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The Impact of Testimonials on Purchase Intentions in a Mock E-commerce Web Site

2012· article· en· W2018219768 on OpenAlexaff
Avishag Spillinger, Avi Parush

Bibliographic record

VenueJournal of theoretical and applied electronic commerce research · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Marketing and Social Media
Canadian institutionsCarleton University
Fundersnot available
KeywordsPurchasingE-commerceProduct (mathematics)BusinessProcess (computing)AdvertisingThe InternetMarketingComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

Purchasing through virtual market is different from the process that takes place in the traditional market. In this market, things are less tangible and more threatening. Therefore, trust becomes crucial and it is established in a different way. This study examined the effect of testimonials on the level of trust in e-commerce. It also examined the impact of product touch level and price on the effect of testimonials. Two mock e-commerce sites were used, one with testimonials and the other without. The experimental approach simulated a complete shopping process with students whose age was between 21 and 30, on a fully functional website, with subjective and objective behavioral measures. The subjective measures were based on two questions that participants were asked along the experiment. The objective measures consisted of metrics such as navigation patterns in the site, number of products in the shopping cart, and readiness to enter credit card number. The presence of testimonials had a greater impact on users with little internet-based shopping experience, was associated with increased trust, and was more significantly pronounced for price than for product touch level. In addition, the results showed that a decreased level of trust was associated with higher prices. The impact of testimonials is accounted for in terms of history sharing and building an online community.

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)0.014
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.696

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.035
GPT teacher head0.410
Teacher spread0.375 · 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 designTheoretical or conceptual
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

Citations11
Published2012
Admission routes1
Has abstractyes

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