MétaCan
Menu
Back to cohort
Record W2225555527 · doi:10.5539/mas.v9n13p176

The Moderating Effect of Religiosity on the Relationship between Trust and Diffusion of Electronic Commerce

2015· article· en· W2225555527 on OpenAlexvenueno aff
Basharat Ali, Nazim Hussain Baluch, Zulkifli Mohamed Udin

Bibliographic record

VenueModern Applied Science · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
Fundersnot available
KeywordsReligiosityIslamBusinessE-commerceProduct (mathematics)Mobile commerceMarketingService (business)ModerationSet (abstract data type)PsychologyLawSocial psychologyPolitical scienceComputer scienceTheology

Abstract

fetched live from OpenAlex

Electronic commerce, an enormous revolution in today’s business world, has genuinely influenced the financial systems, marketplaces, product manufacturing, service and job industries, logistics and consumers’ mind-set. Consumers, posturing different individual characteristics, act differently in showing their trust in e-commerce business mainly due to the nature of the business. As an important predecessor of customers’ readiness to make use of e-commerce, it is essential to maintain consumers’ level of trust. Importantly, religiosity is one of the leading factors in building Muslim consumers’ opinion, both intra-personally and interpersonally, towards new ideas or latest technologies. This is a conceptual study to explore the moderating effect of religiosity on the relationship between trust and diffusion of e-commerce; in particular in Islamic perspective. Exploring the enlightened moderate version of Islamic teachings toward new ideas and innovations including e-commerce, the study aims to highlight applicability of Islamic traits.

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)0.003
metaresearch head score (Gemma)0.018
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 score0.009
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.028
GPT teacher head0.245
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 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

Citations1
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueModern Applied ScienceSame topicIslamic Finance and Banking StudiesFrench-language works237,207