Search results

Delay and disruption claims involve a complex process that often result in disputes, unnecessary expenses and time loss on construction projects. This study aims to review and synthesize the contributions of previous research undertaken in this area and propose future directions for improving the process of delay and disruption claims.

This study adopted a holistic systematic review of literature following Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. A total of 230 articles were shortlisted related to delay and disruption claims in construction using Scopus and Web of Science databases.

Six research themes were identified and critically reviewed including delay analysis, disruption analysis, claim management, contract administration, dispute resolution and delay and disruption information and records. The systematic review showed that there is a dearth of research on managing the wide-ranging information required for delay and disruption claims, ensuring the transparency and uniformity in delay and disruption claims’ information and adopting an end-user’s centred research approach for resolving the problems in the process of delay and disruption claims.

Complexities in delay and disruption claims are real-world problems faced by industry practitioners. The findings will help the research community and industry practitioners to prioritize their energies toward information management of delay and disruption claims.

This study contributes to the body of knowledge in delay and disruption claims by identifying the need for conducting more research on its information requirements and management. Subsequently, it provides an insight on the use of modern technologies such as drones, building information modeling, radio frequency identifiers, blockchain, Bigdata and machine learning, as tools for more structured and efficient attainment of required information in a transparent and consistent manner. It also recommends greater use of design science research approach for delay and disruption claims. This will help to ensure delay and disruption claims are the least complex and less dispute-prone process.

The purpose of this study is to explore the relationship between Lean manufacturing and Industry 4.0 for small and medium size of enterprise in Japan and Brazil.

The authors conducted a quantitative survey (20 companies in Japan and 30 companies in Brazil) combined with a qualitative interview (2 companies in Japan and 15 companies in Brazil).

According to the quantitative study, 90% of them practice Lean manufacturing and 40% of them practice Industry 4.0. In the qualitative study in Brazil, four managers responded that the Lean manufacturing is a prerequisite for Industry 4.0 since any production process with waste cannot be productive, even with sophisticated digitalization technology.

The authors explored further the relationship between “defensive Digital Transformation (DX),” which is based mainly on Lean manufacturing, and “offensive DX,” which relates to customer value creation through Industry 4.0. This study clarifies the relationship and plays as a roadmap to develop better the manufacturing from current status to the vision of Industry 4.0.

Road traffic emissions are generally believed to contribute immensely to air pollution, but the effect of road traffic data sets on air quality (AQ) predictions has not been fully investigated. This paper aims to investigate the effects traffic data set have on the performance of machine learning (ML) predictive models in AQ prediction.

To achieve this, the authors have set up an experiment with the control data set having only the AQ data set and meteorological (Met) data set, while the experimental data set is made up of the AQ data set, Met data set and traffic data set. Several ML models (such as extra trees regressor, eXtreme gradient boosting regressor, random forest regressor, K-neighbors regressor and two others) were trained, tested and compared on these individual combinations of data sets to predict the volume of PM_2.5, PM₁₀, NO₂ and O₃ in the atmosphere at various times of the day.

The result obtained showed that various ML algorithms react differently to the traffic data set despite generally contributing to the performance improvement of all the ML algorithms considered in this study by at least 20% and an error reduction of at least 18.97%.

This research is limited in terms of the study area, and the result cannot be generalized outside of the UK as some of the inherent conditions may not be similar elsewhere. Additionally, only the ML algorithms commonly used in literature are considered in this research, therefore, leaving out a few other ML algorithms.

This study reinforces the belief that the traffic data set has a significant effect on improving the performance of air pollution ML prediction models. Hence, there is an indication that ML algorithms behave differently when trained with a form of traffic data set in the development of an AQ prediction model. This implies that developers and researchers in AQ prediction need to identify the ML algorithms that behave in their best interest before implementation.

The result of this study will enable researchers to focus more on algorithms of benefit when using traffic data sets in AQ prediction.

This study aims to investigate the relationships between big data analytics, management accounting practices and corporate sustainability and, more precisely, the impact of the integration between big data analytics and management accounting on corporate sustainability performance development.

A qualitative case study approach is used in this study with multiple collecting data tools as in-depth interviews and observations, in addition to the content analysis used of the annual reports for the year 2021, of Almarai manufacturing corporate (one of the leaders of food and beverage manufacturing corporates in Saudi Arabia and other countries).

Research findings provide good insights about the significant impact of the effective integration between big data analytics and management accounting on corporate sustainability performance development, big data can assist management accounting to form corporate value-added strategies and activities.

The study is limitedly applied to one manufacturing corporate as a study case; therefore, the findings cannot be generalized. Thus, future research can examine the association between the current study variables with wide-scale applications and with different approaches and in different contexts to enrich the findings. Moreover, future research may focus on the integration between big data analytics and management accounting reports in the meta-verse environment to explore the benefits that corporates could gain from the features and capabilities of meta-verse technology.

There is a research gap regarding the impact of the integration between big data analytics and management accounting practices on corporate sustainability development, as most of the previous studies focused on two variables only of the current study variables; therefore, this study tries to investigate and give important insights about it.

The objective of this paper is to develop a condition-based maintenance (CBM) scheme for pneumatic cylinders. The CBM scheme will detect two common types of air leaking failure modes and identify the leaky/faulty cylinder. The successful implementation of the proposed scheme will reduce energy consumption, scrap and rework, and time to repair.

Effective implementation of maintenance is important to reduce operation cost, improve productivity and enhance quality performance at the same time. Condition-based monitoring is an effective maintenance scheme where maintenance is triggered based on the condition of the equipment monitored either real time or at certain intervals. Pneumatic air systems are commonly used in many industries for packaging, sorting and powering air tools among others. A common failure mode of pneumatic cylinders is air leaks which is difficult to detect for complex systems with many connections. The proposed method consists of monitoring the stroke speed profile of the piston inside the pneumatic cylinder using hall effect sensors. Statistical features are extracted from the speed profiles and used to develop a fault detection machine learning model. The proposed method is demonstrated using a real-life case of tea packaging machines.

Based on the limited data collected, the ensemble machine learning algorithm resulted in 88.4% accuracy. The algorithm can detect failures as soon as they occur based on majority vote rule of three machine learning models.

Early air leak detection will improve quality of packaged tea bags and provide annual savings due to time to repair and energy waste reduction. The average annual estimated savings due to the implementation of the new CBM method is $229,200 with a payback period of less than two years.

To the best of the authors’ knowledge, this paper is the first in terms of proposing a CBM for pneumatic systems air leaks using piston speed. Majority, if not all, current detection methods rely on expensive equipment such as infrared or ultrasonic sensors. This paper also contributes to the research gap of economic justification of using CBM.

The aim of this paper is to provide a narrative review of previous research on tourism demand modelling and forecasting and potential future developments.

A narrative approach is taken in this review of the current body of knowledge.

Significant methodological advancements in tourism demand modelling and forecasting over the past two decades are identified.

The distinct characteristics of the various methods applied in the field are summarised and a research agenda for future investigations is proposed.

本文旨在对先前关于旅游需求建模和预测的研究进行叙述性回顾并对未来潜在发展进行展望。

本文采用叙述性回顾方法对当前知识体系进行了评论。

本文确认了过去二十年旅游需求建模和预测方法论方面的重要进展。

本文总结了该领域应用的各种方法的独特特征, 并对未来研究提出了建议。

El objetivo de este documento es ofrecer una revisión narrativa de la investigación previa sobre modelización y previsión de la demanda turística y los posibles desarrollos futuros.

En esta revisión del marco actual de conocimientos sobre modelización y previsión de la demanda turística y los posibles desarrollos futuros,se adopta un enfoque narrativo.

Se identifican avances metodológicos significativos en la modelización y previsión de la demanda turística en las dos últimas décadas.

Se resumen las características propias de los diversos métodos aplicados en este campo y se propone una agenda de investigación para futuros trabajos.

GraphQL is a new Open API specification that allows clients to send queries and obtain data flexibly according to their needs. However, a high-complexity GraphQL query may lead to an excessive data volume of the query result, which causes problems such as resource overload of the API server. Therefore, this paper aims to address this issue by predicting the response data volume of a GraphQL query statement.

This paper proposes a GraphQL response data volume prediction approach based on Code2Vec and AutoML. First, a GraphQL query statement is transformed into a path collection of an abstract syntax tree based on the idea of Code2Vec, and then the query is aggregated into a vector with the fixed length. Finally, the response result data volume is predicted by a fully connected neural network. To further improve the prediction accuracy, the prediction results of embedded features are combined with the field features and summary features of the query statement to predict the final response data volume by the AutoML model.

Experiments on two public GraphQL API data sets, GitHub and Yelp, show that the accuracy of the proposed approach is 15.85% and 50.31% higher than existing GraphQL response volume prediction approaches based on machine learning techniques, respectively.

This paper proposes an approach that combines Code2Vec and AutoML for GraphQL query response data volume prediction with higher accuracy.

The purpose of this study is to investigate the adoption of waqf technology (Waqftech) using blockchain smart contracts for corporate waqf crowdfunding. Despite the growing interest in Waqftech, Malaysian enterprises have not fully embraced this emerging technology because of uncertainty regarding the benefits it offers to contributors. The research incorporates two theoretical frameworks: the electronic data interchange (EDI) model for firms’ technology adoption, and the triple bottom line theory (TBL) for corporate social responsibility.

A quantitative method using a cross-sectional survey design with a five-point Likert scale questionnaire was used. Data was collected from 210 decision-makers representing small and medium-sized enterprises and analyzed using partial least squares-structural equation modeling.

The findings from this research suggest that Malaysian enterprises are influenced by both corporate and social predictive benefits when using blockchain crowdfunding, but not by environmental benefits. The adoption of blockchain smart contracts does not correlate with predictive environmental benefits because of misconceptions about the disruptive technology’s impact on biological and digital environmental preservation.

This research focuses on organizational behavior rather than individual users of waqf crowdfunding, and it is limited, primarily focusing within Malaysia and regions with similar waqf structures.

The Waqftech framework allows innovative mechanisms for executing corporate waqf investment returns to the intended beneficiaries through the smart contracts’ platform. In addition, this study supports relevant corporate social responsibility and creating shared value technology adoption theories, including EDI and TBL. Aside from this, the study provides empirical implications for waqf management using fintech platforms.

This groundbreaking study focuses on creating a Waqftech model for corporate waqf crowdfunding. The results of this study are important for the development of government policies that support the use of Waqftech in charitable fundraising. More research on biological and digital environmental perspectives is proposed to foster investors’ confidence in the visibility of digital tracking and lead to swift investments in future metaverse fundraising platforms.

The prevention of mental health issues at work represents a significant challenge for organizations. The transformation of workplaces whose future promises to be virtual or hybrid can make the anticipation and prevention of these health issues more challenging, considering the potential distance that it may create between employees and their employers. The recent health crisis undermined individual mental health but also highlighted the importance of new technologies which greatly paved the way for the future of workplaces. This paper aims to examine these new technologies, specifically the use of blockchain technologies in organizations to predict and prevent mental health issues at work, specifically psychological distress, in times of crisis, and beyond. It addresses the main challenges and opportunities and presents research avenues as well as insights for human resource management (HRM) practitioners.

This paper is a viewpoint that addresses the use of blockchain technology in the prevention of employees’ mental health at work in times of crisis and beyond. Literature was used to support this viewpoint and highlight the importance of addressing mental health issues at work and preventing their occurrence in the future.

Blockchain is one of the disruptive new technologies that can be used as a strategic tool for organizations to prevent mental health issues among employees in the workplace in times of crisis, and beyond. It facilitates the collaboration between employees, their organization, healthcare and employee assistance program (EPA) providers, as well as insurance companies. In this context, a specific type of blockchain should be used to support this type of collaboration.

Blockchain can generate both opportunities and challenges for the prevention of mental issues at work. It can transform the future of workplaces and help organizations as well as healthcare and EPA providers to anticipate potential employees’ mental health issues in 2019. Organizations need to address their readiness to implement this new technology and the possible reluctance of their employees to use it. This paper presents insights for managers and HRM practitioners.

The studies that have addressed the use of blockchain in organizations to prevent employees’ mental health issues are sparse. This paper is an attempt to address this gap and examine the challenges as well as the opportunities associated with the use of this disruptive new technology that can significantly reshape the future of workplaces.

The purpose of this study is to analyze state-of-the-art knowledge of artificial intelligence (AI) research in hospitality.

This study adopts the theory-context-methods framework to systematically review 100 AI-related articles recently published (i.e. from 2021 to April 2023) in three top-tier hospitality journals, namely, the International Journal of Contemporary Hospitality Management, International Journal of Hospitality Management and Journal of Hospitality Marketing and Management.

Findings suggest that studies of AI applications in hospitality are mostly theory-driven, whereas most AI methods research adopts a data-driven approach. State-of-the-art AI applications research exhibits the most interest in service robots. In AI methods research, little attention was paid to the amid-service/experience.

This study reveals inadequacies in theory, context and methods in contemporary AI research. More research from hospitality suppliers’ perspectives and research on generative AI applications are advocated in response to the unveiled research gaps and recent AI developments.

This study classifies the most recent AI research in hospitality into two main streams – AI applications research and AI methods research – and discusses the gaps in each research stream and latest AI developments. The paper then suggests future research directions to guide researchers in advancing AI research in hospitality.