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Record W4417001209 · doi:10.54692/amr.2024.114

Customer Trust and Perceived Knowledge leads to Purchase Intention Through the Lens of Attitude Towards Brand

2024· article· W4417001209 on OpenAlexaff

Bibliographic record

VenueAnnals of Management Review · 2024
Typearticle
Language
FieldBusiness, Management and Accounting
TopicConsumer Behavior and Marketing Influence
Canadian institutionsCanadian Institute for International Peace and Security
Fundersnot available
KeywordsMediationRelation (database)Customer knowledgePositive attitudeThrough-the-lens meteringPositive relationshipQuality (philosophy)

Abstract

fetched live from OpenAlex

The purpose of current study is to examine the mediating role of attitude towards brand for relationship of customer trust and perceived knowledge with purchase intention. In this regards, current study proposes three hypotheses of direct relationship, two hypotheses of mediation effects. Current research used the convenience sampling technique from 220 customers of branded cars. Results elucidates that perceived knowledge is better predictor of attitude towards brand as compare to customer trust. Hence, automobile companies deliver the apprise and operative knowledge to the customers that ultimately result in attitude towards brand. Likewise, attitude towards brand act as mediating variable for relation of customer trust and perceived knowledge with purchase intention. Results shows that all three hypotheses of direct relation are acknowledged. Results also indicates that two hypotheses of mediation are also accepted.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.090
GPT teacher head0.366
Teacher spread0.276 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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