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Record W4416284576 · doi:10.63329/av3nz12316

The Servant Leader as a Catalyst: An Empirical Investigation of Servant Leadership in Agile Project Management

2025· article· W4416284576 on OpenAlexaff
Anuraj Sangha

Bibliographic record

VenueScientific Societal & Behavioral Research Journal · 2025
Typearticle
Language
FieldBusiness, Management and Accounting
TopicCollaboration in agile enterprises
Canadian institutionsConcordia UniversityYorkville University
Fundersnot available
KeywordsServant leadershipAgile software developmentShared leadershipStructural equation modelingLeadership styleServantNeuroleadershipProject management

Abstract

fetched live from OpenAlex

Agile project management has become a dominant paradigm for managing complex projects, emphasizing flexibility, customer collaboration, and self-organizing teams. However, traditional, hierarchical leadership models often conflict with Agile’s core values, creating a significant leadership gap. Servant leadership, a philosophy where the leader’s primary goal is to serve the team, is theorized to be a natural fit. This study empirically investigates the impact of servant leadership on key project outcomes within Agile environments. A quantitative, cross-sectional survey was administered to 68 project management professionals, selected via purposive sampling, who were actively working in Agile teams. Data was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results provide robust support for all hypotheses, indicating that servant leadership has a significant positive effect on team performance (β = 0.45, p < 0.001), team member satisfaction (β = 0.52, p < 0.001), and project success (β = 0.38, p < 0.001). These findings underscore that adopting servant leadership is a strategic lever for enhancing team dynamics and achieving superior project outcomes in Agile settings. The study offers practical implications for organizations and lays the groundwork for future research.

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)0.019
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.582
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0190.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.012
Science and technology studies0.0040.004
Scholarly communication0.0090.004
Open science0.0020.001
Research integrity0.0000.002
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 head0.379
GPT teacher head0.476
Teacher spread0.097 · 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; both teacher heads agree on what is shown here.

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
Published2025
Admission routes1
Has abstractyes

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