Intuition in Organizations: New Research Directions
Bibliographic record
Abstract
This 15th annual intuition symposium at AoM showcases new research directions in the discipline. The empirical contributions investigate the role of intuition in academic output and organizational forecasting. The conceptual contributions compare and contrast intuition with tacit knowledge and artificial intelligence. Both research streams are bound together through a contribution about a theoretically grounded training method that bears both conceptual and empirical implications. Specifically, Yaakobi et al. investigate the relationship between intuitive vs. analytical cognitive style of scientists and their research output, highlighting the difference between the number of publications and their impact factor, depending on job complexity. Innes illustrates how intuition contributes to individual foresight in organizational context and evaluates the implication for HR management. Culham investigates a non-western view on intuition, used to develop a training method suitable for a western classroom, and introduces a different understanding of intuition from the engineering discipline. Grant explores the similarities and overlaps between tacit knowledge and intuitive expertise, thus further developing the concept and speculating how the distinction might inform the current debate about artificial intelligence (AI). Finally, Bas and Dörfler compare and contrast AI capabilities and intuition functions, as defined by its six necessary features. Cognitive Style of Scientists and their Publication Performance Author: Erez Yaakobi; Ono Academic College Author: Jacob Weisberg; Bar Ilan U. Author: Talya Miron-Shatz; Ono Academic College Individual Foresight and Intuition in Organizations Author: Melissa Innes; U. of Sunshine Coast Cultivating Oneness as a Path to Intuition Author: Tom Elwood Culham; Beedie School of Business Simon Fraser U. A Knowledge-Based View on Intuition – Relationship between Intuitive Expertise and Tacit Knowledge Author: Michael Grant; Uppsala U. Can AI Have Intuition? Author: Alina Bas; New York U. Author: Viktor Dorfler; U. of Strathclyde Business School
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 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 | 0.016 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.031 |
| Scholarly communication | 0.011 | 0.031 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 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".