Modelling and analysis of factors for implementation of smart cities: TISM approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Anita Singh ◽  
Ashim Raj Singla
Keyword(s):  
Smart Cities ◽  
Matrix Multiplication ◽  
Key Factors ◽  
Rapid Urbanization ◽  
Content Type ◽  
Micmac Analysis ◽  
Expert Opinions ◽  
Structural Framework ◽  
Primary Basis ◽  
Power And Dependence

Purpose The concept of “Smart Cities” is gaining prominence across the world as a solution to effectively address the issues or impediments faced by cities due to rapid urbanization. The purpose of this paper is to identify the key factors which form the primary basis for the implementation of “Smart Cities”. Particularly, this paper aims to analyse the contextual relationship and driving/dependence power of these key factors and model these using the total interpretive structural modelling (“TISM”) framework. Design/methodology/approach The key factors which form the basis for the implementation of Smart Cities were identified through an evaluation of the literature on “Smart Cities” and expert opinions. Thereon, the contextual relationship between these key factors was examined with the help of experts. Thereafter, these key factors were modelled using the total interpretive structured modelling (“TISM”) framework. Cross-impact matrix multiplication applied to classification (MICMAC) analysis was further applied to classify the factors. It is pertinent to note that the driving power and dependence of these key factors were also reviewed. Findings This paper establishes a TISM of the key factors for the implementation of “Smart Cities” which will aid in examining the interrelationship among the factors and will also identify the hierarchy among these factors. On extensive examination of the literature and expert opinions on “Smart Cities”, it can be asserted through TISM that quality of life (F1), e-services adoption (F5) and economic growth (F8) are the leading factors in establishing “Smart Cities”. Furthermore, it must be noted that the MICMAC analysis and driving-dependence graph helps in classifying the key factors as autonomous factors, drivers, linkages and outcomes, which assists in comprehending which factors possess driver power and which are exhibiting dependency. Originality/value The contribution lies in the authentic manner in which this paper attempts to use the TISM approach combined with MICMAC analysis to model key factors for the implementation of “Smart Cities”; which would aid and assist policymakers and practitioners to construct a structural framework for the implementation of “Smart Cities” through identification of drivers, linkages and outcomes.

Smart neighbourhood

2018 ◽  
Vol 9 (2) ◽  
pp. 210-226 ◽  
Author(s):  
Harish Kumar ◽  
Manoj Kumar Singh ◽  
M.P. Gupta ◽  
Jitendra Madaan
Keyword(s):  
Developing Countries ◽  
Urban Development ◽  
Smart City ◽  
Smart Cities ◽  
Matrix Multiplication ◽  
Working Environment ◽  
Sustainable Urban Development ◽  
Key Factors ◽  
Content Type ◽  
City Planners

Purpose This paper aims to identify the key factors to design efficient, healthy and potentially economical neighbourhood places in the surroundings of smart cities to reduce the urban polarization for the sustainable urban development. Design/methodology/approach A two-stage methodology is followed. First, the key factors for neighbourhood are identified from literature studies. The selected factors are validated by sample t-tests. Second, the total interpretive structural modeling is used to interpret the complexity of relationships among various factors. Further, cross-impact matrix multiplication is applied for classification analysis to find the most driving factors for neighbourhood design. Findings The contribution of this research is to show hierarchical relationships among the various factors to design the neighbourhood places as smart from the perspectives of city planners and decision makers. Research limitations/implications The applicability of the research findings is limited to developing countries mainly where population is large and most of cities have high pressure on its infrastructure to fulfil the citizens’ demands. Practical implications This paper will aid policymakers, city planners and government officials to design a sustainable smart city model in which smart neighbourhood would also be the potential solution to decrease pressure on a city’s critical infrastructure especially in developing countries. Social implications A smart city could be considered as the centre point of smart initiatives to develop a place smart, and it should continue beyond the city boundaries to enhance the facilities, services, resources utilization and working environment in neighbourhood places also. Originality/value The study explores the various literature on neighbourhood planning and then link with smart city development as current need of urban development scenario. The authors propose a hierarchical relation framework to develop the neighbourhood places as smart places to meet the future demand of urbanization in developing countries like India.

Total Interpretive Structural Modeling (TISM): approach and application

2017 ◽  
Vol 14 (2) ◽  
pp. 162-181 ◽  
Author(s):  
J. Jena ◽  
Sumati Sidharth ◽  
Lakshman S. Thakur ◽  
Devendra Kumar Pathak ◽  
V.C. Pandey
Keyword(s):  
Design Methodology ◽  
Matrix Multiplication ◽  
Structural Modeling ◽  
Decision Makers ◽  
Interpretive Structural Modeling ◽  
Content Type ◽  
Mutual Dependence ◽  
Step Procedure ◽  
Power And Dependence ◽  
Practical Implications

Purpose The purpose of this paper is to elucidate the methodology of total interpretive structural modeling (TISM) in order to provide interpretation for direct as well as significant transitive linkages in a directed graph. Design/methodology/approach This study begins by unfolding the concepts and advantages of TISM. The step-by-step methodology of TISM is exemplified by employing it to analyze the mutual dependence among inhibitors of smartphone manufacturing ecosystem development (SMED). Cross-impact matrix multiplication applied to the classification analysis is also performed to graphically represent these inhibitors based on their driving power and dependence. Findings This study highlights the significance of TISM over conventional interpretive structural modeling (ISM). The inhibitors of SMED are explored by reviewing existing literature and obtaining experts’ opinions. TISM is employed to classify these inhibitors in order to devise a five-level hierarchical structure based on their driving power and dependence. Practical implications This study facilitates decision makers to take required actions to mitigate these inhibitors. Inhibitors (with strong driving power), which occupy the bottom level in the TISM hierarchy, require more attention from top management and effective monitoring of these inhibitors can assist in achieving the organizations’ goals. Originality/value By unfolding the benefits of TISM over ISM, this study is an endeavor to develop insights toward utilization of TISM for modeling inhibitors of SMED. This paper elaborates step-by-step procedure to perform TISM and hence makes it simple for researchers to understand its concepts. To the best of the authors’ knowledge, this is the first study that analyzes the inhibitors of SMED by utilizing TISM approach.

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.

Analysis of drivers for anti-food waste behaviour - TISM and MICMAC approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Archana Poonia ◽  
Shilpa Sindhu ◽  
Vikas Arya ◽  
Anupama Panghal
Keyword(s):  
Expert Opinion ◽  
Food Waste ◽  
Matrix Multiplication ◽  
Cultural Norms ◽  
Extensive Literature ◽  
Critical Factors ◽  
Content Type ◽  
Factors Affecting ◽  
Micmac Analysis ◽  
Effective Interventions

Purpose This study aims to identify and analyse the interactions among drivers of anti-food waste behaviour at the consumer level. By understanding the mutual interactions among the drivers, an effort is made to identify the most driving and most dependent drivers through the total interpretive structural modelling (TISM) approach. Modelling offers inputs to propose focused interventions for reinforcing the identified drivers of anti-food waste consumer behaviour using the theoretical lens of social practices theory. Design/methodology/approach A proposed model of factors affecting anti-food waste behaviour is arrived at to suggest the most effective anti-food waste behavioural interventions. The factors were identified through an extensive literature search. A hierarchical structure of identified factors has been developed using TISM and MICMAC analysis through expert opinion. Focused marketing strategies towards promoting the identified factors for encouraging anti-food waste behaviour were suggested further. Findings This study identifies nine drivers based on extensive literature review, brainstorming and expert opinion. The TISM hierarchical model portrays the most important and least important drivers of household anti-food waste behaviour. It establishes that fundamental knowledge and socio-cultural norms are the most critical factors to drive the consumers. Marketers can focus on designing effective interventions to enhance the essential knowledge of the consumers and orient the socio-cultural norms towards anti-food waste behaviour. Practical implications This study offers implications for practitioners, policymakers and cause-driven marketing campaigns targeting anti-food waste behaviour. It provides an indicative list of critical factors relevant to household food waste behaviour, which can be used to drive effective marketing campaigns to nudge anti-food waste behaviours. Originality/value The proposed food waste behaviour management model was developed through modelling technique (TISM) and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) analysis, and relating them to marketing interventions is a novel effort in the food waste domain.

A TISM modeling of critical success factors of smartphone manufacturing ecosystem in India

2016 ◽  
Vol 13 (2) ◽  
pp. 203-224 ◽  
Author(s):  
J Jena ◽  
Vijayta Fulzele ◽  
Rachita Gupta ◽  
Fahima Sherwani ◽  
Ravi Shankar ◽  
...  
Keyword(s):  
Critical Success Factors ◽  
Success Factors ◽  
Matrix Multiplication ◽  
Decision Makers ◽  
Interpretive Structural Modeling ◽  
Content Type ◽  
Hierarchical Framework ◽  
Critical Success ◽  
Expert Opinions ◽  
Relevant Factors

Purpose – The purpose of this paper is to identify and analyze various critical success factors (CSFs) that can facilitate smartphone manufacturing in India. This paper further aims to understand the mutual interactions among these CSFs through identification of the hierarchical relationships among them. Design/methodology/approach – A framework for a smartphone manufacturing system has been developed, wherein the hierarchical inter-relationships between identified CSFs have been presented and interpreted using total interpretive structural modeling (TISM). Cross-impact matrix multiplication applied to classification analysis has been further employed to identify the driving power as well as dependence power of these CSFs. Findings – In the present research, 15 CSFs have been identified through literature review and expert opinions. The hierarchical framework developed using TISM has revealed the inter-dependencies among these CSFs. This research further categorizes CSFs into three clusters. The first cluster comprises of CSFs having high dependence power, the second cluster identifies CSFs having high driving power and the third cluster identifies CSFs which act as linkages between the driver CSFs and dependent CSFs. Research limitations/implications – This study has implications for both practitioners and academia. It provides a comprehensive list of CSFs that are relevant to develop a smartphone ecosystem in India. In addition, this study will help decision makers to strategically focus on the main drivers of the ecosystem that requires the immediate attention of decision makers. The methodology employed in this study provides a mechanism to conduct an exploratory study by identifying the factors and analyzing their interactions through the development of a hierarchical framework. Originality/value – The proposed framework developed through qualitative modeling is an effort to understand relevant factors that can promote the smartphone manufacturing ecosystem. This study makes a significant contribution in the literature of smartphone manufacturing, which captures the perspective of different stakeholders.

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.

Modelling the measures of supply chain performance in the Indian automotive industry

2015 ◽  
Vol 22 (4) ◽  
pp. 665-696 ◽  
Author(s):  
Rajesh Katiyar ◽  
Mukesh Kumar Barua ◽  
Purushottam L. Meena
Keyword(s):  
Supply Chain ◽  
Customer Service ◽  
Automotive Industry ◽  
Lead Time ◽  
Matrix Multiplication ◽  
Supply Chain Performance ◽  
Key Factors ◽  
Order Entry ◽  
Interpretive Structural Modeling ◽  
Content Type

Purpose – The purpose of this paper is to investigate the interactions among the key factors of supply chain (SC) in the Indian automotive industry. These key factors are helpful to measure supply chain performance (SCP) and to improve the firm’s effectiveness. Design/methodology/approach – In this paper, an interpretive structural modeling with a fuzzy cross-impact matrix multiplication applied to classification-based approach is used to examine the interactions among the key factors of SCP measurement. Findings – The authors have identified the most dominant key factors used for measuring the performance in automotive SC. The results exhibit that the order lead-time and order entry method are the most significant key factors. These key factors have high driving power to measure SCP whereas the post-transaction measure of customer service and customer query time are highly dependent on other factors. Such relationships among the key factors can help a firm’s top management to make essential judgments in order to solve the overall SC problems and provide a better approach to proactively deal with problems. Originality/value – In this paper, the authors have explored the interactions among the key factors of the SCP in the Indian automotive industry.

Key factors of entrepreneurial university transformation: a model development using ISM method and MICMAC analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Selly Novela ◽  
Rizal Syarief ◽  
Idqan Fahmi ◽  
Yandra Arkeman
Keyword(s):  
Success Factors ◽  
Model Development ◽  
Entrepreneurial University ◽  
Key Factors ◽  
Group Discussions ◽  
Constraint Factor ◽  
Content Type ◽  
Micmac Analysis ◽  
Comprehensive Picture ◽  
The University

PurposeThis study aims to develop a new entrepreneurial university model that will provide a comprehensive picture and explain how universities can become entrepreneurial by considering several factors, both internal and external.Design/methodology/approachThis research collected data by conducting a survey and elaborates on the judgments of informants who are experts in their fields. The gathered data were analyzed with the ISM method and MICMAC analysis.FindingsThis study identifies key factors of entrepreneurial university transformation, specifically, five key success factors and one constraint factor.Research limitations/implicationsThis study has limitations in terms of its in conducting interview techniques. Data collection, which should be maximized in producing agreement through focus group discussions, can more acutely identify factors through consensus.Practical implicationsThis study provides an overview of how universities may transform, following a path to become entrepreneurial by paying attention to the factors driving this transformation. The model demonstrates that while the university plays a central role, the participation of other parties within the ecosystem is also important.Originality/valueThis modeling technique deconstructs a complex system into several sub-systems, so that it can be studied in greater depth. Using ISM and MICMAC, a hard system methodology, this study advances a different and more comprehensive model.

Modeling reconfigurability in supply chains using total interpretive structural modeling

2017 ◽  
Vol 14 (2) ◽  
pp. 194-221 ◽  
Author(s):  
Pallab Biswas
Keyword(s):  
Supply Chain ◽  
Supply Chains ◽  
New Product Development ◽  
Structural Changes ◽  
Structural Modeling ◽  
Global Market ◽  
Interpretive Structural Modeling ◽  
Content Type ◽  
Micmac Analysis ◽  
Expert Opinions

Purpose The purpose of this paper is to identify, analyze, and categorize the major enablers of reconfigurability that can facilitate structural changes within a supply chain in a global scenario. The paper also addresses five reconfigurability dimensions in the perspective of supply chains and the major enablers to attain them. The paper further aims to understand the mutual interactions among these enablers through the identification of hierarchical relationships among them. Design/methodology/approach A framework that holistically considers all the major enablers of reconfigurability has been developed. The hierarchical interrelationships between major enablers have been presented and interpreted using a novel qualitative modeling technique, i.e., total interpretive structural modeling (TISM), which is an extension of ISM. SPSS 22.0 is employed to carry out a one-tailed one-sample t-test further to test the hypotheses for validating the results of TISM. Impact matrix cross-reference multiplication applied to a classification (MICMAC) analysis has been employed to identify the driving and dependence powers of these reconfigurability enablers. Findings In this paper, 15 enablers for reconfigurability paradigm have been identified through literature review and expert opinions. The authors established interrelationships and interdependencies among these enablers and categorized them as enablers of each dimension. New product development and customer satisfaction come at the highest level of priority. The levels of these enablers were obtained using TISM. The authors compared the results with the clusters derived from MICMAC analysis, and the results are found to be well within the acceptable range. Research limitations/implications The study has implications for both practitioners and academia. The work provides a comprehensive list of enablers that are relevant to reconfigure supply chains in today’s volatile global market. This research will also help decision makers to strategically focus on the top-level enablers and their concerned dimensions. The research is based on an automobile company case study and can be extended to products with volatile and changing demands. Originality/value The proposed model for reconfigurability enablers using TISM is a new effort altogether in the area of supply chain management. The novelty of this research lies in its identification of specific enablers to reconfigure a supply chain through different dimensions.

Interpretive structural modelling for attributes of software quality

2017 ◽  
Vol 14 (3) ◽  
pp. 256-269 ◽  
Author(s):  
Adarsh Anand ◽  
Gunjan Bansal
Keyword(s):  
Software Quality ◽  
Matrix Multiplication ◽  
Quality Attributes ◽  
Sample Population ◽  
Structural Modelling ◽  
R Software ◽  
Content Type ◽  
Interpretive Structural Modelling ◽  
Micmac Analysis

Purpose The “quality” of any product or service defines the agility of the product and its life cycle in dynamic environment. The demand of high “quality” becomes an imperative concern, when “software” is acting as a product or a service. Since the nature of the software is intangible and more complex, therefore the assurance of providing accurate results is anxiety for companies. The overall quality of the software is based upon many individual factors (or attributes) that makes software reliable, inclined and a long-lasting product in the marketplace. But how these factors can influence each other is significant to identify. Therefore, the purpose of this paper is to study the quality aspect of the software and analyse the interrelationship of impactful attributes. Design/methodology/approach The analysis has been done through responses sought from software development teams/clients in India. The questionnaire related to the software quality was administered to the sample population. Interconnection among impactful characteristics has been analysed by using a qualitative technique called interpretive structural modelling (ISM). The driving and dependency of the attributes under consideration has been classified using cross-impact matrix multiplication applied to classification (MICMAC) analysis. The procedure of applying ISM method has been automated and provided it as package “ISM” using R software. Findings In general, it is very complex job to determine the most impactful attribute of software quality. By applying ISM and MICMAC analysis on the set of attributes under consideration, it has been found that “reliability” along with “usability” and “performance” is the most influential attribute of software quality and preferred most. Research limitations/implications Though ISM provides an organized modelling framework yet its results are considered less statistically significant. Therefore, it would be interesting to concatenate the present findings with the findings of any analytical methodology; which gives statistically significant results. Practical implications The present proposal deals with the interpretation of the software quality attributes and their contextual relationship but with more effective and efficient manner. It can help management to understand the complexity of relationship amongst attributes (which are quality attributes here) more accurately and precisely. Since today is an era of automation, the manual part is being substituted so as to reduce the labour cost, improve safety, security and product quality to increase production. This study is, therefore, an effort and a helping hand in making the hassle free calculations for obtaining intermediate matrices and doing eventual calculations. Social implications n numbers of parameters can be selected to analyse the interrelationship of any project/study. Eradication human errors in applying transitivity law or applying any other operation in solving problem. The package created here can save precious time of users. Provides well-formatted and readable excel output files that make interpretation easier. Originality/value Software is one such product/service which plays a significant role in this high-technological world, where each and every firm try their best to be on the top of the list of consumers’ preference. For this purpose, companies reduce manual efforts by converting it into qualitative software that provides deliverables in a systematic manner. Therefore, it becomes imperative to study various interrelated quality attributes of the software. On the similar platform, ISM is a widely used technique and just to provide a helping hand in quantification of the qualitative attributes this paper facilitates the readers with algorithm developed using R software.

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