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Record W2991037034 · doi:10.20472/iac.2019.052.012

PACKAGE TRANSPARENCY, OPACITY, AND WINDOWING: AN INVESTIGATION OF THE CANADIAN FOOD INDUSTRY PRACTICES

2019· article· en· W2991037034 on OpenAlexaffabout
Soumaya Cheikhrouhou, Deny Bélisle

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Packaging Perceptions and Trends
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsTransparency (behavior)OpacityProduct (mathematics)Package designMarketingFood productsAdvertisingBusinessComputer scienceEngineeringEngineering drawingMathematicsFood scienceComputer security

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.484

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.010
Science and technology studies0.0100.004
Scholarly communication0.0030.001
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.047
GPT teacher head0.248
Teacher spread0.201 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes2
Has abstractyes

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