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Record W1566752785 · doi:10.5539/ass.v11n18p123

The Influence of Transformational Leadership Style on ICT Adoption in the Nigerian Construction Industry

2015· article· en· W1566752785 on OpenAlexvenueno aff
Abdullahi Y. Waziri, Kherun Nita Ali, Ghali U. Aliagha

Bibliographic record

VenueAsian Social Science · 2015
Typearticle
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsTransformational leadershipStructural equation modelingLeadership styleBusinessCompetitive advantageKnowledge managementPath analysis (statistics)Process (computing)Transactional leadershipInformation and Communications TechnologyPublic relationsMarketingPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Information and communication technology provides construction organizations with new opportunities for improving the process of collaboration, managerial functions and maintenance of competitive advantage. Its adoption has however been faced with problem of incompetent organizational leadership. The purpose of this study, therefore, is to examine the influence of transformational leadership style on IT adoption in construction organizations. Structural equation modelling analytical approach was used to test the presence of a positive and direct relationship between transformational leadership style and IT adoption in organizations. Results strongly supported the hypothesis of a positive and direct relationship with a path coefficient of .79. Part of the recommendation of the study was for managers to diligently exhibit transformational leadership behaviours during technological change in their organizations by paying attention to the needs of their employees, providing ways and reasons for employees to comprehend and analyse problems in different ways, adequate employee motivation and building confidence and trust with employees.

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)0.007
metaresearch head score (Gemma)0.001
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: Empirical
Teacher disagreement score0.790
Threshold uncertainty score0.617

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0010.002
Scholarly communication0.0000.001
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 head0.154
GPT teacher head0.368
Teacher spread0.213 · 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

Citations14
Published2015
Admission routes1
Has abstractyes

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