Interpretive Structural Modeling Research Articles

Evaluating the impact of digital and financial literacy on MSME performance

ABSTRACT The objective of this study was to determine the critical success factors that contribute to the performance of micro-, small, and medium-sized enterprises (MSMEs) in the context of digital and financial literacy. This study employed the total interpretive structural modeling approach to construct a hierarchical model of important success elements and analyze their direct and indirect interconnections. This article specifies six success factors that impact the performance of MSMEs, with the consideration of their effects and value generation leading to enhanced performance. The results indicate that entrepreneurial orientation and digital literacy are the primary components of the developed model, with all factors collectively contributing to overall performance. By identifying and categorizing key components within the strategic model, the study provides MSME founders with a systematic way to enhance business competency, which has significant consequences for business growth and success. The hierarchical model developed in this research assists policy makers and practitioners in improving the performance of MSMEs.

An advanced dual-layered framework for sustainable supply chain performance

Purpose This study aims to evaluate the sustainable supply chain performance indicators. At a macro level, the identification of the sustainable supply chain management (SSCM) performance indicators is done through exhaustive literature survey and interviews with experts. Furthermore, these indicators are evaluated through a hybrid approach, i.e. total weighted interpretive structural modelling (TWISM) followed by analytic hierarchical process (AHP). Design/methodology/approach Micro small and medium enterprises (MSMEs) in India are a major contributor to nation’s GDP. However, this sector struggles to comprehend benefits from implementation of SSCM due to a lack of appropriate performance evaluation metrics. The purpose of this paper is to contribute to the body of knowledge in SSCM by proposing and evaluating a set of SSCM performance indicators. Findings The paper highlights the SSCM performance indicators and concludes that business strategies, implementation planning and impact of stakeholders are the top SSCM performance indicators (SPIs). Therefore, the decision-makers must initially focus on strategic requirements which foster the implementation of SSCM, thereby ensuring profitability for all stakeholders. Research limitations/implications Although the proposed framework was validated through a case study on Indian automobile component manufacturing MSMEs, future research would explore the extension of the framework to other industries. Originality/value The originality of this study lies in the application of the novel TWISM-AHP tool. Furthermore, the SPIs identified in the study, consider the integration of the triple bottom line from the MSME perspective. The TWISM-AHP analysis will be beneficial for SC decision-makers to enhance the SSCM performance based on the identified indicators and their criticality.

Examining the impact of green entrepreneurship drivers on sustainable development in the context of emerging country

PurposeGreen entrepreneurship (GE) is a novel concept in business and enhances environmentally friendly production and operation activities for “sustainable development” (SD). The aim of this study is to determine the drivers that contribute to the growth and success of “micro, small, and medium enterprises” (MSMEs) in the manufacturing sector in India. The study also examines the mutual and cause-and-effect relationships among these identified drivers.Design/methodology/approachThe study used integrated research methodology and identified nine key drivers of GE (GEDs) through extensive literature reviews, theoretical perspectives (i.e. “resource-based view” (RBV), “natural resource-based view” (NRBV) and “critical success factor theory” (CSFT)), and expert opinions. Further, “total interpretive structural modeling” (TISM) and “matrice d'impacts croisés multiplication appliquée á un classment” (MICMAC) analysis are used here to develop a hierarchical model and cluster the drivers, and fuzzy “decision-making trial and evaluation laboratory” (fuzzy-DEMATEL) is used to develop causal relationships among the drivers. Further, a sensitivity analysis is conducted to ensure the robustness of the results.FindingsResults indicated that green manufacturing and operation capability development, green business process management and attitudes toward developing sustainable business models significantly impacted GE and SD. The findings of this study help managers, policymakers, and practitioners gain an in-depth understanding of the drivers of GE.Research limitations/implicationsThe study considers a limited number of drivers and is specific to Indian manufacturing MSMEs only. Further, a limited number of experts from different enterprises are considered for data analysis. This study is also based on interrelationships and their relative importance based on multicriteria decision-making techniques. This study aids government decision-making, policy formulation and strategic decision-making for manufacturing businesses in achieving SD goals. In addition, this research also encourages green entrepreneurs to start eco-driven companies and facilitate the use of environmentally friendly goods to offset environmental challenges and accomplish sustainable development goals.Originality/valueThis study proposes an integrated methodology that will benefit managers, practitioners and others in developing strategies and innovations to improve and develop green practices. This study further helps with responsive, sustainable business development in various manufacturing MSMEs.

Factors Influencing Green Construction Practices in Context of COVID-19 Pandemic: Empirical Evidence from China

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.

Enhancing supply chain resilience through risk mitigation strategies: Evidence from smallholder red chili supply chains in East Java Indonesia

The cultivation of red chili in East Java, Indonesia, has significant economic and social impacts, necessitating proactive supply chain measures. This research aimed to identify priority risk agents, develop effective risk mitigation, and enhance supply chain resilience using the SCOR model, House of Risk, Interpretative Structural Modelling (ISM), and synthesis analysis. Examining 238 respondents—including farmers, collectors, wholesalers, retailers, home-agroindustries, and experts—the findings highlight farmers’ critical role in supply chain resilience despite risks from crop failures, weather fluctuations, and pest infestations. Simultaneous planting led to market oversupply and price drops, but accurate pricing information facilitated quick market adaptation. Wholesalers influenced pricing dynamics and income levels, impacting farmers directly. To improve resilience, three main strategies were developed through ten key elements: proactive strategies (real-time SCM tracking, Weather Early Warning Systems, risk management team formation, and training), resistance strategies (partnerships, chili stock reserves, storage and drying technologies, GAP implementation, post-harvest management, agricultural insurance, and Fair Profit Sharing Agreements), and recovery and growth strategies (flexible distribution channels and customizable distribution centers). Furthermore, the study delves into the mediating and moderating effects between variables within the model. This research not only addresses a knowledge gap but also provides stakeholders with evidence to consider new strategies to enhance red chili supply resilience.

Investigation of the driving power of the barriers affecting BIM adoption in construction management through ISM

Building Information Modelling (BIM) is a rising digital medium that is gaining popularity in the construction sector. Although BIM usage has been mandated in the public construction sector in Saudi Arabia, its adoption remains limited. This paper aims to investigate the driving power of the barriers affecting BIM adoption in construction management through Interpretive Structural Modeling (ISM). A literature review and expert meetings were conducted to identify the potential barriers faced by contractors and consultants in BIM usage in the construction management sector. Two different techniques were utilized to rank the identified barriers to adopting BIM in construction management. The first technique, the Relative Importance Index (RII), evaluates and assesses the barriers from both the contractors and consultants' perspectives. The second technique, Interpretive Structural Modeling (ISM), identifies the driving power of barriers and understands the correlations among them. A total of sixty-nine responses were analyzed using the RII. Cronbach's alpha was utilized to assess the collected data's reliability. The findings from the ISM were validated by experts to establish relationships among the identified barriers. The ISM reveals that the top driving barrier to BIM adoption is the “Lack of skilled and experienced personnel”, followed by “Lack of awareness”, “Communication issues” and “Longer setup time”. The findings of this study could assist local authorities develop an incentive program to encourage contractors and consultants to adopt BIM in construction management.

The Analysis of Trigger Factors of the Environmental Entrepreneurship Process in Saudi Arabia: An Innovative Approach

Environmental entrepreneurship is considered a critical avenue for sustainable development. Despite increasing studies on its benefits and impacts, we lack insights about its corresponding process. There is an increasing need for policymakers to know how to develop this process and what is required for its definition. This research aims to build an innovative theoretical model to explore the trigger factors for the environmental entrepreneurship process in Saudi Arabia, which is the ultimate focus point of this research. To achieve this objective, a deep literature review and a survey of expert entrepreneurs’ opinions were performed. Experts and professionals in entrepreneurship validated the opportunity of readiness factors identified. Data were gathered using a combined approach based on closed-ended questionnaires and arranged interviews according to a schedule. Employing the mixed influence matrix of cross-impact multiplications applied to classification (MICMAC) and the Interpretive Structural Modeling (ISM) approach, the research identifies a hierarchical model of these factors, addressing why and how they interact according to a specific order and priorities. The investigation reveals 15 factors that are structured into three main levels of influence: (1) factors driving the adoption of environmental entrepreneurship orientation, (2) factors related to environmental entrepreneurship intention, and (3) factors for materializing environmental entrepreneurship behavior. The findings emphasize the complementarity between environmental entrepreneurship intention and orientation as a key factor in generating entrepreneurial behavior. This research pioneers the empirical exploration of environmental entrepreneurship as a process. It contributes significantly to theoretical and practical domains by offering a pragmatic framework and better understanding for policymakers and stakeholders to focus on key factors that facilitate this process. This paper is innovative because it uses the integrative ISM–MICMAC approach, supported by a primary and brief bibliometric analysis of entrepreneurship.

Fostering environmentally sustainable business: Analysis of factors from entrepreneurial ecosystem perspective

Sustainable business practices are paramount for long-term environmental preservation and fostering economic growth. Despite the extensive body of literature on various aspects of business sustainability, there remains a noticeable gap in understanding the fundamental factors influencing its adoption. Drawing on the entrepreneurial ecosystem perspective, this study endeavors to examine the hierarchical importance of various factors affecting the environmental sustainability of the business. Employing a comprehensive literature review, we meticulously identified a list of factors and performed analysis using data gathered from experts. In particular, we used Total Interpretive Structural Modelling and MICMAC approach to analyze the identified factors, with an aim to uncover their interlinkage and association. Our findings underscore the critical role played by the government in shaping business sustainability. Additionally, factors such as consumer, technological innovation, and market competition also hold substantial importance. In contrast, factors such as financial considerations, stakeholders, leadership dynamics, human resources, infrastructure, and internal organizational factors appear to have comparatively less influence. Our study not only reveals the significance of these factors but also unveils the intricate web of interrelationships among them. The resulting strategic theoretical framework offers valuable insights for managers and policymakers, highlighting the practical relevance and contextualized interactions among various factors.

Airline business models as complex systems: assessing component interdependencies through interpretive structural modelling

PurposeThis paper explores the intricate interdependencies among core components of airline business models (BMs). In the airline industry, where BMs are complex systems, a successful BM requires an orchestrated configuration of various components. However, there is a paucity of research in BM literature pertaining to the interrelationships among key components of airline BMs.Design/methodology/approachEmploying interpretive structural modelling, we gathered input from experts in Iran to assess the driving power and dependency of elements within airline BMs.FindingsOur findings highlight the significance of operating environment conditions and competitive market dynamics as pivotal external components shaping the foundational structure. Value proposition, customer relationship management, and process monitoring are crucial linkage components that drive power and dependency. Notably, capturing value is positioned with the highest dependency.Practical implicationsWe utilised the ISM technique to visualize interdependencies within airline business models, aiding strategic decision-making. Our findings suggest aligning business and operational strategies with market needs ensures effective value creation and capture, maintaining competitive advantage in the airline industry. In addition, our research reveals critical factors affecting value creation and capture, emphasising monitoring the operating environment and competitive market, and strategically managing value propositions and customer relationship initiatives in the airline industry. We advise adapting business models to external changes for sustained growth and recommend regular monitoring of industry trends and customer expectations.Originality/valueFramed within complexity theory, these insights offer valuable perspectives on identifying and situating critical BM components in the airline industry. The practical implications derived from this study serve as strategic tools for airline managers and potential investors to optimise the design of their airline BMs.

Potential assessment and development obstacle analysis of CCUS layout in China: A combined interpretive model based on GIS-DEMATEL-ISM

Although carbon capture, utilization, and storage (CCUS) has enormous emission reduction potential and application prospects, it still faces many obstacles and challenges in the actual deployment and promotion process. This study aims to evaluate the spatial suitability of the CCUS layout comprehensively and deeply analyze key development obstacles to provide a scientific basis for the promotion and layout of CCUS. Therefore, this article constructs a combination of Geographic Information Systems (GIS), Decision-making trial and evaluation laboratory (DEMATEL) method, and Interpretive structure model (ISM). Firstly, a comprehensive evaluation is conducted on the suitability of the CCUS layout in China based on GIS technology, identifying high-potential areas. The results indicate that when the threshold is 80 % of the highest score for suitability, a total area of 1.46 million square kilometers is suitable for the CCUS project layout. When it dropped to 60 %, this data increased to 2.04 million square kilometers. These areas are mainly distributed in large basin areas with abundant resources. Secondly, the DEMATEL-ISM analysis framework is improved to systematically identify and analyze the key obstacles that constrain the promotion of CCUS in China. The results indicate that there is a very complex relationship between the obstacles that constrain the development of CCUS, with the fundamental reasons being the lagging technological level and imperfect policies and regulations. Finally, goal-oriented suggestions and response measures were proposed, and targeted analysis was conducted on high-suitability areas. This study constructed an interdisciplinary methodology interpretive model to provide decision support for the CCUS layout and targeted strategies to break through current development barriers and promote the rapid development of CCUS.

Investigating the Key Drivers in the Transition to Sustainable Hydrogen Transportation Fuel

Introduction. Hydrogen is a promising energy carrier that can achieve net-zero carbon emissions and decrease fossil fuel dependency. However, the importance of the driving forces in hydrogen adaptation and acceptance during the transition to this technology should be emphasised. When assessing hydrogen as a sustainable transportation fuel, defining the success criteria is vital to comprehend the benefits of hydrogen in the transition process. The transport sector is a priority area for the transition to zero emissions, which requires an understanding of the links between the key drivers of hydrogen adoption in transportation. Aim and tasks. This study aims to identify the key drivers of the hydrogen transition and explore the relationship between the transition drivers to hydrogen as a sustainable transport fuel based on a comprehensive literature review and an interpretative structural modelling approach to determine the relationship between the above-mentioned key drivers. Results. This study identified key drivers for the transition to hydrogen as a sustainable transport fuel using the Interpretive Structural Modelling (ISM) approach. This study examines the potential of hydrogen transition, analyses the driving forces, and examines their interrelationships in terms of driving force and dependent force. The role of technological development and transition awareness in achieving the SDGs is revealed. Robust regulatory agendas and supportive strategies, Workforce development and training based on hydrogen deployment, and Industry partnerships and collaboration are the most fundamental drivers for achieving the transition to hydrogen as a sustainable transport fuel. In addition, the advancement of hydrogen technologies in production, storage, and transportation, as well as hydrogen production and use, aligns with environmental aims, which are key factors that depend mainly on the actions of other driving forces. However, the limitations of this study include subjectivity and potential differences in the driving forces across countries and sectors. Conclusions. The deployment of green hydrogen transition for a clean energy source is critical in carbon-intensive sectors of the economy, which are characterised by key drivers and their interlinkages, such as regulatory strategies, workforce development, and industry partnerships. This has increased the flexibility of energy systems with the collaboration of the stakeholders for developing hydrogen technologies and infrastructure, considering the environmental safety, economic efficiency, and social acceptability of hydrogen.

Influencing Factors of Spatial Ability for Architecture and Interior Design Students: A Fuzzy DEMATEL and Interpretive Structural Model

Spatial ability is not just a skill but a crucial element for architecture and interior design students, significantly impacting their proficiency in tasks involving 2D drawings, 3D components, and artistic expression. Despite extensive research in this area, a gap remains in the understanding of how to effectively cultivate spatial ability through educational interventions. This study, with its unique approach of identifying key influencing factors and their interrelationships within the fuzzy decision-making laboratory analysis method (Fuzzy-DEMATEL) and the interpretative structural model (ISM), fills this gap. The method visualizes cause-and-effect relationships within a structural model and captures the interdependencies between influencing factors. In a collaborative effort between nine universities in 2023–2024, 17 experts selected through purposeful sampling contributed to the development of a comprehensive list of potential influencing factors. After refinement through filtering, comparison with the existing literature, and expert consensus, seven influencing factors of spatial ability for architecture and interior design students from personal traits and STEAM disciplines were identified, which are sketching and hand drawing skills, mathematical skills, video game practice, descriptive geometry skills, augmented reality practice, spatial talk, and gesturing while talking. Sketching and hand drawing skills, mathematical skills, and video game practice come under cause factors of spatial ability, whereas the rest are effect factors. Proceeding with ISM analysis revealed that sketching and hand drawing skills and mathematical skills are located in the input layer and have a continuous impact on spatial ability. Descriptive geometry skills lie in the transition layer, which are considered as deep influencing factors, the rest of the factors lie in the effect layer. This study delves into the theoretical and practical implications of these findings, offering valuable insights for educational policy and practice.

Agriculture 4.0 adoption challenges in the emerging economies: Implications for smart farming and sustainability

To ensure food security in this age of production and supply disruption, the agricultural sectors of emerging economies are gradually adopting more smart technologies to achieve sustainability. However, literature on the challenges of adopting Agriculture 4.0-based smart farming technologies is still very limited. This research, therefore, explores the contextual interrelation among the challenges to adopting Agriculture 4.0-based smart technologies in the agricultural production system from a developing country's perspective and prioritizes the identified challenges. A case study was conducted in Bangladesh, an emerging economy, where data was collected through interviews and focus group discussion sessions. A total of 21 challenges were finalized as relevant to the country's context. The Interpretive Structural Modeling (ISM) technique was deployed to develop a hierarchical structure depicting the challenges' interrelations. The challenges were later ranked based on their relevant weight using the Best-Worst Method (BWM). This study finds technological complexity, lack of collaboration among different stakeholders, inadequate support from the government, and lack of action plans to have very high driving power. Challenges such as high initial investment and operational costs, lack of skilled workforce, and farmers' resistance were found to be dependent challenges. This study is expected to contribute by providing a deeper insight into the challenges of adopting Agriculture 4.0 in emerging economies so that practitioners can take effective mitigating measures to streamline the plant-based agricultural production systems to promote food security and sustainability.

Understanding the drivers of innovative work behaviour among millennial employees in India’s IT sector: some exploratory research findings

PurposeThe study aims to develop a hierarchical model for innovative work behaviour (IWB) that can capture the complex associations among the factors contributing to IWB within the information technology (IT) sector. To accomplish this, the authors rely on an abductive approach using a graph theoretic model, often called interpretive structural modelling (ISM).Design/methodology/approachAfter conducting an in-depth literature review and using the Delphi method, the authors identified 12 factors (11 enablers and IWB as an outcome). The authors collected data through the Delphi approach by sending the questionnaire to 11 experts from academia and the IT sector who have extensive experience and knowledge relevant to the study. The authors then used the ISM method to analyse the relationships among these factors and understand their driving forces.FindingsBased on the ISM model and the Matrice d'Impacts Croisés Multiplication Appliquée à un Classement analysis, the authors have identified that inclusive leadership, proactive personality and knowledge creation ability are the variables with strong driving power but weak dependence. Conversely, IWB has strong dependence but weak driving power. These findings suggest that to foster IWB, the organisation should prioritise inclusive leadership, proactive personality and knowledge-creation ability to succeed in challenging times. The study’s findings contribute to the social exchange theory, which explains IWB in a dynamic setting. Additionally, the study helps address the significant concerns that most IT companies face during times of crisis.Practical implicationsThe study provides valuable guidance for managers and policymakers who are grappling with the challenges of improving IWB in the IT sector. This study is particularly relevant as the industry is currently navigating an economic recession and facing intense competition from other tech companies launching new products and services.Originality/valueThis research holds great significance for top executives, line managers and policymakers in the IT industry. It sheds light on the relevance and importance of various factors facilitating millennials' IWB.

Risk-return analysis of clean energy grid project investment based on integrated ISM and Monte Carlo model

To solve the problem of complex and difficult to quantify factors affecting investment returns and risks in clean energy power grids, this study comprehensively applies the interpretive structural model and Monte Carlo model to the analysis of investment risk-returns in clean energy power grid projects. The interpretive structural model is utilized to analyze project investment returns, while the Monte Carlo model is used to analyze project investment risks. The project investment risk is based on the factor analysis of project investment returns, and key risk factors are identified through 1000 simulations, and the impact of these risks on project returns is quantified. By combining the two, the investability of the project is analyzed. The results showed that grid electricity prices, kilowatt hour subsidies, technology learning rates, total annual sunshine hours, and system power generation efficiency were key factors driving investment returns. The average expected value of investment return was about 20%, and the probability of investment return below 6% was close to 0. The overall project is worth investing in. From this, it can be seen that the research designed investment risk-return analysis methods for clean energy grid projects can effectively distinguish the main factors affecting investment returns and risks, and pre simulate the risk situation of returns. This study can provide reference for investor decision-making.

Augmenting supply chain resilience through AI and big data

PurposeNatural calamities like earthquakes, floods and epidemics/pandemics like COVID-19 significantly disrupt almost all the supply networks, ranging from medicines to numerous daily/emergency use items. Supply Chain Resilience is one such option to overcome the impact of the disruption, which is achieved by developing supply chain factors with Artificial Intelligence (AI) and Big Data Analytics (BDA).Design/methodology/approachThis research examines how organizations using AI and BDA can bring resilience to supply chains. To achieve the objective, the authors developed the methodology to gather useful information from the literature studied and developed the Total Interpretive Structural Modeling (TISM) by consulting 44 supply chain professionals. The authors developed a quantitative questionnaire to collect 229 responses and further test the model. With the analysis, a conceptual and comprehensive framework is developed.FindingsA major finding, this research advocates that supply chain resilience is contingent upon utilizing supply chain analytics. An empirical study provides further evidence that the utilization of supply chain analytics has a positive and favorable effect on the flexibility of demand forecasting to inventory management, resulting in increased efficiency.Originality/valueFew studies demonstrate the impact of advanced technology in building resilient supply chains by enhancing their factors. To the best of the authors' knowledge, no earlier researcher has attempted to infuse AI and BDA into supply chain factors to make them resilient.

Research on the Factors Influencing the Epidemic Resilience of Urban Communities in China in the Post-Epidemic Era

In the aftermath of the COVID-19 pandemic, people are gradually realizing that urban community resilience is pivotal for effectively managing public health emergencies. This study employed grounded theory to establish a theoretical framework for epidemic resilience of urban communities (ERUC) in the post-pandemic era. Subsequently, the decision-making trial and evaluation laboratory (DEMATEL)-interpretive structural modeling (ISM) method is utilized to discern the significance and hierarchical interrelations among influencing factors. The findings delineate that 14 determinants shaping ERUC are organized into five distinct tiers. Notably, nine determinants emerge as principal: vulnerable group; educational attainment; risk perception; medical insurance coverage; communal norms; community emergency response; community services; resident participation; and government efficacy. Among these, the vulnerable group and government efficiency are identified as foundational factors, while medical insurance coverage, resident participation, and community infrastructure are identified as direct influences.

Study on Influencing Factors of Grain Supply Chain Resilience Based on DEMATEL-ISM Method

The essential requirement for the resilience and safety of the food industry supply chain is to achieve independent, controllable, safe, efficient, and sustainable development. Currently, and for a significant period in the future, the food industry supply chain faces several risks, primarily due to increased uncertainties. These risks manifest as the need for optimization in the layout of the food industry, prominent structural shortages, and a lack of full autonomy and control in key links of the industry chain. Such risks can potentially evolve into critical factors that restrict the resilience and security of the food industry supply chain. To effectively address these risks, this paper identifies 12 influencing factors based on four key capabilities of the food supply chain: forecasting ability, response-ability, adaptation ability, and recovery ability. An evaluation index system for the resilience of the food supply chain is constructed, integrating the Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Interpretive Structural Modeling (ISM) methodologies. Analysis of the interactions between these factors reveals that three factors, including supply chain visibility, are fundamental, seven factors, including the level of agricultural mechanization, are intermediate, and dynamic logistics and flexibility are direct factors. Based on these conclusions, this paper proposes several targeted recommendations to enhance the development of the food supply chain. These recommendations aim to improve the supply chain's resilience and ability to withstand potential risks.

Assessing Drivers Influencing Net-Zero Emission Adoption in Manufacturing Supply Chain: A Hybrid ANN-Fuzzy ISM Approach

Nowadays, there is a constant focus on implementing the net-zero emission (NZE) concept in the manufacturing supply chain (MSC). To reduce emissions and improve organisational efficiency, adopting the net-zero concept is a prevalent trend in today’s highly competitive global business environment. Governments and stakeholders are pressuring the manufacturing sector to use natural resources efficiently and reduce environmental impacts. As a result, the manufacturing industry is focusing on cleaner production using net-zero practices. This study aims to identify and analyse the interaction among the drivers of net-zero adoption in the MSC. Through a systematic literature review (SLR), a list of drivers was recognised. To validate these drivers, we conducted an empirical study with 173 respondents from the Indian manufacturing industry. Further, we employed an artificial neural network (ANN) to weigh the nonlinear effect of drivers. Fuzzy interpretive structural modelling (F-ISM) was used to identify the interaction relationships among the drivers and construct a hierarchical structure among these identified drivers. The fuzzy matrix of cross-impact multiplications applied to the classification (F-MICMAC) method was used to categorise these drivers into driving and dependent categories. The outcomes of ANN show that Environmental predictors (100%) emerged as the most significant drivers, followed by Economic drivers (60.38%) and Technological drivers (59.05%). This study is a valuable resource for academia and industry professionals, providing essential insights into how adopting net zero facilitates the manufacturing industry’s ability to achieve net zero across the supply chain.

The Application and Evaluation of the LMDI Method in Building Carbon Emissions Analysis: A Comprehensive Review

The Logarithmic Mean Divisia Index (LMDI) method is widely applied in research on carbon emissions, urban energy consumption, and the building sector, and is useful for theoretical research and evaluation. The approach is especially beneficial for combating climate change and encouraging energy transitions. During the method’s development, there are opportunities to develop advanced formulas to improve the accuracy of studies, as indicated by past research, that have yet to be fully explored through experimentation. This study reviews previous research on the LMDI method in the context of building carbon emissions, offering a comprehensive overview of its application. It summarizes the technical foundations, applications, and evaluations of the LMDI method and analyzes the major research trends and common calculation methods used in the past 25 years in the LMDI-related field. Moreover, it reviews the use of the LMDI in the building sector, urban energy, and carbon emissions and discusses other methods, such as the Generalized Divisia Index Method (GDIM), Decision Making Trial and Evaluation Laboratory (DEMATEL), and Interpretive Structural Modeling (ISM) techniques. This study explores and compares the advantages and disadvantages of these methods and their use in the building sector to the LMDI. Finally, this paper concludes by highlighting future possibilities of the LMDI, suggesting how the LMDI can be integrated with other models for more comprehensive analysis. However, in current research, there is still a lack of an extensive study of the driving factors in low-carbon city development. The previous related studies often focused on single factors or specific domains without an interdisciplinary understanding of the interactions between factors. Moreover, traditional decomposition methods, such as the LMDI, face challenges in handling large-scale data and highly depend on data quality. Together with the estimation of kernel density and spatial correlation analysis, the enhanced LMDI method overcomes these drawbacks by offering a more comprehensive review of the drivers of energy usage and carbon emissions. Integrating machine learning and big data technologies can enhance data-processing capabilities and analytical accuracy, offering scientific policy recommendations and practical tools for low-carbon city development. Through particular case studies, this paper indicates the effectiveness of these approaches and proposes measures that include optimizing building design, enhancing energy efficiency, and refining energy-management procedures. These efforts aim to promote smart cities and achieve sustainable development goals.