Designing Performance Evaluation Indicators for the Effectiveness of Generation-Based E-Learning Case Study: a National Defense Organization)

Document Type : Original Article

Authors

1 PhD Student of Management of Government, Allame Tabatabaee University, Tehran, Iran.

2 , Allame Tabatabaee University, Tehran, Iran.

3 Allame Tabatabaee

4 Allame tabatabaee

Abstract

Objective: The goal of the current study is to design a model for evaluating the effectiveness of e-learning based on multiple generations of employees. Method: This research is considered applied in terms of its objective and falls under the category of mixed research in terms of typology of research with a qualitative and quantitative approach within inductive comparative paradigm. Identification of components through semi-structured interviews based on theoretical saturation was conducted with 14 professors using MAXQDA software. The validity and reliability of the interviews were confirmed, respectively, through the method of relative content validity and Cohen's Kappa index. Also, in the quantitative section, in order to model human capital development, the opinions of 28 human resources managers from the studied organization were initially examined using a non-probability convenience sampling method. Subsequently, the findings were made available to the statistical population. Findings: The coding of interviews led to the identification of 20 key variables, all of which have one or more relationships with other model variables. Furthermore, modeling the identified factors through interpretative structural modeling and Micmac analysis resulted in the formation of eleven levels. Conclusion: The results of this research indicated that effective training courses through electronic learning can be conducted for various generations of employees. Additionally, these results can be utilized by the managers and senior experts of the studied organization who are seeking more effective ways of electronic learning based on the perspectives of different generations of employees.

Keywords

Main Subjects

Smiley face

References

Birkman. (2016). How generational differences impact organizations & teams [Adobe Digital Editions version]. Retrieved from https://birkman.com/wpcontent/ uploads/2016/05/Generational-Differences-PDF.pdf.
Cogin, J. (2012). Are generational differences in work values fact or fiction? Multi-country evidence and implications. The International Journal of Human Resource Management, 23(11), 2268–2294.
Ellison, N. (2014). Bridging the generation gap in the 148th fighter wing maintenance squadron (Master’s thesis). Retrieved from ProQuest Dissertations and Theses database. (UMI No. 1573325).
Foroohar, R. (2016, January 20). The 1 thing on everybody’s mind at Davos 2016.
Glass, A. (2007a). Understanding generational differences for competitive success. Industrial and Commercial Training, 39(2), 98–103.
Grossman, R., & Salas, E. (2011). The transfer of training: what really matters. International Journal of Training and Development, 15(2), 103-120.
Hawkins, J. B. (2011). Bridging the knowledge gap: The effectiveness of compulsory computer-based training in federal employees’ professional education (Doctoral dissertation). Retrieved from ProQuest Dissertations and Theses database. (UMI No. 3489818).
Knight, M. H. (2016). Generational learning style preferences based on computer-based healthcare training (Doctoral dissertation). Retrieved from ProQuest Dissertations and Theses database. (UMI No. 10108947).
Meister, J. C., & Willyerd, K. (2010). The 2020 workplace: How innovative companies attract, develop, and keep tomorrow’s employees today. New York, NY: HarperCollins.
Nichols, M. (2003). A theory of eLearning. Educational Technology & Society, 6, 1-10.
Obeidat, B., Al-Suradi, M., and Tarhini, A. (2016). The Impact of Knowledge Management on Innovation: An Empirical Study on Jordanian Consultancy Firms. Management Research Review, 39(10), 22-42.
Seel, N. M. (Ed.). (2012). Encyclopedia of the Sciences of Learning. Springer.
Defensive Researches and Management
Volume 22, Issue 101 - Serial Number 3
Autumn 2023
November 2024
Pages 161-180

Files

History

  • Receive Date: 07 July 2023
  • Revise Date: 01 December 2023
  • Accept Date: 21 January 2024

Share

How to cite

Statistics