A study on the effects of innovation marketing process for Indonesian SMEs’ in food and beverage sector
Bibliographic record
Abstract
SMEs in the food and beverage sector should be a responsive business for reaching consumers more effectively, expanding the market, and reducing consumer transaction costs. Still, many SMEs’ in the food and beverage sector are hesitant to use social media due to lack of some aspects in their business platforms. The study aims to give an overview model of the innovation marketing process by emphasizing the characteristics of SMEs’, which is simultaneously associated with marketing mix. The proposed model combined the TOE model and characteristics with a marketing mix using SEM on 198 SMEs in the Indonesian food and beverage sector. The findings proved that each technological, environmental, organizational, and characteristics are positively related to each of the people and processes. The management team roles in innovative solutions and contingencies of a complex environment gave the highest positive effects to technology and environment. However, technology does not always facilitate the work process, the quality of result, and process consistency for more efficiency. There are highlight points, i.e. the clear visibility of employee appraisals process for bridging the gap of competencies; using perceived usefulness, perceived ease of use, and perceived trust for generating business ideas, and attracting new customers.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".