Are IT Workers from Mars? An Examination of Their National Culture Dimensions
Bibliographic record
Abstract
The information technology (IT) workforce is characterized by several unique and contextual factors, such as the technology, the occupation itself, and the human factors. Among the human factors, global information systems (IS) studies have examined the role of national culture to explain many workforce differences and nuances across nations. In such cross-cultural research, IS researchers have primarily utilized the published scores of national culture dimensions as provided by the preeminent social psychologist and culture scholar Geert Hofstede and have applied them to various IT populations within a country. Given that the IT profession is unique in many respects, and there is cultural heterogeneity within a country, our study embarked on independently measuring and verifying the national culture values of IT employees in 37 countries. By using the original Hofstede scales, scores were obtained on five national culture dimensions: power distance, uncertainty avoidance, individualism, masculinity, and long-term orientation. We found significant differences between the national culture scores of IT employees and those available in the literature for the general population. Our results are novel and have profound significance. There are major implications for both past and future studies in cross-cultural research as well as for practitioners who interpret and utilize the findings of such research.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.008 |
| Open science | 0.002 | 0.001 |
| 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".