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Record W3047587002 · doi:10.5430/jms.v11n3p1

The Influence of Perceived Internal Marketing on Employees’ Organizational Behaviors

2020· article· en· W3047587002 on OpenAlexvenueno aff
Haiyan Kong, Naipeng Bu, Yue Yuan, Kangping Wang, Liyuan Kong, Wang Jin

Bibliographic record

VenueJournal of Management and Strategy · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsOrganizational commitmentInternal marketingStructural equation modelingMarketingJob performancePsychologyBusinessPerceived organizational supportJob satisfactionSocial psychologyComputer science

Abstract

fetched live from OpenAlex

This study seeks to examine the influence of perceived internal marketing on organizational commitment and job performance. Based on extensive literature review, theoretical framework was proposed, with internal marketing as determinant, organizational commitment the mediator, and job performance the outcome. Targeting Generation-Y employees, a total of 568 valid questionnaires were collected nationwide through online and offline survey. Structural equation modeling was used to analyze the data with AMOS software. The findings indicate that perceived internal marketing, contributed positively to organizational commitment and job performance. Apart from the direct effect on job performance, organizational commitment also mediated the relationship between perceived internal marketing and job performance. This study also examined the four dimensions of internal marketing and effects. This paper concludes with the discussion of theoretical and practical implications, as well as limitation and suggestions for future study.

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)0.000
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 score0.063
Threshold uncertainty score0.289

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.012
GPT teacher head0.229
Teacher spread0.217 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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