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The COVID-19 pandemic exposed vulnerabilities in global healthcare systems and highlighted the need for innovative, technology-driven solutions like Artificial Intelligence (AI). However, previous research on the topic has been limited and fragmented, leading to an incomplete understanding of the 'what', 'where' and 'how' of its application, as well as its associated benefits and challenges. This study proposes a comprehensive AI framework for healthcare and assesses its effectiveness within the UAE's healthcare sector. It provides valuable insights into AI applications for healthcare stakeholders that range from the molecular to the population level. The study covers the different computational techniques employed, from machine learning to computer vision, and the various types of data inputs fed into these techniques, including clinical, epidemiological, locational, behavioural and genomic data. Additionally, the research highlights AI's capacity to enhance healthcare's operational, quality-related and social outcomes, and recognises regulatory policies, technological infrastructure, stakeholder cooperation and innovation readiness as key facilitators of AI adoption. Lastly, we stress the importance of addressing challenges such as data privacy, security, generalisability and algorithmic bias. Our findings are relevant beyond the pandemic in facilitating the development of AI-related policy interventions and support mechanisms for building resilient healthcare sector that can withstand future challenges. [ABSTRACT FROM AUTHOR]

Blockchain technology enables supply chains to share and distribute data in secured and decentralised environments through validators who trust and validate transactions. The use of blockchain was crucial in the context of the healthcare supply chain (HCSC) during COVID-19, where transactions were secure, and links throughout the supply chain were physically disrupted. Current research has focused on the blockchain application in industry, reviewing its architecture and the mechanisms involved. However, limited empirical studies consider HCSCs and the associated implementation challenges. In this sense, our case study of Indian hospitals has engaged with various stakeholders of the HCSC including clinicians, nurses, doctors, hospital managers, and digital healthcare equipment companies to explore the context to answer the following research questions: (i) What is the impact of the blockchain technology in the HCSC context? and (ii) What are the challenges faced in utilising blockchain technologies in the HCSC? Our findings indicate that the challenges blockchain implementation faced were based on variations in patients' treatment needs, data storage and privacy, interoperability, digital transformation, technology resistance, training and skills development, resource restraints and capabilities, and supply chain-wide collaboration. The study emphasises challenges for Blockchain in HCSC setting that both practitioners and academics need to be cognisant of. [ABSTRACT FROM AUTHOR]

Digital platforms have radically transformed how we work, shop, and socialize. Despite their numerous benefits, they may also threaten social justice due to unforeseen consequences of specific design choices, preventing end users from participating equitably in the digital economy. As platform owners compete by leveraging personal data, it remains unclear how digital platforms can be designed to empower end users to control, legally own, and benefit from their data in a privacy-preserving way. Integrating design science research with heuristic theorizing, this study proposes design theory for a end user-centric digital platforms. We derive design theory components from over nine years of data regarding the Dataswyft platform, including five meta-requirements and eight design principles. They reveal how digital platforms can be designed for social justice by empowering end users, both technically and legally, to protect and control their data through a containerized microservice platform infrastructure. This platform design includes multiple data protection layers, end user-driven data collection, reconfiguration, and exchange functionalities, safeguarding mechanisms, and semi-centralized ecosystem governance structures. By evaluating an expository instantiation of the proposed design principles, we demonstrate the applicability and utility of our design theory, paving the way for data self-sovereignty and social sustainability. [ABSTRACT FROM AUTHOR]

Blockchain has gained momentum as a disruptive technology in supply chain management against its introduction as a finance-related instrument. Nevertheless, the developing academic understanding and the limited practical implications lead to insufficient insights into the use of blockchain technology, particularly in the supply chain finance (SCF) domain. Thus, the expected potential of blockchain technology remains underexplored. Accordingly, this study explicates this situation by examining the extant literature findings and web-based big data that can provide evidence about the real needs in supply chains, and investigating how blockchain emerges as a disruptive SCF-oriented technology. The study employs a web analytics method, Search Engine Results Page (SERP) analysis which considers the trends in blockchain technology use and the interactions between blockchain, supply chain and finance appearing in Google searches. The SERP method examined real-time clicks, web traffics and most commonly asked questions about blockchain. The SERP findings revealed that the interest in blockchain technology neither focused on finance nor data privacy as emphasised in the literature but mainly on the benefits of increasing digitalisation and efficiency in supply chains. The results offered practical implications for capturing recent blockchain- and supply chain-related trends and designing more digital and efficient supply chains. [ABSTRACT FROM AUTHOR]

The cloud-edge collaborative manufacturing system (CCMS) connects distributed factories to a cloud centre through cloud-edge collaborative communication, introducing both opportunities and challenges to conventional dynamic job scheduling. Enhancing each factory's scheduling performance by sharing general scheduling knowledge among heterogeneous factories under the consideration of data privacy protection remains challenging. To this end, this paper proposes to solve the dynamic job scheduling in the context of CCMS with a novel federated deep reinforcement learning (FDRL) approach. Within each factory, the scheduling objective involves minimising the makespan and energy consumption, accounting for machine warm-up procedures and real-time dynamics. To handle heterogeneous policy structures, we aggregate their hidden parameters through FDRL, with states, actions, and rewards designed to facilitate the aggregation. The two-phase algorithm, comprising iterative local training and global aggregation, trains the scheduling policies. Constraint items are introduced to the loss functions to smooth local training, and the global aggregation considers production scales and obtained objectives. The proposed approach enhances the solution quality and generalisation of each factory's scheduling policy without exposing original production data. Numerical experiments conducted on sixty scheduling instances validate the superiority of the proposed approach compared to twelve dynamic scheduling methods. Compared to independently trained DRL-based approaches, the proposed FDRL-based approach achieves up to an 8.9% reduction in makespan and a 22.3% decrease in energy consumption through knowledge sharing. [ABSTRACT FROM AUTHOR]

Smart services' data-intensive nature means their convenience for users comes with privacy concerns. This poses challenges for the supply of smart services. This paper analyses the impact of user-differentiated privacy costs on smart service supply strategies. It also discusses the effects of privacy protections and data utilisation capabilities on the improvement of smart service levels. We find that providers tend to segment the market based on user privacy sensitivity to capture the maximum consumer surplus. Strict privacy protection requirements may cause providers to sacrifice service quality. Privacy protection is conducive to improving the level of smart services only when users fully recognise these services' value. Interestingly, our study finds improvements in service levels by providers are driven by two factors. On the one hand, providers choose to improve their service levels to obtain more data and create more profits. On the other hand, to reduce the opportunity costs of early investments to develop services, providers need to improve their capabilities to utilise the value of data, ultimately leading to improved service levels. This study brings a new perspective to the supply of smart services and offers suggestions for how to resolve the contradiction between smart service use and privacy preservation. [ABSTRACT FROM AUTHOR]

This article provides an overview of the regulation of artificial intelligence (AI) in the workplace, with a specific focus on the United States. It explores the challenges policymakers face in promoting the benefits of AI while addressing potential harm and ensuring worker access. Concerns highlighted include the impact on workers' well-being, bias and discrimination, lack of transparency and data privacy, and job displacement. The Biden administration calls for clear governance systems, worker input in AI design and oversight, and protection of workers' rights. The article also examines how other countries approach workplace AI regulation. At the federal level, the US government aims to promote AI development while ensuring trustworthiness and equity, but faces limitations in including workers in decision-making processes. States and cities focus more on individual harms but struggle with capacity limitations and promoting collective rights. Unions can offer policy initiatives to complement traditional mechanisms. The challenge for regulators is to strike a balance between protecting workers and keeping up with AI technology advancements. [Extracted from the article]

Undoubtedly, social media interactions have created phenomenal opportunities for firms, consumers, and brands to engage with one another across the world. These interactions provide marketers with valuable tools to foster deeper connections, personalize experiences, and co-create value with consumers. However, they also pose challenges, including privacy concerns, data security, and misinformation. Drawing from the commitment-trust theory, this research identifies four major opportunities and four significant challenges associated with social media interactions that influence consumer commitment and trust. By developing an integrative theoretical framework and exploring the positive and negative aspects of social media interactions, this research identifies ways to enhance commitment and trust among stakeholders. The research concludes by discussing implications for marketing theory and practice and outlining future research directions. [ABSTRACT FROM AUTHOR]

Privacy concerns may influence many choices consumers make. However, their expressed concerns are sometimes inconsistent with their information-sharing and privacy-protecting behaviors. Many theories have been proposed to explain the paradoxical gap between privacy attitudes and behaviors. Part of the privacy paradox may be explained with two measures that have received limited consideration: impulsiveness and social desirability bias (SDB). Surveys of US adults in 2015 and 2022 included questions to measure several types of privacy concerns along with impulsive tendencies and SDB (N = 2729). Age, education, gender, race, income, and impulsive tendencies were linked with some privacy concerns. If people with above-average concerns also disclose personal information on impulse, it might explain part of the paradox. Large coefficients on the SDB measure suggest that individuals who adjust their responses to be consistent with social norms may also overstate specific privacy concerns. For these individuals, their high expressed concerns may be inconsistent with their privacy behaviors. When researchers try to explain consumer attitudes or actions that involve privacy, multiple privacy concern dimensions should be considered and demographics, impulsive tendencies, and SDB should be included in the models. [ABSTRACT FROM AUTHOR]

In this study, we develop a new paradigm, Circular Lean Six Sigma 4.0 (CLSS4.0) to promote manufacturing sustainability. This paper aims to provide a practical and holistic view of the drivers and barriers that can help companies design an integrated CLSS4.0 model. The paper is based on a qualitative exploratory study using multiple case studies within 12 Moroccan manufacturing firms conducted through semi-structured interviews with top executive managers. The results show that the drivers are related to expected operational and environmental performance, increasing customer requirements, gaining competitive advantage and market growth while barriers are related to insufficient tangible (finance, human and equipment) and intangible (skills and techniques) resources, data privacy, technical issues and management support. The proposed framework identifies the assessment of drivers and barriers and their attributes as a starting point for managers to lead the CLSS4.0 transformation, thereby contributing to its successful implementation. To the best of our knowledge, this study is among the very first studies to discuss the CLSS4.0 drivers and barriers. It could be useful to managers as a diagnostic tool to assess their ability to implement CLSS4.0 before investing in the initiative. [ABSTRACT FROM AUTHOR]

The proliferation of mobile phones in low- and middle-income countries has suddenly and dramatically increased the extent to which the world’s poorest and most vulnerable populations can be observed and tracked by governments and corporations. Millions of historically “off the grid” individuals are now passively generating digital data; these data, in turn, are being used to make life-altering decisions about those individuals — including whether or not they receive government benefits, and whether they qualify for a consumer loan. This paper develops an approach to implementing algorithmic decisions based on personal data, while also providing formal privacy guarantees to data subjects. The approach adapts differential privacy to applications that require decisions about individuals, and gives decision makers granular control over the level of privacy guaranteed to data subjects. We show that stronger privacy guarantees typically come at some cost, and use data from two real world applications — an anti-poverty program in Togo and a consumer lending platform in Nigeria — to illustrate those costs. Our empirical results quantify the tradeoff between privacy and predictive accuracy, and characterize how different privacy guarantees impact overall program effectiveness. More broadly, our results demonstrate a way for humanitarian programs to responsibly use personal data, and better equip program designers to make informed decisions about data privacy.