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Record W4410596693 · doi:10.22214/ijraset.2025.71249

The Impact of Transformational Leadership on Employee Motivation: A Study of Indian Private Sector Organizations

2025· article· en· W4410596693 on OpenAlexaff
Lokendra Chouksey

Bibliographic record

VenueInternational Journal for Research in Applied Science and Engineering Technology · 2025
Typearticle
Languageen
FieldDecision Sciences
TopicLeadership, Behavior, and Decision-Making Studies
Canadian institutionsAlpha Technologies (Canada)
Fundersnot available
KeywordsTransformational leadershipBusinessPrivate sectorBusiness administrationManagementPublic relationsPolitical scienceEconomics

Abstract

fetched live from OpenAlex

Transformational leadership has emerged as a critical factor in enhancing employee motivation in modern organizations. This study explores the influence of transformational leadership behaviours—such as inspirational motivation, intellectual stimulation, idealized influence, and individualized consideration—on employee motivation levels in the Indian private sector. Using a quantitative research design, primary data was collected through a structured questionnaire distributed to 150 employees across various private sector organizations. The findings reveal a significant positive correlation between transformational leadership traits and employee motivation. The study offers valuable insights for human resource professionals and business leaders aiming to foster a motivated and productive workforce through effective leadership styles.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.021
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0020.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.251
GPT teacher head0.492
Teacher spread0.240 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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