Competitiveness and workforce performance: Asia<i>vis-à-vis</i>the “West”
Bibliographic record
Abstract
Purpose The purpose of this eight-country study is to examine what drives performance at the individual worker’s level and compare the explanatory power of such drivers between emerging, newly developed and developed markets around the globe. Design/methodology/approach The study combines established behavioural theory developed in a Western context with three factors anticipated to be most relevant in Asia (competitive attitude, willingness to serve and speed) as drivers of workforce performance. Four thousand working and middle-class respondents from eight countries were sampled. The associations were tested using structural equation modelling, and workforce performance was measured using univariate analysis. Findings Three country clusters emerged from the research: emerging economies in Asia (Indonesia, India), where the three factors powerfully explain performance; “Confucian orbit countries” (China, Japan, Korea), where the factors explain 81-93 per cent; and highly developed Western countries (the USA, the UK, Germany), where the factors account for only 20-29 per cent. Practical implications As well as providing a framework for modelling workforce performance, particularly in Asian countries, the findings indicate that workforce performance should be incorporated in performance indexes. The findings as to which drivers best explain workforce performance in each country can inform workforce recruitment and management, as well as the location of businesses and outsourcing. Originality/value For the first time, the study addresses the anomaly between economic growth and development experienced by Asian countries and their relatively low rankings in global competitiveness indexes by making the link between workforce performance and country performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".