A Metamotivational Approach to Understanding Managers’ Beliefs About Motivationally Diverse Teams in the Domain of Regulatory Mode
Bibliographic record
Abstract
Cultivating effective teams requires managers to integrate the efforts of individuals who often vary in their backgrounds, skills, and identities. One way that team members can differ from each other is in their motivational orientation, or the reasons and ways that people pursue goals. Extant literature demonstrates that the complementary nature of two regulatory mode motivational orientations (locomotion and assessment) can benefit the performance of individuals and teams. Yet relatively little is known about what managers believe about how to manage this type of motivational diversity in teams. In this dissertation, I combine insights from the literature in motivation science, team management, and diversity to propose a novel perspective on managing motivation in teams. The first part of this dissertation (Studies 1-3) examines what people believe about the role of regulatory mode motivation in teams. Study 1 demonstrates that people, on average, recognize the differential benefits of locomotion and assessment for task performance. Using complementary methodologies, Studies 2 and 3 revealed that although people perceive motivationally diverse (vs. homogenous) teams as prone to conflict (Study 2), when prompted to describe their beliefs about motivation in teams they also recognize its potential benefits (Study 3). Following this, in the second part of this dissertation (Studies 4 and 5) I draw on recent advances in the management of team diversity to examine the strategies managers use when managing motivation in teams. In both hypothetical (Studies 4A and 4B) and consequential (Study 5) contexts, managers recognized the differential utility of different kinds of management strategies and were sensitive to intrateam dynamics in motivationally diverse and homogenous teams, but did not vary their use of different kinds of management strategies when managing motivational diversity in teams. By focusing on what managers themselves believe and do when managing motivational diversity in teams, this research offers a novel perspective on an understudied area of team management with implications for the theoretical and practical study of team management.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".