Is marketing digitization important?
Bibliographic record
Abstract
Small industries tend to achieve marketing performance harder than their bigger counterparts, constrained by limited resources when compared to large businesses, especially during the Covid-19 pandemic. These constraints seem to still be relatively difficult to find a way out and result in low marketing performance including small industries in Denpasar. The purpose of the study is to explain the influence of entrepreneurial personality on marketing performance and to investigate the role of digitalization of marketing as a moderation of the influence of entrepreneurial personality on the marketing performance of small industries in Denpasar. The subject of the study was a small industry in Denpasar, represented by the owner / manager as a source. The sample size was set at 150 respondents. Analysis techniques use Moderated Regression Analysis (MRA). The results found that entrepreneurial personality has a significant influence on marketing performance, and digitalization of marketing acts as a quasi-moderation of the influence of entrepreneurial personality on the marketing performance. Small industries should be more active in exploring paid online media since it has a wider reach, such as existing market places or utilizing government-provided facilities, be careful about setting production targets and setting targets for special forces on marketing 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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.002 |
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".