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The purpose of this paper is to introduce a decision support aid for deciding an overseas construction project (OCP) using an adaptive neuro fuzzy inference system (ANFIS).

This study presents an ANFIS approach as a decision support aid for assessment of OCPs. The processing data were derived from 110 simulation cases of OCPs. In total, 21 international factors observed from a Delphi survey were determined as assessment variables to examine the cases. The experts were involved to evaluate and judge whether the company should Go or Not Go for an OCP, based on the different parameter scenarios given. To measure the performance of the ANFIS model, root mean square error (RMSE) and coefficient of correlation (R) were employed.

The result shows that optimum ANFIS model indicating RMSE and R scores adequately near between 0 and 1, respectively, was obtained from parameter set of network algorithm with two input membership functions, Gaussian type of membership function and hybrid optimization method. When the model tested to nine real OCPs data, the result indicates 88.89 percent accurate.

The use of simulation cases as data set in development the model has several advantages. This technique can be replicated to generate other case scenarios which are not available publicly or limited in terms of quantity.

This study evidences that the developed ANFIS model can predict the decision satisfactorily. Therefore, it can help companies’ management to make preliminary assessment of an OCP.

The steel construction market has undergone gradual development in the past decades given its profound impacts on environment, economy and society. The purpose of this paper is to facilitate a better understanding of the major drivers and issues behind the market development of the steel construction industries around the world.

A three-step desktop research was conducted to select relevant research outputs published in the past 20 years. The research methodology in conducting these studies and their research trends were analyzed. Then the potential influencing factors for the market development of steel construction were identified through a content analysis of the selected studies.

A total of 59 articles were identified accordingly. These influencing factors were divided into five main themes: contextual, institutional, industrial, project-related and individual factors. In terms of the frequencies of these factors appeared in previous studies, “continuous development of standards, codes, and specifications” and “advance in product and process technology” were the top two driving forces in the market development of steel construction, while “cost issues” was the most frequently reported obstacle.

The study takes an initiative to establish a practical classification framework that can be dedicated to illuminating the critical issues or success factors affecting the development of the steel construction market. This framework can help policymakers, industry practitioners and researchers achieve sustaining success in steel construction in the developed, emerging and inactive markets.

The highest number of accidents in proportion to the employment rate is found in construction industry among all industries in Pakistan. The purpose of this paper is to identify and prioritize the contributory factors of accident causation that can significantly reduce the rate of accident in the construction industry.

In total, 32 contributory factors of accident causation were identified through a triangulation strategy comprising eight face-to-face semi-structured interviews with the academic and industry experts coupled with a comprehensive literature review. Delphi survey was then conducted among the four respondent groups (clients, contractors, safety official and academia) to prioritize these factors. A consensus was achieved among the respondent groups after conducting two rounds of Delphi survey. Finally, the results were validated using the technique of inter-rater agreement (IRA) analysis.

All the shortlisted accident causation factors were graded as “important” to “extremely important”. Moreover, a “moderate” to “strong level” agreement was developed among the respondent groups. The three most significant factors were highlighted as “poor enforcement of safety rules and regulations by the Government agencies”, “insufficient allocation of safety budget and safety incentives by the client”, and “insufficient provision of safety training and resources by the contractor”.

The findings will help the key stakeholders to prioritize their energies towards achieving zero accident in the construction industry. Moreover, addition of academic experts as one of the respondent groups will enhance the linkages between the academia and the industry practitioners.

Besides highlighting the underlying causes of construction accidents in Pakistan, a detailed methodology is presented in this study for the analysis and validation of the Delphi survey data, which can be extrapolated in other regions and industries for elements prioritization. The findings of the study can also be generalized for other developing countries having similar work environment. The results validation through the use of IRA analysis is an addition to the field of construction safety research. The study also authenticates the applicability of IRA analysis to assess the agreement level among the respondents.

This chapter explores a descriptive theory of multidimensional travel behaviour, estimation of quantitative models and demonstration in an agent-based microsimulation.

A descriptive theory on multidimensional travel behaviour is conceptualised. It theorizes multidimensional knowledge updating, search start/stopping criteria and search/decision heuristics. These components are formulated or empirically modelled and integrated in a unified and coherent approach.

The theory is supported by empirical observations and the derived quantitative models are tested by an agent-based simulation on a demonstration network.

Based on artificially intelligent agents, learning and search theory and bounded rationality, this chapter makes an effort to embed a sound theoretical foundation for the computational process approach and agent-based micro-simulations. A pertinent new theory is proposed with experimental observations and estimations to demonstrate agents with systematic deviations from the rationality paradigm. Procedural and multidimensional decision-making are modelled. The numerical experiment highlights the capabilities of the proposed theory in estimating rich behavioural dynamics.

The digital revolution has brought many challenges and opportunities for the manufacturing firms. The impact of Industry 4.0 technology adoption on sustainable manufacturing and circular economy has been under-researched. This paper aims to review the latest articles in the area of Industry 4.0, sustainable manufacturing and circular economy and further developed a research framework showing key paths.

Qualitative research is performed in two stages. In the first stage, a review of the extant literature is performed to identify the barriers, drivers, challenges and opportunities. In the second stage, a research framework is proposed to integrate Industry 4.0 technology (big data analytics powered artificial intelligence) adoption, sustainable manufacturing and circular economy capabilities.

This research extends the knowledge base by providing a detailed review of Industry 4.0, sustainable manufacturing, and circular economy and proposes a research framework by integrating these three contemporary concepts in the context of supply chain management. Through an exploration of this integrative research framework, the authors propose a future research agenda and seven research propositions.

It is important to understand the interplay between institutional pressures, tangible resources and human skills for Industry 4.0 technology (big data analytics powered artificial intelligence) adoption. Industry 4.0 technology (big data analytics powered artificial intelligence) adoption can positively influence sustainable manufacturing and circular economy capabilities. Managers must also put more attention to sustainable manufacturing to develop circular economic capabilities.

Factory workers and the local communities generally suffer from various adverse effects resulting from the traditional manufacturing process. The quality of the environment is deteriorating to such an extent that people even staying miles away from the factory are also affected due to environmental pollution that is generated from factory operations. Hence, sustainable manufacturing is the only choice left to manufacturers that can help in the transition to a circular economy. The research framework can help firms to enhance circular economy capabilities.

This review paper contains the most updated work on Industry 4.0, sustainable manufacturing and circular economy. It also proposes a research framework to integrate these three concepts.

The purpose of this paper is to examine whether the underlying option impacts an insider’s propensity to purchase and sell before corporate announcements, the proportion of insiders’ trading after announcements relative to before announcements, and the insider’s profitability around corporate announcements.

The authors test whether the timing information and option have impacted on the tendency of insider trade, the percentage of all shares traded by insiders in the post-announcement to pre-announcement periods and the average cumulative abnormal stock returns during the pre-announcement period.

Insiders’ propensity to trade before announcements is higher for stocks without options listed than for stocks with traded options. This result is stronger for unscheduled announcements than for scheduled ones. The proportion of insiders’ trade volume after announcements relative to before announcements in stocks that have not options listed is higher than those in stocks with traded options. The positive relationship between the insiders’ signed volume and the informational content of corporate announcements is stronger in stocks without traded options than in stocks with options listed. Insider trades prior to unscheduled announcement are more profitable than those before scheduled ones.

The paper examines whether there is a difference between the effects of optioned stock and non-optioned stock. Roll et al. (2010) use the relative trading volume of options to stock ratio (O/S) to proxy for informed options trading activity. Future research could explore the impact of O/S. Moreover, the authors examine how insiders with private information use such information to trade in their own firms. Mehta et al. (2017) argue that insiders also use private information to facilitate trading (shadow trading) in linked firms, such as supply chain partners or competitors. Therefore, future research could consider the impact of shadow trading.

Since the insider’s propensity to buy before announcements in stocks without options listed is larger than in stocks with traded options and the relationship is stronger for unscheduled announcements than for scheduled ones, the efforts of regulators should focus on monitoring insider trading in stocks without options listed prior to unscheduled announcements.

First, Lei and Wang (2014) find that the increasing pattern of insider’s propensity to trade before unscheduled announcements is larger than that before scheduled announcements. The authors document the underlying option has impacted the insider’s propensity to purchase and sell, and the relationship is stronger for unscheduled announcements than for scheduled ones. Second, related studies show insider’s trading activity has shifted from periods before corporate announcements to periods after corporate announcements to decrease litigation risk. This paper find the underlying option has influenced the proportion of insiders’ trading after announcements relative to before announcements when the illegal insider trade-related penalties increase.

In this paper, Fantasy Sports (Hereafter FS) is conceptually classified as a concept of gamification for professional sport leagues. From a sporting perspective, FS is often criticized because such online activities may be at the expense of physical activities. Otherwise, gamification can ultimately lead to economic advantages for sport leagues. To further empirically analyse this supposed juxtaposition, an empirical study is presented.

In the empirical study, participation and non-participation in a sport league-related FS league are analysed and the study uses a divided sample (N = 319) for a one-factor Welch-ANOVA. FS effects on sport practice (engaging in doing sport) and usage (engaging with sport) of FS players as well as on gaining and retaining fans are investigated.

Results demonstrate that participating in gamified FS experiences increases sport usage while not harming general sport practice. Furthermore, FS participation increases consumption capital as well as sport fans' loyalty and word of mouth (WOM) towards the league brand. Building on the results, league brands should foster gamified FS applications to retain their fan base and acquire new fans.

The authors’ theoretical contribution indicates the importance of FS as a gamified application and essential marketing tool for professional sport leagues. By introducing the terms sport practice and usage, the authors bridge the traditional logic of sport consumption with innovative approaches around engagement in and with sports. The results refute the prejudice that FS leads to less physical activity due to time substitution or displacement.

With the rapid increase in internet use, most people tend to purchase books through online stores. Several such stores also provide book recommendations for buyer convenience, and both collaborative and content-based filtering approaches have been widely used for building these recommendation systems. However, both approaches have significant limitations, including cold start and data sparsity. To overcome these limitations, this study aims to investigate whether user satisfaction can be predicted based on easily accessible book descriptions.

The authors collected a large-scale Kindle Books data set containing book descriptions and ratings, and calculated whether a specific book will receive a high rating. For this purpose, several feature representation methods (bag-of-words, term frequency–inverse document frequency [TF-IDF] and Word2vec) and machine learning classifiers (logistic regression, random forest, naive Bayes and support vector machine) were used.

The used classifiers show substantial accuracy in predicting reader satisfaction. Among them, the random forest classifier combined with the TF-IDF feature representation method exhibited the highest accuracy at 96.09%.

This study revealed that user satisfaction can be predicted based on book descriptions and shed light on the limitations of existing recommendation systems. Further, both practical and theoretical implications have been discussed.

Computed tomography (CT) scan can provide valuable information in the diagnosis of lung diseases. To detect the location of the cancerous lung nodules, this work uses novel deep learning methods. The majority of the early investigations used CT, magnetic resonance and mammography imaging. Using appropriate procedures, the professional doctor in this sector analyses these images to discover and diagnose the various degrees of lung cancer. All of the methods used to discover and detect cancer illnesses are time-consuming, expensive and stressful for the patients. To address all of these issues, appropriate deep learning approaches for analyzing these medical images, which included CT scan images, were utilized.

Radiologists currently employ chest CT scans to detect lung cancer at an early stage. In certain situations, radiologists' perception plays a critical role in identifying lung melanoma which is incorrectly detected. Deep learning is a new, capable and influential approach for predicting medical images. In this paper, the authors employed deep transfer learning algorithms for intelligent classification of lung nodules. Convolutional neural networks (VGG16, VGG19, MobileNet and DenseNet169) are used to constrain the input and output layers of a chest CT scan image dataset.

The collection includes normal chest CT scan pictures as well as images from two kinds of lung cancer, squamous and adenocarcinoma impacted chest CT scan images. According to the confusion matrix results, the VGG16 transfer learning technique has the highest accuracy in lung cancer classification with 91.28% accuracy, followed by VGG19 with 89.39%, MobileNet with 85.60% and DenseNet169 with 83.71% accuracy, which is analyzed using Google Collaborator.

The proposed approach using VGG16 maximizes the classification accuracy when compared to VGG19, MobileNet and DenseNet169. The results are validated by computing the confusion matrix for each network type.

The purpose of this paper is to propose a stochastic optimisation model for integrating service and maintenance policies in order to solve the queuing problem and the cost of maintenance activities for public transport services, with a particular focus on urban ropeway system.

The authors adopt the following approaches: a discrete-event model that uses a set of interrelated queues for the formulation of the service problem using a cost-based expression; and a maintenance model consisting of preventive and corrective maintenance actions, which considers two different maintenance policies (periodic block-type and age-based).

The work shows that neither periodic block-type maintenance nor an age-based maintenance is necessarily the best maintenance strategy over a long system lifecycle; the optimal strategy must consider both policies.

The maintenance policies are then evaluated for their impact on the service and operation of the transport system. The authors conclude by applying the proposed optimisation model using an example concerning ropeway systems.

This is the first study to simultaneously consider maintenance policy and operational policy in an urban aerial ropeway system, taking up the problem of queuing with particular attention to the unique requirements public transport services.