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Record W4308864462 · doi:10.5267/j.msl.2022.9.003

Assessing the influence of transformational leadership on competitive advantage through important innovations and quality results: Case of railway industry

2022· article· en· W4308864462 on OpenAlexvenueno aff
Tryson Yangailo

Bibliographic record

VenueManagement Science Letters · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicQuality and Supply Management
Canadian institutionsnot available
Fundersnot available
KeywordsTransformational leadershipCompetitive advantageCost leadershipQuality (philosophy)BusinessLeadership styleOrder (exchange)MarketingSample (material)Strategic leadershipKnowledge managementPublic relationsStrategic planningPolitical scienceComputer science

Abstract

fetched live from OpenAlex

The importance of quality and innovation in today’ 21 st century cannot be over emphasised, therefore organisations require leadership styles that can embrace both innovation and quality in order to confront today’s dynamic competitive fierce environment. Sometimes organisations fail to attain or maintain their competitiveness because of limited understanding regarding these strategic variables. There is little research that has attempted to analyse the direct and indirect-relationships among these strategic variables. Therefore this study attempted to bridge the gap in literature by analysing how important-innovations and quality-results as TQM practices influence the association between transformational leadership-style and competitive advantage using sample data collected from TAZARA employees in management using Jamovi. The study shows that transformational leadership style, quality-results and important innovations have significant positive effects on competitive advantage. The results also show that important-innovations and quality-results both individually and collectively partially mediate the relationship between transformational-leadership and competitive- advantage. Managers and advocates of transformational leadership style are emphasised to be aware and cautious of quality results and important innovations at all-time especially in today’s dynamic environment.

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)0.004
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.961
Threshold uncertainty score0.730

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.002
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 head0.080
GPT teacher head0.326
Teacher spread0.247 · 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 designTheoretical or conceptual
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

Citations14
Published2022
Admission routes1
Has abstractyes

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