MétaCan
Menu
Back to cohort
Record W4401167614 · doi:10.2139/ssrn.4911484

Perceptions of Leadership Emoji Use in Virtual Teams: A Cross-Cultural Study

2024· preprint· en· W4401167614 on OpenAlexaff
René Arseneault, Thomas K.B. Koo, Jialiang Yang

Bibliographic record

VenueSSRN Electronic Journal · 2024
Typepreprint
Languageen
FieldComputer Science
TopicDigital Communication and Language
Canadian institutionsDalhousie University
Fundersnot available
KeywordsEmojiCross-culturalPerceptionPsychologyCross-cultural leadershipSocial psychologyComputer scienceLeadership styleSociologyLeadership studiesWorld Wide WebSocial mediaNeuroleadershipAnthropology

Abstract

fetched live from OpenAlex

This research explores cross-cultural differences in perceptions of leadership emoji use and its implication for managing virtual teams. Two randomized controlled experiments tested for the effects of using emojis in computer-mediated workplace communication. Participants from four countries (N = 1488) were randomly assigned to a control or treatment group and asked to read workplace scenarios with or without emoji presence. Leadership perceptions as well as organizational measures, including civility and PO-fit were extended to all participants post-study. GLOBE leadership styles were used to predict the impact of emoji use on our study variables in hierarchical linear modeling. We found leadership emoji use to have a significant effect on several leadership perceptions across our sample. Leadership emoji use significantly increased perceptions of warmth for all countries except South Africa but led to negative perceptions of competence when ‘strong’ emojis (i.e., heart-shaped eyes) were used. German participants were the only country group to be positively impacted by leadership emoji use on organizational outcomes. The stronger emojis used in scenario 2, led to amplified effects as compared with those observed in study 1. Our findings have implications for diversity management and global leadership communication across virtual teams.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.325
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0000.006
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.053
GPT teacher head0.342
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractno

Explore more

Same venueSSRN Electronic JournalSame topicDigital Communication and LanguageFrench-language works237,207