If numbers could “feel”: How well do executives trust their intuition?
Bibliographic record
Abstract
Purpose In the business and data analytics community, intuition has not been discussed widely in terms of its application to executive decision-making. However, the purpose of this paper is to focus on new global research that combines intuition, trust and analytics in terms of how well C-level executives trust their intuition. Design/methodology/approach Our Fulbright research, as described in this paper and performed by colleagues from the United States, Canada, Poland and Italy, examines executives’ as well as other less experienced employees’ preferences for different types of intuition versus data analysis. This study set out to better understand the degree to which executives prefer intuition versus analysis and the relationship between these approaches to decision-making. Our research combines elements of a review, a cross-cultural/cross-company survey study and a biometrics study in interoception. The research team has a multidisciplinary background in business, information technology, strategy, trust management, statistics and neuroscience. Findings Based on our research, the main findings are as follows. The use of and preference for intuition types change as employees gain more experience. However, there may be intuition styles that are more static and trait-like, which are linked to roles, differentiating managers from leaders. Using “inferential intuition” and “seeing the big picture” go hand in hand. Listening to your body signals can promote improved intuition. Cross-cultural differences may impact executive decision-making. Executives often prefer to use their intuition over analysis/analytics. Research limitations/implications This research could be expanded to have a larger sample size of C-level executives. We had 172 responses with 65% C-level executives and 12% directors. However, a recent survey by the Economist Intelligence Unit on intuition used by executives had a sample of 174 executives around the world, which is comparable with our sample size. Practical implications From our research, executives should continue to apply their experiential learning through intuition to complement their use of data in making strategic decisions. We have often discounted the use of intuition in executive decision-making, but our research highlights the importance of making it a critical part of the executive decision-making process. Originality/value Based on the results of our survey and biometrics research, executives apply their intuition to gain greater confidence in their decision-making. Listening to their body signals can also improve their intuitive executive awareness. This complements their use of data and analytics when making executive decisions.
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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 | 0.014 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".