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This study draws on technological frames to provide an understanding of organizational processes of strategizing by exploring how strategizing organizational capabilities for industrial digitalization could be understood through managers' perceptions of digital technology applications. This study complements earlier research focused on industry outcomes by addressing technological frames to understand how strategizing organizational capabilities within industrial digitalization may provide insight into socio-cognitive aspects which may affect technology-induced organizational change.

The single case study uses 14 in-depth interviews collected over two years (October 2020 to February 2022). The study follows an interpretative research design exploring managers' perceptions of industrial digitalization through a digitalization project.

The case study contributes to research by emphasizing socio-cognitive aspects through technological frames exploring how and why managers' perceptions of industrial digitalization affect strategizing organizational capabilities. The study contributes to practice by bringing attention to the disparate views of industrial digitalization. By illustrating how socio-cognitive aspects shape organizational capabilities, this study offers managers valuable insight into the relationship between an organization's capabilities, the individual and the shared structures affecting a digitalization project.

The case study is limited to Swedish manufacturing industries and is not aiming to be transferred or generalized to other industrial contexts or countries.

This study recognizes that strategizing organizational capabilities depends on managers' ability to illuminate the socio-cognitive aspects. Hence, the study contributes to practice by bringing attention to the disparate views among managers on the enhancement efforts made using digital technologies.

The purpose of this study is to examine the state of research into adoption of machine learning systems within the health sector, to identify themes that have been studied and observe the important gaps in the literature that can inform a research agenda going forward.

A systematic literature strategy was utilized to identify and analyze scientific papers between 2012 and 2022. A total of 28 articles were identified and reviewed.

The outcomes reveal that while advances in machine learning have the potential to improve service access and delivery, there have been sporadic growth of literature in this area which is perhaps surprising given the immense potential of machine learning within the health sector. The findings further reveal that themes such as recordkeeping, drugs development and streamlining of treatment have primarily been focused on by the majority of authors in this area.

The search was limited to journal articles published in English, resulting in the exclusion of studies disseminated through alternative channels, such as conferences, and those published in languages other than English. Considering that scholars in developing nations may encounter less difficulty in disseminating their work through alternative channels and that numerous emerging nations employ languages other than English, it is plausible that certain research has been overlooked in the present investigation.

This review provides insights into future research avenues for theory, content and context on adoption of machine learning within the health sector.

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 case describes a hypothetical hedge fund manager who is examining whether to invest in bitcoin. The case discusses potential risks and rewards of investing in bitcoin, the role of bitcoin and digital currencies more broadly, and financial innovation in the space, such as ICOs. It can be taught as part of a second-year MBA elective course in investments, financial institutions/capital markets, or fintech.

Artificial intelligence (AI) allows the brand to co-create value with young customers through mobile apps. However, as many brands claim that their mobile apps are using the most updated AI technology, young customers face app fatigue and start questioning the authenticity of this touchpoint. This paper aims to study the mediating effect of authenticity for the value co-creation of AI-powered branded applications.

Drawing from regulatory engagement theory, this study conceptualize authenticity as the key construct in customers’ value experience process, which triggers customer value co-creation. Two scenario-based online experiments are conducted to collect data from 444 young customers. Data analysis is performed using ANOVA and Process Hayes.

The results reveal that perceived authenticity is an important mediator between media richness (chatbot vs AI text vs augmented reality) and value co-creation. There is no interaction effect of co-brand fit (high vs low) and source endorsement (doctor vs government) on the relationship between media richness and perceived authenticity, whereas injunctive norms (high vs low) strengthen this relationship.

The finding provides insights for marketing managers on engaging young customers suffering from app fatigue. Authenticity holds the key to young customers’ technological perceptions.

This research highlights the importance of perceived authenticity in encouraging young customers to co-create value. Young customers consider authenticity as a motivational force experience that involves customers through the app’s attributes (e.g. media richness) and social standards (e.g. norms), rather than brand factors (e.g. co-brand fit, source endorsement).

In the most recent years, social, innovative, economic and political changes in the European context have characterized consumers' behaviours. The paper aims to understand if the use of electronic commerce differs in a situation characterized by uncertainty.

An innovative approach to categorising online consumer behaviour considers the self-determination theory and basic psychological needs in an uncertain scenario. The research is based on a quantitative analysis obtained by clustering algorithms on a sample of 1,000 digital users in European countries. A structured questionnaire was administered online and distributed through the leading online social platforms and direct mailing.

The results show online activities during changes in consumer behaviour patterns and retailers' strategies. This research will allow online retail managers and practitioners to obtain important information to help them define appropriate customer-oriented strategic actions to enhance value in the electronic context for both customers and firms.

The innovation of this research approaches the categorization of online consumer behaviour by exploiting the self-determination theory in an uncertain scenario. Precisely, the novelty of this research is to highlight three detailed categories of electronic commerce consumers, namely, unwilling, halfback and digital, to collect, store and disseminate information about these categories of Online Consumers Behaviours.

This paper aims to introduce a custom-designed integrated nucleic acid detection polymerase chain reaction (PCR) instrument for clinical detection applications.

The PCR instrument can make rapid, sensitive, low-cost and quantitative molecular diagnosis compared with the current routine test flow from the pipette, series reagent to RT-PCR by manual manipulation. By integrating the multichannel automatic pipetting module, heat amplification module and real-time fluorescence detection module for the first time, the custom-designed integrated nucleic acid detection PCR instrument can achieve sample collection, subpackage, mixing, extracting, measuring and result presentation.

The multichannel automatic pipetting module was assembled with an accuracy of 0.4% (2 microliters) for accuracy measurement. Besides, the accuracy and sensitivity of nucleic acid using integrated low-cost nucleic acid detection PCR instruments were checked with COV-2019 virus (staining method) and African swine fever virus (probe method) under different concentrations.

Because of its high cost, complex system and bulky laboratory settings, including sample subpackage, mixing, extracting, measuring and finally result in presentation, the current nucleic acid detection system is not suitable for field operation and disease diagnosis in remote areas. The group independently designed and assembled an integrated low-cost multichannel nucleic acid detection PCR instrument, including a multichannel automatic pipetting module, a heat amplification module and a real-time fluorescence detection module.

The above equipment showed better reliability compared with commercial qPCR. These results can lay the foundation for functional, fast and low-cost PCR equipment for trace measurements.

This study evaluates how publicly available archival documents shaped online discussions about allegations that thousands of children were kidnapped during the 1950s in Israel, known as the Yemenite children’s affair. It examines if access to historical records leads to more informed and rational public discourse, especially on social media.

Using content analysis, this study examines Facebook posts from media outlets, politicians, NGOs and public groups between 2016 and 2021 to understand how the Israeli State Archives’ release of over 300,000 documents affected support of the kidnapping.

Despite extensive archival information debunking the kidnapping theory, public opinion and discourse largely continued to support it. This suggests a complex interaction between information availability, preexisting beliefs, echo chambers and group allegiances, suggesting that access to factual data alone may not effectively challenge established beliefs in online public settings.

Since data were collected only from Facebook, our conclusions cannot be generalized to other platforms. The study relies only on publicly accessible data and does not establish causality between exposure to information and shifts in opinion. Our findings show that disclosing archival information does not significantly benefit public political discourse on contentious topics but also point to the advantages of mediating information by politicians, NGOs and journalists.

As a unique case study, this research contributes to understanding the role of historical archives in digital-age public discourse. It highlights their potential and limitations in facilitating informed debate and deliberation, emphasizing the complexity of influencing established beliefs with factual data.

The COVID-19 pandemic has affected around 216 countries and territories worldwide and more than 2000 cities in India, alone. The smart cities mission (SCM) in India started in 2015 and 100 smart cities were selected to be initiated with a total project cost of INR 2031.72 billion. Smart city strategies play an important role in implementing the measures adopted by the government such as the issuance of social distancing regulations and other COVID-19 mitigation strategies. However, there is no research reported on the role of smart cities strategies in managing the COVID-19 outbreak in developing countries.

This paper aims to address the research gap in smart cities, technology and healthcare management through a review of the literature and primary data collected using semi-structured interviews.

Each city is unique and has different challenges, the study revealed six key findings on how smart cities in India managed the COVID-19 outbreak. They used: Integrated Command and Control Centres, Artificial Intelligence and Innovative Application-based Solutions, Smart Waste Management Solutions, Smart Healthcare Management, Smart Data Management and Smart Surveillance.

This paper contributes to informing policymakers of key lessons learnt from the management of COVID-19 in developing countries like India from a smart cities’ perspective. This paper draws on the six Cs for the implications directed to leaders and decision-makers to rethink and act on COVID-19. The six Cs are: Crisis management leadership, Credible communication, Collaboration, Creative governance, Capturing knowledge and Capacity building.