Decision-making is an important, yet overlooked, factor in executive hiring
Bibliographic record
Abstract
Purpose Decision-making plays a vital role in business. Yet as a trait, it is often overlooked when identifying, assessing, onboarding and empowering leadership talent. This study, therefore, aims to focus on the dimensions of organisational decision-making and investigate whether it should be an integral factor in the hiring process. The survey also looks at the relationships between decision-making and executive leadership, talent strategy and employee satisfaction. Could good decision-making as a trait be a factor when it comes to retention, improving organisational thinking and thriving leadership? Design/methodology/approach A quantitative and qualitative survey was commissioned and conducted by FT Longitude, part of the Financial Times Group. This included a questionnaire sent out to 400 senior executives at C-suite, C-1 and C-2 levels. The respondents were from companies with more than 1,000 employees in 13 industries and from five countries across the Americas, Europe and Asia-Pacific. This was also accompanied by in-depth interviews with three experts from both the public and private sector. Findings Almost two-thirds (63%) of senior executives have resigned or considered resigning due to frustration with organisational decision-making, while 29% have considered quitting because they were frustrated with the way a company makes decisions. More than a third (34%) resigned for this reason. Yet, a quarter of senior executives say that their decision-making experience was not explicitly discussed before starting their job. Senior executives who were asked about decision-making in interviews were 1.4 times more likely to be satisfied with their jobs. The ability to make decisions should therefore play a central role in hiring senior executives. Originality/value Decision-making as a trait has been neglected when hiring executives. For the first time, this research shows how significant it is for leadership teams. If senior executives are to improve organisational decision-making, this trait needs codifying in HR processes. This has led Kingsley Gate to embed it in recruitment profiling.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".