Utility analysis of character assessment in employee placement
Bibliographic record
Abstract
Purpose There has been a surge of interest in leader character and a push to bring character into mainstream management theory and practice. Research has shown that CEOs and board members have many questions about the construct of leader character. For example, they like to see hard data indicating to what extent character contributes to organizational performance. Human resource management professionals are often confronted with the need to discuss and demonstrate the value of training and development initiatives. The question as to whether such interventions have a dollars-and-cents return on the investment is an important one to consider for any organizational decision-maker, especially given the demand for increased accountability, the push for transparency and tightening budgets in organizations. The authors investigated the potential dollar impact associated with the placement of managers based on the assessment of leader character, and they used utility analysis to estimate the dollar value associated with the use of one instrument – the Leader Character Insight Assessment or LCIA – to measure leader character. Design/methodology/approach The authors used field data collected for purposes of succession planning in a large Canadian manufacturing organization. The focus was on identifying senior management candidates suitable for placement into the most senior levels of leadership in the organization. Peers completed the LCIA to obtain leader character ratings of the candidates. The LCIA is a behaviorally based and validated instrument to assess leader character. Performance assessments of the candidates were obtained through supervisor ratings. Findings The correlation between the leader character measure provided by peers and performance assessed by the supervisor was 0.30 (p < 0.01). Using the data required to calculate ΔU from the Brogden-Cronbach-Gleser model leads to an estimate of CAD $564,128 for the use of the LCIA over the expected tenure of 15 years, which is equivalent to CAD $37,609 yearly; and CAD $375,285 over an expected tenure of 10 years, which is equivalent to CAD $37,529 yearly. The results of the study also indicate that there is still a positive and sizeable return on investment or ROI associated with the LCIA in employee placement even with highly conservative adjustments to the basic utility analysis formula. Originality/value Utility analysis is a quantitative and robust method of evaluating human resource programs. The authors provide an illustration of the potential utility of the LCIA in a selection process for senior managers. They assert that selecting and promoting managers on leader character and developing their character-based leadership will not only leverage their own contributions to the organization but also contribute to a trickle-down effect on employees below them.
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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 | 0.016 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".