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The purpose of this study is to investigate possible drivers of loyalty amongst Islamic banking customers in Gauteng, South Africa. We ponder the relationships of service fairness (a secondorder reflective construct) with perceived value, satisfaction, and loyalty.

Data were obtained from Islamic banking customers in South Africa using interview-administered questionnaires. A total of 350 responses were perceived as being suitable for data analysis. The measurement and structural models were measured through structural equation modelling.

Service fairness and perceived value were found to be important drivers of loyalty within this context.

This study demonstrates that service fairness and perceived value are precursors to the future loyalty intentions of Islamic banking customers. As such, they should be nurtured as key elements of the relationship building process.

The study guides South African Islamic banks and South African banks with Islamic windows to better understand how service fairness (interactional, procedural and distributive) fosters satisfaction, perceived value and loyalty (attitudinal and behavioural).

Enhancing comprehension of the relationship between service fairness and customer loyalty, with satisfaction and perceived value playing intermediary roles, represents an unexplored avenue in academic research within the context of Islamic banking in an emerging African market.

The aim of this study is to empirically explore and analyze the concrete tasks of output measurement and the inherent challenges related to these tasks in a traditional and autonomous professional public work setting – the judicial system.

The analysis of the tasks is based on a categorization of general performance measurement motives (control-motivate-learn) and main stakeholder levels (society-organization-professionals). The analysis is exploratory and conducted as an empirical content analysis on materials and reports produced in two performance improvement projects conducted in European justice organizations.

The identified main tasks in the different categories are related to managing resources, controlling performance deviations, and encouraging improvement and development of performance. Based on the results, key improvement areas connected to output measurement in professional public organizations are connected to the improvement of objectivity and fairness in budgeting and work allocation practices, improvement of output measures' versatility and informativeness to highlight motivational and learning purposes, improvement of professional self-management in setting output targets and producing outputs, as well as improvement of organizational learning from the output measurement.

The paper presents empirically founded practical examples of challenges and improvement opportunities related to the tasks of output measurement in professional public organization.

This paper fulfils an identified need to study how general performance management motives realize as concrete tasks of output measurement in justice organizations.

This chapter explores the nature of military law and IHL during the cold war period. It explores what treaties were completed, Additional Protocols I and II of the 1949 Geneva Conventions, the ad hoc international tribunals of the 1990s and 2000s, and examines the ICJ’s ruling of the legality of nuclear weapons.

The study aims to explore how integrating recent fundamental values (RFVs) from conventional accounting studies enhances the accuracy of a machine learning (ML) model for predicting stock return movement in Indonesia.

The study uses multilayer perceptron (MLP) analysis, a deep learning model subset of the ML method. The model utilizes findings from conventional accounting studies from 2019 to 2021 and samples from 10 firms in the Indonesian stock market from September 2018 to August 2019.

Incorporating RFVs improves predictive accuracy in the MLP model, especially in long reporting data ranges. The accuracy of the RFVs is also higher than that of raw data and common accounting ratio inputs.

The study uses Indonesian firms as its sample. We believe our findings apply to other emerging Asian markets and add to the existing ML literature on stock prediction. Nevertheless, expanding to different samples could strengthen the results of this study.

Governments can regulate RFV-based artificial intelligence (AI) applications for stock prediction to enhance decision-making about stock investment. Also, practitioners, analysts and investors can be inspired to develop RFV-based AI tools.

Studies in the literature on ML-based stock prediction find limited use for fundamental values and mainly apply technical indicators. However, this study demonstrates that including RFV in the ML model improves investors’ decision-making and minimizes unethical data use and artificial intelligence-based fraud.

Warehouses are under pressure to operate as efficiently as possible. In pursuit of attaining high efficiency in the order picking process, the warehouse manager must take several planning decisions, typically supported by a central planning system. However, highly centralised work erodes the autonomy of warehouse workers, interfering with worker well-being and productivity. This study holistically explores the impact of a work system with more decision autonomy for order pickers.

We conduct a unique field experiment in a real-world warehouse and use a within-subjects design to compare two work systems, one with worker autonomy and one without. 18 permanent employees participate in our study, in which we measure both psychosocial and physical well-being as well as productivity. Post-experimental interviews are conducted to delve deeper into the observed effects.

Our study illustrates that involving order pickers in operational decisions can benefit their job satisfaction and motivation without compromising productivity. Although we fail to find significance at the conventional level (α = 0.05), we do find marginally significant effects of our treatment on physical well-being aspects. Furthermore, our intervention invoked a highly positive user experience.

We show that slightly loosening tight process control results in organisational and individual benefits without endangering smooth operational flows. The warehouse in this paper acknowledged this and decided to permanently work according to this philosophy.

This study is the first to holistically explore the effects of a participatory work setting in a real-world warehouse.

This paper reviews the literature on disability inclusion (DI) in supply and demand chains of hospitality and tourism (H&T) organisations. The purpose of this study is to assess disability support and interventions within H&T organisations. Through the assessment, we identified gaps to recommend H&T scholars’ and practitioners’ knowledge of DI from new perspectives.

An integrative review was conducted to examine the published evidence on DI in H&T organisations. This study used high-ranking H&T journals from the Scopus and Web of Science databases between 2001 and 2023. In total, 101 empirical papers met the criteria for the review analysis.

DI focuses heavily on customer disabilities, with scant research on DI in H&T employment. The review emphasises the critical need for empirical research into the varied disability employment ecosystem within H&T organisations, focusing on social integration for inclusive workplaces.

This study contributes to the H&T literature, which previously overlooked the disability context in diversity. The research offers strategies for creating inclusive environments in the H&T industry for disabled consumers and producers.

Experts may adjust their assessments through communication and mutual influence, and this dynamic evolution relies on the spread of internal trust relationships. Due to differences in educational backgrounds and knowledge experiences, trust relationships among experts are often incomplete. To address such issues and reduce decision biases, this paper proposes a probabilistic linguistic multi-attribute group decision consensus model based on an incomplete social trust network (InSTN).

In this paper, we first define the new trust propagation operators based on the operations of Probability Language Term Set (PLTS) with algebraic t-conorm and t-norm, which are combined with trust aggregation operators to estimate InSTN. The adjustment coefficients are then determined through trust relations to quantify their impact on expert evaluation. Finally, the particle swarm algorithm (PSO) is used to optimize the expert evaluation to meet the consensus threshold.

This study demonstrates the feasibility of the method through the selection of treatment plans for complex cases. The proposed consensus model exhibits greater robustness and effectiveness compared to traditional methods, mainly due to the effective regulation of trust relations in the decision-making process, which reduces decision bias and inconsistencies.

This paper introduces a novel probabilistic linguistic multi-attribute swarm decision consensus model based on an InSTN. It proposes a redefined trust propagation and aggregation approach to estimate the InSTN. Moreover, the computational efficiency and decision consensus accuracy of the proposed model are enhanced by using PSO optimization.

With the change in climate and increased pollution, there has been a need to reduce environmental carbon emissions. This research aims to develop a framework for reducing environmental carbon footprints to improve business performance.

This study uses Scientific Procedures and Rationales for the Systematic Literature Reviews (SPAR-4-SLR) approach. Articles are searched in the Scopus database using various keywords and their combinations. It resulted in 651 articles initially. After applying different screening criteria, 61 articles were considered for the final study.

This study provided four themes and sub-themes within each category. This research also used theories, methodologies and context (TMC) framework to provide future research questions. This study used the antecedents, decisions and outcomes (ADO) framework for synthesising the findings. The ADO framework will help to achieve carbon neutrality and improve firms' supply chain (SC) performance.

This study provides theoretical implications by highlighting the various theories that can be used in future research. This study also states the practical implications for the achievement of carbon neutrality by the firms.

This study contributes to the literature linking carbon neutrality with business performance.

The current study explores the role of social psychological factors on organizational commitment. It examined the relationships between organizational fairness, inclusion, diversity, sexism, psychological safety and affective commitment.

The study adopted a cross-sectional survey method where data were collected from staff across six higher education institutions in the United Kingdom. A total of 416 responses were collected and the data were analyzed by employing independent sample t-tests to explore gender differences regarding organizational diversity, inclusion, fairness, sexism, psychological safety and affective commitment. The mediating role of psychological safety in the effects of organizational diversity, fairness, inclusion and sexism on staff’s commitment to the organization was evaluated through a series of simple mediation models. Finally, the mediated effect of organizational social psychological factors on affective commitment through psychological safety, moderated by gender was tested.

As hypothesized, men had higher perceptions of organizational inclusion and diversity and viewed their organization as less sexist. Psychological safety mediated the positive effects of organizational diversity, fairness, inclusion and sexism on staff’s affective commitment to the organization. Organizational diversity and sexism played a bigger part in how women evaluate their organizational environment as safe to take risks and be themselves and consequently on how they commit to the organization. On the other hand, organizational fairness and inclusion seem more closely tied to men’s evaluation of their organization as safe to be themselves and consequently stronger commitment.

The findings shed light on the underlying mechanisms that shape affective commitment, that can lead to more inclusive work environments and contribute to systemic change in the Higher Education context.

While corporate social responsibility (CSR) has been gaining support for the role it plays in employee outcomes, such as organizational identification (OID), the view of CSR from a social identity perspective is underdeveloped. This conceptual chapter explores the role of social identity processes grounded in organizational justice to develop a model of CSR attributions and the moderating role these attributions play in organizational member outcomes. CSR is understood as the relational processes happening with stakeholders, and these relationships engage specific organizational identity orientations. The social identity process flows from there, resulting in CSR attributions including strategic, relational, and virtuous. Using social identity, organizational identity, and organizational justice, this chapter makes two specific contributions: a CSR attribution typology grounded in organizational justice and the moderating impact of these attributions between activated justice dimensions and resulting organizational member outcomes.