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Record W3082508326 · doi:10.5267/j.msl.2020.8.035

Gender perspectives in individual and organizational factors: A study of millennial employees in creative industries

2020· article· en· W3082508326 on OpenAlexvenueno aff
Widya Parimita, Dedi Purwana, Usep Suhud

Bibliographic record

VenueManagement Science Letters · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessCreative industriesPsychologyMarketingOrganizational changePublic relationsPolitical science

Abstract

fetched live from OpenAlex

This study aims to analyze individual and organizational factors from the perspective of gender amongst millennial employees in creative industries. The populations in this study are employees in the creative industries with sample limited to employee from millennial generation category. Based on these considerations, the sample taken in this study were 202 respondents. The research proves that exogenous variables (ethical behavior of leaders and internal communication) have positive and significant effects for endogenous variables (psychological capital, work attachment, meaning of work) on employees (millennial generation) of the creative industry sector. The results also showed that psychological capital was only determined by the ethical behavior of the leadership, whereas psychological capital was the most dominant variable in determining work engagement. Meanwhile, internal communication provides the most dominant influence on the meaning of work. This research is able to contribute by generating novelty research in the form of literature studies using gender roles. From the gender point of view, millennial employees who are female have the highest value for internal communication, work engagement and meaning of work. Meanwhile, the highest value of psychological capital is dominated by male respondents. Ethical behavior of leaders viewed from a gender perspective has the same value between male and female respondents.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score0.293

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.155
GPT teacher head0.310
Teacher spread0.154 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

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