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Artificial Intelligence Models and Employee Lifecycle Management: A Systematic Literature Review Cover

Artificial Intelligence Models and Employee Lifecycle Management: A Systematic Literature Review

Open Access
|Sep 2022

References

  1. Akhavian, R., & Behzadan, A. H. (2016). Smartphone-based construction workers‘ activity recognition and classification. Automation in Construction, 71, 198-209. https://doi.org/10.1016/j.autcon.2016.08.015
  2. Anh, N. T., Tu, N. D., Solanki, V. K., Giang, N. L., Thu, V. H., Son, L. N., . . . Nam, V. T. (2020). Integrating employee value model with churn prediction. International Journal of Sensors Wireless Communications and Control, 10(4), 484-493. https://doi.org/10.2174/2210327910666200213123728
  3. Ardabili, S., Mosavi, A., Mahmoudi, A., Gundoshmian, T. M., Nosratabadi, S., & Várkonyi-Kóczy, A. R. (2019). Modelling temperature variation of mushroom growing hall using artificial neural networks. Paper presented at the International Conference on Global Research and Education.10.20944/preprints201908.0201.v1
  4. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32. https://doi.org/10.1023/A:1010933404324
  5. Chuang, Y.-C., Hu, S.-K., Liou, J. J., & Tzeng, G.-H. (2020). A data-driven MADM model for personnel selection and improvement. Technological and Economic Development of Economy, 26(4), 751-784. http://dx.doi.org/10.3846/tede.2020.1236610.3846/tede.2020.12366
  6. Colomo-Palacios, R., González-Carrasco, I., López-Cuadrado, J. L., Trigo, A., & Varajao, J. E. (2014). I-Competere: Using applied intelligence in search of competency gaps in software project managers. Information Systems Frontiers, 16(4), 607-625. http://dx.doi.org/10.1007/s10796-012-9369-610.1007/s10796-012-9369-6
  7. Deb, S. K., Jain, R., & Deb, V. (2018, January). Artificial intelligence―creating automated insights for customer relationship management. In 2018 8th international conference on cloud computing, data science & engineering (Confluence) (pp. 758-764). IEEE. http://dx.doi.org/10.1109/CONFLUENCE.2018.844290010.1109/CONFLUENCE.2018.8442900
  8. Efron, B., & Hastie, T. (2016). Computer age statistical inference (Vol. 5): Cambridge University Press.10.1017/CBO9781316576533
  9. Fallucchi, F., Coladangelo, M., Giuliano, R., & William De Luca, E. (2020). Predicting employee attrition using machine learning techniques. Computers, 9(4), 86. https://doi.org/10.3390/computers9040086
  10. Friedman, J., Hastie, T., & Tibshirani, R. (2001). The elements of statistical learning. vol. 1 Springer Series in Statistics. New York.10.1007/978-0-387-21606-5_1
  11. Giaglis, G. M. (2001). A taxonomy of business process modeling and information systems modeling techniques. International Journal of Flexible Manufacturing Systems, 13(2), 209-228. https://doi.org/10.1023/A:1011139719773
  12. Illéssy, M., Huszár, Á., & Makó, C. (2021). Technological Development and the Labour Market: How Susceptible Are Jobs to Automation in Hungary in the International Comparison? Societies, 11(3), 93. https://doi.org/10.3390/soc11030093
  13. Irfan, D., Tang, X., Narayan, V., Mall, P. K., Srivastava, S., & Saravanan, V. (2022). Prediction of Quality Food Sale in Mart Using the AI-Based TOR Method. Journal of Food Quality, 2022. https://doi.org/10.1155/2022/6877520
  14. Jain, P. K., Jain, M., & Pamula, R. (2020). Explaining and predicting employees’ attrition: a machine learning approach. SN Applied Sciences, 2(4), 1-11. https://doi.org/10.1007/s42452-020-2519-4
  15. Jayadi, R., Firmantyo, H. M., Dzaka, M. T. J., Suaidy, M. F., & Putra, A. M. (2019). Employee Performance Prediction using Naïve Bayes. International Journal of Advanced Trends in Computer Science and Engineering, 8(6):3031-3035. https://doi.org/10.30534/ijatcse/2020/106912020
  16. Jebelli, H., Khalili, M. M., Hwang, S., & Lee, S. (2018). A supervised learning-based construction workers’ stress recognition using a wearable electroencephalography (EEG) device. Paper presented at the Construction research congress.10.1061/9780784481288.005
  17. Jiang, H., Cheng, Y., Yang, J., & Gao, S. (2022). AI-powered chatbot communication with customers: Dialogic interactions, satisfaction, engagement, and customer behavior. Computers in Human Behavior, 134, 107329. https://doi.org/10.1016/j.chb.2022.107329
  18. Jiangang, D., Huan, Z., Jiuru, S., & Yu, Z. (2022). A Review and Prospects of Customer Behavior under AI Service. Foreign Economics & Management, 44(03), 19-35. https://doi.org/10.16538/j.cnki.fem.20211017.101
  19. Kaewwiset, T., Temdee, P., & Yooyativong, T. (2021). Employee Classification for Personalized Professional Training Using Machine Learning Techniques and SMOTE. Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunication Engineering, 376-379. https://doi.org/10.1109/ECTIDAMTNCON51128.2021.9425754
  20. Khayer Zahed, R., Teimouri, H., & Barzoki, A. S. (2021). Designing a model of strategic training system with talent management approach: the case of Iranian National Tax Administration. International Journal of Business Innovation and Research, 24(4). https://doi.org/10.1504/IJBIR.2021.114080
  21. Khera, S. N., & Divya. (2018). Predictive modelling of employee turnover in Indian IT industry using machine learning techniques. Vision, 23(1), 12-21. https://doi.org/10.1177/0972262918821221
  22. Leitner-Hanetseder, S., Lehner, O. M., Eisl, C., & Forstenlechner, C. (2021). A profession in transition: Actors, tasks and roles in AI-based accounting. Journal of Applied Accounting Research, 22 (3), 539-556. https://doi.org/10.1108/JAAR-10-2020-0201
  23. Li, N., Kong, H., Ma, Y., Gong, G., & Huai, W. (2016). Human performance modeling for manufacturing based on an improved KNN algorithm. The International Journal of Advanced Manufacturing Technology, 84(1-4), 473-483. https://doi.org/10.1007/s00170-016-8418-6
  24. Li, X., Chi, H.-l., Lu, W., Xue, F., Zeng, J., & Li, C. Z. (2021). Federated transfer learning enabled smart work packaging for preserving personal image information of construction worker. Automation in Construction, 128, 103738. https://doi.org/10.1016/j.autcon.2021.103738
  25. Liu, J., Li, J., Wang, T., & He, R. (2019). Will Your Classmates and Colleagues Affect Your Development in the Workplace: Predicting Employees‘ Growth Based on Interpersonal Environment. Paper presented at the 2019 IEEE Fifth International Conference on Big Data Computing Service and Applications (Big-DataService). https://doi.org/10.1109/BigDataService.2019.00016
  26. Liu, J., Long, Y., Fang, M., He, R., Wang, T., & Chen, G. (2018). Analyzing employee turnover based on job skills. Paper presented at the Proceedings of the International Conference on Data Processing and Applications. https://doi.org/10.1145/3224207.3224209
  27. Liu, J., Wang, T., Li, J., Huang, J., Yao, F., & He, R. (2019). A Data-driven Analysis of Employee Promotion: The Role of the Position of Organization. Paper presented at the 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC). https://doi.org/10.1109/SMC.2019.8914449
  28. Long, Y., Liu, J., Fang, M., Wang, T., & Jiang, W. (2018). Prediction of employee promotion based on personal basic features and post features. Paper presented at the Proceedings of the International Conference on Data Processing and Applications. https://doi.org/10.1145/3224207.3224210
  29. Makó, C., & Illéssy, M. (2020). Automation, Creativity, and the Future of Work in Europe: A Comparison between the Old and New Member States with a Special Focus on Hungary. Intersections: East European Journal of Society and Politics, 6(2), 112-129. https://doi.org/10.17356/ieejsp.v6i2.625
  30. Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2010). Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. International Journal of Surgery, 8(5), 336-341. https://doi.org/10.1136/bmj.b2535
  31. Morozevich, E. S., Kuznetsova, Y. A., Kubrikova, A. S., Livak, N. S., & Makarov, A. I. (2022). Employee’s Competence Profile for Adaptive Organization Management. Organizacija, 55(1), 3-16. https://doi.org/10.2478/orga-2022-0001
  32. Moyo, S., Doan, T. N., Yun, J. A., & Tshuma, N. (2018). Application of machine learning models in predicting length of stay among healthcare workers in underserved communities in South Africa. Human resources for health, 16(1), 1-9. https://doi.org/10.1186/s12960-018-0329-1
  33. Murphy, K. P. (2012). Machine learning: a probabilistic perspective: MIT press.
  34. Nosratabadi, S., Ardabili, S., Lakner, Z., Mako, C., & Mosavi, A. (2021). Prediction of Food Production Using Machine Learning Algorithms of Multilayer Perceptron and ANFIS. Agriculture, 11(5), 408. https://doi.org/10.3390/agriculture11050408
  35. Nosratabadi, S., Mosavi, A., Duan, P., Ghamisi, P., Filip, F., Band, S. S., Gandomi, A. H. (2020). Data science in economics: comprehensive review of advanced machine learning and deep learning methods. Mathematics, 8(10), 1799. https://doi.org/10.3390/math8101799
  36. Nosratabadi, S., Szell, K., Beszedes, B., Imre, F., Ardabili, S., & Mosavi, A. (2020). Comparative Analysis of ANN-ICA and ANN-GWO for Crop Yield Prediction. In the 2020 RIVF International Conference on Computing and Communication Technologies (RIVF). https://doi.org/10.1109/RIVF48685.2020.9140786.
  37. Olan, F., Arakpogun, E. O., Suklan, J., Nakpodia, F., Damij, N., & Jayawickrama, U. (2022). Artificial intelligence and knowledge sharing: Contributing factors to organizational performance. Journal of Business Research, 145, 605-615. https://doi.org/10.1016/j.jbusres.2022.03.008
  38. Praveen, U., Farnaz, G., & Hatim, G. (2019). Inventory management and cost reduction of supply chain processes using AI based time-series forecasting and ANN modeling. Procedia Manufacturing, 38, 256-263. https://doi.org/10.1016/j.promfg.2020.01.034
  39. Peisl, T., & Shah, B. (2019). The impact of blockchain technologies on recruitment influencing the employee lifecycle. Paper presented at the European Conference on Software Process Improvement. https://doi.org/10.1007/978-3-030-28005-5_54
  40. Raschka, S. (2015). Python machine learning: Packt Publishing Ltd.
  41. Ren, S., Patrick Hui, C. L., & Jason Choi, T. M. (2018). AI-based fashion sales forecasting methods in big data era. In Artificial intelligence for fashion industry in the big data era (pp. 9-26). Springer, Singapore.10.1007/978-981-13-0080-6_2
  42. Schapire, R.E. (2013). Explaining AdaBoost. In: Schölkopf, B., Luo, Z., Vovk, V. (eds) Empirical Inference. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41136-6_5
  43. Singer, G., & Cohen, I. (2020). An objective-based entropy approach for interpretable decision tree models in support of human resource management: The case of absenteeism at work. Entropy, 22(8), 821. https://doi.org/10.3390/e22080821
  44. Singh, V. K., Singh, P., Karmakar, M., Leta, J., & Mayr, P. (2021). The journal coverage of Web of Science, Scopus and Dimensions: A comparative analysis. Scientometrics, 126(6), 5113-5142. https://doi.org/10.1007/s11192-021-03948-5
  45. Susmita, E. K. K. A., & Singh, P. (2022). Predicting HR Professionals’ Adoption of HR Analytics: An Extension of UTAUT Model. Organizacija, 55(1), 77-93. https://doi.org/10.2478/orga-2022-0006
  46. Xie, Q. (2020). Machine learning in human resource system of intelligent manufacturing industry. Enterprise Information Systems, 16 (2), 264-284. https://doi.org/10.1080/17517575.2019.1710862
  47. Yadav, S., Jain, A., & Singh, D. (2018). Early prediction of employee attrition using data mining techniques. Paper presented at the 2018 IEEE 8th International Advance Computing Conference (IACC). https://doi.org/10.1109/IADCC.2018.8692137
  48. Zaman, E. A. K., Kamal, A. F. A., Mohamed, A., Ahmad, A., & Zamri, R. A. Z. R. M. (2018). Staff Employment Platform (StEP) Using Job Profiling Analytics. Paper presented at the International Conference on Soft Computing in Data Science. https://doi.org/10.1007/978-981-13-3441-2_30
  49. Zhao, Y., Hryniewicki, M. K., Cheng, F., Fu, B., & Zhu, X. (2018). Employee turnover prediction with machine learning: A reliable approach. Paper presented at the Proceedings of SAI intelligent systems conference. https://doi.org/10.1007/978-3-030-01057-7_56
  50. Zhe, I. T. Y., & Keikhosrokiani, P. (2021). Knowledge workers mental workload prediction using optimised ELANFIS. Applied Intelligence, 51(4), 2406-2430. https://doi.org/10.1007/s10489-020-01928-5
DOI: https://doi.org/10.2478/orga-2022-0012 | Journal eISSN: 1581-1832 | Journal ISSN: 1318-5454
Language: English
Page range: 181 - 198
Submitted on: Sep 15, 2021
Accepted on: Jul 23, 2022
Published on: Sep 23, 2022
Published by: University of Maribor
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2022 Saeed Nosratabadi, Roya Khayer Zahed, Vadim Vitalievich Ponkratov, Evgeniy Vyacheslavovich Kostyrin, published by University of Maribor
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.