Revista de la Academia de Gestión Estratégica

1939-6104

Abstracto

Employee's Learning Commitment and Self-efficacy

Tahir Masood Qureshi

Purpose of the Study: The study is designed to investigate the relationship between employee learning commitments leading to employee self-efficacy that in turn leads to other positive outcomes. The study is built around employee learning commitment, knowledge sharing practices, and self-efficacy.

Design/Methodology/Approach: This mixed method cross-sectional study shall empirically test the hypothesis based on the research questions on the sample drawn from the higher education sector.

Findings: Analysis revealed that employee learning commitment is significantly related to employee self-efficacy. The mediation of employee skills and abilities is also proven significant in the same relationship. Moreover, the findings also indicate that employee adaptability & responsiveness and employee skills, knowledge & abilities mediate the relationship between employee learning commitment and employee self-efficacy.

Research Limitations: The findings of this study cannot be generalized with much confidence in non-educational based work settings since it is built around the higher education system in the Middle East.

Practical Implications: The major implications of the study are to lead to better training resources developed for employees to enhance their skills, improve their understanding of abilities and enable them to use their skills for the best of their interests in the education sector. Such implications will directly impact economic and community development.

Originality/Value: Previously published research work focused on knowledge sharing and its outcomes. However, there has not been sufficient exploration in the knowledge sharing and learning process leading to employee self- efficacy specifically in the education sector of the Middle East affected by employees' diverse skills, abilities and adaptability of new methods and teaching approaches. 

Descargo de responsabilidad: este resumen se tradujo utilizando herramientas de inteligencia artificial y aún no ha sido revisado ni verificado.