In-Depth Study of the Strategic Interaction between Electronic Commerce, Innovation, and Attainment of Competitive Advantage in the Context of SMEs
Bibliographic record
Abstract
E-commerce has experienced significant growth in recent years. The advancement of information and communication technology has enabled businesses, including Small, and Medium Enterprises (SMEs), to conduct their operations online more efficiently and effectively. This research aims to analyze the influence of e-commerce and innovation on the competitive advantage of SMEs. This study employs a quantitative approach using Structural Equation Modeling (SEM) method supported by Partial Least Squares (PLS). The quantitative approach was chosen to allow for the quantitative and objective measurement of the variables involved. An online Likert scale survey was conducted among SMEs in Semarang City from September to October 2023, resulting in 152 initial respondents. After excluding 11 respondents who did not meet the study's requirements, the final sample size was 141 SMEs. The results of the study indicate that the utilization of e-commerce and innovation significantly influences the competitive advantage of SMEs in Semarang City. Through e-commerce, SMEs can reach a wider market, optimize operations, and strengthen their brand image. MSMEs in Semarang City should focus on developing responsive and engaging e-commerce platforms, enhancing targeted online marketing and promotion efforts, investing in research and development of new products and services, adopting new technologies to improve operational efficiency and product quality, as well as fostering mutually beneficial partnerships to expand market reach and resources.
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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 | 0.002 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".