Understanding Marketing Responses to a Tax on Sugary Drinks: A Qualitative Interview Study in the United Kingdom, 2019
Bibliographic record
Abstract
BACKGROUND: The World Health Organization (WHO) recommends that countries implement fiscal policies to reduce the health impacts of sugary drinks. Few studies have fully examined the responses of industry to these policies, and whether they support or undermine health benefits of sugary drinks taxes. We aimed to explore the changes that sugary drinks companies may make to their marketing, and underlying decision-making processes, in response to such a tax. METHODS: Following introduction of the UK Soft Drinks Industry Levy (SDIL) in 2018, we undertook one-to-one semi-structured interviews with UK stakeholders with experience of the strategic decision-making or marketing of soft drinks companies. We purposively recruited interviewees using seed and snowball sampling. We conducted telephone interviews with 6 representatives from each of industry, academia and civil society (total n=18), which were transcribed verbatim and thematically analysed. Four transcripts were double-coded, three were excluded from initial coding to allow comparison; and findings were checked by interviewees. RESULTS: Themes were organised into a theoretical framework that reveals a cyclical, iterative and ongoing process of soft drinks company marketing decision-making, which was accelerated by the SDIL. Decisions about marketing affect a product's position, or niche, in the market and were primarily intended to maintain profits. A product's position is enacted through various marketing activities including reformulation and price variation, and non-marketing activities like lobbying. A soft drinks company's selection of marketing activities appeared to be influenced by their internal context, such as brand strength, and external context, such as consumer trends and policy. For example, a company with low brand strength and an awareness of trends for reducing sugar consumption may be more likely to reformulate to lower-sugar alternatives. CONCLUSION: The theoretical framework suggests that marketing responses following the SDIL were coordinated and context-dependent, potentially explaining observed heterogeneity in responses across the industry.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".