Organizational Consequences of Misperceptions about Sensitive Topics
Bibliographic record
Abstract
Conversations addressing conflicts, disagreements, and sensitive topics are instrumental for both individual and team decision-making in organizational settings. Nevertheless, discussions of difficult or sensitive topics are often avoided due to a common misconception that such dialogues diminish decision-making efficiency, exacerbate conflicts, and strain relationships. In this symposium, we present novel research on organizational and interpersonal contexts where people fail to talk about and effectively manage sensitive topics. These topics are often controversial, including the request to initiate a negotiation, changing one’s political views, and engaging with large-scale societal problems through reporting or helping. In particular, the papers presented will show that people (1) overestimate how likely negotiation counterparts are to withdraw a deal if one attempts to negotiate, and as a result, avoid negotiating; (2) overestimate how likely ingroup members are to penalize one for changing one’s mind about controversial political topics, which leads to self-censorship; (3) have conflicting perceptions of victims’ motivations in reporting about similar events, which affects trust and perceptions of accuracy; (4) underestimate the sensitivity and impact of big problems, leading to lower helping; (5) may overestimate the mere effect of apologies on reducing medical lawsuits. Moreover, this set of papers shows the detrimental consequences of such misperceptions, particularly for missed opportunities for disclosure and for economic and relational benefits. Taken together, this symposium highlights the fraught nature of sensitive topics, and points to avenues for improving the effective flow of information within organizations. Negotiators’ Inflated Perception of Their Likelihood of Jeopardizing a Deal Author: Einav Hart; George Mason U. Author: Julia Bear; Stony Brook U.-State U. of New York Author: Zhiying Ren; The Wharton School, U. of Pennsylvania Intragroup Illusions: Overestimating the Social Costs of Political Belief Change Author: Trevor Spelman; Northwestern Kellogg School of Management Author: Abdo Elnakouri; Northwestern U. Author: Nour Kteily; Northwestern Kellogg School of Management Author: Eli Finkel; Kellogg School of Management, Northwestern U. Motivated to Uncover the Truth: When Past Experiences of Victimization Boost Trust Author: Jennifer Abel; Harvard Business School Author: Julian Jake Zlatev; Harvard Business School The Bigger the Problem the Littler Author: Lauren Eskreis-Winkler; Northwestern Kellogg School of Management Author: Luiza Peres; Kellogg School of Management, Northwestern U. Author: Ayelet Fishbach; professor Apologies: Is Their Effect in Reducing Lawsuits for Medical Malpractice a Misperception? Author: Nelly Arbel Groissman; Technion - Israel Institute of Technology Author: Eran Dorfman; Technion - Israel Institute of Technology Author: Elad Yom Tov; Bar Ilan U. Author: Paul Feigin; Technion - Israel Institute of Technology Author: Anat Rafaeli; Technion Israel Institute of Technology
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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 | 0.045 | 0.165 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".