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Record W3018235202 · doi:10.5539/hes.v10n2p145

Role of University Principals’ Leadership Strategies on Teachers’ and Management Performance: Mediating Role of Support and Rewards in Australia and Pakistan

2020· article· en· W3018235202 on OpenAlexvenueno aff
Urooj Fatima, Junchao Zhang, Daniyal Khan

Bibliographic record

VenueHigher Education Studies · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOrganizational Leadership and Management Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsTransformational leadershipTransactional leadershipLikert scalePsychologyLeadership styleEducational leadershipPublic relationsPedagogyPolitical scienceSocial psychology

Abstract

fetched live from OpenAlex

Leadership strategies of principals in educational institutes is considered an important factor in order to increase support and rewards. Universities in Pakistan suffer from the problems of low support and rewards, due to which most of the teachers and management staff members remain dissatisfied with their principals. This study investigated whether transformational or transactional leadership is better for providing support and rewards to teachers and management staff, along with evaluation of principles’ leadership qualities. For that, 5-point Likert scale questionnaire was utilised to assess the performance of principals by investigating 75 management and 75 teachers of 3 universities of Pakistan. Critical review approach was used for comparison between Pakistan and Australia. It was found that transformational leadership is much better as compared to transactional leadership, because it improves interaction and support, as observed in Australia. However, teachers and management staff members in Pakistan reported dissatisfaction, when asked about leadership role of their principals. They said that their principals never motivate them, support them nor reward them in contrast to leadership approaches in Australia.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.464
Threshold uncertainty score0.490

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.077
GPT teacher head0.297
Teacher spread0.220 · 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.

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

Citations1
Published2020
Admission routes1
Has abstractyes

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