Modeling the barriers toward the growth of higher education institutions

2020 ◽  
Vol 20 (2) ◽  
pp. 243-264
Author(s):  
Faisal Talib ◽  
Zillur Rahman
Keyword(s):  
Higher Education ◽  
Structural Model ◽  
Higher Education Institutions ◽  
Decision Makers ◽  
Interpretive Structural Modeling ◽  
Climate Change Research ◽  
Content Type ◽  
Micmac Analysis ◽  
Top Managers ◽  
World Class

PurposeIn recent years, sustainable issues have gained increasing importance in the higher education sector across the country. To remain competitive, higher education institutions (HEIs) have to engage themselves in providing world-class opportunities for higher education and research so that the beneficiaries may not be at a disadvantage in the international level. However, many HEIs still encounter barriers that can affect their growth. The purpose of this study is to identify and interpret the most significant barriers affecting the growth of HEIs in India and understand the mutual interactions and interdependence among them.Design/methodology/approachIn this study, 12 barriers affecting the growth of HEIs in India were identified and sorted by literature review, and these were validated through discussion with experts. Contextual relationships were drawn between them during brainstorming sessions. A hierarchical, seven-level structural model of these barriers affecting the growth of HEIs was developed by utilizing the total interpretive structural modeling (TISM) approach.FindingsThe results of this study showed “lack of climate change research” as the topmost outcome of the model having the highest dependence and lowest driving power. “Facilitator domination over academic experts” occupied the bottom-level input, compared to other barriers of the model, with highest driving power and lowest dependence. Further, Matrice d'Impacts Croisés – Multiplication Appliquée à un Classement (MICMAC) analysis was also performed to categorize the identified barriers for better understanding. Under MICMAC analysis, five barriers were categorized as driver barriers, four barriers as dependent barriers and the rest as linkage barriers, while none as autonomous. A TISM-based model that partitioned the barriers into different levels was developed, showcasing direct and important transitive relations.Practical implicationsThe TISM-based model developed in this study may provide a more realistic approach to the problems faced by decision-makers, academicians and top managers in the growth of HEIs in India. Thus, it will provide a roadmap to focus on the most influential barriers and reduce or eliminate them.Originality/valueThe present study identified major barriers that when dealt with can help overcome the effect of other barriers preventing the growth of HEIs in India. It also introduces a model to identify and manage the barriers by understanding the effectiveness of each barrier and minimizing the overall effect in Indian HEIs. Moreover, the analysis of interdependence and interactions studied among these barriers may also help decision-makers, academicians and top managers to find deeper insights, prioritize and rectify them, to re-establish the growth of HEIs and gain a competitive advantage.

Modeling the barriers of Indian telecom services using ISM and MICMAC approach

2017 ◽  
Vol 11 (2) ◽  
pp. 188-209 ◽  
Author(s):  
Faisal Talib ◽  
Zillur Rahman
Keyword(s):  
Literature Review ◽  
Clear Understanding ◽  
Interpretive Structural Modeling ◽  
Holistic View ◽  
Content Type ◽  
Micmac Analysis ◽  
Top Managers ◽  
Effective Performance ◽  
Relationship Model ◽  
Telecom Sector

Purpose The purpose of this paper is to identify the potential barriers of telecom services and develop relationships among them using interpretive structural modeling (ISM) in the Indian telecom sector. Further, this paper intends to find the driving and dependence powers of telecom barriers, using MICMAC (Matriced' Impacts Croise′s Multiplication Applique′ea′un Classement) analysis. Design/methodology/approach A group of experts from telecom sector and academia was consulted and the ISM was used to develop the contextual relationship among identified barriers of telecom services. The results of ISM are used as an input to MICMAC analysis, to further classify identified barriers based on their driving and dependence powers. Findings This paper has identified 11 barriers of telecom services from the literature review and opinion of experts, and a contextual relationship model was developed using the ISM technique. The paper further studied barriers based on their driving and dependence powers using MICMAC analysis that classifies them into four clusters. The results identified the barriers such as “lack of adequate investment in infrastructure expansion”, “interference of private operators”, “legal and regulatory issues”, “lack of maintenance culture” and “lack of financial resources and high cost” as strategic-level barriers that, if dealt with carefully, can help overcome the effect of other barriers preventing the effective performance of the Indian telecom sector. Research limitations/implications Although, there are a number of barriers that affect the performance of telecom services, existing theories, models and frameworks have focused on identification of factors, their implementation and benefits, and thus, these theories lack a holistic view in understanding those barriers which hinder the performance of the telecom sector. Top-managers and policymakers need to be more concerned about these barriers so that the performance of the Indian telecom sector could be improved. Originality/value A thorough literature review has revealed that no such study has been undertaken that provides any clear understanding of the barriers of telecom services, linkages between them and hierarchical relationships among the barriers.

Modelling and analysis of barriers affecting the implementation of lean green agile manufacturing system (LGAMS)

2019 ◽  
Vol 26 (2) ◽  
pp. 498-529 ◽  
Author(s):  
Rahul Sindhwani ◽  
Varinder Kumar Mittal ◽  
Punj Lata Singh ◽  
Ankur Aggarwal ◽  
Nishant Gautam
Keyword(s):  
Manufacturing Systems ◽  
Manufacturing System ◽  
Agile Manufacturing ◽  
Extensive Literature ◽  
Interpretive Structural Modeling ◽  
Content Type ◽  
Micmac Analysis ◽  
Top Managers ◽  
Discrete Manner ◽  
Practical Implications

Purpose Many types of research have already investigated the lean, green or agile manufacturing systems in a discrete manner or as combinations of two of them. In today’s competitive scenario, if industry wants to perpetuate its name in the market, then it has to supervene proper thinking and smart approach. Therefore, the combination of lean, green and agile manufacturing systems can provide better and beneficial results. The purpose of this paper is to discern the barriers to the combined lean green agile manufacturing system (LGAMS), understand their interdependence and develop a framework to enhance LGAMS by using total interpretive structural modeling (TISM) and MICMAC (Matriced’ Impacts Croise’s Multiplication Appliquée a UN Classement) Analysis. Design/methodology/approach This paper uses TISM methodology and MICMAC analysis to deduce the interrelationships between the barriers and rank them accordingly. A total of 13 barriers have been identified through extensive literature review and discussion with experts. Findings An integrated LGAMS has been presented that balances the lean, green and agile paradigms and can help supply chains become more efficient, streamlined and sustainable. Barriers are identified while referring to all three strategies to showcase the clear relevance. TISM models the barriers in different levels showcasing direct and important transitive relations. Further, MICMAC analysis distributes the barriers in four clusters in accordance with their driving and dependence power. Research limitations/implications The inferences have been drawn from a model developed on the basis of inputs from a small fraction of the industry and academia and may show variations when considering the whole industry. Practical implications The outcome of this research can contribute to bringing the change to the manufacturing systems used in most developing nations. Also, top managers considering adoption of LGAMS can be cautious of the most influential barriers. Originality/value A TISM-based model of the barriers to an integrated LGAMS has been proposed with evaluation of the influence of the barriers.

Modeling the performance measures of world class manufacturing using interpreting structural modeling

2015 ◽  
Vol 10 (1) ◽  
pp. 4-22 ◽  
Author(s):  
Abhijeet Keshaorao Digalwar ◽  
Anil Jindal ◽  
Kuldip Singh Sangwan
Keyword(s):  
Performance Measures ◽  
Model Performance ◽  
Structural Modeling ◽  
Decision Makers ◽  
Interpretive Structural Modeling ◽  
Content Type ◽  
Practical Applications ◽  
Manufacturing Organizations ◽  
World Class ◽  
World Class Manufacturing

Purpose – The purpose of this paper is to study the performance measures of world class manufacturing (WCM) and to establish relationship among them using interpretive structural modeling (ISM). Design/methodology/approach – The research paper presents a blend of theoretical framework and practical applications. In the paper, 16 performance measures are identified from literature survey and experts’ opinion, and then these are validated by questionnaire survey in India. Finally, ISM is used to obtain structural relationship among these performance measures of WCM. Findings – The results of the survey and the ISM methodology have been used to evolve the mutual relationships among these performance measures. Practical implications – The adoption of such an ISM-based model on WCM performance measures in manufacturing organizations would help managers, decision-makers and practitioners of WCM in better understanding of these performance measures and to focus on appropriate performance measures while implementing WCM in their organizations. Originality/value – Performance measures are of paramount importance for the implementation of WCM practices. Knowing the key performance measures and relationship among them can help many organizations to implement WCM practices. It is one of the foremost attempts to model performance measures of WCM. The paper provides useful insights into the WCM practitioners, consultants and researchers.

Factors influencing organizational agility in higher education

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shalini Menon ◽  
M. Suresh
Keyword(s):  
Higher Education ◽  
Organizational Structure ◽  
Human Resource ◽  
Structural Model ◽  
Readiness To Change ◽  
Matrix Multiplication ◽  
Extensive Literature ◽  
Organizational Agility ◽  
Interpretive Structural Modeling ◽  
Content Type

PurposeThe purpose of this paper is to explore the factors that can facilitate agility in higher education and to analyze the interrelationship between the factors.Design/methodology/approachA structured model of factors facilitating agility in higher education was developed using total interpretive structural modeling (TISM). Cross-impact matrix multiplication (MICMAC) analysis helped in classifying the factors on the basis of their driving and dependency power.FindingsAn extensive literature review and expert opinion helped in identifying eight enablers that can promote agility in higher education. The ability to sense the environment, organizational structure, adoption of ICT, organizational learning, human resource strategies, leadership, readiness to change and collaboration with the stakeholders were the eight factors identified. The structural model revealed leadership as the most crucial enabler followed by human resource strategies and organizational structure.Research limitations/implicationsThe model has incorporated and prioritized all the crucial drivers of agility that can help universities and colleges design, adopt and implement policies and practices that would facilitate agility.Originality/valueSo far, the research on agility in higher education has looked into each factor in isolation. This research provides a comprehensive list of the factors and establishes the interplay between the factors making this study new and original.

Application of total interpretive structural modeling for analyzing factors of additive manufacturing and industry 4.0 integration

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Vishal Ashok Wankhede ◽  
Vinodh S.
Keyword(s):  
Additive Manufacturing ◽  
Industry 4.0 ◽  
Structural Model ◽  
Matrix Multiplication ◽  
Structural Modeling ◽  
Influential Factors ◽  
Time Data ◽  
Interpretive Structural Modeling ◽  
Content Type ◽  
Micmac Analysis

Purpose The purpose of this paper is to develop a model based on the total interpretive structural modeling (TISM) approach for analysis of factors of additive manufacturing (AM) and industry 4.0 (I4.0) integration. Design/methodology/approach AM integration with I4.0 is attributed due to various reasons such as developing complex shapes with good quality, real-time data analysis, augmented reality and decentralized production. To enable the integration of AM and I4.0, a structural model is to be developed. TISM technique is used as a solution methodology. TISM approach supports establishing a contextual relationship-based structural model to recognize the influential factors. Cross-impact matrix multiplication applied to classification (MICMAC) analysis has been used to validate the TISM model and to explore the driving and dependence power of each factor. Findings The derived structural model indicated the dominant factors to be focused on. Dominant factors include sensor integration (F9), resolution (F12), small build volumes (F19), internet of things and lead time (F14). MICMAC analysis showed the number of driving, dependent, linkage and autonomous factors as 3, 2, 12 and 3, respectively. Research limitations/implications In the present study, 20 factors are considered. In the future, additional factors could be considered based on advancements in I4.0 technologies. Practical implications The study has practical relevance as it had been conducted based on inputs from industry practitioners. The industry decision-makers and practitioners may use the developed TISM model to understand the inter-relationship among the factors to take appropriate measures before adoption. Originality/value The study on developing a structural model for analysis of factors influencing AM and I4.0 is the original contribution of the authors.

A framework to enhance agile manufacturing system

2017 ◽  
Vol 24 (2) ◽  
pp. 467-487 ◽  
Author(s):  
Rahul Sindhwani ◽  
Vasdev Malhotra
Keyword(s):  
Manufacturing System ◽  
Structural Model ◽  
Agile Manufacturing ◽  
Top Management ◽  
Management Support ◽  
Interpretive Structural Modeling ◽  
Content Type ◽  
Micmac Analysis ◽  
Model Hierarchy ◽  
Or Organization

Purpose The purpose of this paper is to identify and analyze the interactions among different enablers of agile manufacturing system (AMS). The existing enablers available in the past literature are scattered and not able to meet specific requirements of the customers. So, it becomes a necessity to encapsulate these enablers in appropriate proportions to enable traditional organizations to AMS. To fill this gap total interpretive structural modeling (TISM) and MICMAC analysis-based framework model have been developed to understand the mutual interactions between among the enablers. Design/methodology/approach Identification of enablers followed by application of TISM, which is an innovative version of ISM and MICMAC analysis, is used to study and analyze the mutual interactions between identified enablers. Findings The result reveals that top management support, organizational structure and information technology integration have strong driving power and weak dependence power and are at the lowest level in the TISM model hierarchy, while the outcome enablers of AMS have low-driving power but have high-dependence power. Research limitations/implications This model is developed on the basis of inputs from few experts and may not reflect the opinion of whole industry community. Practical implications Top management must stress on enablers having strong driving power for efficient implementation of AMS. Managers in the area of manufacturing may drive useful insights from the empirical study presented in this paper. Managers should plan an effective strategy for proper implementation of AMS which makes organization more agile, productive, competitive and profitable. Originality/value TISM-based framework structural model has been proposed for industry or organization which is a new effort for implementation of AMS.

Development of framework for just-in-time implementation in maintenance

2018 ◽  
Vol 24 (4) ◽  
pp. 488-510 ◽  
Author(s):  
Sandeep Phogat ◽  
Anil Kumar Gupta
Keyword(s):  
Structural Equation ◽  
Structural Model ◽  
Model Development ◽  
Equation Modeling ◽  
Just In Time ◽  
Interpretive Structural Modeling ◽  
Content Type ◽  
Micmac Analysis ◽  
Binary Correlation ◽  
Power And Dependence

Purpose The purpose of this paper is to propose an interpretive structural modeling (ISM) model which highlights the relationships between the identified just-in-time (JIT) elements useful for the implementation of JIT in maintenance and understand mutual influences of these identified JIT elements on JIT implementation in maintenance. Further, this paper seeks to identify dependence power and driving power of identified JIT elements using an ISM and Matrice d’Impacts Croisés Multiplication Appliquée á un Classement (MICMAC) analysis. Design/methodology/approach The methodology used in the paper is ISM with a view to evolving mutual relationships among JIT elements. The identified JIT elements have been further classified, based on their dependence power and driving power using MICMAC analysis. Findings This paper has developed the relationships among 16 identified JIT elements using the ISM methodology. Further, this paper analyses the driving power and dependence power of identified JIT elements with the help of MICMAC analysis. The incorporated approach is developed here, as the ISM provides only binary correlation among identified JIT elements. The MICMAC analysis is adopted here as it is useful in specific examination related to driving and the dependence power of identified JIT elements. The ISM developed model and MICMAC analysis finding are validated with the help of industrial experts. Research limitations/implications The weightage and validation for the ISM and MICMAC analysis are obtained throughout the opinion of academics and industry experts. Further hypothesis may be conducted to examine the validity of the planned model, and structural model may also be validated statistically with the help of structural equation modeling. Practical implications The ISM model development and MICMAC analysis of identified JIT elements provide academics and maintenance managers a macro picture of the profits gained by the organizations by the implementation of JIT in maintenance of an organization. Originality/value The results will be useful for maintenance managers to understand the process of implementation of JIT in maintenance and to gain benefits after the implementation of JIT in maintenance of an organization.

Sustainable industry 4.0 – an exploratory study for uncovering the drivers for integration

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
N. Harikannan ◽  
S. Vinodh ◽  
Anand Gurumurthy
Keyword(s):  
Industry 4.0 ◽  
Structural Model ◽  
Public Awareness ◽  
Sustainable Manufacturing ◽  
Matrix Multiplication ◽  
Interpretive Structural Modeling ◽  
Content Type ◽  
Micmac Analysis ◽  
Societal Pressure ◽  
Practical Implications

Purpose The concept of sustainable manufacturing has been adopted by manufacturing organizations to develop eco-friendlier products and processes. In recent times, industries are progressing toward Industry 4.0 (I4.0). Guided with smart intelligent devices, I4.0 can possibly decrease excess production, material movement and consumption of energy. If so, it is hypothesized that there is a good synergy between I4.0 and sustainability, which warrants an integrated approach for implementation. This amalgamation is termed as “Sustainable industry 4.0.” Hence, this paper aims to systematically identify and analyze the drivers for this integration. Design/methodology/approach This paper presents the analysis of 20 drivers identified from literature review for simultaneous deployment of I4.0 and sustainable manufacturing. Interpretive structural modeling (ISM) is used to derive the structural model for analyzing the causal association between drivers. Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) analysis is being performed to group the drivers. Findings The results showed that the dominant drivers derived are societal pressure and public awareness (D18), government policies on support I4.0 (D12), top management involvement and support (D15) and government promotions and regulations (D16). Also, the MICMAC analysis revealed many driving, dependent, linkage and autonomous drivers. Research limitations/implications The opinion from experts with combined expertise on I4.0 and sustainability was obtained. The respondent size could be increased in future studies. Practical implications The study has been done based on inputs from industry practitioners. Managerial and practical implications are presented. ISM shows that the drivers for deploying sustainable I4.0 are highly inter-related. It also reveals the pre-requisites for each level of the drivers. Originality/value The idea of analyzing the drivers for sustainable I4.0 is the original contribution of the authors.

Academia a new knowledge supplier to the industry! Uncovering barriers in the process

2019 ◽  
Vol 16 (5) ◽  
pp. 715-733 ◽  
Author(s):  
Ankur Kashyap ◽  
Rajat Agrawal
Keyword(s):  
Higher Education ◽  
Supply Chain ◽  
Structural Model ◽  
Practical Implication ◽  
Hybrid Approach ◽  
Research Problem ◽  
Expert Elicitation ◽  
Interpretive Structural Modeling ◽  
Content Type ◽  
Knowledge Component

Purpose In the era of Industry 4.0, knowledge component plays a vital role in manufacturing. For tacking the new complexities of the business, a concept of knowledge supply chain (KSC) is being proposed, which takes into account of knowledge component. Higher education institutes (HEIs) which are primary creator of knowledge are important foundations of such supply chain and act as the “knowledge supplier.” The purpose of this paper is to focus on why the HEIs are failed to become knowledge supplier in developing country like India. Design/methodology/approach This research paper adopts a resource-based theory to explore the concept and identify barriers which obstructs the progress of HEIs to become prominent knowledge supplier to industry. To tackle the research problem, an integrated hybrid approach of interpretive structural modeling–analytic hierarchy process is used. Expert elicitation was engaged to find out the prominence of each barrier and the interrelations among them. Findings Based on literature review, eight critical barriers were recognized. The findings put forward a four layer structural model. Based on this model, various remedial actions are also suggested to eliminate the barriers or lessen their negative effects on KSC. Practical implications This study finds its practical implication in higher education reforms as the identified barriers could enhance the decision-making quality regarding academia–industry interaction. Social implications Using the results of the study, HEIs could improve their social sustainability as they have different stakeholders covering wider sections of society and one being industry. Originality/value Most of the existing studies talk about short-term interactions like technology transfer. This study takes into account the barriers which are acting as roadblocks in long-term knowledge supplying role of HEIs.

TISM-based analysis of important factors for additive manufacturing in healthcare:a case study

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
S. Logesh ◽  
S. Vinodh
Keyword(s):  
Health Care ◽  
Additive Manufacturing ◽  
Structural Model ◽  
Matrix Multiplication ◽  
Extensive Literature ◽  
Interpretive Structural Modeling ◽  
Content Type ◽  
Micmac Analysis ◽  
Factors Influencing

Purpose This paper aims to focus on developing a theoretical framework for the analysis of factors influencing additive manufacturing (AM) in the health-care domain. Design/methodology/approach A total of 18 factors are considered through extensive literature review and the relationship between each factor is studied using total interpretive structural modeling (TISM) and the model is logically developed. TISM model is developed using appropriate expert inputs. In addition, cross-impact matrix multiplication applied to classification (MICMAC) analysis is conducted to group the factors. Findings It was found that “ease of design” and “research and development” are the two most important factors with the highest driving power and dependencies. Through MICMAC analysis, the significance of factors is studied. Practical implications The study has been done based on inputs from academic experts and industry practitioners. The inferences from the study have practical relevance. Originality/value The development of a structural model for the analysis of factors influencing AM in the health-care domain is the original contribution of the authors.

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