{"title":"Predicting Employee Productivity based on Work Ethics and Organizational Learning","authors":"Samaneh Faregh, R. Jahanian, Mahtab Salimi","doi":"10.52547/IJETHICS.2.4.48","DOIUrl":null,"url":null,"abstract":"Background: Today, employee productivity is an important issue for organizations and the role of work ethics and learning in productivity is considered important. Therefore, the purpose of this study was to predict employee productivity based on work ethics and organizational learning. Method: The method of the present study was descriptive-correlation. The statistical population included the employees of the Social Security Organization (404 people) whose sample size was selected based on Cochran's formula and random sampling method (n=205). The research instruments were Hersey and Blanchard (1983) employee productivity questionnaire, Gregory (1990) work ethic and Nife (2001) organizational learning questionnaire, the reliability of which was obtained by Cronbach's alpha test (0.84). Descriptive statistics were analyzed with SPSS26 software and inferential statistics were analyzed with Amos24. Results: Data analysis showed that the variables of work ethic and organizational learning can predict 0.45 variance of the criterion variable (employee productivity). Also, work ethic and organizational learning had an impact factor of 0.51 and 0.43, respectively, on employee productivity (p <0.05). Conclusion: According to the results, it can be said that work ethic and organizational learning are effective on employee productivity. Therefore, to increase organizational productivity, more attention should be paid to work ethic and organizational learning.","PeriodicalId":45480,"journal":{"name":"International Journal of Embedded Systems","volume":"2 1","pages":"48-56"},"PeriodicalIF":0.5000,"publicationDate":"2021-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Embedded Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.52547/IJETHICS.2.4.48","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
引用次数: 1
Abstract
Background: Today, employee productivity is an important issue for organizations and the role of work ethics and learning in productivity is considered important. Therefore, the purpose of this study was to predict employee productivity based on work ethics and organizational learning. Method: The method of the present study was descriptive-correlation. The statistical population included the employees of the Social Security Organization (404 people) whose sample size was selected based on Cochran's formula and random sampling method (n=205). The research instruments were Hersey and Blanchard (1983) employee productivity questionnaire, Gregory (1990) work ethic and Nife (2001) organizational learning questionnaire, the reliability of which was obtained by Cronbach's alpha test (0.84). Descriptive statistics were analyzed with SPSS26 software and inferential statistics were analyzed with Amos24. Results: Data analysis showed that the variables of work ethic and organizational learning can predict 0.45 variance of the criterion variable (employee productivity). Also, work ethic and organizational learning had an impact factor of 0.51 and 0.43, respectively, on employee productivity (p <0.05). Conclusion: According to the results, it can be said that work ethic and organizational learning are effective on employee productivity. Therefore, to increase organizational productivity, more attention should be paid to work ethic and organizational learning.
期刊介绍:
With the advent of VLSI system level integration and system-on-chip, the centre of gravity of the computer industry is now moving from personal computing into embedded computing. Embedded systems are increasingly becoming a key technological component of all kinds of complex technical systems, ranging from vehicles, telephones, audio-video-equipment, aircraft, toys, security systems, medical diagnostics, to weapons, pacemakers, climate control systems, manufacturing systems, intelligent power systems etc. IJES addresses the state of the art of all aspects of embedded computing systems with emphasis on algorithms, systems, models, compilers, architectures, tools, design methodologies, test and applications.