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The COVID-19 global health crisis forced border closings, strained resources and tightened funding, forcing humanitarian organisations to innovate. This paper aims to identify gaps in the literature on innovation in humanitarian supply chains, and to develop an appropriate framework for future research through a systematic literature review. We use a systematic literature review approach and synthesis the discussion of innovation in humanitarian supply chains after reviewing 43 papers. The synthesis identifies the different contexts for and outcomes of innovation in humanitarian supply chains. Our findings indicate that research on innovation in humanitarian supply chains is an underdeveloped topic. Gaps we identified in regards to the humanitarian context are: (1) a limited discussion of the contribution by the beneficiary to the supply chain; (2) a limited discussion of reconstruction innovations; (3) a lack of study on field application for complex innovations; (4) the lack of discussion of the role of individual knowledge in humanitarian supply chain innovation and finally (5) a lack of study of position innovations where humanitarian organisations use supply chains as a way to market effectively towards donors. [ABSTRACT FROM AUTHOR]

In recent years, emerging technologies have gained popularity and being implemented in different fields. Thus, critical leading-edge technologies such as artificial intelligence and other related technologies (blockchain, simulation, 3d printing, etc.) are transforming the operations and other traditional fields and proving their value in fighting against unprecedented COVID-19 pandemic outbreaks. However, due to this relation's novelty, little is known about the interplay between emerging technologies and COVID-19 and its implications to operations-related fields. In this vein, we mapped the extant literature on this integration by a structured literature review approach and found essential outcomes. In addition to the literature mapping, this paper's main contributions were identifying literature scarcity on this hot topic by operations-related fields; consequently, our paper emphasizes an urgent call to action. Also, we present a novel framework considering the primary emerging technologies and the operations processes concerning this pandemic outbreak. Also, we provided an exciting research agenda and four propositions derived from the framework, which are collated to operations processes angle. Thus, scholars and practitioners have the opportunity to adapt and advance the framework and empirically investigate and validate the propositions for this and other highly disruptive crisis. [ABSTRACT FROM AUTHOR]

Motivated by the unique opportunity that a crisis period provides for capturing and reassessing specific entrepreneurial behaviors, this paper takes on the debate about the entrepreneurial attributes of informal ventures "IVent" and their endeavor to formalize. In doing so, Cronbach's alpha rationales and contingency analysis are applied to quantitative data from street vendors of the main markets of Cameroon to investigate informal entrepreneurial behavioral dynamics and their impact on a hypothetical willingness for change during the COVID-19 pandemic. It was uncovered that IVent have activated entrepreneurial attributes of resilience, intention and orientation. Also, specific composites or variables of entrepreneurial orientation and resilience have been found more supportive of IVent's endeavor to formalize than others. The novelties in that regard are: (1) While most of the variables related to the composite of innovativeness reinforce the desire to formalize, the variable of "routine" plays a hindering role. (2) The composite of proactiveness is not significantly related to the willingness to formalize. (3) The composite of risk-taking has proven significant to IVent's transformation aims. And (4) contrary to all other variables of the attribute of resilience and self-efficacy, the variable of behavior control appears to hamper the aim to move out of the shadows. It is argued that facilitating Ivent's dynamics and aspiration to formalize necessitates policymakers to advance policies that support the development of the uncovered determining composites. Furthermore, academia should undertake an extensive investigation into the role that the captured non-significant variables, attributes, and composites may play in fostering IVent's shadowed business lifecycle. [ABSTRACT FROM AUTHOR]

This paper studies continuing optimal lockdowns (can also be interpreted as quarantines or self-isolation) in the long run if a disease (Covid-19) is endemic and immunity can fail, that is, the disease has SIRS dynamics. We model how disease related mortality affects the optimal choices in a dynamic general equilibrium neoclassical growth framework. An extended welfare function that incorporates loss from mortality is used. In a disease endemic steady state, without this welfare loss even if there is continuing mortality, it is not optimal to impose even a partial lockdown. We characterize how the optimal restriction and equilibrium outcomes vary with the effectiveness of the lockdown, the productivity of working from home, the rate of mortality from the disease, and failure of immunity. We provide the sufficiency conditions for economic models with SIRS dynamics with disease related mortality–a class of models which are non-convex and have endogenous discounting so that no existing results are applicable. [ABSTRACT FROM AUTHOR]

This paper reflects on an educator's perceived experiences and observations on the complex process of 'passage' when students transitioning from high school into their first-year of post-secondary education often struggle to adapt to academic writing standards. It relies on literature to further explore such a process. Written communication has become increasingly popular in formal academic and professional settings, stressing the need for effective formal writing skills. The development of online tools for aiding writing is not a new concept, but a new software development known as ChatGPT, may add to the many challenges academic writing has faced over the years. This paper reflects on the students' struggles as they navigate different courses seeking to adapt their writing skills to formal and structured written academic requirements. The COVID-19 pandemic forced many recent high school students into virtual education, uncertain of its effectiveness in developing the writing skills high school graduates require in academia. Many unknowns exist in using ChatGPT in academic contexts, especially in writing. ChatGPT can generate texts independently, raising concerns about plagiarism and its impact on students' critical thinking and writing skills. This paper hopes to contribute to pedagogical discussions on the current challenges surrounding the use of artificial intelligence technology and how better to support beginner writers in academia. [ABSTRACT FROM AUTHOR]

Digital Contact Tracing (DCT) has been proved to be an effective tool to counteract the new SARS-CoV-2 or Covid-19. Despite this widespread effort to adopt the DCT, less attention has been paid to the organisation of the health logistics system that should support the tracing activities. Actually, the DCT poses a challenge to the logistics of the local health system in terms of number of daily tests to be collected and evaluated, especially when the spreading of the virus is soaring. In this paper we introduce a new optimisation problem called the Daily Swab Test Collection (DSTC) problem, that is the daily problem of collecting swab tests at home in such a way to guarantee a timely testing to people notified by the app to be in contact with a positive case. The problem is formulated as a variant of the team orienteering problem. The contributions of this paper are the following: (i) the new optimisation problem DSTC that complements and improves the DCT approach proposed by Ferretti et al. (Science https://doi.org/10.1126/science.abb6936, 2020), (ii) the DSCT formulation as a variant of the TOP and a literature review highlighting that this variant can have useful application in healthcare management, (iii) new realistic benchmark instances for the DSTC based on the city of Turin, (iv) two new efficient and effective hybrid algorithms capable to deal with realistic instances, (v) the managerial insights of our approach with a special regard on the fairness of the solutions. The main finding is that it possible to optimise the underlying logistics system in such a way to guarantee a timely testing to people recognised by the DCT. [ABSTRACT FROM AUTHOR]

Since 2019n-CoV has swept the whole world, people's daily life is affected seriously and the public space system is facing to major challenges. Community public space should be re-evaluated and optimized as people change the way they use it. This research conducted a nationwide questionnaire survey in China, which collected the opinions of residents on community public space from the perspective of epidemic prevention and social needs to select research indicators. The questionnaire data were processed through the frequency analysis method, precedence chart and TOPSIS. We found: (1) under the background of the epidemic, people pay more attention to the satisfaction of leisure activities in community public space and the control of social spacing during activities; (2) the current assessment result of community public space risk of 2019-nCoV exposure is 0.386, which is relatively high; and (3) the exposure risk of the community can be effectively reduced by controlling for the two indicators of social distance and social facility sterilization. When the social distance is 1.8–3 m and the facility sterilization is once a day, the community public space minimizes exposure risk. In view of the above results, this paper selects the Wuhan start-up area of Wuhan, China, as an example for carrying out the optimization design of community public space based on the epidemic and the design the space optimization model for the two indicators of social distance and facility disinfection from both software and hardware aspects. [ABSTRACT FROM AUTHOR]

Future state prediction for nonlinear dynamical systems is a challenging task. Classical prediction theory is based on a, typically long, sequence of prior observations and is rooted in assumptions on statistical stationarity of the underlying stochastic process. These algorithms have trouble predicting chaotic dynamics, "Black Swans" (events which have never previously been seen in the observed data), or systems where the underlying driving process fundamentally changes. In this paper we develop (1) a global and local prediction algorithm that can handle these types of systems, (2) a method of switching between local and global prediction, and (3) a retouching method that tracks what predictions would have been if the underlying dynamics had not changed and uses these predictions when the underlying process reverts back to the original dynamics. The methodology is rooted in Koopman operator theory from dynamical systems. An advantage is that it is model-free, purely data-driven and adapts organically to changes in the system. While we showcase the algorithms on predicting the number of infected cases for COVID-19 and influenza cases, we emphasize that this is a general prediction methodology that has applications far outside of epidemiology. [ABSTRACT FROM AUTHOR]

Parental decision making is necessary for contracting medical interventions that require personal risk–benefit evaluation, and for overseeing matters of education. In the nineteenth century, exemptions from obligatory vaccination were granted for religious and conscientious reasons. Then and today, religion and moral values play marginal roles in vaccine hesitancy and denialism. Rather, the key values invoked by vaccine hesitants and denialists are liberty and pluralism. Neither is compatible with limiting adolescents' choice. Because vaccination does not require assessment of personal medical risks, because it does not need to occur within the sphere of the doctor–patient relationship, and because the risk involved is within the range of their daily activities, adolescents have the right to free access to vaccination without legal requirement of parental involvement. Drawing on the development of Common Law, and on the development of respect for personal conscience in the history of ideas, this paper does not promote an argument that grants public health an overriding moral power. Rather, this paper rejects the presumption that vaccination of adolescents might involve a conflict between parental authority and public health. Free access to vaccination is compatible with the law and ethics of adolescents' evolving autonomy in relation to healthcare. [ABSTRACT FROM AUTHOR]

Mining is an important enterprise that will supply essential minerals for the country's infrastructural development. Mining is a key industry and a development engine in the Indian economy. The epidemic has had both immediate and long-term effects on the labour and industries in India. Short-term effects include the cessation of mineral production, salary loss due to absenteeism and pandemic limitations, unemployment, and the selling of minerals on the market. The reputation and economic health of industries, fresh mineral deposit exploration, and new mining complex development have all been impacted in long-term. Aside from the mining industry-specific impact of COVID-19, the social fabric of the mining workers has been impacted since they are living in an environment of fear and uncertainty. This paper attempts to analyse the effect of COVID-19 on extractive industries in India. It also focuses the steps which taken by the Indian government. The paper concluded with the negative effect of COVID-19 which disrupts the mining sector and the contribution declined during COVID-19. However, for instance from mineral rich-Odisha, the mining sector takes up by its growth in post COVID-19 period with remarkable increment from negative growth to high positive rate. [ABSTRACT FROM AUTHOR]

Since the successful containment of COVID-19 in Wuhan in late March 2020, China had implemented a nationwide highly stringent and restrictive zero-COVID policy to manage the pandemic until the sudden swift away from it in early December 2022. How did the Chinese Communist Party discursively construct it as a 'normal' and legitimate policy? Using interpretivism and poststructuralist political theory, this paper examines how Chinese political elites constructed a discourse of danger for the COVID pandemic, with the dominant discursive narratives full of xenophobic and nationalist languages. The discourse framed 'foreigners' as 'threats' to Chinese people's health, advocated that China should rely on home-made vaccines and medicines and, more importantly, argued that the Chinese Communist rule demonstrates 'institutional superiority' over Western governance. This xenophobic and nationalist discourse has lingered on after the dismantling of the zero-COVID policy. There are grounds for us to concern whether China is seeking self-reliance rather than integrating itself with the world. A Chinese decoupling from the world—a nationalist self-reliance policy similar with that in the Mao era—is not unthinkable. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has caused unprecedented socio-economic devastation. With widespread displacement of population/ migrants, considerable destruction of property, increase in mortality, morbidity, and poverty, infectious disease outbreaks and epidemics have become global threats requiring a collective response. Project Management is, however, a relatively less explored discipline in the Third Sector, particularly in the domain of humanitarian assistance or exploratory projects. Via a systematic literature review and experts' interviews, this paper explores the essence of humanitarian projects in terms of the challenges encountered and the factors that facilitate or hinder project success during crises like Covid-19. Additionally, the general application of project management in international assistance projects is analysed to determine how project management can contribute to keeping the project orientation humane during a crisis. The analysis reveals that applying project management tools and techniques are beneficial to achieve success in humanitarian assistance projects. However, capturing, codifying, and disseminating the knowledge generated in the process and placing the end-users at the centre of the project life cycle is a prerequisite. While the latter can seem obvious, the findings demonstrate that the inadequate inclusion of beneficiaries is one of the main reasons that prevent positive project outcomes leading to unsustainable outcomes. The key finding of this paper is that the lack of human-centred approaches in project management for humanitarian assistance and development projects is the main reason such projects fail to achieve desired outcomes. [ABSTRACT FROM AUTHOR]

Classifying tweets containing valuable information about COVID-19 is crucial for developing monitoring systems that provide the latest updates. Existing approaches for informative tweet classification considers only the last layer vector of a special token by ignoring the vectors of other tokens and the token vectors from the previous layers. The paper addresses this drawback by proposing a novel approach which (i) makes use of all the token vectors from the last four layers and (ii) leverages additional information in the form of POS tags and informative words. Experiment results show that the proposed approach outperforms all the existing approaches and achieves an accuracy of 92% and F1-score of 92.01% on the COVID-19 informative tweets dataset. The uniqueness of this paper is the attempt to leverage token vectors from the last four layers, additional information in the form of POS tags and informative words from COVID-19 informative tweets for classification. [ABSTRACT FROM AUTHOR]

This paper examines the extent to which refugee girls and young women were able to access learning during COVID-19 education closures in Pakistan, and the role that EdTech played in their learning access. It is based on findings from a survey with 403 Afghan refugee students, along with in-depth interviews with six young female refugees. The research shows that, while the majority of female refugee students were able to continue accessing education in some form during school closures, learning access was nevertheless limited, and a sizable minority were not engaged in any learning during this time. Teacher and institutional support was either absent or inadequate for many students, and infrastructure and devices that serve to support remote learning were not always reliable or accessible. Although male respondents were less likely than females to engage in independent study during the closures, refugee girls and young women were significantly less likely than their male counterparts to own the devices they needed for learning. The findings demonstrate how targeted investment in specific types of EdTech and teacher professional development, as well as supporting educational institutions in the establishment of remote learning opportunities, could help sustain learning during future periods of educational restriction. [ABSTRACT FROM AUTHOR]

The Covid-19 pandemic negatively affected many sectors including aviation and travel. Travel bans and forced lockdowns prevented transportation activity, especially air travel. Accordingly, huge amounts of emission reductions occurred. On the other hand, travel restrictions are not the only cause of emissions reductions. Changing travel intention in the era of Covid-19 is another important factor that affects aviation emissions. This paper aims to investigate the Landing/Take-Off (LTO) emission changes at Turkish airports. An emission inventory has been implemented for the years 2019 and 2020 to reveal the impacts of Covid-19 on aviation emissions. Domestic, international, and cargo flights have been included in the inventory. According to the results, total emissions of SO2, CO2, CO, NOx, NMVOC, CH4, N2O, and PM2.5 have decreased in 2020 compared to 2019 by 49.8%, 49.7%, 41.0%, 52.6%, 40.0%, 33.8%, 49.8%, and 50.3%, respectively. Total CO2 reductions in the Q2, Q3, and Q4 periods of 2020 compared to that of 2019 are 87%, 50% and 43%, respectively. Another aim of this paper is to find the underlying reasons for emission reductions. For Turkish airports, emission reductions have resulted from travel bans in Q2. After the relaxation of restrictions with the declaration of the "New Normal" in Turkey, flight traffic rebounded to a certain level but was lower than 2019 levels. Therefore, changing travel intention is the main cause of emission reductions in Q3 and Q4 of 2020. The results of this study contribute to both the areas of air pollution and tourism management. [ABSTRACT FROM AUTHOR]

The ubiquity of handheld devices and easy access to the Internet help users get easy and quick updates from social media. Generally, people share information with their friends and groups without inspecting the posts' veracity, which causes false information propagation in the network. Moreover, detecting false news and rumors in such a massive load of unstructured information is a very tedious task. Results, many literature papers explored different machine learning and deep learning approaches to detect the presence of rumors on social media networks. Although detection of misleading news and rumors is not sufficient, therefore, we have proposed a model for the detection and prevention of transmitted rumors in this paper. In this paper, we use blockchain technology to verify the credibility of information and design a framework with four layers: network layer, blockchain layer, machine layer, and device layer, to prevent the propagation of rumors in the network. We also use deep learning techniques to identify the anomalies in the network. The Bi-directional Long Short Term Memory (Bi-LSTM) model is used to prevent the introduction of new rumors by continuously monitoring incoming messages in the network. The experimental results demonstrate that the proposed Bi-LSTM model outperforms state-of-the-art machine learning methods and recent baseline work. Performance is compared over different metrics such as accuracy, precision, recall, f1-score, and specificity. Experiment results show that our Bi-LSTM model outperforms all the other approaches and achieved 99.63 % accuracy. Additionally, the probability of incorrect detection is significantly low with only 0.13% false positive. [ABSTRACT FROM AUTHOR]

COVID-19 pandemic is an active epidemic disease and is evolving in various variants of SAR-COV-2. The most critical point for COVID-19 is to break the transmission chain by early detection and isolation. It necessitates monitoring human interactions using remote technologies as a proactive strategy to minimize the spread. This paper presents a framework for user behaviour recognition and localization in a heterogeneous environment leveraging artificial intelligence for COVID-19 contact tracing. This approach designed and developed a mobile application to acquire the mobile sensors (received signal strength indicator (RSSI), accelerometer, gyroscope, and speed), sensors response, and machine learning-based user behaviour classification to assess the spread of COVID-19 with better proximity detection. This paper presents a machine learning-based free-space path loss (FSPL) model to estimate the distance between users holding mobile devices. The framework considers comprehensive user interaction scenarios depending upon multiple parameters, including the randomness of a user's interaction (device-device communication), device position, environmental obstacles, and device type. This platform classifies user behaviour on a train, bus, walking, and sitting at one, two, and three-meter from the suspected patient. The proposed system outperforms existing models, estimates the distance up to three meters, and accurately classifies each user behaviour. This system can send alert notifications to all users within three meters of a person whose COVID-19 test is positive. This approach uses machine learning models and finds that the random forest classifier shows higher accuracy of 97.95% for user behaviour classification and uses the LSTM model for distance estimation from one to three meters between users with the highest accuracy of 98.07%. [ABSTRACT FROM AUTHOR]

The three closely related COVID-19 outcomes of incidence, intensive care (IC) admission and death, are commonly modelled separately leading to biased estimation of the parameters and relatively poor forecasts. This paper presents a joint spatiotemporal model of the three outcomes based on weekly data that is used for risk prediction and identification of hotspots. The paper applies a pure spatiotemporal model consisting of structured and unstructured spatial and temporal effects and their interaction capturing the effects of the unobserved covariates. The pure spatiotemporal model limits the data requirements to the three outcomes and the population at risk per spatiotemporal unit. The empirical study for the 21 Swedish regions for the period 1 January 2020–4 May 2021 confirms that the joint model predictions outperform the separate model predictions. The fifteen-week-ahead spatiotemporal forecasts (5 May–11 August 2021) show a significant decline in the relative risk of COVID-19 incidence, IC admission, death and number of hotspots. [ABSTRACT FROM AUTHOR]

Objectives: The aim of this study was to explore and learn from the experiences of Métis women, Two-Spirit, and gender-diverse people accessing health and social services in Victoria, British Columbia, during the COVID-19 pandemic., Methods: This paper comes from a larger study exploring Métis women, Two-Spirit, and gender-diverse people's experiences accessing health and social services in Victoria. Using a by-and-for Métis approach that employed a conversational interview method, we conducted interviews with Métis women, Two-Spirit, and gender-diverse people who lived in and/or accessed services in Victoria in December 2020 and January 2021. This paper focuses specifically on data addressing how COVID-19 impacted these participants., Results: A total of 24 Métis women, Two-Spirit, and gender-diverse people participated in the study. Overall, three themes specific to COVID-19 were identified. First, participants described the detrimental impacts of COVID-19 on their ability to connect with their Métis community and practice their culture, as well as their overall feelings of isolation. Second, participants highlighted some of the ways that COVID-19 has exacerbated existing barriers to culturally safe healthcare. Last, participants spoke about the mixed economic impacts that COVID-19 has had for them, sharing insight into the ways in which gender, in particular, has shaped their financial instability., Conclusion: Improving access to culturally safe health and social services by incorporating the experiences and expertise of Métis women, Two-Spirit, and gender-diverse people is crucial to mitigating the disproportional negative impacts of the pandemic and improving overall health outcomes within Métis communities across Canada., (© 2024. The Author(s).)

Critical agency (CA) refers to an individual's feeling of power in relation to social inequalities. Research has demonstrated that high CA is associated with positive adolescent outcomes, however, less is known about what supports are important for its development. Moreover, a large majority of the literature is based on studies from the US and various countries in Africa; although the UK is saturated with inequalities there is little research within a UK context. In this paper we examine (a) the validity of using an existing measure of CA with a sample of UK adolescents and (b) the extent to which resilience supports account for variance in CA. Our analysis identified two distinct factors of CA: justice-oriented and community-oriented. High CA in both factors was explained by resilience supports associated with peer relationships (p < 0.01). Our findings push us towards new relational, ecological ways of understanding adolescent CA. We close by instantiating a translational framework for those devising policies in support of youth resilience and CA. [ABSTRACT FROM AUTHOR]

This paper investigates the resilience of small and medium-sized enterprises (SMEs) in relation to the COVID-19 pandemic, particularly the influence exerted by certain factors related to management control, integrated information systems (enterprise resource planning [ERP]), information and communication technology (ICT) systems, and financial resources. For this purpose, leveraging from the dynamic capability theory, in late spring 2020, a questionnaire was sent to limited-liability SMEs in Verona and Vicenza provinces in Italy operating in the manufacturing, construction, and distribution sectors. Respondents were asked to answer a set of questions and to evaluate the resilience of their firms as of January 1, 2020; May 1, 2020; and one year later, as of July 2021. Using a multivariate regression model to analyze data from the 143 questionnaires received, we found a positive influence of budgeting, business continuity tools, availability of supplementary financial resources, and resilience of the entrepreneur on both COVID-19 resilience and the organizational resilience of SMEs. SMEs with a high intention to use ERP systems also had high resilience levels. ICT use for smart working was not statistically significant. Despite the limitations concerning the sample and period considered, we believe that this study contributes to both academic debate, by illustrating the influence of certain managerial and organizational factors on the resilience of SMEs, and practice, by supporting a greater empirical use of management control tools, such as budgets and contingency plans, and integrated information systems (ERP). Our study supports the creation of financial buffers and strengthening the entrepreneur's resilience for improving SME resilience. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the convergence pattern of sovereign bond yields in European Union (EU) countries during the Covid-19 pandemic. To this purpose we employ the Phillips and Sul convergence methodology. We further test whether the above pattern was affected by the intensity of government responses against the Covid-19 dispersion. Our findings indicate the existence of two convergence clubs. Both clubs are composed of countries that have previously been classified as either 'core' or 'periphery' countries, indicating that in this particular health crisis, this classification is too simplistic. Moreover, our results suggest that the intensity of Covid-19 containment policies had little to no effect in shaping the convergence pattern of sovereign bond yields in the EU. These conclusions are robust even after accounting for the size of the fiscal sector of each country. [ABSTRACT FROM AUTHOR]

The paradigm shifts in HRM during the outbreak of COVID-19 involve new challenges for organizations, whereas it remains unclear how psychological contract fulfillment works on employees' attitudes and behaviors in the new working settings from organizations. This paper explores the impacts of psychological contract fulfillment on employees' work attitudes and behaviors (work engagement, intrinsic motivation, and affective commitment) during the COVID-19 outbreak, and examines the mediating mechanism of perceived organizational support between psychological contract fulfillment and these work-related variables. For the research, a cross-sectional research design and quantitative analysis were adopted. Data were collected via survey questionnaires and from 405 respondents working remotely during the COVID-19 outbreak. The findings revealed that psychological contract fulfillment positively impacted employees' work engagement, intrinsic motivation, and affective commitment during the COVID-19 outbreak, and indicated that perceived organizational support significantly but partially mediates the positive associations between PCF and these work-related variables. [ABSTRACT FROM AUTHOR]

Disruption from the COVID-19 pandemic has caused major upheavals for manufacturing, and has severe implications for production networks, and the demand and supply chains underpinning manufacturing operations. This paper is the first of its kind to pull together research on both—the pandemic-related challenges and the management interventions in a manufacturing context. This systematic literature review reveals the frailty of supply chains and production networks in withstanding the pressures of lockdowns and other safety protocols, including product and workforce shortages. These, altogether, have led to closed facilities, reduced capacities, increased costs, and severe economic uncertainty for manufacturing businesses. In managing these challenges and stabilising their operations, manufacturers are urgently intervening by—investing in digital technologies, undertaking resource redistribution and repurposing, regionalizing and localizing, servitizing, and targeting policies that can help them survive in this altered economy. Based on holistic analysis of these challenges and interventions, this review proposes an extensive research agenda for future studies to pursue. [ABSTRACT FROM AUTHOR]

The governments' isolation measures to contain the transmission of COVID-19 imposed a dilemma for the people at the bottom of the pyramid. Since these people have very unreliable sources of income, a dilemma arises: they must either work under risky conditions or refrain from work and suffer from income cuts. Emergency donations of food and cleaning supplies in a pandemic context might be overlooked by government and civil society actors. This paper aims to model the effects of donations on mitigating the negative effects of COVID-19 on vulnerable communities. Applying the system dynamics method, we simulated the behaviour of the pandemic in Rio de Janeiro (Brazil) communities and the impacts that donations of food and cleaning supplies have in these settings. We administered surveys to the beneficiaries and local organisations responsible for the final distribution of donations to gather information from the field operations. The results show that increasing access to cleaning supplies in communities through donations can significantly reduce coronavirus transmission, particularly in high-density and low-resource areas, such as slums in urban settings. In addition, we also show that food donations can increase the vulnerable population's ability to afford necessities, alleviating the stress caused by the pandemic on this portion of the population. Therefore, this work helps decision-makers (such as government and non-governmental organisations) understand the impacts of donations on controlling outbreaks, especially under COVID-19 conditions, in a low-resource environment and, thus, aid these hard-to-reach populations in a pandemic setting. [ABSTRACT FROM AUTHOR]

The COVID 19 has brought unprecedented changes in the way we communicate. There is a greater accent on Virtual communication. This paper aims to establish a relationship between Emotional intelligence and the effectiveness of Virtual communication on Decision making. This empirical study is based on a sample drawn from 296 working professionals at five different levels of organizational hierarchy. A standardized questionnaire (ɑ = 0.824) was used to collect the responses of Emotional intelligence, Virtual communication, and Decision-making effectiveness. Hierarchical regression using PROCESS Macro model 1 was used to identify the moderating effect of Emotional intelligence on Virtual communication and Decision making effectiveness. Since the p-value (p ≤.007) is found significant, Emotional intelligence acts as a moderator that affects the strength of the relationship between Virtual communication effectiveness and Decision making. Validation of Task Technology fit theory is the theoretical implication of the study. Manipulation of individual dimensions in the model can reduce the dependence on technology for task completion with enhanced performance effectiveness. The findings are relevant to educators, consultants, and any professional who need to adapt Virtual communication platforms on an ongoing basis. Since work-life balance is projected as a constraint in this study, policymakers can consider policy amendments to reduce the stress caused due to Virtual communication which intrudes into their personal space. This empirical study is the first of its kind to benchmark the organizational practice of Emotional intelligence training to enhance Virtual communication and Decision making effectiveness during unprecedented times of pandemic. [ABSTRACT FROM AUTHOR]

This paper aims to introduce a novel family of probability distributions by the well-known method of the T–X family of distributions. The proposed family is called a "Novel Generalized Exponent Power X Family" of distributions. A three-parameters special sub-model of the proposed method is derived and named a "Novel Generalized Exponent Power Weibull" distribution (NGEP-Wei for short). For the proposed family, some statistical properties are derived including the hazard rate function, moments, moment generating function, order statistics, residual life, and reverse residual life. The well-known method of estimation, the maximum likelihood estimation method is used for estimating the model parameters. Besides, a comprehensive Monte Carlo simulation study is conducted to assess the efficacy of this estimation method. Finally, the model selection criterion such as Akaike information criterion (AINC), the correct information criterion (CINC), the Bayesian information criterion (BINC), the Hannan–Quinn information criterion (HQINC), the Cramer–von-Misses (CRMI), and the ANDA (Anderson–Darling) are used for comparison purpose. The comparison of the NGEP-Wei with other rival distributions is made by Two COVID-19 data sets. In terms of performance, we show that the proposed method outperforms the other competing methods included in this study. [ABSTRACT FROM AUTHOR]

This paper examines the subjective well-being (SWB) of children and adolescents (10‒18 years old) during the COVID-19 pandemic in Indonesia for two periods (May to July 2020 and March to May 2021), using cross-sectional data from two distinct samples of N = 1,011 (M age = 14.61) and N = 1,640 (M age = 14.86), respectively. Its aims are twofold: (1) to examine the state of SWB among Indonesian children, including its cognitive component (measured using the CW-SWBS), positive affect (PA), and negative affect (NA), and the participants' satisfaction with their contact with friends and how they spend their time; and (2) to compare the evolution of these SWB-related aspects from the first to the second year of the COVID-19 pandemic. Data were collected using Google Forms and convenience and snowball sampling. Results showed that boys displayed significantly higher mean SWB scores than girls, while elementary students displayed significantly higher mean scores for the cognitive component than middle and high school students for both data collection periods. Boys also displayed significantly higher mean PA scores than girls. There were significant school grade differences on PA and NA, depending on the period of study. In the first year of the COVID-19 pandemic, children and adolescents displayed lower scores on satisfaction with their contact with friends than in the second year of the COVID-19 pandemic. These results suggest that children and adolescents adapted to the COVID-19 situation during the second year, and this adaptation protected their SWB from further decrease, as defended by the homeostasis theory. [ABSTRACT FROM AUTHOR]

Social networks are popular for advertising, idea sharing, and opinion formation. Due to COVID-19, coronavirus information disseminated on social media affects people's lives directly. Individuals sometimes managed it well, but it often hampered daily activities. As a result, analyzing people's feelings is important. Sentiment analysis identifies opinions or sentiments from text. In this paper, we present an effective model that leverages the benefits of Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) to categorize Arabic tweets using a stacked ensemble learning model. First, the tweets are represented as vectors using a word embedding model, then the text feature is extracted by CNN, and finally the context information of the text is acquired by BiLSTM. Aravec, FastText, and ArWordVec are employed separately to assess the impact of the word embedding on the our model. We also compare the proposed method to various deep learning models: CNN, LSTM, and BiLSTM. Experiments are performed on three different Arabic datasets related to COVID-19 and vaccines. Empirical findings show that the proposed model outperformed the other models' results by achieving F-measures of 76.76%, 87.%, and 80.5% on the SenWave, AraCOVID19-SSD, and ArCovidVac datasets, respectively. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to analyzing the dynamics of COVID-19 using nonstandard finite difference (NSFD) schemes. Our model incorporates both asymptomatic and symptomatic infected individuals, allowing for a more comprehensive understanding of the epidemic's spread. We introduce an unconditionally stable NSFD system that eliminates the need for traditional Runge–Kutta methods, ensuring dynamical consistency and numerical accuracy. Through rigorous numerical analysis, we evaluate the performance of different NSFD strategies and validate our analytical findings. Our work demonstrates the benefits of using NSFD schemes for modeling infectious diseases, offering advantages in terms of stability and efficiency. We further illustrate the dynamic behavior of COVID-19 under various conditions using numerical simulations. The results from these simulations demonstrate the effectiveness of the proposed approach in capturing the epidemic's complex dynamics. [ABSTRACT FROM AUTHOR]

The COVID-19 (Coronavirus Disease 2019) pandemic has led to several mandatory prevention regulations, changes in healthcare services, unprecedented unemployment rates, financial stress, and emotional worries. Given the increasing cases of COVID-19, coupled with the drastic physical and psychological changes within postpartum mothers during the postpartum period, this paper aims to present an overview of the postpartum depression (PPD) among postpartum women during the COVID-19 pandemic. The postpartum period is characterized by drastic physical changes and substantial demands on parental role adjustment, and it places enormous stress on the mothers and makes them vulnerable to mental health problems. A literature search was conducted in four electronic databases (ScienceDirect, Scopus, Wiley, and SAGE) with different combinations of keywords were used. As the result, 25 articles that involved 10,515 postpartum women from 14 countries were extracted. Results have recorded PPD prevalence ranged from 6.4% to 56.9% during the COVID-19 pandemic. Limited social support, social isolation, fear of COVID-19 exposure, or infection for themselves, newborns, have worsening PPD symptoms among postpartum women. In brief, early detection, appropriate and timely intervention is needed to prevent and identify PPD among postpartum women during the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

This study explores the factors that influence the dissemination process of and public susceptibility to fake news amidst COVID-19. By adopting a qualitative approach that draws on 21 interviews with social media users from the standpoint of source credibility and construal level theories, our findings highlight motives of news sharers, platform features, and source credibility/relatedness as major factors influencing the dissemination of and public susceptibility to fake news. The paper further argues that public susceptibility to fake news can be mitigated by building an integrated approach that combines a tripartite strategy from an individual, institutional and platform level. For example, educating the public on digital resilience and enhancing awareness around source credibility can help individuals and institutions reflect on news authenticity and report fake news where possible. This study contributes to fake news literature by integrating concepts from information management, consumer behaviour, influencer marketing and mindfulness to propose a model to help authorities identify and understand the key factors that influence susceptibility to fake news during a public crisis such as COVID-19. [ABSTRACT FROM AUTHOR]

In this paper, we consider an SEIS epidemic model with infectious force in latent and infected period, which incorporates by nonlinear incidence rates. The local stability of the equilibria is discussed. By means of Lyapunov functionals and LaSalle's invariance principle, we proved the global asymptotic stability of the disease-free equilibrium and the endemic equilibrium. An application is given and numerical simulation results based on real data of COVID-19 in Morocco are performed to justify theoretical findings. [ABSTRACT FROM AUTHOR]

This paper analyzes the emergence of two well-defined peaks during the morning peak period in the traffic flow diurnal curve. It selects six California cities as research targets, and uses California employment and household travel survey data to explain how and why this phenomenon has risen during the pandemic. The final result explains that the double-humped phenomenon results from the change in the composition of commuters during the morning peak period after the outbreak. [ABSTRACT FROM AUTHOR]

COVID-19 is a modern virus that has spread all over the world and is affecting billions of people. Timely and accurate detection is of great significance to slow down the spread of the virus and treat infected people effectively. Deep learning methods have been shown promising in COVID-19 detection due to their accurate feature extraction ability, which can enhance the classification ability of the detection model with high accuracy. Existing studies mostly focus on information compression and feature extraction, which inevitably leads to original information loss. This paper proposes a novel end-to-end COVID-19 classification model called MSCGRU, which is based on the fusion of multi-faceted global features with local information of original images. Firstly, multi-scale CNN is used to extract multi-scale global features from multiple channels, and these features are fused with local information to obtain comprehensive features. Secondly, GRU is adopted to extract deep abstract features from comprehensive features. The experimental results demonstrate that the proposed MSCGRU greatly strengthens the learning ability and achieves better prediction performance compared with other methods. The accuracy of multi-classification is 98.2%, and the accuracy of binary classification is 100%. [ABSTRACT FROM AUTHOR]

By the end of 2021, more than 168 million students across the globe had missed a year of face-to-face schooling due to the COVID-19 pandemic. In NSW, Australia, most students engaged in learning from home for eight weeks during 2020 and a further 14 weeks during 2021. This study provides robust empirical evidence on how two years of disruptions to schooling affected student learning. Drawing on matched data for 3,827 Year 3 and 4 students from 101 NSW government schools, this paper compares student achievement growth in mathematics and reading for 2019 (pre-pandemic) and 2021 (second year of the pandemic) student cohorts. While overall there was no significant difference between cohorts, when analysed by socio-educational advantage, we were surprised to find that students in the lowest band achieved approximately three months' additional growth in mathematics. Arguably, grave concerns about the potentially dire impact of COVID-19 on the learning of disadvantaged students were met by investments that made a difference. We argue that targeted funding and system-wide initiatives to support more equitable outcomes should remain a priority after the pandemic if Australia is to meet its aspirations for excellence and equity. [ABSTRACT FROM AUTHOR]

Despite the historically documented regularity in human mobility patterns, the relaxation of spatial and temporal constraints, brought by the widespread adoption of telecommuting and e-commerce during the COVID-19 pandemic, as well as a growing desire for flexible work arrangements in a post-pandemic work, indicates a potential reshaping of these patterns. In this paper, we investigate the multifaceted impacts of relaxed spatio-temporal constraints on human mobility, using well-established metrics from the travel behavior literature. Further, we introduce a novel metric for schedule regularity, accounting for specific day-of-week characteristics that previous approaches overlooked. Building on the large body of literature on the impacts of COVID-19 on human mobility, we make use of passively tracked Point of Interest (POI) data for approximately 21,700 smartphone users in the US, and analyze data between January 2020 and September 2022 to answer two key questions: (1) has the COVID-19 pandemic and its associated relaxation of spatio-temporal activity patterns reshaped the different aspects of human mobility, and (2) have we achieved a state of stable post-pandemic "new normal"? We hypothesize that the relaxation of the spatiotemporal constraints around key activities will result in people exhibiting less regular schedules. Findings reveal a complex landscape: while some mobility indicators have reverted to pre-pandemic norms, such as trip frequency and travel distance, others, notably at-home dwell-time, persist at altered levels, suggesting a recalibration rather than a return to past behaviors. Most notably, our analysis reveals a paradox: despite the documented large-scale shift towards flexible work arrangements, schedule habits have strengthened rather than relaxed, defying our initial hypotheses and highlighting a desire for regularity. The study's results contribute to a deeper understanding of the post-pandemic "new normal", offering key insights on how multiple facets of travel behavior were reshaped, if at all, by the COVID-19 pandemic, and will help inform transportation planning in a post-pandemic world. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has left an indelible mark on societies worldwide, challenging governments to respond swiftly and effectively to mitigate its impact. India, with its vast population and complex healthcare landscape, faced unique challenges in formulating and implementing a pandemic response strategy. The article examines India's policy response to the COVID-19 pandemic and explores the valuable lessons it offers for shaping a more resilient and prepared society in a post-COVID world. It provides a comprehensive analysis of India's multifaceted approach to managing the pandemic, highlighting key elements such as lockdowns, testing and contact tracing, healthcare infrastructure, vaccination drives, and economic relief measures. By delving into both the successes and shortcomings of these policies, it seeks to extract valuable insights for policymakers and public health officials globally. As the world transitions into a post-COVID era, the lessons learned from India's experience offer a roadmap for building stronger healthcare systems, improving disaster preparedness, and enhancing social safety nets. The article underscores the importance of proactive governance, community engagement, data-driven decision-making, and international collaboration in the face of global health crises. The paper demonstrates that India's journey through the pandemic provides a wealth of knowledge that can inform policy development, foster greater resilience, and help societies better navigate the uncertainties of a post-COVID world. By reflecting on the successes and challenges of India's response, this article offers actionable insights for shaping a more equitable, sustainable, and prepared society in the wake of the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

This paper specifically investigates the effects of US government emergency actions on the investor sentiment–financial institution stock returns relationship. Despite attempts by many studies, the literature still provides no answers concerning this nexus. Using a new firm-specific Twitter investor sentiment (TS) metric and performing a panel smooth transition regression for daily data on 66 S&P 500 financial institutions from January 1 to December 31, 2020, we find that TS acts asymmetrically, nonlinearly, and time varyingly according to the pandemic situation and US states’ responses to COVID-19. In other words, we uncover the nexus between TS and financial institution stock returns and determine that it changes with US states’ reactions to COVID-19. With a permissive government response (the first regime), TS does not impact financial institution stock returns; however, when moving to a strict government response (the overall government response index exceeds the 63.59 threshold), this positive effect becomes significant in the second regime. Moreover, the results show that the slope of the transition function is high, indicating an abrupt rather than a smooth transition between the first and second regimes. The results are robust and have important policy implications for policymakers, investment analysts, and portfolio managers. [ABSTRACT FROM AUTHOR]

In this paper, we describe conversion factors (CF) for the calculation of biologically effective irradiances (BEI) from erythemal irradiance for three effects: photosynthesis of previtamin D3, psoriasis healing, and inactivation of the SARS-Cov-2 virions. CFs were empirically derived from measurements of spectral solar ultraviolet (UV) radiation during all sky conditions at four mid-latitude sites in the Northern Hemisphere, namely Aosta, Belsk, Reading, and San Diego. These CFs were found to depend on solar zenith angle (SZA) and total column ozone, but are largely independent (within ± 5% for SZA < 60°) of local conditions such as surface albedo, visibility and other local atmospheric patterns. The values of these empirical CFs are consistent with analytical CFs derived with radiative transfer calculations (model FastRT) for clear-sky and overcast conditions. To validate these analytical CFs, one-hour radiant exposures for the three biological effects were calculated from erythemal measurements at Reading between 2012 and 2021 and compared with similar exposures calculated directly from the spectral UV measurements. The two datasets agreed within 10% for SZA < 65°, demonstrating the utility of the conversion method. These results suggest that the proposed analytical CFs can be used with confidence to estimate radiant exposures for the three biological effects from measurements of the UV Index at any northern mid-latitude site. [ABSTRACT FROM AUTHOR]

In the wake of ongoing challenges faced by the disruption of COVID-19, the current study attempts to investigate fintech growth during COVID-19 in the Middle East and North African (MENA) region. The study applies descriptive analysis, content analysis, and keyword selection criteria to segregate current challenges and future prospects of fintech growth in the MENA region. Our study comprises 250 research articles, web reports, news articles, opinion papers, and commentaries. The study reported privacy issues, cybercrimes, financial disruption and instability, exploitation of social norms and values, rising inequalities, and non-compliance of regulatory authorities as major challenges posed by fintech startups in the MENA region. On the contrary, the future prospects of FinTech growth in the MENA region are employment opportunities, decentralization, cost-effectiveness, financial outreach, networking, and breaking traditional financial biases. The study recommended multiple implications for policymakers, regulation bodies, countries in the MENA region, and fintech developers. [ABSTRACT FROM AUTHOR]

This paper addresses the notion of electronic economics by examining financial technology (fintech) firms' performance and corporate governance quality, using data from the United States. The findings support our maintained assumption that due to the economics of electronic platforms perused by financial technology firms, these firms outperform firms from other industries (non-Fintech). The final sample comprises 1,712 company-year observations between 2010 and 2019 (pre-COVID-19). The evidence suggests that our corporate governance quality index, developed from Organisation of Economic Cooperation and Development (www.oecd.org/corporate/corporate-governance-factbook.htm, 2019) guidelines, accurately captures corporate governance quality in the United States, principally due to the inclusion of an anti-bribery policy indicator, in the index. The results suggest that this evidence is not merely an artefact due to the corporate governance quality index potentially capturing priced risk factors. Our findings reveal that fintech firms have superior corporate governance quality than non-fintech and that fintech firms place more reliance on internal versus external corporate governance mechanisms, vis-a-vis companies in other industries. [ABSTRACT FROM AUTHOR]

The influence of Covid-19 pandemic crisis on rates of return is analyzed in this paper in the light of possible data manipulation related to reporting systems provided by the administration in the USA, Turkey and Poland. The study used various methods of analyzing the relationship of a discrete, non-discrete and dichotomous data nature between the studied variables. As a result, the strongest reaction of the market was observed in Turkey followed by the USA and Poland. It can be concluded that the reaction of the surveyed markets was influenced by the data manipulations. The added value of the article is related to the use of various methods to study phenomena and detect the impact of data manipulation on the markets. [ABSTRACT FROM AUTHOR]

This paper evaluates the stock performance of Islamic banks relative to their conventional counterparts during the initial phase of the COVID-19 crisis (from December 31, 2019, to March 31, 2020). Using 426 banks from 48 countries, we find that stock returns of Islamic banks were about 10–13% higher than those of conventional banks after controlling for a host of the bank- and country-level variables. This study explains the Islamic banks' superior crisis stock performance by exploring the potential role of pre-crisis bank efficiency. In a univariate analysis, we document higher non-parametric Data Envelopment Analysis (DEA) efficiency levels for Islamic banks than conventional banks in the year preceding the COVID-19 crisis. Our multivariate regressions show that the risk-adjusted DEA efficiency scores can explain crisis stock returns for Islamic banks but not conventional banks. The evidence is robust to alternative measures of stock returns, efficiency models, and other empirical strategies. Finally, we present insight on the importance of key bank characteristics in determining the stock returns of conventional banks during the crisis period. [ABSTRACT FROM AUTHOR]

This paper addresses the interpretability problem of non-parametric option pricing models by using the explainable artificial intelligence (XAI) approach. We study call options written on the S&P 500 stock market index across three market regimes: pre-COVID-19, COVID-19 market crash, and post-COVID-19 recovery. Our comparative option pricing exercise demonstrates the superiority of the random forest and extreme gradient boosting models for each market regime. We also show that the model's pricing accuracy has worsened from the pre-COVID-19 to the recovery period. Moneyness was the most important price determinants across the market regimes, while the implied volatility and time-to-maturity inputs contributed intermittently to a lesser extent. During the COVID-19 crash, open interest gained more economic importance due to the increased behavioral tendencies of traders consistent with market distress. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic entailed restrictions that hampered face-to-face interactions and social gatherings. In this paper, we examine whether loneliness increased to different extents among age groups due to these restrictions, and if these differences were mediated by specific life course conditions. Based on longitudinal data from the Swiss Household Panel, our results show that loneliness increased disproportionately among younger individuals during the pandemic. This finding aligns with the social convoy model and the socioemotional selectivity theory, which postulate a decline of social network size over the life course. It also corresponds to findings indicating a decrease in contact frequency with increasing age. Individuals aged 30 years and above experienced a lower increase in loneliness when they lived in shared households; however, this protective effect was not observed for younger individuals. Living together with a partner, being male, and not anticipating health complications in case of a COVID-19 infection moderated the increases of loneliness, but they were independent of age. [ABSTRACT FROM AUTHOR]

Unexpected and sudden spread of the novel Coronavirus disease (COVID-19) has opened up many scopes for researchers in the fields of biotechnology, health care, educational sectors, agriculture, manufacturing, service sectors, marketing, finance, etc. Hence, the researchers are concerned to study, analyze and predict the impact of infection of COVID-19. The COVID-19 pandemic has affected many fields, particularly the stock markets in the financial sector. In this paper, we have proposed an econometric approach and stochastic approach to analyze the stochastic nature of stock price before and during a COVID-19-specific pandemic period. For our study, we considered the BSE SENSEX INDEX closing pricing data from the Bombay Stock Exchange for the period before and during COVID-19. We have applied the statistical tools, namely descriptive statistics for testing the normal distribution of data, unit root test for testing the stationarity, and GARCH and stochastic model for measuring the risk, also investigated drift and volatility (or diffusion) coefficients of the stock price SDE by using R Environment software and formulated the 95% confidence level bound with the help of 500 times simulations. Finally, the results have been obtained from these methods and simulations are discussed. [ABSTRACT FROM AUTHOR]

The COVID-19 has placed pandemic modeling at the forefront of the whole world's public policymaking. Nonetheless, forecasting and modeling the COVID-19 medical waste with a detoxification center of the COVID-19 medical wastes remains a challenge. This work presents a Fuzzy Inference System to forecast the COVID-19 medical wastes. Then, people are divided into five categories are divided according to the symptoms of the disease into healthy people, suspicious, suspected of mild COVID-19, and suspicious of intense COVID-19. In this regard, a new fuzzy sustainable model for COVID-19 medical waste supply chain network for location and allocation decisions considering waste management is developed for the first time. The main purpose of this paper is to minimize supply chain costs, the environmental impact of medical waste, and to establish detoxification centers and control the social responsibility centers in the COVID-19 outbreak. To show the performance of the suggested model, sensitivity analysis is performed on important parameters. A real case study in Iran/Tehran is suggested to validate the proposed model. Classifying people into different groups, considering sustainability in COVID 19 medical waste supply chain network and examining new artificial intelligence methods based on TS and GOA algorithms are among the contributions of this paper. Results show that the decision-makers should use an FIS to forecast COVID-19 medical waste and employ a detoxification center of the COVID-19 medical wastes to reduce outbreaks of this pandemic. [ABSTRACT FROM AUTHOR]

This paper problematizes the notion of "intent" through the concept of "involuntary resistance". Departing from the narratives of employees in nursing homes in Sweden during the Covid-19 pandemic in 2020 and 2021, we suggest that neoliberal norms and a local management that capitalizes on social hierarchies (sex, age, class, etc.) were the context of the strong biopolitical state management that occurred due to the Covid-19 pandemic. The friction between different forms of governing became a seedbed for an involuntary resistance with an unclear intent against the state recommendations. This sheds light upon the need to (re)frame the current dominance of specific types of knowledge that are constructed in the field of resistance. We suggest that new paths of thought are needed—within social sciences—that work towards a wider conceptualizing of resistance, which embraces practices that lie outside the common thought of dissent. [ABSTRACT FROM AUTHOR]

This paper draws from data collected from 500+ surveys, distributed twice from the same respondents (2020 and 2021), and forty-five face-to-face interviews (2022). The location studied is a metropolitan county in Colorado (USA). The research examined the discourses and practices having to do with organic and natural food consumption—note, too, the data were collected at the height of the COVID-19 pandemic. The findings upend conventional understandings of, and frameworks used to explain, consumer behavior. What are often presented as motivations in prior studies are shown, instead, to be justifications; rationalizations after-the-fact. The paper troubles decision-making frameworks that cast motivations, attitudes, and intentions as "antecedents" to consumer behavior. Rather, the findings point to the significance of social networks, and in particular network diversity, for understanding and explaining the sayings (discourses) and doings (practices) of "individual" consumers. Discourses linked to health are also shown to be salient variables, though when situated within social networks those discourses are shown to have politics. Particular attention is devoted to explaining dietary shifts among those who reported the largest increases in the consumption of organic and natural foods between 2020 and 2021/22. The paper concludes discussing what the data mean from the standpoint of envisioning just and inclusive food system futures and agrifood policy that delivers on those ends. [ABSTRACT FROM AUTHOR]