The impact of digital marketing and brand articulating capability for enhancing marketing capability
Bibliographic record
Abstract
Small and Medium Enterprises (SMEs) have a central role in the Indonesian economy. SMEs are a driver of the Indonesian economy and non-oil exports and have a significant role in absorbing labor. The SME sector in Indonesia generally has several obstacles, one of which is marketing constraints. The ability to build and communicate brands to customers tends to be low, so it has not been able to bind customers and affect the marketing performance of SMEs. This ability during the pandemic has worsened, as can be seen from the deteriorating marketing performance of SMEs. This study attempts to fill the research gap between digital marketing and marketing performance. This study offers the concept of Brand Articulating Capability to bridge the gap between Digital Marketing in increasing Marketing Performance. Three hypotheses were developed and tested in a sample frame of 230 SMEs in South Sulawesi, Indonesia. The analysis was carried out using Structural Equation Modelling to test the research. The study's findings support the model using the following variables: Digital Marketing has a significant effect on Marketing Performance, Digital Marketing has a significant impact on Brand Articulating Capability, and the Brand Articulating Capability variable has a mediating and strategic role in improving marketing performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".