How Consumer Motivations to Participate in Sharing Economy Differ Across Developed and Developing Countries: A Comparative Study of Türkiye and Canada
Bibliographic record
Abstract
Extreme and fundamental changes in the economy and social life in the 2000s, fueled by technological development, pushed people toward new ways of consumption known as “Sharing Economy” (SE). Consumers’ motivations to participate in SE are still not completely clear because of SE’s relatively short history and hazy boundaries. This study aimed to contribute to closing that gap. This research also looks at how consumers’ motives for SE differ across countries. Data from 678 people (440 in Istanbul, Türkiye, and 238 in Toronto, Canada) were collected and analyzed using partial least squares structural equation modeling (PLS-SEM). The results indicate that economic benefits, modern lifestyle, enjoyment, and ecological sustainability concerns substantially impact consumers’ participation in SE in both Türkiye and Canada. However, consumers in both countries are unaffected by product diversity, ubiquitous availability, sense of belonging, or convenience. In addition, altruism influences Turkish consumers but not Canadians; this could be explained by Türkiye’s being a Middle Eastern country with a feminine cultural structure. Even though Türkiye and Canada are very different in economic, social, cultural, and historical terms, their outcomes are remarkably similar. These identical findings indicate that consumers’ stimulations are similar in participating SE regardless of their country of origin. This paper is unique as it is the first research comparing Turkish and Canadian consumers’ motivations. This study is significant for both literature and practitioners in that it contributes to better understanding consumer incentives in SE.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 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".