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Record W7133540946 · doi:10.23762/fso_vol12_no3_3

The Impact of Varying Styles of Leadership on Team Dynamics and Project Success

2024· article· en· W7133540946 on OpenAlexaff
Harmandeep Kaur, Adnan ul Haque, Pavlos Gkasis

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2024
Typearticle
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsYorkville University
Fundersnot available
KeywordsServant leadershipLeadership styleAgile software developmentShared leadershipDynamics (music)Structural equation modelingAutocracyTransactional leadership

Abstract

fetched live from OpenAlex

The present study examines the impact that distinct styles of leadership have on team dynamics and project success. By combining purposive, convenience, and networking sampling techniques to avoid overreliance on one single sampling technique, we used partial least squares structural equation modelling on a sample of 188 responses obtained from a semi-structured matrix-based questionnaire. Our findings revealed that autocratic leadership has no significant impact on team dynamics or project success. All other styles considered (i.e. servant leadership, agile leadership, ethical leadership, and laissez-faire leadership) have a statistically significant impact on team dynamics and project success. Furthermore, servant leadership and agile leadership have a strong positive correlation with team dynamics and project success, whereas ethical leadership exhibits a moderate positive linkage with team dynamics and project success. Laissez-faire leadership and autocratic leadership have weak positive correlations with team dynamics and project success. Lastly, team dynamics impact project outcomes in a statistically significant manner. Our analysis concludes that the most effective leadership style among all the above is servant leadership, which has a statistically significant positive impact on team dynamics and project success.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.396
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0020.002
Open science0.0030.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.505
GPT teacher head0.618
Teacher spread0.113 · 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 designObservational
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 abstractyes

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