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Contemporary technological disruptions are espoused as though they stimulate sustainable growth in the built environment through the Green Internet of Things (G-IoT). Learning from discipline-specific experiences, this paper articulates recent advancements in the knowledge and concepts of G-IoT in relation to the construction and smart city sectors. It provides a scoping review for G-IoT as an overlooked dimension. Attention was paid to modern circularity, cleaner production and sustainability as key benefits of G-IoT adoption in line with the United Nations’ Sustainable Development Goals (UN-SDGs). In addition, this study also investigates the current application and adoption strategies of G-IoT.

This study uses the Preferred Reporting Items for Systematic and Meta-Analyses (PRISMA) review approach. Resources are drawn from Scopus and Web of Science repositories using apt search strings that reflect applications of G-IoT in the built environment in relation to construction management, urban planning, societies and infrastructure. Thematic analysis was used to analyze pertinent themes in the retrieved articles.

G-IoT is an overlooked dimension in construction and smart cities so far. Thirty-three scholarly articles were reviewed from a total of 82 articles retrieved, from which five themes were identified: G-IoT in buildings, computing, sustainability, waste management and tracking and monitoring. Among other applications, findings show that G-IoT is prominent in smart urban services, healthcare, traffic management, green computing, environmental protection, site safety and waste management. Applicable strategies to hasten adoption include raising awareness, financial incentives, dedicated work approaches, G-IoT technologies and purposeful capacity building among stakeholders. The future of G-IoT in construction and smart city research is in smart drones, building information modeling, digital twins, 3D printing, green computing, robotics and policies that incentivize adoption.

This study adds to the normative literature on envisioning potential strategies for adoption and the future of G-IoT in construction and smart cities as an overlooked dimension. No previous study to date has reviewed pertinent literature in this area, intending to investigate the current applications, adoption strategies and future direction of G-IoT in construction and smart cities. Researchers can expand on the current study by exploring the identified G-IoT applications and adoption strategies in detail, and practitioners can develop implementation policies, regulations and guidelines for holistic G-IoT adoption.

The purpose of this study is to investigate the determinants of behavioral intention to use smartphone diet applications within the emerging market. Specifically, it focuses on the Privacy Calculus Model constructs, encompassing perceived risk and perceived benefit, as well as the pivotal elements of trust and self-efficacy. It also explores the moderating influence of experience on the influencing factors and intention to use a diet application.

In a survey with 572 respondents, data analysis was conducted using partial least squares (PLS) structural equation modeling.

The findings reveal that perceived risk exerts a significant negative influence on behavioral intention. Conversely, perceived benefit, trust and self-efficacy exhibit a positive impact on behavioral intention. Moreover, the study delves into the moderating role of users' experience, which is found to significantly influence these relationships, suggesting that user experience plays a pivotal role in shaping the adoption dynamics of diet applications.

The limitations of this study may include the sample size and the specific focus on the emerging market of Saudi Arabia. The implications of the findings are relevant for scholars, developers, marketers, and policymakers seeking to promote the use of smartphone diet applications.

This study adds value by exploring the determinants of behavioral intention in the context of smartphone diet applications, and it is a first attempt to test the moderating role of users' experiences, providing valuable insights for various stakeholders in the field.

Despite the opportunities of digital twins (DTs) for smart buildings, limited research has been conducted regarding the facility management stage, and this is explained by the high complexity of accurately representing and modelling the physics behind the DTs process. This study thus organises and consolidates the fragmented literature on DTs implementation for smart buildings at the facility management stage by exploring the enablers, applications and challenges and examining the interrelationships amongst them.

A systematic literature review approach is adopted to analyse and synthesise the existing literature relating to the subject topic.

The study revealed six main categories of enablers of DTs for smart building at the facility management stage, namely perception technologies, network technologies, storage technologies, application technologies, knowledge-building and design processes. Three substantial categories of DTs application for smart buildings were revealed at the facility management stage: efficient operation and service monitoring, efficient building energy management and effective smart building maintenance. Subsequently, the top four major challenges were identified as being “lack of a systematic and comprehensive reference model”, “real-time data integration”, “the complexity and uncertainty nature of real-time data” and “real-time data visualisation”. An integrative framework is finally proposed by examining the interactive relationship amongst the enablers, the applications and the challenges.

The findings could guide facility managers/engineers to fairly understand the enablers, applications and challenges when DTs are being implemented to improve smart building performance and achieve user satisfaction at the facility management stage.

This study contributes to the knowledge body on DTs by extending the scope of the existing studies to identify the enablers and applications of DTs for smart buildings at the facility management stage and the specific challenges.

With the increasing number of mobile applications, efficiently recommending mobile applications to users has become a challenging problem. Although existing mobile application recommendation approaches based on user attributes and behaviors have achieved notable effectiveness, they overlook the diffusion patterns and interdependencies of topic-specific mobile applications among user groups. mobile applications among user groups. This paper aims to capture the diffusion patterns and interdependencies of mobile applications among user groups. To achieve this, a topic-aware neural network-based mobile application recommendation method, referred to as TN-MR, is proposed.

In this method, first, the user representations are enhanced by introducing a topic-aware attention layer, which captures both the topic context and the diffusion history context. Second, it exploits a time-decay mechanism to simulate changes in user interest. Multitopic user representations are aggregated by the time decay module to output the user representations of cascading representations under multiple topics. Finally, user scores that are likely to download the mobile application are predicted and ranked.

Experimental comparisons and analyses were conducted on the actual 360App data set, and the results demonstrate that the effectiveness of mobile application recommendations can be significantly improved by using TN-MR.

In this paper, the authors propose a mobile application recommendation method based on topic-aware attention networks. By capturing the diffusion patterns and dependencies of mobile applications, it effectively assists users in selecting their applications of interest from thousands of options, significantly improving the accuracy of mobile application recommendations.

This study examined the determinants of mobile health (M-Health) application, adoption, usage and discontinuation among corporate workers diagnosed with hypertension and diabetes in Ghana.

The diffusion innovation and reasoned action theories were employed using an exploratory design. Three hundred corporate workers diagnosed with diabetes and hypertension from three health facilities for the past six months were sampled for the study using a multi-stage sampling technique and administered questionnaires. Descriptive statistics and logistic regression tools were employed in the analysis of data.

The study found a significant number of factors influencing m-health applications adoption, usage and discontinuity. These factors include nature and demand of job, perceived advantage, compatibility, complexity, triability, aesthetics and trust. Aesthetics emerged as the strongest predictive factor for the adoption, usage and discontinuity of use among diabetic and hypertensive corporate workers. With the adoption of M-Health applications, compatibility, as well as nature and demand of job, were significant predictors. With the usage of M-Health applications, complexity, triability, aesthetics and trust were significant predictors. Moreover, perceived advantage, compatibility, complexity and triability influenced significantly the choice to discontinue using M-Health applications. The study concluded that M-Health application functionalities play a valuable role in patients’ intention to adopt, use and discontinue the use of an M-Health application in Ghana.

This exploratory study offers in-depth insight into how major M-Health application features affect its adoption, usage and discontinuity, providing crucial information for future research and the improvement of chronic condition healthcare delivery.

This study aims to explore how the application of digital technology and information technology can help firms improve their innovation performance and examines the mediating mechanisms of supply chain agility and supply chain integration.

This study conducted a questionnaire survey of 320 business managers in an automotive cluster in China and analyzed the collected data using structural equations.

Digital technology applications (DTA) have a positive impact on innovation performance, while supply chain agility and integration mediate this impact. In addition, information technology applications (ITA) also has a positive impact on innovation performance, while supply chain agility and integration mediate between the two. Supply chain agility (SCA) and supply chain integration (SCI) significantly enhance the positive impact of technology adoption on firms' innovation performance.

This study confirms the impact of digital technology and information technology applications on innovation performance and explores the mediating role played by supply chain agility and integration.

Against the background of the I50 paradigm, this paper queries in what ways blockchain and blockchain-based applications deployed in the smart city context facilitate the integration of the I50 paradigm in smart urban contexts.

A mixed methods approach is applied. First, by means of desk research and thematic literature review, a conceptual model integrating the I50 paradigm, smart city and blockchain-based solutions is built. Second, science mapping bibliometric analysis (SciMat) based on keywords’ co-occurrence is applied to a sample of 491 research articles to identify key domains of blockchain-based applications’ use in smart city. Third, a semi-systematic literature review complements insights gained through SciMat. Fourth, the findings are interpreted through the precepts of the conceptual model devised earlier.

The key blockchain-based applications in smart cities pertain to two domains, i.e. the foundational, service facilitation-oriented domain, including security (and safety), networks, computing, resource management and the service delivery-oriented domain, including mobility, energy and healthcare. Blockchain serves as the key building block for applications developed to deliver functions specific to each of the thus identified domains. A substantial layering of blockchain-based tools and applications is necessary to advance from the less to the more complex functional domains of the smart city.

At the conceptual level, the intricacies of the (making of the) I50 paradigm are discussed and a case for I50 – smart city – blockchain nexus is made. Easton’s input–output model as well as constructivism is referenced. At the empirical level, the key major domains of blockchain-based applications are discussed; those that bear the prospect of integrating the I50 paradigm in the smart city are highlighted. At the methodological level, a strategic move is made aimed at restoring the literature review’s role as subservient to the key line of exploration, to justify and ultimately support it, rather than to showcase the literature review as the ultimate purpose for itself.

This paper introduces the YouWalk-UOS mobile application, a tool that revolutionises the assessment of urban open spaces (UOS). The paper demonstrates how integrating real-time, on-ground observations with users’ reactions into a digital platform can transform the evaluation of urban open spaces. It seeks to address the existing shortcomings of traditional UOS assessment methods and underscore the need for innovative, adaptable and inclusive approaches.

Emphasizing the necessity of UOS for mental and physical health, community interaction and social and environmental resilience in cities, the methodology involves a comprehensive analysis of a number of theoretical frameworks that have historically influenced urban open space conceptualisation, design and assessment. The approach includes a critical review of traditional UOS assessment methods, contrasting them with the capabilities of the proposed YouWalk-UOS application. Building on the reviewed theoretical frameworks, the methodology articulates the application’s design, which encompasses 36 factors across three assessment domains: functional, social and perceptual and provides insights into how technology can be leveraged to offer a more holistic and participatory approach to urban space assessment.

YouWalk-UOS application represents an important advancement in urban space assessment, moving beyond the constraints of traditional methods. The application facilitates a co-assessment approach, enabling community members to actively participate in the evaluation and development of their urban environments. Findings highlight the essential role of technology in making urban space assessment more user-centred, aligning more closely with community needs and aspirations.

The originality lies in the focus on the co-assessment approach and integration of mobile technology into urban open space assessment, a relatively unexplored area in urban design literature. The application stands out as an innovative solution, offering a new perspective on engaging communities in co-assessing their environments. This research contributes to the discourse on urban design and planning by providing a fresh look at the intersection of technology, user engagement and urban space assessment.

The purpose of this paper is to develop a software-library in the R programming language that implements the concepts of the interpretive coordinate, interpretive axis and interpretive plane. This allows for the automatic and reliable interpretation of results from the multiple correspondence analysis (MCA) as previously proposed and published. Consequently, the users can seamlessly apply these concepts to their data, both via R commands and a corresponding graphical interface.

Within the context of this study, and through extensive literature review, the advantages of developing software using the Shiny library were examined. This library allows for the development of full-stack applications for R users without the need for knowledge of the corresponding technologies required for the development of complex applications. Additionally, the structural components of a Shiny application were presented, leading ultimately to the proposed software application.

Software utilizing the Shiny library enables nonexpert developers to rapidly develop specialized applications, either to present or to assist in the understanding of objects or concepts that are scientifically intriguing and complex. Specifically, with this proposed application, the users can promptly and effectively apply the scientific concepts addressed in this study to their data. Additionally, they can dynamically generate charts and reports that are readily available for download and sharing.

The proposed package is an implementation of the fundamental concepts of the exploratory MCA method. In the next step, discoveries from the geometric data analysis will be added as features to provide more comprehensive information to the users.

The practical implications of this work include the dissemination of the method’s use to a broader audience. Additionally, the decision to implement it with open-source code will result in the integration of the package’s functions by other third-party user packages.

The proposed software introduces the initial implementation of concepts such as interpretive coordination, the interpretive axis and the interpretive plane. This package aims to broaden and simplify the application of these concepts to benefit stakeholders in scientific research. The software can be accessed for free in a code repository, the link to which is provided in the full text of the study.

ChatGPT is a versatile technology with practical use cases spanning many professional disciplines including marketing. Being a recent innovation, however, there is a lack of academic insight into its tangible applications in the marketing realm. To address this gap, the current study explores ChatGPT’s application in marketing by mining social media data. Additionally, the study employs the stages-of- growth model to assess the current state of ChatGPT’s adoption in marketing organizations.

The study collected tweets related to ChatGPT and marketing using a web-scraping technique (N = 23,757). A topic model was trained on the tweet corpus using latent Dirichlet allocation to delineate ChatGPT’s major areas of applications in marketing.

The topic model produced seven latent topics that encapsulated ChatGPT’s major areas of applications in marketing including content marketing, digital marketing, search engine optimization, customer strategy, B2B marketing and prompt engineering. Further analyses reveal the popularity of and interest in these topics among marketing practitioners.

The findings contribute to the literature by offering empirical evidence of ChatGPT’s applications in marketing. They demonstrate the core use cases of ChatGPT in marketing. Further, the study applies the stages-of-growth model to situate ChatGPT’s current state of adoption in marketing organizations and anticipate its future trajectory.