Bibliographic record
Abstract
On a global scale, households are purchasing from meal kit delivery services like HelloFresh and Goodfood at an ever-growing rate. A meal kit promises beginner cooks and busy individuals an affordable, convenient, and approachable option for meal planning and preparation. This paper investigates how meal kit delivery services align and position themselves against other food industry players such as grocery stores, grocery delivery platforms, and online food delivery services, using HelloFresh as a case study. Meal kit delivery services are platforms which operate by intermediating between various parties and generate profit by the expansion of their customer bases through their networks. The goal of this paper is to explore the marketing strategies employed by HelloFresh to yield profit through platform growth and global dominance objectives. To pursue this goal, a walkthrough method on the HelloFresh user interface and a discursive analysis of various company reports and news articles from HelloFresh were conducted. By leveraging the benefits of convenience, affordability, and availability of meal kits, it was found that HelloFresh does not compete with its competitors, but rather with grocery stores, grocery delivery platforms, and online food delivery services. I argue that HelloFresh’s competition strategy successfully positions its services as a desirable alternative to consumers while capturing profit through various avenues and masking shortcomings with its competitors. The findings of this study aim to assist platform scholars with understanding the marketing strategies and multiple avenues of profit generation on meal kit platforms.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".