SWOT Analysis of Web 2.0 Entrepreneur Strategy in Enhancing Competitive Advantage: A Case Study of IACE Food Start-up Business in Surakarta
Bibliographic record
Abstract
Web 2.0 technology that supports social media is now one type of media used in marketing strategies for entrepreneurs that is considered effective because of its worldwide impact. The wide range of businesses offered through social media today are not always well-known brands but also start-up endeavors. Analysis of data from social media can provide information that is very useful for a business, but maybe it is a beginner’s business able to compete in the market with this marketing strategy? This study provides evidence of how effective SWOT analysis of social media strategy can improve a business’ competitive advantage. The research data obtained are qualitative data relating to the use of social media (Instagram and website) from the results of interviews with employees of start-up businesses engaged in the field of food and which are members of food start-up of the Indonesia Agency for Creative Economy (IACE) that is well-known as BEKRAF (Badan Ekonomi Kreatif) in Surakarta. The research was carried out by conducting SWOT analysis of social-media usage data and then through strategy formulation, separating the strategies referring to cost advantage and differentiation advantage. The researchers present the results of the research by providing recommendations for social-media strategies to achieve competitive advantage for start-up entrepreneurs in Surakarta. Keywords: SWOT, Web 2.0, food start-up business, competitive advantage, entrepreneur
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".