ISM Model Research Articles

Forecasting and Impact Analysis of the Development Trends of China's New Energy Electric Vehicles Based on Time Series Causal Analysis

New energy electric vehicles with numerous properties have experienced explosive growth in recent years. This research constructs an ISM model to analyze the key factors influencing the new energy vehicle market and develops both a Winters' additive model and an ARIMA model to forecast the development of the NEV industry from 2023 to 2033. The results show that, the development of the new energy vehicles in the following ten years as a growth trend. Moreover, Granger causality tests, conducted within the VAR framework, revealed that NEV sales growth and market share significantly impact policy effectiveness and market dynamics. Simultaneously, the environmental impact was assessed using a LCA model, which indicates that the development of new energy vehicles is beneficial to the ecological environment. The results contribute to the theoretical and empirical understanding of the NEV market, providing valuable insights for policy makers and industry stakeholders.

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.

Towards sustainable automobile ecosystem in India: Integrated analysis of technical, economic, and ESG dimensions

Global environmental concerns are leading to paradigm shift in the automobile eco-system. This comprehensive study of the automobile ecosystem, incorporates technical, economic, and environmental-social-governance (ESG) dimensions. An “integrated multi-criteria decision-making” (MCDM) methodology using “interpretive structural modelling” (ISM) and “fuzzy analytical hierarchy process” (FAHP) is deployed, The ISM models highlights prominent drivers and critical barriers, establishes their hierarchy, and depicts interrelationships. The FAHP rankings for drivers favours electric vehicles and hydrogen fuel vehicles over others, while barriers promote hybrids and alternate fuels like Biofuel, CNG/LNG. Unlike previous works, this study considers all eco-system factors, both critical drivers and barriers, and demonstrates hierarchy using a unique V-model. This is an innovative extension of V-model from traditional systems engineering to strategic project management domain. This holistic approach, in sync with the natural state of things, will enable government, industry, and academia in formulating a strategic framework towards future automobile sustainability.

Measurement of the LED Lighting Industry Supply Chain Vulnerability and Identification of Key Influencing Factors

The current supply chain vulnerability has become a prominent factor affecting the stable development of the economy in various countries and is a significant issue with overall and long-term significance. This study constructs a network model to measure the vulnerability and identify key influencing factors in the LED lighting industry supply chain based on the entropy-CRITIC–ISM method. By reviewing literature and using expert interviews, an indicator system for measuring the vulnerability of the LED lighting industry supply chain was constructed. The relative weights of each evaluation indicator were determined using the entropy-CRITIC method, identifying the main factors influencing supply chain vulnerability. Based on the ISM model, the structure of the vulnerability of the LED lighting industry supply chain is revealed and presented in a hierarchical-directed topological graph. The results indicate that the ratio of key equipment suppliers from overseas, the spatial aggregation degree of the supply chain, the growth rate of LED lighting industry manufacturers, and the international market share of LED lighting industry products are deep-seated factors influencing the vulnerability of the LED lighting industry supply chain. The conclusions provide a quantitative method for objectively understanding the vulnerability of the LED lighting industry supply chain and offer specific policy references for filling the gaps and strengthening the industrial basic capabilities of the LED lighting industry

Investigating the intention and behavior of vegetable farmers to adopt IoT technology: survey—based evidence from China

A key instrument for upgrading China’s agriculture is the Internet of Things (IoT). To solve the problem of IoT technology promotion, farmers’ intentions to adopt IoT technology must be transformed into behavior, and their intentions and behaviors must be unified. The multivariate logistic model was used to analyses the factors influencing farmers’ intention to adopt IoT technology and behavioral deviation based on survey data of vegetable farmers in Jiangxi Province. The ISM model was used to investigate the relationship and hierarchy between the factors influencing farmers’ intention to adopt IoT technology and behavioral deviation. The findings revealed that first, a significant deviation exists between vegetable farmers’ IoT technology adoption intention and behavior, with 72.69% of those surveyed having the intention to adopt, but did not follow through. Second, among the 12 factors confirmed as remarkably influencing the deviation, expected return is the most superficial factor; intermediate influencing factors include vegetable price volatility, adoption cost, credit support, related knowledge and skills, and technology maturity; secondary factors include social capital, whether vegetables are registered trademarks, planting scale, planting method, and IoT related equipment supply; and age is the deepest root factor.

Buying a Wealthy Dream: Determinants of Rural Residents' Lottery Purchase Behavior in China.

The expansion of illicit lottery activities has caused significant harm to both agricultural production and the livelihood of rural residents. An analysis of the factors that influence rural residents' participation in underground lotteries can provide crucial insight for regulating the lottery industry's development. This study examines the present state of rural residents' participation in underground lotteries, investigates the factors that impact their participation using the Double-Hurdle model, and further employs the ISM model to evaluate the correlations and hierarchical structure among the factors, using field survey data collected from 603 rural residents in S Province, China. The findings reveal that 53.07% of the respondents have participated in underground lotteries. Risk preference, information acquisition, social networks, age, education, family burden, percentage of agricultural labor, agricultural business scale, and household income significantly affect whether residents purchase lotteries (WPL). All factors, except the percentage of agricultural labor and agricultural business scale, also significantly impact underground lottery spending (LS). Among the significant influencing factors, rural residents' risk preference is the direct surface factor, whereas agricultural business scale, household income, information acquisition, and social networks are the middle indirect factors, and residents' age, education, family burden, and percentage of agricultural labor are the deep-rooted factors. It is recommended to regulate the lottery industry's development can be achieved by enhancing entertainment and cultural activities, expanding economic opportunities, enhancing rural education, increasing law awareness, and improving the public welfare lottery business model. The conclusions offer a valuable reference point for the standardized development of the lottery industry and the promotion of social stability in rural areas.

Urban and Rural Influencing Factors Based on the ISM Model and Their Improvement Suggestions

Urban and Rural Influencing Factors Based on the ISM Model and Their Improvement Suggestions

Transition from Small-scale to Large-scale Dynamo in a Supernova-driven, Multiphase Medium

Magnetic fields are now widely recognized as critical at many scales to galactic dynamics and structure, including multiphase pressure balance, dust processing, and star formation. Using imposed magnetic fields cannot reliably model the interstellar medium's (ISM) dynamical structure nor phase interactions. Dynamos must be modeled. ISM models exist of turbulent magnetic fields using small-scale dynamo (SSD). Others model the large-scale dynamo (LSD) organizing magnetic fields at the scale of the disk or spiral arms. Separately, neither can fully describe the galactic magnetic field dynamics nor topology. We model the LSD and SSD together at a sufficient resolution to use the low explicit Lagrangian resistivity required. The galactic SSD saturates within 20 Myr. We show that the SSD is quite insensitive to the presence of an LSD and is even stronger in the presence of a large-scale shear flow. The LSD grows more slowly in the presence of SSD, saturating after 5 Gyr versus 1–2 Gyr in studies where the SSD is weak or absent. The LSD primarily grows in warm gas in the galactic midplane. Saturation of the LSD occurs due to α-quenching near the midplane as the growing mean-field produces a magnetic α that opposes the kinetic α. The magnetic energy in our models of the LSD shows a slightly sublinear response to increasing resolution, indicating that we are converging toward the physical solution at 1 pc resolution. Clustering supernovae in OB associations increases the growth rates for both the SSD and the LSD, compared to a horizontally uniform supernova distribution.

Study on the Identification of Obstacle Paths for Digital Economy Promotion in Underdeveloped Regions based on ISM-MICMAC Modeling

Identifying the obstacles in the path of barriers to the promotion of the digital economy in less-developed regions can provide insight into the underlying causes that lead to the limited development of the digital economy. This helps the government and related organizations to formulate targeted policies and measures, which can be targeted to address the different barriers in solving the digital economy promotion problems and improve the development of the digital economy. A total of 26 influencing factors of barriers to the promotion of digital economy from six dimensions are categorized into three levels: surface, middle, and bottom according to the ISM model, and the influencing factors are categorized into three clusters of influencing factors, namely, independent, autonomous and connected, using the MICMAC method. The results of the study show that: (1) the digital divide factors such as the lack of digital economy professionals and the relatively weak economic foundation are the underlying factors hindering the promotion of the digital economy, and have the deepest impact on the obstacles in the path of digital economy promotion. (2) Inadequate education system, lack of government funding, and lack of related policy support are the linkage factors in the path of barriers to the promotion of the digital economy, with greater dependence and driving force, which need to be focused on.

Research on the Path of High-Quality Development of Student Management under the Explanatory Structural Model Oriented to Solve the Dilemma of Collaborative Governance of Student Management in Colleges and Universities

Abstract This paper firstly studies the student management model, analyzes the mechanism of explaining the operation of the structural model, and proposes the DEMATEL method. Secondly, the ISM model is established on the basis of DEMATEL, and the cross-matrix multiplication method is introduced to explore the diffusion of interrelationships among factors in the system. And finally, the construction of the DEMATEL-ISM model is completed. Then, 11 experts were selected to set up an ISM group to construct the relevant reachability matrix after determining the key factors of the problem, and a multi-layer recursive order structure model was established based on the high-quality management influencing factors. Finally, an empirical examination was conducted to examine the overall effect of student management quality in S colleges and universities. Among the key factors of student management, classroom management was identified as the most critical factor affecting the quality of student management, with the ratio of the most important and the important summed up to 76.1%. Among the classroom student participation in higher education, the highest score was given to participatory behavior, with an interval between [2,5] and a mean value of 3.4808. The quality of student management in higher education was characterized by a high level of dissatisfaction among students and teachers, which was 41.94% and 51.28%, respectively. It can be seen that daily management, moral education, teacher management, and teacher competence are insufficient. Colleges and universities need to strengthen the construction of these 4 aspects and put forward relative coping strategies for the deficiencies that exist.

Research on the Coupled Relationship of Factors Influencing Construction Workers’ Unsafe Behaviors: A Hybrid DEMATEL-ISM-MICMAC Approach

The unsafe behavior of construction workers is the most direct reason of frequent construction site accidents. In order to improve the safety management of construction sites and figure out the causal relationship among the influencing factors in the field of construction workers’ unsafe behaviors, literature research, questionnaire survey, decision-making trial, and evaluation laboratory—interpretive structural modeling—crossimpact matrix multiplication applied to classification (DEMATEL-ISM-MICMAC) methods were used in combination in this thesis. The analysis of collected data was carried out in three dimensions: individual, organizational environment, and safety management. A framework about influencing factors of construction workers’ unsafe behaviors was constructed. DEMATEL-ISM was used to construct the explanatory structural model, which to analyze the influence relationships and hierarchical relationships among factors. MICMAC method was used to analyze the driving dependency. ISM model consists of six parts, the bottom of which includes three influencing factors: work environment, safety supervision, and concernment of superior. With characteristics of high drive and low dependence, the bottom layer are the root causes of construction workers’ unsafe behaviors. Work environment and concernment of superior are the core indicators of it. The intermediate layer with a low drive and low dependence covers six factors: psychological status, physical health, professional skills, organizational climate, work quota, and safety plan. It is the indirect factor to influence construction workers’ unsafe behaviors. The top layer is composed of safety awareness, safety education, and technical delivery. Safety awareness is the core of individual dimension, showing the characteristics of low drive and high dependence, is the direct factor to influence construction workers’ unsafe behaviors. Based on the DEMATEL-ISM-MICMAC method, the methodology to reduce construction workers’ unsafe behaviors was proposed.

The Galactic supernova remnant CTB 1: optical and X-ray emission analysis

ABSTRACT We present the kinematic results for the supernova remnant (SNR) CTB 1, obtained with the PUMA equipment of the 2.1-m telescope at San Pedro Mártir National Astronomical Observatory, Baja California, Mexico. The observations were made on the H α line using a Fabry–Perot interferometer. The kinematics derived from the Fabry–Perot velocity cubes allowed us to estimate a kinematic distance for this SNR. We estimate that CTB1 is about 3.8 kpc from the sun, corresponding to a linear diameter of 19 pc, with an expansion velocity of $286 \rm ~ km ~s^{-1}$. Additionally, we analyse the diffuse X-ray emission from CTB 1 using data from the XMM–Newton observatory. From the best fits to the spectrum of southwest and northeast regions, we obtained a hot plasma temperature $\sim 0.1-0.3 ~\rm keV$ with an overabundance in O and Ne and a lower abundance in Fe, which could be caused by a SNR with a stellar progenitor between 10 to $70 \, \rm {\rm M}_{\odot }$. The initial energy of the supernova explosion is estimated between (0.6–1.0) × 1050 erg for soft X-ray emission. The X-ray emission also presents a non-thermal component of low surface brightness in the hard X-ray band. Images in radio and X-ray emission allow us to classify CTB 1 as mixed-morphology supernova type. The evolution models show that the cloudy ISM model is the only one that reproduces the mixed morphology type observed in the SNR CTB 1.

What promote farmers to adopt green agricultural fertilizers? Evidence from 8 provinces in China

The utilization of green agricultural fertilizers is a crucial measure in promoting sustainable farming practices, facilitating the development of green agriculture and driving rural revitalization. However, the current adoption rate of green fertilizer in agricultural production remains low. Based on 960 questionnaires and combining individual, product and environment factors, this study used Logistic model to determine the influencing factors of farmers' decisions of green agricultural fertilizers, and used ISM model to analyze the hierarchical relationship among the influencing factors. The results revealed that out of 20 factors that included internal and external influences on farmers' choice of green agricultural fertilizers, 17 were found to significantly impact farmers' behavior. Notably, factors such as crop types, planting method, product price, get information from friends' recommendations and retailers' recommendations, and land fertility emerged as the surface factors affecting farmers' decisions. Furthermore, the deep factors were the farmer's gender and higher educational level, which were the farmer's own factor, and the others were the intermediate factor. The driving mechanism of each factor is different, and the effect is different. The study findings are expected to shed light on future research and policy-making efforts aimed at effectively promoting green agricultural fertilizers to accelerate the green transformation of agriculture and drive rural revitalization.

Mechanisms influencing the learning effect of ISM network under big data regression algorithm

Abstract Exploring the impact mechanism of online learning effectiveness can effectively optimize and improve the impact to enhance online learning effectiveness. This paper describes a big data Gaussian process regression model under big data technology, and global a priori sparse approximation and local approximation solve the predicted distribution results. The training conditional probabilities and test conditional probabilities of the hidden variables are optimized using the induced point method and the spectral approximation method. Then, a simple analysis of the network learning behavior applied by the ISM model is introduced using the big data Gaussian process regression model, and the key, direct and fundamental factors that may affect the network learning are mined. Finally, relative data analysis was conducted based on the relevant influencing factors mined by the ISM model. From the number of course visits, 34.54% of the learners’ visits to the course are mainly concentrated between 100-199 times. Regarding the length of discussions, the per capita participation in online discussions was 37.32 minutes, and most learners mainly focused on 0-50 minutes. It indicates that using big data Gaussian regression model can analyze the influencing factors of online learning through ISM model, and the targeted improvement and optimization can enhance the effectiveness of online learning.

Evaluation of Safety Management of Smart Construction Sites from the Perspective of Resilience

In the context of green, low-carbon, and sustainable construction, the safety management of smart construction sites has been a key issue. Current related research mainly focuses on the application of technology, but lacks methods to evaluate the safety management level. Therefore, this research aims to construct a smart construction site safety management evaluation model from a resilience perspective. First, this research identified and screened the indicators initially based on the 4R resilience characteristics and 4M theory by analyzing the policy texts of smart construction site safety management. Then, through expert consultation, the ISM model of resilience indicators was established to determine the evaluation indicator system of smart construction site safety management. Next, the weight of each indicator was determined with the help of the analytic network process, and the evaluation criteria of the indicators were formulated according to the existing specifications and expert interviews; then, the evaluation model of smart construction site safety management was established. Finally, the feasibility of the model was proved through a case study. The findings of the research show that in terms of weights, management has the highest score, followed by media, man, and machine. However, more resilience measures are used for the safety management of machine than the other three in policy texts. Obviously, there is a deviation between weights and resilience characteristics. These findings help reveal the current situation of safety management at smart construction sites, which is of great significance for improving resilience. The findings also help smart construction sites to realize the upgrading of safety, efficiency, and greenness, and promote the sustainable development of smart construction sites as well as the construction industry.

Barriers to Adopting Lean Methodology in the Portuguese Construction Industry

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.

Statistical Analysis of Export Waybill Data of Cross-Border E-Commerce Based on ISM Model

Abstract This paper uses the expository structural model (ISM) as the basis for research. Firstly, it briefly analyzes the business structure of our cross-border e-commerce and foreign trade business. The specific organizational structure of the enterprise will encounter a variety of interference. This paper constructs a set of enterprise supply chain performance evaluation indexes based on balanced scoring. The writing improves the information level, logistics ability, customer status maintenance, and other vital links. Simulation results show that this method can improve the overall performance of foreign trade documents.

Influencing Factors of Agricultural Products Cold Chain Logistics Development Based on ISM Model

Influencing Factors of Agricultural Products Cold Chain Logistics Development Based on ISM Model

Analysis of Influencing Factors of Farmers’ Homestead Revitalization Intention from the Perspective of Social Capital

Revitalizing idle rural residential bases is essential for improving the utilization rate of residential resources, activating pastoral land resources assets, increasing farmers’ property income, and stimulating rural development vitality. Social capital is one of the essential social resources owned by farmers, which is closely related to rural social governance and farmers’ daily lives and plays an indispensable role in revitalizing residential land. Based on the theory of social capital and the survey data of 316 farm households in Shaanxi Province in 2022, this study first empirically analyzes the influencing factors of social capital and its constituent dimensions on farm households’ willingness to revitalize their home-steads using a Logit regression model, and then empirically analyzes the hierarchical structure relationship among the influencing factors using an ISM model. The results of the study show that: (1) Social capital and its three constituent dimensions of social network (“Contact with relatives, neighbors, and families” and “Contact with village committee staff”), social trust (“Trust in friends and relatives” and “Trust in village committee and other organizations”), and social participation (“Participation in meetings of village committees and other organizations”) have significant positive effects on farmers’ willingness to revitalize their residential land. (2) There are differences in the effects of different dimensions of social capital on farmers’ willingness to revitalize their homesteads: social trust as a deep-seated cause affects farmers’ social networks and social participation, which in turn affects farmers’ knowledge of homestead revitalization policies and ultimately farmers’ willingness to revitalize their homesteads. Based on the findings of the study, to improve farmers’ willingness to revitalize their homestead bases, government departments should base their efforts on the social capital characteristics of farmers, increase the cultivation of farmers’ social capital, and strengthen the publicity of the work of revitalizing homestead bases and related policies.

Identification and ranking of enablers to green technology adoption for manufacturing firms using an ISM-MICMAC approach.

The debate about the adoption of green technologies (GTs) is trending due to increasing environmental concerns across the globe. In the context of the manufacturing industry, the research about enablers to GT adoption while using the ISM-MICMAC approach is scarce. Hence, the empirical analysis of GT enablers is being done in this study using a novel ISM-MICMAC method. Firstly, the research framework is developed by using the ISM-MICMAC methodology. Then, enablers to GTs were extracted from existing studies and validated by experts. Results showed that the most significant enabler to GT adoption is providing incentives for green manufacturers (1) in the ISM model. So, manufacturing firms must take initiatives for GTs to mitigate the negative environmental impact of industrialization without losing their earnings. This research brings substantial empirical scholarship to understand GT enablers and their contribution in GT enabler incorporation in the manufacturing industry of developing economies.