The effect of e-WOM on purchase intention in e-commerce in Indonesia through the expansion of the information adoption model
Bibliographic record
Abstract
Technological advancements in Indonesia have been on the rise, and the advent of technology has brought about significant changes in various aspects of society. The integration of technology into human life has greatly enhanced various activities, making information exchange and communication more accessible. As we observe the continuous evolution of technology, it has created new business prospects for companies to harness the power of the internet in providing online shopping services that connect consumers, service providers, and intermediary traders. E-commerce has emerged as a vital platform supporting commercial transactions via the internet. Prior to making a purchase, consumers frequently turn to social media for product reviews, where electronic word of mouth enables users to share their product-buying experiences. This study seeks to investigate the impact of user-generated information through electronic word of mouth on purchase intentions within the e-commerce landscape of Indonesia, utilizing the Information Acceptance Model (IACM). Employing a causal descriptive research approach, this study targeted e-commerce users in Indonesia, with 155 respondents participating. Data collection was carried out through Google Forms, and the collected data was analyzed using SmartPLS 3. Hypothetical testing included t-tests, p-tests, and path coefficient assessments. The study's findings revealed that variables such as information quantity, information needs, attitudes toward information, information usability, and information adoption all play a significant role in influencing purchase intentions, while information quality and information credibility had no discernible impact on purchase intentions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".