Exploring Digital Marketing Strategies in Catering Industry
Bibliographic record
Abstract
With the development and popularity of the Internet, digital marketing has gradually become popular in various industries. Following the development of the times, the catering industry applies digital marketing as the main marketing method. Starbucks, as one of the major companies in the restaurant industry, has complete digital marketing strategies that can be utilized as the research case in this paper. By exploring the advantages and disadvantages of Starbucks’ digital marketing strategy in China, combining qualitative research and quantitative research, this paper provided ideas for other brands in the catering industry to improve their digital marketing models. This paper first introduced the digital marketing strategy of the catering industry and Starbucks and the recent research related to those. Then, the paper was followed by a detailed introduction of the research tools, methods, and model. Using SPSS 25 to build a structural equation model (SEM), the path coefficients showed the relationship of variables. This paper concluded that Starbucks uses its digital marketing strategy to attract customers. This strategy heavily influenced people’s purchase intentions; however, customers’ willingness to share Starbucks products on social media was not strong enough to fulfill Starbucks' expectation of attracting potential customers through existing customers' promotions on social media. This paper figured out how to solve Starbucks's problem and how other brands in the catering industry would learn to correct it.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".