Motivational factors and the impact of e-commerce adoption on business performance: Evidence from traditional drink SMEs in Indonesia
Bibliographic record
Abstract
This study scrutinizes the motivational factors of e-commerce adoption using the Integrated Model of E-commerce Adoption in SMEs (IMAES) and analyzes its impact on business performance. This is quantitative research. Survey technique used to collect data. The Special Region of Yogyakarta, Indonesia, was chosen purposively as the center of traditional drinks, and 330 SMEs of traditional drinks in all districts/cities were proportionally taken as samples. Structural Equation Modeling was used as the data analysis method with PLS tools. This study shows that buyer, competitor behavior, relative advantage, organizational readiness, perceived ease, benefit observability, compatibility, ICT organizational, and innovativeness level significantly and positively influence e-commerce adoption. Risk perception and complexity have a significant and negative effect on e-commerce adoption. E-commerce adoption has a significant and positive impact on business performance, operational performance, financial performance, and marketing performance. Stakeholder collaboration is required to increase e-commerce adoption.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".