The Influence of User Generated Content on Purchase Intention of Automobiles in Sri Lanka
Bibliographic record
Abstract
The purpose of this study to identify the influence of user generated content on purchase intention of automobiles in Sri Lanka. The said study is based on the theory of Uses and Gratification and supportive findings. This study has been addressed the survey type research method and structured questionnaire was used to collect data and utilize sample frame of automobile followers on Facebook. Measuring the influence of user generated content on purchase intention through developed hypotheses. Researchers have used analysis technique of descriptive analysis, regression, and frequency test where statistical package for social science (SPSS) was used as the main analytical software. The results of the study discuss the main eight element affect the purchase intention of automobiles, but the two elements not strongly affect the purchase intention which were namely, homophily and trust. These two elements have weak relationship with the purchase intention. Mainly consumer resonance mediating the user generated characteristics to purchase intention of automobiles. Hence this study has been significantly contributing to the existing knowledge explaining the need, motivation (users and gratification) - consumer resonance-intention linkage in customer behaviour.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".