Optimizing Linguistic Diversity in Highly Multicultural Engineering Design Teams
Bibliographic record
Abstract
Abstract Psychological safety, cognitive styles, multicultural competencies and innovation in highly multicultural engineering design teamsEngineering design is a process that frequently takes place in teams. Innovation is known to bemore likely in cohesive teams that draw on each individual’s strengths, whereas teams in whichmembers feel excluded or silenced are less likely to produce innovative results. We examineindividuals and teams in an undergraduate engineering class to determine how linguisticdiversity, multicultural competency, psychological safety, and cognitive styles correlate witheach other and with innovation over the course of a design project.Our student population is very diverse, with over 30% of students most comfortable speakingMandarin Chinese and approximately another 15% of the class regularly using other languagesthan English. While linguistic and cultural diversity are positively correlated with innovation inthe long term [1], in the short term, it can lead to communication problems and a lack ofpsychological safety [2, 3]. Psychological safety, in turn, is positively correlated with innovation[2, 4, 5]. We manipulate team formation in order to maximize diversity in work groups,including linguistic diversity. We also assess students for psychological safety in their teams,their cognitive styles, and their multicultural competencies (using the Multicultural PersonalityQuestionnaire (MPQ)). High multicultural competency is positively correlated withpsychological safety in multicultural contexts [6]; we posit that teams with higher average MPQscores will both score higher in psychological safety and innovate more than teams with lowMPQ scores. We also hypothesize that teams featuring a predominantly connective cognitivestyle will produce more innovative results than those with a predominantly sequential cognitivestyle, as other literature suggests [2, 5, 7, 8]. Since neither cognitive style is statistically relatedto psychological safety [2], we also hypothesize that the teams that will be the most innovativewill be those that exhibit high psychological safety and a mostly connective cognitive style.Finally, we hypothesize that teams on the whole will be more innovative than teams in controlclasses due to the elimination of cultural uniformity in teams. This uniformity tends to increasepsychological safety at the expense of exposing team members to unusual new ideas – ideas thatare known to fuel innovation [9].References[1] S. K. Crotty and J. M. Brett, "Fusing creativity: cultural metacognition and teamwork in multicultural teams," Negotiation and Conflict Management Research, vol. 5, pp. 210- 234, 2012.[2] C. Post, E. De Lia, N. DiTomaso, T. M. Tirpak, and R. Borwankar, "Capitalizing on thought diversity for innovation," Research Technology Management, vol. 52, pp. 14-25, 2009.[3] Y. R. F. Guillaume, J. F. Dawson, S. A. Woods, C. A. Sacramento, and M. A. West, "Getting diversity at work to work: what we know and what we still don't know," Journal of Occupational and Organizational Psychology, vol. 86, pp. 123-141, 2013.[4] R. K. Sawyer, Explaining creativity: the science of human innovation, 2nd ed. New York: Oxford University Press, 2012.[5] C. Post, "Deep-level composition and innovation: the mediating roles of psychological safety and cooperative learning," Group and Organization Management, vol. 37, pp. 555- 588, 2012.[6] K. I. van der Zee and J. P. van Oudenhoven, "The multicultural personality questionnaire: a multidimensional instrument of multicultural effectiveness," European Journal of Personality, vol. 14, pp. 291-309, 2000.[7] E. Miron-Spektor, M. Erez, and E. Naveh, "The effect of conformist and attentive-to- detail members on team innovation: reconciling the innovation paradox," Academy of Management Journal, vol. 54, pp. 740-760, 2011.[8] M. M. Jabri, "The development of conceptually independent subscales in the measurement of modes of problem solving," Educational and Psychological Measurement, vol. 51, pp. 975-983, 1991.[9] G.-A. Amoussou, M. Porter, and S. J. Steinberg, "Assessing creativity practices in design," presented at the 41st ASEE/IEEE Frontiers in Education Conference, Rapid City, SD, 2011.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".