E-commerce Industry’s R&D, Market Operating and Performance: Based on Worldwide Listed Companies’ Empirical Analysis
Bibliographic record
Abstract
Based on the characteristics of the e-commerce industry, this paper proposes the conception of operating intensity and explores the relationships between R&D intensity, operating intensity and firms’ performance. Multiple regression analysis approach is adopted based on the unbalanced panel dataset of global e-commerce listed companies in 49 countries in 2001-2015. Our findings suggest that suitable R&D intensity contributes positively to e-commerce firms’ performance, and with a lag. Operating intensity contributes an inverted U shape to e-commerce firms’ performance. We also find that interaction between R&D and operating intensity’s effects on firms’ performance is positively significant in global samples. In Group 7 sample, R&D intensity contributes positively to e-commerce firms’ performance, but BRICS sample’s is negative. Operating intensity contributes positively to firms’ performance both in Group 7 sample and BRICS sample. Marginal utility of operating intensity on firms’ performance in Group 7 sample is bigger than in BRICS. The results imply that R&D investment of e-commerce listed companies in BRIC countries has not converted to benefit, however it drags down the firms’ performance.
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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 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".