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Constructing a Closed-Loop Model of “Online Social Fission–Offline Transaction” for Small and Medium Retail Enterprises

2025· article· W7117126052 on OpenAlexaboutno aff
Li Li

Bibliographic record

VenueFrontiers in Management Science · 2025
Typearticle
Language
FieldBusiness, Management and Accounting
TopicConsumer Retail Behavior Studies
Canadian institutionsnot available
Fundersnot available
KeywordsRenminbiPoint (geometry)ClothingPoint of saleRegression analysisDual (grammatical number)ChinaExchange rateQuarter (Canadian coin)

Abstract

fetched live from OpenAlex

In the face of the dual challenges of rising customer acquisition costs for small and medium retail enterprises globally (508 RMB per person in China and approximately 480 USD per person in the United States) and a social fission conversion rate of less than 5%, this study focuses on the proposition of “zero-cost content-driven growth” and constructs and empirically tests a five-step closed-loop model of “online social fission–offline transaction.” A framework integrating “AR visual content stimulation (S) –lightweight situational inducement (O) –private domain retention and transmission (R)” is proposed. Based on a 94-day longitudinal tracking of 30 multi-category stores in three Chinese cities (Wuhan, Xiangyang, and Lhasa), including 9 jewelry stores, 12 clothing stores, and 9 cosmetics stores, 423,000 micro-behavioral data points were collected (comprising 28,000 AR shares, 336,000 exposures, and 41,500 clicks). Structural equation modeling using Smart-PLS 4.0 and segmented regression analysis using Stata 17 were conducted. The results show that: (1) Zero-cost AR sharing has a significant positive correlation with the conversion rate of “exposure–click” (β=0.011, p<0.001, R2=0.34), with a 1.1% increase in conversion for every additional 100 shares, maintaining stable gains even when marginal costs are zero; (2) The cost of in-store gifts has an inverse U-shaped relationship with the conversion rate of “click–transaction” (inflection point at 41.2 RMB, 95% CI [38.7,43.5]), with the conversion rate peaking at 18.7% in the 35-45 RMB range (dropping to 11.2% below 30 RMB and to 13.9% above 50 RMB); (3) The intensity of private domain operations has a partial mediating effect on the “transaction – repurchase – re-fission” path (indirect effect = 0.39, Boot SE = 0.04, 95% CI [0.31,0.48]), accounting for 42% of the total effect; (4) Cross-regional robustness tests show that the customer acquisition costs for the experimental groups in Wuhan, Xiangyang, and Lhasa are 167 RMB per person, 172 RMB per person, and 168 RMB per person, respectively, a 66.5% average reduction compared to the control group (503 RMB per person), with ROI remaining stable at 1:15.2 to 1:15.7 (ANOVA, F=1.23, p=0.29). This study not only provides small and medium retail enterprises with a lightweight growth solution under a budget of “≤50 RMB per customer” but also expands the theoretical boundaries of the SOR framework in the context of “zero-cost visual content stimulation,” offering empirical evidence for cross-cultural retail digitalization research in China. (Sung, E. C., 2021)

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.004
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.031
GPT teacher head0.272
Teacher spread0.241 · 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 designSimulation or modeling
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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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