## 2 The Methodology of ISM and MICMAC  
ISM was first developed by [PERSON] [34] to breakdown and analyze the complex interrelationships in a network which has multiple cause events. [PERSON] [35] introduced the initial concept, definition and methodology of ISM and illustrated the utility of ISM from the perspective of a complex system. In recent studies, ISM is used as an analysis tool which can explore the interrelationship between different objectives for decision making [36; 37; 38; 39; 40; 41; 42; 43], break complex, unclear and fuzzy systems into a graph network. ISM has also been implemented to a number of empirical cases in various areas. For instance, [PERSON] and [PERSON] [44] explored the interacted factors among the main obstacles which influence the implementation of \"reverse logistics\" in the vehicle machinery field. [PERSON] et al. [45] built a hierarchy network model used for an assessment of the dynamics and changing status of different risk factors in the process of supply chain management. Generally, ISM is often integrated with other analysis tools of factors, such as DEMATEL (Decision Making Trial and Evaluation Laboratory), ELECTRE (Elimination and Choice Translating Reality) [46], fuzzy MICMAC [47] and SPIN (Situation, Problem, Implication, and Need) [46] etc. In this study, MICMAC analysis is selected to be integrated with ISM under the consideration of simple size and flexible implementation. MICMAC analysis is designed to classify the CSFs into different categories or clusters, such as autonomous,dependent, linkages and independent, according to their \"driver and dependence powers\", which indicate the degrees of interactions between CSFs [48; 49; 50; 51; 52; 53; 54].  
There are five steps in conducting ISM:  
* Step1: investigation and identification are made to generate the list of CSFs though the combination of literature review and case studies.
* Step2: interrelations between CSFs are explored by semi-structured interviews. In this step, a causal relationship analysis is processed through pairwise comparisons and the results are presented in the causal relationship network of CSFs.
* Step3: the causal relationship network of CSFs is converted to the adjacency matrix (A). Thereafter, a reachability matrix (R) is obtained from A and constructed to investigate the transitivity among CSFs. This process is undertaken via Boolean algebraic algorithms implemented in MATLAB software. Thereafter, a partition process is taken to break down the R into different levels. The location and distribution of CSFs in the final model are determined.
* Step4: skeleton-matrix (S) is implemented to determine the directional links between CSFs in different levels.
* Step5: the final model is generated with the obtained directional links to illustrate the causal relationship between each CSF.  
After generating the ISM, the result of ISM can be integrated into a MACMIC analysis to understand the degrees of interactions between CSFs by: (1) calculating the driver and dependence powers via a summation of values in the corresponding row or column of R; (2) establishing the four-quadrant matrix with a \(x\)-\(y\) axis, where the \(x\) axis refers to the dependence powers of each CSF and the \(y\) axis refers to the driver powers of each CSF; (3) assigning each CSF into different categories. The flowchart in completing ISM and MACMIC analysis is presented in Figure 1.  
Figure 1: The flowchart in completing ISM and MACMIC analysis.