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Considering the ongoing discourse on diversity, equity and inclusion, brands aim to develop marketing campaigns that demonstrate respect for all individuals. Despite these intentions, many advertisements still provoke strong negative reactions from consumers due to brand transgressions in social media marketing campaigns that violate these values. The purpose of this paper is to analyze the repercussions that such social media marketing campaigns have on brands, categorizing these campaigns as brand transgressions in social media advertising.

This research uses a mixed-method design that includes semi-structured interviews (Study 1), a content analysis (Study 2) and an online experiment (Study 3).

This paper clarifies the elements that qualify as brand transgressions in advertising within the diversity, equity and inclusion discourse. The negative electronic word-of-mouth (e-WOM) associated with brand transgressions in advertising comprises negative emotions (e.g. anger, contempt, disgust and hate) and behavioural intentions to penalize the brand (e.g. negative word-of-mouth, brand avoidance and protest behaviours). The negative e-WOM stemming from these transgressions amplifies the adverse consequences for consumer–brand relationships by negatively influencing other consumers through sympathy towards the offended parties.

This paper offers brand managers guidelines for preventing and managing negative consumer reactions towards brands based on their responses to marketing campaigns that contradict the principles of diversity, equity and inclusion.

This paper contributes to the literature on brand transgressions related to diversity, equity and inclusion values by exploring their impact on consumer–brand relationships and highlighting the pivotal role of sympathy in perpetuating negative consequences.

This study aimed to identify and analyse the key factors influencing the adoption of e-government services and to discern their implications for various stakeholders, from policymakers to platform developers.

Through a comprehensive review of existing literature and detailed analysis of multiple studies, this research organised the influential factors based on their effect: highest, direct and indirect. The study also integrated findings to present a consolidated view of e-government adoption drivers.

The research found that users' behaviour, attitude, optimism bias and subjective norms significantly shape their approach to e-government platforms. Trust in e-Government (TEG) emerged as a critical determinant, with security perceptions being of paramount importance. Additionally, non-technical factors, such as cultural, religious and social influences, play a substantial role in e-government adoption decisions. The study also highlighted the importance of performance expectancy, effect expectancy and other determinants influencing e-government adoption.

While numerous studies have explored e-government adoption, this research offers a novel classification based on the relative effects of each determinant. Integrating findings from diverse studies and emphasising non-technical factors introduce an interdisciplinary approach, bridging the gap between information technology and fields like sociology, anthropology and behavioural sciences. This integrative lens provides a fresh perspective on the topic, encouraging more holistic strategies for enhancing e-government adoption globally.

The paper aims to address the gap in literature on effective disaster governance in Southeast Asia, with a focus on COVID-19 management in Malaysia. It explores the intersection of localized hazard understandings, disaster governance and policy implications, emphasizing systemic vulnerability, resilience enhancement and improving risk communication and resource management. The goal is to offer insights and frameworks for policymakers, researchers and practitioners to navigate disaster governance complexities in Southeast Asia, marked by unique challenges and vulnerabilities.

The study employs an analytical descriptive methodology, involving a comprehensive literature review of academic articles, official reports and case studies related to disaster governance in Malaysia. It focuses on evaluating Malaysia's disaster governance's adaptability, inclusivity and coordination, considering policy implementation, stakeholder engagement and resource allocation. This approach facilitates a detailed analysis of the effectiveness of Malaysia's strategies in integrating environmental and health disaster management systems.

The findings reveal that Malaysia's disaster governance, particularly during COVID-19, has been challenged by the need for rapid adaptation and coordination. While early lockdowns and a robust vaccination program were effective, they also highlighted issues in healthcare system capacity and socioeconomic impacts. The study emphasizes the importance of integrated, resilient disaster management strategies that consider public health, environmental risks and socio-economic factors. It underscores the need for improved stakeholder collaboration, infrastructure resilience and climate change mitigation in disaster governance.

The research is limited by a lack of specific data on Malaysia's unique socio-cultural, economic and political dynamics in disaster response. This gap hinders the development of nuanced, context-specific policies and practices for effective disaster governance, emphasizing the need for more region-focused studies.

The study highlights the need for Malaysia to develop more resilient, integrated disaster governance frameworks, emphasizing local community empowerment, sustainable infrastructure and climate change mitigation. It calls for enhanced regional cooperation in Southeast Asia for effective disaster preparedness and response.

The research suggests that disaster governance should prioritize social resilience and inclusivity, addressing the varying impacts on different societal segments. Emphasis on mental health, community engagement and equitable resource distribution is vital for comprehensive disaster management and societal well-being in Southeast Asia.

This paper uniquely contributes to the understanding of disaster governance in Southeast Asia by combining empirical research and systematic reviews focused on Malaysia. It offers a novel perspective on integrating environmental and health disaster management, highlighting systemic vulnerabilities and resilience strategies. The study's value lies in its emphasis on the region's unique challenges, providing valuable insights for policymakers, researchers and practitioners in developing effective, context-specific disaster governance frameworks in Southeast Asia.

Authorities have set up numerous security checkpoints during times of armed conflict to control the flow of commercial and humanitarian trucks into and out of areas of conflict. These security checkpoints have become highly utilized because of the complex security procedures and increased truck traffic, which significantly slow the delivery of relief aid. This paper aims to improve the process at security checkpoints by redesigning the current process to reduce processing time and relieve congestion at checkpoint entrance gates.

A decision-support tool (clearing function distribution model [CFDM]) is used to minimize the effects of security checkpoint congestion on the entire humanitarian supply network using a hybrid simulation-optimization approach. By using a business process simulation, the current and reengineered processes are both simulated, and the simulation output was used to estimate the clearing function (capacity as a function of the workload). For both the AS-IS and TO-BE models, key performance indicators such as distribution costs, backordering and process cycle time were used to compare the results of the CFDM tool. For this, the Kerem Abu Salem security checkpoint south of Gaza was used as a case study.

The comparison results demonstrate that the CFDM tool performs better when the output of the TO-BE clearing function is used.

The efforts will contribute to improving the planning of any humanitarian network experiencing congestion at security checkpoints by minimizing the impact of congestion on the delivery lead time of relief aid to the final destination.

School effectiveness has attracted some currency in educational research globally since the 1960s though such studies mostly point to the efforts of principal leadership as the basis for promoting effective schools. However, in the case of Ghana, there is a lack of research conducted in the area, and due to that, this study sought to explore internal public perspectives of what constitutes school effectiveness in the Colleges of Education in the Upper East Region of Ghana.

This study employed the convergent parallel mixed-method design otherwise called concurrent mixed-method design. The population for the study comprised second and third-year students, tutors and leadership of the colleges. In total, 308 respondents constituted the sample size. The breakdown is 257 students in all, 41 tutors and 10 leaders of the colleges. Two instruments, namely, an in-depth interview guide and a questionnaire were used to elicit responses to address the object of this study.

The study revealed that the characteristics of effective schools include the high academic performance of students and a good show of disciplined behavior by both students and staff in the colleges among others.

To the best of our knowledge, during the search for studies conducted on school effectiveness, there is no scientific study done in Ghana highlighting the attributes of effective educational institutions. Most of the studies conducted in the area of educational studies only focused on principal leadership, educational access, participation and equity at the level of pre-tertiary institutions.

The study aspires to enhance comprehension of the intricate interplay between supply chain management (SCM) and resilience in family businesses, thereby offering valuable insights to managers and policymakers endeavouring to foster resilience in uncertain environments.

Commencing from the premise that family businesses (FBs) prioritize the preservation of socio-emotional wealth (SEW) when formulating strategic decisions, this study endeavours to advance understanding of supply chain practices adopted by FBs and their direct impact on resilience during crisis situations or economically challenging periods. Through an exploratory case study of nine FBs, the present research reveals four pivotal strategies in SCM that contribute to their resilience: (i) reorganization of inventory management; (ii) cultivating close relationships with suppliers; (iii) emphasizing product quality and customer retention; and (iv) implementing cost reduction measures to bolster resilience. The aim of the study is to provide an in-depth understanding of the intricate interplay between SCM and resilience in FBs, thereby offering valuable insights to managers and policymakers endeavouring to foster resilience in uncertain environments.

Our approach offers a theoretical framework for SCM aligned with prior research on the interplay between characteristics of family businesses and resilience strategies. Furthermore, this paper illustrates how factors such as the emphasis on high-quality products and services by family businesses contribute to achieving non-economic objectives that owners adopt to reconcile family and business needs, creating intrinsic added value for the company. It reveals various challenges in SCM, including inventory organization changes, supplier closures and the significance of customer retention. Family businesses are implementing product and technology enhancements and leveraging digitization to enhance supply chain processes.

This paper contributes significantly to the field of FBs by highlighting the crucial role of SCM in enhancing business resilience during crises. It empirically examines how the SEW characteristics of FBs influence the reconfiguration of their supply chains to enhance resilience, presenting a theoretical model for this context. Our theoretical framework employs an SEW perspective to elucidate how FBs respond to the challenges posed by the COVID-19 pandemic by adapting their SCM processes to safeguard their social and emotional legitimacy, organizational visibility and reputation. These adaptations gain particular relevance during crises or turbulent conditions, potentially leading to alterations in how FBs formulate their supply chain strategies and manage supply chain-related processes.

This study aims to ensure reliable analysis of dynamic responses in asphalt pavement structures. It investigates noise reduction and data mining techniques for pavement dynamic response signals.

The paper conducts time-frequency analysis on signals of pavement dynamic response initially. It also uses two common noise reduction methods, namely, low-pass filtering and wavelet decomposition reconstruction, to evaluate their effectiveness in reducing noise in these signals. Furthermore, as these signals are generated in response to vehicle loading, they contain a substantial amount of data and are prone to environmental interference, potentially resulting in outliers. Hence, it becomes crucial to extract dynamic strain response features (e.g. peaks and peak intervals) in real-time and efficiently.

The study introduces an improved density-based spatial clustering of applications with Noise (DBSCAN) algorithm for identifying outliers in denoised data. The results demonstrate that low-pass filtering is highly effective in reducing noise in pavement dynamic response signals within specified frequency ranges. The improved DBSCAN algorithm effectively identifies outliers in these signals through testing. Furthermore, the peak detection process, using the enhanced findpeaks function, consistently achieves excellent performance in identifying peak values, even when complex multi-axle heavy-duty truck strain signals are present.

The authors identified a suitable frequency domain range for low-pass filtering in asphalt road dynamic response signals, revealing minimal amplitude loss and effective strain information reflection between road layers. Furthermore, the authors introduced the DBSCAN-based anomaly data detection method and enhancements to the Matlab findpeaks function, enabling the detection of anomalies in road sensor data and automated peak identification.

Throughout human history, the occurrence of disasters has been inevitable, leading to significant human, financial and emotional consequences. Therefore, it is crucial to establish a well-designed plan to efficiently manage such situations when disaster strikes. The purpose of this study is to develop a comprehensive program that encompasses multiple aspects of postdisaster relief.

A multiobjective model has been developed for postdisaster relief, with the aim of minimizing social dissatisfaction, economic costs and environmental damage. The model has been solved using exact methods for different scenarios. The objective is to achieve the most optimal outcomes in the context of postdisaster relief operations.

A real case study of an earthquake in Haiti has been conducted. The acquired results and subsequent management analysis have effectively assessed the logic of the model. As a result, the model’s performance has been validated and deemed reliable based on the findings and insights obtained.

Ultimately, the model provides the optimal quantities of each product to be shipped and determines the appropriate mode of transportation. Additionally, the application of the epsilon constraint method results in a set of Pareto optimal solutions. Through a comprehensive examination of the presented solutions, valuable insights and analyses can be obtained, contributing to a better understanding of the model’s effectiveness.

This paper is a case study of student response systems used in large-class teaching. It considers the benefits, including the engagement of students and academic gains such as reduced administration. The constraints and impacts in classroom teaching are noted, drawing upon the experience of two teachers with their learning captured as a means of dissemination of practice to support other teachers who may be considering adopting and later adapting such practices (Gribble and Beckmann, 2023).

An autoethnographical account was undertaken using an action-learning approach as a sense-making exercise. These accounts enabled a depth of insight beyond the anecdotal evidence experienced by an individual teacher alone.

The findings show that while student response systems have constraints, these can be addressed by putting pedagogical concerns in front of any technology deployment, reaping benefits for students and teachers. Once engaged in using the system, students become more willing to enter further discussions. However, the limitations of both systems indicate that there may be a need for multiple systems to be available based on the pedagogical needs of the class.

The exploration of student response systems and outcomes of positive engagement by students in classroom settings provides insight to those wishing to explore such systems for use in large-class teaching settings.

This work extends discussions surrounding interactivity using student response systems. Additionally, practical insights from the users into their experiences with their students in using such systems provide alternatives for engagement in delivering large-class learning at scale.

This paper aims to use Bayesian network (BN) methodology complemented by machine learning (ML) and what-if analysis to investigate the impact of digital technologies (DT) on logistics service quality (LSQ), employing the service quality (SERVQUAL) framework.

Using a sample of 244 Italian firms, this study estimates the probability distributions associated with both DT and SERVQUAL logistics, as well as their interrelationships. Additionally, BN technique enables the application of ML techniques to uncover hidden relationships, as well as a series of what-if analyses to extract more knowledge.

The results show that the average probability of firms investing in DT for analytics (DTA) is higher than that of investing inDT for immersive experiences (DTIE). Furthermore, adopting both offers only a moderate likelihood of successfully implementing SERVQUAL logistics. Additionally, certain technologies may not directly influence some SERVQUAL dimensions. The application of ML reveals hidden relationships among technologies, enhancing the predictions of SERVQUAL logistics. Finally, what-if analyses provide further insights to guide decision-making processes aimed at enhancing SERVQUAL logistics dimensions through DTA and DTIE.

This research delves into the influence of DTIE and DTA on SERVQUAL logistics, thereby filling a gap in the existing literature in which no study has explored the intricate relationships between these technologies and SERVQUAL dimensions. Methodologically, we pioneer the integration of BN with ML techniques and what-if analysis, thus exploring innovative techniques to be used in logistics and supply-chain studies.