MétaCan
Menu
Back to cohort
Record W2969972247 · doi:10.4018/jeco.2019100103

The Influence of Online Reviews and Brand Trust and Customer Equity

2019· article· en· W2969972247 on OpenAlexaff
Glenn Asano, V. Cheng, Joan Helen Rhodes, Peter Lok

Bibliographic record

VenueJournal of Electronic Commerce in Organizations · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Marketing and Social Media
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsBrand equityBusinessEquity (law)MarketingCustomer equitySample (material)The InternetBrand awarenessAdvertisingCustomer retentionComputer scienceService qualityService (business)

Abstract

fetched live from OpenAlex

Using a restricted probability sample of 269 participants, the key findings were: (a) that negative online reviews have a higher negative impact on customer equity than positive online reviews; this is a significant finding because previous findings were mainly short-term focus (on willingness to purchase) and long-term measurement such as ‘customer equity' could provide management with new knowledge on negative online reviews; (b) ‘brand equity' driver has the greatest impact on customer equity as compared to the other two drivers (‘value' and ‘relationship'). This is a significant new finding which could assist management in redirecting its resources; (c) brand trust was unexpectedly found to have a negative relationship with the drivers of customer equity. This could be that long-term outcome as brand trust may prove to be difficult to measure in a cross-sectional study. Furthermore, online reviews have no significant relationship with ‘brand trust.' This may be that brand trust may be more important in short-term outcomes with online reviews.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.276
Threshold uncertainty score0.376

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.011
GPT teacher head0.320
Teacher spread0.309 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Electronic Commerce in OrganizationsSame topicDigital Marketing and Social MediaFrench-language works237,207