Document Type : Original Research Manuscripts


1 Adaptive Management, Department of Management,Dehaghan Branch,Islamic Azad University ,Dehaghan, Isfahan, Iran.

2 Department of Management,Dehaghan Branch,Islamic Azad University ,Dehaghan, Iran


The current research was conducted to apply knowledge extraction in the classification of jobs to identify the key role players using a mixed method (qualitative and sufficient data). The application of expert systems or decision support systems based on organizational data is increasing in the selection and hiring of personnel. The data was derived from in-depth and semi-structured interviews with 17 subject experts in bank human resources, who were selected based on purposeful sampling.
Data analysis was done based on the Strauss and Corbin model in the form of open, axial, and selective coding in the Atlas TI8 software. The results showed that the classification of jobs for the key role players in public and private banks includes causal conditions (requirement of talent substitution, human resource management developments, and organizational challenges), intervening conditions  (organizational limitations and fear and resistance), and contextual conditions (strengthens and drivers) strategies (developmental, supportive and creating) and short-term and long-term consequences are among the components of the job classification model for the key role players in public and private banks. Next, based on the database with the CART method, the data mining of job classification was done. Regarding the performance of the model, it showed variance values of 311.92 and a risk value of 288.19. The predictions in the model explained 28.9% of the differences observed in the variable "employment status of A employees' category".


Main Subjects

  1. Akhavan Kharazian, M., Shahbazi, M., & Fatehi, M. (2017). Performance Evaluation of Knowledge Workers at R&D department in Outsourcing Conditions. Journal of Production and Operations Management, 14(1), 139-15
  2. Akhwan Khorazian, M., Shahbazi, M., Fatehi, M. (2018). Discovering the optimal pattern of hiring knowledge workers using the integrated approach of DEA and CART. Production and Operations Management, 10(1), 65-82. Doi: 10.22108/jpom.2019.106503.1080
  3. Appadoo, K., Soonnoo, M. B., & Mungloo-Dilmohamud, Z. (2020, December). Job recommendation system, machine learning, regression, classification, natural language processing. In 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) (pp. 1-6). IEEE.
  4. Asensio del Arco, E., Perán López, J., & Rodríguez, Y. (2016). From Corporate Social Responsibility to Social Entrepreneurship: A New Methodology. Revista Española De Pedagogía,76(270), 225–245.
  5. Asplund, K. (2020). When profession trumps potential: The moderating role of professional identification in employees’ reactions to talent management. The International Journal of Human Resource Management, 31(4), 539-561.  Doi:10.1080/09585192.2019.1570307
  6. Azar, A., Ahmadi, P., & Sabat, M. (2009). Designing a human resources selection model with a data mining approach (Case: Recruitment of candidates for the entrance exams of a commercial bank in Iran). Information Technology Management, 2(4), 3-22. [in Persian].
  7. Bacanli, F. (2016). Career decision-making difficulties of Turkish adolescents. International Journal for Educational and Vocational Guidance, 16, 233-250. Doi:10.1007/s10775-015-9304-8
  8. Chow, A., Eccles, J. S., & Salmela-Aro, K. (2012). Task value profiles across subjects and aspirations to physical and IT-related sciences in the United States and Finland. DevelopmentalPsychology,48(6), 1612.  Doi:10.1037/a0030194
  9. Clarke, M., & Scurry, T. (2020). The role of the psychological contract in shaping graduate experiences: a study of public sector talent management programmes in the UK and Australia. The International Journal of Human Resource Management, 31(8), 965-991. Doi:10.1080/09585192.2017.1396545
  10. Expósito-Casas, E., González-Benito, A. & López-Martín, E. Data mining to detect variables associated with the occupational aspirations of Spanish 15-year-old students. Int J Educ Vocat Guidance(2022).  Doi:10.1007/s10775-022-09554-y
  11. Glaser, B. G., & Strauss, A. L. (2017). The discovery of grounded theory: Strategies for qualitative research. Routledge.
  12. Gronbach, K. (2021). The chief human resources officer is the new chief financial officer. Journal of Cultural Marketing Strategy, 5(2), 98-102. ‏
  13. Hajiheydari, N., Khabiri, H., & Talafi Daryani, M. (2017). A Framework for Data Mining Approach Applications in Human Resource Management. Iranian Journal of Management, 12(47), 21-50.
  14. Hirschfeld, R., & Van Scotter, J. (2019). Vocational behavior from the dark side. Journal of Vocational Behavior,110, 303–316.  Doi:10.1016/j.jvb.2018.10.019
  15. Horváthová, P., Velčovská, Š., Kauerová, L., & Larsen, F. R. (2019). Evaluation of Key Positions and Employees Management Level in Manufacturing Industry—The Czech Case. Sustainability, 12(1), 242.
  16. Kabwe, C., & Okorie, C. (2019). The efficacy of talent management in international business: The case of European multinationals. Thunderbird International Business Review, 61(6), 857-872. ‏
  17. Kaffash, S., & Marra, M. (2017). Data envelopment analysis in financial services: a citations network analysis of banks, insurance companies and money market funds. Annals of Operations Research, 253 (1), 307-344.
  18. Kaliannan, M., Darmalinggam, D., Dorasamy, M., & Abraham, M. (2022). Inclusive talent development as a key talent management approach: A systematic literature review. Human Resource Management Review, 100926. ‏
  19. Karatop, B., Kubat, C., & Uygun, Ö. (2015). Talent management in manufacturing system using fuzzy logic approach. Computers & Industrial Engineering, 86, 127-136. Doi: 10.1016/j.cie.2014.09.015
  20. Khanyile, R., Marima, R., Mbeje, M., Mutambirwa, S., Montwedi, D., & Dlamini, Z. (2023). AI Tools Offering Cancer Clinical Applications for Risk Predictor, Early Detection, Diagnosis, and Accurate Prognosis: Perspectives in Personalised Care. In Artificial Intelligence and Precision Oncology: Bridging Cancer Research and Clinical Decision Support (pp. 293-312). Cham: Springer Nature Switzerland.
  21. Lauermann, F., Tsai, Y. M., & Eccles, J. S. (2017). Math-related career aspirations and choices within Eccles et al.’s expectancy–value theory of achievement-related behaviors. Developmental Psychology,53(8), 1540.  Doi:10.1037/dev0000367
  22. Lent, R. W., Brown, S. D., & Hackett, G. (2000). Contextual supports and barriers to career choice: A social cognitive analysis. Journal of Counseling Psychology,47(1), 36–49. Doi:10.1037/0022-0167.47.1.36
  23. Li, Z. (2017). Human Resource Hybrid Recommendation Algorithm Based on Spark. South China University of Technology, Guangzhou, China.
  24. Lukovac, V., Pamučar, D., Popović, M., & Đorović, B. (2017). Portfolio model for analyzing human resources: An approach based on neuro-fuzzy modeling and the simulated annealing algorithm. Expert Systems with Applications, 90, 318-331. Doi: 10.1016/j.eswa.2017.08.034
  25. Madanchian, M., & Taherdoost, H. (2022). The Impact of Digital Transformation Development on Organizational Change. In Driving Transformative Change in E-Business Through Applied Intelligence and Emerging Technologies (pp. 1-24). IGI Global. Doi:10.4018/978-1-6684-5235-6.ch001
  26. Malik, A. R., & Singh, P. (2014). ‘High potential’ programs: Let's hear it for ‘B’ players. Human Resource Management Review, 24(4), 330-346. ‏ Doi:10.1016/j.hrmr.2014.06.001
  27. Manresa, A., & Escobar Rivera, D. (2021). Excellence in sustainable management in a changing environment. Sustainability, 13(4), 2296.
  28. Manresa, A., & Escobar Rivera, D. (2021). Excellence in sustainable management in a changing environment. Sustainability, 13(4), 2296.
  29. Mau, W. C., & Li, J. (2018). Factors influencing STEM career aspirations of underrepresented high school students. The Career Development Quarterly,66(3), 246–258.  Doi:10.1002/cdq.12146
  30. Moore, R., & Burrus, J. (2019). Predicting STEM major and career intentions with the theory of planned behavior. The Career Development Quarterly,67(2), 139–155.  Doi:10.1002/cdq.12177
  31. Murcia, N. N., Ferreira, F. A., & Ferreira, J. J. (2022). Enhancing strategic management using a “quantified VRIO”: Adding value with the MCDA approach. Technological Forecasting and Social Change, 174, 121251. Doi: 10.1016/j.techfore.2021.121251
  32. Nene, S. E. (2020). Knowledge Audit Methodologies: The importance of knowledge management infrastructure (Doctoral dissertation, Stellenbosch: Stellenbosch University).
  33. Nina, S. (2019). What the Public Sector Could Learn from the Private One. Repüléstudományi Közlemények, 31(2), 193-201. ‏ Doi:10.32560/rk.2019.2.14
  34. NOMNGA, P., & NGQULU, N. (2021, May). Advancing Artificial Intelligence to Combat Escalating Cyberspace Human Rights Violations in Africa. In 2021 IST-Africa Conference (IST-Africa) (pp. 1-9). IEEE.
  35. Qazi, N., Kazi, A. S., Kazi, S., Anand, V., Qureshi, S. R., & Inayat, A. (2021). EXCLUSIVE TALENT MANAGEMENT PHILOSOPHY AND DIFFERENTIATING WORKFORCE: A STUDY OF BANKING SECTOR, SINDH, PAKISTAN. Int.(Lahore), 33(2),93-96.
  36. Rathmann, K., Loter, K., & Vockert, T. (2020). Critical events throughout the educational career: The effect of grade retention and repetition on school-aged children’s well-being. International Journal of Environmental Research and Public Health,17(11), 4012.  Doi:10.3390/ijerph17114012
  37. Research, S. (2017). On job recommendation of AI companies based on data mining. Value Engineering, 36(34), 42-44.
  38. Schlosser, F. (2015). Identifying and differentiating key employees from owners and other employees in SMEs. Journal of Small Business Management, 53(1), 37-53. ‏ Doi:10.1111/jsbm.12066
  39. Shah Hosseini, M. and Janati Far, H. (2022). The effect of empowering senior managers on the recognition of key employees (Case study of Sodid Pipe and Equipment Company). The second international conference on new challenges and solutions in industrial engineering, management and accounting, Damghan. [in Persian].
  40. Strohmeier, S., & Piazza, F. (2013). Domain driven data mining in human resource management: A review of current research. Expert Systems with Applications, 40 (7), 2410-2420. Doi: 10.1016/j.eswa.2012.10.059
  41. Traicoff, D., Pope, A., Bloland, P., Lal, D., Bahl, J., Stewart, S., ... & Sandhu, H. (2019). Developing standardized competencies to strengthen immunization systems and workforce. Vaccine, 37(11), 1428-1435. Doi: 10.1016/j.vaccine.2019.01.047
  42. Vaiman, V., Haslberger, A., & Vance, C. M. (2015). Recognizing the important role of self-initiated expatriates in effective global talent management. Human Resource Management Review, 25(3), 280-286. ‏
  43. Vardi, S., & Collings, D. G. (2023). What's in a name? talent: A review and research agenda. Human Resource Management Journal. Doi: 10.1111/1748-8583.12500
  44. Wowczko, I. A. (2015, November). Skills and vacancy analysis with data mining techniques. In Informatics, 2 (4), 31-49. MDPI. Doi: 10.3390/informatics2040031
  45. Zhu, H. (2021). Research on human resource recommendation algorithm based on machine learning. Scientific Programming, 2021, 1-10.
  46. Zhu, X., Seaver, W., Sawhney, R., Ji, S., Holt, B., Sanil, G. B., & Upreti, G. (2017). Employee turnover forecasting for human resource management based on time series analysis. Journal of Applied Statistics, 44 (8), 1421-1440.