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Record W7026768960

Awe-full Research: Opening Minds in High-Stakes Meetings Through Wonder

2025· article· en· W7026768960 on OpenAlexaboutno aff

Bibliographic record

VenueJournal of the Association for Information Systems · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicMedia Influence and Health
Canadian institutionsnot available
Fundersnot available
KeywordsWonderPhase (matter)Plan (archaeology)Psychological interventionTest (biology)Baseline (sea)Vendor
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.780
Threshold uncertainty score0.476

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.104
GPT teacher head0.350
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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