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Anthropomorphism is the innate human tendency to attribute human or human-like characteristics to non-human entities or objects. Even though it is widely used by marketing practitioners, there is a scarcity of academic research that systematically attempts to capture this phenomenon. The aim of the current study is to investigate anthropomorphism in product packages of the 2010 Nielsen’s Top 100 grocery brands in the UK.

This study uses a mixed-method design combining quantitative content analysis and correspondence analysis. The former methodology allowed for documentation of anthropomorphic package elements, whereas the latter facilitated the study of structural relationships between anthropomorphic cues and product-related attributes such as type, category and target market.

The findings reveal that anthropomorphism is widely used in the packaging of grocery brands in the sample investigated. Moreover, the evidence shows that there appears to be an association between anthropomorphism and product-related attributes.

The current study contributes to both theory and practice. It illuminates the under-investigated interface of anthropomorphism and marketing by capturing anthropomorphic elements appearing in product packaging. The combination of anthropomorphic package elements and product-related attributes may assist managers in designing their packages to provide unique product experiences.

This study serves as a roadmap for both academics and practitioners wishing to engage in a fruitful dialogue on the emerging area of anthropomorphic marketing.

The purpose of this study is to evaluate the performance of mutual funds during the COVID-19 pandemic with environmental, social and governance (ESG) criteria. The main research question is whether mutual fund performance differs with respect to the level of the mutual fund’s ESG score.

The data set contains global fund data, and mutual fund performance is analyzed using two types of data envelopment analysis (DEA) models: the DEA portfolio index (DPEI) and the range direction measure (RDM) DEA. Propensity score matching and logistic regression are also applied.

The results reveal that: nonequity mutual funds present significantly higher performance compared to the performance of equity mutual funds; mutual funds with high ESG scores are associated with significantly higher performance compared to those with low to medium ESG scores; funds with high ESG scores experience higher performance irrespective of their type; and efficiency scores derived from the RDM DEA are significantly higher than those derived from the DPEI model.

Investors, fund managers and market participants can benefit from the findings of this study and improve their investment decision-making process, including more sustainable funds in their portfolios. Regulators and policymakers should further promote or even require the inclusion of more sustainable investments in the financial products offered by institutional investors. The main limitation of the study is related to data availability regarding the ESG score of mutual funds.

To the best of the authors’ knowledge, this is the first study that provides robust evidence in support of a positive association between ESG scores and mutual fund performance during the pandemic-induced crisis applying a DEA methodology.

Refugee camps are not easily welcomed by local communities. The purpose of this paper is to outline a structured approach to support the decision-making process for siting refugee camps in mainland Greece using multiple criteria, including local opposition. A suitability analysis generates a list of potential sites and a multiple criteria evaluation is applied. The motivation is the development of a methodology that can support choices and policies regarding the refugee camps siting problem, incorporating the need to address local opposition.

The proposed methodology combines geographic information systems (GIS) with multiple criteria decision-making (MCDM) techniques. These are used to develop a location classification and ranking model based on related criteria and subcriteria, attributes and weights. The region of Peloponnese in Greece is selected as a case study to validate the approach.

The lack of predefined candidate sites for refugee camps necessitates, initially, tackling a site search problem to generate a pool of potential sites through a suitability analysis. Subsequently, using the GIS the pool yields a subset of potential sites, satisfying all the criteria to setup a refugee camp. Through the current analysis the suitability of the single existing refugee camp site in Peloponnese can be evaluated. Finally, a “with and without” analysis, excluding the social criterion, depicts the changes in the candidate sites pool and their scores.

There is a lack of relevant literature taking into account the local opposition or sociopolitical implications as decision criteria. The selection of the appropriate criteria is a complex process that involves the cooperation of many experts. The main criteria, subcriteria and their attributes were determined according to existing literature and authors' informed judgment.

The proposed methodology can help decision-makers to setup a decision-making system and process for identifying refugee camps' sites using multiple criteria, including local opposition.

The purpose of this paper is to introduce an innovative procedure for digital historical documents image binarization based on image pre‐processing and image condition classification. The estimated results for each class of images and each method have shown improved image quality for the six categories of document images described by their separate characteristics.

The applied technique consists of five stages, i.e. text image acquisition, image preparation, denoising, image type classification in six categories according to image condition, image thresholding and final refinement, a very effective approach to binarize document images. The results achieved by the authors' method require minimal pre‐processing steps for best quality of the image and increased text readability. This methodology performs better compared to current state‐of‐the‐art adaptive thresholding techniques.

An innovative procedure for digital historical documents image binarization based on image pre‐processing, image type classification in categories according to image condition and further enhancement. This methodology is robust and simple, with minimal pre‐processing steps for best quality of the image, increased text readability and it performs better compared to available thresholding techniques.

The technique consists of limited but optimized pre‐processing sequential steps, and attention should be given in document image preparation and denoising, and on image condition classification for thresholding and refinement, since bad results in a single stage corrupt the final document image quality and text readability.

The paper contributes in digital image binarization of text images suggesting a procedure based on image preparation, image type classification and thresholding and image refinement with applicability on Byzantine historical documents.