Analysis of factors affecting project communications with a hybrid DEMATEL-ISM approach (A case study in Iran)

Municipal solid waste management: Identification and analysis of technology selection criteria using Fuzzy Delphi and Fuzzy DEMATEL technique

Municipal solid waste management (MSWM) poses a considerable challenge to developing countries like Bangladesh because of the rising waste generation rates and lack of effective management practices such as illegal open dumping and informal waste collection. One of the crucial factors in the successful management of MSW is to select the appropriate technology which is a complex multi-criteria and laborious process. Despite the global emphasis on the importance of MSWM in the literature, there is a lack of studies conducted in developing countries that effectively identify and analyze the critical performance criteria for appropriate technology selection. This research aims to address this shortcoming by identifying, and prioritizing the selection criteria and finally investigating the inter-relationship between them and the degree to which they affect or are affected by one another. First, a thorough literature review and expert consultation were employed to determine a set of 21 key criteria using the Fuzzy Delphi method (FDM). Later, taking into account the imprecise and subjective nature of the DEMATEL method on human judgements, the Fuzzy DEMATEL technique was employed to investigate the cause-effect relationships among the identified criteria. The findings of the study demonstrated that 14 criteria were categorized as causal elements that have the most significant influence on the MSWM technology selection process and 7 criteria were categorized as effect. The selection of MSWM technology demands greater consideration of the top three ranked criteria, namely T4- Access to Technology (AT), T8- Feasibility (F), and the Ec6-Infrastructure requirements (IR). By identifying the pertinent criteria, structures and interrelationships, the outcome of the study can facilitate a better understanding of causal relationships among the criteria that require specific consideration from the decision-makers and allow them to select appropriate MSW management technology.

Comprehensive factor analysis and risk quantification study of fall from height accidents

Working at heights poses frequent and significant risks, demanding scientific approaches for investigating fall-from-height (FFH) incidents and proposing preventive measures to enhance building safety. Nevertheless, ongoing research on analyzing the causal factors behind fall-from-height accidents lacks a comprehensive qualitative and quantitative assessment of the interplay between these factors. To bridge this gap, this study introduces an integrated risk analysis model. Utilizing incident reports and leveraging the multi-case rootedness theory, the model initially identifies influential elements. Subsequently, employing the Grey Decision Making Laboratory (Grey-DEMATEL) and Interpretive Structural Modeling (ISM) techniques, a hierarchical network is constructed, followed by the transformation of this hierarchical network model into a Bayesian Network (BN) model using GeNie2.0 software. Ultimately, the study was based on data from 420 accident cases and analyzed the causes and diagnosis of the accidents. The findings indicate that A5 (Low-security awareness) is the most significant factor contributing to falls from great heights and that the connection between the components is dynamic and non-linear rather than simply independent and linear. Furthermore, the study established a likelihood of occurrence of such incidents of up to 57 % and ranked the probability of occurrence of each contributing component in the case of a fall from height. This study presents a scientifically valid method for analyzing fall-from-height accidents. Experimental results confirm the model's applicability, empowering contractors to improve safety management by accessing precise risk information and prioritizing preventive measures against interrelated accidents. The model facilitates informed decision-making for contractors to effectively mitigate fall-from-height risks and establish a safer working environment.

A hybrid STAMP-fuzzy DEMATEL-ISM approach for analyzing the factors influencing building collapse accidents in China

To explore the factors influencing recent construction collapse accidents, this study utilizes a sample of 355 reports on building collapse accidents from 2012 to 2022. The investigation employs the systems-theoretic accident modeling and processes (STAMP) model to retrieve 22 key causal factors of accidents from the physical, operational, managerial, and supervisory layers. Subsequently, an improved decision-making trial and evaluation laboratory (DEMATEL)-interpretive structural modeling (ISM) method is used to analyze the relationships and strengths of these influencing factors, providing a comprehensive understanding of the logical connections between the causes of building collapse accidents. The results indicate that the deep-rooted causes of building collapse accidents are primarily lax safety management at the enterprise level and the exchange of interests at the government regulatory level, which in turn affect workers at the operational level and the physical aspects of accidents on-site. Furthermore, integrating the STAMP model and the triangular fuzzy DEMATEL-ISM model overcomes the limitations of the traditional STAMP model, allowing for a more focused identification of key factors.

Exploring the restrictive factors for the development of the construction waste recycling industry in a second-tier Chinese city: a case study from Jinan

Recycling construction waste in urban cities has attracted wide attention in the government department and building industry worldwide owing to numerous benefits in economics, the environment, and society for sustainable development. Most of the research was focused on the scope of first-tier/new first-tier cities. At the same time, less attention has been paid to second-tier cities, which should be further considered because of their significant potential for increasing construction waste. Jinan is one of the pilot cities for construction waste treatment in China. This city has been chosen as the case in this study to explore critical restrictive factors for developing the construction waste recycling industry (CWRI) in second-tier cities with the adoption of combined methods of the DEMATEL-ISM-EWM. It was revealed by the results that (1) the government is the main stakeholder. In addition, the legislation, management system, incentive/support for stakeholders, and technical standards were closely associated with CWRI development in Jinan. Low landfill fees and natural ore tax rates are unique and vital restrictive factors that should receive more attention. (2) Other key restrictive factors that should be given priority were (i) the lack of green design for designers, (ii) poor classification and management of construction waste for constructors on site, (iii) tight operating funds and insufficient publicity for recycling enterprises, and (iv) insufficient research funds for research institutions. Finally, a comparison of the similarities and differences of the restrictive factors on CWRI development between second-tier and first-tier/new first-tier cities was conducted, and a series of reasonable suggestions were provided, considering the perspective of stakeholders. The research will offer a valuable reference for industry practitioners and academics interested in CWRI.

Quantitative risk assessment of college campus considering risk interactions

Because of the more emerging risks and stronger risk interactions, the risk of college campuses as well as students and staff received more and more attention. Current works on campus risk mostly focus on single-category factors, and few of them considered risk interactions. Therefore, an integrated model for assessing comprehensive risks on the campus is proposed to put forward risk reduction strategies. First, a comprehensive risk identification of the college campus is conducted by integrating the modified egg model and the fault tree. Then, DEMATEL (Decision-Making Trial and Evaluation Laboratory) is applied to quantify the complex risk interactions and determine the influential causes for further modelling. Finally, the Bayesian network is established for cause diagnosis, consequence prediction, and risk reduction. The identified most sensitive cause is alcohol use. In the case of the four sensitive causes simultaneously occurring, the probability of high campus risk will increase from 21.9% of the original to 39.4%. Moreover, an efficiency analysis of different risk reduction strategies is performed to determine the most efficient risk reduction strategy. The results indicate that the proposed methodology may of great significance for the risk reduction of the college campus in the changing age.

How digitalization in banking improve service supply chain resilience of e-commerce sector? a technological adoption model approach

Supply chain resilience (SCR) is crucial for organizational sustainability and growth. This paper explores the factors of digitalization in the banking sector for achieving resilience in the e-commerce service supply chain. Most previous researchers have explored supply chain resilience in e-commerce using structural equation modeling. This study identifies and evaluates the crucial factors of digital banking that improve resilience in the e-commerce service supply chain using the Interpretive Structural Modeling (ISM) and Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique. A conceptual framework is created based on the literature review and technology adoption theory. The framework comprises the critical digital technology adoption factors for the resilient service supply chain in Pakistan’s e-commerce sector. The results show that the most critical driving factors are “performance expectancy (F15),” “e-cost effectiveness (F5),” and “Trust (F8)” The research findings have several implications for decision-makers, and practitioners, providing insights into how digitalization in the banking sector can help in designing resilient supply chains in the e-commerce sector.

DELAY MITIGATION STRATEGIES AND THE IMPLICATION ON THE CONSTRUCTION INDUSTRY: A SYSTEMATIC LITERATURE REVIEW

Every project’s goal has always been to be successful. Failure to complete projects on time has become a major problem in the last few decades. Construction delays are a common and serious problem that needs to be investigated further. This issue has implications for the construction industry and the economy’s growth, as well as the long-term development of countries. Many studies have not highlighted the strategies implemented by organisations to lessen the impact of project delays. As such, a systematic literature analysis (SLR) was performed in this study by selecting publications of articles from indexed journals on the Web of Science and Scopus to identify the efficacy of strategies utilised when mitigating problems that postpone the overall construction. The method applied for this SLR is using the Reporting Standards for Systematic Evidence Syntheses (ROSES) analysis protocol. Through this protocol, several steps need to be followed accordingly to acquire an accurate number of articles that have to be reviewed. The findings identified five leading aspects of strategies that should be adopted to reduce construction project delays, namely: communication; management; information systems and technology; enforcement of law and order; and financials. The implications of these strategies are also discussed in this research.

Sustainability assessment of waste cooking oil-based biodiesel plant in developing economy based on F-DEMATEL and F-ISM approaches

Developing clean and renewable energy resources for long-term usage is a crucial concern in developing countries. In this regard, biodiesel technology is drawing a lot of public interest throughout the world. The issues associated with the sustainability of a biodiesel plant are intimately connected with restricted natural resources, political influences, energy independence, economic prosperity and global stability. Therefore, this paper aspires to identify the most influential factors that affect the sustainability of a biodiesel plant and describe interactions among them. The study focuses on identifying 36 factors based on an extensive literature review, questionnaire, experts' opinion and website in the context of developing economies. These factors include social, environmental and economic aspects. A framework based on fuzzy-interpretive structural modelling (F-ISM) and fuzzy-decision-making trial and evaluation laboratory (F-DEMATEL) is applied to investigate the identified factors. The F-ISM approach allows a better understanding of the contextual interactions between the factors and classify them based on their driving and dependent powers. The fuzzy-DEMATEL technique facilitates in identifying the influential and influenced factors and categorizes them into cause-and-effect groups. The findings of the study suggest that 'Legal and regulatory compliances', 'Political constraints', 'International relations', 'Health and education' and 'Public safety and security' are the five most influential factors that are needed to be addressed for the sustainability of biodiesel plant. The paper also presents a few strategies, which can be applied as a guiding step by the decision-makers to formulate policies for the effective implementation of sustainable growth of the biodiesel plant.

Artificial intelligence healthcare service resources adoption by medical institutions based on TOE framework

Objectives

This study used the Technology-Organization-Environment (TOE) framework to identify the factors involved in the decisions made by integrated medical and healthcare organizations to adopt artificial intelligence (AI) elderly care service resources.

Method

This study identified the Decision-making Trial and Evaluation Laboratory-Interpretive Structural Modeling (DEMATEL-ISM) method was used to construct a multilayer recursive structural model and to analyze the interrelationships between the levels. A MICMAC quadrant diagram was used for a cluster analysis.

Results

The ISM recursive structural model was divided into a total of seven layers. The bottom layer contained the four factors of High risk of data leakage (T1), Lack of awareness of the value and benefits of AI healthcare technology (T5), Lack of management leadership support (O1), and Government policies (E1). Having a low dependency but high driving force, these factors are the root causes of adoption by healthcare organizations. The topmost layer contained the most direct factors, which had a high dependency but the low driving force, influencing adoption: Competitive pressures (E2), Lack of patient trust (E5), and Lack of excellent partnerships (E7). Healthcare organizations are more concerned with technology and their environments when deciding to adopt intelligent healthcare resources.

Conclusion

The combination of the three methods of DEMATEL-ISM-MICMAC construction models provides new ideas for smart healthcare services for hospitals. The DEMATEL method favors the construction dimension of the micro-model, while the ISM method favors the construction dimension of the macro-model. Combining these two methods may reduce the loss of information within the system, simplify the matrix calculation workload, and improve the efficiency of operations while decomposing the complex problems into several sub-problems in a more comprehensive and detailed way. Conducting cluster analysis of the adoption determinants utilizing MICMAC quadrant diagrams may provide strong methodological guidance and decision-making recommendations for government departments, senior decision-makers in healthcare organizations, and policy-makers in associations in the senior care industry.

Analysis of Factors Influencing Miners' Unsafe Behaviors in Intelligent Mines using a Novel Hybrid MCDM Model

Coal mine accidents seriously affect people’s safety and social development, and intelligent mines have improved the production safety environment. However, safety management and miners’ work in intelligent mines face new changes and higher requirements, and the safety situation remains challenging. Therefore, exploring the key influencing factors of miners’ unsafe behaviors in intelligent mines is important. Our work focuses on (1) investigating the relationship and hierarchy of 20 factors, (2) using fuzzy theory to improve the decision-making trial and evaluation laboratory (DEMATEL) method and introducing the maximum mean de-entropy (MMDE) method to determine the unique threshold scientifically, and (3) developing a novel multi-criteria decision-making (MCDM) model to provide theoretical basis and methods for managers. The main conclusions are as follows: (1) the influence degree of government regulation, leadership attention, safety input level, safety system standardization, and dynamic supervision intensity exert the most significant influence on the others; (2) the causality of government regulation, which is the deep factor, is the highest, and self-efficacy displays the smallest causality, and it is the most sensitive compared to various other factors; (3) knowledge accumulation ability, man–machine compatibility, emergency management capability, and organizational safety culture has the highest centrality among the individual factors, device factors, management factors, and environmental factors, respectively. Thus, corresponding management measures are proposed to improve coal mine safety and miners’ occupational health.

Modeling the Constraints to the Utilization of the Internet of Things in Managing Supply Chains of Off-Site Construction: An Approach toward Sustainable Construction

Despite persistent calls for cleaner production and improved automation of construction processes, the adoption of the Internet of Things (IoT) in managing the supply chains of off-site construction businesses has been discouraged due to various constraints. This paper methodically identifies and prioritizes the crucial factors that impede the application of the Internet of Things (IoT) in off-site construction. Content analysis and an expert-based evaluation strategy were used to identify and evaluate the constraints affecting Internet of Things adoption in off-site construction. The ISM, MICMAC, and DEMATEL techniques were used to analyze the data. This study identifies the “lack of clear strategy for governing IoT utilization in supply chain management” as the most significant factor that impedes the application of the Internet of Things (IoT) in off-site construction businesses. The outcomes also provide a rich source of insights into off-site construction businesses to clearly recognize the implications of utilizing IoT technologies in managing the supply chains of businesses and what to expect when applying IoT technologies and solutions. While this paper advocates for improved green construction practices, cleaner production, and automation in the construction industry, it has set the stage for integrating IoT technologies in the supply chain management of off-site construction businesses.

Barriers to implementing digitalization in the Indian construction industry

Purpose

This research paper aims to analyze the critical barriers to implementing digitalization in the Indian construction industry as Indian construction companies are lagging in the implementation of digital technologies in the work environment.

Design/methodology/approach

In this research paper, a qualitative research approach is adopted, and multiple detailed interviews are conducted with industry and academic experts. Further, multi-criteria decision-making (MCDM) techniques are used to finalize the prioritization among various alternatives. The fuzzy-decision-making trial and evaluation laboratory (Fuzzy-DEMATEL) and interpretive structural modeling (ISM) techniques are employed to find the exact relationship among the identified alternatives.

Findings

This study identifies 14 critical barriers from an extensive literature review and multiple interviews with industry professionals, and further driving and critical barriers are identified.

Research limitations/implications

In this research paper, an exploratory study with a limited number of respondents from a large Indian construction company is carried out. Further, a detailed longitudinal analysis can be done to assess the subjectivity of the participants with more advanced statistical tools. However, this research discusses several points pertaining to the implementation of digitalization in the construction industry. The research further identifies the critical barriers to digitalization in the Indian construction industry.

Practical implications

The finding of the study has two-pronged implications. First, it provides a road-map to the construction industry by highlighting the engagement of top management as the key focus area for successful digitalization. Second, the finding also shows similarity of the digitalization process to the adoption of process improvement techniques like lean and total quality management (TQM), wherein the top management plays a crucial role in ushering in the implementation of a disruptive change.

Originality/value

The research is unique in two ways. First, this is one of the very few attempts to understand digitalization in the Indian context. Second, the research also demonstrates that the combination of fuzzy DEMATEL and ISM techniques can be successfully employed in the emerging field of construction digitalization research.

Using ANN to Predict the Impact of Communication Factors on the Rework Cost in Construction Projects

The construction sector has a large impact on the environment and available resources. Natural resources and energy consumption occurs not only during the operation of the facility, but also during its construction. In addition, this situation often occurs when work already completed requires rework. In such cases, not only the reuse of resources and energy occurs but also generation of waste. Many studies support the relationship between communication and project efficiency, which is expressed in the cost of rework. At present there is no available tool to quantify the evaluation of this relationship. This study aims to fill this knowledge gap. The article purpose was to create ANNs (artificial neural networks) for assessing and predicting the impact of communication factors on rework costs in construction projects. During the data collection phase, 12 factors that influence communication were identified and assessed. The level of rework costs in 18 construction projects was also calculated. We used ANN, which is a two-layer feedforward network with a sigmoid transfer function in the hidden layer and a linear transfer function in the output layer. The network input layer consists of 12 neurons while the hidden layer consists of 10 neurons and one output neuron. The optimal results of the mean square error and correlation were shown by the Levenberg–Marquardt algorithm. The proposed model can be used by project management as the integration decision support tool aimed at decreasing the number of reworks and reducing energy and resource consumption in construction projects.

Improved DEMATEL-ISM integration approach for complex systems

Purpose

Decision Making Trial and Evaluation Laboratory (DEMATEL) and Interpretive Structural Modeling (ISM) are commonly used separately, but also may be combined per their common characteristics to identify causal relationships and hierarchical structure among factors in complex systems with a relatively small computational burden. The purpose of this study is to establish an improved DEMATEL-ISM integration approach to remedy the disadvantages of the traditional DEMATEL-ISM integration method. A case study was conducted to compare the proposed improved integration approach against the traditional integration method, and to validate its feasibility and effectiveness.

Methods

The proposed improved DEMATEL-ISM integration approach has two main parts: a threshold determination via maximum mean de-entropy (MMDE) method and an additional transitivity check process. The factors influencing China’s rural-urban floating population’s willingness to participate in social insurance was analyzed as a case study.

Results

The traditional and improved methods show notable differences in the hierarchical factor structure and the inner influence relationship among factors that they respectively reveal. The traditional integration approach results in some irrationality, while the improved approach does not.

Originality

This study confirms the importance of proper threshold determination and reachability matrix transitivity checking during DEMATEL-ISM integration. The improved approach includes a scientific threshold determination method based on the MMDE method, plus a transitivity check of the reachability matrix with necessary corrections to ensure its soundness. It can be straightforwardly operated at a relatively low computational burden while providing accurate analysis results.

Modelling the inhibitors of cold supply chain using fuzzy interpretive structural modeling and fuzzy MICMAC analysis

The Cold Supply Chain (CSC) is an integral part of the supply chain of perishable products. The aim of this research is to examine the inhibitors that have a major impact on the performance of CSC operations in the United Arab Emirates (UAE). This study provides a synthesis and suggests a hierarchical model among CSC inhibitors and their respective relevance. The hierarchical synthesis of twelve (12) primary CSC inhibitors is achieved through a comprehensive literature review and consultation with academics and CSC professionals. This study used semi-structured interviews, a fuzzy interpretive structural modeling (FISM) and a Fuzzy-MICMAC (FMICMAC) analysis to explore and establish the relationship between and among identified inhibitors. FISM is used to examine the interaction between inhibitors, while FMICMAC analysis is used to examine the nature of inhibitors on the basis of their dependence and driving power. The results of the FISM and FMICMAC analysis show the inter-relationships and relative dominance of identified inhibitors. The results show that some inhibitors are of high strategic importance due to their high driving power and low dependence. These inhibitors seek more management attention in order to improve their effectiveness. The result of a hierarchical model helps to understand the influence of a particular inhibitor on others. 'Higher capital and operating costs' occupy the highest level in the FISM model. The 'fragmented cold supply chains', 'lack of skilled labor', 'inadequate information system infrastructure' and 'lack of commitment by top level management' had strong driving power but weak dependence, which characterizes them as independent inhibitors. Management should be extra careful when dealing with these inhibitors as they influence the effects of other variables at the top of the FISM hierarchy in the overall management of the cold supply chain. The study also suggests a number of recommendations for addressing these inhibitors in cold supply chains operating in the UAE. With due attention and care for these inhibitors, the operation of the cold supply chains is likely to be even more successful.

Integrated Evaluation of the Impediments to the Adoption of Coconut Palm Wood as a Sustainable Material for Building Construction

Coconut palm wood is commonly regarded as a sustainable building material. Nonetheless, its adoption as a green building material by the construction industry is limited, particularly in West Africa. This paper analyses the impediments to the effective adoption of coconut palm wood in residential building construction. The research data was collected from literature reviews, expert-based surveys, and semi-structured interviews with specialists from the construction sector, African Timber Organization, governmental institutions related to forestry and construction, university professors, and researchers in the areas of sustainable building materials and construction. Thirteen crucial impediments were identified and an integrated evaluation of the impediments was conducted using the Interpretive Structural Modelling (ISM) technique to examine the hierarchical structure of the relations between the impediments. A further technique, Cross-impact Matrix Multiplication Applied to Classification (MICMAC), was used to categorize the impediments from a driving to driven perspective. This categorization provides a unique profile for the impediments, which is different from that of conventional evaluation techniques for evaluating impediments. The findings of this paper offer useful guide to practitioners and policy makers in formulating effective policies, regulations, and standards that will promote the development and wide adoption of coconut palm wood in the construction industry.