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This paper aims to illustrate the extensive benefits of qualitative data analysis as a rarely undertaken process in post-occupancy evaluation surveys. As a result, there is limited evidence of what occupants say about their buildings, especially for operational parameters, as opposed to how they rate them. While quantitative analyses provide useful information on how workers feel about workplace operational factors, qualitative analyses provide richer information on what aspects of the workplace workers identify as influential to their comfort, well-being and productivity.

The authors analysed 6,938 comments from office buildings worldwide on workers’ perception of workplace operational factors: design, storage, needs, space at desks and storage in their work environments. These factors were analysed based on the buildings’ design intent and use, and the associated comments were coded into positive, negative and balanced comments. The authors used a combination of coding, descriptive analysis, content analysis and word cloud to dissect the comments.

The findings showed that whereas workers rated these operational factors favourably, there were significantly more negative comments about each factor. Also, the Chi-square test showed a significant association (p < 0.01) between the satisfaction scale and the type of comments received for all the operational factors. This means that when a factor is rated high in the satisfaction score (5–7), there were fewer negative and more positive comments and vice versa. The word cloud analysis highlighted vital aspects of the office environment the workers mostly commented on, such as open plan design, natural lighting, space and windows, toilets, facilities, kitchens, meeting room booking systems, storage and furniture.

This study highlights the importance of dissecting building occupants’ comments as integral to building performance monitoring and measurement. These emphasise the richness and value of respondents’ comments and the importance of critically analysing them. A limitation is that only 6,938 comments were viable for analysis because most comments were either incomplete with no meaning or were not provided. This underlines the importance of encouraging respondents to comment and express their feelings in questionnaire surveys. Also, the building use studies questionnaire data set presents extensive opportunities for further analyses of interrelationships between demographics, building characteristics and environmental and operational factors.

The findings from this study can be applied to future projects and facility management to maintain and improve office buildings throughout their life cycle. Also, these findings are essential in predicting the requirements of future workplaces for robust workplace designs and management.

The authors identified specific comments on the performance of workplaces across the globe, showing similarities and differences between sustainable, conventional, commercial and institutional buildings. Specifically, the analysis showed that office workers’ comments do not always corroborate the ratings they give their buildings. There was a significantly higher percentage of negative comments than positive comments despite the high satisfaction scores of the operational factors.

The digital contents (d-contents) are vulnerable to various threats either natural or manmade. Digital preservation is the plethora of a wide array of strategies necessary for the long-term preservation of digital objects. This study was carried out to assess the digital preservation practices for information resources in university libraries of Pakistan.

A quantitative survey based on a structured questionnaire was carried out to conduct the study. The questionnaire containing two sets of strategies (general and technical) was distributed amongst the whole population and received 90% response rate.

Overall, progressive implementation of general digital preservation practices was noted in these libraries like checking the digital collections for viruses, keeping the digital media in fire/water/theft proof locations, restricting unauthorized access, maintaining ideal humidity and temperature, and checking the digital media for functionality. Amongst the technical practices, only replication was in practice at a progressive rate, followed by metadata recording and media refreshing that was sometimes practiced in these libraries. The other technical practices were rarely or never practiced in these libraries. Significant variances in general and technical digital preservation practices were noted based on their physical locations (regional distribution).

The study contributes a comprehensive set of digital preservation practices divided into general and technical types to conduct similar studies in other parts of the world.

The findings stress the need for national and institutional policies, funding streams and skill enhancement of library staff.

The study fills the literature gap and contributes a comprehensive set of digital preservation practices divided into general and technical types to conduct similar studies in other parts of the world.

The peer-review history for this article is available at: https://publons.com/publon/10.1108/OIR-02-2023-0074

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.

The purpose of this paper is to characterize the interfaces between manufacturing companies and the Internet of Things (IoT) suppliers involved in their digital servitization.

This paper builds on an explorative case study of a manufacturing firm and its IoT suppliers. This paper relies on the Industrial Network Approach to study interfaces between buying firms and their suppliers.

This paper identifies three distinct types of supplier interfaces: connected, digital and digital-physical. They all contain technical resource interfaces with additional organizational and/or technical complexities that need to be managed. Connectivity, an Agile approach to software development and strong technical dependence emerged as key factors that impact the interactions between manufacturing firms and IoT suppliers and how their resources are combined.

This paper offers managerial implications regarding the importance of internal organization (such as appropriate cross-functional teams) to manage the dynamics of collaborations required by digital technologies, maintain interactions with IoT suppliers and identify and manage interdependences between IoT suppliers. Building close relationships with suppliers of crucial infrastructure (e.g. IoT cloud platform and data security systems) can also be beneficial for manufacturing firms to reduce risks. Finally, attention should be given to IoT technology strategy, which impacts both digital and digital-physical supplier interfaces.

In digital servitization, manufacturing firms are heavily reliant on external resources for IoT technology. Despite this, few studies have investigated the characteristics of their interfaces with IoT suppliers, how these can be managed and how resources are combined.

This paper aims to propose a new method to derive custom dynamic cyber risk metrics based on the well-known Goal, Question, Metric (GQM) approach. A framework that complements it and makes it much easier to use has been proposed too. Both, the method and the framework, have been validated within two challenging application domains: continuous risk assessment within a smart farm and risk-based adaptive security to reconfigure a Web application firewall.

The authors have identified a problem and provided motivation. They have developed their theory and engineered a new method and a framework to complement it. They have demonstrated the proposed method and framework work, validating them in two real use cases.

The GQM method, often applied within the software quality field, is a good basis for proposing a method to define new tailored cyber risk metrics that meet the requirements of current application domains. A comprehensive framework that formalises possible goals and questions translated to potential measurements can greatly facilitate the use of this method.

The proposed method enables the application of the GQM approach to cyber risk measurement. The proposed framework allows new cyber risk metrics to be inferred by choosing between suggested goals and questions and measuring the relevant elements of probability and impact. The authors’ approach demonstrates to be generic and flexible enough to allow very different organisations with heterogeneous requirements to derive tailored metrics useful for their particular risk management processes.

The purpose of this paper Computing is a recent emerging cloud model that affords clients limitless facilities, lowers the rate of customer storing and computation and progresses the ease of use, leading to a surge in the number of enterprises and individuals storing data in the cloud. Cloud services are used by various organizations (education, medical and commercial) to store their data. In the health-care industry, for example, patient medical data is outsourced to a cloud server. Instead of relying onmedical service providers, clients can access theirmedical data over the cloud.

This section explains the proposed cloud-based health-care system for secure data storage and access control called hash-based ciphertext policy attribute-based encryption with signature (hCP-ABES). It provides access control with finer granularity, security, authentication and user confidentiality of medical data. It enhances ciphertext-policy attribute-based encryption (CP-ABE) with hashing, encryption and signature. The proposed architecture includes protection mechanisms to guarantee that health-care and medical information can be securely exchanged between health systems via the cloud. Figure 2 depicts the proposed work's architectural design.

For health-care-related applications, safe contact with common documents hosted on a cloud server is becoming increasingly important. However, there are numerous constraints to designing an effective and safe data access method, including cloud server performance, a high number of data users and various security requirements. This work adds hashing and signature to the classic CP-ABE technique. It protects the confidentiality of health-care data while also allowing for fine-grained access control. According to an analysis of security needs, this work fulfills the privacy and integrity of health information using federated learning.

The Internet of Things (IoT) technology and smart diagnostic implants have enhanced health-care systems by allowing for remote access and screening of patients’ health issues at any time and from any location. Medical IoT devices monitor patients’ health status and combine this information into medical records, which are then transferred to the cloud and viewed by health providers for decision-making. However, when it comes to information transfer, the security and secrecy of electronic health records become a major concern. This work offers effective data storage and access control for a smart healthcare system to protect confidentiality. CP-ABE ensures data confidentiality and also allows control on data access at a finer level. Furthermore, it allows owners to set up a dynamic patients health data sharing policy under the cloud layer. hCP-ABES proposed fine-grained data access, security, authentication and user privacy of medical data. This paper enhances CP-ABE with hashing, encryption and signature. The proposed method has been evaluated, and the results signify that the proposed hCP-ABES is feasible compared to other access control schemes using federated learning.

This chapter provides an integrated overview of digital implementation in smart city ecosystems, focusing on the data ecosystem, the data value chain, and the digital implementation in smart cities. It acknowledges the opportunities and challenges presented by the growth of big data and its impact on delivering public services and coordinating local ecosystems in smart cities. This chapter introduces the concept of a smart city data ecosystem and explores the implementation of digital solutions, highlighting the role of local management and internal and external providers. It examines the digitalization of processes and services, technological infrastructure, and social media in the context of the smart city, emphasizing the interaction between local and national data ecosystems. This chapter incorporates interviews with smart city managers, policymakers, and technicians involved in the digital development process, offering valuable insights into the practical implementation and usage of data management within smart cities and the potential impact on urban development.

The purpose of this paper is to analyse the current state of research on the integration of blockchain and building information modelling (BIM) in the Architecture, Engineering, Construction and Operations (AECO) industry as a means of identifying gaps between the existing paradigm and practical applications for determining future research directions and improving the industry. The study aims to provide clear guidance on areas that need attention for further research and funding and to draw academic attention to factors beyond the technical dimension.

A mixed-method systematic review is used, considering multiple literature types and using a sociotechnical perspective-based framework that covers three dimensions (technic, process and context) and three research elements (why, what and how). Data are retrieved and analysed from the Web of Science and Scopus databases for the 2017–2023 period.

While blockchain has the potential to address security, traceability and transparency and complement the system by integrating supporting applications, significant gaps still exist between these potentials and widespread industry adoption. Current limitations and further research needs are identified, including designing fully integrated prototypes, empirical research to identify operational processes, testing and analysing operational-level models or applications and developing and applying a technology acceptance model for the integration paradigm. Previous research lacks contextual settings, real-world tests or empirical investigations and is primarily conceptual.

This paper provides a comprehensive, critical systematic review of the integration of blockchain with BIM in the construction industry, using a sociotechnical perspective-based framework which can be applied in future reviews. The study provides insight into the current state and future opportunities for policymakers and practitioners in the AECO industry to prepare for the transition in this disruptive paradigm. It also provides a phased plan along with a clear direction for the transition to more advanced applications.

Deep learning (DL) is on the rise because it can make predictions and judgments based on data that is unseen. Blockchain technologies are being combined with DL frameworks in various industries to provide a safe and effective infrastructure. The review comprises literature that lists the most recent techniques used in the aforementioned application sectors. We examine the current research trends across several fields and evaluate the literature in terms of its advantages and disadvantages.

The integration of blockchain and DL has been explored in several application domains for the past five years (2018–2023). Our research is guided by five research questions, and based on these questions, we concentrate on key application domains such as the usage of Internet of Things (IoT) in several applications, healthcare and cryptocurrency price prediction. We have analyzed the main challenges and possibilities concerning blockchain technologies. We have discussed the methodologies used in the pertinent publications in these areas and contrasted the research trends during the previous five years. Additionally, we provide a comparison of the widely used blockchain frameworks that are used to create blockchain-based DL frameworks.

By responding to five research objectives, the study highlights and assesses the effectiveness of already published works using blockchain and DL. Our findings indicate that IoT applications, such as their use in smart cities and cars, healthcare and cryptocurrency, are the key areas of research. The primary focus of current research is the enhancement of existing systems, with data analysis, storage and sharing via decentralized systems being the main motivation for this integration. Amongst the various frameworks employed, Ethereum and Hyperledger are popular among researchers in the domain of IoT and healthcare, whereas Bitcoin is popular for research on cryptocurrency.

There is a lack of literature that summarizes the state-of-the-art methods incorporating blockchain and DL in popular domains such as healthcare, IoT and cryptocurrency price prediction. We analyze the existing research done in the past five years (2018–2023) to review the issues and emerging trends.

As the size of the population is growing and the capacity of the planet Earth is limited, human beings are searching for sustainable and technology-enabled solutions to support society, ecology and economy. One of the solutions has been developing smart sustainable cities. Smart sustainable cities are cities as systems, where their infrastructure, different subsystems and different functional domains are virtually connected to the information and communication technologies (ICT) and internet via sensors and devices and the Internet of Things (IoT), to collect and process real-time Big Data and make efficient, effective and sustainable solutions for a democratic and liveable city for its various stakeholders. This chapter explores the concepts and practices of sustainable smart cities across the globe and explores the use of technologies such as IoT, Blockchain technology and Cloud computing, etc. their challenges and then presents a view on business models for sustainable smart cities.