Retracing Expectancy Theory: An Evolution of Management Studies’ Second Best Known Motivation Theory
Bibliographic record
Abstract
Victor Vroom has long been credited as originating expectancy theory, the idea that people are motivated to behave in certain ways so long as their efforts lead to successful performance that is then exchanged for valuable or attractive rewards. Expectancy theory has become widely accepted in practice as an applied framework for managers to understand how employees engage in their work while being motivated to perform their best. It is now an orthodoxy in management studies, featuring prominently in scholarship over the past half century and in management textbooks still to this day. However, a close excavation of the origins of expectancy theory and its development over time reveals a much different version than the one Vroom is said to have founded. We explore the antecedents of Vroom’s ideas and leverage our analysis to illustrate a reassessment of expectancy theory in management studies, especially how it has evolved in and around Vroom’s influence for several decades. We invite a rethink of who and what has been included in the annals of management theory and encourage those writing textbooks to employ a more thoughtful, inclusive approach in storying histories of management thought.
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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 | 0.016 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.028 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".