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Record W4200548367 · doi:10.5430/jbar.v11n1p1

The Impact of Social Responsibility on the Market Value for Services Corporations, Service Type as a Marketing Variable

2021· article· en· W4200548367 on OpenAlexvenueno aff
Ali Mustafa Magablih

Bibliographic record

VenueJournal of Business Administration Research · 2021
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessSocial responsibilityMarketingCorporate social responsibilityVariablesRegression analysisValue (mathematics)Test (biology)Descriptive statisticsOrder (exchange)Service (business)Product typeStock exchangeVariable (mathematics)Product (mathematics)AccountingFinancePublic relationsStatistics

Abstract

fetched live from OpenAlex

The study aims to know the impact of social responsibility as a cost and also to show the market value of the Jordanian services corporations listed on the Amman Stock Exchange. “Services type” was used as a variable for the relationship rate in this study. The descriptive approach was used and applied to the data of 37 companies during the period from 2012-2019. The researcher also used statistical methods such as the arithmetic mean and standard deviation to describe the study data, and the test of linear regression and correlation analysis, in order to test the study hypotheses. Among the most important results that have been reached, there is an impact of social responsibility as costs and the disclosure of the market value of services companies. The study also showed a modified effect of services type on the relationship between social responsibility disclosure and market value.Based on the preceding, the study recommended expanding the social responsibility disclosure, which helps the company build a strong name as a desirable institution, which enhances the image of the company and the name of the product in the services market and among customers.

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)0.007
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.814
Threshold uncertainty score0.790

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.064
GPT teacher head0.381
Teacher spread0.317 · 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
Published2021
Admission routes1
Has abstractyes

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