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Record W3139151303 · doi:10.5267/j.ijdns.2021.3.001

The effect of transformational leadership climate on employee engagement during digital transformation in Indonesian banking industry

2021· article· en· W3139151303 on OpenAlexvenueno aff
Shinta Winasis, Djumarno Djumarno, Setyo Riyanto, Eny Ariyanto

Bibliographic record

VenueInternational Journal of Data and Network Science · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEmployee Performance and Leadership
Canadian institutionsnot available
Fundersnot available
KeywordsTransformational leadershipBusinessMarketingSustainabilityDigital transformationLeadership stylePublic relationsPolitical scienceEcology

Abstract

fetched live from OpenAlex

The industrial era 4.0 requires companies to radically conduct changes related to technology. This is so that the company can follow the market's everchanging appetite and is essential to maintaining the company's sustainability. The digital transformation that occurs is fundamental, changing existing procedures and arrangements and creating a new business ecosystem. In carrying out a company transformation, it requires full support and commitment from workers. And the process needs to be conducted by competent leaders, in line with the spirit of change. In this study, the relationship between transformational leadership climate and employee engagement was measured. The research was conducted on banking service companies that have made radical changes related to technology for 2 years. The results showed that the climate created by leaders who adopt transformational leadership styles has a positive and significant effect on employee engagement. This research is a preliminary study, and has limitations, including the number of samples and types of companies studied.

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)0.004
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.058
GPT teacher head0.295
Teacher spread0.237 · 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

Citations42
Published2021
Admission routes1
Has abstractyes

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