Artificial Intelligence Model for Human Capital Management

Authors

Didik Hadiyatno , Dwi Susilowati , Nadi Hernadi Moorcy , Imam Arrywibowo , Tutik Yuliani

DOI:

10.29303/jppipa.v9i10.5083

Published:

2023-10-25

Issue:

Vol. 9 No. 10 (2023): October

Keywords:

Artificial Intelligence, Human Capital, Management

Research Articles

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How to Cite

Hadiyatno, D. ., Susilowati, D. ., Moorcy, N. H. ., Arrywibowo, I. ., & Yuliani, T. . (2023). Artificial Intelligence Model for Human Capital Management. Jurnal Penelitian Pendidikan IPA, 9(10), 8280–8286. https://doi.org/10.29303/jppipa.v9i10.5083

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Abstract

Artificial Intelligence is a technology that allows machines to learn and adapt quickly from given data without having to be explicitly programmed. AI has found its place in many industries and has great potential to improve the efficiency of human resources within organizations. In this article, we will discuss how the use of artificial intelligence can help improve human resource efficiency. This review is a literature search with an elaborative approach. This approach is a methodological effort by organizing the logic flow of the discussion with various compatible literature sources. Reliable literature sources come from journals, books, articles, and other sources relevant to this discourse. The result of the study is that human resource management needs to adjust to the needs of the organization and adjust the culture or corporate culture to the technological culture. Human interaction with machines is an inevitable necessity. The existence of artificial intelligence is very helpful in human resource management, for example in recruitment systems, training, data analysis, performance analysis, and operational duty efficiency.

References

Ahmed, I., Jeon, G., & Piccialli, F. (2022). From artificial intelligence to explainable artificial intelligence in industry 4.0: a survey on what, how, and where. IEEE Transactions on Industrial Informatics, 18(8), 5031–5042. Retrieved from https://ieeexplore.ieee.org/abstract/document/9695219

Ajeesh, A. K., & Rukmini, S. (2023). Posthuman perception of artificial intelligence in science fiction: an exploration of Kazuo Ishiguro’s Klara and the Sun. AI & SOCIETY, 38(2), 853–860. https://doi.org/10.1007/s00146-022-01533-9

Arora, M., Prakash, A., Mittal, A., & Singh, S. (2021). HR analytics and artificial intelligence-transforming human resource management. 2021 International Conference on Decision Aid Sciences and Application (DASA), 288–293. https://doi.org/10.1109/DASA53625.2021.9682325

Ashta, A., & Herrmann, H. (2021). Artificial intelligence and fintech: An overview of opportunities and risks for banking, investments, and microfinance. Strategic Change, 30(3), 211–222. https://doi.org/10.1002/jsc.2404

Barthelme, U., & Furbach, U. (2023). A Different Look at Artificial Intelligence: On Tour with Bergson, Proust and Nabokov. Springer Nature.

Bongard, J., & Levin, M. (2021). Living things are not (20th century) machines: updating mechanism metaphors in light of the modern science of machine behavior. Frontiers in Ecology and Evolution, 9, 147. https://doi.org/10.3389/fevo.2021.650726

Chen, Z. (2023). Artificial intelligence-virtual trainer: Innovative didactics aimed at personalized training needs. Journal of the Knowledge Economy, 14(2), 2007–2025. https://doi.org/10.1007/s13132-022-00985-0

Chytiri, A.-P. (2019). Human Resource Managers’ Role in the Digital Era. SPOUDAI Journal of Economic and Business, 69(1), 62–72. Retrieved from https://dora.dmu.ac.uk/server/api/core/bitstreams/ea2d44b0-1211-4672-9c14-b594046a1ac1/content

Drukker, L., Noble, J. A., & Papageorghiou, A. T. (2020). Introduction to artificial intelligence in ultrasound imaging in obstetrics and gynecology. Ultrasound in Obstetrics & Gynecology, 56(4), 498–505. https://doi.org/10.1002/uog.22122

Hu, Z. (2020). Research on fintech methods based on artificial intelligence. Journal of Physics: Conference Series, 1684(1). https://doi.org/10.1088/1742-6596/1684/1/012034

Köchling, A., & Wehner, M. C. (2020). Discriminated by an algorithm: a systematic review of discrimination and fairness by algorithmic decision-making in the context of HR recruitment and HR development. Business Research, 13(3), 795–848. https://doi.org/10.1007/s40685-020-00134-w

Laurim, V., Arpaci, S., Prommegger, B., & Krcmar, H. (2021). Computer, whom should i hire?--acceptance criteria for artificial intelligence in the recruitment process. Proceedings of the 54th Hawaii International Conference on System Sciences. Retrieved from http://hdl.handle.net/10125/71288

Lee, K.-F. (2021). A Human Blueprint for AI Coexistence. Retrieved from https://library.oapen.org/bitstream/handle/20.500.12657/47279/1/9783030541736.pdf#page=253

Malik, A., Srikanth, N. R., & Budhwar, P. (2020). Digitisation, artificial intelligence (AI) and HRM. In Human resource management: Strategic and international perspectives, Sage London.

Mazurchenko, A., & Maršíková, K. (2019). Digitally-powered human resource management: Skills and roles in the digital era. Acta Informatica Pragensia, 8(2), 72–86. https://doi.org/10.18267/j.aip.125

O’Connor, S., Yan, Y., Thilo, F. J. S., Felzmann, H., Dowding, D., & Lee, J. J. (2023). Artificial intelligence in nursing and midwifery: A systematic review. Journal of Clinical Nursing, 32(13–14), 2951–2968. https://doi.org/10.1111/jocn.16478

OECD. (2021). Artificial Intelligence, Machine Learning and Big Data in Finance: Opportunities, Challenges, and Implications for Policy Makers. OECD Business and Finance Outlook 2020 : Sustainable and Resilient Finance., 1–72.

Prakash, N., Krishna, G., & Mores, G. (2019). Digitalization of HRM practice in the present scenario. International Journal of Research in Management Studies, 4(1), 1–5. Retrieved from http://www.ijrms.com/olvolume4issue1/NBhanuPrakash-GandhamSriRamaKrishna-GSamuelMores-1.pdf

Ramanathan, A., Ma, H., Parvatikar, A., & Chennubhotla, S. C. (2021). Artificial intelligence techniques for integrative structural biology of intrinsically disordered proteins. Current Opinion in Structural Biology, 66, 216–224. https://doi.org/10.1016/j.sbi.2020.12.001

Raschka, S., Patterson, J., & Nolet, C. (2020). Machine learning in python: Main developments and technology trends in data science, machine learning, and artificial intelligence. Information, 11(4), 193. https://doi.org/10.3390/info11040193

Sakka, F., El Maknouzi, M. E. H., & Sadok, H. (2022). Human resource management in the era of artificial intelligence: future HR work practices, anticipated skill set, financial and legal implications. Academy of Strategic Management Journal, 21, 1–14. Retrieved from https://www.abacademies.org/articles/human-resource-management-in-the-era-of-artificial-intelligence-future-hr-work-practices-anticipated-skill-set-financial-and-legal-13536.html

Sarkar, C., Das, B., Rawat, V. S., Wahlang, J. B., Nongpiur, A., Tiewsoh, I., Lyngdoh, N. M., Das, D., Bidarolli, M., & Sony, H. T. (2023). Artificial intelligence and machine learning technology driven modern drug discovery and development. International Journal of Molecular Sciences, 24(3), 2026. https://doi.org/10.3390/ijms24032026

Siregar, H., Setiawan, W., & Dirgantari, P. D. (2020). Isu Proses Bisnis Berbasis Artificial Intelligence untuk Menyosong Era Industri 4.0. Jurnal Bisnis Strategi, 29(2), 89–100. https://doi.org/10.14710/jbs.29.2.89-100

Sığırcı, Ö. (2021). Artificial Intelligence in Marketing: A Review of Consumer-AI Interactions. Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry, 342–365. https://doi.org/10.4018/978-1-7998-6985-6.ch016

Strohmeier, S. (2020). Digital human resource management: A conceptual clarification. German Journal of Human Resource Management, 34(3), 345–365. https://doi.org/10.1177/2397002220921131

Tong, S., Jia, N., Luo, X., & Fang, Z. (2021). The Janus face of artificial intelligence feedback: Deployment versus disclosure effects on employee performance. Strategic Management Journal, 42(9), 1600–1631. https://doi.org/10.1002/smj.3322

Umi, K. (2022). Pengenalan Kecerdasan Buatan (Artificial Intelligence) Kepada Para Remaja. Universitas Bina Darma. Retrieved from http://eprints.binadarma.ac.id/15964/

Varadaraj, D. A., & Al Wadi, D. B. M. (2021). A Study on Contribution of Digital Human Resource Management towards Organizational Performance. The International Journal of Management Science and Business Administration, 7(5), 43–51. https://doi.org/10.18775/ijmsba.1849-5664-5419.2014.75.1004

Wu, L., Sun, C., & Fan, F. (2022). Multi-criteria framework for identifying the trade-offs and synergies relationship of ecosystem services based on ecosystem services bundles. Ecological Indicators, 144, 109453. https://doi.org/10.1016/j.ecolind.2022.109453

Xia, J., Yan, Y., & Ji, L. (2022). RETRACTED ARTICLE: Research on control strategy and policy optimal scheduling based on an improved genetic algorithm. Neural Computing and Applications, 34(12), 9485–9497. https://doi.org/10.1007/s00521-021-06415-7

Xie, F. (2022). Human Resource Data Integration System Based on Artificial Intelligence Environment. Journal of Environmental and Public Health, 2022. https://doi.org/10.1155/2022/1650583

Zhang, B., Zhu, J., & Su, H. (2023). Toward the third generation artificial intelligence. Science China Information Sciences, 66(2), 121101. https://doi.org/10.1007/s11432-021-3449-x

Zhang, J., & Chen, Z. (2023). Exploring Human Resource Management Digital Transformation in the Digital Age. Journal of the Knowledge Economy, 29. https://doi.org/10.1007/s13132-023-01214-y

Author Biographies

Didik Hadiyatno, Universitas Balikpapan

Dwi Susilowati, Universitas Balikpapan

Nadi Hernadi Moorcy, Universitas Balikpapan

Imam Arrywibowo, Universitas Balikpapan

Tutik Yuliani, Universitas Balikpapan

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Copyright (c) 2023 Didik Hadiyatno, Dwi Susilowati, Nadi Hernadi Moorcy, Imam Arrywibowo, Tutik Yuliani

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