bias against women, https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scrapssecret- ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G
15. Bondarouk, T., Harms, R., Lepak, D.: Does e-HRM lead to better HRM service? The International Journal of Human Resource Management 28, 1332–1362 (2017)
16. Laumer, S., Gubler, F., Maier, C., Weitzel, T.: Job Seekers’ Acceptance of Job Recommender Systems: Results of an Empirical Study. Proceedings of the Annual Hawaii International Conference on System Sciences (2018)
17. Laumer, S., Stetten, A. von, Eckhardt, A., Weitzel, T.: Online Gaming to Apply for Jobs. The Impact of Self- and E-Assessment on Staff Recruitment. 42nd Hawaii International Conference on System Sciences (2009)
18. Wirtky, T., Laumer, S., Eckhardt, A., Weitzel, T.: On the Untapped Value of e-HRM – a Literature Review. Communications of the Association for Information Systems 38, 20–83 (2016)
19. Venkatesh, Thong, Xu: Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly 36, 157–178 (2012)
20. Mölk, A., Auer, M.: Designing brands and managing organizational politics: A qualitative case study of employer brand creation. European Management Journal 36, 485–496 (2018)
21. Ochmann, J., Laumer, S., Franke, J.: The power of knowledge: A literature review on socio-technical perspectives on organizational knowledge management. Proceedings of the 25th Americas Conference on Information Systems. Cancun, Mexico (2019)
22. Russell, S.J., Norvig, P., Davis, E., Edwards, D.: Artificial intelligence. A modern approach. Pearson, Boston (2016)
23. Yu, H., Liu, C., Zhang, F.: Reciprocal recommendation algorithm for the field of recruitment. Journal of Information & Computational Science 8, 4061–4068 (2011)
24. IBM: IBM Watson Recruitment, https://www.ibm.com/talent-management/hrsolutions/ recruiting-software
25. Faliagka, E., Iliadis, L., Karydis, I., Rigou, M., Sioutas, S., Tsakalidis, A., Tzimas, G.: Online consistent ranking on e-recruitment: seeking the truth behind a well-formed CV. Artificial Intelligence Review 42, 515–528 (2014)
26. Derous, E., Ryan, A.M., Serlie, A.W.: Double Jeopardy Upon Resumé Screening: When Achmed Is Less Employable Than Aïsha. Personnel Psychology 68, 659–696 (2015)
27. Ruta, C.D.: HR portal alignment for the creation and development of intellectual capital. The International Journal of Human Resource Management 20, 562–577 (2009)
28. Bondarouk, T.V., Ruël, H.J.M.: Electronic Human Resource Management: challenges in the digital era. The International Journal of Human Resource Management 20, 505–514 (2009)
29. Xu, P., Barbosa, D.: Matching Résumés to Job Descriptions with Stacked Models. In: Bagheri, E., Cheung, J.C.K. (eds.) Advances in Artificial Intelligence, 10832, pp. 304–309. Springer International Publishing, Cham (2018)
30. Venkatesh, Morris, Davis: User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly 27, 425 (2003)
31. Huang, J., Martin-Taylor, M.: Turnaround user acceptance in the context of HR selfservice technology adoption: an action research approach. The International Journal of Human Resource Management 24, 621–642 (2013)
32. Buettner, R.: Getting a job via career-oriented social networking markets. Electronic Markets 27, 371–385 (2017)
33. Gao, Y., Li, H., Luo, Y.: An empirical study of wearable technology acceptance in healthcare. Industrial Management & Data Systems 115, 1704–1723 (2015)
34. Ajzen, I., Fishbein, M.: Attitude-behavior relations: A theoretical analysis and review of empirical research. Psychological Bulletin 84, 888–918 (1977)
35. Bolton, R.N., Parasuraman, A., Hoefnagels, A., Migchels, N., Kabadayi, S., Gruber, T., Komarova Loureiro, Y., Solnet, D.: Understanding Generation Y and their use of social media: a review and research agenda. Journal of Service Management 24, 245–267 (2013)
36. Hella, S., Mol, S.T.: E-Recruitment: A study into applicant perceptions of an online application system. International Journal of Selection and Assessment 17, 311–323 (2009)
37. Lapointe, Rivard: A Multilevel Model of Resistance to Information Technology Implementation. MIS Quarterly 29, 461 (2005)
38. Guest, G., Bunce, A., Johnson, L.: How Many Interviews Are Enough? Field Methods 18, 59–82 (2006)
39. Myers, M.D.: Qualitative research in business & management. Sage, Los Angeles (2010)
40. Mayring, P.: Qualitative content analysis: theoretical foundation, basic procedures and software solution. Klagenfurt (2014)
41. Gruzd, A., Staves, K., Wilk, A.: Connected scholars: Examining the role of social media in research practices of faculty using the UTAUT model. Computers in Human Behavior 28, 2340–2350 (2012)
42. Schreier, M.: Qualitative content analysis in practice. Sage, Los Angeles (2012)
43. Andersen, P.H., Kragh, H.: Sense and sensibility: Two approaches for using existing theory in theory-building qualitative research. Industrial Marketing Management 39, 49– 55 (2010)
44. Davison, H.K., Burke, M.J.: Sex Discrimination in Simulated Employment Contexts: A Meta-analytic Investigation. Journal of Vocational Behavior 56, 225–248 (2000)
45. Bolander, P., Sandberg, J.: How Employee Selection Decisions are Made in Practice. Organization Studies 34, 285–311 (2013)
46. Wirth, J., Maier, C., Laumer, S., Weitzel, T.: Perceived information sensitivity and interdependent privacy protection: a quantitative study. Electronic Markets 29, 359–378 (2019)
47. Martin, K.D., Murphy, P.E.: The role of data privacy in marketing. Journal of the Academy of Marketing Science 45, 135–155 (2017)
48. Rainie, L., Duggan, M.: Privacy and Information Sharing (2016)
49. Laumer, S., Maier, C., Gubler, F.T.: Chatbot acceptance in healthcare: Explaining user adoption of conversational agents for disease diagnosis. Proceedings of the 27th European Conference on Information Systems (ECIS) (2019)
50. Pavlou, P.: Consumer Acceptance of Electronic Commerce. Integrating Trust and Risk with the Technology Acceptance Model. Consumer Acceptance of Electronic Commerce 7, 101–134 (2003)
51. Martin, G., Gollan, P.J., Grigg, K.: Is there a bigger and better future for employer branding? Facing up to innovation, corporate reputations and wicked problems in SHRM. The International Journal of Human Resource Management 22, 3618–3637 (2011)
52. Jasperson, Carter, Zmud: A Comprehensive Conceptualization of Post-Adoptive Behaviors Associated with Information Technology Enabled Work Systems. MIS Quarterly 29, 525– 557 (2005)
53. Davis, F.D.: Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly 13, 319 (1989)
54. Burton-Jones, A., Stein, M., Mishra, A.: IS Use. MIS Quarterly Research Curations (2017)
55. Maier, C., Laumer, S., Wirth, J., Weitzel, T.: Technostress and the hierarchical levels of personality: a two-wave study with multiple data samples. European Journal of Information Systems 28, 496–522 (2019)
56. Tarafdar, M., Maier, C., Laumer, S., Weitzel, T.: Explaining the link between technostress and technology addiction for social networking sites: A study of distraction as a coping behavior. Information Systems Journal 62, 51 (2019)