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The purpose of this paper is to investigate the rates of return and the risks of different types of educational paths – all leading to a tertiary educational degree. The paper seeks to distinguish a purely academic educational path from a purely vocational path and a mixed path with loops through both systems.

The paper studies the labor market outcome to compare earnings and calculate net return rates as well as risk measures to investigate whether different educational paths are characterized by different risk‐return trade‐offs. Entrepreneurs are separated from employees in order to examine whether for the same combination of education the labour market outcomes differ between the two groups.

The empirical results are based on the Swiss Labor Force Survey (SLFS) and demonstrate that mixed educational paths are well rewarded in the labor market. However, for entrepreneurs a high return is also associated with a high income variance.

The findings provide evidence for the existence of complementarities between vocational and academic education. Further research on mixed educational paths might provide more insight into this presumed relationship.

Since the results indicate that mixed educational paths are a worthwhile strategy, the permeability of a national education system is a very important educational policy issue.

The study is innovative in three ways: first, it focuses on complete educational paths and not just the highest educational degree. Second, an alternative measure, the Baldwin rate of return, is used to assess the profitability attached to different educational paths. Third, the income risk associated with each educational path is calculated.

The purpose of this paper is to model the paths of risks associated with building information modeling (BIM) adoption in the Chinese architecture, engineering and construction industry.

A total of 16 risks were identified from the literature review and grouped into nine categories. The data were collected through a questionnaire survey with 95 professionals in China. The partial least square structural equation modeling was used to analyze the data.

The results suggested the risk categorization was confirmed, and that 15 hypothetical risk paths were statistically significant, which formed 13 chains of risk paths. “Inadequate relevant knowledge and expertise” was the primary root risk category of all the 13 chains of risk paths. Additionally, “technological issues,” “poor information sharing and collaboration,” and “liability for data input” had direct effect on the “cost overrun with BIM,” while all the other risks indirectly influence cost via these three risk categories.

Most of the respondents were designers because few clients and contractors have adopted BIM. In addition, the impact and likelihood of risks were accessed by respondents’ judgment based on their experience, which is a common problem of risk management research. As this study focuses on the Chinese architecture, engineering and construction industry, there would be geographical limitation on the findings.

This study provides practitioners with a clear understanding of the risks associated with BIM adoption and enables practitioners to take measures to mitigate the root risks and assure the potential benefits of BIM.

Although there have been studies on the risks associated with BIM adoption, most of them lacked empirical evidence and failed to examine the interactions between risks. This study is different from these prior studies, because it focuses on the interrelationships between risks and identifies the risk paths and root risks using the empirical data. Therefore, this study expands the literature relating to both BIM and risk management. Also, this study enables practitioners to take measures to mitigate the root risks and assure the potential benefits produced by BIM, thereby contributing to the practice.

The aim of this study is o examine the advantages and disadvantages of different existing scoring systems in the cybersecurity domain and their applicability to the AEC industry and to systematically apply a scoring system to determine scores for some of the most significant construction participants.

This study proposes a methodology that uses the Common Vulnerability Scoring System (CVSS) to calculate scores and the likelihood of occurrence based on communication frequencies to ultimately determine risk categories for different paths in a construction network. As a proof of concept, the proposed methodology is implemented in a construction network from a real project found in the literature.

Results show that the proposed methodology could provide valuable information to assist project participants to assess the overall cybersecurity vulnerability of construction and assist during the vulnerability-management processes. For example, a project owner can use this information to get a better understanding of what to do to limit its vulnerability, which will lead to the overall improvement of the security of the construction network.

It has to be noted that the scoring systems, the scores and categories adopted in the study need not necessarily be an exact representation of all the construction participants or networks. Therefore, caution should be exercised to avoid generalizing the results of this study.

The proposed methodology can provide valuable information and assist project participants to assess the overall cyber-vulnerability of construction projects and support the vulnerability-management processes. For example, a project owner can use this approach to get a better understanding of what to do to limit its cyber-vulnerability exposure, which will ultimately lead to the overall improvement of the construction network's security. This study will also help raise more awareness about the cybersecurity implications of the digitalization and automation of the AEC industry among practitioners and construction researchers.

Given the amount of digitized services and tools used in the AEC industry, cybersecurity is increasingly becoming critical for society in general. In some cases, (e.g. critical infrastructure) incidents could have significant economic and societal or public safety implications. Therefore, proper consideration and action from the AEC research community and industry are needed.

To the authors' knowledge, this is the first attempt to measure and assess the cybersecurity of individual participants and the construction network as a whole by using the Common Vulnerability Scoring System.

The existence of hidden and dangerous points after the fire leads to loss of command of the rescue teams, casualties, and financial loss. This paper aims to identify these points by taking into account their contributing factors that can help to better manage, prevent and prepare for the risks even after their occurrence.

In this study, the fire overhaul was investigated by identifying the risks of this stage and ranking them in terms of impact. This paper introduces danger points using empirical knowledge (questionnaire) along with theoretical knowledge (literature review) and suggested solutions. Finally, by investigating the dangers and their effects on each other, it introduced three main danger paths. The main problem is the lack of applied knowledge, the relationship between theoretical knowledge at this stage of fire and its application in firefighting operations. To solve this problem, a model was developed to determine the relationships between the effects on firefighters’ health during fire overhaul using structural equation modeling (SEM) in the building. To develop this model, dangerous points were first identified, categorized and weighted, and then the effect of each of these points and their interactions was determined.

This paper contributes to the literature by identifying hazardous points and investigating their impacts on firefighters’ health using the SEM method. This study identifies 17 points that can be traversed through three paths.

Hazardous points could affect the success of the fire overhaul operation. Adopting a flexible model during this stage can enhance safety.

The results of this study can be used to rank and classify fire overhaul hazards in buildings and to increase firefighters’ safety and other related factors.

Previous research has little specific guidance on how to improve large infrastructures’ risk analysis. This paper aims to propose a practical risk analysis framework across the project lifecycle with Bayesian Networks (BNs).

The framework includes three phases. In the qualitative phase, primary risks were identified by literature reviews and interviews; questionnaires were used to determine key risks at each project stage and causal relationships between stage-related risks. In the quantitation, brainstorming and questionnaires, and techniques of ranked nodes/paths, risk map and Bayesian truth serum were adopted. Then, a BN-based risk assessment model was developed, and risk analysis was conducted with AgenaRisk software.

Twenty key risks across the lifecycle were determined: some risks were recurring and different risks emerged at various stages with the construction and feasibility most risky. Results showed that previous stages’ risks significantly amplified subsequent stages’ risks. Based on the causality of stage-related risks, a qualitative model was easily constructed. Ranked nodes/paths facilitated the quantification by requiring less statistical knowledge and fewer parameters than traditional BNs. As articulated by a case, this model yielded very simple and easy-to-understand representations of risks and risk propagation pathways.

Rare research has developed a BN risk assessment model from the perspective of project stages. A structured model, a propagation network among individual risks, stage-related risks, and the final adverse consequence, has been designed. This research provides practitioners with a realistic risk assessment approach and further understanding of dynamic and stage-related risks throughout large infrastructures’ lifecycle. The framework can be modified and used in other real-world risk analysis where risks are complex and develop in stages.

A number of previous studies have investigated international construction project risks and have proposed risk mitigation measures without examining their interdependence. The purpose of the current study is to identify the influence of various risk mitigation measures on macro-level risk factors in the international marketplace.

The authors initially identified 26 risk variables and nine risk mitigation measures through a literature review, which were then verified for their pertinence to international projects by three experts. Subsequently, 105 questionnaire survey responses were collected and analysed using factor analysis and structural equation modelling to test the interrelations between the risk variables and mitigation measures.

The findings suggest that joint ventures with local partners is emerged as the most critical risk mitigation measure that influences the international projects, which are exposed to political, project and firm-specific risk factors. Further, it is worth noting that among the recognised risk mitigation measures in international projects, offering more local employment is the least critical mitigation measure in the international projects.

The findings of this study are based on the macro-risk factors encountered by Indian construction firms in international projects, mostly from specific Asian and African regions. Thus, the opinions of construction firms from the developed countries might be different.

The main contribution of this study to existing knowledge is empirical evidence of the interrelationships between risk mitigation measures and risk factors that are portrayed as latent variables of different manifest risk variables. The generated model can assist construction firms in emphasising several risk mitigation methods, in order to reduce risk and enhance performance in international construction projects.

This paper aimed to explain why individuals still tend to disclose their privacy information even when privacy risks are high and whether individuals disclose or withhold information following the same logic.

This study develops a configurational decision tree model (CDTM) for precisely understanding individuals' decision-making process of privacy disclosure. A survey of location-based social network service (LBSNS) users was conducted to collect data, and fuzzy-set qualitative comparative analysis (fsQCA) was adopted to validate the hypotheses.

This paper identified two configurations for high and low disclosure, respectively, and found that the benefits and the risks did not function independently but interdependently, and the justice would play a crucial role when both the benefits and the risks were high. Furthermore, the authors found that there were asymmetric mechanisms for high disclosure and low disclosure, and males focused more on perceived usefulness, while females concerned more about perceived enjoyment, privacy risks and perceived justice.

This paper further extends privacy calculus model (PCM) and deepens the understanding of the privacy calculus process from a configurational perspective. In addition, this study also provides guidance for future research on how to adopt the configurational approach with qualitative comparative analysis (QCA) to revise and improve relevant theories for information systems (IS) behavioral research.

The purpose of this paper is to propose a novel risk assessment approach that considers the inter‐relationship between supply chain risks and the structure of network at the same time. To reduce the impact of the supply chain risk and enhance the flexibility of transportation route finding during the product delivery, the authors propose a way to model the risk propagation and how to integrate it with the supply chain network using Bayesian Belief Network (BBN). The key risk indicators (KRI) of each vertex and edge of the supply chain network which are measured or computed by the proposed approach can be utilized to develop the optimal transportation route in the execution phase.

BBN is utilized to illustrate the relations among supply chain risks which may take place in a certain vertex. To apply the BBN to the supply chain network, the authors develop the framework to integrate BBN and the supply chain network by using the general functions that describe the characteristics of the risk factors and inter‐relationships between vertices.

By using the proposed risk assessment and dynamic route‐finding approach, it is possible to reduce the unexpected cost from the supply chain risk and overcome the limitations of previous risk management strategies which focus on developing counter plans and assume the independency of supply chain risks.

The proposed approach describes how to develop KRI‐BBN to model the risk propagation and to integrate the KRI‐BBN and supply chain network. The KRIs directly measured or computed by KRI‐BBN in real time can be utilized to alternate supply chain execution plans such as inventory management, demand management and product flow management. Transportation problem considering risk is developed to show how to apply the proposed approach and numerical experiments are conducted to prove the cost effectiveness.

The contribution of this paper lies in the way of developing KRI‐BBN to assess the supply chain risk and modelling of the risk propagation by integrating KRI‐BBN with supply chain network. With the proposed risk assessment approach, it is able to alternate the transportation route to minimize the unexpected cost and transportation cost simultaneously.

Supply chain risks (SCRs) have uncertainty and interdependency characteristics that must be incorporated into the risk assessment stage of the SCR management framework. This study aims to develop SCR networks and determine the major risk drivers that impact the performance of the sago starch agro-industry (SSA).

The risk and performance variables were collected from the relevant literature and expert consultations. The Bayesian network (BN) approach was used to model the uncertain and interdependent SCRs. A hybrid method was used to develop the BN structure through the expert’s knowledge acquisitions and the learning algorithm application. Sensitivity analyses were performed to examine the significant risk driver and their related paths.

The analyses of model indicated several significant risk drivers that could affect the performance of the SSA. These SCR including both operational and disruption risks across sourcing, processing and delivery stage.

The implementation of the methodology was only applied to the Indonesian small-medium size sago starch agro-industry. The generalization of findings is limited to industry characteristics. The modelled system is restricted to inbound, processing and outbound logistics with the risk perspective from the industry point of view.

The results of this study assist the related actors of the sago starch agro-industry in recognizing the major risk drivers and their related paths in impacting the performance measures.

This study proposes the use of a hybrid method in developing SCR networks. This study found the significant risk drivers that impact the performance of the sago starch agro-industry.

Non-financial corporate debt is one of the important sources of systematic risk in the real economy. Assessing a measure of systematic risk in corporation debt is currently a key challenge. In this regard, we propose a two-tier risk contagion networks model.

Assessing a measure of systematic risk in corporation debt is currently a key challenge. In this regard, we propose a two-tier risk contagion networks model based on four dimensions: concept definition, data structure, risk contagion network construction, and risk measurement indicators construction. We take the Jiangsu bond issuer guarantee network as a sample area.

Taking the Jiangsu bond issuer guarantee network as a sample area, we find that there is a strong correlation between the debts of non-financial corporation in China, and it is easy to become a potential regional systematic risk source. In addition, our empirical research also reveals that external risk exposure and node degree of network are two key indicators when identifying key risk-contagion enterprises.

The main contributions of this study are two-fold. First, this article proposes a two-tier risk contagion networks model to measure systematic risk in non-financial corporation. Second, this article describes the structure of the corporate risk contagion network.