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Record W3096783621 · doi:10.5267/j.msl.2020.9.044

The moderating effect of entrepreneurial marketing in the relationship between business intelligence systems and competitive advantage in Jordanian commercial banks

2020· article· en· W3096783621 on OpenAlexvenueno aff
Emad Ali Kasasbeh, Khaled Khalaf Alzureikat, Shaher Falah Al-Roud, Wasfi Abdul Kareem Alkasassbeh

Bibliographic record

VenueManagement Science Letters · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBig Data and Business Intelligence
Canadian institutionsnot available
Fundersnot available
KeywordsCompetitive advantageBusinessCompetitive intelligenceMarketingIndustrial organizationModerationPoint (geometry)Positive relationshipKnowledge managementComputer sciencePsychology

Abstract

fetched live from OpenAlex

The paper investigates the Competitive Advantage in the Jordanian Commercial Banks (JCB) and its relationship with the Business Intelligence Systems (BIS) through the moderate influence of the Entrepreneurial Markets. Business Intelligence Systems (BIS), can extract a better knowledge for the performance and future gains for the organization. Therefore, it can pin point the impasse which in turn results in a suitable result for the problem in hand. As a result, the organization success will increase due to the efficiency of management performance. This study explores the Intelligent System (IS) relationship with Competitive Advantage (CA) through monitoring the moderating role of Entrepreneurial Marketing (EM). A survey is implemented for the data collection & analysis with 300 questionnaires, and (PLS) is used for the analyses. The results indicate that BIS was definitely related to Competitive Advantage, and EM moderated the connection between BIS and Competitive Advantage.

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)0.004
metaresearch head score (Gemma)0.001
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 score0.025
Threshold uncertainty score0.761

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.048
GPT teacher head0.283
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 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

Citations9
Published2020
Admission routes1
Has abstractyes

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