MétaCan
Menu
Back to cohort
Record W4386237923 · doi:10.4236/ti.2023.143010

The Mediating Role of Risk, Credibility, and Convenience in the Relationship between Initial Trust and Purchase Intention in Online Shopping

2023· article· en· W4386237923 on OpenAlexvenueno aff
Vijayakumaran Kathiarayan

Bibliographic record

VenueTechnology and Investment · 2023
Typearticle
Languageen
FieldDecision Sciences
TopicTechnology Adoption and User Behaviour
Canadian institutionsnot available
Fundersnot available
KeywordsCredibilityMediationRisk perceptionBusinessSource credibilityComputer-assisted web interviewingE-commerceMarketingAdvertisingPerceptionPsychologyComputer sciencePolitical science

Abstract

fetched live from OpenAlex

The concept of e-commerce, or online shopping, has witnessed significant growth in recent years, driven by factors such as convenience and cost-effec-tiveness. However, concerns related to initial trust, perceived risks, credibility of online platforms, and convenience have hindered some consumers from making purchases online. This study aims to investigate the relationship between initial trust and purchase intention among online shoppers, with a focus on the mediating factors of risk, credibility, and convenience. The research will be conducted in the Klang Valley region of Malaysia, targeting trainees who are active online shoppers. Data will be collected through a structured questionnaire, and statistical methods such as regression analysis and mediation analysis will be employed for data analysis. The study aims to provide valuable insights into the factors that shape initial trust and their impact on purchase intention, contributing to the existing body of knowledge in the field of e-commerce. The findings of this research will have practical implications for online retailers, enabling them to develop strategies to enhance trust, reduce perceived risk, establish credibility, and improve convenience, ultimately driving purchase intention among online shoppers.

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)0.003
metaresearch head score (Gemma)0.007
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 score0.221
Threshold uncertainty score0.792

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
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 head0.171
GPT teacher head0.408
Teacher spread0.237 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueTechnology and InvestmentSame topicTechnology Adoption and User BehaviourFrench-language works237,207