Impact of Social Media in Coffee Retail Business
Bibliographic record
Abstract
Retail coffee business has been growing fast in the Middle East countries particularly during the last decade. High penetration rates of mobile communication devices such as smart phones and high usage of Social Media make the coffee retails in the region with Fee WIFI access, very attractive off-line and on-line social venues. The growth of internet in Arab countries is continuous and offers many e-commerce opportunities for retail businesses to penetrate, grow and achieve loyalty. This article aims to test how coffee retail businesses can optimize social media usage in order to increase their customer base, reach higher level of customer satisfaction and hence increase rate of customer loyalty in the long run. The literature review focuses on social media engagement and use of businesses, small businesses and retailers in the world and in the middle-east, coffee shop industry and coffee shop service expectations of customers. The primary data collection is targeted to test the constructs identified in the secondary data for coffee retail business as well as social media engagement of subjects who frequent coffee shops. Proposed conceptual model suggests that social media engagement of coffee shops customers through retailers’ social media and internet presence will lead to higher satisfaction about the shop and consequently transform them into loyal customers. Therefore, finally the model suggests that coffee shops should continuously seek to optimize their social media enabled marketing activities in order to achieve their business objectives.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".