Reported study summary | Four-E retention logic | SEM
Influencer Marketing Lab: Retention Signals on UGC Platforms
This manuscript-aligned page summarizes a UGC influencer study in Vietnam using the Four-E construct logic (CX, CEX, BE, CE) and split-sample validation. The model is estimated with EFA and CFA/SEM (overall n = 565; EFA n = 200; CFA/SEM n = 365), with retention explained primarily through engagement quality.
Overall sample: n = 565 Split: EFA n = 200 | CFA/SEM n = 365 Cross-sectional survey + SEM workflow
Reported
Background
Influencer campaigns on UGC platforms often optimize reach metrics before clarifying how trust, experience, and expectation matching translate into retention. The study addresses this gap by testing a structured Four-E logic and by positioning engagement quality as the central bridge between exposure and return behavior.
RQ1
Which antecedent constructs most strongly contribute to consumer engagement quality (CE) in UGC influencer campaigns?
RQ2
Does CE act as the direct pathway to customer retention (CR) after influencer exposure?
RQ3
How should the reported structural logic be translated into campaign action order without inventing statistical claims?
Construct Development Logic (Paper Order)
CE
Consumer Engagement
CE is framed as interaction depth and quality, not only view count. It functions as the model bridge to retention.
CX
Content Experience
CX captures perceived informational and narrative quality of creator content in the UGC stream.
CEX
Expectation Alignment
CEX reflects whether creator claims and audience expectations match delivered product or service experience.
BE
Brand Equity Transfer
BE captures transfer of creator credibility and symbolic value to the promoted brand context.
Hypotheses Summary
H1 CE → CRH2 CX → CEH3 CEX → CEH4 BE → CEH5a/H5b/H5c mediation via CE
Reported
Method
The method follows a cross-sectional online survey protocol with split-sample construct validation and structural testing. The workflow keeps exploration and confirmation separate, then estimates path and mediation logic using SEM.
Design
Cross-sectional online survey targeting active UGC platform users in Vietnam.
Inclusion Criteria
Respondents had to be active users who regularly consume influencer-led UGC content and can evaluate campaign experience and trust signals.
Collection Period
Reported as one online collection wave in the manuscript screenshots. Exact calendar dates are not visible in the available screenshot source set.
Split-Sample Logic
Exploratory factor analysis on n = 200, followed by CFA and SEM on n = 365, with total sample n = 565.
Modeling Workflow
EFA establishes factor structure, CFA confirms measurement consistency, and SEM tests direct and mediated retention pathways.
Period
Single cross-sectional online wave (exact dates not shown in screenshots).
Total Sample
Overall n = 565.
Split Samples
EFA n = 200; CFA/SEM n = 365.
Model Family
Measurement validation (EFA/CFA) and structural equation modeling.
Reported Outcomes
CE model R2 = 55%; CR model R2 = 70%.
Measurement Note
Construct items for CX, CEX, BE, CE, and CR were developed through the study instrument and validated through split-sample factor procedures before structural path interpretation.
Reported + Inferred
Results
Reported Interpretation
CE model R2 = 55% and CR model R2 = 70% are reported values used consistently on this page.
CE is the direct bridge to CR in the tested structure.
H5a/H5b/H5c are interpreted as mediation routes where CX, CEX, and BE contribute to retention through CE.
Inference is limited to qualitative interpretation and does not claim unavailable coefficient tables.
Annotated model view of reported Four-E paths with qualitative pathway-strength tiers.Open base versionReportedBase reported layout showing directional paths from CX, CEX, and BE to CE and from CE to CR.
Reported + Inferred
Figure 2. Hypothesis-to-Action Mapping
Action map aligning reported path anchors with operational sequencing lanes.
Annotated action map translating hypothesis paths into an implementation sequence.Open base versionReported + InferredBase matrix linking hypothesis labels to preliminary action lanes.
External Creator Cases (Explanatory Only)
The following creators are retained as hybrid evidence cards and are explicitly external explanatory cases, not observations from the survey sample.
Disclosure and comparison framing support expectation alignment first, then drive engagement through question-answer and product clarification interactions.
Creator-brand association supports brand equity transfer, while narrative packaging maintains content experience quality during sponsored integration.
External explanatory case, not in survey sample.
Managerial Translation
Managerial Implications
Managerial recommendations are derived from reported model logic and constrained qualitative inference. The main implication is to build campaign sequencing around engagement quality as the operational bridge to retention.
Engagement-First Optimization
Use CE as the operating KPI for campaign diagnostics: improve interaction depth, response quality, and recurring participation before scaling exposure spend.
Expectation-Alignment and Trust Safeguards
Deploy disclosure discipline, claim verification, and message-product matching to reduce expectation gaps before conversion-focused pushes.
Brand-Equity Transfer Governance
Formalize creator-brand fit criteria and monitor spillover risk so BE transfer improves retention instead of creating short-cycle hype only.
Sequencing Rule
Fit and authenticity first.
Engagement loops second.
Conversion optimization last.
Reported + Inferred
Limitations Note
The visual package combines reported structure with explicit qualitative inference to improve managerial readability. Exact public coefficient tables are not visible in the available screenshot source archive, so this page does not claim numeric path coefficients or p-values.
Source Attribution
Paper screenshot source: archived local screenshot evidence set.
