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This paper evaluates the factors influencing the utilization of the Internet of Things (IoT) for sustainable facilities management (SFM) practices in Saudi Arabia.

A mixed approach, combining a literature review, pilot-testing and questionnaire survey, was adopted to evaluate the factors. Twenty-seven factors were identified and grouped into four groups: technical, business and organizational, operational and security and privacy. The questionnaire was distributed to 30 facilities managers and 30 IoT specialists, totaling 60 practitioners, to determine the effect index of each factor. The practitioners' consensus on the ranking of the factors was then determined.

The study identifies the top-ranking factors as: “Difficulty in ensuring data security and protection,” “Difficulty in ensuring data privacy and confidentiality” and “Limited awareness and understanding of IoT benefits and capabilities.” These factors highlight the challenges to successful IoT implementation in the FM sector. The FM sector could benefit from utilizing IoT while maintaining the security, privacy and effectiveness of building operations by successfully addressing these concerns. A high level of consensus on the ranking of the factors was observed between facilities managers and IoT specialists. This was substantiated by a Spearman’s rank correlation coefficient of 0.79.

This study enriches the literature by combining practical insights from facilities managers with technical expertise from IoT specialists on the factors impacting IoT implementation in the Saudi Arabian FM sector. Beyond academic contributions, it provides practical insights for industry professionals, fostering a culture of knowledge-sharing and guiding future research in this field.

This study investigates the pathways for adopting IoTs and BDA technologies to improve healthcare management.

The study relied on 445 healthcare professionals' perspectives to explore different causal pathways to IoTs and BDA adoption and usage for daily healthcare management. The Fussy-set Qualitative Comparative Analysis was adopted to explore the underlying pathways for healthcare management.

The empirical analysis revealed six different configural paths influencing the acceptance and use of IoTs and BDA for healthcare improvement. Two key user topologies from the six configural paths, digital literacy and ease of use and social influence and behavioural intentions, mostly affect the paths for using digital health technologies by healthcare physicians.

Despite this study's novel contributions, limitations include the fsQCA methodology, perceptual data and the context of the study. The fsQCA methodology is still evolving with different interpretations, although it reveals new insights and as such further studies are required to explain the configural paths of social phenomena. Additionally, future research should consider other constructs beyond the UTAUT and digital literacy to illustrate configural paths to healthcare technology acceptance and usage. Again, the views of healthcare professionals are perceptual data. Hence future research on operational data will support significant contributions towards pathways to accept and use emerging technologies for healthcare improvement. Lastly, this study is from a developing country perspective where emerging digital healthcare technology is still emerging to support healthcare management. Hence, more investigation from other cross-country analyses of configural paths for digital technology deployment in healthcare will enhance the conversation with IoTs and BDA for healthcare management.

Holistically, the acceptance and use of healthcare technologies and platforms is not solely on their capabilities, but a combination of distinct factors driven by users' perspectives. This offers healthcare administrators and institutions to essentially reflect on the distinct combinations of conditions favourable to health professionals who can use IoTs and BDA for healthcare improvement.

This study is among the few scholarly works to empirically investigate the configural paths to support healthcare improvement with emerging technologies. Using fsQCA is a unique contribution to existing information system literature for configural paths for healthcare improvement with emerging digital technologies.

Predictive digital twin technology, which amalgamates digital twins (DT), the internet of Things (IoT) and artificial intelligence (AI) for data collection, simulation and predictive purposes, has demonstrated its effectiveness across a wide array of industries. Nonetheless, there is a conspicuous lack of comprehensive research in the built environment domain. This study endeavours to fill this void by exploring and analysing the capabilities of individual technologies to better understand and develop successful integration use cases.

This study uses a mixed literature review approach, which involves using bibliometric techniques as well as thematic and critical assessments of 137 relevant academic papers. Three separate lists were created using the Scopus database, covering AI and IoT, as well as DT, since AI and IoT are crucial in creating predictive DT. Clear criteria were applied to create the three lists, including limiting the results to only Q1 journals and English publications from 2019 to 2023, in order to include the most recent and highest quality publications. The collected data for the three technologies was analysed using the bibliometric package in R Studio.

Findings reveal asymmetric attention to various components of the predictive digital twin’s system. There is a relatively greater body of research on IoT and DT, representing 43 and 47%, respectively. In contrast, direct research on the use of AI for net-zero solutions constitutes only 10%. Similarly, the findings underscore the necessity of integrating these three technologies to develop predictive digital twin solutions for carbon emission prediction.

The results indicate that there is a clear need for more case studies investigating the use of large-scale IoT networks to collect carbon data from buildings and construction sites. Furthermore, the development of advanced and precise AI models is imperative for predicting the production of renewable energy sources and the demand for housing.

This paper makes a significant contribution to the field by providing a strong theoretical foundation. It also serves as a catalyst for future research within this domain. For practitioners and policymakers, this paper offers a reliable point of reference.

Internet of things (IoT) is the technology enabler in smart city adoption and creates circular economy (CE). The purpose of this chapter is to find out the relationship nexus between IoT, smart city and CE and its practical constraints in execution. This chapter elaborately maps the empirical evidence available in the literature survey of smart city development, information and communication technology (ICT) and CE. The contributions from outstanding scholars and current topics are analyzed using high citations index. The authors referred the topics with scholarly publishers and validated the data. Following that, significant technology subjects such as big data and analytics, as well as intelligent information systems, are individually examined. The findings are the challenges in implementing IoT and its interoperability capacity due to interlinking of different devices and systems. Another key challenger as well as drivers are data volume and quality, privacy, complexity and governance. The research can help policymakers, IT infrastructure provider, bureaucrats and all other concerned stakeholders to get sensitized on the implementation of IoT at smart city infrastructure. It also describes about the importance of collaboration and embracing open innovation (OI) while implementing IoT. This study is innovative in its approach and referred literature in the field of IoT, smart city, and CE by high citation index (HCI). It also researched about the various constraints in building CE through technology deployment and discusses about multilayer and multifunctional collaboration.

This paper systematically reviews the evolution of Internet of Things (IoT) research in business and management over the past decade and a half. It synthesizes current knowledge, identifies major themes, gaps, and future opportunities to guide scholars on potential research directions within this exponentially growing domain.

A structured systematic literature review methodology filtered IoT publications across business/management journals using Scopus database. Detailed thematic and bibliometric analyses chronologically mapped the progress of peer-reviewed articles from 2005–2023. Both quantitative metrics and qualitative coding inductively revealed historical trends, topics, applications and research implications.

Analysis uncovered six primary IoT research themes - business models, technology, data, customers, organizations, and sustainability. Dominant focuses were found on technological enablers, business model innovation and customer experience transformations. While technical aspects are well-documented, strategic technology integrations and organizational change management require greater emphasis.

Focus restricted to academic articles published in management journals risks missing relevant papers published in other fields. Screening process involved some subjectivity. Lacks geographic analysis of research contexts. The rapidly evolving nature of technology domain risks findings’ generalizability.

Key enablers and success factors that we identified may support managerial decision making when it comes to IoT adoption.

We discuss advancing IoT innovation through ethics and sustainability lenses and these may help ensure responsible adoption.

This analysis weaves together the extant literature and offers an evidence-based research agenda for management scholars by chronicling the state, evolution, influential factors, and future opportunities within IoT literature. It highlights major thematic shifts and priority gaps to address.

Despite IoT’s huge potential, enterprises’ ability to leverage it is their competitive advantage. Thus, competitive differentiation is primarily predicated on leveraging IoT toward customer needs. To examine the research gap, this study aims to explore the drivers of customer satisfaction and how they are affected by the interaction between IoT capabilities.

A mixed-method research framework is applied to assess the impact of IoT capabilities on customer satisfaction. Based on the theoretical underpinning of the resource-based view and dynamic capability, the study highlights the importance of IoT capabilities in active resource allocation and effective resource utilization. First, DEMATEL is used capture the interrelationship between IoT capabilities. Further, the impact of each IoT capabilities on customer satisfaction is studied using CoCoSo method.

The study highlights the importance of IoT capabilities in active resource allocation and effective resource utilization. The findings are enriched through the complementarity of resources in a dynamic business-to-business-to-customer (B2B2C) scenario. The authors expand the IoT capabilities from conventional business-to-business (B2B) or business-to-customer (B2C) scenario to tri-nodal B2B2C relationship triangle.

Based on the findings, the authors offer a business transformation strategy for firms in key areas of customer satisfaction by leveraging IoT. The study can help management prioritize and develop key IoT capabilities to meaningfully increase customer satisfaction metrics.

Building on the dynamic capabilities and resource-based view of the firm, an integrated decision-making research model is proposed. In addition, this study investigates the product and service capabilities unlocked using IoT capabilities. This work can be considered one of the leading attempts to improve customer satisfaction using IoT capabilities from traditional dyadic (B2B or B2C) structure to triadic (B2B2C) framework.

This study aims to examine the effects of Internet of Things (IoT) technology on efficiency and patient care in Italian and Israeli intensive care units (ICUs). The goal is to study how IoT might improve care settings by controlling health dynamics and responding to life-threatening circumstances.

This survey-based research explores IoT use, challenges and adaptability in ICUs in both countries. Interviews and surveys of ICU health-care workers are used to get both quantitative and qualitative data on integrating experiences and perspectives.

The research found significant variations between Italy and Israel due to technology infrastructures and health-care practices. Israel shows a more concentrated deployment in a major medical centre with advanced but limited uptake, whereas Italy shows application throughout ICUs highlighting regional health-care system disparities. Interoperability, data security and IoT training are common difficulties.

This research has limitations. One drawback is the geographical dispersion of study sites, with a bigger sample size in Italy than in Israel. This discrepancy may affect findings applicability. However, these preliminary findings provide a foundation for further research into the complexities of deploying IoT in various health-care settings.

This study compares IoT integration in two national health-care systems, adding to health-care technology literature. Regional variations affect technology adoption, but IoT may enhance ICU operations and patient care, according to one research. This study helps health-care practitioners, academics and policymakers understand the pros and cons of IoT in health care.

Unlocking the potential of Big Data Analytics (BDA) has proven to be a transformative factor for the Architecture, Engineering and Construction (AEC) industry. This has prompted researchers to focus attention on BDA in the AEC industry (BDA-in-AECI) in recent years, leading to a proliferation of relevant research. However, an in-depth exploration of the literature on BDA-in-AECI remains scarce. As a result, this study seeks to systematically explore the state-of-the-art review on BDA-in-AECI and identify research trends and gaps in knowledge to guide future research.

This state-of-the-art review was conducted using a mixed-method systematic review. Relevant publications were retrieved from Scopus and then subjected to inclusion and exclusion criteria. A quantitative bibliometric analysis was conducted using VOSviewer software and Gephi to reveal the status quo of research in the domain. A further qualitative analysis was performed on carefully screened articles. Based on this mixed-method systematic review, knowledge gaps were identified and future research agendas of BDA-in-AECI were proposed.

The results show that BDA has been adopted to support AEC decision-making, safety and risk assessment, structural health monitoring, damage detection, waste management, project management and facilities management. BDA also plays a major role in achieving construction 4.0 and Industry 4.0. The study further revealed that data mining, cloud computing, predictive analytics, machine learning and artificial intelligence methods, such as deep learning, natural language processing and computer vision, are the key methods used for BDA-in-AECI. Moreover, several data acquisition platforms and technologies were identified, including building information modeling, Internet of Things (IoT), social networking and blockchain. Further studies are needed to examine the synergies between BDA and AI, BDA and Digital twin and BDA and blockchain in the AEC industry.

The study contributes to the BDA-in-AECI body of knowledge by providing a comprehensive scope of understanding and revealing areas for future research directions beneficial to the stakeholders in the AEC industry.

The Internet of Things (IoT) platform empowers the digital transformation of the manufacturing industry by providing information technology services. Simultaneously, it enters the market by offering smart products to consumers. In light of different service fee scenarios, this article explores the optimal decision-making for the platform. It investigates the pricing models and entry decisions of IoT platforms.

In this study, we have formulated a game-theoretic model to scrutinize the influence of the IoT platform ventured into the smart device market on the pre-existing suppliers operating under subscription-based and usage-based pricing agreements.

Our outcome shows that introducing an IoT platform’s smart device has a differential effect on manufacturers depending on their contract type. Notably, our research indicates that introducing the platform’s own smart device within the subscription-based model does not negatively impact the profitability of incumbent manufacturers, so long as there is a noticeable discrepancy in the quality of the smart devices. However, our findings within the usage-based model demonstrate that despite the variance in smart device quality differentiation, the platform’s resolution to launch their device and impose their pricing agreements adversely affects established manufacturers. Additionally, we obtain valuable Intel regarding the platform’s entry strategies and contractual inclinations. We demonstrate that the platform is incentivized to present its smart device when reasonable entry costs remain. Furthermore, the platform prefers subscription-based contracts when the subscription fee is relatively high in non-platform entry and entry cases.

These findings hold significant practical implications for firms operating in an IoT-based supply chain.

The new technology aspects of Industry 4.0 (I4.0), such as digital technologies including artificial intelligence (AI), block chain, big data analysis and the internet of things (IoT) as a digital cosmos, have the potential to fundamentally transform the future of business and supply chain management. By augmenting the functional components of the food supply chain (FSC), these technologies can transform it into an intelligent food supply chain (iFSC). The purpose of this study is to identify the I4.0 utilization for FSC to become an iFSC. Additionally, it suggests future research agendas to bridge the academic knowledge gaps.

This study utilizes the bibliometric analysis methodology to investigate the techno-functional components of iFSC in the context of I4.0. The study followed steps of bibliometric analysis to assess existing components’ knowledge in the area of intelligent food supply chain management. It further reviews the selected articles to explore the need for I4.0 technologies’ adoption as well as its barriers and challenges for iFSC.

This study examines the integration of emerging technologies in FSC and concludes that the main emphasis is on the adoption of blockchain and internet of things technology. To convert it into iFSC, it should be integrated with I4.0 and AI-driven FSC systems. In addition to traditional responsibilities, emerging technologies are acknowledged that are relatively uncommon but possess significant potential for implementation in FSC. This study further outlines the challenges and barriers to the adoption of new technologies and presents a comprehensive research plan or collection of topics for future investigations on the transition from FSC to iFSC. Utilizing artificial intelligence techniques to enhance performance, decision-making, risk evaluation, real-time safety, and quality analysis, and prioritizing the elimination of barriers for new technologies.

The uniqueness of this study lies in the provision of an up-to-date review of the food supply chain. In doing so, the authors have expanded the current knowledge base on the utilization of all I4.0 technologies in FSC. The review of designated publications yield a distinctive contribution by highlighting hurdles and challenges for iFSC. This information is valuable for operations managers and policymakers to consider.