The influence of social commerce information sharing on purchase intention and perceived risk: the mediating role of customer relationship quality and the moderating role of online reviews in the Turkish market
Bibliographic record
Abstract
Social commerce is an effective instrument for enterprises aiming to expand their customer base and enhance revenues. By mastering the implementation of social media platforms (SMPs) and surmounting the accompanying hurdles, brands may achieve significant achievements in social commerce. The article investigates the impact of social commerce information sharing (SCIN) on purchase intention and perceived risk in Turkey. This examines the influence of SCIN on customer relationship quality (CRQ) dimensions, including brand trust, commitment, and satisfaction. The study precisely intends to investigate the mediating of CRQ dimensions in the relationship between SCIN and purchase intention. The study also examines the mediator role of perceived risk in the relationship between SCIN and purchase intention. Also, the study examines how online reviews moderate the relationship between SCIN and customer outcomes such as purchase intention and perceived risk. The current study employs a sample of 314 participants from Turkey to explore the relationship between SCIN, brand trust, commitment, satisfaction, purchase intention, and perceived risk. The proposed conceptual model is tested using the Structural Equation Modeling-AMOS statistical approach. The results show that SCIN strongly predicts perceived risk, purchase intention, and CRQ dimensions, such as brand trust, commitment, and satisfaction. Furthermore, the study reveals that perceived risk does not directly mediate the relationship between SCIN and purchase intention. Instead, it confirms that purchase intention is a significant consequence of CRQ dimensions and perceived risk. The results also indicate that online reviews do not moderate the relationship between SCIN and customer outcomes, such as perceived risk and purchase intention. In summary, this study underscores the pivotal role of SCIN in influencing the decision-making process of Turkish customers, particularly in the context of making purchases. The findings carry significant practical implications for marketers of SMPs aiming to influence Turkish consumers, providing valuable insights to enhance their strategy in the Turkish market.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".