Embracing Structure in Organizations: The Interplay Between Perceptions and Benefits of Structure
Bibliographic record
Abstract
The idea that structure —explicit and predetermined rules that are imposed to guide behavior in situations and tasks— can elevate and improve performance is well established in the field of management. However, what is largely absent from the literature is an investigation of individuals’ perceptions and attitudes towards the structures that are often embedded in tasks and situations necessary for our work. As perceptions are consequential antecedents of behavior, how individuals perceive these structured devices may have important implications for the tasks and experiences they choose to engage in and support. The papers in this symposium build on prior work on the topic of structure by making three important contributions: 1) they begin to investigate how people perceive the impact of adding structure on enjoyment and effectiveness; 2) they demonstrate how structure can provide interpersonal benefits— topic preparation improves conversations and precommitment strategies facilitate the development of interpersonal trust; and 3) they show how structural attributions shape perceptions of others, the self, and support for policy. Ultimately, the work presented in this symposium highlights the power of perceptions and how they might hinder our ability to capitalize on the benefits that structure can confer in organizations and society. Structure—The Unwanted Ally: Perceptions and Preferences for Structured vs. Unstructured Tasks Author: Kelly Harrington; Kellogg School of Management, Northwestern U. Author: Loran F. Nordgren; Northwestern U. The Power of Forethought: Brainstorming Flexible Topics Improves Conversations Author: Nicole Abi-Esber; Harvard Business School Author: Alison Wood Brooks; Harvard U. Precommitment Allows Leaders to Maintain Trust When De-Escalating Commitment Author: Ariella Kristal; Harvard Business School Author: Charles Adam Dorison; - Who’s responsible? How structural attributions affect how we see ourselves and others Author: Sophia Pink; The Wharton School, U. of Pennsylvania
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".