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Record W2154577533 · doi:10.5539/ijms.v3n4p95

Examining the Impact of Internal Marketing on Organizational Citizenship Behavior

2011· article· en· W2154577533 on OpenAlexvenueno aff
Mehdi Abzari, Tohid Ghujali

Bibliographic record

VenueInternational Journal of Marketing Studies · 2011
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsnot available
Fundersnot available
KeywordsOrganizational citizenship behaviorMarketingPath analysis (statistics)Internal marketingBusinessCitizenshipPopulationStructural equation modelingOrganizational commitmentPsychologySocial psychologySociologyPolitical scienceStatisticsMathematics

Abstract

fetched live from OpenAlex

Internal marketing tries to satisfy needs of employees by considering them as the organization's internal market. According to performed researches satisfaction of the organization's employees needs in the best manner through internal marketing activities could be leaded to forming and improving of employees' extra-role behaviors in addition to improvement of their in-role behaviors. Organizational citizenship behaviors are one of the extra-role behaviors of employees in the organization. Thus, the present survey examines the relationship between internal marketing and organizational citizenship behavior and the impact of internal marketing dimensions on the amount of appearing of employees' citizenship behavior. Population of this research includes employees of Melli Bank in Isfahan city of Iran. after sampling of 220 persons, Questionnaire is used for data collection. Two-hundred fifteen (215) returned questionnaires have been analyzed. Structural equations technique and path analysis (regression model) have been used to test hypotheses by applying of Amos Graphic software. Results showed that all five hypotheses are accepted with 95% confidence.

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)0.006
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
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.088
GPT teacher head0.323
Teacher spread0.235 · 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

Citations17
Published2011
Admission routes1
Has abstractyes

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