Search results

Globally, there is a growing proportion of disabled people as a result of different circumstances. This growth generates attention and leads to ways to integrate the affected population into society. Addressing such disability and integration is particularly important at buildings level, enabling and expanding the scope of activities for people with disabilities (PWDs). The rising number of PWDs and the need to integrate them into society create a need for action to improve their living condition and integration into society. This study aims to examine the issue of accessibility for PWDs in higher education facilities in Qatar.

Addressing accessibility at buildings level is particularly important in higher education because it enables inclusion in training and education and increases the potential for productive engagement in society. The study aims to develop an objective tool to assess and measure accessibility in educational institutions. Five selected buildings were examined and evaluated at Qatar University based on proximity, multi-use, vertical and horizontal circulation availability. The survey respondents were randomly selected. An existing assessment method was used in surveying respondents, including those with and without disabilities.

A comparative study was conducted to explore the discrepancy between facility users with and without disability, indicating the gap in existing tools.

The developed tool generates the same outcome when conducted by different assessors, indicating the level of compliance and percentage met as a benefit, not a focus. It allows professionals and non-professionals with minimal experience to conduct the assessment.

Prefabricated components sustainable supplier (PCSS) selection is critical to the success of prefabricated projects. However, limited studies have addressed the uncertainty and complexities during the selection process, particularly in multi-criterion group decision-making (MCGDM) circumstances. Hence, the research aims to develop a group decision-making model using a modified fuzzy MCGDM approach for PCSS selection under uncertain situation.

The proposed study develops a framework for sorting decisions in PCSS selection by using the hesitant fuzzy technique for order preference by similarity to ideal solution (HF-TOPSIS) method. The maximum consistency (MC) model is used to calculate the weights of decision makers (DMs) based on the cardinality and sequence of decision data.

The proposed framework has been successfully applied and illustrated in the case example of CB01 contract section in Hong Kong-Zhuhai-Macao Bridge (HZMB) megaproject. The results show various complicated decision-making scenarios can be addressed through the proposed approach. The MC model is able to calculate the weights of DMs based on the cardinality and sequence of decision data.

The research contributes to improving accuracy and reliability decision-making processes for PCSS selection, especially under hesitant and fuzzy situations in prefabricated megaprojects.

This paper aims to explore consumers’ voluntary disposition practices through swapping events organised by community-based enterprises. The paper investigates consumers’ decision-making strategies and factors affecting voluntary clothing disposition via public swapping events across the UK.

This paper investigates UK swapping events, through conducting 18 semi-structured consumer interviews. Data were transcribed and analysed using the seven-step guide proposed by Easterby-Smith et al. (2018).

Findings indicate that within community-based enterprises an implicit social contract emerges between the enterprises and swappers which has an influence on the clothing brought to swaps, thereby impacting the competence and meaning elements of practice. This is linked to peer-pressure susceptibility which affects consumers’ participation in swapping. The findings further reveal an emerging consumer strategy aiding decision-making process regarding items brought to swaps. The use of a particular strategy is found to be linked with the respective level of swapping expertise.

Though the interviews provide a rich narrative, this paper is limited by its sample size meaning data cannot be generalised. Although the data is limited by singular country perspective, research participants were recruited from across the UK, thus, offering a broad picture of the swapping practice.

This paper contributes to and advances an understanding of swapping events organised by community-based enterprises. The theory of social practice lens offers a unique viewpoint on the elements influencing the consumers’ decision-making process with reference to voluntary disposition.

Introduction: When people need to take decisions, being economic decisions or otherwise, their decisions tend to rely on information the brain has already processed, and this includes the resources that the person has already invested. This is called sunk cost bias in the behavioural economics literature. On the other hand, mental practices could lead to the mental accounting bias, where people allocate a different value to a fixed amount of money, depending on circumstances.

Purpose: In this chapter, both biases mental accounting and sunk cost are investigated for the tourism industry in Turkey.

Methodology: The topic is researched through scenario-based questions and the Chi-square Automatic Interaction Detector (CHAID) method is applied.

Findings: As a result, it could be reported that people, regardless of gender, fall into sunk cost and mental accounting biases in decisions relating to their vacations. Mental accounting biases can be primarily explained using the scenario questions posed rather than gender, education, and income while sunk cost bias is explained by status, ‘being s university student’ and ‘income level’.

Practical implications: Rapid price changes in the tourism industry can disturb consumers who are mental accounting and sunk cost biased. So, they can change their holiday preferences or be dissatisfied with it and give negative feedback.

The purpose of this study is to explain the following questions: First, whether the executive equity incentive has an impact on enterprise innovation and digital transformation; Second, if there is any influence, whether there is difference between state-owned enterprises and private enterprises in the research conclusions; Third, whether the digital transformation of enterprises has had an intermediary effect between executive equity incentive and enterprise innovation; Fourth, whether the proportion of independent directors in the corporate governance mechanism has a regulatory effect.

In the context of China's promotion of “digital China” construction and high-quality development of economic innovation, this paper takes Shanghai and Shenzhen A-share listed companies in 2011–2019 as a sample, empirically studies the linear and nonlinear relationship between executive equity incentive and enterprise digital transformation and innovation, and further considers the regulatory effect of heterogeneous property rights and the proportion of independent directors, with a view to improving the reform of China's enterprise equity incentive system make contributions to enterprise innovation and digital transformation.

The results show that executive equity incentive has a positive role in promoting enterprise digital transformation and innovation, and enterprise digital transformation has a positive role in promoting enterprise innovation; Digital transformation of enterprises has a partial intermediary effect between executive equity incentive and enterprise innovation.

First, it expands the research on the economic consequences of enterprise salary incentive system. Second, it expands the research on the specific role path of enterprise digital economy transformation. Third, provide new ideas for the reform of corporate governance mechanism.

Through this study, the aim is to provide reference for the improvement of CEO related theories, the reform of internal governance mechanisms and compensation systems in enterprises, and ultimately contribute to economic and social development and the achievement of dual carbon goals.

In the context of accelerating the implementation of the “dual carbon” goal and promoting sustainable economic and social development, this paper builds a panel data model based on the panel data of 31 provinces in China from 2011 to 2019 to empirically test the impact of CEO stability on green innovation and total factor productivity of enterprises.

The results show that CEO stability has a positive role in promoting enterprise total factor productivity and green innovation and enterprise green innovation also has a positive role in promoting total factor productivity; Heterogeneity testing found that CEO stability has no significant impact on green innovation in state-owned enterprises. Corporate green innovation has a partial mesomeric effect between CEO stability and total factor productivity; The proportion of independent directors has a negative moderating effect on CEO stability and corporate green innovation, while equity incentive has a positive moderating effect on the relationship between CEO stability, corporate total factor productivity and green innovation.

First, it develops the research on CEO stability and its economic consequences. Second, it expands the impact of CEO stability on heterogeneous enterprises. Third, provides new ideas for the reform of internal governance mechanism and salary system of enterprises.

The authors consider the quasi maximum likelihood (MLE) estimation of dynamic panel models with interactive effects based on the Ahn et al. (2001, 2013) quasi-differencing methods to remove the interactive effects. The authors show that the quasi-difference MLE (QDMLE) over time is inconsistent when N→∞ whether T is fixed or goes to infinity. On the other hand, the QDMLE is consistent and asymptotically unbiased if the difference is taken over individuals when T is large whether N is fixed or large. Monte Carlo studies are conducted to compare the performance of the QDMLE using different quasi-difference methods.

This paper aims to present a literature review of the most recent optimisation methods applied to Credit Scoring Models (CSMs).

The research methodology employed technical procedures based on bibliographic and exploratory analyses. A traditional investigation was carried out using the Scopus, ScienceDirect and Web of Science databases. The papers selection and classification took place in three steps considering only studies in English language and published in electronic journals (from 2008 to 2022). The investigation led up to the selection of 46 publications (10 presenting literature reviews and 36 proposing CSMs).

The findings showed that CSMs are usually formulated using Financial Analysis, Machine Learning, Statistical Techniques, Operational Research and Data Mining Algorithms. The main databases used by the researchers were banks and the University of California, Irvine. The analyses identified 48 methods used by CSMs, the main ones being: Logistic Regression (13%), Naive Bayes (10%) and Artificial Neural Networks (7%). The authors conclude that advances in credit score studies will require new hybrid approaches capable of integrating Big Data and Deep Learning algorithms into CSMs. These algorithms should have practical issues considered consider practical issues for improving the level of adaptation and performance demanded for the CSMs.

The results of this study might provide considerable practical implications for the application of CSMs. As it was aimed to demonstrate the application of optimisation methods, it is highly considerable that legal and ethical issues should be better adapted to CSMs. It is also suggested improvement of studies focused on micro and small companies for sales in instalment plans and commercial credit through the improvement or new CSMs.

The economic reality surrounding credit granting has made risk management a complex decision-making issue increasingly supported by CSMs. Therefore, this paper satisfies an important gap in the literature to present an analysis of recent advances in optimisation methods applied to CSMs. The main contribution of this paper consists of presenting the evolution of the state of the art and future trends in studies aimed at proposing better CSMs.

In this study the authors aim to outline new ways of information extraction for automated valuation models, which in turn would help to increase transparency in valuation procedures and thus contribute to more reliable statements about the value of real estate.

The authors hypothesize that empirical error in the interpretation and qualitative assessment of visual content can be minimized by collating the assessments of multiple individuals and through use of repeated trials. Motivated by this problem, the authors developed an experimental approach for semi-automatic extraction of qualitative real estate metadata based on Comparative Judgments and Deep Learning. The authors evaluate the feasibility of our approach with the help of Hedonic Models.

The results show that the collated assessments of qualitative features of interior images show a notable effect on the price models and thus over potential for further research within this paradigm.

To the best of the authors’ knowledge, this is the first approach that combines and collates the subjective ratings of visual features and deep learning for real estate use cases.

This paper investigates the probable differential impact of the confirmed cases of COVID-19 on the equities markets of G7 and Nordic countries to ascertain possible interdependencies, diversification and safe haven prospects in the era of the COVID-19 pandemic over the short-, intermediate- and long-term horizons.

The authors apply a unique methodology in a denoised frequency-domain entropy paradigm to the selected equities markets (Li et al. 2020).

The authors’ findings reinforce the operability of the entrenched market dynamics in the COVID-19 pandemic era. The authors divulge that different approaches to fighting the pandemic do not necessarily drive a change in the deep-rooted fundamentals of the equities market, specifically for the studied markets. Except for an extreme case nearing the end (start) of the short-term (intermediate-term) between Iceland and either Denmark or the US equities, there exists no potential for diversification across the studied markets, which could be ascribed to the degree of integration between these markets.

The authors’ findings suggest that politicians should pay closer attention to stock market fluctuations as well as the count of confirmed COVID-19 cases in their respective countries since these could cause changes to market dynamics in the short-term through investor sentiments.

The authors measure the flow of information from COVID-19 to G7 and Nordic equities using the entropy methodology induced by the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), which is a data-driven technique. The authors employ a larger sample period as a result of this, which is required to better comprehend the subtleties of investor behaviour within and among economies – G7 and Nordic geographical blocs – which largely employed different approaches to fighting the COVID-19 pandemic. The authors’ focus is on diverging time horizons, and the ICEEMDAN-based entropy would enable us to measure the amount of information conveyed to account for large tails in these nations' equity returns. Furthermore, the authors use a unique type of entropy known as Rényi entropy, which uses suitable weights to discern tailed distributions. The Shannon entropy does not account for the fact that financial assets have fat tails. In a pandemic like COVID-19, these fat tails are very strong, and they must be accounted for.