The impact of country culture on the adoption of new forms of work organization
Bibliographic record
Abstract
Purpose This paper aims at understanding the relationship between the adoption of new forms of work organizations (NFWOs) and measures of country impact, in terms of national culture and economic development. Design/methodology/approach The adoption of NFWO practices is measured through data from the fourth edition of the International Manufacturing Strategy Survey, while Hofstede's measures are adopted for national culture, and gross national income (GNI) per capita is used as an economic development variable. Multivariate linear regression is applied to investigate relationships, using company size as a control variable. A cluster analysis is utilized to identify groups of countries with similar cultural characteristics and to highlight different patterns of adoption of NFWO practices. Findings The authors show that it is possible to explain different patterns in the adoption of NFWO practices when considering company size and cultural variables. GNI is instead only significant for some practices and does not always positively influence the adoption of NFWO. On the other hand, cultural variables are linked to all the practices, but there is no dominant dimension to explain higher or lower NFWO adoption. Research limitations/implications Results are limited because only Hofstede's cultural variables are used and manufacturing performance is not considered. Therefore, it is not possible to discriminate between more or less successful NFWO variations. Practical implications This paper provides managers with insights on how to take into account cultural variables when transferring organizational models to different countries. Originality/value This paper contributes to previous studies showing the importance of including several contextual variables, country impact in particular, in the study of operations management.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".