Self-Determination Theory Can Help You Generate Performance and Well-Being in the Workplace: A Review of the Literature
Bibliographic record
Abstract
The Problem According to self-determination theory (SDT), employees can experience different types of motivation with respect to their work. The presence of the different types of motivation is important given that, compared with controlled regulation (introjected and extrinsic motivation), autonomous regulation (intrinsic and identified motivation) leads to a host of positive individual and organizational outcomes. Despite this empirically validated phenomenon, managers remain unaware of the outcomes of motivation in the workplace and of the practices that can foster autonomous regulation through psychological need satisfaction. The focus of the article will be to review relevant literature to reveal the benefits that SDT principles can bring to the workplace. The Solution Managers are encouraged to promote autonomous regulation first by assessing their employees’ motivation for a particular outcome and by structuring three elements of the work environment (job design, interpersonal relationships/leadership, and compensation) in such a way as to facilitate need satisfaction (autonomy, competence, and relatedness). Some questions we try to answer are as follows: What are the outcomes of different motivation types in the workplace? Why are an employee’s basic psychological needs important to consider? What kinds of tools are available to assess employees’ motivation with regard to their work? Which work practices are likely to encourage autonomous regulation? The Stakeholders Employees, managers (individuals in direct contact with employees), leaders (individuals who oftentimes are in a position to influence organizational strategies and processes) and human resource development (HRD) practitioners interested in stimulating optimal functioning at work.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".