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The purpose of this paper is to identify the themes that emerged from retail supply chain (RSC) literature during the coronavirus disease 2019 (COVID-19) pandemic that inform future mitigation and recovery strategies.

This study analyses contributions in the RSC literature using four databases: Emerald, Elsevier (Science Direct), Wiley and Taylor & Francis. The systematic review approach resulted in identifying 74 articles covering 2020 to 2022.

Four themes emerged from the RSC literature on COVID-19. The first theme highlighted the factors that exacerbated the effects of the COVID-19 pandemic on the RSC. The second theme focussed on the types of disruptions that occurred in the RSC during the pandemic. The third theme demonstrated the recovery strategies used to reduce the impact of COVID-19 on the RSC. The fourth theme identified proposed mitigation strategies for the RSC post-COVID-19 outbreak.

The study provides a deeper understanding of how RSC managers could successfully reduce the effects of the COVID-19 pandemic by dealing with interruptions. Based on the reviewed studies and the four themes that evolved from RSC literature on COVID-19 throughout 2020–2022, 11 key RSC strategies and lessons have been recommended to decision-makers in the retail industry.

This is the first study to identify the themes that emerged from RSC literature during the COVID-19 pandemic to inform future mitigation and recovery strategies. The resulting themes add to the existing body of knowledge and establish the need for further research into other sectors that might be affected by future pandemics.

Construction industry (CI) has great prominence for the world economy, and it is expected that, with the use of the innovative technologies and approaches of Industry 4.0 (I4.0), the new industrial paradigm, construction can reach higher levels of productivity. This study aims to develop a model (readiness model) to assess the level of use of I4.0 technologies by the construction sector in Brazil and its most relevant applications.

The methodology used was bibliographic research, design-science research and a survey to validate the model, carried out with 162 companies, considered among the main ones in the sector in Brazil. The literature review revealed 13 technologies of I4.0 applied to construction; hence, the views of industry experts were based on these technologies.

The Digital Advancement Within CoNstruction (DAWN) readiness model was proposed, showing that among the 13 evaluated technologies of I4.0 and their applications, the Brazilian construction companies had a low level of utilization; both high and middle-income companies presented this low level of use; some technologies with a greater number of scientific publications were less used in practice in the Brazilian construction.

The originality and theoretical contribution are to present a readiness model to assess the level of use of I4.0 technologies and their most relevant applications in the CI in countries with an economy similar to Brazil’s, making it possible to measure the level of adoption of these technologies.

One of the most important components of healthcare is the timely delivery of pharmaceutical products, such as life-saving medicines. However, disruptions like COVID-19 bring new challenges and risks to the pharmaceutical supply chain (PSC) and healthcare organizations that impact their operational performance. This study focuses on mitigating risks in India's generic medicine supply chain (GMSC) as a result of various disruptions, which can assist policymakers develop appropriate plans and strategies to build resilience in the Jan Aushadhi Scheme (JAS) of micro, small and medium enterprises (MSMEs) in order to improve their overall performance.

Risk-causing vulnerabilities and resilience capabilities are identified from the literature review and expert's opinions. Following that, the vulnerabilities are classified into cause-and-effect vulnerabilities, and supply chain resilient capabilities (SCRCs) are measured using a hybrid fuzzy DEMATEL and best worst method (FDEMATEL-BMW) framework.

The outcome of the study reveals that transportation breakdown, loss of human resources and loss of suppliers are the potential risk-causing vulnerabilities that lead to vulnerabilities like shortages of medicines, loss of in-hand stock qualities and loss of sales/revenue. In addition, the analysis suggests that the sustainability of an organization with maximum weightage is the critical factor for building resilience in GMSC followed by flexibility, agility and visibility.

The integration of resilience into Jan Aushadhi GMSC can help in managing disruptions efficiently and effectively to mitigate risk and optimize MSMEs overall performance.

To the best of the authors’ knowledge, this work will be the first of its kind to model resilience in GMSC of MSMEs using a hybrid framework.

Oppositional brand loyalty (OBL) has become widespread and has thus received growing scholarly attention in recent years. Although opposition behaviors have distinct manifestations, their differential antecedents remain underexplored. Drawing from the identity-based motivation model, this article aims to examine the impact of brand identification and brand disidentification on the two distinct dimensions of OBL (i.e. brand avoidance and trash talk) according to their different intensity and activation levels (passive or active). In addition, article aims to examine the moderating role of perceived inter-group rivalry.

The authors collected survey data and obtained 392 valid questionnaires from smartphone users. Structural equation modeling was used for hypothesis testing.

The results show that brand identification has a significant positive effect on avoidance of rival brands, but not on trash talk; brand disidentification has a significant positive effect on both avoidance and trash talk of rival brands; and perceived inter-group rivalry positively moderates the relationship between brand disidentification and trash talk, but does not alter the relationship between brand disidentification and brand avoidance.

This study extends the literature on OBL by investigating differential antecedents of two distinct dimensions of brand opposition. It provides more complete understanding of the formation of OBL by examining the role of users’ relationship with a focal brand, its competing brand and inter-group relationship.

The health crisis has highlighted the shortcomings of the industry sector which has revealed its vulnerability. To date, there is no guarantee of a return to the “world before”. The ability of companies to cope with these changes is a key competitive advantage requiring the adoption/mastery of industry 4.0 technologies. Therefore, companies must adapt their business processes to fit into similar situations.

The proposed methodology comprises three steps. First, a comparative analysis of the existing CPSs is elaborated. Second, following this analysis, a deep learning driven CPS framework is proposed highlighting its components and tiers. Third, a real industrial case is presented to demonstrate the application of the envisioned framework. Deep learning network-based methods of object detection are used to train the model and evaluation is assessed accordingly.

The analysis revealed that most of the existing CPS frameworks address manufacturing related subjects. This illustrates the need for a resilient industrial CPS targeting other areas and considering CPSs as loopback systems preserving human–machine interaction, endowed with data tiering approach for easy and fast data access and embedded with deep learning-based computer vision processing methods.

This study provides insights about what needs to be addressed in terms of challenges faced due to unforeseen situations or adapting to new ones. In this paper, the CPS framework was used as a monitoring system in compliance with the precautionary measures (social distancing) and for self-protection with wearing the necessary equipments. Nevertheless, the proposed framework can be used and adapted to any industrial or non-industrial environments by adjusting object detection purpose.

To provide human judgment input for computer algorithm development, this study examines the relative importance of source, content, and style cues in predicting the truthfulness ratings of two common types of online health information: news stories and institutional news releases.

This study employed a multi-method approach using (1) a manual content analysis of 400 randomly selected online health news stories and news releases from HealthNewsReview.org and (2) an online experiment comparing truthfulness ratings between news stories and news releases.

Using content analysis, the authors found significant differences in the importance of source, content, and style cues in predicting truthfulness ratings of news stories and news releases: source and style cues predicted truthfulness ratings better than content cues. In the experiment, source credibility was the most important predictor of truthfulness ratings, controlling for individual differences. Experts have higher ratings for news media stories than news releases and lay people have no differences in rating the two news formats.

It is important for health educators to curb consumer trust in misinformation and increase health information literacy. Rather than solely reporting scientific evidence, educators should focus on addressing cues people use to judge the truthfulness of health information.

This is the first study that directly compares human judgments of health news stories and news releases. Using both the breadth of content analysis and experimental causality testing, the authors evaluate the relative importance of source, content, and style cues in predicting truthfulness ratings.

This study, a narrative literature review, aims to examine the combined benefits of the active and passive use of social media (SM) for well-being (WB), physical and mental health during the COVID-19 pandemic.

A search strategy has been carried out in the databases: Riss, PubMed, Medline, Scopus and Google Scholar, including all the articles published until 19 October 2023.

SM offers various benefits, including global risk awareness, health information, social connections and support. With the natural increase in physical inactivity due to COVID-19 social restrictions, SM has been identified as an appropriate tool for promoting physical activity (PA) at home to improve health.

It suggests that the combined use of active and passive benefits of SM could potentially play an important role in public health by increasing individuals’ health behaviours. In addition, dissemination, sharing and social interaction of information provided by YouTube can encourage healthy behaviours, contribute to WB, physical and mental health and raise public health awareness.

The findings presented in this study highlight the combined benefits of differentiating the features of SM use. Compared to other SM platforms, YouTube can be used as a useful tool for home-based PA that promotes health by enabling people to remain active and avoid barriers to PA due to social restrictions during the global crisis. In addition, some recommendations from the findings may help protect against potential risks and improve public health outcomes during global crises, such as the COVID-19 pandemic, among the general public using SM.

The purpose of this paper is to develop an appropriate machine learning model for predicting soil compaction degree while also examining the contribution rates of three influential factors: moisture content, electrical conductivity and temperature, towards the prediction of soil compaction degree.

Taking fine-grained soil A and B as the research object, this paper utilized the laboratory test data, including compaction parameter (moisture content), electrical parameter (electrical conductivity) and temperature, to predict soil degree of compaction based on five types of commonly used machine learning models (19 models in total). According to the prediction results, these models were preliminarily compared and further evaluated.

The Gaussian process regression model has a good effect on the prediction of degree of compaction of the two kinds of soils: the error rates of the prediction of degree of compaction for fine-grained soil A and B are within 6 and 8%, respectively. As per the order, the contribution rates manifest as: moisture content > electrical conductivity >> temperature.

By using moisture content, electrical conductivity, temperature to predict the compaction degree directly, the predicted value of the compaction degree can be obtained with higher accuracy and the detection efficiency of the compaction degree can be improved.

The purpose of this study is to examine the respondents’ evaluation of the pervasiveness of fake news through various SM platforms in Kuwait. The authors also examined the respondents’ attitudes toward most fake news on SM. A total of 1,539 Kuwaitis were selected.

The questionnaire was the major tool for this study. The respondents, from whom demographic information was obtained, were asked about which SM platforms most frequently spread fake news, their attitudes toward the subjects most frequently involved in spreading fake news, their degree of use of the six SM platforms and interest in various subjects, and the attitudes toward the negative nature of SM news. SPSS was used for the data analysis.

The results showed that WhatsApp was the most likely to be used to disseminate fake news; Twitter and Instagram ranked second. The younger subjects were affected more by text and voice clips than the older ones.

The study, hopefully, produces new knowledge on the subject of fake news in social media, especially in the Arab world, since there are few studies conducted in the region. The study showed that WhatsApp was the SM tool most likely to be responsible for disseminating fake news in Kuwait, which may shed light on the usage of this application to be a news tool, rather than merely an interpersonal communication medium.

This study aims to analyze how, under the stakeholder theory, corporate social responsibility (CSR) might favor the emergence of circular economy (CE) in the Agri-food sector, which is a relevant context, as it is technologically dynamic and requires paying attention to all the stakeholders.

An exploratory, qualitative research design has been adopted to study the phenomenon in detail, as it facilitates the understanding of complex phenomena such those under investigation and helps enrich existing theory with new insights from real-world cases to add theoretical generalizations to the existing body of research in the field.

The results of this study highlight that companies adopting CSR models are oriented toward circularity.

This study provides useful indications to managers and policymakers as to how to favor the two approaches (CSR and CE) and benefit all the stakeholders.

While there is wide scholarly and managerial interest toward CSR and CE, previous research has mainly analyzed CE and CSR as two independent phenomena. Therefore, there is a lack of understanding about how the two areas are linked. Following previous studies that have started to theoretically argue an interconnection between CSR and CE, in this research, it has been empirically investigated, and further explored theoretically, whether CSR can implicitly encourage the emergence of CE approaches.