SMEs and Electronic Commerce: The Case of Istanbul
Bibliographic record
Abstract
Electronic commerce has been increasingly popular and has become a must tool for the enterprises. Since the Internet had started to be used commonly by all segments of the society, electronic commerce gained a crucial significance as a practical way for people to meet their needs. The main purpose of this study is to examine the perception of and the expectations from electronic commerce by Small- and medium-sized enterprises (SMEs) and to evaluate the effect of the obstacles to e-commerce in the innovation processes and performance. Within this study, the impact of the innovation processes and innovation data sources in the field of electronic commerce on the performance of the enterprises is analyzed. A survey comprising of 50 questions was conducted by the participation of middle and higher level managers of the SMEs in Istanbul. In total, 277 surveys were examined. Reliability and validity of these surveys were checked via SPSS-17 and evaluated by using the methods of factor analysis, correlation analysis, and regression analysis. As a result, it has been confirmed that factors of innovation processes and innovation data sources have a meaningful relation to the performance of companies. Therefore, the importance and necessity of investing in innovation processes and data sources for increasing the company performance are verified by this study.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".