Impact of job satisfaction on training motivation

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This study aims to introduce new insights regarding factors influencing employees’ level of training motivation through investigating the impact of job satisfaction dimensions (pay, fringe benefits, contingent rewards, promotion, supervision, co-workers, operating conditions, nature of work and communication) on training motivation.
Data were collected from 342 participants from six Jordanian ministries. The multiple regression technique was utilized to examine the predictive power of job satisfaction dimensions on training motivation. At the next stage, three sequential MR analysis rounds were conducted, each time using a different construct of training motivation (valence, instrumentality and expectancy) as a dependent variable.
The results indicate that the dimensions of job satisfaction explain a low but significant variance of the overall training motivation model. Furthermore, it was found that only three dimensions of job satisfaction (nature of work, supervision and co-workers) respectively had a positive and significant impact on training motivation, while contingent rewards had a significant but negative impact.
Regarding training motivation constructs, results indicate that the nature of work and supervision were the elements that have an impact on all constructs of training motivation. Finally, it was found that co-workers and contingent rewards had a significant impact on the training motivation constructs of expectancy (confidence in ability to learn) and valence (the perceived value of training outcomes), but no impact on instrumentality (rewards associated with learning).

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    • Figure 1. Proposed theoretical framework to test the impact of job satisfaction on training motivation
    • Table 1. Findings of confirmatory factor analysis
    • Table 2. Shared variance among the research dimensions
    • Table 3. Reliability scores for the aggregate purified scales for all constructs
    • Table 4. Findings of the descriptive statistics
    • Table 5. Assumptions of multiple regression
    • Table 6. Multiple regressions results