We have identified various variables for effective customer involvement in green concept implementation in the supply chain from the literature review and experts' opinions. Literature was reviewed to identify various variables for effective customer involvement in green concept implementation in supply chain. Experts from academia and industry were invited to idea engineering workshop, and brainstorming session was conducted where 13 variables relevant to effective customer involvement in green concept implementation in the supply chain were identified. A questionnaire-based survey was conducted to rank these variables. Using the ISM approach and MICMAC analysis technique, it had been identified that three variables (‘Education Level of Customers,’ ‘Customer Income,’ and ‘Customer Intelligence’) were falling into the category of autonomous variables, hence discarded, and research work had been continued with the remaining ten variables. Autonomous variables have weak driver power and weak dependence. These variables are relatively disconnected from the system, with which they have only few links, which may not be strong, and hence, may be discarded (Ravi and Shankar 2005; Mudgal et al. 2009, 2010; Luthra et al. 2011).
Identification of variables for effective customer involvement in green concept implementation in the supply chain
These variables identified for effective customer involvement in green concept implementation in the supply chain are as follows: ‘Awareness Level of Customers,’ ‘Encouragement and Support of Customers,’ ‘Motivation by Organization Sales Network,’ ‘Positive Perception about Top Management Commitment and Openness in Policy towards Greening,’ ‘Effective Advertisement and Marketing Campaign towards Green Efforts of Organization,’ ‘IT Enablement and Effective Communication,’ ‘Environment-Friendly Distribution,’ ‘Effective Training Program Schedule for Customer,’ ‘Green Labeling and Use of Green Packaging Material,’ and ‘Recycling and Reuse Efforts of Organization.’ The abovesaid identified variables are explained in the following subsections.
Awareness level of customers
Customers have been reported as strong drivers for greening activities in the literature (Green et al. 1996). Producing environment-friendly products and creating awareness among consumers are some of the ways through which companies can contribute towards nature conservation. Customer demands have a strong influence on the decisions that companies take towards eco-design (Alhola 2008). To obtain the most sustainable solution, the environment consideration of properties of products and services must meet customer requirement (Zhu et al. 2008a, 2008b).
Encouragement and support of customers
Some studies have found that ultimate individual consumer interest in the environment and environmentally sound products is quite substantial, even though there has been a slight decline (Reijonen 2011). Implementation of environmental technology may build a positive brand image, mitigate environmental liabilities associated with a firm's products and services, and influence the mindset of customers and investors (Rao and Holt 2005). In the USA, an estimated 75% of consumers claim that their purchases are influenced by reputation, and 80% would be willing to pay more for environment-friendly products (Lamming and Hamapson 1996; Chien and Shih 2007a, 2007b).
Motivation by organization sales network
Customers are in direct contact with the organizations' sales personnel in most of the cases and may be informed, influenced, and convinced about the green products and services offered by the organizations. As one of the results of the brainstorming session, this factor was strongly recommended by the participants of the session.
Positive perception about top management commitment and openness in policy towards greening
Top management may be held responsible directly and indirectly for each activity at all the levels of the organizations (Singh and Kant 2008). Top management commitment is necessary for supporting GSCM ideas, practices, and cooperation across organizational functions (Sarkis et al. 2007; Zhu et al. 2007a, 2007b), and success of any strategic program needs to be derived from top management (Yu and Hui 2008). Top management has a significant ability to support actual formation and implementation of green initiatives across the organization. Top management may provide continuous support for GSCM in the strategic and action plans for successful implementation (Ravi and Shankar 2005; Mudgal et al. 2009). Positive perception about top management commitment and openness in policy towards greening may be achieved by publishing sincere green efforts of organization.
Effective advertisement and marketing campaign towards green efforts of organization
Organizations may advertise environment-friendly products and services to create awareness among customers. Customers aware of green products may prefer to purchase green products, which may further increase an organization's reputation and sales volumes (Luthra et al. 2011). Newspapers, hording, magazines, printed material distribution (leaflets, booklets, etc.), and various audiovisual media (e.g., radio, television,cinema) may be a few media for advertisement and marketing campaign for making the customers more aware of green efforts of the organization. In India, few retail organizations are providing recycled paper and jute bags (with green slogans) for carrying their products.
IT enablement and effective communication
IT enablement may be required for processing and updating accurate information of products, materials, and other resources (Sarkis et al. 2007) and for supporting various GSCM activities (Ravi and Shankar 2005). Informal linkages and improved communication may help the organization to adopt green practices (Yu 2007; Yu and Hui 2008), and increased environmental performance in GSCM may be achieved by information sharing of improved quality (Wu et al. 2010).
Environment-friendly or green distribution is the process of moving a product from its manufacturing source to its customers with a low impact on the environment. Reverse logistics is identified as the process of planning, implementing, and controlling flows of raw materials, in-process inventory, and finished goods from a manufacturing, distribution, or use point to a point of recovery or point of proper disposal (Ilgin and Gupta 2010; Srivastva 2007). The use of green fuel-like compressed natural gas-driven vehicles may exhibit seriousness about green efforts of an organization.
Effective training program schedule for customers
Training and education are the prime requirements for achieving successful implementation of GSCM in any organization (Ravi and Shankar 2005; Sarkis et al. 2007; Wu et al. 2010). Trained personnel may contribute in training the customers, leading to better customer involvement in GSCM implementation.
Green labeling and use of green packing material
Environment-friendly packing refers to use of recyclable or dissolvable materials for packing and has a clear objective of encouraging business to market greener products (Fielding 2001), and it may be a good way to make the customers better informed about environmental choices while purchasing. Eco-labeling is a voluntary scheme designed to encourage businesses to market environment-friendly products and services (Mudgal et al. 2009).
Recycling and reuse efforts of organization
Recycling is the process of collecting used products, components, and materials from the field and separating them into categories of like materials (recyclable and nonrecyclable), and recyclable materials may be processed into recycled products, components, and materials. Reuse is the process of collecting used materials, products, or components from the field, and distributing or selling them as used. Waste management and recyclability evaluation methods may help in managing and minimizing waste and improving the environment (Ilgin and Gupta 2010; Srivastva 2007). Lean is a competitive practice that reduces costs, improves the environment, and improves quality (Bhetja et al. 2011). The use of lean or flexible manufacturing may help in the continuous improvement and elimination of waste in all forms and has great potential for reciprocal benefits to firm environmental management practices (Mudgal et al. 2009).
Based upon the abovesaid variables for effective customer involvement in green concept implementation in the supply chain, a research questionnaire was designed. The questionnaire was developed taking into account the experts' opinions. A first draft was reviewed by experts from the academia and industry. Their feedback was used to improve the questions and eliminate redundancies. A second version was developed. These variables were tested for content validity and reliability through the pretesting of the questionnaire. Content validity is the technique used to ensure that the measures adequately quantify the concepts that they are supposed to be tested (Sekaran 2003). Reliability concerns the extent to which an experience, test, or any measuring procedure yields the same results on repeated trials (Carmines and Zeller 1979). Reliability evaluates the accuracy of measures through assessing the internal stability and consistency of items in each variable (Hair et al. 2009). Validity of the variables was pretested among selected experts from the academia and industry. The reliability of measures was also pretested by applying Cronbach's alpha coefficients on the responses from experts. All values of the coefficients fall within the range of 0.60 to 0.80, ensuring an acceptable level of reliability (Nunnally 1987). The results from this pretest were used to further improve the questionnaire. After a discussion with the experts and the pretest, we kept our questionnaire very simple and short due to low rate of responses from respondents. After pretesting, the final version will be used in the survey. The population of this study consists of academicians, manufacturing firms, and valuable customers from North Indiasince the population size was very large. After identifying the target population, it was necessary to determine the sample size. The sample size was taken using the following mathematical relationship for proportions (Israel 1996; Rea and Parker 2005; Sanchez Gomez 2011).
where Z= Z value in normal distribution tables (1.96 for 95% confidence level), p= the estimated proportion of the population that presents the characteristics (0.5 is used as a conservative value, higher or lower values yield a smaller required sample size), and H= the precision level or margin of error, expressed as decimal (10%= 0.1). Then, sample size = (1.96)2× 0.5(1–0.5)/(0.1)2 =96.04 or sample size= 96.
Therefore, approximately 96 complete questionnaires were needed. A questionnaire-based study was carried out, and respondents were asked to rank the variables on a five-point Likert scale (where ‘1’ means not important and ‘5’ means most important). This research was conducted from August 2011 to November 2012 at the North India zone. We used convenience sampling as well as random sampling. Due to difficulties of mail surveys and the possibility of respondents to misunderstand the questionnaire items, we used convenience sampling through interviewing various academicians, top/middle level managers/engineers of industries, and customers. One hundred seventy-eight completed questionnaires were collected via interviews. Further, to test the convenience sampling bias, we carried out random surveys through e-mail; 643 questionnaires were sent to various academicians, top/middle level managers/engineers of various industries, and customers. After reminder emails in addition to telephonic calls, 171 questionnaires were received. Twenty-seven questionnaires were incomplete and were discarded. This gives an overall response rate of 22.4%. A response rate of 20% is considered for positive assessment of the surveys (Malhotra and Grover 1998). A total of 322 questionnaires were considered for further research work.
Interpretive structural modeling
The mathematical foundations of the ISM methodology can be found in reference works (Harary et al. 1965), while the philosophical basis for the development of this approach has been presented by Warfield (1974). ISM has been used for policy analysis (Sage 1977) and, in recent years, for management research (Mandal and Deshmukh 1994; Jharkharia and Shankar 2005; Ravi and Shankar 2005; Sushil 2005; Sarkis et al. 2007; Mudgal et al. 2009, 2010; Diabat and Kannan 2011; Luthra et al. 2011). ISM was first proposed by J. Warfield in 1974 to analyze the complex socioeconomic systems. Its basic idea is to use the experts' practical experience and knowledge to decompose a complicated system into several subsystems and construct a multilevel structural model. The ISM is interpretive as the judgment of the selected group for the study decides whether and how the factors are interrelated. ISM generally has the following steps (Ravi and Shankar 2005; Sage 1977; Warfield 1974):
Step 1: Variables affecting the system are listed.
Step 2: From the variables identified in step 1, the contextual relationships among the variables are found.
Step 3: A structural self-interaction matrix (SSIM) is developed for variables, which indicated pairwise relationships among variables of the system.
Step 4: A reachability matrix is developed from the SSIM, and the matrix is checked for transitivity. The transitivity of the contextual relation is a basic assumption made in ISM. It states that if variable A is related to variable B and variable B is related to variable C, then variable A is necessarily related to variable C.
Step 5: The reachability matrix obtained in step 4 is partitioned into different levels.
Step 6: Based on the contextual relationships given above in the reachability matrix, a directed graph is drawn and the transitive links are removed.
Step 7: The resultant diagraph is converted into an ISM by replacing variable nodes with statements.
Step 8: The ISM model developed in step 7 is reviewed to check for conceptual inconsistency, and necessary modifications are made.
The flow chart for the ISM methodology is shown in Figure 1.
Matrice d’impacts croises-multipication applique´ an classment (cross-impact matrix multiplication applied to classification) is abbreviated as MICMAC (Mudgal et al. 2009). In the MICMAC analysis, the dependence power and driver power of the variables are analyzed. Variables will be classified into four clusters. The four clusters are autonomous, dependent, linkage, and driver/independent. In the final reachability matrix, the driving power and dependence power of each of the variables will be plotted. Autonomous variables (first cluster) have weak driving power and weak dependence power. These variables can be disconnected from the system. The second clusters named dependent variables have weak driving power and strong dependence power. The third cluster named linkage variables has strong driving power and strong dependence power. The fourth cluster named independent variables has strong driving power and weak dependence power.