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Green construction practices (GCPs) are essential for the construction industry to achieve carbon neutral and sustainable development. However, the promotion of GCPs faces multifaceted challenges, particularly within the context of recent global uncertainties. The COVID-19 pandemic has wrought substantial disruption upon the construction sector, which makes it a good candidate as a case study for enhancing future risk management strategies. Currently, there is limited research on the factors influencing GCPs in the global uncertainty context. To bridge this research gap, this study first identifies 26 factors affecting GCPs in the context of the COVID-19 pandemic through a comprehensive literature review. Subsequently, based on feedback from 22 experts, Interpretative Structural Modeling (ISM) and Matrice d’Impacts Croisés Multiplication Appliquée à un Classement (MICMAC) methodologies are adopted to illustrate the intricate relationships among influencing factors and further classify their relative importance. The results underscore the pivotal role of factors such as technology development, the difficulty of construction, materials, and equipment performance, as well as identify 13 factors that have a fundamental impact. This research provides insights for decision-makers to enhance risk management strategies for GCPs in the global uncertainty context, prioritize the determinants, and facilitate the optimal allocation of resources to advance GCPs.

This research paper presents a comprehensive study on optimizing the critical artificial intelligence (AI) factors influencing cost management in civil engineering projects using a multi-criteria decision-making (MCDM) approach. The problem addressed revolves around the need to effectively manage costs in civil engineering endeavors amidst the growing complexity of projects and the increasing integration of AI technologies. The methodology employed involves the utilization of three MCDM tools, specifically Delphi, interpretive structural modeling (ISM), and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC). A total of 17 AI factors, categorized into eight broad groups, were identified and analyzed. Through the application of different MCDM techniques, the relative importance and interrelationships among these factors were determined. The key findings reveal the critical role of certain AI factors, such as risk mitigation and cost components, in optimizing the cost management processes. Moreover, the hierarchical structure generated through ISM and the influential factors identified via MICMAC provide insights for prioritizing strategic interventions. The implications of this study extend to informing decision-makers in the civil engineering domain about effective strategies for leveraging AI in their cost management practices. By adopting a systematic MCDM approach, stakeholders can enhance project outcomes while optimizing resource allocation and mitigating financial risks.

Nowadays, the importance of logistics management has been increasingly realized in industry and society. However, current educational approaches in logistics management seem unable to effectively equip students with the necessary skills to cope with practical issues after graduation. Recently, contest-based education has attracted logistics management educators’ attention, but how it can be effectively utilized in this discipline is largely unclear. To fill this gap, this study followed a system approach and analyzed the factors influencing student performance in logistics management contests in China using interpretive structural modelling (ISM) and Matrice d’ Impacts Croisés-Multiplication Appliquée á un Classement (MICMAC). The results suggest that the driving forces for improving student performance in contests are the instructors’ encouragement and their previous experience in instructing contests. Also, the contestants’ previous experience in academic contests, team leadership, and effectiveness of communication between instructors and contestants are critical influencing factors. Based on the results, the educational strategies for effective utilization of contest-based education in logistics management are discussed. This study contributes to the existing literature by using a system modeling approach to clarify the mechanisms of contest-based education adoption in logistics management as well as informing university teachers and higher education institutes about strategies to improve their education quality.

Tourism is an upcoming industry with a high potential for future growth. Many factors influencing destination image (DI) are affecting tourism development. Therefore, studying factors influencing DI is essential. This research study aims to model the factors influencing DI in the tourism industry. A total of 15 factors were the focus of the study. Data on the factors were collected from 10 tourism industry experts. An integrated modeling approach was adopted using the Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Interpretive Structural Modelling (ISM). Interrelationships between the factors, causal effects, relative importance weights, ranks, and driving and dependence powers were analyzed to develop causal and interpretive structures. The developed model revealed that political stability is foundational to other aspects of DI. Then, factors related to safety and security, policy rules and regulations, tourism infrastructure, information and communication technologies, environment, economic development and affluence, fairs, exhibits, and festivals in a destination should be considered. These affect others related to health and hygiene, transport infrastructure, natural and cultural resources, human resources, quality of services, prices, hospitality, friendliness, and receptiveness aspects in a destination. The developed model provides a valuable framework for decision-makers in the tourism industry to enhance and shape the DI.

The Lean methodology allows for the streamlining of management and production systems to reduce costs. In the case of the construction sector, the goal is to optimize processes, reduce waste, increase product quality, and increase client satisfaction. These have been areas where construction, as a sector, is struggling to deliver substantial results. Despite the potential benefits associated with Lean approaches, there has been significant resistance from the construction industry. The main objective of this research is to identify the barriers to applying Lean methodology in the Portuguese construction sector. This will contribute to understanding why Lean is not gaining traction among construction stakeholders and help to identify improvement areas replicable in similar markets. Initially, the barriers were identified based on a comprehensive literature review, and those barriers were evaluated based on their importance using a survey responded to by construction sector professionals. Based on the survey’s results, 15 barriers were considered critical. Additionally, a combined ISM model and MICMAC analysis was developed to study the relations between these barriers and the driving and dependence power of each one of the critical barriers. The results obtained show that the main barriers are the lack of support and commitment from top management, a lack of organizational communication, a lack of communication and transparency between stakeholders, unsuitable organizational structures, a lack of adequate Lean awareness and understanding, management resistance to change, and employees’ aversion to change and fear of new procedures.

This study aims to identify the critical parameters for implementing a sustainable artificial intelligence (AI) cloud system in the information technology industry (IT). To achieve this, an AHP-ISM-MICMAC integrated hybrid multi-criteria decision-making (MCDM) model was developed and implemented. The analytic hierarchy process (AHP) was used to determine the importance of each parameter, while interpretive structural modeling (ISM) was used to establish the interrelationships between the parameters. The cross-impact matrix multiplication applied to classification (MICMAC) analysis was employed to identify the driving and dependent parameters. A total of fifteen important parameters categorized into five major groups have been considered for this analysis from previously published works. The results showed that technological, budget, and environmental issues were the most critical parameters in implementing a sustainable AI cloud system. More specifically, the digitalization of innovative technologies is found to be the most crucial among the group from all aspects, having the highest priority degree and strong driving power. ISM reveals that all the factors are interconnected with each other and act as linkage barriers. This study provides valuable insights for IT industries looking to adopt sustainable AI cloud systems and emphasizes the need to consider environmental and economic factors in decision-making processes.

For many years, entrepreneurs were considered the change agents of their societies. They use their initiative and innovative minds to solve problems and create value. In the aftermath of the digital transformation era, a new group of entrepreneurs have emerged who are called transformational entrepreneurs. They use various digital platforms to create value. Surprisingly, despite their importance, they have not been sufficiently investigated. Therefore, this research scrutinizes the elements affecting transformational entrepreneurship in digital platforms. To do so, the authors have considered a two-phase method. First, interpretive structural modeling (ISM) and Matrices d’Impacts Croises Multiplication Appliqué a Un Classement (MICMAC) are used to suggest a model. ISM is a qualitative method to reach a visualized hierarchical structure. Then, four unsupervised machine learning algorithms are used to ensure the accuracy of the proposed model. The findings reveal that transformational leadership could mediate the relationship between the entrepreneurial mindset and thinking and digital transformation, interdisciplinary approaches, value creation logic, and technology diffusion. The GMM in the full type, however, has the best accuracy among the various covariance types, with an accuracy of 0.895. From the practical point of view, this paper provides important insights for practitioners, entrepreneurs, and public actors to help them develop transformational entrepreneurship skills. The results could also serve as a guideline for companies regarding how to manage the consequences of a crisis such as a pandemic. The findings also provide significant insight for higher education policymakers.

The supply chain performance measurement system (SCPMS) is considered an integral and important part of supply chain management (SCM) for an effective and efficient supply chain (SC). The healthier and more flexible SCPMS is based on the enablers from which the SC metrics are taken. The identification of such enablers must be aligned with the strategic objectives of the organization and mapped to the SC measurement objectives. Hence, this study identifies a set of enablers and ranks them. Interpretive structural modeling (ISM)-based methodology is used in the current study to model the SCPMS implementation enablers. MICMAC analysis is further employed to categorize and comprehend the importance of each SCPMS enabler. ISM offers relationship modeling of SCPMS enablers, whereas MICMAC helps with classifying them into four categories. The Delphi method is then used to validate the ISM model. The SCPMS variable is also used in prioritization using stepwise weight assessment ratio analysis (SWARA). Practicing managers may benefit when developing and installing the SCPMS to satisfy the requirements of supply chain 4.0 for Industry 4.0 (I4.0). They will also be able to recognize and allocate resources while implementing SCPMS. The prioritization using SWARA provides an important ranking according to its importance. The SCPMS enablers of ‘top management support’, ‘SC 4.0 performance measurement awareness’, and ‘managerial readiness’ are found to be significant.

In recent years, the performance of free-and-open-source software (FOSS) for image processing has significantly increased. This trend, as well as technological advancements in the unmanned aerial vehicle (UAV) industry, have opened blue skies for both researchers and surveyors. In this study, we aimed to assess the quality of the sparse point cloud obtained with a consumer UAV and a FOSS. To achieve this goal, we also process the same image dataset with a commercial software package using its results as a term of comparison. Various analyses were conducted, such as the image residuals analysis, the statistical analysis of GCPs and CPs errors, the relative accuracy assessment, and the Cloud-to-Cloud distance comparison. A support survey was conducted to measure 16 markers identified on the object. In particular, 12 of these were used as ground control points to scale the 3D model, while the remaining 4 were used as check points to assess the quality of the scaling procedure by examining the residuals. Results indicate that the sparse clouds obtained are comparable. MicMac® has mean image residuals equal to 0.770 pixels while for Metashape® is 0.735 pixels. In addition, the 3D errors on control points are similar: the mean 3D error for MicMac® is equal to 0.037 m with a standard deviation of 0.017 m, whereas for Metashape®, it is 0.031 m with a standard deviation equal to 0.015 m. The present work represents a preliminary study: a comparison between software packages is something hard to achieve, given the secrecy of the commercial software and the theoretical differences between the approaches. This case study analyzes an object with extremely complex geometry; it is placed in an urban canyon where the GNSS support can not be exploited. In addition, the scenario changes continuously due to the vehicular traffic.

Past research reveals that many lean implementation barriers hinder lean implementation in small and medium-sized enterprises (SMEs). Among many sectors, the manufacturing sector suffers more as it generates more waste while carrying out manufacturing processes. Many manufacturing units make unsuccessful attempts to implement lean principles in their manufacturing systems. Hence, such units must eliminate the prevailing lean barriers to accomplish successful lean implementation. Moreover, the contextual relationship of lean barriers must be studied to understand the effect of such barriers. This paper uses interpretive structural modeling (ISM) to explore lean barriers, their relationships, and their influence on other lean barriers. The present research also reveals the most significant classification of lean barriers into various categories of independent, dependent, autonomous, and linkage using the (MICMAC) Matrice d’Impacts Croisés Multiplication Appliquée á un Classement analysis. ISM and MICMAC together provide relationship modeling and reveal the interrelationship between each lean implementation barrier and its categories, respectively. The ISM model is validated using the Delphi technique. The interpretative ranking process (IRP) is used to rank the barriers. The three significant lean implementation barriers revealed through the IRP include “lack of lean understanding”, “lack of strong quality policy”, and “risk of sustainable practice implementation”. The present research will help practicing managers of SMEs in the manufacturing sector to understand the mutual influence of lean barriers before introducing lean implementation. It is suggested that SMEs work on independent barriers so that dependent barriers can also be overcome with the least amount of resources and effort.

As the core link of Engineering Procurement Construction (EPC) projects, procurement involves a huge amount of money and has a wide range of influence. Once the procurement risks of EPC projects occur, it is easy to cause a domino effect, which can have a huge impact on the cost, schedule and quality of the construction project. Therefore, this paper took international EPC projects as the research object, identifying 25 risk factors related to procurement through case analysis, literature analysis and expert interviews. Integrated Interpretative Structural Modelling (ISM) and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) method was used to establish the interrelationships between risk factors. Based on the results, procurement risk transmission paths for international EPC projects are developed, and key risk factors are found. Moreover, corresponding risk management suggestions are put forward. The research results impel contractors of the international EPC projects to pay close attention to the key risk factors in the project procurement process. After recognizing the risk transfer path, contractors can take measures to avoid risk to reduce the possibility of project failure as soon as possible. This research can provide support for managing procurement risk in the international EPC project.

The paper describes application of the cross-impact method in the process of selecting locations and technologies used in a geothermal system based on energy accumulated in a dry rock formation, where CO2 is used as the working medium. The survey is based on the opinion of a group of 20 experts representing different fields of earth and technical sciences. They represent Norway and Poland, where the location of such a system is considered. Based on experts’ experience and opinions, all factors that seem to be significant were classified into the following groups: targets, key factors, results, determiners, motor and brakes, regulating factors, external factors, auxiliary factors, and autonomous factors. Direct influences between variables were indicated. Due to major differences in geological conditions in Poland and Norway, the factor of on- or offshore technology was pointed out as the primary determiner. Among key factors, the system operation’s long-term safety and level of technological readiness were indicated. As a target factor, an interest of local authority was pointed out. Among the variables that are important when selecting locations for this type of system, nine are essential: (1) Formal constraints related to local nature protection areas—this variable is essential in the case of an onshore system; (2) Availability of CO2 sources; (3) Level of geological recognition; (4) The distance of the CO2-EGS from a thermal energy user and electricity grid; (5) Existing wells and other infrastructure; (6) Depth of the EGS system; (7) Water depth if offshore, this variable is only important when offshore systems are involved; (8) Physical parameters of reservoir rocks; (9) Reservoir temperature.

Currently, the use of free and open-source software is increasing. The flexibility, availability, and maturity of this software could be a key driver to develop useful and interesting solutions. In general, open-source solutions solve specific tasks that can replace commercial solutions, which are often very expensive. This is even more noticeable in areas requiring analysis and manipulation/visualization of a large volume of data. Considering that there is a major gap in the development of web applications for photogrammetric processing, based on open-source technologies that offer quality results, the application presented in this article is intended to explore this niche. Thus, in this article a solution for photogrammetric processing is presented, based on the integration of MicMac, GeoServer, Leaflet, and Potree software. The implemented architecture, focusing on open-source software for data processing and for graphical manipulation, visualization, measuring, and analysis, is presented in detail. To assess the results produced by the proposed web application, a case study is presented, using imagery acquired from an unmanned aerial vehicle in two different areas.

Imaging techniques and Image Based-Modeling (IBM) practices in the field of Cultural Heritage (CH) studies are nowadays no longer used as one-shot applications but as various and complex scenarios involving multiple modalities; sensors, scales, spectral bands and temporalities utilized by various experts. Current use of Structure from Motion and photogrammetric methods necessitates some improvements in iterative registration to ease the growing complexity in the management of the scientific imaging applied on heritage assets. In this context, the co-registration of photo-documentation among other imaging resources is a key step in order to move towards data fusion and collaborative semantic enrichment scenarios. This paper presents the recent development of a Totally Automated Co-registration and Orientation library (TACO) based on the interoperability of open-source solutions to conduct photogrammetric-based registration. The proposed methodology addresses and solves some gaps in term of robustness and versatility in the field of incremental and global orientation of image-sets dedicated to CH practices.

The driving force of super-gentrification shapes a complex system in which multiple dynamic factors interact with each other. This paper takes the dynamic factor system of super-gentrification as the research object and uses the Interpretative Structure Modeling (ISM) to analyze these dynamic factors. The levels of these dynamic factors and the interaction between them are determined. The Cross Impact Matrix Multiplication Applied to a Classification (MICMAC) analysis is also conducted to determine the dependence power and driving power of these dynamic factors. Through analysis, it is concluded that the dynamic factors of super-gentrification are distributed on six levels. Among these dynamic factors, Transformation of Industrial Structure and Occupational Structure in Urban Central Areas, Housing Needs of Overseas Elites, Investment Needs, Development of the Real Estate Market, and Unique Areas and Lifestyle Preferences are the fundamental dynamic factors affecting super-gentrification. The findings of this paper can enrich the existing theoretical research on the driving force of super-gentrification and can provide a reference for policy makers to promote urban landscape sustainability to some extent.

The recent development of operational small unmanned aerial systems (UASs) opens the door for their extensive use in forest mapping, as both the spatial and temporal resolution of UAS imagery better suit local-scale investigation than traditional remote sensing tools. This article focuses on the use of combined photogrammetry and “Structure from Motion” approaches in order to model the forest canopy surface from low-altitude aerial images. An original workflow, using the open source and free photogrammetric toolbox, MICMAC (acronym for Multi Image Matches for Auto Correlation Methods), was set up to create a digital canopy surface model of deciduous stands. In combination with a co-registered light detection and ranging (LiDAR) digital terrain model, the elevation of vegetation was determined, and the resulting hybrid photo/LiDAR canopy height model was compared to data from a LiDAR canopy height model and from forest inventory data. Linear regressions predicting dominant height and individual height from plot metrics and crown metrics showed that the photogrammetric canopy height model was of good quality for deciduous stands. Although photogrammetric reconstruction significantly smooths the canopy surface, the use of this workflow has the potential to take full advantage of the flexible revisit period of drones in order to refresh the LiDAR canopy height model and to collect dense multitemporal canopy height series.