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The purpose of this paper is to explore consumers’ views and expectations on circular clothing. This paper also clarifies how the remanufacturing process should be communicated and circular fashion marketed to consumers.

The research methodology consisted of consumer interviews, utilising an online innovation platform (Owela) to involve consumers and workshops with project partners and with external stakeholders.

Consumers’ interest towards recycling and sustainable solutions has increased. They appreciate the idea of recycling textile waste to produce new clothes; circular products should become “the new normal”. Consumers are asking for more visible and concrete information about circular clothing and how their behaviour has affected the environmental aspects of textile production. The communication should be timed correctly by using multiple communication channels and also paying attention to the shopping experience. In addition, digital services alongside circular clothing could create additional value for consumers.

In this study, only consumers from Finland were involved. The results might be different in different parts of Europe and especially worldwide.

This study focusses on circular clothing – an area that has not been studied much before. Also, consumers involved in this study were of a different age compared to most of the previous studies, where the focus has been mainly on young college students.

This study aims to examine the impact of workplace safety (WPS) on employee retention (ER) in the health-care sector in Azad Jammu and Kashmir (AJ&K), Pakistan. At the same time, a mediation relationship through job satisfaction (JS) and employee loyalty (EL) was also tested.

Structured questionnaires were used to collect the data from 300 doctors, using purposive sampling technique analysed using partial least squares (Smart-PLS 3).

This study’s findings supported all hypotheses, such as WPS has a significant positive relationship with ER. In addition, a mediation relationship between JS and EL was also confirmed. Furthermore, a serial mediation effect of JS and EL between WPS and ER was also confirmed in this study.

This study might not fit organisations from other regions due to regional norms. In the future, this study’s model may be tested on other regions and segments of the health-care sector, such as nurses, management staff and support staff.

The present study is unique because it is based on a newly formulated framework, WPS → JS → EL → ER, under the social exchange theory, which has not been tested before.

In a safe environment, doctors will feel relaxed, stay longer and provide better services; resultantly, patients will get better treatment.

This study tested the sequential mediation effect through JS and EL for the first time in ER, which was missing previously, to the best of the authors’ knowledge. This will add more insights to the safety-retention literature in health-care settings. Furthermore, this study is also the first attempt to explore the relationship between WPS and ER in the health-care sector in AJ&K.

This paper aims to explore the drivers behind the accuracy of self-reported home valuations in the Warsaw (Poland) housing market.

In order to achieve the research goal, firstly, unique data on subjective residential property values estimated by their owners were compared with market-justified ones. The latter was calculated using geographically weighted regression, which allowed for taking into account spatially heterogeneous buyers' housing preferences. An ordered logit model was then used to identify the factors influencing the probability of the occurrence of bias towards over or undervaluation.

The results of the study revealed that, on average, homeowners overvalued their properties by only 1.94%, and the fraction of interviewees estimating their properties accurately ranges from 20% to 68%, depending on the size of the margin of error adopted. The drivers of the valuation bias variation were the physical, locational and neighbourhood attributes of the property as well as the personal characteristics of the respondents, for which their age and employment situation played a key role.

In contrast to previous studies, this is the first to examine drivers behind the accuracy of self-reported home valuations in a Central and Eastern Europe country. In addition, this work is the first to consider heterogeneous housing preferences when calculating objective property values.

Hedonic value is commonly conceded as a determinant of behavioral intentions toward location-based advertising (LBA). However, the careful consideration of a mechanism behind hedonic motivation and its subsequent impact on continuance intention is inadequate. This study aims to explore the formation of hedonic value and its motivation for prolonged usage toward LBA.

A sample of 486 mobile users was recruited to evaluate the research model using structural equation modeling (SEM).

Results reveal that perceived utility and promotional offers are the strongest indicators of hedonic value. Moreover, social support and contextual convenience play an essential role in heightening hedonic value. Furthermore, the research lenses attempt to clarify the direct, indirect influences of hedonic value, irritation and perceived credibility on continuance intention.

The findings offer practitioners an understanding of how to improve hedonic value and continuance intention and develop effective LBA strategies in emerging markets.

This study narrows the gap of current literature by formulating a hedonic value-based continuance intention model based on uses and gratifications theory (UGT). Additionally, this work illuminates the insights into hedonic value toward LBA by identifying its motivations, including perceived utility, promotional offers, social support and contextual convenience.

The subject of this paper is the phenomenon of social media aesthetics, which can be perceived as a tool for promoting and building the image of libraries, especially in terms of merchandising. The aim of this paper is to analyse the potential of the dark academia social media trend in the promotion of academic libraries.

The article is based on a review of the social networking sites YouTube and Instagram and an analysis of network resources using the Brand24 tool.

Resources that are described by Internet users as “dark academia” are popular in social media. Dark academia as an aesthetic concept creates potential for the promotion of academic libraries, especially those that are more traditional in terms of their architecture, décor or how they offer their services.

The paper concerns a phenomenon which, although popular socially, has not yet been scientifically analysed in the literature on the subject. Since the topic is new and there is no scientific literature on it, the author had to base the paper on less standard sources of information (e.g. analysis of the content of social media). The article is a review, an introduction, as well as an invitation to further discussion. The author's aim is not to comprehensively cover this topic but only to draw attention to an interesting and rarely discussed issue that has great potential for practical activities.

The topic has great potential for the practical improvement of the promotional activities of libraries, especially older, more traditional libraries, to create a strong and positive image on the basis of characteristics often perceived as weaknesses.

Social media services are powerful social impact tools. Showing the potential role of social media aesthetics for cultural institutions could serve to make the public more aware of the role of the proper use of social media for promotion and image building.

The use of social media aesthetics is very rarely discussed in the subject literature.

The study aims to develop a model that supports the application of data science techniques for real estate professionals in the fourth industrial revolution (4IR) era. The present 4IR era gave birth to big data sets and is beyond real estate professionals' analysis techniques. This has led to a situation where most real estate professionals rely on their intuition while neglecting a rigorous analysis for real estate investment appraisals. The heavy reliance on their intuition has been responsible for the under-performance of real estate investment, especially in Africa.

This study utilised a survey questionnaire to randomly source data from real estate professionals. The questionnaire was analysed using a combination of Statistical package for social science (SPSS) V24 and Analysis of a Moment Structures (AMOS) graphics V27 software. Exploratory factor analysis was employed to break down the variables (drivers) into meaningful dimensions helpful in developing the conceptual framework. The framework was validated using covariance-based structural equation modelling. The model was validated using fit indices like discriminant validity, standardised root mean square (SRMR), comparative fit index (CFI), Normed Fit Index (NFI), etc.

The model revealed that an inclusive educational system, decentralised real estate market and data management system are the major drivers for applying data science techniques to real estate professionals. Also, real estate professionals' application of the drivers will guarantee an effective data analysis of real estate investments.

Numerous studies have clamoured for adopting data science techniques for real estate professionals. There is a lack of studies on the drivers that will guarantee the successful adoption of data science techniques. A modern form of data analysis for real estate professionals was also proposed in the study.

The purpose of this paper is to investigate the functionality and effectiveness of the Carbon Risk Real Estate Monitor (CRREM tool). The aim of the project, supported by the European Union’s Horizon 2020 research and innovation program, was to develop a broadly accepted tool that provides investors and other stakeholders with a sound basis for the assessment of stranding risks.

The tool calculates the annual carbon emissions (baseline emissions) of a given asset or portfolio and assesses the stranding risks, by making use of science-based decarbonisation pathways. To account for ongoing climate change, the tool considers the effects of grid decarbonisation, as well as the development of heating and cooling-degree days.

The paper provides property-specific carbon emission pathways, as well as valuable insight into state-of-the-art carbon risk assessment and management measures and thereby paves the way towards a low-carbon building stock. Further selected risk indicators at the asset (e.g. costs of greenhouse gas emissions) and aggregated levels (e.g. Carbon Value at Risk) are considered.

The approach described in this paper can serve as a model for the realisation of an enhanced tool with respect to other countries, leading to a globally applicable instrument for assessing stranding risks in the commercial real estate sector.

The real estate industry is endangered by the downside risks of climate change, leading to potential monetary losses and write-downs. Accordingly, this approach enables stakeholders to assess the exposure of their assets to stranding risks, based on energy and emission data.

The CRREM tool reduces investor uncertainty and offers a viable basis for investment decision-making with regard to stranding risks and retrofit planning.

The approach pioneers a way to provide investors with a profound stranding risk assessment based on science-based decarbonisation pathways.

In the literature there are numerous tests that compare the accuracy of automated valuation models (AVMs). These models first train themselves with price data and property characteristics, then they are tested by measuring their ability to predict prices. Most of them compare the effectiveness of traditional econometric models against the use of machine learning algorithms. Although the latter seem to offer better performance, there is not yet a complete survey of the literature to confirm the hypothesis.

All tests comparing regression analysis and AVMs machine learning on the same data set have been identified. The scores obtained in terms of accuracy were then compared with each other.

Machine learning models are more accurate than traditional regression analysis in their ability to predict value. Nevertheless, many authors point out as their limit their black box nature and their poor inferential abilities.

AVMs machine learning offers a huge advantage for all real estate operators who know and can use them. Their use in public policy or litigation can be critical.

According to the author, this is the first systematic review that collects all the articles produced on the subject done comparing the results obtained.

Trust has become a major issue among online shoppers. This underresearched subject will predictably determine the success or failure of e-commerce vendors. The lack of face-to-face interaction, the inability to inspect goods and services prior to purchase, and the asynchronous exchange of goods and money all contribute to the perceived risk of purchasing online and the resulting need for trust. Trust is particularly critical for small and new Internet ventures confronted by the liability of newness (Stinchcombe 1965). Lacking, among other things, a name that is readily recognized in the marketplace, entrepreneurial Internet ventures require trust if they are to succeed. The research presented in this article addresses this issue by building on the work of McKnight and colleagues and considering the effects of propensity to trust on trusting beliefs. Specifically, the author predicts that propensity to trust will significantly affect perceived ability, benevolence, and integrity but only for those individuals with limited direct experience. Based on a sample of web survey participants, the author found that propensity to trust significantly impacted perceived ability and benevolence for individuals with limited direct experience only. No statistically significant results were found for the effects of propensity to trust on perceived integrity.