Soft skills requirements in software development jobs: a cross‐cultural empirical study
Bibliographic record
Abstract
Purpose Most of the studies carried out on human factor in software development concentrate primarily on personality traits. However, soft skills which largely help in determining personality traits have been given comparatively little attention by researchers. The purpose of this paper is to find out whether employers' soft skills requirements, as advertised in job postings, within different roles of software development, are similar across different cultures. Design/methodology/approach The authors review the literature relating to soft skills before describing a study based on 500 job advertisements posted on well‐known recruitment sites from a range of geographical locations, including North America, Europe, Asia and Australia. The study makes use of nine defined soft skills to assess the level of demand for each of these skills related to individual job roles within the software industry. Findings It was found that in the cases of designer, programmer and tester, substantial similarity exists for the requirements of soft skills, whereas only in the case of system analyst is dissimilarity present across different cultures. It was concluded that cultural difference does not have a major impact on the choice of soft skills requirements in hiring new employee in the case of the software development profession. Originality/value Specific studies concerning soft skills and software development have been sporadic and often incidental, which highlights the originality of this work. Moreover, no concrete work has been reported in the area of soft skills and their demand as a part of job requirement sets in diverse cultures, which increases the value of this paper.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".