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The technology used by an organization is significantly influenced by the organization’s preferred competitive capabilities. The Internet of things (IoT) is an important technology, which is implemented by most prominent business organizations. The purpose of this paper is to investigate the relationship between an organization’s strategies and the IoT architectures implemented by the organization.

This study has been carried out on primary data collected with the help of a structured questionnaire. The data have been analyzed by statistical techniques like cluster analysis and discriminant analysis through SPSS.

The empirical investigation of data revealed that there is a relationship between organizational strategy and IoT architectures. The three-layered architecture of the IoT is most suitable for caretakers; the three-, four- or five- layered architectures are suitable for marketeers; whereas innovators find it more suitable to use five- or more-layered architecture of the IoT. This paper draws the conclusion based on maximum likelihood rather than using statistical analyses like ANOVA. The idea behind using the maximum likelihood estimate is that there are many subjective parameters in deciding the architectures of the IoT. These subjective parameters are difficult to quantify, so it is not possible to apply ANOVA on these parameters.

This study considers three organizational strategies; the relationship between other organizational strategies and IoT architecture will be studied in future.

This study offers multiple opportunities to practitioners and consulting firms of the IoT to adopt a suitable IoT architecture according to the organizational strategy. This study equips IoT development engineers to select suitable technology for data capturing, data transmission, and data management and access for an IoT architecture.

Although a lot of work has already been done on the architecture of IoT for different industries and businesses, to the best of our knowledge, this is the first study that relates organizational strategies to IoT architectures. This study applies to all the major industry types.

The purpose of this paper is to investigate the implications for supply chain risk management (SCRM) by applying internet of things (IoT). Therefore, the impact and effects on the SCRM process, as well as the internal and external pathway and the outcome of SCRM are examined.

This study adopts a multiple case study methodology with twelve companies from the manufacturing industry. This study is guided by the information processing theory (IPT) and a theory-grounded research framework to provide insights into information requirements and information processing capabilities for IoT-supported SCRM.

The studied cases demonstrate an increase in data availability in the companies that contribute to improved process transparency and process management. Furthermore, the process steps, risk transparency, risk knowledge and risk strategies have been enhanced, which enabled improved SCRM performance by fitting information requirements and information processing capabilities, thus allowing for competitive advantage.

This study offers in-depth insights for SCRM managers into the structure of IoT systems, primary use cases and changes for the process itself. Furthermore, implications for employees, incentives and barriers are identified, which could be used to redesign SCRM.

This study addresses the requirement for additional empirical research on technology-enhanced SCRM, supported by IPT as a theoretical foundation. The radical change of SCRM by IoT is demonstrated while discussing the human role, implications for SCRM strategies and identifying relevant topics for future development.

Particular attention to the issue of information management will be required to meet the expected growth in IoT-devices and the data they generate. As government agencies start collecting and using such information, they must also deal with the issue of privacy, to comply with laws and regulations. The approach discussed here shows that record-keeping principles may form part of a solution to the issue of managing IoT-data for government agencies.

This study uses the generally accepted record-keeping principles as a basis for a high-level discussion on how IoT-data can be managed. This is followed by a presentation and discussion on how the Norwegian record-keeping standard, Noark, can be extended to highlight practical issues.

Record keeping has principles that are relevant to the management of IoT-data. Further an implementation of the chosen use-cases is possible based on an existing record keeping standard. Record keeping is one of many information science approaches that can manage IoT-data.

The main limitations are that the discussion cannot cover all types of IoT-devices, nor can all issues be captured with a limited choice of examples. The results should be seen within the context of the types of devices discussed and limited to the chosen use-cases. However, the level of abstraction used means the results may be applicable to similar scenarios.

The approach shows that record-keeping principles may be used as an approach to manage IoT-data. This discussion is useful when compared with other information science approaches, e.g. big-data or semantic Web approaches. The practicalities of a record-keeping approach are also discussed and relevant strengths and weaknesses are showed.

The purpose of this paper is to demonstrate how Internet of Things (IoT) technology can enable highly distributed elevator equipment servicing by using remote-monitoring technology to facilitate a shift from traditional corrective maintenance (CM) and time-based maintenance (TBM) to more predictive, condition-based maintenance (CBM) in order to achieve various benefits.

Literature review indicates that CBM has advantages over conventional CM and TBM from a theoretical perspective, but it depends on continuous monitoring enhancement via advanced IoT technology. An in-depth case study was carried out to provide practical evidence that IoT enables elevator firms to achieve CBM.

From a theoretical perspective, the CBM of elevators makes business sense. The challenges lie in data collection, data analysis and decision making in real-world business contexts. The main findings of this study suggest that CBM can be commercialized via IoT in the case of elevators and would improve the safety and reliability of equipment. It would, thus, make sense from technological, process and economic perspectives.

Our longitudinal real-world case study demonstrates a practical way of making the CBM of elevators widespread. Integrating IoT and other advanced technology would improve the safety and reliability of elevator equipment, prolong its useful life, minimize inconvenience and business interruptions due to equipment downtime and reduce or eliminate major repairs, thus greatly reducing maintenance costs.

The main contribution of this paper lies in the empirical demonstration of the benefits and challenges of CBM via IoT relative to conventional CM and TBM in the case of elevators. The authors believe that this study is timely and will be valuable to firms working on similar research or commercialization strategies.

Due to the rapid growth of blockchain technology in recent years, the fusion of blockchain and the Internet of Things (BIoT) has drawn considerable attention from researchers and industrial practitioners and is regarded as a future trend in technological development. Although several authors have conducted literature reviews on the topic, none have examined the development of the knowledge structure of BIoT, resulting in scattered research and development (R&D) efforts.

This study investigates the intellectual core of BIoT through a co-citation proximity analysis–based systematic review (CPASR) of the correlations between 44 highly influential articles out of 473 relevant research studies. Subsequently, we apply a series of statistical analyses, including exploratory factor analysis (EFA), hierarchical cluster analysis (HCA), k-means clustering (KMC) and multidimensional scaling (MDS) to establish the intellectual core.

Our findings indicate that there are nine categories in the intellectual core of BIoT: (1) data privacy and security for BIoT systems, (2) models and applications of BIoT, (3) system security theories for BIoT, (4) frameworks for BIoT deployment, (5) the fusion of BIoT with emerging methods and technologies, (6) applied security strategies for using blockchain with the IoT, (7) the design and development of industrial BIoT, (8) establishing trust through BIoT and (9) the BIoT ecosystem.

We use the CPASR method to examine the intellectual core of BIoT, which is an under-researched and topical area. The paper also provides a structural framework for investigating BIoT research that may be applicable to other knowledge domains.

This study aims to investigate the ethical issues related to the internet of Things (IoT) deployment in small- and medium-sized enterprises (SMEs) from an individual employee's perspective. To provide researchers and practitioners with concrete tools for examining these matters, an ethical framework dedicated to IoT is introduced.

First, the applicability of Mason's original privacy, accuracy, property and accessibility (PAPA) framework is studied in the IoT context. Second, issue category additions are proposed based on the identified coverage limitations of PAPA.

While the original PAPA framework can be utilised as a generic ethical evaluation tool, it lacks coverage of several IoT-specific issue areas. To thoroughly address the ethical risks associated with IoT, two additional categories are introduced.

The new framework requires further validation to ensure its applicability and to identify potential modification requirements in continuously evolving IoT ecosystems.

Considering the lack of ethical IoT frameworks, this study provides organisations with a practical framework for analysing the ethical issues in IoT deployment.

Ethical standards for IoT have not been sufficiently addressed in the current literature and frameworks, making the ethical considerations dependent on subjective stances. Thus, there is an acute demand for a practical framework that outlines the general ethical standards, helping its users to thoroughly address the potential ethical issues.

While the use of IoT keeps growing in SMEs, there is an apparent lack of ethical guidelines. This study contributes to the gap by introducing a preliminary framework for both practical use and further theoretical development.

The Internet of Things (IoT), which enables smart objects to collect and exchange data, has a variety of application domains used in everyday life including healthcare. As a set of promising next-generation technologies in the healthcare domain, Healthcare-related Internet of Things (H-IoT) promises to facilitate better healthcare by offering data-driven insights. While effective in practice at large, emerging data concerns arise because of the inscrutable black-box systems. Inspired by the notion of human data interaction, this paper seeks to understand how people engage with the H-IoT data that is about and produced by themselves and to elucidate the main data issues and challenges involved in the development of H-IoT.

This work conducted a comprehensive survey and integrated the method of content analysis by systematically review the recently published H-IoT research work in the healthcare domain.

This study thoroughly surveyed more than 300 research studies published in the last decades and classified seven H-IoT end-user groups, and three H-IoT data types that are important to H-IoT comprehension. Attention to human data interaction, our study also highlights several critical issues associated with this notion in the context of H-IoT.

This study will support H-IoT research by characterizing the data issues and challenges exist in the context of H-IoT user and data interaction. The findings will provide insights in designing for effective interactions with data in the H-IoT.

Current knowledge management (KM) systems cannot be used effectively for decision-making because of the lack of real-time data. This study aims to discuss how KM can benefit by embedding Internet of Things (IoT).

The paper discusses how IoT can help KM to capture data and convert data into knowledge to improve the parking service in transportation using a case study.

This case study related to intelligent parking service supported by IoT devices of vehicles shows that KM can play a role in turning the incoming big data collected from IoT devices into useful knowledge more quickly and effectively.

The literature review shows that there are few papers discussing how KM can benefit by embedding IoT and processing incoming big data collected from IoT devices. The case study developed in this study provides evidence to explain how IoT can help KM to capture big data and convert big data into knowledge to improve the parking service in transportation.

The Internet of Things-based digital twin (IoT-DT) technologies offer a transformative approach to building retrofitting for reducing operational carbon (ROC) emissions. However, a notable gap exists between the potential and adoption of the two emerging technologies, further exacerbated by the nascent state of research in this domain. This research aims to establish the best practices that innovatively strengthen the identified enablers to decisively tackle challenges, ensuring the efficient implementation of IoT-DT for ROC emissions in buildings.

This study adopted a mixed-method approach. Questionnaire data from 220 multidiscipline professionals were analysed via structural equation modelling analysis, while interview data obtained from 18 stakeholders were analysed using thematic content analysis. The findings were triangulated for cohesive interpretation.

After the analysis of questionnaire data, a structural model was established, depicting the critical challenges (inadequate data security, limited technical expertise and scalability issues) and key enablers (robust data security measures, skill development and government incentives) of implementing IoT-DT for ROC. Sequentially, analysis of in-depth interview data revealed the IoT-based DT best practices (safeguarding data, upskilling and incentivization). Upon triangulating the questionnaire and interview findings, this study explicitly highlights the potential of the established best practices to strategically strengthen enablers, thereby mitigating challenges and ensuring the successful implementation of IoT-based DT for ROC emissions in buildings.

This study provides practical guidance for stakeholders to effectively implement IoT-DT in ROC in buildings and contributes significantly to climate change mitigation.

Introduction: The internet of things (IoT) is the emerging technology of interconnected objects that can be termed as ‘things’ used to exchange data, connecting with different devices on the internet. It is the future where connected devices are controlled remotely. The insurance sector is one of the leading industries providing financial protection services to their customers to recover losses. Like others, the insurance industry uses the services very efficiently to solve their customer-centric problems and provide the best services to them. IoT in insurance is enhancing customer services.

Purpose: To determine how the insurance industry utilises the different IoT technologies to provide the best services and solutions to their users. The insurance sector is working on other areas of expertise to offer outstanding facilities to their clientele.

Methodology: We reviewed published material covering five years on IoT and insurance and customer services in the media, newspapers, journal publications, and the web. We determined how the insurance sector adapted the new terminology to contribute its best services to the users.

Findings: We observed that IoT services and technologies benefit the insurance industry and the clientele. This shows excellent results in the growth of the sector and heightened facilities for the consumers.