Consequences of Construals: Functions and Adaptions
Bibliographic record
Abstract
People can have mental representations of situations, people, and tasks at different levels, focusing on the details (i.e., construing concretely) or the big picture (i.e., construing abstractly). Past research has demonstrated that abstract and concrete construals are adaptive in different situations to meet task-related needs. However, this research often fails to consider the complicated mental shifts that must occur in organizational contexts, wherein competing demands exert pressure on workers to shift based on business needs. To address this gap in the literature, recent research has taken a functional view of construal level, examining construal shifts across varying organizational contexts (beyond simple tasks) to explicate better when and how construal level can improve organizational functioning. This symposium presents five lines of research that use diverse methodologies and samples to explore when abstract and concrete construals are better suited to organizational demands, how people shift their construals to adapt to varying situations, and the outcomes of construal adaptations across several levels of organizational behavior, including communicators, entrepreneurs, teams, and firms. This symposium aims to provide an opportunity for knowledge sharing and discussion among researchers interested in examining the functions of construal level in organizations. “I” am More Concrete Than “We”: Linguistic Abstraction and First-Person Pronoun Usage Presenter: Yidan Yin; U. of Southern California -Marshall School of Business Presenter: Cheryl Wakslak; U. of Southern California Presenter: Priyanka D. Joshi; San Francisco State U. Doing it All: Managing the Tension of Concrete and Abstract Business Demands for Female Entrepreneur Presenter: Samantha Dodson; U. of Utah, David Eccles School of Business Presenter: Rachael Goodwin; Syracuse U. Whitman School of Management Presenter: Arielle M. Newman; Syracuse U. Whitman School of Management Micro foundations of Sensing Capabilities: From Managerial Cognition to Team Behavior Presenter: Jean-François Harvey; HEC Montreal Presenter: Jean-Nicolas Reyt; McGill U. Big-Picture and Detailed-Oriented Roles in Working Dyads: Subjective Costs and Objective Benefits Presenter: Ashli Carter; NYU Stern School of Business Presenter: Tyler Talbot; U. of Utah Presenter: Samantha Dodson; U. of Utah, David Eccles School of Business Presenter: Kristina Diekmann; U. of Utah Presenter: Batia Mishan Wiesenfeld; New York U. Shifting Gears: The Influence of CEO Construal Shifts on Firm Strategic Conformity Presenter: Daniel Gamache; U. of Georgia Presenter: Adam Steinbach; U. of South Carolina Presenter: Lingling Pan; U. of Pittsburgh Presenter: Farhan Iqbal; U. of Georgia Presenter: Russell Eric Johnson; Eli Broad School of Business, Michigan State U.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".