Motivation Backfire: When Motivation Leads to Unexpected Organizational Outcomes
Bibliographic record
Abstract
When leveraged correctly, motivation can be a powerful organizational tool. More motivated employees are likely to persist longer on tasks, (Bargh et al., 2001), pay more attention to details (Botvinick & Braver, 2015), and expend more effort (Amabile, Barsade, Mueller, & Staw, 2005; Isen, Daubman, & Nowicki, 1987) than those who are less motivated. Organizations are also instrumental in designing motivational systems. For example, people were less motivated to volunteer when asked to volunteer for 200 hours, compared to when the same 200 hours was broken up over 4 hours per week or 8 hours every 2 weeks (Rai et al., 2022). While much research has examined the positive effects of motivation, there is also research suggesting that motivation can backfire. For example, highly motivated people are more likely to “choke under pressure” (Beilock & Gray, 2007) and offering money for prosocial behaviors can decrease their uptake (Gneezy & Rustichini, 2000). Understanding the contexts in which motivation can lead to negative outcomes is a key organizational concern. In this symposium, we bring together leading and emerging scholars with the aim to answer three questions: (1) when does motivation lead to negative organizational outcomes, (2) are people’s expectations for motivation’s impact on organizational outcomes calibrated to reality, and (3) once these negative outcomes are anticipated, can interventions be designed to overcome them? Together, the papers in this symposium offer important empirical insights into how conventional wisdom about the effects of motivation can lead us astray. Practically, the collection of papers also provides managers and policymakers with insights as to how to design motivational systems that are consistent with their desired outcomes. Team Passion Peaks: The Double-Edge Sword of Momentarily High Team Passion Author: Anne Margit Reitsema; Harvard Business School Author: Kai Krautter; Harvard U. Author: Jon Michael Jachimowicz; Harvard Business School From Warm Glow to Cold Chill: The Effect of Choice Framing on Donation Interest Author: Ilana Brody; UCLA Anderson School of Management Author: Hengchen Dai; UCLA Anderson School of Management Author: Jana Gallus; UCLA Anderson School of Management Motivation Myopia: The Overestimation of Motivation’s Impact on Performance Author: Eliana Polimeni; Northwestern Kellogg School of Management Author: Loran F. Nordgren; Northwestern U. Goal Fusion Increases Motivation Author: Jiabi Wang; U. of Chicago Booth School of business Author: Ayelet Fishbach; professor
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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 | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| 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".