Customer citizenship behavior and customer perceived value in China: the mediating role of value co-creation experience
Bibliographic record
Abstract
Abstract The accelerating advancement of the digital economy has shortened the distance between customers and organizations, prompting customers to participate in value creation process. The role of customers has changed from passive receiver to value co-creator. This study explores the impact of customer citizenship behavior (CCB) on co-creation experience and then on customer perceived value (CPV) based on value co-creation theory under the service-dominant logic (SDL). We introduce co-creation experience as a mediating variable and propose a mediated model of CCB on CPV. We then conduct two rounds of data collection with a total of 642 matched questionnaires in virtual brand communities to test the proposed model. The findings show that different dimensions of CCB exert distinct influences on different dimensions of value co-creation experience. Moreover, different dimensions of co-creation experiences have different impacts on CPV. The findings also show that co-creation experience mediates the relationship between CCB and CPV. This study offers theoretical and practical insights for organizations to enhance customers’ roles in value co-creation process.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".