Brand capabilities in digital marketing: The key to enhancing marketing performance
Bibliographic record
Abstract
This study presents a structural equation model to describe the interrelationships between Digital Marketing, Brand Articulating Capability, Brand Resonance Capability, and Marketing Performance. It employs a cross-sectional study to examine the relationships among those research variables. This study utilized a survey methodology, which involved gathering data from 292 participants who were representatives of small and medium enterprises (SMEs) in South Sulawesi, Indonesia. Out of the 292 questionnaires distributed, 270 were considered appropriate for analysis. Four hypotheses were formulated and examined by employing structural equation modeling (SEM) analysis, which revealed several noteworthy findings. The results suggest that SMEs with a stronger focus on digital marketing are more likely to enhance their capacity to articulate their brand, leading to improved marketing effectiveness. In contrast, those with a higher level of Digital Marketing tend to acquire brand resonance capabilities. The findings of this study highlight the significance of the Brand Articulating Capability and Brand Resonance factors in improving the impact of Digital Marketing on Marketing Performance, hence facilitating the growth of SMEs.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".