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In this paper, the philosophy of John Rawls, known as “Justice as Fairness,” is discussed. This philosophy examines the responsibilities social actors hold toward their societal organizations. From an Islamic perspective, justice is pivotal in safeguarding collective interests, aligning with Rawls' conviction that just societies nurture happiness and foster well-being across various life aspects. To achieve customer welfare, our viewpoint underscores the importance of justice in reflecting on consumer well-being from both Rawls’ theory and Islamic perspectives.

We adopt a conceptual approach where secular views of Rawls’ “Theory of Justice” are merged with the Islamic view, resulting in novel insights regarding the hermeneutics involved in the notion of justice and the preservation of consumer well-being.

Our analysis reveals that John Rawls' “A Theory of Justice” aligns with the Islamic perspective in several respects yet diverges in others, notably in the concepts of pre-creation consciousness and divine guidance. These distinctions are emphasized in our paper.

Our paper presents a perspective on justice founded on the concepts of the “Original Position” and the “Veil of Ignorance.” The commentary explores consumer well-being by integrating Rawls' principles with an analysis that elucidates the role justice plays in enhancing societal welfare.

This study aims to enhance merging of additive manufacturing (AM) techniques with powder injection molding (PIM). In this way, the prototypes could be 3D-printed and mass production implemented using PIM. Thus, the surface properties and mechanical performance of parts produced using powder/polymer binder feedstocks [material extrusion (MEX) and PIM] were investigated and compared with powder manufacturing based on direct metal laser sintering (DMLS).

PIM parts were manufactured from 17-4PH stainless steel PIM-quality powder and powder intended for powder bed fusion compounded with a recently developed environmentally benign binder. Rheological data obtained at the relevant temperatures were used to set up the process parameters of injection molding. The tensile and yield strengths as well as the strain at break were determined for PIM sintered parts and compared to those produced using MEX and DMLS. Surface properties were evaluated through a 3D scanner and analyzed with advanced statistical tools.

Advanced statistical analyses of the surface properties showed the proximity between the surfaces created via PIM and MEX. The tensile and yield strengths, as well as the strain at break, suggested that DMLS provides sintered samples with the highest strength and ductility; however, PIM parts made from environmentally benign feedstock may successfully compete with this manufacturing route.

This study addresses the issues connected to the merging of two environmentally efficient processing routes. The literature survey included has shown that there is so far no study comparing AM and PIM techniques systematically on the fixed part shape and dimensions using advanced statistical tools to derive the proximity of the investigated processing routes.

This study aims to investigate the influence of information and communication technology (ICT) capabilities on supply chain fraud and sustainability within the context of Ghana’s small and medium-sized enterprises (SMEs). Additionally, the research explores the mediating role of supply chain fraud in the relationship between ICT capabilities and supply chain sustainability.

Data were collected from 102 respondents within Ghana’s SME sector, and the research employed the dynamic capability theory as the conceptual framework. The study utilized partial least squares-structural equation modeling (PLS-SEM) to develop and analyze the proposed model.

The results of the study reveal a significant reduction in supply chain fraud attributable to enhanced ICT capabilities within Ghanaian SMEs. Moreover, ICT capabilities exert a significant positive influence on supply chain sustainability. Importantly, supply chain fraud emerges as a mediator, elucidating its role at the nexus of supply chain sustainability and ICT capabilities.

This research contributes to the limited body of evidence on the interconnectedness of ICT capabilities, supply chain fraud and supply chain sustainability, particularly within the context of Ghanaian SMEs. Notably, this study pioneers an examination of the mediating impact of supply chain fraud on the relationship between ICT capabilities and supply chain sustainability.

This study examines how assurors make sense of sustainability assurance (SA) work and how interactions with assurance team members and clients shape assurors’ sensemaking and their actual SA work.

To obtain detailed accounts of how SA work occurs on the ground, this study explores three SA engagements by interviewing the main actors involved, both at the client firms and at their Big Four assurance providers.

Individual assurors’ (i.e. partners and other team members) sensemaking of SA work results in the crafting of their logics of action (LoAs), that is, their meanings about the objectives of SA work and how to conduct it. Without organizational socialization, team members may not arrive at shared meanings and deviate from the team-wide assurance approach. To fulfill their objectives for SA work, assurors may engage in socialization with clients or assume a temporary role. Yet, the role negotiations taking place in the shadows of the scope negotiations determine their default role during the engagement.

Two options are available to help SA statement users gauge the relevance of SA work: either displaying the SA work performed or making it more uniform.

This study theoretically grounds how assurors make sense of SA work and documents how (the lack of) professional socialization, organizational socialization and socialization of frequent interaction partners at the client shape actual SA work. Thereby, it unravels the SA work concealed behind SA statements.

A region’s transforming care partnership identified that autistic adults without an intellectual disability (ID) may be falling through gaps in services when presenting with a significant emotional and/or behavioural need in the absence of a mental health diagnosis. The region’s intensive support teams (ISTs) for adults with ID therefore piloted a short-term “behavioural support service” for this population. The purpose of this paper is to evaluate this pilot.

This study represents a mixed-methods service evaluation over a four year pilot period. The quantitative component examined referral rates and demographic data of accepted and declined referrals; and length of referral episodes and Health of The Nation Outcomes Scores (HoNOS) for accepted referrals. The qualitative component used thematic analysis to identify key themes relating to reasons for referral, clinical/therapeutic needs, and the models of support that most informed assessments and interventions at individual and systems levels.

The ISTs accepted 30 referrals and declined 53. Most accepted referrals were male (83%), and under 24 years old (57%). Average HoNOS scores were above the thresholds generally associated with hospital admission. Key qualitative themes were: transitional support; sexual risks/vulnerabilities; physical aggression; domestic violence; and attachment, trauma and personality difficulties. Support mostly followed psychotherapeutic modalities couched in trauma, attachment and second- and third-wave cognitive behavioural therapies. Positive Behaviour Support (PBS) did not emerge as a model of preference for service users or professionals.

This project represents one of the first of this type for autistic adults without an ID in the UK. It provides recommendations for future service development and research, with implications for Transforming Care policy and guidance.

Financial services industry is increasingly showing interest in automated financial advisors, or robo-advisors, with the aim of democratizing access to financial advice and stimulating investment behavior among populations that were previously less active and less served. However, the extent to which consumers trust this technology influences the adoption of rob-advisors. The resemblance to a human, or anthropomorphism, can provide a sense of social presence and increase trust.

In this paper, we conduct an experiment (N = 223) to test the effect of anthropomorphism (low vs medium vs high) and gender (male vs female) of the robo-advisor on social presence. This perception, in turn, enables consumers to evaluate personality characteristics of the robo-advisor, such as competence, warmth, and persuasiveness, all of which are related to trust in the robo-advisor. We separately conduct an experimental study (N = 206) testing the effect of gender neutrality on consumer responses to robo-advisory anthropomorphism.

Our results show that consumers prefer human-alike robo-advisors over machinelike or humanoid robo-advisors. This preference is only observed for male robo-advisors and is explained by perceived competence and perceived persuasiveness. Furthermore, highlighting gender neutrality undermines the positive effect of robo-advisor anthropomorphism on trust.

We contribute to the body of knowledge on robo-advisor design by showing the effect of robot’s anthropomorphism and gender on consumer perceptions and trust. Consequently, we offer insightful recommendations to promote the adoption of robo-advisory services in the financial sector.

Sarcasm is a linguistic expression that usually carries the opposite meaning of what is being said by words, thus making it difficult for machines to discover the actual meaning. It is mainly distinguished by the inflection with which it is spoken, with an undercurrent of irony, and is largely dependent on context, which makes it a difficult task for computational analysis. Moreover, sarcasm expresses negative sentiments using positive words, allowing it to easily confuse sentiment analysis models. This paper aims to demonstrate the task of sarcasm detection using the approach of machine and deep learning.

For the purpose of sarcasm detection, machine and deep learning models were used on a data set consisting of 1.3 million social media comments, including both sarcastic and non-sarcastic comments. The data set was pre-processed using natural language processing methods, and additional features were extracted and analysed. Several machine learning models, including logistic regression, ridge regression, linear support vector and support vector machines, along with two deep learning models based on bidirectional long short-term memory and one bidirectional encoder representations from transformers (BERT)-based model, were implemented, evaluated and compared.

The performance of machine and deep learning models was compared in the task of sarcasm detection, and possible ways of improvement were discussed. Deep learning models showed more promise, performance-wise, for this type of task. Specifically, a state-of-the-art model in natural language processing, namely, BERT-based model, outperformed other machine and deep learning models.

This study compared the performance of the various machine and deep learning models in the task of sarcasm detection using the data set of 1.3 million comments from social media.

Political Science in the United States has focused too much on variable-oriented, quantitative methods and thus lost its ability to ask “big questions.” Stein Rokkan (d. 1979) was an eminent comparativist who asked big questions and provided such qualitative tools as conceptual maps, grids, and clustered comparisons. Ibn Khaldun (d. 1406), arguably the first social scientist, also asked big questions and provided a universal explanation about the dialectical relationship between nomads and sedentary people. This article analyzes to what extent Ibn Khaldun's concepts of asabiyya and sedentary culture help understand the rise and fall of the Muslim civilization. It also explores my alternative, class-based perspective in Islam, Authoritarianism, and Underdevelopment. Moreover, the article explores how Rokkan's analysis of cultural, geographical, economic, and religio-political variations within Western European states can provide insights to the examination of such variations in the Muslim world.

This study aims to obtain methods to identify and find the place of damage, which is one of the topics that has always been discussed in structural engineering. The cost of repairing and rehabilitating massive bridges and buildings is very high, highlighting the need to monitor the structures continuously. One way to track the structure's health is to check the cracks in the concrete. Meanwhile, the current methods of concrete crack detection have complex and heavy calculations.

This paper presents a new lightweight architecture based on deep learning for crack classification in concrete structures. The proposed architecture was identified and classified in less time and with higher accuracy than other traditional and valid architectures in crack detection. This paper used a standard dataset to detect two-class and multi-class cracks.

Results show that two images were recognized with 99.53% accuracy based on the proposed method, and multi-class images were classified with 91% accuracy. The low execution time of the proposed architecture compared to other valid architectures in deep learning on the same hardware platform. The use of Adam's optimizer in this research had better performance than other optimizers.

This paper presents a framework based on a lightweight convolutional neural network for nondestructive monitoring of structural health to optimize the calculation costs and reduce execution time in processing.