Barriers analysis for customer resource contribution in value co-creation for service industry using interpretive structural modeling

2020 ◽  
Vol 15 (3) ◽  
pp. 1137-1166
Author(s):  
Ranjit Roy Ghatak
Keyword(s):  
Structural Model ◽  
Service Industry ◽  
Structural Modeling ◽  
Service Industries ◽  
Interpretive Structural Modeling ◽  
Content Type ◽  
Management Framework ◽  
Creation Process ◽  
Resource Contribution ◽  
Successful Integration

Purpose Co-creating services with the customer has recently appeared as an alternative strategy to achieve competitive advantage. Developing and sustaining a gainful experience requires sharing of knowledge, skills and resources between the firm and its customers. Managing value co-creation throws substantial challenge and difficulties. This study aims to investigate the barriers to customer resource contribution in value co-creation in service industries and find their interrelationships for developing an effective management framework for removal of those barriers. Design/methodology/approach A systematic literature review led to the identification of 26 barriers, which were further confirmed through expert opinion. The study used interpretative structural modeling (ISM) approach and Matrice d’Impacts croises-multipication applique (MICMAC), for analyzing the contextual relationships and develop a hierarchical model of the barriers. Findings ISM approach led to the development of a 13-level structural model. The barriers were further classified into autonomous, driver, linkage and dependent barriers using the MICMAC analysis. The framework offers a means to fulfill the expectations of the customers, thus leading to successful integration of the customer in the value creation process. Removal of the barriers has also been discussed. Practical implications The framework provides a direction and a tool to meet the expectations of the customers and lead to successful integration of the customer. Originality/value The study addresses a gap in the literature for the need of a structured framework for managing the value co-creation process in the service industry

Analysis of interaction among the barriers to 5S implementation using interpretive structural modeling approach

2017 ◽  
Vol 24 (7) ◽  
pp. 1834-1853 ◽  
Author(s):  
Rajesh Attri ◽  
Bhupender Singh ◽  
Sunil Mehra
Keyword(s):  
Structural Equation ◽  
Structural Model ◽  
Structural Modeling ◽  
Equation Modeling ◽  
Successful Implementation ◽  
Interpretive Structural Modeling ◽  
Content Type ◽  
Modeling Approach ◽  
Manufacturing Organizations ◽  
Top Management Commitment

Purpose The purpose of this paper is to ascertain and analyze the interactions among different barriers of 5S implementation in manufacturing organizations. Design/methodology/approach In this paper, 15 barriers affecting the implementation of 5S in manufacturing organizations have been identified from literature analysis and discussion with academic and industrial experts. Afterwards, identified barriers were validated by using nation-wide questionnaire-based survey. Then, interpretive structural modeling (ISM) approach has been utilized to find out the interaction among the identified barriers in order to develop hierarchy-based model. Findings The research identifies several key barriers which have high driving power and weak dependence power. In this concern, these barriers entail extreme care and handling for successful implementation of 5S. Financial constraints, lack of top management commitment, and no proper vision and mission are found to be the key barriers. Research limitations/implications The developed ISM model is based on experts’ opinion. This developed hierarchy-based model requires further validation by using structural equation modeling approach or by performing detailed case studies. Originality/value In this paper, ISM-based structural model has been recommended for Indian manufacturing organizations, which is a novel exertion in the area of 5S implementation.

Understanding determinants of social networking service fatigue: an interpretive structural modeling approach

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lin Xiao ◽  
Ting Pan ◽  
Jian Mou ◽  
Lihua Huang
Keyword(s):  
Social Networking ◽  
Impression Management ◽  
Structural Model ◽  
Structural Modeling ◽  
Extensive Literature ◽  
Privacy Concern ◽  
Social Networking Service ◽  
Interpretive Structural Modeling ◽  
Content Type ◽  
Factors Influencing

PurposeThe purpose of this paper is to build a comprehensive structural model to demonstrate the interrelationships of factors influencing social networking service (SNS) fatigue and to identify the varying degrees of influence.Design/methodology/approachA total of 14 factors influencing SNS fatigue are identified through an extensive literature review. Interpretive structural modeling (ISM) and Matrice d'Impacts Croisés Multiplication Appliqué à un Classement (MICMAC) analysis are employed to build a hierarchical model and classify these factors into four clusters.FindingsThe results revealed that ubiquitous connectivity and immediacy of feedback are key factors contributing to SNS fatigue through their strong influence on other factors. Privacy concern, impression management concern and work–life conflict lead directly to SNS fatigue. In contrast, system feature overload and system pace of change are relatively insignificant in generating SNS fatigue.Originality/valueThis study represents an initial step toward comprehensively understanding the interrelationships among the factors leading to SNS fatigue and reveals how determinants of SNS fatigue are hierarchically organized, thus extending existing research on SNS fatigue. It also provides logical consistency in the ISM-based model for SNS fatigue by grouping identified factors into dependent and independent categories. Moreover, it extends the applicability of the integration of the ISM and MICMAC approaches to the phenomenon of SNS fatigue.

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 enablers for vertical integration to enhance rural employability

2019 ◽  
Vol 34 (4) ◽  
pp. 690-702 ◽  
Author(s):  
Rahul Priyadarshi ◽  
Srikanta Routroy ◽  
Girish Kant
Keyword(s):  
Vertical Integration ◽  
Rural Areas ◽  
Structural Model ◽  
Aloe Vera ◽  
Structural Modeling ◽  
Value Addition ◽  
Interpretive Structural Modeling ◽  
Content Type ◽  
Fuzzy Matrix ◽  
The Right

Purpose The purpose of the paper is to identify, analyze and select the enablers for vertical integration of Aloe vera supply chain (AVSC) so that rural employability will be enhanced in the context of Rajasthan, India. Design/methodology/approach Interpretive structural modeling (ISM) was proposed to develop a structural model to identify the right enablers for enhancing the rural employability and business prospects. Also, fuzzy-matrix cross-reference multiplication applied to classification (F-MICMAC) was applied to segregate the enablers into four clusters on the basis of their driving and dependence power. Finally, the significant enablers were selected. Findings Out of identified 13 enablers, three enablers (i.e. institute for training and research, transportation infrastructure and government incentives for value addition) were appearing at the bottom of the ISM structural model and also in the driving quadrant of driver-dependent diagram. Therefore, they are the significant enablers for vertical integration of AVSC to enhance the rural employability in the context of Rajasthan, India. Research limitations/implications The interactions among enablers are not statistically validated. However, the empirical analysis and total interpretive structural modeling may be used for this purpose. Practical implications The outcomes of the study will provide the guidelines for implementation of vertical integration at the village level to enhance rural employability in the context of Rajasthan, India in specific. Originality/value Although a few studies have been reported in the literature related to value-addition process (vertical integration), but the modeling of enablers to segregate and identify the appropriate enablers for vertical integration of AVSC for enhancing employability at the rural areas is unique.

scholarly journals Factors Affecting the Lut Desert Tourism in Iran: Developing an Interpretive-Structural Model

2021 ◽  
Vol 13 (13) ◽  
pp. 7245
Author(s):  
Beniamino Murgante ◽  
Mohammad Eskandari Sani ◽  
Sara Pishgahi ◽  
Moslem Zarghamfard ◽  
Fatemeh Kahaki
Keyword(s):  
Sampling Method ◽  
Structural Model ◽  
Tourism Development ◽  
Cost Effective ◽  
Structural Modeling ◽  
Interpretive Structural Modeling ◽  
Factors Affecting ◽  
Purposive Sampling ◽  
Interpretive Structural Model ◽  
Lut Desert

The Lut desert is one of the largest and most attractive deserts in Iran. The value of desert tourism remains unclear for Iran’s economy and has only recently been taken into consideration by the authorities, although its true national and international value remains unclear. This study was aimed at investigating the factors that influence tourism development in the Lut desert. Data collected through the purposive sampling method was analyzed using Interpretive Structural Modeling and the MICMAC Analysis. According to the results, cost-effective travel expenses, security, and safety provided in the desert, together with appropriate media advertising and illustration of the Lut desert (branding) are the leading factors that influence tourism in the Lut desert in Iran. This paper highlighted the importance of desert tourism, especially in this region.

Barriers to adoption of reverse logistics: a case of construction, real estate, infrastructure and project (CRIP) sectors

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sudhir Ambekar ◽  
Dipayan Roy ◽  
Amit Hiray ◽  
Anand Prakash ◽  
Vishal Singh Patyal
Keyword(s):  
Real Estate ◽  
Reverse Logistics ◽  
Delphi Study ◽  
Structural Model ◽  
Interpretive Structural Modeling ◽  
Content Type ◽  
Organizational Policies ◽  
Technology System ◽  
Inadequate Information ◽  
Scarcity Of Resources

PurposeThis study attempts to identify and analyse the barriers to implementing a reverse logistics (RL) system in Indian Construction, Real estate, Infrastructure and Project (CRIP) sectors and present a structured model to identify interdependencies among them.Design/methodology/approachThe barriers to implementing RL in CRIP sectors in India were identified using a Delphi study. The interdependencies were identified using Interpretive Structural Modeling (ISM). Further, using the Matriced' Impacts Croisés Multiplication Appliquée à un Classement (MICMAC) analysis, the barriers were classified on the basis of their driving power and interdependencies.FindingsThe study has identified ten barriers that can hamper the application of an RL system in CRIP sectors. The finding of the ISM model shows that macro level barriers such as lack of awareness of reverse logistics, insufficient government policies and unavailability of standard codes stimulate each other and also drive all other barriers. The organization-specific barriers operating at the strategic/tactical level, namely, company's rigid mechanism, lack of awareness of economic profits, inadequate company’s organizational policies and lack of training, reluctance from stakeholders, scarcity of resources and finance from company are found at the intermediate level of hierarchy and they can be influenced by the barriers at the lower level and influence the barriers on the and higher levels. The operational level barrier namely “Inadequate Information Technology system” is at the top of the hierarchy and can be driven by all the barriers at the lower level.Research limitations/implicationsThe present findings are based on the opinions of experts only from Indian CRIP sectors so the results may require to be validated in other contexts.Practical implicationsThe structural model presenting the interdependencies will be a guide for the CRIP supply chain professionals in understanding and ranking the barriers they may face while implementing the RL system.Originality/valueThe study contributes to the existing literature by providing a set of barriers and their interdependencies faced during the implementation of an RL system implementation in CRIP sectors. It is one of the first studies which identifies barriers applicable to the CRIP firms in India and models their inter-dependencies. Additionally Consequently, these firms can make a move forward towards a circular economy by overcoming these interlinked barriers.

Sustainable logistics barriers of fruits and vegetables

2018 ◽  
Vol 25 (8) ◽  
pp. 2589-2610 ◽  
Author(s):  
Rakesh Raut ◽  
Bhaskar B. Gardas
Keyword(s):  
Supply Chain ◽  
Value Chain ◽  
Fruits And Vegetables ◽  
Structural Model ◽  
Fresh Produce ◽  
Sustainable Transportation ◽  
Three Dimensions ◽  
Interpretive Structural Modeling ◽  
Content Type ◽  
Sustainable Logistics

PurposeThe reduction of food wastage at every stage of a fresh produce supply chain helps in achieving balance among all three dimensions (social, ecological and economic) of the sustainability and helps in stimulating the growth and development in the agricultural domain. The purpose of this paper is to address the causal factors of post-harvesting losses (PHLs) occurring in the transportation phase.Design/methodology/approachThrough exhaustive literature survey and expert opinions, 12 crucial barriers to sustainable transportation of fruits and vegetables (F&V) are identified. The interpretive structural modeling (ISM) methodology, a multi-criteria decision-making (MCDM) approach, is employed for developing a structural model of the identified barriers.FindingsThe results of the analysis highlighted that two factors, namely, the non-availability of refrigerated vehicles, and excessive loading on the vehicles, are the most significant barriers to sustainable transportation which are found to have the highest driving power.Research limitations/implicationsThe results of the present research are applicable to the F&V supply chains only. The established interrelation among the identified factors depends on the judgments given by the experts which could be biased. The developed ISM model is intended to guide the policy and decision makers for formulating the policies for the performance improvement of the fresh produce value chain.Originality/valueIt is the first research of its kind focusing on the model development of critical factors causing PHLs in the transportation phase of the agricultural fresh produce supply chain using MCDM process.

Factors affecting strategic plan implementation using interpretive structural modeling (ISM)

2018 ◽  
Vol 31 (5) ◽  
pp. 406-414 ◽  
Author(s):  
Mohammadkarim Bahadori ◽  
Ehsan Teymourzadeh ◽  
Hamidreza Tajik ◽  
Ramin Ravangard ◽  
Mehdi Raadabadi ◽  
...  
Keyword(s):  
Strategic Planning ◽  
Planning Process ◽  
Strategic Plan ◽  
Structural Modeling ◽  
Interpretive Structural Modeling ◽  
Plan Implementation ◽  
Content Type ◽  
Factors Affecting ◽  
Strategic Planning Process ◽  
The Relationship

PurposeStrategic planning is the best tool for managers seeking an informed presence and participation in the market without surrendering to changes. Strategic planning enables managers to achieve their organizational goals and objectives. Hospital goals, such as improving service quality and increasing patient satisfaction cannot be achieved if agreed strategies are not implemented. The purpose of this paper is to investigate the factors affecting strategic plan implementation in one teaching hospital using interpretive structural modeling (ISM).Design/methodology/approachThe authors used a descriptive study involving experts and senior managers; 16 were selected as the study sample using a purposive sampling method. Data were collected using a questionnaire designed and prepared based on previous studies. Data were analyzed using ISM.FindingsFive main factors affected strategic plan implementation. Although all five variables and factors are top level, “senior manager awareness and participation in the strategic planning process” and “creating and maintaining team participation in the strategic planning process” had maximum drive power. “Organizational structure effects on the strategic planning process” and “Organizational culture effects on the strategic planning process” had maximum dependence power.Practical implicationsIdentifying factors affecting strategic plan implementation is a basis for healthcare quality improvement by analyzing the relationship among factors and overcoming the barriers.Originality/valueThe authors used ISM to analyze the relationship between factors affecting strategic plan implementation.

An integrated interpretive structural modeling and a graph-theoretic approach for measuring the supply chain complexity in the Indian automotive industry

2018 ◽  
Vol 29 (3) ◽  
pp. 478-514 ◽  
Author(s):  
Kavilal E.G. ◽  
Shanmugam Prasanna Venkatesan ◽  
Joshi Sanket
Keyword(s):  
Supply Chain ◽  
Automotive Industry ◽  
Structural Modeling ◽  
Theoretic Approach ◽  
Passenger Vehicle ◽  
Interpretive Structural Modeling ◽  
Content Type ◽  
Graph Theoretic ◽  
Graph Theoretic Approach ◽  
Numerical Index

Purpose Easily employable quantitative supply chain complexity (SCC) measures considering the significant dimensions of complexity as well as the drivers that represent those dimensions are limited in the literature. The purpose of this paper is to propose an integrated interpretive structural modeling (ISM) and a graph-theoretic approach to quantify SCC by a single numerical index considering the interdependence and the inheritance of the SCC drivers. Design/methodology/approach In total, 18 SCC drivers identified from the literature are clustered according to the significant dimensions of complexity. The interdependencies established through ISM and inheritance values of SCC drivers are mapped into a Variable Permanent Matrix (VPM). The permanent function of this VPM is then computed and the resulting single numerical index is the measure of SCC. Findings A scale is proposed by computing the minimum and maximum threshold values of SCC with the help of expert opinions of the Indian automotive industry. The complexity of commercial and passenger vehicle sectors within the automotive industry is measured and compared using the proposed scale. From the results, it is identified that the number of suppliers, increase in spare-parts due to shortened product life-cycle and demand uncertainties increase the SCC of the passenger vehicle sector, while number of parts, products and processes, variety of products and process and unreliability of suppliers increase the complexity of the commercial vehicle sector. The result indicates that various SCC drivers have a different impact on determining the SCC level of these two sectors. Originality/value The authors propose an integrated method that can be readily applied to measure and quantify SCC considering the significant dimensions of complexity as well as the interdependence and the inheritance of the SCC drivers that contribute to those dimensions. This index further helps to compare the complexity of the supply chain which varies between industries.

The prioritization of technologies and public R&D roles between the manufacturing and service industries in the fourth industrial revolution

foresight ◽  
2019 ◽  
Vol 21 (6) ◽  
pp. 680-694 ◽  
Author(s):  
Jinwon Kang ◽  
Jong-Seok Kim ◽  
Seonmi Seol
Keyword(s):  
Artificial Intelligence ◽  
Manufacturing Industry ◽  
Market Failure ◽  
Industrial Revolution ◽  
Service Industry ◽  
Service Industries ◽  
Delphi Survey ◽  
Smart Factory ◽  
Content Type ◽  
Fourth Industrial Revolution

Purpose The purpose of this study is to reveal the similarities and differences between the manufacturing and service industries in their prioritization of technologies and public research and development (R&D) roles, along with the complementation of properties of technology and public R&D role in the context of Fourth Industrial Revolution. Design/methodology/approach Two rounds of Delphi surveys were designed to meet the purpose of this study, which used rigorous triangulation techniques. The Delphi method was combined with the brainstorming method in the first-round Delphi survey, while the second-round Delphi survey focused on experts’ judgments. Finally, language network analysis was performed on the properties of technology and public R&D roles to complement the data analyses regarding prioritization. Findings This study identifies different prioritizations of five similar key technologies in each industry, so that it can note different technological impacts to the two industries in the Fourth Industrial Revolution. Smart factory technology is the first priority in the manufacturing industry, whereas artificial intelligence is the first priority in the service industry. The properties of the three common technologies: artificial intelligence, big data and Internet of things in both industries are summarized in hyper-intelligence on hyper-connectivity. Moreover, it is found that different technological priorities in the service and manufacturing industries require different approaches to public R&D roles, while public R&D roles cover market failure, system failure and government failure. The highest priority public R&D role for the service industry is the emphasis of non-R&D roles. Public R&D role to solve dy-functions, focus basic technologies and support challenging areas of R&D is prioritized at the highest for the manufacturing industry. Originality/value This study of the different prioritizations of technologies in the manufacturing and service industries offers practical lessons for executive officers, managers and policy-makers. They, by noting the different technological impacts in the manufacturing and service industries, can prepare for current actions and establish the priority of technology for R&D influencing the future paths of their industries in the context of the Fourth Industrial Revolution. While managers in the service industry should pay greater attention to the technological content of hyper-intelligence and hyper-connectivity, managers in the manufacturing industry should consider smart factory and robot technology.

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