Risk Factors of Building Apartments for University Talent  through the Agent Construction Mode in China: Interrelationship and Prioritization

Liu, Zhengrong; Jiang, Wendan; Wu, Yuzhe; Peng, Yi

doi:10.3390/su8040325

Cited by 4 publications

(4 citation statements)

References 19 publications

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“…It uses the basics of graph theory and employs interactive learning to exploit theoretical, conceptual, and computational leverage to illustrate a contextual relationship among variables. Thus, ISM identifies inter-relationships among variables to build a concrete model [23,24].…”

Section: Methodsmentioning

confidence: 99%

Relationship Modeling of Critical Success Factors for Enhancing Sustainability and Performance in E-Learning

et al. 2018

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E-learning, a technology-mediated learning approach, is a pervasively adopted teaching/learning mode for transferring knowledge. Some of the motivational factors for its wide adoption are time and location independence, user-friendliness, on-demand service, resource richness, and multi-media and technology driven factors. Achieving sustainability and performance in its delivery is of paramount importance. This research utilizes the critical success factors (CSFs) approach to identify the sustainable E-learning implementation model. Fifteen CSFs have been identified through the literature review, expert opinions, and in-depth interviews. These CSFs have been modeled for interdependence using interpretive structural modeling and Matriced’ Impacts Croise’s Multiplication Appliquée a UN Classement (MICMAC) analysis. Further, the model has been validated through in-depth interviews. The present research provides quantification of CSFs of E-learning in terms of their driving and dependence powers and their classification thorough MICMAC analysis. The E-learning system organizers may focus on improving upon the enablers such as organizational infrastructure readiness, efficient technology infrastructure, appropriate E-learning course design, course flexibility, understandable relevant content, stakeholders’ training, security, access control and privileges, commitment, and being user–friendly and well-organized, in order to enhance the sustainability and performance in E-learning. This study will also help E-learning stakeholders in relocating and prioritizing resources.

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Section: Methodsmentioning

confidence: 99%

Relationship Modeling of Critical Success Factors for Enhancing Sustainability and Performance in E-Learning

et al. 2018

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“…For instance, Jha and Devaya [35] modeled the risks faced by Indian construction companies assessing international projects. Liu et al [36] identified risk factors of building apartments for University talent through the agent construction mode in China. In this vein, Hachicha and Elmsalmi [37] proposed an integrated approach based-structural modeling for risk prioritization in the supply network management.…”

Section: The Supply Chain Risk Managementmentioning

confidence: 99%

Prioritization of the Best Sustainable Supply Chain Risk Management Practices Using a Structural Analysis Based-Approach

2021

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Companies attempt to improve the performance of their supply chain (SC) by distinguishing and presenting feasible sustainable development practices (SDP). Considering SDP without focusing on sustainability risks may disturb the company’s future. Very few studies in the extant literature have dealt with the impact of (SDP) on the supply chain risk management (SCRM). In fact, the aim of this paper is to classify and prioritize SDPs according to their priority for better risk management and effective SC performance. The proposed approach comprises two phases. First, 14 SDPs are identified and selected from the literature. Second, MICMAC (Matrice d’impacts croisés multiplication appliquée à un classement) method as a structural analysis method applies to identify and assess sustainable supply chain risk management (SSCRM) practices which reduce risk in the SC. The input data for each phase are based on Delphi technique, which is a process group used to collect the opinions of experts in the field. The aim of the proposed approach is to prioritize SSCRM practices and classify them into influential, non-influential, independent and dependent practices and their mutual relationships. The six key findings SSCRM practices from direct and indirect classification include the following elements: (1) Delayed differentiation, (2) Information sharing with upstream and/or downstream partners, (3) Simplification of product dismantling/anticipation of product end of life, (4) Supplier/subcontractor’s performance assessment, (5) establishing shared supply management and (6) establishment of contracts with transporters.

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“…Nevertheless, the Chinese construction industry is a major construction market globally. Furthermore, the Chinese construction market is different from other construction industries in many respects, including the participants' poor education and their uneven level of quality, as well as the imperfection of the laws and regulations [32][33][34][35]. Thus, it is both important and necessary to extract the behavioral risks among principal participants in Chinese construction.…”

Section: Behavioral Risk In the Construction Industrymentioning

confidence: 99%

Critical Behavioral Risk Factors among Principal Participants in the Chinese Construction Industry

Pan

Jia

2018

Sustainability

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The main participants in construction projects are the client, contractors, material suppliers, and consultants such as the project supervisor. They play the most important roles in implementing construction projects, and their behavior has a significant impact on the project’s performance. Because each participant has their own particular interests, by virtue of proprietary information advantage, each individual participant is driven to achieve maximum benefit, which can result in improper behavior with respect to each other. The risk of this resulting in moral hazard and adverse selection based on information asymmetry is called behavioral risk among principal construction participants. Behavior is affected by various risk factors; successful implementation of construction projects depends on effective management of the key risk factors. This paper identifies and ranks the critical behavioral risk factors from the perspective of principal construction participants in the Chinese construction industry. The data used for analysis is based on an interview and questionnaire survey. Factor analysis is conducted with the assistance of SPSS17.0. Forty-one potential behavioral risk factors are identified, with 30 of those being critical, including “client changes project objective or investment direction”, “designer uses technological capability advantage to obtain profit”, and others. These findings contribute to the understanding of risk management in the construction industry in China. They also serve as a useful reference for further studies on the subject.

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Risk Factors of Building Apartments for University Talent through the Agent Construction Mode in China: Interrelationship and Prioritization

Cited by 4 publications

References 19 publications

Relationship Modeling of Critical Success Factors for Enhancing Sustainability and Performance in E-Learning

Relationship Modeling of Critical Success Factors for Enhancing Sustainability and Performance in E-Learning

Prioritization of the Best Sustainable Supply Chain Risk Management Practices Using a Structural Analysis Based-Approach

Critical Behavioral Risk Factors among Principal Participants in the Chinese Construction Industry

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