Applying cultural intelligence to religious symbols in multinationals
Bibliographic record
Abstract
Purpose The purpose of this paper is to describe how religious symbols might impede employees’ motivational cultural intelligence (CQ) in some international contexts, and how multinational managers might employ this knowledge to respond in a manner that mitigates risks to knowledge sharing. Design/methodology/approach The paper uses several theories (e.g. CQ, social categorization, expectancy, and contact theories) to develop a conceptual model about the nature of the risk to employees’ motivational CQ. It then draws on models of acculturation to explore how multinational corporation managers might respond. Findings It is conjectured that the salience of religious-based value conflict, learned both vicariously and through direct experiences, will adversely impact motivational CQ, and that the introduction of religious symbols may exacerbate this relationship. A framework of possible interventions is offered, and each intervention approach is evaluated in terms of how it may mitigate or exacerbate the risks raised by the model. Research limitations/implications The proposed model requires empirical validation. Practical implications Multinationals are advised how (and why) to treat the preservation of motivational CQ as central to any intervention in the conflict over religious symbols. Social implications An uninformed response to controversy over religious symbols could impede knowledge sharing and potentially exacerbate broader societal tensions (UN Global Compact, 2013). Therefore, this paper addresses a clear socio-economic need. Originality/value Controversy over the use of religious symbols in the workplace has generated considerable international media attention, but has been neglected by cross-cultural management research.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".