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The rapid expansion of internet usage and device connectivity has underscored the importance of understanding the public’s cyber behavior and knowledge. Despite this, there is little research that examines the public’s objective knowledge of secure information security practices. The purpose of this study is to examine how objective cyber awareness is distributed throughout society.

This study draws on a large national survey of adults to examine the relationship between individual factors – such as demographic attributes and socioeconomic resources – and information security awareness. The study estimates several statistical models using weighted logistic regression to model objective information security awareness.

The results indicate that socioeconomic resources such as income and education have a significant effect on individuals’ information security awareness with richer and more highly educated individuals exhibiting greater awareness of important security practices and tools. Additionally, age and gender represent consistent and clear informational gaps in society as older individuals and females are significantly less knowledgeable about an array of information security practices than younger individuals and males, respectively.

The findings have important implications for our understanding of information security behavior and user vulnerability in an increasingly digital and connected society. Despite the growing importance of cybersecurity for all individuals in nearly all domains of daily life, there is substantial inequality in awareness about secure cyber practices and the tools and techniques used to protect one’s self from attacks. While digital technology will continue to permeate many aspects of daily life – from financial transactions to health services to social interactions – the findings here indicate that some users may be far more exposed and vulnerable to attack than others.

This study contributes to our understanding of general user information security awareness using a large survey and statistical models to generalize about the public’s information security awareness across multiple domains and stimulates future research on public knowledge of information security. The findings indicate that some users may be far more exposed and vulnerable to attack than others. Despite the growing importance of cybersecurity for all individuals in nearly all domains of daily life, there is substantial inequality in awareness about secure cyber practices and the tools and techniques used to protect one’s self from attacks.

This paper aims to propose a new method to derive custom dynamic cyber risk metrics based on the well-known Goal, Question, Metric (GQM) approach. A framework that complements it and makes it much easier to use has been proposed too. Both, the method and the framework, have been validated within two challenging application domains: continuous risk assessment within a smart farm and risk-based adaptive security to reconfigure a Web application firewall.

The authors have identified a problem and provided motivation. They have developed their theory and engineered a new method and a framework to complement it. They have demonstrated the proposed method and framework work, validating them in two real use cases.

The GQM method, often applied within the software quality field, is a good basis for proposing a method to define new tailored cyber risk metrics that meet the requirements of current application domains. A comprehensive framework that formalises possible goals and questions translated to potential measurements can greatly facilitate the use of this method.

The proposed method enables the application of the GQM approach to cyber risk measurement. The proposed framework allows new cyber risk metrics to be inferred by choosing between suggested goals and questions and measuring the relevant elements of probability and impact. The authors’ approach demonstrates to be generic and flexible enough to allow very different organisations with heterogeneous requirements to derive tailored metrics useful for their particular risk management processes.

The purpose of this paper is to give an overview of artificial intelligence (AI) and other AI-enabled technologies and to describe how COVID-19 affects various industries such as health care, manufacturing, retail, food services, education, media and entertainment, banking and insurance, travel and tourism. Furthermore, the authors discuss the tactics in which information technology is used to implement business strategies to transform businesses and to incentivise the implementation of these technologies in current or future emergency situations.

The review provides the rapidly growing literature on the use of smart technology during the current COVID-19 pandemic.

The 127 empirical articles the authors have identified suggest that 39 forms of smart technologies have been used, ranging from artificial intelligence to computer vision technology. Eight different industries have been identified that are using these technologies, primarily food services and manufacturing. Further, the authors list 40 generalised types of activities that are involved including providing health services, data analysis and communication. To prevent the spread of illness, robots with artificial intelligence are being used to examine patients and give drugs to them. The online execution of teaching practices and simulators have replaced the classroom mode of teaching due to the epidemic. The AI-based Blue-dot algorithm aids in the detection of early warning indications. The AI model detects a patient in respiratory distress based on face detection, face recognition, facial action unit detection, expression recognition, posture, extremity movement analysis, visitation frequency detection, sound pressure detection and light level detection. The above and various other applications are listed throughout the paper.

Research is largely delimited to the area of COVID-19-related studies. Also, bias of selective assessment may be present. In Indian context, advanced technology is yet to be harnessed to its full extent. Also, educational system is yet to be upgraded to add these technologies potential benefits on wider basis.

First, leveraging of insights across various industry sectors to battle the global threat, and smart technology is one of the key takeaways in this field. Second, an integrated framework is recommended for policy making in this area. Lastly, the authors recommend that an internet-based repository should be developed, keeping all the ideas, databases, best practices, dashboard and real-time statistical data.

As the COVID-19 is a relatively recent phenomenon, such a comprehensive review does not exist in the extant literature to the best of the authors’ knowledge. The review is rapidly emerging literature on smart technology use during the current COVID-19 pandemic.

The COVID-19 pandemic is not a foodborne infectious disease, but it has dramatically impacted food safety practices worldwide due to its potential for transmission through contaminated surfaces and food. Accordingly, the Omicron variant seems to have affected food-related activities and behaviours and disturbed food supply networks since its appearance in November 2021. Hence, this paper aims to assess how the Omicron variant impacted food safety knowledge, attitudes and practices amongst adult consumers in five countries: Bosnia and Herzegovina, North Macedonia, Serbia, Montenegro and Russia.

The study is based on an online survey. The questionnaire was developed and revised based on previous research on the impact of previous COVID-19 waves on food-related activities in several countries. The questionnaire was distributed through the SurveyMonkey platform from January 15 to February 25, 2022. It consisted of 29 multiple-choice and one-option questions organised into three sections. A total of 6,483 valid responses were received. Statistical Package for Social Sciences (SPSS) version 25.0 was used to analyse the survey results.

According to the survey findings, food safety practices evolved during the Omicron wave in the studied countries. Firstly, less than half of the sample used a face mask whilst purchasing food. Secondly, regarding food safety knowledge, the survey results suggest that there is still a lack of knowledge in the studied countries. Thirdly, the survey indicates a lack of knowledge amongst the respondents regarding food safety attitudes. For instance, more than a third of the sample (34.4%) are unsure whether the COVID-19 virus can be transmitted through food. These results are surprising and alarming, especially considering that our sample has a higher education than the population of the studied countries.

The main limitation of this research is the sample bias. Survey participants were randomly chosen, enrolled voluntarily and not rewarded. As a result, the questionnaire was self-administered and completed exclusively by people motivated by an interest in the topic. Consequently, our survey does not represent the general population of the studied countries. People with a high degree of education and women, for example, were overrepresented in our sample.

This study is unique in that it is the first to gather information and analyse people’s perceptions of the effects of the Omicron variant on food safety. As a result, the findings of this survey offer a solid basis for future investigations into the impact of the pandemic on food safety in the Balkan region and Russia. This study can help further understand the changes during the COVID-19 pandemic. It provides crucial insights that can be used to guide future decision-making and policy development regarding improving food safety practices. This and other future studies will be a foundation for organisational and government readiness for future shocks, crises and pandemics. The effects of the present Ukrainian conflict on agricultural systems and supply chains throughout the globe (e.g. increased food prices) show that this is timely, urgent and highly required.

The purpose of this paper is to analyse and classify the literature that contributed to three questions, namely, what are the benefits of big data analytics (BDA) in the field of supply chain management (SCM) and logistics, what are the challenges in BDA applications in the field of SCM and logistics and what are the determinants of successful applications of BDA in the field of SCM and logistics.

This paper conducts a systematic literature review (SLR) to analyse the findings of 44 selected papers published in the period from 2016 to 2020, in the area of BDA and its impact on SCM. The designed protocol is composed of 14 steps in total, following Tranfeld (2003). The selected research papers are categorized into four themes.

This paper identifies sets of benefits to be gained from the use of BDA in SCM, including benefits in data analytics capabilities, operational efficiency of logistical operations and supply chain/logistics sustainability and agility. It also documents challenges to be addressed in this application, and determinants of successful implementation.

The scope of the paper is limited to the related literature published until the beginning of Corona Virus (COVID) pandemic. Therefore, it does not cover the literature published since the COVID pandemic.

This paper contributes to the academic research by providing a roadmap for future empirical work into this field of study by summarising the findings of the recent work conducted to investigate the uses of BDA in SCM and logistics. Specifically, this paper culminates in a summary of the most relevant benefits, challenges and determinants discussed in recent research. As the field of BDA remains a newly established field with little practical application in SCM and logistics, this paper contributes by highlighting the most important developments in contemporary literature practical applications.

This study aims to examine how the theory of planned behaviour and technology acceptance theory can be used to understand the adoption of non-pharmaceutical interventions (NPIs). The relationships between attitudes, behavioural intentions towards using NPIs, actual use of NPIs and word-of-mouth (WOM) were examined and compared between early and late adopters.

A survey was conducted to test the hypotheses with partial least squares structural equation modelling (n = 278).

The results indicate that relationships between attitudes, intentions and behavioural intentions were positive and significant in the whole data set – and that there were differences between the early and late adopters. WOM had no substantial relationship with actual usage and early adopters’ behavioural intentions.

This research gives a better sense of how WOM impacts attitudes, behavioural intentions and actual usage among early and late adopters of NPIs and highlights the effectiveness of WOM, especially among late adopters of NPIs. Furthermore, using the TAM allows us to make specific recommendations regarding encouraging the use of NPIs. A new three-stage communications model is introduced that uses early adopters as influencers to reduce the NPI adoption time by late adopters.

COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The main purpose of this work is, COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The COVID-19 diagnosis is required to detect at the beginning stage and special attention should be given to them. The fastest way to detect the COVID-19 infected patients is detecting through radiology and radiography images. The few early studies describe the particular abnormalities of the infected patients in the chest radiograms. Even though some of the challenges occur in concluding the viral infection traces in X-ray images, the convolutional neural network (CNN) can determine the patterns of data between the normal and infected X-rays that increase the detection rate. Therefore, the researchers are focusing on developing a deep learning-based detection model.

The main intention of this proposal is to develop the enhanced lung segmentation and classification of diagnosing the COVID-19. The main processes of the proposed model are image pre-processing, lung segmentation and deep classification. Initially, the image enhancement is performed by contrast enhancement and filtering approaches. Once the image is pre-processed, the optimal lung segmentation is done by the adaptive fuzzy-based region growing (AFRG) technique, in which the constant function for fusion is optimized by the modified deer hunting optimization algorithm (M-DHOA). Further, a well-performing deep learning algorithm termed adaptive CNN (A-CNN) is adopted for performing the classification, in which the hidden neurons are tuned by the proposed DHOA to enhance the detection accuracy. The simulation results illustrate that the proposed model has more possibilities to increase the COVID-19 testing methods on the publicly available data sets.

From the experimental analysis, the accuracy of the proposed M-DHOA–CNN was 5.84%, 5.23%, 6.25% and 8.33% superior to recurrent neural network, neural networks, support vector machine and K-nearest neighbor, respectively. Thus, the segmentation and classification performance of the developed COVID-19 diagnosis by AFRG and A-CNN has outperformed the existing techniques.

This paper adopts the latest optimization algorithm called M-DHOA to improve the performance of lung segmentation and classification in COVID-19 diagnosis using adaptive K-means with region growing fusion and A-CNN. To the best of the authors’ knowledge, this is the first work that uses M-DHOA for improved segmentation and classification steps for increasing the convergence rate of diagnosis.

Pandemics are a serious challenge for humanity, as their social and economic impacts can be tremendous. This study aims to understand how innovation based in the sharing economy (SE) business models can contribute to overcoming the challenges arising from the Covid-19 pandemic.

Following a netnographic approach, the authors studied the computer-mediated social interactions of internet-based virtual innovation communities.

This study found that the SE business models contribute to overcome the challenges of the Covid-19 pandemic by redistributing idle resources to lessen the impacts of confinement. This was achieved through process innovations and an innovative use of the network, which enabled fast-open and decentralized innovation processes, and quick implementation of innovations. This innovation process is based on a decentralized decision-making approach, clear rules, informal relationship among community members and open communication channels, as well as in evasive strategies to avoid facing challenges, institutional restrictions and barriers in the adoption of innovations.

This study was limited to a virtual innovation community of highly specialized and educated experts and nine community projects focused on institutional contexts of a developed country. Future research should focus on the institutional contexts of less specialized communities and developing countries and study other community innovation projects in pandemics to understand the processes of fast-open, decentralized and evasive innovation and the importance of relational capabilities for innovation in digital contexts.

The findings can guide innovation managers and public policymakers in implementing effective strategies and policies to overcome pandemic challenges using SE business models. This research also provides important insights into the types and processes of innovation in organizations that create solutions to overcome social and business challenges during pandemics. In addition, this study highlights the contributions of netnographic approaches to conducting research on innovation and in pandemic periods when measures of confinement are in place.

This study uses an innovative framework to map the types of innovation and highlights two different types of innovation processes.

This study aims to highlight that security and flexibilities remain the main points of contention in the cordiality business. This research points to planning a framework that empowers hotel users to get to the room using a mobile access key. Advancing secured facilities, mobile phone “Near Field Communication” (NFC) innovation as the entrance device by carrying out an application containing an imitated mobile key for explicit verification access is used.

The proposed system is evaluated by triangulation of experimental, numerical and rational evaluation using partial least square structural equation modeling (PLS-SEM) with Malaysian hotel guests and employees.

The discoveries with the hypothesis supported validated that the suggested solution can eliminate physical cards, boost protection and encourage a contactless ecosystem. Theoretical, management and societal contributions are discussed here.

This experiment comes with the constraints that it was conducted in only two hotels and does not fully reflect the choices of a wider range of travellers. Secondly, the cost of existing NFC smart locks is still relatively high, and along with the development of technology, the price will decrease when supply exceeds demand.

To promote high-security attributes, NFC technology as the access system by implementing an application containing an emulated smart key for specific authentication access is used. The host-card emulation enables cost-effectiveness profit and initiating a defence system in the pandemic era.

To promote high-security attributes, NFC technology is used as the access system by implementing an application containing an emulated smart key for specific authentication access. The host-card emulation enables cost-effectiveness profit and initiating a defence system in the pandemic era.

The novelty of this study comes from the use of commonly available smartphone NFC features that are yet to be applied in the tourism ecosystem. The research provokes the applied concept of mobile smartkeys.