Volume 5, Issue 1 (Spring 2022)
Iranian Journal of Educational Sociology 2022, 5(1): 172-182 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Hosseini Zare S R, Toloie Oshloghi A, Motadel M R, Radfar R. (2022). Identification the Components of Evaluation of Employee Performance Smart Pattern (Case Study: Bank of Industry and Mine). Iranian Journal of Educational Sociology. 5(1), 172-182. doi:10.61186/ijes.5.1.172
URL: http://iase-idje.ir/article-1-1113-en.html
URL: http://iase-idje.ir/article-1-1113-en.html
1- PhD Student, Department of Information Technology Management, UAE Branch, Islamic Azad University, Dubai, UAE
2- Professor, Department of Industrial Management, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran
3- Assistant Professor, Department of Industrial Management, Islamic Azad University, Central Tehran Branch, Tehran, Iran
4- Professor, Department of Industrial Management, University of Management and Economics, Science and Research, Islamic Azad University, Tehran, Iran
2- Professor, Department of Industrial Management, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran
3- Assistant Professor, Department of Industrial Management, Islamic Azad University, Central Tehran Branch, Tehran, Iran
4- Professor, Department of Industrial Management, University of Management and Economics, Science and Research, Islamic Azad University, Tehran, Iran
Abstract: (1579 Views)
Purpose: Evaluation of employee performance plays an effective role in organizational productivity and effectiveness. As a result, the purpose of this study was identification the components of evaluation of employee performance smart pattern.
Methodology: This study in terms of purpose was applied and in terms of implementation method was qualitative. The research population was specialist managers and experts evaluation of employee performance of the bank of industry and mine in 2020 year, which according to the principle of theoretical saturation number of 16 people was selected by purposeful sampling method. The research tool was a semi-structured interview, which whose validity was confirmed by the triangulation method and its reliability was calculated by the agreement coefficient method between the two coders 0.82. Finally, data were analyzed based on open, axial and selective coding methods.
Findings: The findings showed that components of evaluation of employee performance smart pattern had 50 open codes in 12 axial codes and 3 selective codes. The selective codes included the factors of functional (with 4 axial codes of optimal function, quality of work, active and effective participation in meetings and planning, forearm and follow-up), job behavior (with 7 axial codes of observing administrative regulations and discipline, proper behavior and encounter with clients and colleagues, cooperation and accountability, analysis of issues and providing appropriate solutions, self-propulsion and innovation, increased job information and skills and information transfer) and ethical behavior (with 1 axial code of observing ethics and Islamic behavior). Finally, according to the identified components, the evaluation of employee performance smart pattern was painted.
Conclusion: The results of this research have practical implications for the specialists and officials of the Bank of Industry and Mine, and they with the help of identified components for evaluation of employee performance smart pattern can provide the ground for improving the performance of the organization.
Methodology: This study in terms of purpose was applied and in terms of implementation method was qualitative. The research population was specialist managers and experts evaluation of employee performance of the bank of industry and mine in 2020 year, which according to the principle of theoretical saturation number of 16 people was selected by purposeful sampling method. The research tool was a semi-structured interview, which whose validity was confirmed by the triangulation method and its reliability was calculated by the agreement coefficient method between the two coders 0.82. Finally, data were analyzed based on open, axial and selective coding methods.
Findings: The findings showed that components of evaluation of employee performance smart pattern had 50 open codes in 12 axial codes and 3 selective codes. The selective codes included the factors of functional (with 4 axial codes of optimal function, quality of work, active and effective participation in meetings and planning, forearm and follow-up), job behavior (with 7 axial codes of observing administrative regulations and discipline, proper behavior and encounter with clients and colleagues, cooperation and accountability, analysis of issues and providing appropriate solutions, self-propulsion and innovation, increased job information and skills and information transfer) and ethical behavior (with 1 axial code of observing ethics and Islamic behavior). Finally, according to the identified components, the evaluation of employee performance smart pattern was painted.
Conclusion: The results of this research have practical implications for the specialists and officials of the Bank of Industry and Mine, and they with the help of identified components for evaluation of employee performance smart pattern can provide the ground for improving the performance of the organization.
Type of Study: Research Article |
Subject:
Special
Received: 2021/10/30 | Accepted: 2021/12/14 | Published: 2022/04/3
Received: 2021/10/30 | Accepted: 2021/12/14 | Published: 2022/04/3
References
1. Abbasi A, RanayeeKordshooli H, Asgharijahromi S. (2017). Designing employee's performance evaluation model on the basis of organizational performance models for Iranian public organizations. Journal of Research in Human Resources Management. 8(4): 213-235. [Persian]
2. Alaei A, Farajpour Khanaposhtani Gh. (2018). Provide an approach based on fuzzy multi-criteria decision making to evaluate and ranking employees. Journal of Business Management. 10(38): 108-136. [Persian]
3. Behboudi O, Rojui M, Zarei A, Shojaee Baghini G. (2019). Designing the model of evaluation of marketing performance in the tourism industry of Iran. Journal of Tourism and Development. 7(4): 61-82. [Persian]
4. Chong VK, Loy CY, Masschelein S, Woodliff DR. (2018). The effect of performance evaluation schemes on predicted transfer prices: Do leadership tone and perceived fairness concerns matter? Management Accounting Research. 41: 11-19. [DOI:10.1016/j.mar.2018.02.003]
5. Frederiksen A, Lange F, Kriechel B. (2017). Subjective performance evaluations and employee careers. Journal of Economic Behavior & Organization. 134: 408-429. [DOI:10.1016/j.jebo.2016.12.016]
6. Hasani M, Azar A, Shahrabi J, Moghbel Baarz A. (2017). Designing agent-based modeling in process performance measurement. Transformation Management Journal. 9(18): 66-89. [Persian]
7. Islami X, Mulolli E, Mustafa N. (2018). Using Management by Objectives as a performance appraisal tool for employee satisfaction. Future Business Journal. 4(1): 94-108. [DOI:10.1016/j.fbj.2018.01.001]
8. Kataoka T, Morikawa K, Takahashi K. (2019). Strategic human resource management simulation considering work elements, skills, learning and forgetting. Procedia Manufacturing. 39: 1633-1640. [DOI:10.1016/j.promfg.2020.01.278]
9. Liu Z, Ma Y, Zheng H, et al. (2022). Human resource recommendation algorithm based on improved frequent itemset mining. Future Generation Computer Systems. 126: 284-288. [DOI:10.1016/j.future.2021.08.017]
10. Lopez YP, Dohrn S, Posig M. (2020). The effect of abusive leadership by coaches on division I student-athletes' performance: The moderating role of core self-evaluations. Sport Management Review. 23(1): 130-141. [DOI:10.1016/j.smr.2019.07.001]
11. Nejad Irani F, Makhdoomi H, Rahimi Gh, Hojati SA. (2021). Explaining and designing a performance appraisal model for the staff of Kurdistan University of Medical Sciences with a data theory approach. Journal of Teaching Research. 8(4): 13-34. [Persian]
12. Orr MT, Hollingworth L, Beaudin B. (2019). Performance assessment for school leaders: Comparing field trial and implementation results. Journal of Educational Administration. 58(1): 38-59. [DOI:10.1108/JEA-05-2018-0087]
13. Pardo C, Sven Ivens B, Pagani M. (2020). Are products striking back? The rise of smart products in business markets. Industrial Marketing Management. 90: 205-220. [DOI:10.1016/j.indmarman.2020.06.011]
14. Rajabzadeh A, Khodadad Hosseini SH, Parvizian K. (2005). Designing personnel evaluation in banking system: Using AHP. Commercial Strategies. 12(14): 47-66. [Persian]
15. RanayeeKorshooli H, Saghapour A. (2011). Designing a evaluating employee performance model based on organizational performance management patterns. Public Administration Perspective. 2(6): 95-114. [Persian]
16. Rojuee M, Ramezani M, Hesari MR, BorBor Jafari M. (2017). Designing performance evaluation indicators by using AHP and BSC approaches (Case of study: Social security organization of Mashhad). Social Welfare. 17(64): 131-158. [Persian]
17. Ronaghi MH, Feyzi K. (2013). A performance assessment model for business intelligence by using fuzzy analysis. Journal of Science & Technology Parks and Incubators. 9(34): 1-10. [Persian]
18. Rusu G, Avasilcai S, Hutu CA. (2016). Organizational context factors influencing employee performance appraisal: A research framewor. Procedia - Social and Behavioral Sciences. 221: 57-65. [DOI:10.1016/j.sbspro.2016.05.090]
19. Shanshan D, Chang L. (2018). Performance evaluation research on social capital improving the livelihood economy in West China. Computers & Electrical Engineering. 71: 408-415. [DOI:10.1016/j.compeleceng.2018.07.011]
20. Stollberger J, Heras ML, Rofcanin Y, Bosch MJ. (2019). Serving followers and family? A trickle-down model of how servant leadership shapes employee work performance. Journal of Vocational Behavior. 112: 158-171. [DOI:10.1016/j.jvb.2019.02.003]
21. Wang Y. (2020). Construction and simulation of performance evaluation index system of Internet of Things based on cloud model. Computer Communications. 153: 177-187. [DOI:10.1016/j.comcom.2020.02.016]
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
.jpg)
