Search results

1 – 10 of 384
To view the access options for this content please click here
Article
Publication date: 10 August 2020

Abhilasha Meena, Sanjay Dhir and Sushil

This study aims to identify and prioritize various growth-accelerating factors in the Indian automotive industry. It further develops a hierarchical model to examine the…

Abstract

Purpose

This study aims to identify and prioritize various growth-accelerating factors in the Indian automotive industry. It further develops a hierarchical model to examine the mutual interactions between the factors, their dependence and their driving power.

Design/methodology/approach

This study first identifies the growth-accelerating factors and then uses the modified total interpretive structural modeling (m-TISM) framework, which is an extended version of TISM. It further uses MICMAC analysis to analyze the mutual interrelation between the identified factors.

Findings

This study highlights the interrelation amongst the factors using m-TISM model. A hierarchical model shows the level of autonomous, dependence, linkage and independent factors considering the Indian automotive industry. This study also provides the understanding related to the interdependence of growth-accelerating factors.

Research limitations/implications

The government and practitioners could evaluate the growth-accelerating factors which have higher driving power for implementing efficient policies and strategy formulation. By implementing m-TISM model in the Indian automotive industry, auto manufacturers can become more productive and profitable. Future studies could use other methods such as expert opinion to derive the factors, and further model could be verified using structural equation modeling technique.

Originality/value

This study uses a novel m-TISM framework for the analysis of growth-accelerating factors in the context of the Indian automotive industry. It further provides a detailed theoretical and conceptual understanding relating to the philosophy and establishes an interrelation amongst these under-researched growth-accelerating factors.

To view the access options for this content please click here
Article
Publication date: 11 February 2019

Vineet Jain and Vimlesh Kumar Soni

The purpose of this paper is to identify the flexible manufacturing system performance variables and analyze the interactions among these variables. Interpretive…

Abstract

Purpose

The purpose of this paper is to identify the flexible manufacturing system performance variables and analyze the interactions among these variables. Interpretive structural modeling (ISM) has been reported for this but no study has been done regarding the interaction of its variables. Therefore, fuzzy TISM (total ISM) has been applied to deduce the relationship and interactions between the variables and driving and dependence power of these variables are examined by fuzzy MICMAC.

Design/methodology/approach

Fuzzy TISM and fuzzy MICMAC analysis have been applied to deduce the relationship and interactions among the variables and driving and dependence power of these variables are examined by fuzzy MICMAC.

Findings

In total, 15 variables have been identified from the extensive literature review. The result showed that automation, use of automated material handling, an effect of tool life and rework percentage have high driving power and weak dependence power in the fuzzy TISM model and fuzzy MICMAC analysis. These are also at the lowest level in the hierarchy in the fuzzy TISM model.

Originality/value

Fuzzy TISM model has been suggested for manufacturing industries with fuzzy MICMAC analysis. This proposed approach is a novel attempt to integrate TISM approach with the fuzzy sets. The integration of TISM with fuzzy sets provides flexibility to decision-makers to further understand the level of influences of one criterion over another, which was earlier present only in the form of binary (0, 1) numbers; 0 represents no influence and 1 represents influence.

Details

Journal of Modelling in Management, vol. 14 no. 1
Type: Research Article
ISSN: 1746-5664

Keywords

To view the access options for this content please click here
Article
Publication date: 8 May 2017

J. Jena, Sumati Sidharth, Lakshman S. Thakur, Devendra Kumar Pathak and V.C. Pandey

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…

Downloads
2268

Abstract

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.

To view the access options for this content please click here
Article
Publication date: 13 February 2019

Zuby Hasan, Sanjay Dhir and Swati Dhir

The purpose of this paper is to examine the elements of asymmetric motives, i.e., initial cross-border joint venture (CBJV) conditions and relative partner characteristics…

Abstract

Purpose

The purpose of this paper is to examine the elements of asymmetric motives, i.e., initial cross-border joint venture (CBJV) conditions and relative partner characteristics in emerging nations. The two main objectives of the present research are to identify the elements affecting asymmetric motives in Indian bilateral CBJV and to construct modified total interpretive structural modelling (TISM) for the identified elements of asymmetric motives.

Design/methodology/approach

For the current study, the qualitative technique named total interpretive structural modelling was used. The TISM (Sushil, 2012) is a novel extension of interpretive structural modelling (ISM) where ISM helps to understand the “what” and “how” of research (Warfield, 1974) and TISM answers the third question, i.e., “why” in the form of TISM; further checks for the correctness of TISM are given in Sushil (2016). TISM provides a hierarchical model of the elements selected for study and the interpretation of each element by iterative process and also a digraph that systematically depicts the relationship among various elements. TISM is an innovative modelling technique used by researchers in varied fields (Srivastava and Sushil, 2013; Wasuja et al., 2012; Nasim, 2011; Prasad and Suri, 2011). Steps involved in TISM are shown in Figure 1. It uses reachability matrix and partitioning of elements similar to ISM. Also, along with traditional TISM, the modified TISM process was also used where both paired comparisons and transitivity checks were done simultaneously which helped in minimising the redundant comparisons being made in the original process. Furthermore, for identifying the elements of study, SDC Platinum database was used, which was taken from research papers of major journals namely British Journal of Management, Administrative Science Quarterly, Strategic Management Journal, Management Science, Academy of Management Journal and Organization Science (Schilling, 2009). The database included all joint ventures that were formed in India, having India as one of the partner firms during fiscal year April 2000 and March 2010. From these, 361 CBJVs and 76 domestic joint ventures were identified. Although 54 CBJVs were excluded from these, a total number of 307 CBJVs were studied in the current research. Among these 307 CBJVs, 201 were from super-advanced nations (G7), 40 CBJVs from developing nations and 66 CBJVs from other developed nations. As 65 per cent of the CBJVs came from G7 nations (France, Italy, Japan, Canada, Germany, USA and UK), in the current study, we tried to examine Indian CBJVs with G7 partners only for a period of ten years as mentioned above.

Findings

The results of the study indicate that asymmetric motives are directly affected by critical activity alignment and interdependency. Thus, we can conclude that critical activity alignment of partners in CBJV is an antecedent of CBJV motive and thereby minimises the number of asymmetric motives. Bottom level variables such as culture difference and relative capital structure are considered as strong drivers of asymmetric motives. Diversification, resource heterogeneity and inter-partner conflict are middle level elements. Effect of these elements on asymmetric motives can only be improved and enhanced when improvement in bottom level variables is found. It has been believed that as the relative capital structure among firm increases, CBJVs’ asymmetric motives also increase, the reason being that as the difference in capital structure occurs, gradual change in bargaining power will also occur.

Originality/value

TISM used in the present study provides valuable insights into the interrelationship between identified elements through a systematic framework. The methodology of TISM used has its implications for researchers, academicians as well for practitioners. Further study also examines driver-dependent relationship among elements of interest, i.e., relative partner characteristics and initial CBJV conditions by using MICMAC analysis, which can be viewed as a significant step in research related to bilateral CBJV.

To view the access options for this content please click here
Article
Publication date: 24 October 2021

Sreenivasa Sekhar Josyula, M. Suresh and R. Raghu Raman

Organizations are fast adopting new technologies such as automation, analytics and artificial intelligence, collectively called intelligent automation, to drive digital…

Abstract

Purpose

Organizations are fast adopting new technologies such as automation, analytics and artificial intelligence, collectively called intelligent automation, to drive digital transformation. When adopting intelligent automation, there is a need to understand the success factors of these new technologies and adapt agile software development (ASD) practices to meet customer expectations. The purpose of this paper is to explore the success factors of intelligent automation and create a framework for managers and practitioners to meet dynamic business demands. Total interpretive structural modeling (TISM) framework is a suitable approach to integrate quantitative measurement with qualitative semi-structured interviews capturing the context of the individual organization environment.

Design/methodology/approach

This paper identified agility factors and their interrelationships using a TISM framework. TISM results were validated using a one-tailed t-test to confirm the interrelationships between factors. Furthermore, the agility index of a case project organization was assessed using a graph-theoretic approach (GTA) to identify both the triggering factors for agility success and improvement proposals.

Findings

Results showed that leadership vision, organization structure and program methodology were driving factors. The TISM model was validated statistically and the agility index of the intelligent automation case project organization was calculated to be79.5%. Here, a GTA was applied and the triggering factors for improvement of the agility index were identified.

Research limitations/implications

The limitations of the study are described along with the opportunities for future research as the field evolves through the rapid innovation of technology and products.

Practical implications

The increasing role of digital transformation in enterprise strategy and operations requires practitioners to understand how ASD practices must be planned, measured and/or improved over time through the implementation of automation, analytics and artificial intelligence programs. The TISM digraph provides a framework of hierarchical structure to organize the influencing factors, which assists in achieving organizational goals. This study highlights the driving factors which contribute to the success of intelligent automation projects and project organizations.

Originality/value

This is a first attempt to analyze the interrelationships among agility factors in intelligent automation projects (IAP) using TISM and the assessment of the agility index of a case IAP organization using a GTA.

Details

International Journal of Organizational Analysis, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1934-8835

Keywords

To view the access options for this content please click here
Article
Publication date: 15 December 2020

Rishabh Rajan, Sanjay Dhir and Sushil

In the rapidly changing business world, innovation plays a vital role for organizations to gain a competitive advantage. Various factors associated with technology…

Abstract

Purpose

In the rapidly changing business world, innovation plays a vital role for organizations to gain a competitive advantage. Various factors associated with technology management and innovations in organizations are diverse in the existing literature. Therefore, there is a need to bridge these gaps in the fitting proportions toward innovations within organizations. The primary objective of this study is to identify, explain and interpret the relationships between the identified technology-related factors that are important for innovations in organizations.

Design/methodology/approach

In this study, a modified total interpretive structural modeling (M-TISM) methodology was used to examine and analyze the various interactions between identified factors for innovations in organizations. However, the argumentation of the links is relatively weak in M-TISM. In order to compensate for this, M-TISM is additionally altered by an “Argumentation-based Modified TISM”. Hence, this research strengthens the modified TISM methodology by incorporating argumentation and total interpretation of the relationships between the identified factors.

Findings

A total of six major factors were identified using a literature review. Results suggest that workforce technical skills, technological infrastructure, technological alliances, technology transfer and top management support have an impact on innovation in organizations. Results also suggest that top management support and the technological infrastructure of an organization have a greater impact on innovation.

Research limitations/implications

For policymakers and practitioners, this study provides a suggestive list of critical factors, which may help to develop policies or guidelines for improving innovation in organizations. Policymakers should focus on technological infrastructure and collaborations to enhance innovations and productions within the organizations. For academicians, this study provides a modified TISM model that shows the impact of technology-related factors on innovations. Future researchers could expand this study by adding a greater number of technological factors and validate this model in other industries.

Originality/value

This study fills a gap in the literature by interpreting the various relationships among the identified factors and innovations. The model has been validated through a panel of seven experts from the Indian automotive industry of multiple organizations. This study is useful in the automobile industry as it determines what and how technology-related factors affect innovations, process improvement and R&D production for organizations.

Details

Benchmarking: An International Journal, vol. 28 no. 6
Type: Research Article
ISSN: 1463-5771

Keywords

To view the access options for this content please click here
Article
Publication date: 9 January 2019

Nisha Bamel, Sanjay Dhir and Sushil Sushil

The purpose of this paper is to identify the inter-partner dynamics-based enablers of joint venture (JV) competitiveness. In addition, this paper models the interactions…

Abstract

Purpose

The purpose of this paper is to identify the inter-partner dynamics-based enablers of joint venture (JV) competitiveness. In addition, this paper models the interactions among identified enablers/factors to project the strength of their relationship with JV competitiveness.

Design/methodology/approach

ISM- and total interpretive structural modeling (TISM)-based fuzzy TISM approach has been used to examine the interactions and strength of interactions among identified enablers of JV competitiveness.

Findings

The analysis concludes that inter-partner dynamics-based enablers, such as partner fit, power symmetry and trust, have strong driving power and low dependence power and are at the lowest level of hierarchy in fuzzy TISM model. Variables like collaborative communication, organizational learning and absorptive capacity are linkage variables and they have high dependence as well as driving power and they lie in the second level of fuzzy TISM hierarchy. Strategic flexibility is found to have high dependence power and has weak driving power. The outcome variable JV competitiveness found to have zero driving power and highest dependence power.

Practical implications

The findings have implications for practitioners and policy makers. JVs may achieve competitiveness by managing identified enablers (inter-partner dynamics).

Originality/value

Present paper is one among the few efforts that address the issue of JV competitiveness (post-formation of JV). Methodologically also, this study is one among few initial efforts of using modified fuzzy TISM to explore and understand the linkage among enablers and outcome variables. Modified fuzzy TISM process carries out transitivity checks along with the successive pair-wise comparisons and simplifies the fuzzy TISM approach.

Details

Benchmarking: An International Journal, vol. 26 no. 1
Type: Research Article
ISSN: 1463-5771

Keywords

To view the access options for this content please click here
Article
Publication date: 4 November 2021

Archana Poonia, Shilpa Sindhu, Vikas Arya and Anupama Panghal

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…

Abstract

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.

Details

Journal of Indian Business Research, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1755-4195

Keywords

To view the access options for this content please click here
Article
Publication date: 9 September 2021

Anita Singh and Ashim Raj Singla

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…

Abstract

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.

Details

Journal of Modelling in Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1746-5664

Keywords

To view the access options for this content please click here
Article
Publication date: 26 July 2021

Vishal Ashok Wankhede and Vinodh S.

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…

Abstract

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.

1 – 10 of 384