Digital marketing, digital orientation, marketing capability, and information technology capability on marketing performance of Indonesian SMEs
Bibliographic record
Abstract
This research analyzes the effect of digital marketing, digital orientation, marketing capabilities, and information technology capabilities on marketing performance of Indonesian SMEs. The methods are quantitative methods and data analysis techniques using AMOS 23 software based on Structural Equation Modeling (SEM). The method of selecting the sample uses the purposive sampling methods. The study uses data based on questionnaires to 338 SMEs respondents in Madura, Indonesia. The results of data analysis show that the digital marketing had a positive and significant effect on the marketing performance, the digital orientation had a positive and significant effect on the marketing performance, marketing capabilities had a positive and significant effect on the marketing performance and information technology capabilities had a positive and significant effect on the marketing performance. The theoretical implication of the research is that it finds additional knowledge of marketing strategy in the field of small medium enterprise. Then, marketing management by integrating marketing capabilities and information technology to optimize digital marketing on SMEs 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 | 0.001 | 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) | 0.003 | 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".