The study explores the transparency status of sustainability disclosures among luxury and mass-market fashion brands, examining the possibilities that the type of information disclosed is selectively prioritized, and higher differences in supply chain disclosure exist among differently positioned retail brands. Fashion Transparency Index 2017 represents the first comprehensive index that ranked 100 of the most affluent fashion brands according to the levels of corporate and supply chain transparency. To explore the index raw-data study employs non-parametric methods, the Hodges-Lehmann median differences and the Probability of Superiority. For 100 brands, the average corporate and supply chain transparency joint score was just 19.6% (out of 100%). The type of information disclosed was selectively prioritized, and there was an 80% chance that brands were scored higher in corporate than in supply chain transparency. A comparison of the mass-market (n = 27) and luxury (n = 13) sub-sample revealed higher transparency of supply chain disclosures among mass-market brands. Benchmarking the transparency of sustainability disclosures serves as a point of reference against which current and future disclosures may be assessed. Consumers may use these findings to question current transparency status and facilitate further industry change. Retailers might use identified critical areas to re-negotiate priorities for future disclosure.

Purpose: The purpose of this quantitative study is to identify disclosure strategies for transparency in sustainability reporting to support strategic thinking around transparency in the fashion industry. This research has two specific research objectives: to capture progress towards greater transparency across sustainability reporting areas, across fashion brands and years, and to identify strategic approaches for transparency in sustainability reporting by revealing common patterns in business disclosure.Design/methodology/approach: The authors cross-sectionally analyzed secondary data using four consecutive Fashion Transparency Indices (2017–2020). Brands' strategies for transparency in sustainability reporting were examined through the stakeholder theory lens.Findings: Findings confirm the presence of four approaches to disclosure: measurable, ambiguous, policy-only and secretive strategy. The disclosure was disproportionally distributed between 30% brands as transparency leaders and 70% brands as transparency laggards. The most transparent brands were not necessarily those rated highest by the index but those whose progress toward transparency was traceable over the years.Research limitations/implications: The study has overcome the limitation of the verifiability approach, supporting the requirement for diachronic and strategic disclosure assessments.Practical implications: As most brands hesitantly disclose sustainability information, stakeholders cannot know whether business policies equate to more than a corporate wish list. If there is no inspection for mandatory business disclosure, and if there is no penalty for disclosure violations, some fashion retailers will continue to generate profits while operating in an uncompliant and “opaque” manner.Originality/value: The framing of disclosure strategies for transparency in sustainability reporting is the first scholarly effort to investigate diachronically sustainability disclosure among a big sample of major fashion brands.

Multilevel modeling techniques have gained traction among experimental psychologists for their ability to account for dependencies in nested data structures. Increasingly, these techniques are extended to the analysis of binary data (e.g., correct or incorrect responses). Despite their popularity, the information in logistic multilevel models is often underutilized when researchers focus solely on fixed effects and ignore important heterogeneity that exists between participants. In this tutorial, we review four techniques for estimating and quantifying the relative degree of between-person variability in logistic multilevel models in an accessible manner using real data. First, we introduce logistic multilevel modeling, including the interpretation of fixed and random effects. Second, we review the challenges associated with the estimation and interpretation of within- and between-participant variation in logistic multilevel models, particularly computing the intraclass correlation coefficient (ICC), which is usually a first, simple step in a linear MLM. Third, we demonstrate four existing methods of quantifying the ICC in logistic multilevel models and discuss their relative advantages and disadvantages. Fourth, we present bootstrapping methods to make statistical inference about these ICC estimates. To facilitate reuse, we developed R code to implement the discussed techniques, which is provided throughout the text and as supplemental materials.

Plastic packaging dominates the US grocery industry. This realization raises the question of whether consumers are purchasing food that is not wrapped in conventional plastic but environmentally friendly packaging. This quantitative study adapted the Theory of Planned Behavior to investigate the relationship between consumers’ socio-demographics, purchase intention, and purchasing behavior regarding environmentally friendly grocery packaging. The survey was distributed through Qualtrics, and a sample of 487 eligible US grocery consumers was gathered. The study uncovers some novel findings. First, the results suggest that consumers’ subjective norms substantially stimulate environmentally friendly grocery packaging purchase intentions, influencing actual purchasing behavior. Second, we discovered that purchase intention and perceived behavioral control are likely working in conjunction to help bridge the intention-behavior gap in environmentally friendly consumption. Third, this study supplied a fresh perspective on socio-demographics’ role in environmentally friendly consumption, confirming that predominantly younger, unmarried consumers are more prone to purchase grocery items packaged in environmentally friendly materials. We hope that these study findings provide marketers with fresh insights into the characteristics of consumers willing to purchase grocery items packaged in environmentally friendly materials.

The standard regression technique for modeling binary response variables in education research is logistic regression. The odds ratios from these models are used to quantify and communicate variable effects. These effects are sometimes pooled together as in a meta-analysis. We argue that this process is problematic as odds ratios calculated from different models and studies are not directly comparable. As an alternative, we recommend the linear probability model for computing risk differences and a Poisson working model for computing risk ratios. These effect sizes are comparable across models and studies. However, standard approaches for estimating these models have their problems, hence, we motivate and present modified estimation techniques for estimating these models that mitigate these problems.

Accounting for model misspecification in Bayesian structural equation models is an active area of research. We present a uniquely Bayesian approach to misspecification that models the degree of misspecification as a parameter—a parameter akin to the correlation root mean squared residual. The misspecification parameter can be interpreted on its own terms as a measure of absolute model fit and allows for comparing different models fit to the same data. By estimating the degree of misspecification simultaneously with structural parameters, the uncertainty about structural parameters reflects the degree of model misspecification. This results in a model that produces more reliable inference than extant Bayesian structural equation modeling. In addition, the approach estimates the residual covariance matrix that can be the basis for diagnosing misspecifications and updating a hypothesized model. These features are confirmed using simulation studies. Demonstrations with a variety of real-world examples show additional properties of the approach.