Search results

The purpose of this paper is to perform a comparative analysis of the delay associated in running two real-time machine learning-based applications, namely, a video quality assessment (VQA) and a phishing detection application by using the edge, fog and cloud computing paradigms.

The VQA algorithm was developed using Android Studio and run on a mobile phone for the edge paradigm. For the fog paradigm, it was hosted on a Java server and for the cloud paradigm on the IBM and Firebase clouds. The phishing detection algorithm was embedded into a browser extension for the edge paradigm. For the fog paradigm, it was hosted on a Node.js server and for the cloud paradigm on Firebase.

For the VQA algorithm, the edge paradigm had the highest response time while the cloud paradigm had the lowest, as the algorithm was computationally intensive. For the phishing detection algorithm, the edge paradigm had the lowest response time, and the cloud paradigm had the highest, as the algorithm had a low computational complexity. Since the determining factor for the response time was the latency, the edge paradigm provided the smallest delay as all processing were local.

The main limitation of this work is that the experiments were performed on a small scale due to time and budget constraints.

A detailed analysis with real applications has been provided to show how the complexity of an application can determine the best computing paradigm on which it can be deployed.

The circular economy business models (CEBMs) provide ways for firms operating in the construction industry to move from a linear to a circular approach. Thus, this study aims to explore CEBM research within the construction sector to show the focus area of studies, highlighting new areas that require attention.

This study adopted a bibliometric approach, using the Scopus database as the data source. The keywords used for paper extraction from the database were “circular economy business” OR “circular business” AND “model” OR “models” AND “construction industry” OR “building industry”. The VOSviewer software was then used to prepare a co-occurrence and co-authorship map based on the bibliographic data gathered.

The study’s findings reveal five research clusters in the construction industry. These clusters include circular construction intelligence, modular business modelling, eco-construction, sustainable construction economics and smart energy-efficient buildings. The two most cited scholars had two publications each, while the top journals are the Journal of Cleaner Production and Sustainable Production and Consumption. This study concludes that there is a need for research within the construction sector to focus on CEBMs’ archetypes and frameworks. This will enable a smooth transition from linear to circular business models in the sector.

The information was gathered from a single database, Scopus; hence, using other databases, including Web of Science, Google Scholar and Dimensions, might produce more articles for examination and, consequently, different findings on the subject under investigation.

These findings would assist researchers in considering the areas mentioned, which are yet to receive attention, and, by extension, enhance economic development while maintaining environmental sustainability.

This paper made a significant contribution to the body of knowledge by identifying scholars and platforms that have been instrumental in advancing CEBM research and highlighting new areas that require attention in the construction sector.

The purpose of this paper is to explore the concept of data assets and how companies can assetize their data. Using the literature review methodology, the paper first summarizes the conceptual controversies over data assets in the existing literature. Subsequently, the paper defines the concept of data assets. Finally, keywords from the existing research literature are presented visually and a foundational framework for achieving data assetization is proposed.

This paper uses a systematic literature review approach to discuss the conceptual evolution and strategic imperatives of data assets. To establish a robust research methodology, this paper takes into account two main aspects. First, it conducts a comprehensive review of the existing literature on digital technology and data assets, which enables the derivation of an evolutionary path of data assets and the development of a clear and concise definition of the concept. Second, the paper uses Citespace, a widely used software for literature review, to examine the research framework of enterprise data assetization.

The paper offers pivotal insights into the realm of data assets. It highlights the changing perceptions of data assets with digital progression and addresses debates on data asset categorization, value attributes and ownership. The study introduces a definitive concept of data assets as electronically recorded data resources with real or potential value under legal parameters. Moreover, it delineates strategic imperatives for harnessing data assets, presenting a practical framework that charts the stages of “resource readiness, capacity building, and data application”, guiding businesses in optimizing their data throughout its lifecycle.

This paper comprehensively explores the issue of data assets, clarifying controversial concepts and categorizations and bridging gaps in the existing literature. The paper introduces a clear conceptualization of data assets, bridging the gap between academia and practice. In addition, the study proposes a strategic framework for data assetization. This study not only helps to promote a unified understanding among academics and professionals but also helps businesses to understand the process of data assetization.

This paper aims to provide a literature review on the cloud-based platforms for the education sectors. The several aspects of cloud computing adoption in education, remote/distance learning and the application of cloud-based design and manufacturing (CBDM) have been studied and theorised.

A four-step methodology was adopted to analyse and categorise the papers obtained through various search engines. Out of 429 research articles, 72 papers were shortlisted for the detailed analysis.

Many factors that influence cloud computing technology adoption in the education sector have been identified in this paper. The research findings on several research items have been tabulated and discussed. Based on the theoretical research done on cloud computing for education, cloud computing for remote/distance learning and CBDM, cloud computing could enhance the educational systems in mainly developing countries and improve the scope for remote/distance learning.

This study is limited to papers published only in the past decade from 2011 to 2020. Besides, this review was unable to include journal articles published in different languages. Nevertheless, for the effective teaching and learning process, this paper could help understand the importance and improve the process of adopting cloud computing concepts in educational universities and platforms.

This study is a novel one as a research review constituting cloud computing applications in education and extended for remote/distance learning and CBDM, which have not been studied in the existing knowledge base.

The coronavirus disease 2019 (COVID-19) pandemic has had a big impact on organisations globally, leaving organisations with no choice but to adapt to the new reality of remote work to ensure business continuity. Such an unexpected reality created the conditions for testing new applications of smart home technology whilst working from home. Given the potential implications of such applications to improve the working environment, and a lack of research on that front, this paper pursued two objectives. First, the paper explored the impact of smart home applications by examining the factors that could contribute to perceived productivity and well-being whilst working from home. Second, the study investigated the role of productivity and well-being in motivating the intention of remote workers to use smart home technologies in a home-work environment in the future.

The study adopted a cross-sectional research design. For data collection, 528 smart home users working from home during the pandemic were recruited. Collected data were analysed using a structural equation modelling approach.

The results of the research confirmed that perceived productivity is dependent on service relevance, perceived usefulness, innovativeness, hedonic beliefs and control over environmental conditions. Perceived well-being correlates with task-technology fit, service relevance, perceived usefulness, perceived ease of use, attitude to smart homes, innovativeness, hedonic beliefs and control over environmental conditions. Intention to work from a smart home-office in the future is dependent on perceived well-being.

The findings of the research contribute to the organisational and smart home literature, by providing missing evidence about the implications of the application of smart home technologies for employees' perceived productivity and well-being. The paper considers the conditions that facilitate better outcomes during remote work and could potentially be used to improve the work environment in offices after the pandemic. Also, the findings inform smart home developers about the features of technology which could improve the developers' application in contexts beyond home settings.

Cloud computing services gained huge attention in recent years and many organizations started moving their business data traditional server to the cloud storage providers. However, increased data storage introduces challenges like inefficient usage of resources in the cloud storage, in order to meet the demands of users and maintain the service level agreement with the clients, the cloud server has to allocate the physical machine to the virtual machines as requested, but the random resource allocations procedures lead to inefficient utilization of resources.

This thesis focuses on resource allocation for reasonable utilization of resources. The overall framework comprises of cloudlets, broker, cloud information system, virtual machines, virtual machine manager, and data center. Existing first fit and best fit algorithms consider the minimization of the number of bins but do not consider leftover bins.

The proposed algorithm effectively utilizes the resources compared to first, best and worst fit algorithms. The effect of this utilization efficiency can be seen in metrics where central processing unit (CPU), bandwidth (BW), random access memory (RAM) and power consumption outperformed very well than other algorithms by saving 15 kHz of CPU, 92.6kbps of BW, 6GB of RAM and saved 3kW of power compared to first and best fit algorithms.

The proposed multi-objective bin packing algorithm is better for packing VMs on physical servers in order to better utilize different parameters such as memory availability, CPU speed, power and bandwidth availability in the physical machine.

The current era of the fourth industrial revolution has attracted significant research on the use of digital technologies in improving construction project delivery. However, less emphasis has been placed on how these digital tools will influence the management of the construction workforce. To this end, using a review of existing works, this chapter explores the fourth industrial revolution and its associated technologies that can positively impact the management of the construction workforce when implemented. Also, the possible challenges that might truncate the successful deployment of digital technologies for effective workforce management were explored. The chapter submitted that implementing workforce management-specific digital platforms and other digital technologies designed for project delivery can aid effective workforce management within construction organisations. Technologies such as cloud computing, the Internet of Things, big data analytics, robotics and automation, and artificial intelligence, among others, offer significant benefits to the effective workforce management of construction organisations. However, several challenges, such as resistance to change due to fear of job loss, cost of investment in digital tools, organisational structure and culture, must be carefully considered as they might affect the successful use of digital tools and by extension, impact the success of workforce management in the organisations.

The purpose of this paper is to explain how enterprise resource planning (ERP) implementation evolves by cloud computing in different industries with different delivery models of cloud ERP. This paper also investigates infrastructure as a service (IaaS) as a delivery approach for cloud ERP. Case research on IaaS is rarely found in the literature. In addition, this paper intends to reveal how this transformation from on-premises to the cloud would influence the ERP implementation process.

A multiple-case study is conducted to identify the different deployed models of cloud ERP systems in the implementation projects. The influences of emerging cloud computing technology on ERP implementation are investigated by interviewing consultants related to the projects.

The findings illustrate that not only software as a service (SaaS) but also IaaS and platform as a service cloud computing services are widely applied in cloud ERP implementation. This study also indicates that certain technical limitations of cloud ERP might have a positive effect on the outcome of ERP implementation.

This study investigates how cloud computing influences ERP implementation from different aspects. The result identifies both SaaS and IaaS as two different approaches widely adopted in cloud ERP implementation. Besides, this study has discussed in-depth and analyzed these two cloud ERP paradigms in five factors, including functionality, performance, portability, security, cost and customization. The classification and suggestions are original to the literature.

This paper aims to explore key management actions for implementing security on the cloud, which is a critical issue as many organizations are moving business processes and data on it. The cloud is a flexible, low cost and highly available technology, but it comes with increased complexity in maintaining the cloud consumer’s security. In this research, a model was built to assist strategic decision-makers in choosing from a diverse range of actions that can be taken to manage cloud security.

Published research from 2010 to 2022 was reviewed to identify alternatives to management actions pertaining to cloud security. Analytical hierarchical process (AHP) was applied to rate the most important action(s). For this, the alternatives, along with selection criteria, were summarized through thematic analysis. To gauge the relative importance of the alternatives, a questionnaire was distributed among cloud security practitioners to poll their opinion. AHP was then applied to the aggregated survey responses.

It was found that the respondents gave the highest importance to aligning information security with business needs. Building a cloud-specific risk management framework was rated second, while the actions: enforce and monitor contractual obligations, and update organizational structure, were rated third and fourth, respectively.

The research takes a general view without catering to specialized industry-based scenarios.

This paper highlights the role of management actions when implementing cloud security. It presents an AHP-based multi-criteria decision-making model that can be used by strategic decision-makers in selecting the optimum mode of action. Finally, the criteria used in the AHP model highlight how each alternative contributes to cloud security.

This paper aims to study the economic effects of Cloud Computing for a sample of Israeli firms. The authors propose a framework that considers how this technology affects firm performance also introducing the indirect economic effects that take place through cloud-complementary technologies such as Big Data and Machine Learning.

The model is estimated through structural equation modeling. The data set consists of the microdata of the survey of information and communication technologies uses and cyber protection in business conducted in Israel by the Central Bureau of Statistics.

The results point to Cloud Computing as a crucial technology to increase firm performance, presenting significant direct and indirect effects as the use of complementary technologies maximizes its impact. Firms that enjoy most direct economic gains from Cloud Computing appear to be the smaller ones, although larger enterprises seem more capable to assimilate complementary technologies, such as Big Data and Machine Learning. The total effects of cloud on firm performance are quite similar among manufacturing and service firms, although the composition of the different effects involved is different.

This paper is one of the very few analyses estimating the impact of Cloud Computing on firm performance based on country microdata and, to the best of the authors’ knowledge, the first one that contemplates the indirect economic effects that take place through cloud-complementary technologies such as Big Data and Machine Learning.