Evaluation of the Consumers’ Trust Effect on Viral Marketing Acceptance Based on the Technology Acceptance Model
Bibliographic record
Abstract
Integrating marketing principles with IT suggests new developed models for the marketing world such as viral marketing (VM). On the other hand, lots of prior studies have theoretically articulated and empirically examined the fact that trust is the essence of any human interaction especially for the online activities. Grounded on the viral marketing and trust literature, this descriptive and applied study, was targeted to integrate trust and viral marketing through the technology acceptance model (TAM). The purpose of this study is to evaluate the consumers’ trust effect on viral marketing acceptance. Respondents to the questionnaire are 69 experts responsible for the selection and purchase of the medical equipment in Isfahan University of Medical Sciences, Iran. This study is originated from the perspective of the graduated consumers whose behaviors look different from the average. Applying a census-based methodology and statistical analysis, especially path analysis, the study showed that trust plays an important role in the attitude toward engaging in VM and in the intention of the consumers to engage and finally to actual use of VM by the experts. This study verified TAM too. It was followed by some managerial implications for the companies in order to design their viral marketing campaigns to be more successful. Finally, considering the findings and the limitations of this study, some academic suggestions for further researches have been made.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".