Building marketing performance through digital marketing and database-based networking capability in Indonesian SMEs
Bibliographic record
Abstract
This study aims to bridge the theoretical gap in the relationship of business ability to build relationships with marketing performance in the digital economy era, marked by the application of IT in its business activities. The progress of the digital economy is currently increasing in response to the Covid-19 pandemic. Data collection was conducted by distributing questionnaires to the owners and managers of SMEs in the city of Medan. Questionnaires were distributed to 175 respondents using non-probability sampling and purposive sampling techniques. Testing models and hypotheses using Structural Equation Model (SEM) was conducted with AMOS software version 23. The results show that SMEs are more likely to build and develop database-based networks to improve marketing performance. It is empirically proven that digital marketing, strategy quality, and relationship-building skills improve SMEs' marketing performance in Medan City. Theoretically, this study contributes in highlighting the strategy quality and relational database as moderating variables in the relationship between relational capability and marketing performance in digital business.
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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 | 0.002 |
| 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) | 0.002 | 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".