Fayez M. Shafloot

Fayez M. Shafloot

Doctoral Dissertation Announcement


Candidate: Fayez M. Shafloot

Degree of: Doctor of Philosophy

Department: Interdisciplinary Ph.D. in Evaluation

Title: The Relationship Among Training Policy, Knowledge Transfer, and Performance Improvement: A Study of Private Sector Organizations in the Kingdom of Saudi Arabia

Committee:
Dr. Chris L. S. Coryn, Chair
Dr. Larry Blackmer
Dr. Arlen R. Gullickson

Date: Monday, February 20, 2012 Noon to 2:00 p.m.
4410 Ellsworth Hall

Abstract:
The purpose of this study was to explore the relationship between training policy variables (i.e., needs assessment, trainee preparation, training program review, accountability, and management support), knowledge transfer, and performance improvement. The study focused on the private sector in Saudi Arabia and used mixed methods design to collect data: surveys and interviews.
As a whole, the results showed that the correlation coefficients for the relationships between training policy and knowledge transfer and performance improvement independently were moderate and statistically significant. Training Accountability factors had the largest correlation with Knowledge Transfer, followed by Management Support while Trainee Preparation and Training Program Review each had the smallest correlations with Knowledge Transfer.
On the other hand, the correlation between Management Support and Performance Improvement was the largest among all variables while it was the smallest between Accountability and Performance Improvement. Moreover, the results revealed that the model predicting Knowledge Transfer was statistically significant and accounted for approximately 38% of the variance in Knowledge Transfer as well as it was a statistically significant model for predicting Performance Improvement and accounted for approximately 25% of the variance. Overall, this study supports the extant literature in many aspects, and it provides an understanding of the relationship between variables and how training policy variables predict knowledge transfer and performance improvement.

 

 

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