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Factor investment is booming in global asset management, especially environmental, social, and governance (ESG), dividend yield, and volatility factors. In this chapter, we use data from the US securities market from 2003 to 2019 to predict dividends and volatility factors through machine learning and historical data–based methods. After that, we utilize particle swarm optimization to construct the Markowitz portfolio with limits on the number of assets and weight restrictions. The empirical results show that that the prediction ability using XGBoost is superior to the historical factor investment method. Moreover, the investment performance of our portfolio with ESG, high-yield, and low-volatility factors outperforms baseline methods, especially the S&P 500 ETF.

The study aims to identify the possible risk factors for electricity grids operational disruptions and to determine the most critical and influential risk indicators.

A multi-criteria decision-making best-worst method (BWM) is employed to quantitatively identify the most critical risk factors. The grey causal modeling (GCM) technique is employed to identify the causal and consequence factors and to effectively quantify them. The data used in this study consisted of two types – quantitative periodical data of critical factors taken from their respective government departments (e.g. Indian Meteorological Department, The Central Water Commission etc.) and the expert responses collected from professionals working in the Indian electric power sector.

The results of analysis for a case application in the Indian context shows that temperature dominates as the critical risk factor for electrical power grids, followed by humidity and crop production.

The study helps to understand the contribution of factors in electricity grids operational disruptions. Considering the cause consequences from the GCM causal analysis, rainfall, temperature and dam water levels are identified as the causal factors, while the crop production, stock prices, commodity prices are classified as the consequence factors. In practice, these causal factors can be controlled to reduce the overall effects.

From the results of the analysis, managers can use these outputs and compare the risk factors in electrical power grids for prioritization and subsequent considerations. It can assist the managers in efficient allocation of funds and manpower for building safeguards and creating risk management protocols based on the severity of the critical factor.

The research comprehensively analyses the risk factors of electrical power grids in India. Moreover, the study apprehends the cause-consequence pair of factors, which are having the maximum effect. Previous studies have been focused on identification of risk factors and preliminary analysis of their criticality using autoregression. This research paper takes it forward by using decision-making methods and causal analysis of the risk factors with blend of quantitative and expert response based data analysis to focus on the determination of the criticality of the risk factors for the Indian electric power grid.

The purpose of this paper is to investigate the extent to which interdisciplinary (HASS, i.e. non-STEM) factors—in particular, accounting, stakeholder management and accountability—enable, influence and motivate large human exploration ventures, principally in maritime and space fields, utilizing Columbus’s and Chinese explorations of the 1400s as the primary setting.

The study analyzes archival data from narrative and interpretational history, including both academic and non-academic sources, that relate to two global historical events, the Columbus and Ming Chinese exploration eras (c. 1400–1500), as a parallel to the modern “Space Race”. Existing studies on pertinent HASS (Humanities and Social Sciences) and STEM (Science, Technology, Engineering and Mathematics) enablers, influencers and motivators are utilized in the analysis. The authors draw upon the concepts of stakeholder theory and the construct of accountability in their analysis.

Findings suggest that non-STEM considerations—politics, finance, accountability, culture, theology and others—played crucial roles in enabling Western Europe (Columbus) to reach the Americas before China or other global powers, demonstrating the pivotal importance of HASS factors in human advancements and exploration.

In seeking to answer those questions, this study identifies only those factors (HASS or STEM) that may support the success or failure in execution of the exploration and development of a region such as the New World or Space. Moreover, the study has the following limitation. Relative successes, failures, drivers and enablers of exploratory ventures are drawn almost exclusively from the documented historical records of the nations, entities and individuals (China and Europe) who conducted those ventures. A paucity of objective sources in some fields, and the need to set appropriate boundaries for the study, also necessitate such limitation.

It is observable that many of those HASS factors also appear to have been influencers in modern era Space projects. For Apollo and Soyuz, success factors such as the relative economics of USA and USSR, their political ideologies, accountabilities and organizational priorities have clear echoes. What the successful voyages of Columbus and Apollo also have in common is an appetite to take risks for an uncertain return, whether as sponsor or voyager; an understanding of financial management and benefits measurement, and a leadership (Isabella I, John F. Kennedy) possessing a vision, ideology and governmental apparatus to further the venture’s goals.

Whilst various historical studies have examined influences behind the oceangoing explorations of the 1400s and the colonization of the “New World”, this article takes an original approach of analyzing those motivations and other factors collectively, in interdisciplinary terms (HASS and STEM). This approach also has the potential to provide a novel method of examining accountability and performance in modern exploratory ventures, such as crewed space missions.

Social risk management has recently come to the fore as a significant feature of project management. This prominence is particularly evident in urban construction projects that take place in cultural heritage and tourism historic sites. Accordingly, this study aims to adopt social network analysis (SNA) to investigate social risks in construction projects occurring in urban districts rife with historically and culturally significant tourism sites.

The present study analyzed a real case study in Iran as an emergent economy and a developing country. Primarily, the study reviewed previous literature on social risks and relevant stakeholders. Next, the judgments of experts through the content validity ratio analysis confirmed 12 social risks and 9 key stakeholders. Finally, SNA is used to determine the relations between the social risks and stakeholders as well as the significance of each risk.

The investigation demonstrated that the most important social risks in the construction projects of the case study are “Psychological disorders”, “Environmental pollution” and “Cultural conflicts”.

The findings could help policymakers, urban planners and project managers in developing countries with a rich cultural heritage to reduce social risks and improve the efficiency of their projects.

To the best of the authors’ knowledge, the present study is one of the first instances to investigate construction projects implemented in densely populated urban areas hosting cultural heritage and historic tourism sites.

Clarifying the risk evolution mechanism of housing construction for work-safety management is essential. Existing studies have inadequately discussed the risk-accumulation process in housing construction. Therefore, this study aimed to use the complex network theory and risk allocation mechanisms to explore the evolution of risk factors.

The authors analysed a database of housing construction accidents in China from 2015 to 2020 to identify risk factors. Moreover, the causal relationship between risk factors was determined through a systematic analysis of the logical sequence of risk factors. A complex network was used to construct a risk network for housing construction accidents (RNHCA).

The risk matrix method was used to define the factor risk threshold, and a risk value was assigned based on the correlation between risk factors. This contributes to the examination of the evolution mechanism of risk networks in the process of risk factor transmission. The case verification results show that the RNHCA quantitative assessment model can better evaluate the system risk status of housing construction accidents. Furthermore, this model can identify the key risk factors and risk chains with high risk in the evolution of the risk network.

Accident investigation reports need to be classified and processed to analyse the evolution law of risk networks under different scales of construction project, such as high-rise buildings, middle-rise buildings, and low-rise buildings.

This study clarified the risk evolution process of complex systems in housing construction and provided a new method for analysing accidents.

This study clarifies the risk value allocation of risk factors in the transmission process and reveals the process of risk factor evolution in housing construction. This study explains the individual risk factors that form a systemic risk through the transmission chain. Moreover, this paper clarified the transformation relationship between system risk and accidents. The paper also provided a new perspective for risk analysis.

The study examines tourists' psychological and social risk and shared beliefs – devotion, concerns and entertainment – at a religious and cultural heritage destination. It also examines how shared beliefs impact tourists’ nostalgia. Further, it examines whether nostalgia affects choice deferral and revisit intentions. Finally, it investigates how moderation of place attachment strengthens the link between shared beliefs – devotion, concerns, entertainment and nostalgia.

The data were collected from 439 inbound tourists, with 272 completing online questionnaires and 167 participating in face-to-face survey. Data analysis was performed using Amos 24.0 and SPSS 25.0, employing structural equation modeling (SEM) and the PROCESS macro.

The findings suggest that perceived psychological and social risk negatively impacts tourists' shared beliefs – devotion, concerns and entertainment – which positively impacts nostalgia. Positive nostalgic association boosts revisit intention and hampers choice deferral. The data also show how strong place attachment strengthens the relationship between shared beliefs – devotion, concerns and entertainment – and tourists’ perceived nostalgia.

This work contributes to information behavior using S-O-R theory. It analyzes the psychological and social risks of destination visits and how nostalgia affects shared beliefs and revisit intentions. Management and policymakers at destination enterprises can use the findings to design measures to enhance revisit intentions despite risk considerations.

Pakistan's destination tourism is underutilized amid its religious and cultural heritage significance. The literature has ignored how perceived psychological and social risk affects travelers' shared beliefs and nostalgic feelings. Thus, this study suggests and validates these linkages utilizing stimulus-organism-response (S-O-R) theory in Pakistan's unique environment with inbound tourists.

The overall objective of this study is envisaged to provide decision makers with actionable insights and access to multi-risk maps for the most in-danger stave churches (SCs) among the existing 28 churches at high spatial resolution to better understand, reduce and mitigate single- and multi-risk. In addition, the present contribution aims to provide decision makers with some information to face the exacerbation of the risk caused by the expected climate change.

Material and data collection started with the consultation of the available literature related to: (1) SCs' conservation status, (2) available methodologies suitable in multi-hazard approach and (3) vulnerability leading indicators to consider when dealing with the impact of natural hazards specifically on immovable cultural heritage.

The paper contributes to a better understanding of place-based vulnerability with local mapping dimension also considering future threats posed by climate change. The results highlight the danger at which the SCs of Røldal, in case of floods, and of Ringebu, Torpo and Øye, in case of landslide, may face and stress the urgency of increasing awareness and preparedness on these potential hazards.

The contribution for the first time aims to homogeneously collect and report all together existing spread information on architectural features, conservation status and geographical attributes for the whole group of SCs by accompanying this information with as much as possible complete 2D sections collection from existing drawings and novel 3D drawn sketches created for this contribution. Then the paper contributes to a better understanding of place-based vulnerability with local mapping dimension also considering future threats posed by climate change. Then it highlights the danger of floods and landslides at which the 28 SCs are subjected. Finally it reports how these risks will change under the ongoing impact of climate change.

This study aims to develop an interface management risk interaction modeling and analysis methodology applicable to complex systems in high-speed rail construction projects, reveal the interaction mechanism of interface management risk and provide theoretical support for project managers to develop appropriate interface management risk response strategies.

This paper introduces the association rule mining technique to improve the complex network modeling method. Taking China as an example, based on the stakeholder perspective, the risk factors and significant accident types of interface management of high-speed rail construction projects are systematically identified, and a database is established. Then, the Apriori algorithm is used to mine and analyze the strong association rules among the factors in the database, construct the complex network, and analyze its topological characteristics to reveal the interaction mechanism of the interface management risk of high-speed rail construction projects.

The results show that the network is both scale-free and small-world, implying that construction accidents are not random events but rather the result of strong interactions between numerous interface management risks. Contractors, technical interfaces, mechanical equipment, and environmental factors are the primary direct causal factors of accidents, while owners and designers are essential indirect causal factors. The global importance of stakeholders such as owners, designers, and supervisors rises significantly after considering the indirect correlations between factors. This theoretically explains the need to consider the interactions between interface management risks.

The interaction mechanism between interface management risks is unclear, which is an essential factor influencing the decision of risk response measures. This study proposes a new methodology for analyzing interface management risk response strategies that incorporate quantitative analysis methods and considers the interaction of interface management risks.

The traditional failure mode and effect analysis (FMEA) has some limitations, such as the neglect of relevant historical data, subjective use of rating numbering and the less rationality and accuracy of the Risk Priority Number. The current study proposes a machine learning–enhanced FMEA (ML-FMEA) method based on a popular machine learning tool, Waikato environment for knowledge analysis (WEKA).

This work uses the collected FMEA historical data to predict the probability of component/product failure risk by machine learning based on different commonly used classifiers. To compare the correct classification rate of ML-FMEA based on different classifiers, the 10-fold cross-validation is employed. Moreover, the prediction error is estimated by repeated experiments with different random seeds under varying initialization settings. Finally, the case of the submersible pump in Bhattacharjee et al. (2020) is utilized to test the performance of the proposed method.

The results show that ML-FMEA, based on most of the commonly used classifiers, outperforms the Bhattacharjee model. For example, the ML-FMEA based on Random Committee improves the correct classification rate from 77.47 to 90.09 per cent and area under the curve of receiver operating characteristic curve (ROC) from 80.9 to 91.8 per cent, respectively.

The proposed method not only enables the decision-maker to use the historical failure data and predict the probability of the risk of failure but also may pave a new way for the application of machine learning techniques in FMEA.