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This paper aims to describe a case study of a formative study for the development of a social marketing campaign for increased biowaste sorting. In following the social marketing principles, it provides insights for practitioners willing to implement behaviour change interventions.

The case study describes the scoping review, expert interviews, online survey and focus groups, which were integrated to comprehend the specificities of biowaste sorting in the South Suburbs of Athens. This mixed-method research design enabled a comprehensive understanding of biowaste sorting practices that would not have been attainable through a single method.

This study found that the “descriptive norm” and “perceived controllability” significantly influenced biowaste sorting. Differential challenges of at-home and out-of-home sorting were also identified. The demand for more information was also highlighted. The tailored intervention comprises of a mix of behavioural modelling, persuasive communication, education and enablement.

By concretely showcasing how the integration of multiple research methods through the application of social marketing principles can guide the design of a tailored behaviour change intervention, this paper offers an outline for informed decision-making and strategic planning in the realm of (environmental) behaviour change.

Non-profit organizations (NPOs) are exposed to a highly competitive environment in which they are forced to grow their commercial activity to acquire additional financial resources. This study aims to create an understanding of how NPOs involved in textile reuse as a revenue-generating programme manage their reverse supply chains (RSC).

The research involves an embedded single-case study of NPOs in Finland involved in post-use textile collection. The main data sources are semi-structured interviews and participant observations.

This study is inspired by the microfoundations movement and identifies the underlying microfoundations of the NPOs’ capabilities for managing RSC for textile reuse. The study contributes to the literature by demonstrating NPOs’ lower-level, granular practices and their adaptations for achieving quality outcomes in textile reuse.

The findings have context sensitivity and apply to the NPOs which operate in a context similar to Finland, such as in other Nordic countries.

This study continues the discussion on the adoption of “business-like” practices in the NPOs’ pursuit of additional revenue streams to finance humanitarian work. The findings of this study can also be transferred to the growing area of domestic textile circularity.

Using the case of NPOs in textile reuse, the study illustrates how RSC management can serve a social, non-profit cause and transform unwanted textile products into a source of fundraising for humanitarian work.

This enriches the understanding of NPOs’ practices within the scope of revenue-generating programmes by examining one of them – textile reuse through charity shops from an RSC perspective.

This study aims to examine own-race bias (ORB) in prospective person memory (PPM) and explore whether the effects of ORB were moderated by two factors that are salient to real-world missing person appeals (MPAs): the number of appeals an individual encounters and the frequency in which these appeals are encountered.

A mixed experimental design was used whereby 269 Caucasian participants studied MPAs (4 or 8 appeals) for various frequencies (once or three times), which featured both white and non-white missing individuals. Participants then completed a PPM sorting task that required them to identify missing individuals as a secondary objective.

ORB was not observed for prospective person memory performance, although participants did demonstrate a greater conservative bias for appeals involving different ethnicities. The main effect of number and frequency of appeals on PPM was significant, however, these variables did not moderate ORB.

The current study has limitations that should be taken into consideration. There was an underrepresentation of non-white ethnicities within the sample which limits the ability to determine if ORB effects vary across ethnicities. Additionally, experimental simulations of missing person identifications still lack ecological validity and thus future innovative methods are required to study missing person identifications more realistically.

This study demonstrates that PPM performance may not be influenced by ORB effects overall as found within previous generic memory tasks, although the influence of the number and frequency of appeals presented continues to demonstrate the need to improve MPAs to maximise public facial recognition and identification of missing persons.

The purpose of this study is to explore the antecedents of uncaptured sustainable value and strategies to generate opportunities to capture it in the circular supply chain of post-consumer used clothing.

This study is based on an inductive analysis of 21 semi-structured interviews conducted with various stakeholders in the circular clothing supply chain (for-profit and not-for-profit) using the value mapping approach, as previously applied in the literature on sustainable business models.

Fifteen antecedents of uncaptured sustainable value, and thirteen value opportunity strategies were revealed that hinder or generate multi-dimensional value types. Economic value is impacted the most, while there is lack of explicit understanding of the impact of these antecedents and strategies on environmental and social value capture. From a multi-stakeholder perspective, the ecosystem is emerging as new for-profit actors are developing novel process technologies, while not-for-profit actors are consolidating their positions by offering new service options. There is also an emerging “coopetition” between the different stakeholders.

More granularity in the different types of uncaptured value could be considered, and external supply chain stakeholders, such as the government, could be included, leading to more detailed value mapping.

This research provides practitioners with a value-mapping tool in circular clothing supply chains, thus providing a structured approach to explore, analyse and understand uncaptured value and value opportunities.

This extended value perspective draws upon the value-mapping approach from the sustainable business model literature and applies it in the context of the circular clothing supply chain. In doing so, this research illustrates circular clothing supply chains in a new way that facilitates an improved understanding of multi-dimensional and multi-stakeholder value for embedded actors.

Investors are inattentive to continuous information as opposed to discrete information, resulting in underreaction to continuous information. This paper aims to examine if the well-documented return predictability of the strategies based on the ratio of short-term to long-term moving averages can be enhanced by conditioning on information discreteness. Anchoring bias has been the popular explanation for the source of underreaction in the context of moving averages-based strategies. This paper proposes and studies another possible source based on investor inattention that can potentially result in superior performance of these strategies.

The paper uses portfolio sorting as well as Fama-MacBeth cross-sectional regressions. For examining the role of information discreteness in the return predictability of the moving average ratio, the sample stocks are double-sorted based on the moving average ratio and information discreteness measure. The returns to these portfolios are computed using standard approaches in the literature. The regression approach controls for various well-known return predictors.

This study finds that the equally-weighted monthly returns to the long-short moving average ratio quintile portfolios increase monotonically from 0.54% for the discrete information portfolio to 1.37% for the continuous information portfolio over the 3-month holding period. This study observes a similar pattern in risk-adjusted returns, value-weighted portfolios, non-January returns, large and small stocks, for alternative holding periods and the ratio of 50-day to 200-day moving average. The results are robust to control for well-known return predictors in cross-sectional regressions.

To the best of the authors’ knowledge, this is the first paper to document the significant role of investor inattention to continuous information in the return predictability of strategies based on the moving average ratios. There are many underreaction anomalies that have been reported in the literature, and the paper's results can be extended to those anomalies in subsequent research.

The findings of this paper have important practical implications. Strategies based on moving averages are an extremely popular component of a technical analyst's toolkit. Their profitability has been well-documented in the prior literature that attributes the performance to investors' anchoring bias. This paper offers a readily implementable approach to enhancing the performance of these strategies by conditioning on a straightforward measure of information discreteness. In doing so, this study extends the literature on the role of investor inattention to continuous information in anomaly profits.

While there is considerable literature on technical analysis, and especially on the performance of moving averages-based strategies, the novelty of this paper is the analysis of the role of information discreteness in strategy performance. Not only does the paper document robust evidence, but the findings suggest that the investor’s inattention to continuous information is a more dominant source of underreaction compared to anchoring. This is an important result, given that anchoring has so far been considered the source of return predictability in the literature.

This case study explores different perspectives on integrating the Sustainable Development Goals (SDGs) in universities to achieve university transformations. This study recognises that university actors think differently about the purpose of universities, hold diverse perspectives on the SDGs, and, thus, prefer specific types of SDG integration.

Using Q methodology, 29 participants from one university expressed their perspectives by sorting 50 statements covering different types of SDG integration. Statements were based on academic and grey literature on SDG integration in universities, and interviews with university actors from a previous study. After the sorting task, participants were interviewed to understand the reasons behind the placement of particular statements.

The study identifies three perspectives held by the study participants. Perspective 1 emphasises the value of the SDGs and supports a deep integration of the Goals in their university. Perspective 1 also advocates for incorporating the SDGs into the university’s identity. Perspective 2 sees the university’s purpose as more comprehensive than the SDGs; thus, the university should develop knowledge regardless of its relevance to the SDGs. This perspective supports a pragmatic integration of the SDGs – favouring actions that benefit the university without introducing significant changes. Finally, Perspective 3 argues that the university should approach the SDGs through social justice and empowerment lenses. This perspective also questions the suitability of the SDGs for universities, arguing that the SDGs fail to challenge current structures underpinning the unsustainability of the world.

Although previous studies have analysed diverse ways of understanding the SDGs in universities, to the best of the authors’ knowledge, this study is the first to treat the SDGs as a governance framework of 17 goals and adopt a whole-institution approach to study universities.

The study aimed to assess whether transgender women, who were students at a university in London, England, faced hiring discrimination when seeking employment.

Three comparable university classmates—a cisgender woman, a cisgender man and a transgender woman—studying Engineering applied to the same job openings. Similarly, another set of three university classmates—a cisgender woman, a cisgender man and a transgender woman—studying Social Work applied to the same job openings. The degree of discrimination was quantified by calculating the difference in the number of interview invitations received by each group.

When three comparable university classmates apply for the same job openings, the rate of interview invitations differs based on gender identity. For cisgender women, the invitation rate is 31.3%, while for cisgender men, it stands at 35.1%. However, for transgender women, the rate drops significantly to 10.4%. Additionally, transgender women face further challenges in male-dominated sectors (STEM), where their chance of being invited for a job interview is even lower compared to those in female-dominated sectors, with a reduction of 8.7 percentage points. The study also reveals that firms with written equality policies on gender identity diversity show a 25.7 percentage point increase in invitation rates for transgender women compared to firms without such policies. Furthermore, the research highlights that negative beliefs among job recruiters regarding various aspects of transgender women, including their gender identity status, disclosure, job performance, vocational relationships and turnover, contribute to their exclusion from job interviews. Moreover, for transgender women who do receive interview invitations, these tend to be for lower-paid jobs compared to those received by cisgender women (by 20%) and cisgender men (by 21.3%). This wage sorting into lower-paid vacancies suggests a penalty in terms of lower returns on education, which could drive wage and income differences.

Transgender women received a higher number of job interview invitations when firms had written equality policies on gender identity diversity. This outcome can help policymakers identify actions to reduce the exclusion of transgender people from the labour market.

The study gathers information from job recruiters to quantify the roots of hiring discrimination against transgender women. It also enables an examination of whether workplaces' written equality policies on gender identity diversity are related to transgender women’s invitations to job interviews.

Many studies point out that a complication in the professional training is connected with the division of education into the formal and non-formal part. There is a need to integrate these two components to achieve a greater learning effect. The aim of the study is to find out whether students perceive non-formal education as important and to identify which form of extracurricular education they prefer. The aim of this study is to find the relationships among the variables of the educational process of young people.

Around 844 Czech secondary and tertiary students participated in the study. The participants expressed their attitudes towards non-formal education through a questionnaire. Welch’s ANOVA test and Games-Howell’s Post-Hoc test were used to verify the hypotheses. Pearson’s correlation coefficient was used to determine correlation relationships.

Students perceive non-formal education as important. The importance was perceived significantly least by high school and beginning university students in technical fields. Digital forms of education are favoured by students. Significant differences in this area are connected with the different field of study. Students are interested especially in the particular area of non-formal education that is connected with their field of study.

The study provides desirable results in relation to the possible adaptation of education to the needs of students. The article reminds that digital education is promoted because of its flexibility and changing competencies. Specific forms of non-formal education will be appropriate to differentiate according to the fields and the age of students.

To overcome the limitations of traditional multi-attribute decision making (MADM) methods, which only provide deterministic rankings of decision objects, this paper proposes a novel multi-attribute 3WD model. This model presents three-parameter interval grey number decision-theoretic rough sets (TPIGNDTRSs), aiming to offer a reasoned interpretation of loss functions in grey environments and ensure objective assessment of conditional probabilities.

Firstly, the traditional equivalence relation is replaced with the probabilistic dominance relation (PDR), categorizing decision objects into two state sets in DTRS for more objective conditional probabilities. Secondly, as the three-parameter interval grey number (TPIGN) introduces the most probable value on the basis of the traditional two-parameter interval grey number, it provides a more comprehensive method for describing grey information. Consequently, integrating TPIGN into DTRS refines the interpretations of loss functions in grey environments. Finally, by utilizing two main sorting techniques, relative kernel and degree of accuracy ranking and possibility ranking, two types of 3WD rules with TPIGNDTRSs, are constructed.

This study has successfully developed and validated a new multi-attribute 3WD model. The model was tested in two distinct domains: evaluating innovation efficiency in high-tech enterprises and recommending movies in a practical case. The findings reveal that the model can effectively integrate relevant information of high-tech enterprises, provide the government with enterprise-level assessments, and gather consumer preferences to recommend the most suitable movies.

This study treats the loss function as grey information in the 3WD model but overlooks the grey nature of evaluation values, limiting its applicability. Additionally, the model’s reliance on subjective expert judgments and historical data to establish the loss function may affect its objectivity. The implications of this research are that the novel model overcomes traditional MADM limitations, enhancing decision-making quality and efficiency in complex and grey scenarios. The model’s successful application in evaluating high-tech enterprises and recommending movies illustrates its dual value in both theory and practice.

Initially, the model proposed in this study is of significant importance for the development of the 3WD field, as it successfully addresses the challenges of uncertain loss functions and unknown conditional probabilities in grey information environments. Moreover, by integrating the 3WD model with MADM problems, it has broken through the bottlenecks of traditional MADM methods, offering new perspectives and strategies for solving MADM issues. Therefore, this research not only advances theoretical research but also provides powerful tools for practical applications.

The widespread adoption of online purchasing has prompted increasing concerns about product safety, and regulators are beginning to hold e-commerce sites accountable for dangerous product defects. For online consumers, understanding the many inherent safety risks among the extensive array of products they browse is a formidable task. The authors attempt to address this problem via a client-side software artifact that warns shoppers about potential product safety hazards at the point of sale.

In this study, the authors built four candidate designs and assessed their effectiveness by means of a large randomized controlled experiment (n = 466). The authors define effectiveness as significant changes in dependent variables associated with health behaviors and technology adoption.

The authors find that all of the designs score high on adoption likelihood, that designs incorporating highlighting and scoring are better at increasing safety knowledge and that simpler designs are better at enhancing safety awareness.

These findings will inform the design of safety information dissemination systems and open new areas of safety awareness enhancement research. More generally, the authors introduce a novel method of testing text visualization variations and their impact on behavioral decisions.