Explore the vast range of titles available.

Extant literature has increased our understanding of the multifaceted nature of the digital divide, showing that it entails more than access to information and communication resources. Research indicates that digital inequality mirrors to a significant extent offline inequality related to socioeconomic resources. Bridging digital divides is critical for sustainable digitalized societies. Ιn this paper, we present a literature review of Information Systems research on the digital divide within settings with advanced technological infrastructures and economies over the last decade (2010–2020). The review results are organized in a concept matrix mapping contributing factors and measures for crossing the divides. Building on the results, we elaborate a research agenda that proposes [1] extending established models of digital inequalities with new variables and use of theory, [2] critically examining the effects of digital divide interventions, and [3] better linking digital divide research with research on sustainability.

Artificial Intelligence (AI) are a wide-ranging set of technologies that promise several advantages for organizations in terms off added business value. Over the past few years, organizations are increasingly turning to AI in order to gain business value following a deluge of data and a strong increase in computational capacity. Nevertheless, organizations are still struggling to adopt and leverage AI in their operations. The lack of a coherent understanding of how AI technologies create business value, and what type of business value is expected, therefore necessitates a holistic understanding. This study provides a systematic literature review that attempts to explain how organizations can leverage AI technologies in their operations and elucidate the value-generating mechanisms. Our analysis synthesizes the current literature and highlights: (1) the key enablers and inhibitors of AI adoption and use; (2) the typologies of AI use in the organizational setting; and (3) the first- and second-order effects of AI. The paper concludes with an identification of the gaps in the literature and develops a research agenda that identifies areas that need to be addressed by future studies.

Rapid advances in technology have spurred tremendous progress in developing the next generation of Industry 4.0 that was initially introduced in 2011 as a German strategic initiative for revolutionizing the manufacturing sector. Ten years have passed since 2011. In these ten years, numerous new and promising technologies and applications have been developed. The original concept of Industry 4.0, including the conceptual framework, technology framework, and enabling technologies, has experienced tremendous changes. As such, the new generation of Industry 4.0 emerges, which is also called Industry 5.0. Today, we are on the cusp of the Industry 4.0 evolution supported by a new set of enabling technologies. In such evolution of Industry 4.0, future Industry 4.0 requires a combination of recently emerging new technologies, which is giving rise to the emergence of the next generation of Industry 4.0 or Industry 5.0. Such technologies originate from different disciplines, including Artificial Intelligence (AI), 5G/6G, Quantum Computing, and others. The technologies in the original Industry 4.0 framework, such as Cyber-Physical Systems, IoT, etc., will be affected by Artificial Intelligence (AI), 5G/6G, and Quantum Computing. At this present moment, the emergence of a new era of Industry 4.0 can be seen. In this paper, we briefly survey the main emerging enabling technologies in Industry 4.0 as it relates to industries.

From the Preface As society rushes to digitize sensitive information and services, it is imperative that we adopt adequate security protections. However, such protections fundamentally conflict with the benefits we expect from commodity computers. In other words, consumers and businesses value commodity computers because they provide good performance and an abundance of features at relatively low costs. Meanwhile, attempts to build secure systems from the ground up typically abandon such goals, and hence are seldom adopted [Karger et al. 1991, Gold et al. 1984, Ames 1981]. In this book, a revised version of my doctoral dissertation, originally written while studying at Carnegie Mellon University, I argue that we can resolve the tension between security and features by leveraging the trust a user has in one device to enable her to securely use another commodity device or service, without sacrificing the performance and features expected of commodity systems.We support this premise over the course of the following chapters. Introduction. This chapter introduces the notion of bootstrapping trust from one device or service to another and gives an overview of how the subsequent chapters fit together. Background and related work. This chapter focuses on existing techniques for bootstrapping trust in commodity computers, specifically by conveying information about a computer's current execution environment to an interested party. This would, for example, enable a user to verify that her computer is free of malware, or that a remote web server will handle her data responsibly. Bootstrapping trust in a commodity computer. At a high level, this chapter develops techniques to allow a user to employ a small, trusted, portable device to securely learn what code is executing on her local computer. While the problem is simply stated, finding a solution that is both secure and usable with existing hardware proves quite difficult. On-demand secure code execution. Rather than entrusting a user's data to the mountain of buggy code likely running on her computer, in this chapter, we construct an on-demand secure execution environment which can perform security sensitive tasks and handle private data in complete isolation from all other software (and most hardware) on the system. Meanwhile, non-security-sensitive software retains the same abundance of features and performance it enjoys today. Using trustworthy host data in the network. Having established an environment for secure code execution on an individual computer, this chapter shows how to extend trust in this environment to network elements in a secure and efficient manner. This allows us to reexamine the design of network protocols and defenses, since we can now execute code on end hosts and trust the results within the network. Secure code execution on untrusted hardware. Lastly, this chapter extends the user's trust one more step to encompass computations performed on a remote host (e.g., in the cloud).We design, analyze, and prove secure a protocol that allows a user to outsource arbitrary computations to commodity computers run by an untrusted remote party (or parties) who may subject the computers to both software and hardware attacks. Our protocol guarantees that the user can both verify that the results returned are indeed the correct results of the specified computations on the inputs provided, and protect the secrecy of both the inputs and outputs of the computations. These guarantees are provided in a non-interactive, asymptotically optimal (with respect to CPU and bandwidth) manner. Thus, extending a user's trust, via software, hardware, and cryptographic techniques, allows us to provide strong security protections for both local and remote computations on sensitive data, while still preserving the performance and features of commodity computers.

Based on a critical review of the Unified Theory of Acceptance and Use of Technology (UTAUT), this study first formalized an alternative theoretical model for explaining the acceptance and use of information system (IS) and information technology (IT) innovations. The revised theoretical model was then empirically examined using a combination of meta-analysis and structural equation modelling (MASEM) techniques. The meta-analysis was based on 1600 observations on 21 relationships coded from 162 prior studies on IS/IT acceptance and use. The SEM analysis showed that attitude: was central to behavioural intentions and usage behaviours, partially mediated the effects of exogenous constructs on behavioural intentions, and had a direct influence on usage behaviours. A number of implications for theory and practice are derived based on the findings.

Information technologies exist to enable us to either do things we have not done before or do familiar things more efficiently. Metaverse (i.e. extended reality: XR) enables novel forms of engrossing telepresence, but it also may make mundate tasks more effortless. Such technologies increasingly facilitate our work, education, healthcare, consumption and entertainment; however, at the same time, metaverse bring a host of challenges. Therefore, we pose the question whether XR technologies, specifically Augmented Reality (AR) and Virtual Reality (VR), either increase or decrease the difficulties of carrying out everyday tasks. In the current study we conducted a 2 (AR: with vs. without) × 2 (VR: with vs. without) between-subject experiment where participants faced a shopping-related task (including navigating, movement, hand-interaction, information processing, information searching, storing, decision making, and simple calculation) to examine a proposed series of hypotheses. The NASA Task Load Index (NASA-TLX) was used to measure subjective workload when using an XR-mediated information system including six sub-dimensions of frustration, performance, effort, physical, mental, and temporal demand. The findings indicate that AR was significantly associated with overall workload, especially mental demand and effort, while VR had no significant effect on any workload sub-dimensions. There was a significant interaction effect between AR and VR on physical demand, effort, and overall workload. The results imply that the resources and cost of operating XR-mediated realities are different and higher than physical reality.

The Metaverse has the potential to form the next pervasive computing archetype that can transform many aspects of work and life at a societal level. Despite the many forecasted benefits from the metaverse, its negative outcomes have remained relatively unexplored with the majority of views grounded on logical thoughts derived from prior data points linked with similar technologies, somewhat lacking academic and expert perspective. This study responds to the dark side perspectives through informed and multifaceted narratives provided by invited leading academics and experts from diverse disciplinary backgrounds. The metaverse dark side perspectives covered include: technological and consumer vulnerability, privacy, and diminished reality, human–computer interface, identity theft, invasive advertising, misinformation, propaganda, phishing, financial crimes, terrorist activities, abuse, pornography, social inclusion, mental health, sexual harassment and metaverse-triggered unintended consequences. The paper concludes with a synthesis of common themes, formulating propositions, and presenting implications for practice and policy.

Background The growing application of artificial intelligence (AI) in healthcare has brought technological breakthroughs to traditional diagnosis and treatment, but it is accompanied by many risks and challenges. These adverse effects are also seen as ethical issues and affect trustworthiness in medical AI and need to be managed through identification, prognosis and monitoring. Methods We adopted a multidisciplinary approach and summarized five subjects that influence the trustworthiness of medical AI: data quality, algorithmic bias, opacity, safety and security, and responsibility attribution, and discussed these factors from the perspectives of technology, law, and healthcare stakeholders and institutions. The ethical framework of ethical values-ethical principles-ethical norms is used to propose corresponding ethical governance countermeasures for trustworthy medical AI from the ethical, legal, and regulatory aspects. Results Medical data are primarily unstructured, lacking uniform and standardized annotation, and data quality will directly affect the quality of medical AI algorithm models. Algorithmic bias can affect AI clinical predictions and exacerbate health disparities. The opacity of algorithms affects patients’ and doctors’ trust in medical AI, and algorithmic errors or security vulnerabilities can pose significant risks and harm to patients. The involvement of medical AI in clinical practices may threaten doctors ‘and patients’ autonomy and dignity. When accidents occur with medical AI, the responsibility attribution is not clear. All these factors affect people’s trust in medical AI. Conclusions In order to make medical AI trustworthy, at the ethical level, the ethical value orientation of promoting human health should first and foremost be considered as the top-level design. At the legal level, current medical AI does not have moral status and humans remain the duty bearers. At the regulatory level, strengthening data quality management, improving algorithm transparency and traceability to reduce algorithm bias, and regulating and reviewing the whole process of the AI industry to control risks are proposed. It is also necessary to encourage multiple parties to discuss and assess AI risks and social impacts, and to strengthen international cooperation and communication.

The use of mobile applications (apps) has been growing in the world of technology, a phenomenon related to the increasing number of smartphone users. Even though the mobile apps market is huge, few studies have been made on what makes individuals continue to use a mobile app or stop using it. This study aims to uncover the factors that underlie the continuance intention to use mobile apps, addressing two theoretical models: Expectation confirmation model (ECM) and the extended unified theory of acceptance and use of technology (UTAUT2). A total of 304 questionnaires were collected by survey to test the theoretical framework proposal, using structural equation modelling (SEM). Our findings indicate that the most important drivers of continuance intention of mobile apps are satisfaction, habit, performance expectancy, and effort expectancy.

Background The lack of interoperability between health information systems reduces the quality of care provided to patients and wastes resources. Accordingly, there is an urgent need to develop integration mechanisms among the various health information systems. The aim of this review was to investigate the interoperability requirements for heterogeneous health information systems and to summarize and present them. Methods In accordance with the PRISMA guideline, a broad electronic search of all literature was conducted on the topic through six databases, including PubMed, Web of science, Scopus, MEDLINE, Cochrane Library and Embase to 25 July 2022. The inclusion criteria were to select English-written articles available in full text with the closest objectives. 36 articles were selected for further analysis. Results Interoperability has been raised in the field of health information systems from 2003 and now it is one of the topics of interest to researchers. The projects done in this field are mostly in the national scope and to achieve the electronic health record. HL7 FHIR, CDA, HIPAA and SNOMED-CT, SOA, RIM, XML, API, JAVA and SQL are among the most important requirements for implementing interoperability. In order to guarantee the concept of data exchange, semantic interaction is the best choice because the systems can recognize and process semantically similar information homogeneously. Conclusions The health industry has become more complex and has new needs. Interoperability meets this needs by communicating between the output and input of processor systems and making easier to access the data in the required formats.