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The current body of empirical research regarding the impact of trust in the cybersecurity commitment of institutions on digital payment usage has focused solely on a macro-level analysis, overlooking the intricate dynamics between institutions' cybersecurity commitments and the trust levels of digital payment users. In light of this limitation, this study aims to offer a more comprehensive understanding of this complex relationship.

A case study was conducted on digital payment users in India through the critical realist lens. To gather data, interviews and focus group discussions were conducted with digital payment users from various regions of the country.

The citizen-centric outcomes of the national cybersecurity commitment (performance and responsiveness) are the most prominent and impactful trust indicators. These outcomes play a crucial role in shaping digital payment users' perception and trust in the cybersecurity commitment of public institutions. Individuals' value positions also influence trust judgments, as it is essential to recognize the value tensions that may arise due to security implementation and their congruence with citizens' values.

The findings of this study have significant implications for policymakers. They are potentially an artifact of the security and perception of digital payment users and the cultural uniqueness of digital payment users in India.

The study proposes a holistic understanding of the relationship between institutions' cybersecurity commitments and the trust levels of digital payment users. It offers a qualitative evaluation of how digital payment users perceive and construe efficient information security management implemented by public institutions.

This study aims to empirically examine the relationship between Internet use and personal privacy risk perceptions, the mediating effect of trust and the moderating effect of satisfaction on that relationship, which is exactly conducive to the practice of personal information protection.

A moderated mediation model will be employed to test the hypothesized relationships using the 2017 Chinese Society Survey data.

The authors find that Internet use positively relates to citizens' risk perceptions toward privacy security, and trust partially mediates the relationship between Internet use and privacy risk perception. In addition, the analysis of moderating effects showed that satisfaction with social life significantly enhances the negative impact on individuals' privacy risk perceptions of interpersonal trust. The positively moderating effect of satisfaction with local governments' work mainly reveals the relationship between interpersonal trust (or institutional trust) and citizens' privacy risk perception. Moreover, satisfaction with Internet platforms positively moderates the relationship between consumer trust and privacy risk perception.

This article contributes to the social risk amplification framework by applying it to the personal privacy information protection field, which was rarely discussed before. It also enriches privacy research by identifying the internal mechanism of how Internet use influences citizens' risk perceptions towards privacy information leakage.

This paper aims to develop a multi-objective problem for scheduling the operations of trucks entering and exiting cross-docks where the number of unloaded or loaded products by trucks is fuzzy logistic. The first objective function minimizes the maximum time to receive the products. The second objective function minimizes the emission cost of trucks. Finally, the third objective function minimizes the number of trucks assigned to the entrance and exit doors.

Two steps are implemented to validate and modify the proposed model. In the first step, two random numerical examples in small dimensions were solved by GAMS software with min-max objective function as well as genetic algorithms (GA) and particle swarm optimization. In the second step, due to the increasing dimensions of the problem and computational complexity, the problem in question is part of the NP-Hard problem, and therefore multi-objective meta-heuristic algorithms are used along with validation and parameter adjustment.

Therefore, non-dominated sorting genetic algorithm (NSGA-II) and non-dominated ranking genetic algorithm (NRGA) are used to solve 30 random problems in high dimensions. Then, the algorithms were ranked using the TOPSIS method for each problem according to the results obtained from the evaluation criteria. The analysis of the results confirms the applicability of the proposed model and solution methods.

This paper proposes mathematical model of truck scheduling for a real problem, including cross-docks that play an essential role in supply chains, as they could reduce order delivery time, inventory holding costs and shipping costs. To solve the proposed multi-objective mathematical model, as the problem is NP-hard, multi-objective meta-heuristic algorithms are used along with validation and parameter adjustment. Therefore, NSGA-II and NRGA are used to solve 30 random problems in high dimensions.

The purpose of this paper is to explore and establish the contours of trust in learning analytics and to establish steps that institutions might take to address the “trust deficit” in learning analytics.

“Trust” has always been part and parcel of learning analytics research and practice, but concerns around privacy, bias, the increasing reach of learning analytics, the “black box” of artificial intelligence and the commercialization of teaching and learning suggest that we should not take stakeholder trust for granted. While there have been attempts to explore and map students’ and staff perceptions of trust, there is no agreement on the contours of trust. Thirty-one experts in learning analytics research participated in a qualitative Delphi study.

This study achieved agreement on a working definition of trust in learning analytics, and on factors that impact on trusting data, trusting institutional understandings of student success and the design and implementation of learning analytics. In addition, it identifies those factors that might increase levels of trust in learning analytics for students, faculty and broader.

The study is based on expert opinions as such there is a limitation of how much it is of a true consensus.

Trust cannot be assumed is taken for granted. This study is original because it establishes a number of concerns around the trustworthiness of learning analytics in respect of how data and student learning journeys are understood, and how institutions can address the “trust deficit” in learning analytics.

This article seeks to discuss trust within the context of public health crises using an autopoietic systems perspective that positions communication as one of its core concepts. This article will explore trust studies conducted during public health crises in this Millennium (from SARS to COVID-19 pandemics), including their problems; briefly summarize Luhmann's concept of Vertrauen; and use this concept to analyze trust issues during the COVID-19 pandemic.

This article will explore trust studies conducted during public health crises from SARS to COVID-19 pandemics, including their problems. The perspective used is an explication of Niklas Luhmann's theory regarding Vertrauen which was derived as a framework for reading empirical facts on trust issues during the COVID-19 pandemic. The research design and exploration stages were inspired by the theory of autopoiesis systems by Niklas Luhmann.

From a systems perspective, the COVID-19 pandemic has highlighted the extraordinary complexity of the linkages between social systems. Trust will continue to evolve dynamically as new variants emerge in society. Consequently, the pandemic has provided the momentum necessary for maximally exploring the concept of trust. Indonesia thus experienced significant obstacles when making and implementing disaster mitigation policies. Owing to the lack of a trust system, greater emphasis was given to control and power. There has been little preparedness to create and reinforce public trust, and this in turn has stifled efforts to stop the spread of COVID-19.

This study of trust, communication and public health crises has provided space to reflect on the development of trust within the social system. This study shows that trust can prove to be a very important factor in resolving a crisis. However, the complexity of the interrelationships of the social system can affect the quality of trust. The context of Indonesia's social system which is very complex due to population density and the dynamics of the development of its social system which is very diverse as an archipelagic country has contributed to the originality of the study of trust in times of crisis in a growing contemporary society.

The purpose of this paper is to understand the evolutionary influences on corporate social responsibility (CSR) literature at a global level and propose the future studies required to enhance the CSR literature.

This paper combines the methodologies of narrative review and historical analysis. Drawing on the archival information, this paper synthesises data from multiple sources to bring out an enhanced understanding of the external influences on the development of CSR literature.

The findings suggest that the CSR literature in the previous decades has been influenced by several management domains like strategic management, marketing management and organisational behaviour. The future research is likely to be more influenced by the perspectives of national business system, politico-legal context and practical considerations related to implementation.

This review paper presents a case for studying the practical aspects of CSR implementation and the changing nature of the external context of CSR.

The paper offers unique value by combining different review methodologies and abstraction at a global level. This paper is a significant addition to better understand the impact of business events on the progress of CSR and the external influence on CSR literature.

The purpose of this study is to extensively explore the vehicular network paradigm, challenges faced by them and provide a reasonable solution for securing these vulnerable networks. Vehicle-to-everything (V2X) communication has brought the long-anticipated goal of safe, convenient and sustainable transportation closer to reality. The connected vehicle (CV) paradigm is critical to the intelligent transportation systems vision. It imagines a society free of a troublesome transportation system burdened by gridlock, fatal accidents and a polluted environment. The authors cannot overstate the importance of CVs in solving long-standing mobility issues and making travel safer and more convenient. It is high time to explore vehicular networks in detail to suggest solutions to the challenges encountered by these highly dynamic networks.

This paper compiles research on various V2X topics, from a comprehensive overview of V2X networks to their unique characteristics and challenges. In doing so, the authors identify multiple issues encountered by V2X communication networks due to their open communication nature and high mobility, especially from a security perspective. Thus, this paper proposes a trust-based model to secure vehicular networks. The proposed approach uses the communicating nodes’ behavior to establish trustworthy relationships. The proposed model only allows trusted nodes to communicate among themselves while isolating malicious nodes to achieve secure communication.

Despite the benefits offered by V2X networks, they have associated challenges. As the number of CVs on the roads increase, so does the attack surface. Connected cars provide numerous safety-critical applications that, if compromised, can result in fatal consequences. While cryptographic mechanisms effectively prevent external attacks, various studies propose trust-based models to complement cryptographic solutions for dealing with internal attacks. While numerous trust-based models have been proposed, there is room for improvement in malicious node detection and complexity. Optimizing the number of nodes considered in trust calculation can reduce the complexity of state-of-the-art solutions. The theoretical analysis of the proposed model exhibits an improvement in trust calculation, better malicious node detection and fewer computations.

The proposed model is the first to add another dimension to trust calculation by incorporating opinions about recommender nodes. The added dimension improves the trust calculation resulting in better performance in thwarting attacks and enhancing security while also reducing the trust calculation complexity.

This study aims to investigate the rising trend of virtual influencers – digitally created characters with human-like attributes. It aims to evaluate and compare their effectiveness with human influencers in terms of brand attitudes and purchase intentions. It uncovers the mechanisms underlying the differences in effectiveness observed between virtual influencers and their human counterparts.

The research uses a 2 (influencer type: human vs virtual) × 3 (product type: functional vs symbolic vs experiential) between-subjects design. Through a pilot study (n = 334) and a main study (n = 352), the research examines the interactive effects of influencer and product type on brand attitude and purchase intentions. Hypotheses were developed and tested using moderating mediation models centered on authenticity and product–endorser fit.

Virtual influencers are perceived as less authentic than human influencers across all product types, negatively influencing brand attitude and purchase intention. However, the extent to which influencer type affects brand attitudes and purchase intentions, mediated by product–endorser fit, varies based on the product type.

This research emphasizes two key mechanisms – authenticity and product–endorser fit – influencing the effectiveness of virtual influencers. It suggests that aligning virtual influencers with suitable product types can offset their perceived authenticity deficit, significantly affecting their endorsement effectiveness.

Debates regarding climate change risk perception (CCRP), particularly its scale and impact on social and environmental sustainability, have continued for decades. CCRP is experiencing a renaissance with an increased focus on environmentally relevant behaviors to mitigate the effects of climate change. However, CCRP lacks investigation from the employee perspective. Supported by the social exchange and value–belief–norm theories, this study aims to address the impact of employees’ CCRP on their proenvironmental behavior (PEB) via the moderating roles of environmental values and psychological contract breach.

The nonprobability convenience sampling technique was used to collect survey data from a sample of 299 employees across 138 manufacturing firms in Pakistan.

The results show that employees’ CCRP positively impacts their PEB and that this relationship is moderated by their environmental values and psychological contract breach. Specifically, environmental values strengthen the CCRP–PEB relationship, while psychological contract breach weakens it.

The findings of the study emphasize useful guidance for managers and practitioners as a future avenue to restructure the climate change framework by emphasizing the conditions (i.e. environmental values and psychological contract breach). In doing so, the study is beneficial for managers and practitioners in helping to increase employees’ PEB through the development of climate change action plans.

To the best of the authors’ knowledge, this study is one of the first investigations into CCRP–employees’ PEB nexus in the developing country context. The study incorporates social exchange and value–belief–norm theory, which serve as the CCRP’s theoretical underpinnings. The findings advance the new knowledge about a firm’s social responsibility to achieve the sustainable development goals outlined in the UN’s 2030 Agenda.

This study aims to systematically review various behavioral biases that impact an investor’s decision-making process. The prime objective of this paper is to thematically explore the behavioral bias literature and propose a comprehensive framework that can elucidate a more reasonable explanation of changes in financial markets and investors’ behavior.

Systematic literature review (SLR) methodology is applied to a portfolio of 71 peer-reviewed articles collected from different electronic databases between 2007 and 2021. Content analysis of the extant literature is performed to identify the research themes and existing gaps in the literature.

This research identifies publication trends of the behavioral biases literature and uncovers 24 different biases that impact individual investors’ decision-making. Through thematic analysis, an attribute–consequence–impact framework is proposed that explains different biases leading to individual investors’ irrationality. The study further proposes directions for future research by applying the theory–characteristics–context–methodology framework.

The results of this research will help scholars and practitioners in understanding the existence of various behavioral biases and assist them in identifying potential strategies which can evade the negative effects of these biases. The findings will further help the financial service providers to understand these biases and improve the landscape of financial services.

The essence of the current paper is the application of the SLR method on 24 biases in the area of behavioral finance. To the best of the authors’ knowledge, this study is the first attempt of its kind which provides a methodical and comprehensive compilation of both cognitive and emotional behavioral biases that affect the individual investor’s decision-making.