Fuzzy Interpretive Structural Modeling Research Articles

SmartISM 2.0: A Roadmap and System to Implement Fuzzy ISM and Fuzzy MICMAC

Interpretive structural modeling (ISM) is a widely used technique to establish hierarchical relationships among a set of variables in diverse domains, including sustainability. This technique is generally coupled with MICMAC (Matrice d’Impacts Croisés Multiplication Appliquée á un Classement (cross-impact matrix multiplication applied to classification)) to classify variables in four clusters, although the manual application of the technique is complex and prone to error. In one of the previous works, a novel concept of reduced conical matrix was introduced, and the SmartISM software was developed for the user-friendly implementation of ISM and MICMAC. The web-based SmartISM software has been used more than 48,123 times in 87 countries to generate ISM models and MICMAC diagrams. This work attempts to identify existing approaches to fuzzy ISM and fuzzy MICMAC and upscale the SmartISM to incorporate fuzzy approaches. The fuzzy set theory proposed by Zadeh 1965 and Goguen 1969 helps the decision makers to provide their input with the consideration of vagueness in the real environment. The systematic review of 32 studies identified five significant approaches that have used different linguistic scales, fuzzy numbers, and defuzzification methods. Further, the approaches have differences in either using single or double defuzzification, and the aggregation of inputs of decision makers either before or after defuzzification, as well as the incorporation of transitivity either before or after defuzzification. A roadmap was devised to aggregate and generalize different approaches. Further, two of the identified approaches have been implemented in SmartISM 2.0 and the results have been reported. Finally, the comparative analysis of different approaches using SmartISM 2.0 in the area of digital transformation shows that, with a wide flexibility of fuzzy scales, the results converge and improve the confidence in the final model. The roadmap and SmartISM 2.0 will help in the implementation of fuzzy ISM and fuzzy MICMAC in a more robust and informed way.

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.

Fuzzy Interpretive Structural Modeling regarding Factors Affecting Employees’ Experience Management in Mashhad University of Medical Sciences

Background: Employees are considered internal customers of the organization, and today, employee’s experience has become a prominent topic in the field of human resources. This study aims to model the factors affecting employees’ experience management in Mashhad University of Medical Sciences using fuzzy interpretive structural modeling. Methods: This applied study was descriptive-analytical conducted in 2023. 10 faculty members, managers, and staff of Mashhad University of Medical Sciences were selected using a purposive sampling method. Interpretive Structural Modeling (ISM) questionnaire consisting of a 7x7 matrix was then given to them, and they were asked to find the contextual relationship between the variables based on the pairwise comparison scale. In the analysis section, fuzzy interpretive structural modeling and MICMAC analysis were used to structure and analyze the status of the factors. Results: 7 organized themes (organizational, physical environment, technological, job, managerial, cultural, and individual factors) were identified for employees’ experience in Mashhad University of Medical Sciences. Findings showed that cultural and managerial factors are at the lowest level of the structure and have more influence and less dependence. In other words, these are the most influential factors on employees’ experience. Job, technology, and organizational factors were placed at the second level. In contrast, individual and physical factors were at the highest level of the structure and had less influence and more dependence on other factors. Conclusion: The findings indicate that Mashhad University of Medical Sciences should focus on strengthening and improving cultural and managerial factors to improve employees’ experience. Additionally, attention to job, technology, and organizational factors, along with addressing individual employee’s needs and providing a favorable physical environment, are essential for employees’ effective experience management at the university. This study, by providing a comprehensive model of factors influencing employees’ experience, assists universities and other organizations in gaining a deeper understanding of this concept and developing effective strategies for its enhancement.

Can Industry 4.0-enabled smart manufacturing help firms in emerging economies move toward carbon-neutrality?

The manufacturing industry plays a pivotal role in the economic growth of all countries. Most manufacturing firms consume large amounts of energy and fail to adopt sustainable practices. However, Industry 4.0 is expected to help firms move toward carbon neutrality. Hence, this study focuses on 10 manufacturing firms in India and the opinions of industry experts to analyze the factors that motivate organizations to implement carbon neutrality. First, the driving factors of the shift toward carbon neutrality were investigated and ranked using the fuzzy analytic hierarchy process. Fuzzy interpretive structural modeling was then used to explain the interrelationships among the identified factors, providing a seven-level hierarchy model with 14 factors. Focusing on “Sustainable Business Value”, “Environmental Legitimacy” and “Government Policies” can help managers to develop strategies that are both socially responsible and profitable. These key findings have significant strategic implications for industry and policymakers.

Addressing Challenges: Adopting Blockchain Technology in the Pharmaceutical Industry for Enhanced Sustainability

The growing importance of sustainability in organizational success, particularly in the pharmaceutical industry, underscores the need for leveraging technologies such as blockchain methods to enhance sustainability indicators across environmental, social, and economic pillars. This study aims to identify and understand the challenges hindering the adoption of blockchain technology in the pharmaceutical sector for improving sustainability performance, addressing two research topics: the specific challenges faced by blockchain adoption in this context and the interdependencies among these challenges. Employing a two-step approach, the study compiles challenges through a literature review, refines them via expert opinions, and establishes their interrelationships using methodologies like fuzzy interpretive structural modeling (FISM) and cross-impact matrix multiplication applied to classification (MICMAC). The research contributes to unraveling the complex relationships and dependencies within the system, providing a structured framework for improved decision making and strategic planning. It fills a literature gap as the first attempt to outline driving and dependent factors related to the challenges of adopting blockchain technology for sustainability enhancement in the pharmaceutical sector, offering insights that can significantly impact brand image, company perception, and consumer value.

Analysis of factors influencing carbon emissions of urban households: A case study of old urban community in northern China

AbstractCO2 emissions reduction of urban residents is one of the main ways to mitigate climate change. Clarifying the key factors of carbon emissions can provide a reference basis for the government to formulate carbon reduction policies. The old urban community that widely exist in most cities in northern China is taken as the research object. Based on the fuzzy interpretive structural model and structural equation model, a new method is proposed to explore the influencing factors of household carbon emissions (HCEs). It is found that behavior change attitude and low‐carbon personal cognition are the two most important factors affecting HCE. Elderly families, high‐income families, and low‐education families are the groups with higher per capita HCE. Different suggestions were proposed to promote HCE reduction for the high‐income young tenants and low‐income elderly local residents in the community. In addition, by introducing multi‐agent judgment, the coupling model proposed in this study reduces the subjectivity caused by excessive reliance on single agent judgment in previous studies. It enhances the credibility of research conclusions and provides a good methodological reference for research in similar fields.

Hybrid business offerings in small internationalisers: a mixed-method analysis of internal capabilities through hesitant fuzzy information

PurposeIn this research, the initial list of internal capabilities in small and medium-sized enterprises (SMEs) leading to success in international markets has been extracted. Then, the most relevant capabilities to international SMEs under servitisation and hybrid offerings have been screened. Next, the selected capabilities have been classified, and ultimately the relationship amongst the capabilities has been analysed. The conceptual model for SMEs participating in international markets with hybrid offerings has been illustrated.Design/methodology/approachA literature review has been employed to extract the initial list of internal capabilities to address the research objectives. Then, a novel hesitant fuzzy Delphi (HFD) method has been developed to select the most relevant capabilities for SMEs for hybrid offerings in international markets by using the experts opinions. Subsequently, a novel hesitant fuzzy interpretive structural modelling (HFISM) has been developed to classify the capabilities, design a level-based conceptual model and present the relationship amongst the prominent capabilities.FindingsAfter the literature review, sixteen internal capabilities leading to success in the international market via hybrid offerings have been extracted. Then, eight selected capabilities were chosen for further investigation by applying 15 expert opinions and via the HFD approach. According to HFISM results, a level-based conceptual model was emanated, and “ability to take advantage of international opportunities”, “financial strength”, “technology level” and “efficient innovation management” were considered as the most fundamental capabilities resulting in successful hybrid offerings in international markets.Originality/valueAlongside the multi-layer decision-making approach developed in this manuscript to analyse the internal capabilities roles in hybrid offering success towards international markets, to the best knowledge of the authors, the hesitant fuzzy approaches developed in this article have not been previously presented by any other scholar. A novel HFD approach has been designed for consensus amongst the experts under uncertain circumstances. Furthermore, a novel HFISM has been suggested and employed in this research to comprehend the relationship amongst the internal capabilities.

A decision support method for credit risk based on the dynamic Bayesian network

PurposeCredit risk evaluation is a crucial task for banks and non-bank financial institutions to support decision-making on granting loans. Most of the current credit risk methods rely solely on expert knowledge or large amounts of data, which causes some problems like variable interactions hard to be identified, models lack interpretability, etc. To address these issues, the authors propose a new approach.Design/methodology/approachFirst, the authors improve interpretive structural model (ISM) to better capture and utilize expert knowledge, then combine expert knowledge with big data and the proposed fuzzy interpretive structural model (FISM) and K2 are used for expert knowledge acquisition and big data learning, respectively. The Bayesian network (BN) obtained is used for forward inference and backward inference. Data from Lending Club demonstrates the effectiveness of the proposed model.FindingsCompared with the mainstream risk evaluation methods, the authors’ approach not only has higher accuracy and better presents the interaction between risk variables but also provide decision-makers with the best possible interventions in advance to avoid defaults in the financial field. The credit risk assessment framework based on the proposed method can serve as an effective tool for relevant policymakers.Originality/valueThe authors propose a novel credit risk evaluation approach, namely FISM-K2. It is a decision support method that can improve the ability of decision makers to predict risks and intervene in advance. As an attempt to combine expert knowledge and big data, the authors’ work enriches the research on financial risk.

Transitioning drivers from linear to circular economic models: evidence of entrepreneurship in emerging nations

PurposeVarious publications have extensively documented the advantages of a circular economy in ensuring sustainability and limiting climate change. Despite academic records emphasising the need to adopt this business strategy, entrepreneurs in developing countries prefer linear economies. This reluctance is attributable to several factors, including insufficient infrastructure and technology, limited financial access, inadequate education systems and the prevalence of informal enterprises. Therefore, a thorough analysis of the underlying economic, political and social conditions is required to identify the drivers of circular economies (CEs) and their contribution to entrepreneurship in developing countries.Design/methodology/approachIn this study, the authors first conducted a comprehensive quantitative literature review based on LangChain to identify the critical CE drivers from the social, technological and organisational perspectives. Based on the input from the expert panel of Iranian academic and industry professionals, the authors applied an integrated fuzzy interpretive structural modelling and cross-impact matrix multiplication approach to classification (Fuzzy-ISM-MICMAC) to investigate the chronology of entrepreneurial drivers.FindingsLevel-based model results reveal entrepreneurial drivers in developing nations and their interrelationships, specifically underlining the importance of supply chain factors and stakeholder preferences. Thus, the differences between the perception of the main drivers in developed and developing economies can be identified, with the former paying particular attention to legislative and financial factors. The study's findings contribute to conserving resources, reducing waste and adopting more sustainable corporate practices, thereby assisting developing countries in achieving development goals.Originality/valueThis study employs an innovative quantitative systematic literature review approach that relies on a large language model to identify the drivers of the CE. Furthermore, it adopts a systematic approach to examine the enablers of the CE rather than a narrow and individual perspective of the entrepreneurial drivers. The study employs the fuzzy ISM MICMAC technique to showcase the prioritisation of entrepreneurial prospects in emerging economies.

A comprehensive approach for the evaluation of the impact of blockchain on photovoltaic supply chain using hybrid data analytic method

Blockchain technology has emerged as a disruptive and transformative innovation, drawing significant interest from organizations for its potential advantages. This study employs an exploratory research approach, utilizing a hybrid data analytics method, to investigate the impact of blockchain on the photovoltaic supply chain. Our research employs a comprehensive methodology, combining both quantitative and qualitative techniques. The research journey begins with the identification of crucial indicators, followed by the collection of data through a carefully designed questionnaire and the utilization of the Delphi method for expert insights. Subsequently, we employ advanced data analytics techniques, specifically the fuzzy Analytic Network Process (ANP) and fuzzy Interpretive Structural Modeling (ISM) methods, to prioritize these indicators and unveil their intricate relationships. Our findings underscore the paramount importance of several key criteria among the twelve identified indicators. These include data security, effective data management, data quality assurance, robust system documentation, and streamlined access protocols. Notably, customer satisfaction emerges as a pivotal factor, exerting substantial influence within our model. Moreover, our analysis underscores that data security stands as the foundational element in this multifaceted framework. This study offers valuable insights for stakeholders in the photovoltaic supply chain, aiding in the strategic adoption of blockchain technology to enhance transparency, efficiency, and overall performance. It serves as a robust framework for organizations seeking to navigate the evolving landscape of sustainable energy supply chains while harnessing the potential of blockchain innovation.

Analysis and Empirical Study of Factors Influencing Urban Residents’ Acceptance of Routine Drone Deliveries

The usage of drone delivery couriers has multiple benefits over conventional methods, and it is expected to play a big role in the development of urban intelligent logistics. Many courier companies are currently attempting to deliver express delivery using drones in the hopes that this new type of tool used for delivery tasks will become the norm as soon as possible. However, most urban residents are currently unwilling to accept the use of drones to deliver express delivery as normal. This study aims to find out the reasons for the low acceptance of the normalization of drone delivery by urban residents and formulate a more reasonable management plan for drone delivery so that the normalization of drone delivery can be realized as soon as possible. A research questionnaire was scientifically formulated which received effective feedback from 231 urban residents in Jinjiang District, Chengdu City. A binary logistic model was used to determine the factors that can significantly influence the acceptance of residents. In addition, the fuzzy interpretive structural model(Fuzzy-ISM) was used to find out the logical relationship between the subfactors inherent to these influencing factors. It was concluded that when the infrastructure is adequate, increasing public awareness and education, enhancing the emergency plan, lowering delivery costs, enhancing delivery efficiency and network coverage, and bolstering the level of safety management can significantly raise resident acceptance of unmanned aerial vehicle(UAV) delivery. Given the positional characteristics of the subfactors in the interpretive structural model(ISM) and matrices impacts croises-multiplication appliance classemen(MICMAC) in this study, we should first make sure that the drone delivery activities can be carried out in a safe and sustainable environment with all the necessary equipment, instead of focusing on increasing the residents’ acceptance right away, in the future work of regularized drone urban delivery has not yet started the construction phase. There should be more effort put into building the links that will enable acceptance to be improved with higher efficiency, which will be helpful to the early realization of the normalization of drone urban delivery if there is already a certain construction foundation in the case where the drone delivery environment is up to standard and hardware conditions are abundant.

A fuzzy Interpretive Structural Modeling approach for implementing IoT and achieving the United Nations Sustainable Development Goals

The growing demand for data-driven decision-making and the emergence of new business models has led to a rise in digital technologies. As a result, businesses have become more aware of the impact of their activities on the environment, including the production of digital technologies. Sustainability has become a significant concern for businesses worldwide, pushing them to explore ways to integrate sustainable practices into their operations and address this issue. Digitization has emerged as an essential tool, with the Internet of Things (IoT) technology leading the way. Organizations can improve decision-making and enhance operational efficiency with IoT, but aligning its implementation with the United Nations’ Sustainable Development Goals is essential. Concepts like Industry 4.0 and Circular Economy principles have emerged to create sustainable business models, but their implementation faces various challenges. Businesses must adopt analytical methods and tools to support strategic planning. This study applies the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) method to identify twelve key challenges businesses face in this IoT adoption. A survey was used for gathering expert opinion from industry specialists working in the field of IoT for conducting advanced analytical techniques like Fuzzy Interpretive Structural Modeling (F-ISM) and Matrice d’Impacts Croisés Multiplication Appliqués à un Classement (MICMAC) to highlight the most critical challenges, including loss of value appropriation, Low incentive to change, Less adoption of sustainable practices and Lack of interoperability. While IoT holds great promise for supporting sustainable development, the result highlights the urgent need for stakeholder collaboration to overcome these challenges and unlock its full potential. The findings of this study benefit organizations heavily dependent on digital services like IoT by providing them with a comprehensive framework for effectively implementing IoT towards sustainable development.

Modelling the operational maturity challenges faced by online food ordering and delivery enterprises during Covid-19 lockdown in Oman: a fuzzy interpretive structural modelling approach

PurposeThis research aims to ascertain the various operational maturity challenges faced by the online food ordering and delivery enterprises (OFODE), their nature and their interactive relationships. In particular, this paper aims to (a) identify the most relevant operational maturity challenges faced by the OFODE during the COVID-19 lockdown in Oman, (b) explore and establish any likely structural relationship among these challenges and (c) put them into logical clusters.Design/methodology/approachExperts helped to reduce the 18 initially identified maturity challenges to 13 most pressing ones. Mutual relationships, dominance of interactions and their classifications were explored using fuzzy interpretive structural modeling (FISM) and fuzzy MICMAC analysis.FindingsThe study of situation-specific operational maturity challenges convinced the authors to propose a distinct FISM model that depicts the relationship among these challenges. Keeping commissions and fees reasonable emerges as the challenge which all other challenges seemingly culminate into. One of the most important situation-specific challenges (i.e. customer confidence about infection free delivery) emerges as a linkage challenge which aggravates as well as is aggravated by certain challenges.Research limitations/implicationsBesides enriching literature, the proposed model has implications for practitioners particularly when the similar lethal waves are experienced anywhere. The number of respondents, subjective approach, specific context as well as the geographical area coverage are the key limitations.Originality/valueTo the best of the authors’ knowledge, this study is the first known scientific effort which attempts to model the operational maturity challenges faced by the OFODE during COVID-19 lockdown period. The authors used the FISM modeling approach to forge these interrelated challenges into a structural model.

Developing a hierarchical framework for assessing the strategic effectiveness of sustainable waste management in the Somaliland construction industry.

This study contributes to develop a hierarchical framework for assessing the strategic effectiveness of waste management in the construction industry. This study identifies a valid set of strategic effectiveness attributes of sustainable waste management (SWM) in construction. Prior studies have neglected to develop a strategic effectiveness assessment framework for SWM to identify reduce, reuse, and recycle policy initiatives that ensure waste minimization and resource recovery programs. This study utilizes the fuzzy Delphi method to screen out nonessential attributes in qualitative information. This study initially proposes a set of 75 criteria; after two rounds of assessment, consensus regarding 28 criteria is achieved among experts, and the 28 criteria are validated. Fuzzy interpretive structural modeling divides the attributes into various elements. The modeling constructs a six-level model that depicts the interrelationships among the 28 validated criteria as a hierarchical framework, and it finds and ranks the optimal drivers for practical improvement. This study integrates the best-worst method to measure the weights of different criteria in the hierarchical strategic effectiveness framework. The findings reveal that waste management operational strategy, construction site waste management performance, and the mutual coordination level are the top aspects for assessing strategic effectiveness in the hierarchical framework. In practice, the waste reduction rate, the recycling rate, water and land usage, the reuse rate, and noise and air pollution levels are identified to assist policymakers in evaluation. The theoretical and managerial implications are discussed.

Modelling and analysing the enablers of digital resilience for small and medium enterprises

PurposeSmall and medium enterprises (SMEs) significantly contribute to economic growth, development, exports and employment of the nations. To maintain competitiveness in today's market, SMEs must explore and identify enablers to enhance their digital transformation process. This paper aims to shed light on some essential enablers SMEs can use to implement digital resilience successfully.Design/methodology/approachThe quantitative assessment and validation of the enablers have been done using powerful and novel techniques, namely, the Delphi method, “fuzzy interpretive structural modelling” (F-ISM) method and “cross-impact matrix multiplication applied to classification (MICMAC)” analysis. The F-ISM model is developed using the information drawn from digital transformation experts and practitioners involved in the digital transformation process for SMEs. Furthermore, the F-ISM model provides four paths to complete the pathway to digital resilience.FindingsThe F-ISM and MICMAC analysis revealed four ways to enhance the digital transformation process in SMEs. These enterprises can utilise these path assessments to become digitally resilient in the present dynamic scenario. To enhance digital resilience among SMEs, the study identified ten enablers. Among these, “management competencies” was the most crucial, followed by “knowledge management” and “monitoring and controlling”.Research limitations/implicationsThe present study is limited in that the data used to develop the models were collected from a small group of industry experts whose opinions may not exhibit the comprehensive views of the population.Practical implicationsThe findings can help SMEs enhance the digital transformation process by taking up different pathways to integrate the various enablers of digital resilience depending on resource availability.Originality/valueThe results indicate the most critical and influential enablers for enhancing digital resilience among SMEs. This research can be valuable to academicians, industry practitioners and researchers for guiding their future work.

Enablers for Growth of Cryptocurrencies: A Fuzzy–ISM Benchmarking

Cryptocurrencies and their market capitalisation have experienced vibrant growth in the last few years. Their total market cap is more than USD 858 billion as of the date of writing and is growing, with nearly 21,984 tradeable cryptos in 530 exchanges. It is emerging as one of the biggest threats to the traditional fundraising market. The issue of the industry’s long-term viability and steady expansion is of paramount importance. Even though unsustainable and uneven growth could help boost economic activity in the short term, it would be detrimental in the long run because of the risk of extinction. This paper is one of the first attempts to identify the factors contributing to the growth of the cryptocurrency market and their effects. This paper is based on the hybrid MCDM methodology of research and uses fuzzy–ISM (interpretive structural modelling). This method is divided into three phases: identification, expert opinion, and interpretation. Sixteen factors were chosen from the previous literature and suggestions from industry professionals. Seven barriers have been framed based on the fuzzy–ISM analysis to better understand the impacts of and interrelationships among the identified barriers. The factors are further classified using fuzzy MICMAC into four major categories based on the drive power and dependence power extracted from the fuzzy matrix. This paper explains the importance of all identified factors as enablers of the acceptance of cryptocurrencies for investment and fundraising.

Green, resilient, agile, and sustainable fresh food supply chain enablers: evidence from India.

The online version contains supplementary material available at 10.1007/s10479-023-05176-x.

Survival and sustenance strategy of primary agricultural cooperative credit societies in India: a fuzzy interpretive structural modelling approach

PurposePrimary agricultural cooperative credit societies (PACSs) in India are considered to be the backbone of the last mile delivery of rural credit for landless and marginal farmers and the poorest sections of the society. Contribution of PACS in social welfare is well documented. However, in spite of being such important form of organization at the grass root level, the authors find a typical baffling phenomenon which is fluctuating growth of PACS. Given that PACS have not been able to establish a consistent growth rate, thus hindering its trajectory to become a strong and consolidated organizational form, it is natural to investigate and identify those success factors that are critical for a newly formed PACS to survive and sustain. Hence it is necessary to undertake a strategic assessment of the function of PACS and hence identify those critical success factors (CSFs). This paper aims to address this issue.Design/methodology/approachThe authors review the existing literature and conduct interview with experts and other stakeholders with the purpose to gain a view of the strategic situation of PACS in India, especially with respect to their birth and death processes and identify a set of factors that are critical to the survival and sustenance of PACS. Based on the inputs from the experts, we modelled the interrelationships amongst these CSFs using interpretive structural modelling (ISM). Further, fuzzy Matriced’ Impacts Croise's Multiplication Appliquée a UN Classement (MICMAC) analysis is performed to cluster these CSFs into four quadrants based on their influence and dependence in the system.FindingsThe findings point out that a hierarchical interconnected structure exists amongst the CSFs that influence the survival and sustenance of the PACSs. “Quality and Motivation of Management” was found to be driving all other factors so far the survival of any PACS is considered. Also the authors have found that “Loan Recovery” is the most sensitive factor that needs to be taken care of for the sustenance of PACS. These findings are in the expected line and commensurate with the conventional wisdom yet to be documented till date.Originality/valueIt is a forward-looking analysis that tries to find out the CSFs for the survival of PACS in order to face the changing economic scenario of India which is still agrarian in nature. Both in terms of research question as well as methodology, to the best of the authors’ knowledge, this is first of its kind so far PACS are concerned being one of the most important but neglected entities in India; so far the last mile rural agricultural credit delivery is concerned, this study will help the policy makers to develop a holistic viewpoint in relation to long term sustenance of PACS.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-05-2022-0331

Designing a self-management model of the career of higher education graduates with a focus on new perspectives career

The present study was conducted with the aim of designing a self-management model of the career path of higher education graduates, focusing on new perspectives on the career path and based on mixed research, which was applied in terms of purpose and descriptive survey in terms of nature and method. The statistical population of the qualitative and quantitative part of the research included all entrepreneurial graduates and professors who are members of the faculty of entrepreneurship faculties of universities of a country, and in the qualitative phase, 35 of them were selected based on the principle of theoretical adequacy and using the Purposive sampling method. The data collection tool in the qualitative part was a semi-structured interview, and in the quantitative part, a researcher-made questionnaire based on the results of the qualitative phase of the research, whose validity was confirmed by using the CVR coefficient and its reliability was confirmed by calculating the inconsistency rate. Qualitative data analysis was done using thematic analysis method and Atlas.ti software, as a result of which 10 main effective factors in career self-management were identified. In the quantitative part, the model was designed in six levels using Matlab software and fuzzy interpretive structural modeling method, in which self-awareness and entrepreneurial self-confidence with the greatest power of guidance was placed as the main effective factor in self-management of the career path in the sixth level of the model. Clarity of goals and expectations was another effective factor that was in the fifth level of the model, and physical dynamics and personal marketing were the variables that were in the fourth level of the model. In the continuation of leveling, the three variables of psychological skills, human skills and adaptation and environmental awareness were placed in the third level, academic knowledge in the second level, and cognitive and information skills in the first level of the model.

Ranking Risks of BOT Toll Road Investment Projects in Indonesia Using Fuzzy Interpretive Structural Modelling

The Government of Indonesia implemented the Build, Operate, and Transfer (BOT) model, relying on private investment to bridge the financing gap in developing public infrastructure facilities, including toll roads. Toll road investments, like other greenfield infrastructure projects, are typically characterized by high project risk, which discourages private sector investment. Many previous studies have investigated the various risk events in toll road investment projects, but only a few have assessed the interrelationships of risk events in the Indonesian context. This study fills this knowledge gap by determining which risk event influences other events most. Fuzzy interpretive structural modelling combined with the matrix impact of cross-references multiplication applied to a classification method was used to determine the hierarchy of risk events and analyze their influences on other risk events. A total of fourteen risk events were identified and analyzed. An unclear output specification was found to be the most significant risk event, with the biggest driving power affecting other risks. The findings and limitations of this study point the way forward for future research.