Consumers’ value co-creation in sharing economy: The role of social support, consumers’ ethical perceptions and relationship quality
Bibliographic record
Abstract
The ancient phenomenon of ‘sharing’ has become mainstream, and transformed the traditional consumer behavior due to proliferation of online sharing economy platforms. Millions of people participate in popular sharing economy platforms (SEPs) such as Airbnb and Uber. Although sharing economy research has gained interest, yet a holistic model that explains the formation of consumer value co-creation intentions on such platforms remains absent. The purpose of this study is to develop a model of the antecedents of consumers value co-creation intentions at SEPs and evaluate it empirically. Building on social support theory, relationship quality theory, value co-creation and marketing ethics literature, we propose a theoretical model that explains the formation of consumers’ value co-creation intentions. Empirical data was collected from n = 342 Generation Y consumers and analyzed using structural equation modeling (SEM). The results reveal that social support influences ethical perceptions, which further influences value co-creation. Ethical perceptions also influence consumers' trust, satisfaction and commitment with the SEP. However, trust and commitment do not influence value co-creation intentions. Our study contributes to the literature on sharing economy by providing a holistic model of the antecedents of consumers’ value co-creation intentions. We also offer important insights for SEP managers.
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 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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".