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Record W2889225253 · doi:10.14738/abr.66.4167

The Relationship of Servant Leadership in the Classroom and Student Perceptions of University Quality of Instruction

2018· article· en· W2889225253 on OpenAlexaboutno aff
Betsy Sparks

Bibliographic record

VenueArchives of Business Research · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement Theory and Practice
Canadian institutionsnot available
Fundersnot available
KeywordsServant leadershipPerceptionPsychologyQuality (philosophy)Structural equation modelingServantManagementMedical educationPedagogyTransactional leadershipSocial psychologyComputer scienceMedicine

Abstract

fetched live from OpenAlex

In this study, we examined students' perceptions of certain servant leader behaviors exhibited by faculty and the associated perception of effective university instruction. Five servant leadership dimensions were considered: altruistic calling, emotional healing, wisdom, persuasive mapping, and organizational stewardship. Students in the United States who were in undergraduate or graduate programs or had completed a program within the last month were invited to complete the survey instrument based on the Servant Leadership Questionnaire developed by Barbuto and Wheeler [1] and the University of Winnipeg’s Quality of University Instruction Questionnaire (UW-QUIQ) developed by Clark [4]. We used a structural equation model and found there was a positive relationship with professors exhibiting servant leadership dimensions and quality of instruction. Business professors in this study exhibited lower servant-leadership attributes in some areas and lower levels of effective university instruction than their counterparts, mandating the need for change. We suggest that these results provide insight into improving business school instruction.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.260
GPT teacher head0.374
Teacher spread0.114 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations9
Published2018
Admission routes1
Has abstractyes

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