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Record W4412870396 · doi:10.59075/hjsnvc46

Impact of Green Transformational Leadership and Green Transactional Leadership on Green Organizational Citizenship Behaviour by Mediating Role of Green Self-Efficacy

2025· article· en· W4412870396 on OpenAlexaff
Saeed Ahmad, Sabah Younus

Bibliographic record

Venue˜The œcritical review of social sciences studies · 2025
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsStudent Biotechnology Network
Fundersnot available
KeywordsTransformational leadershipTransactional leadershipOrganizational citizenship behaviorPsychologyManagementPolitical scienceSocial psychologyOrganizational commitmentEconomics

Abstract

fetched live from OpenAlex

The aim of this research paper is to apply the results and findings of the examination carried out by studying the impact of green transformational leadership and green transactional leadership on green organizational citizenship behaviour by the mediating role of green self-efficacy in the food and beverage sector of Pakistan. Research data was gathered by using a survey method consisting of a structured questionnaire which was given to the respondents comprising of managers and employees of various organizations. The research data gathered from convenience sampling was examined by making use of the software of smart PLS. The findings of the study concluded that both green styles of leadership, i.e. green transformational leadership and green transactional leadership positively impacted green organizational citizenship behaviour by the mediating role of green self-efficacy. The research also concluded that green transformational leadership has a more positive impact on green organizational citizenship behaviour as compared to green transactional leadership. In summary, these findings can assist managers and leaders in developing and implementing strategies which are effective to improve the awareness, behaviour and attitude of the employees in contributing towards environmental sustainability in the workplace.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.632
Threshold uncertainty score0.599

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.075
GPT teacher head0.343
Teacher spread0.267 · 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
Published2025
Admission routes1
Has abstractyes

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