Awe-full Research: Opening Minds in High-Stakes Meetings Through Wonder
Bibliographic record
Abstract
Did you know Minnesota has more shoreline than California, Florida, and Hawaii combined? Or that Montreal, beating Boston, has the highest concentration of post-secondary students of all the major cities in North America? No wonder it is a great city for academic conferences and track discussions! These surprising facts are more than trivia—they can induce awe, a powerful emotion defined as a sense of wonder capable of reshaping our mental models or the way we think (Keltner & Haidt, 2003). Awe can lead to increased openness, perspective-taking, and creative thinking—qualities that are often missing in high-stakes collaborative meetings, where biases, fixed mindsets, and rigid agendas can limit innovation (Perlow et al., 2017). Although strategies like virtual reality, meditation, and yoga have been shown to promote openness, we seek to address a practical gap. Specifically, we aim to identify how and whether simple, affordable, and scalable interventions prime openness, creativity, and shared understanding in collaborative IS meetings where business outcomes are on the line, such as requirements gathering, vendor negotiations, strategic IT planning, and system implementations. We propose a multi-phase study to answer these questions. In Phase 1, we plan to pilot test awe-inducing stimuli across the five senses using small focus groups, followed by a survey to determine the most effective stimuli per sense. In Phase 2 we will, in random order, assign participants all seven effective stimuli (within subjects design) identified during Phase 1, using observation, interviews, and surveys to measure awe responses and effectiveness. Phase 3 will use small groups (treatment and control) of randomly assigned students participating in lab-based, collaborative, brainstorming sessions simulating high stakes via time pressure, competition, and big rewards based on performance. Observations, interviews and surveys will assess effects on openness, creativity, and performance. By our conference presentation, we will have completed Phases 1 and 2 and identified the most effective scalable interventions for inspiring awe. While initial participants are students, we aim to extend Phase 3 to field studies, particularly among professionals in IS roles. Our ultimate goal is to develop accessible interventions to prime high-stakes meetings for open minds and innovation.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".