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Self-Determination Theory and Workplace Outcomes: A Meta-Analysis
- McAnally, Kaylyn
- Advisor(s): Hagger, Martin S
Abstract
Employers have prioritized promotion of adaptive workplace outcomes in their employees to maximize employee and organizational productivity. Motivational theories such as self-determination theory provide a framework to identify potentially modifiable motivational determinants of these outcomes. The theory predicts that satisfaction of basic psychological needs (autonomy, competence, relatedness) and autonomous forms of motivation will be linked to adaptive workplace outcomes (e.g., improved job satisfaction and performance). In the current meta-analysis, we aimed to test a model in which need support impacted work related outcomes through need satisfaction and autonomous and controlled forms of motivation using synthesized data derived from the extant research applying the theory in workplace contexts. Studies (k = 192) reporting relations between self-determination theory constructs and workplace outcomes were identified in a database search. The matrix of meta-analytically synthesized correlations among the theory constructs and outcomes was used as input for a meta-analytic structural equation model testing hypothesized effects. Results showed effects of need support on adaptive workplace outcomes through the mediation of need satisfaction and autonomous motivation. Controlled motivation was related to burnout, but not to adaptive workplace outcomes. Model effects also varied according to work type, employee type, and cultural orientation moderator variables. The value of this analysis is three-fold; (1) this analysis provides a meta-analytic synthesis of research applying SDT in the workplace, (2) tests a model outlining the indirect effects of perceived autonomy support on workplace outcomes mediated by need satisfaction and forms of motivation from SDT, and (3) reports analyses examining effects of key moderator variables on the averaged correlations among SDT constructs and outcomes and the parameter estimates in the model test.