Job Attribute Preference of Executives: A Conjoint Analysis
Bibliographic record
Abstract
This research explores the job attribute preferences of executives in Bangladesh. Unlike most past researches that deployed isolated estimation methods, this research used conjoint analysis, a marketing research tool to measures the relative utilities and trade-off matrices of different job attributes. Data was collected from 140 executive MBA students from a premier business school in Dhaka using a questionnaire presenting an array of hypothetical job offers. Salary & benefit and person-job match are found to be the top two most preferred job attributes. Workenvironment and company- reputation are indicated as the two least important job attributes. Simulation was run to demonstrate the trade-offs people make in their job choice decisions. Case-wise conjoint results show no significant difference among different demographic groups (e.g. married-single, have-don’t have dependents and others) in terms of the order of importance of the job attributes. However, the value of the relative importance was found to be slightly different for different demographic groups.This research is important for academics as it demonstrate a new technique to analyze job attribute preferences. Managers can use the results of this study for designing jobs to attract and retain the best talents of the market. They can use the simulation process demonstrated here for optimizing overall preference of their job offers.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".