Do top executives’ immigration status and multiculturalism perceptions matter? A diversity mindsets perspective
Bibliographic record
Abstract
Purpose This study assesses whether cultural diversity indicators – the top executive’s immigration status and management perception of workforce multiculturalism influencing creativity – are related to employer-reported firm outcomes and projections. Design/methodology/approach Using data from a representative survey of 801 Atlantic Canadian employers, this paper conducts regression analyses to test associations between two diversity variables and five objective and subjective measures of firm performance: revenue change and employment change in the previous three years, projected revenue change and employment change over the next three years, and projected provincial economic growth/decline over the next three years. Findings General support for the hypotheses was found. Firms led by immigrant CEOs or owners are more likely to report recent employment growth and anticipate both revenue and provincial economic growth. Although not all statistically significant, employers viewing multiculturalism as enhancing creativity had a positive sign with performance gains. Research limitations/implications While we contend that the unique features of our survey data outweigh any potential drawbacks, we acknowledge some limitations to our analysis. First, particularly noteworthy is the cross-sectional nature of our data, which suggested that our hypothesized relationships reflect synergistic associations and not necessarily causal relationships. Second, the data is self-reported. To minimize bias/erroneous responses, we ensured that all responses were anonymized and engaged with a third independent party to conduct the surveys over the phone. Furthermore, we did not ask participants for numeric figures or for past revenue and employment. Nonetheless, we recognize that a degree of social desirability cannot be ruled out. Practical implications Our paper provides solid evidence of a logical synergistic outcome of diversity climates with typically neglected firm-level performance outcomes, which carries significant policy and practical implications for diversity management and multiculturalism policies. Originality/value The paper contributes to relatively scarce research of a similar nature focusing on Canada. It also goes beyond the financial indicators that the diversity-performance literature is centered on and adds value through its unique survey data variables.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".