Abstract
This experimental study investigates the use of artificial intelligence (AI) technology and its role in improving human resources management, particularly the selection and recruitment process. The importance of this study lies in the using AI technologies within the business environment has significantly increased because of the contentious technological developments and the revolution of the internet of things that have been imposed in business environments, which made it imperative for organizations to keep pace with these developments and work with them to create an innovative and competitive business model. The study focuses on the artificial intelligence dimensions represented in technological skills, automation, and expert systems in improving the effectiveness of human resources recruitment and selection. Due to the nature of the study, a qualitative methodology was found to be more appropriate to achieve the objectives of the study. Accordingly, semi-structured interviews were conducted with a sample of twenty-five specialists in human resources management at Zain Telecom Company in Kingdom of Bahrain. The findings reveal that AI technology had achieved a high level of effectiveness in Zain Telecom Company, in addition to the presence of a positive qualitative role for AI technology in its dimensions (technological skills, automation, and expert systems) to improve the effectiveness of selection and staffing at an entrepreneurial organization operating in Bahrain (i.e. Zain Telecom Company). It has been concluded that AI gives promising answers for scouts to advance ability procurement by assuming control over the long run, burning-through redundant undertakings, for example, sourcing and screening candidates, to improve the nature of the recruiting cycle and kill human predispositions. Enlarged knowledge will be utilized broadly and progressively to create better and more successful outcomes; subsequently, routine authoritative positions will be supplanted by smart AI innovations and will slowly vanish.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Aleksic, S. (2015, July). Towards fifth-generation (5G) optical transport networks. In 2015, 17th International Conference on Transparent Optical Networks (ICTON) (pp. 1–4). IEEE.
Al Kurdi, O. F. (2021). A critical comparative review of emergency and disaster management in the arab world. Journal of Business and Socio-economic Development,1(1), 24–46. https://doi.org/10.1108/JBSED-02-2021-0021.
Alston, M., & Bowles, W. (2003). Research for social workers: An introduction to methods (2nd edn). London: Routledge.
Armstrong, M. (2014). Human recourse management practic. London: Kogan Page.
Ary, D., Jacobs, L., Sorensen, C., & Walker, D. (2014). Introduction to research in education. London: Wadsworth.
Bowen, G. (2009). Document analysis as a qualitative research method. Qualitative Research Journal, 9(2), 27–40.
Boyatzis, R. (1998). Transforming qualitative research information: Thematic analysis and code development. London: Sage Publication.
Braun, V., & Clarke, V. (2012). Thematic analysis. In H. Cooper, P. M. Camic, D. L. Long, A. T. Panter, D. Rindskopf, & K. J. Sher (Eds.), APA handbook of research methods in psychology, Vol. 2: Research designs: Quantitative, qualitative, neuropsychological, and biological (pp. 57–71). Washington, DC: American Psychological Association.
Brynjolfsson, E., & McAfee, A. (2014). The second machine age. USA: Norton Company.
Chigbu, U. E. (2019). Visually hypothesising in scientific paper writing: Confirming and refuting qualitative research hypotheses using diagrams. Publications, 7(1), 22.
Christopher, C., Martens, C., Ontañón, S., Mirowski, P., Mathewson, K. W., & Farrugia, S. (2019, October). Playable experiences at the 15th AAAI conference on artificial intelligence and interactive digital entertainment (AIIDE’19). In Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 15(1), 234–238.
Corbin, J., & Strauss, A. (2008). Basics of qualitative research: Techniques and procedures for developing grounded theory. London: SAGE Publication.
Creswell, J. (1998). Qualitative inquiry and research design: Choosing among five traditions. London: Sage Publication.
Creswell, J. (2003). Research design: Qualitative, quantitative, and mixed method approaches. London: Sage Publication.
Denscombe, M. (2009). The good research guide. England: McGraw-Hill Education.
Denzin, N. (2009). Overcoming resistances to qualitative inquiry. Sage Journal, 2(3).
Erixon, F. (2018). The economic benefits of globalization for business and consumers. European Centre for International Political Economy.https://ecipe.org/publications/the-economic-benefits-of-globalization- for-business-and-consumers/. Accessed 15.12.2019.
Fernandez, A., Herrera, F., Cordon, O., & Francesco, M. (2019). Evolutionary fuzzy systems for explainable artificial intelligence: Why, when, what for, and where to? IEEE Computational Intelligence Magazine, 14(1), 69–81.
Gartner Report (2017). Critical Capabilities for Business Intelligence and Analytics Platforms report, February 2017. Retrieved from: 286 Jerzy Kisielnicki, Anna Maria Misiak http://www.pages.pyramidanalytics.com/gartner-mqand-critical-capabilities-2017.html?utm_medium=cpc&utm_source=solutionsreview&utm_campaign=Gartner2016&utm_content=300x250.
Greene, J., & Hall, J. (2010). Handbook of mixed methods in social & behavioral research. California (pp. 119–143).
Grewal, D. (2014). A critical conceptual analysis of definitions of artificial intelligence as applicable to computer engineering. IOSR Journal of Computer Engineering, 16(2), 9–13.
Groover, M. (2016). Automation production systems computer integrated manufacturing. USA: Pearson.
Hancock, B., Ockleford, E., & Windridge, K. (2009). An introduction to Qualitative research. National institute for Health research. THE NHR RDS EM/YH.
Jatobá, M., Santos, J., Gutierriz, I., Moscon, D., Fernandes, P. O., & Teixeira, J. P. (2019). Evolution of artificial intelligence research in human resources. Procedia Computer Science, 164, 137–142.
Johansson, J., & Herranen, S. (2019). The application of artificial intelligence in human resources management. Business Administration Thesis, Jonkoping University, Sweden.
Kaplan, A., & Haenlein, M. (2019). On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15–25.
Khatri, S., Pandey, D. K., Penkar, D., & Ramani, J. (2020). Impact of artificial intelligence on human resources. In N. Sharma, A. Chakrabarti, & V. Balas (Eds.), Data Management, Analytics and Innovation. Advances in Intelligent Systems and Computing (1016). Singapore: Springer. https://doi.org/10.1007/978-981-13-9364-8_26, last accessed 2020/12/21.
Kim, S., David, J., & Xu, M. (2018). The fourth industrial revolution: Opportunities and challenges. International Journal of Financial Research, 9(2), 90–95.
Kirk, J., & Miller, M. (1986). Reliability and validity in qualitative research. London: Sage Publication.
Kurzweil, R. (2005). The singularity is near. New York: Viking Press.
Laudon, J., & Laudon, K. (2004). Management information system. England: Pearson Education Limited.
Lecompte, M., & Preissle, J. (1993). Ethnography and qualitative design in educational research. New York: Press Academic.
Lgwenagu, C. (2016). Fundamentals of research methodology and data collection. Mauritius: Lambert Academic Publishing.
Ma, H., & Wang, J. (2021). Application of artificial intelligence in intelligent decision-making of human resource allocation. In J. MacIntyre, J. Zhao, & X. Ma (Eds.), The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy. SPIOT 2020. Advances in Intelligent Systems and Computing (Vol. 1282). Cham: Springer. https://doi.org/10.1007/978-3-030-62743-0_28, last accessed 2021/01/12.
Marks, D., & Yardley, L. (2004). Research methods for clinical and health psychology. London: Sage Publication.
Maxwell, J. (1996). Applied social research methods series-qualitative research design: An interactive approach. London: Sage Publication.
Meskó, B., Hetényi, G., & Győrffy, Z. (2018). Will artificial intelligence solve the human resource crisis in healthcare? BMC Health Services Research, 18(545). https://doi.org/10.1186/s12913-018-3359-4, last accessed 2020/12/21.
Mochol, M., Jentzsch, A., & Wache, H. (2007). Suitable employees wanted? Find them with semantic techniques. In Proceedings of workshop on making semantics web for business at European Semantic Technology Conference (ESTC2007), Vienna, Austria.
Mondy, R., & Noe, R. (2005). Human resource management. USA: Pearson Prentice Hall.
Nweke, E., & Orji, N. (2009). A handbook of political science. Department of Political Science, Ebonyi State University.
Oravec, J. (2014). Expert systems research. International Journal of Design for Learning, 5(2).
Robinson, M. (2018). Artificial intelligence in hiring-understanding attitudes and perspectives of HR practitioners. United States: Wilmington University.
Russel, S., & Norvig, P. (2003). Artificial intelligence—A modern approach. Englewood Cliffs, New Jersey: Alan Apt.
Saunders, M., Lewis, P., & Thornhill, A. (2009). Research methods for business students (4th ed.). Essex: Pearson Education Limited.
Schroeder, R. (2007). Operations management (p. 91). United States: McGraw-Hill.
Schwab, K. (2016). The fourth industrial revolution. Geneva, Switzerland: World Economic Forum.
Schwab, K. (2019). The global competitiveness report. Switzerland: World Economic Forum.
Shahid, M., & Li, G. (2019). Impact of artificial intelligence in marketing: A perspective of marketing professionals of Pakistan. Global Journal of Management and Business Research, 19(2).
Siggelkow, N. (2007). Persuasion with case studies. Academy of Management Journal, 50(1), 20–24.
Stake, R. (1995). The art of case study research. California: Thousand Oaks.
Stavors, C., & Westberg, K. (2009). Using triangulation and multiple case studies to advance relationship marketing theory. Qualitative Market Research Journal, 12(3), 307–320.
Strohmeier S., & Piazza, F. (2015). Artificial intelligence techniques in human resource management—A conceptual exploration. In C. Kahraman & S. Çevik Onar (Eds.), Intelligent techniques in engineering management. Intelligent systems reference library (Vol. 87). Cham: Springer. https://doi.org/10.1007/978-3-319-17906-3_7, 2020/12/10.
Tecuci, G. (2012). Artificial intelligence. WIREs. Computational Statistics, 4(2), 168–180.
Teece, D. J. (2016). Dynamic capabilities and entrepreneurial management in large organizations: Toward a theory of the (entrepreneurial) firm. European Economic Review, 86, 202–216.
The World Economic Forum. (2019). https://www.weforum.org/.
Tkachenko, V., Kuzior, A., & Kwilinski, A. (2019). Introduction of artificial intelligence tools into the training methods of entrepreneurship activities. Journal of Entrepreneurship Education, 22(6).
Vardarlier, P., & Zafer, C. (2020). Use of artificial intelligence as business strategy in recruitment process and social perspective. In U. Hacioglu (Eds.), Digital business strategies in blockchain ecosystems. Contributions to management science. Cham: Springer. https://doi.org/10.1007/978-3-030-29739-8_17, last accessed 2021/01/12.
Varshney, M., & Galhan, S. (2018). Instrumentation, Automation, IOT and emerging technologies for engineers. Independently published.
Wall, T. D., & Wood, S. J. (2005). The romance of human resource management and business performance, and the case for big science. Human Relations, 58(4), 429–462.
Wang, J., Dexter, D., Zhiao, S., & Bing, Z. (2013). WEB-based Gene set analysis toolkit. Nucleic Acids Research, 41(10), 77–83
Yin, R. (2003). Case study research: Design and methods. California: Thousand Oaks.
Zieliński, C., Stefańczyk, M., Kornuta, T., Figat, M., Dudek, W., Szynkiewicz, W., & Iturburu, M. (2017). Variable structure robot control systems: The RAPP approach. Robotics and Autonomous Systems, 94, 226–244.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Aldulaimi, S.H., Abdeldayem, M.M., Mowafak, B.M., Abdulaziz, M.M. (2021). Experimental Perspective of Artificial Intelligence Technology in Human Resources Management. In: Hamdan, A., Hassanien, A.E., Khamis, R., Alareeni, B., Razzaque, A., Awwad, B. (eds) Applications of Artificial Intelligence in Business, Education and Healthcare . Studies in Computational Intelligence, vol 954. Springer, Cham. https://doi.org/10.1007/978-3-030-72080-3_26
Download citation
DOI: https://doi.org/10.1007/978-3-030-72080-3_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-72079-7
Online ISBN: 978-3-030-72080-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)