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Record W4205243499 · doi:10.1145/3494583.3494594

Research on the impact of promotional activities and relationship commitments on customer participation from the perspective of consumers

2021· article· en· W4205243499 on OpenAlexaff
Shu-Che Chi, Cheng-Ying Chang, Cheng-Yi Chang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBusinessPromotion (chess)MarketingEmpirical researchPerspective (graphical)Sales promotionIncentiveDatabase transactionProduct (mathematics)Structural equation modelingResearch ObjectTransaction costEconomicsMicroeconomicsFinance

Abstract

fetched live from OpenAlex

This research begins from the viewpoint of the safeguard measures in transaction cost theory, exploring from the perspective of consumers how retailers should choose suitable promotional activities to promote incentive compatibility between retailers and consumers, enhance bilateral relationship commitments, and thereby elevate customer participation for the benefit of a win-win result. This research categorizes retailer's promotion activities into monetary promotion and non-monetary promotion. The empirical research results applied to the retail industry provide concrete contributions in both theory and practice.This study opts pet product sales industry as the research empirical object and takes pet product retailers and the consumers as the units of analysis. The research uses the structural equation model for the verification of the research hypothesis. The study results found that customer participation is enhanced when both monetary and non-monetary promotional activities are used. Also, relationship commitment has a positive and significant impact on customer participation. There is partial intermediary effect on relationship promises when monetary promotion activities are used to influence customer participation. Non-monetary promotional activities have no intermediary effect on customer participation and relationship promises.

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)0.009
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 score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.192
GPT teacher head0.409
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
Published2021
Admission routes1
Has abstractyes

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