PACKAGE TRANSPARENCY, OPACITY, AND WINDOWING: AN INVESTIGATION OF THE CANADIAN FOOD INDUSTRY PRACTICES
Bibliographic record
Abstract
Package often represents the consumers' first contact with the product on the point of purchase (Underwood & Klein, 2002). Besides, consumers exposure to package often continues until its full consumption (Chandon, 2013). Package design elements have been shown to be a critical source of information consumers use to forge expectations and make choices about products and brands (Greenleaf & Raghubir, 2008; Orth & Malkewitz, 2008). While the marketing literature has seen a recent interest in the study of the effect of package design elements on product evaluation (e.g., Koo & Suk, 2016; Lui et al., 2017; Rundh, 2013), research on package transparency has been scarce (Deng and Srinivasan, 2013). However, understanding the use of transparency is key as it corresponds to a strong trend where consumers want to see what they are buying (Schrmann, 2008) and it has been shown to influence the amount of product consumed (Deng & Srinivasan, 2013). This study aims at contributing to the marketing research on structural package design elements, in particular transparency, by investigating the Canadian food industry's practices. A quantitative content analysis of 1,500 packages belonging to product categories where the use of transparency, opacity, and on-package windows is prevalent has been undertaken. This research offers a comprehensive understanding of the wide array of transparency, opacity and windowing practices adopted by food manufacturers and producers in different contexts. It highlights several future research avenues in terms of understanding the role of package opacity level, shape and location of windows, and substituting or complementing a displayed image on consumer product and brand judgement. From a managerial standpoint, it offers a broad view of the current use of transparency in several industries and underlines the advantages and downsides of the use of this package design element by food producers and manufacturers.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".