SmartISM: Implementation and Assessment of Interpretive Structural Modeling

: Interpretive Structural Modeling (ISM) is a technique to establish the interrelationships between elements of interest in a speciﬁc domain through experts’ knowledge of the context of the elements. This technique has been applied in numerous domains and the list continues to grow due to its simplistic concept, while sustainability has taken the lead. The partially automated or manual application of this technique has been prone to errors as witnessed in the literature due to a series of mathematical steps of higher-order computing complexity. Therefore, this work proposes to develop an end-to-end graphical software, SmartISM, to implement ISM technique and MICMAC (Matrice d’Impacts Crois é s Multiplication Appliqu é e á un Classement (cross-impact matrix multiplication applied to classiﬁcation)), generally applied along with ISM to classify variables. Further, a scoping review has been conducted to study the applications of ISM in the previous studies using Denyer and Tranﬁeld’s (2009) framework and newly developed SmartISM. For the development of SmartISM, Microsoft Excel software has been used, and relevant algorithms and VBA (Visual Basic for Applications) functions have been illustrated. For the transitivity calculation the Warshall algorithm has been used and a new algorithm reduced conical matrix has been introduced to remove edges while retaining the reachability of variables and structure of digraph in the ﬁnal model. The scoping review results demonstrate 21 different domains such as sustainability, supply chain and logistics, information technology, energy, human resource, marketing, and operations among others; numerous types of constructs such as enablers, barriers, critical success factors, strategies, practices, among others, and their numbers varied from 5 to 32; number of decision makers ranged between 2 to 120 with a median value of 11, and belong to academia, industry, and/or government; and usage of multiple techniques of discourse and survey for decision making and data collection. Furthermore, the SmartISM reproduced results show that only 29 out of 77 studies selected have a correct application of ISM after discounting the generalized transitivity incorporation. The outcome of this work will help in more informed applications of this technique in newer domains and utilization of SmartISM to efﬁciently model the interrelationships among variables.


Introduction
Every discipline is expanding its frontier and multiple disciplinary approaches have become essential to solve complex problems. This leads to the study of a large number of constructs of interests simultaneously. These constructs may have been identified in theory or practice. Warfield [1][2][3][4] in the 1970's developed a technique to establish an interrelationship model between variables known as interpretive structural modeling (ISM). The holistic picture of important constructs in the structured form derived from ISM technique helps the practitioners to solve the problem effectively. This technique is widely used due to its simplistic procedure and profound value addition in problem solving in different domains.

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Development of SmartISM, a software tool for ISM and MICMAC using Microsoft Excel and VBA. • Scoping review of applications of ISM on existing studies to identify application domains, types and numbers of variables studied, composition of decision makers, decision making and data collection techniques, and accuracy of ISM application using SmartISM.
The remainder of the paper is organized into the following sections: literature review, research methodology, development of SmartISM using Microsoft Excel, results, discussion, and conclusion.

Literature Review
There are numerous studies in the literature that illustrate the ISM technique. They can be summarized into the seven steps approach with an additional eighth step for MICMAC analysis, as given in the following subsection. The next subsection illustrates the existing available automation of the ISM. The last subsection presents some studies that have reviewed the implementations of ISM.

ISM and MICMAC Techniques
The interpretive structural modelling (ISM) can be defined as constructs' directional structuring technique based on contextual interrelationships defined by domain experts, utilizing computerized conversion of relations into a pictorial model using matrix algebra and graph theory. It may be explained in the series of steps as follows, which will assist in automating all the processes of the ISM technique.

Elements or Constructs or Variables
Identification of elements or constructs of the subject being studied is the most important of all activities. Similarly, the establishment of their definition along with the theoretical boundaries or scope is very critical. Elements must be explained with the details of their definition, objectives, and possible indications or measurements. These elements are generally identified by literature review, expert opinions, and/or surveys. Some of the unique approaches have been use of thematic analysis [58], upper echelon theory [11], contingency theory [59], content analysis [52], strengths, weaknesses, opportunities, and threats (SWOT) analysis [30], idea engineering workshop [40,60], and Delphi technique [37,61]. One study [42] has defined the source, understanding, and interpretation for each variable.

Decision Makers (DMs)
DMs play a very significant role in ISM as the whole process and outcome are dependent upon their input. There are three important aspects for the selection of a group of DMs such as size, expertise, and diversity. The group of DMs should be representative of all of the stakeholders in the domain of the problem. They should have sound experience of domain and expert level knowledge of variables being studied. The literature shows the number of DMs ranging between 2 [62,63] to 120 [64] with a median value of 11, and very few studies [16,30,41,65] have taken DMs from academia, industry, and government together.

Structural Self-Interaction Matrix (SSIM)
Elements or constructs are interrelated with one of the four relations such as x influences y, y influences x, x and y mutually influence each other, or x and y are unrelated. These relations are almost universally represented by 'V', 'A', 'X', and 'O' characters respectively in the SSIM. These relationships are assigned by DMs based on contextual relationships during pairwise comparison on variables. The number of comparisons is nC2 (mathematical combination), where n is the number of variables in the domain of study. Finally, an n by n matrix is formed with nC2 cells filled with A, V, X, and O symbols and the remaining cells are blank. Most studies have used these standard symbols except few such as [35]. As this is the basic matrix and required for all other steps therefore has been documented in most of the studies except few such as [15,66].

Reachability Matrix (RM) and Final Reachability Matrix (FRM)
RM is the representation of SSIM in binary form. V, A, X, and O symbols of SSIM are replaced with 1, 0, 1, and 0, digits respectively. At their transposed positions by row with column and column with row, 0, 1, 1, and 0 digits are placed, respectively. The constructs are assumed to influence self, so ones are placed at the diagonal positions. The resultant RM is checked for transitive relations. Transitivity is the basic assumption in the ISM such as if variable x influences y and y influences z then x will influence z transitively. This is second-order or two-hop transitivity whereas generalized transitivity means x is related to z through one or more variables. The transitive relations hence identified are represented in the RM with 1*s to distinguish from original 1s and the resulting matrix is known as FRM. FRM also consists of driving and dependence powers of each variable by counting 1s and 1*s in rows and columns respectively. Very few studies mention usage of some software for transitivity calculations such as [56,57]. However, one of the most frequent reasons for incorrect ISM calculations have been wrong transitivity calculations, such as in studies [9,13,16,[23][24][25][26][27][28][29][30][31][32][33][34][35][36][37]. Therefore, this study proposes the use of an established Warshall algorithm [67] for transitivity calculations.

Level Partitioning
This is a very important step to develop the hierarchical directional structure among the variables. Reachability, antecedent, and intersection sets are derived for all the variables from the FRM. For a specific variable, a reachability set consists of itself and all the variables it influences, and an antecedent set consists of itself and all the variables influencing it. Thereafter, the intersection set of reachability and antecedent set is calculated. Variables having the same reachability and intersection sets are given the top rank and are removed for the next iteration and the process is repeated until all variables are ranked. Some studies such as [31,[38][39][40][41][42] in the literature had incorrect leveling for variables.

Conical Matrix (CM) and Digraph
CM is row and column wise ordered FRM based on ranks or levels of variables identified in the level partitioning step. Further the levels of each variable are also recorded at the end of row and column in CM. This matrix helps in drawing the digraph to get the first visual output of the hierarchical directional structure of variables. Circular nodes are drawn with variable numbers. Further they are connected with directional edges based upon 1s or 1*s in the CM between pairs of variables. Fewer studies have mentioned CM and digraph [12,20,27,35,65,[68][69][70], as the digraph resembles the final model with a lesser number of edges. The importance of the digraph further goes down in automatic calculation of transitivity.

Reduced Conical Matrix (RCM) and Final ISM Model
Digraph is converted into a final model by replacing the node numbers with names of the variables and representing nodes in the rectangular shapes. Moreover, efforts are made to remove maximum edges from digraph while maintaining the levels and structure of variables and reachability of variables. This is done to improve the readability of the final model. Several studies have committed mistakes at this step either by adding extra edges [11,14,[43][44][45][46][47][48] or omitting edges [49][50][51][52] that have affected the reachability of the variables. Therefore, a new algorithm, reduced conical matrix (RCM), has been devised to remove maximum possible edges without affecting structure and reachability of variables, as explained in the fourth section. This RCM is used for making the final ISM model. The final model may further be subjected to validations by different means such as review by DMs, interviews from different sets of participants, or statistical validations. MICMAC (Matrice d'Impacts Croisés Multiplication Appliquée á un Classement (cross-impact matrix multiplication applied to classification)) in the simplest terms is a variable classification technique. Variables are mapped onto a two-dimensional grid based on their dependence and driving power values, represented on horizontal and vertical axes respectively. The range of these values is between 1 and total number of variables and the axes are bifurcated at mid-points, resulting in four quadrants numbered anti clockwise. These quadrants classify variables into autonomous, dependent, linkage, and independent categories. The autonomous variables are not connected with the remaining system of variables whereas linkage variables are sensitive and strongly connected with independent and dependent variables. The final hierarchical ISM model coupled with the MICMAC analysis greatly improves the understanding of variables. Therefore, most studies have carried out MICMAC analysis except few such as [19,39,47,71,72].

Implementation of ISM
As originally proposed by Warfield [1][2][3][4], the ISM requires its steps to be executed with the assistance of a computer [6]. Some of the more recent studies demonstrate specialized software or routines being developed for ISM. The article [56] mentions the development of the ISM software package in R software. This software package takes the SSIM input in the comma separated (.csv) excel file and provides two outputs in excel file format, namely, "ISM_Matrix" for FRM step to incorporate transitivity calculations and "ISM_Output" for partitioning step to identify the levels of the variables. Similarly, some studies such as [57] have used MATLAB software to calculate the FRM and partitioning steps. The previous studies have attempted to automate FRM and partitioning steps, leading to partial automation of ISM. As pointed out earlier in absence of automation, the final model may introduce errors in edges regardless of correct FRM and leveling, leading to wrong reachability of variables. Further, having all the steps being carried out automatically shows the prompt results to researchers and decision makers for further possible iterations. Therefore, there exists a need to develop an end-to-end graphical software to implement ISM and MICMAC and identify the required algorithms for it.

Assessment of ISM Applications
The ISM technique is being applied in a range of domains [53][54][55]. The review article [54] provides 10 different application domains for ISM. It further provides additional parameters such as integration with other MCDM approaches. Similarly the review article [55] identified ISM applications in 14 domains without industry or organizations, 20 industrial sectors, and 4 other areas. Furthermore, among other characteristics, it mentions integration with other MCDM approaches, and the presence of constructs for cost and/or quality. These reviews haven't focused on operationalization of ISM technique. Therefore, there exists a gap to identify the methodology of steps of applications of ISM in the existing articles such as nature and number of variables, compositions of DMs, decision making and data collection techniques, and accuracy of ISM results.

Research Methodology
This study addresses two objectives, firstly the development of SmartISM for the implementation of ISM, as explained in the following section. The second objective is the scoping review of literature to identify the scope of ISM and MICMAC applications and the assessment of applications of ISM using SmartISM tool. For the scoping review the five-step framework of Denyer and Tranfield (2009) [73] has been adopted as explained in the following paragraphs, Figure 1. The review process also generated the data necessary for the assessment of application of ISM using SmartISM. plementation of ISM, as explained in the following section. The second objective is the scoping review of literature to identify the scope of ISM and MICMAC applications and the assessment of applications of ISM using SmartISM tool. For the scoping review the five-step framework of Denyer and Tranfield (2009) [73] has been adopted as explained in the following paragraphs, Figure 1. The review process also generated the data necessary for the assessment of application of ISM using SmartISM. Step I: Question formulation: Formulating questions requires identification of context, interventions, mechanisms, and outcomes. In this study, context is considered to be domain, and decision makers; interventions are variables of interest in problem domain; mechanisms are techniques for data collection from DMs; and outcomes are the ISM outputs and MICMAC diagram. In essence there will be the following research questions that will help in addressing the second objective of this study. Step I: Question formulation: Formulating questions requires identification of context, interventions, mechanisms, and outcomes. In this study, context is considered to be domain, and decision makers; interventions are variables of interest in problem domain; mechanisms are techniques for data collection from DMs; and outcomes are the ISM outputs and MICMAC diagram. In essence there will be the following research questions that will help in addressing the second objective of this study. Step II: Locating Studies: As the ISM based studies are huge, only quality sources were considered, rather than an exhaustive search. As per the objectives, articles that had significant discussion and documentation on ISM application as mentioned in Step I were needed. As defined earlier, the steps of ISM are structural self-interaction matrix, initial and final reachability matrix, level partitioning, conical matrix, digraph, and final model. It was observed that an article going into the details of level partitioning had sufficient demonstration of ISM. Therefore, "Interpretive Structural Modeling" + partitioning keywords were used on ScienceDirect database and it resulted in 300 articles up to the year of 2021, of which 291 belonged to review and research articles.
Step III: Study selection and evaluation: These articles were further perused for the relevance to present study and classified into different groups such as definition only, other techniques, no partitioning, non-related, incomplete outputs, and desired study. As the articles were growing nonlinearly each year, therefore, after the year of 2017, a random selection of five articles was preferred to keep the dataset manageable. It resulted in 77 articles in the desired study group that were considered further in this study.
Step IV: Analysis and Synthesis: This step has two components: first the analysis of articles for context, interventions, and mechanisms was performed, as explained in step one, by extracting relevant information as shown in Table 2. Second was the extraction of SSIM from the 77 selected articles to reimplement the ISM technique using SmartISM. The results from the SmartISM were compared with the outcomes illustrated in the article for SSIM, RM, FRM, LP, CM, digraph, final model and edges in the final model, and MICMAC and the variations are summarized in Table 3.
Step V: Reporting and using the results: Results of analysis and synthesis are reported in results and discussion section. They have been provided in a fashion that will assist in informed-adoption and application of ISM and MICMAC, and utilization of SmartISM for academicians, practitioners, and policy makers alike.

Articles' Details
Articles' publication years range from 2005 to 2021. As the articles are increasing non-linearly, therefore 2017 onwards only five articles were randomly chosen for each year. The publication sources having two or more articles have been shown in Table 1. Journals in the area of sustainability have the maximum number of articles. Journal of cleaner production had published 18 articles out of 77 selected articles.

Development of SmartISM Using Microsoft Excel
Microsoft Excel provides an excellent environment for graphical representation and modelling of virtually any conceptual framework of any discipline. It has some important features such as cellular addressable input sheets, interactive output, vector graphic objects, integral atomic access of data in multiple ways, many inbuilt data processing functions, backend VBA (Visual Basic for Applications) interface to code any logic or algorithm, mechanisms for development of event driven interfaces, ubiquitous tool and ease of use, and widespread ecosystem of support and training. Hence it makes a natural choice for practitioners, decision makers, and researchers to develop their problem-solving models in Microsoft Excel. Its applications in business statistics and decision making are widely documented. Following are some advanced applications of MS Excel in different domains such as Genetic Analysis [74], Finite Element Analysis in Engineering [75], and Pharmacokinetic Pharmacodynamic fields of Pharmacology [76]. On the flip side it has a drawback to support multiple real-time concurrent users. This section explains the functions and features of VBA to develop SmartISM, an end-to-end graphical software to automate processes of ISM and MICMAC with the help of pseudo codes. Additionally, the demonstration video for SmartISM has been attached as a Supplementary Material, see Video S1.
Firstly, the SSIM matrix defined by DMs is entered in Excel, and serves as the basic input for other steps of ISM. For n variables, the size of SSIM will be n by n. DMs will compare n(n + 1)/2 or n C 2 unique pairs of variables and assign one of the relationships using symbols V, A, X, or O, as explained earlier. Thereafter, eight VBA macros will derive matrices of RM, FRM, CM, and RCM; level partitioning; and draw diagrams of digraph, final model, and MICMAC. RM is a binary form of SSIM using conversion rules for V, A, X, and O as explained earlier and keeping 1s at the diagonal positions of the matrix, as described in the following pseudo code. RM also contains the driving and dependence powers for each variable.
[i] ← 0 //count non-zero elements in rows and columns and append to show the driving and //dependence powers The second function FRM requires calculation of transitive relations among variables. For manual calculation, RM can be visualized as a digraph with variables representing nodes and 1s in the RM representing the directed edges. By tracing different paths, transitive relations can be identified. For a large number of variables the process would be tedious and leads to errors, whereas a simple Warshall algorithm [67] for transitive closure can be used to automate it. This algorithm results in generalized transitivity if applied in-place, otherwise it will give second-order or two-hop transitivity. Transitive relations are marked with 1* in FRM, see the pseudo code for main logic in the following paragraph. Moreover, the 1s and 1*s are counted in rows and columns to calculate the driving and dependence powers respectively for each variable.
The next step is to calculate the ranks of the variables through level partitioning. A new matrix LP is defined with five columns namely elements (Mi), reachability set R(Mi), antecedent set A(Mi), intersection set R(Mi)∩A(Mi) and level, and n rows. For a specific variable Mi in FRM, non-zero cells in the row comprise its reachability set and their corresponding identifiers are kept in the LP row of the same variable Mi. Similarly, non-zero cells in the column comprise its antecedent set and their corresponding identifiers are kept in the LP row of the same variable Mi. The intersection sets are calculated for all variables and variables having the same reachability and intersection sets are given first rank. In the next iteration, identifiers of all the ranked variables are removed from reachability, antecedent, and intersection sets. Again, variables having the same reachability and intersection sets are given the second rank and iteration continues until all the variables are ranked. The iteration results may be copied in one Microsoft Excel Sheet.
Function LP //initiate a matrix LP of size n by 5 to keep element number, reachability set, antecedent //set, intersection set and levels for each of the n elements; levels will remain empty For i = Once the variables are ranked, a digraph can be developed easily by positioning the variables as per their ranks with the help of CM. CM is row and column wise sorted FRM as per variables' ranks or levels. Directed edges can be drawn between variables as per non-zero cells in the CM. Two shape objects Oval and Connector are needed to automate the drawing of digraph. Positing of ovals needs to be carefully assigned, as there can be multiple ovals in one level. The simplest way to identify the needed objects in drawing is to auto record a macro and draw a sample. Afterwards, the macro can be manually edited and static names of the objects can be made dynamic for easy handling in the loop structures of VBA. The pseudo codes for the functions for CM and digraph is as follows. The final model represents variable names in the rectangular boxes in place of their identifiers in ovals and tries to remove maximum possible transitive links from the digraph. Transitive reduction is a technique to reduce the number of transitive links. Transitive reduction is complicated, specifically for the directed cyclic graphs, and the algorithm may even distort the structure of the digraph. Therefore, an algorithm was designed to develop a reduced conical matrix (RCM) that removes maximum links without changing the structure of digraph and reachability of elements. The main logic is to remove incoming links from second lower-level variables from the CM and results in RCM, see the pseudo code for the main logic in the following paragraph. RCM was used to draw automated final ISM model using Rectangle and Connector shape objects, as in the following pseudo code.
Function RCM //copy the content of CM into RCM RCM ← CM //start loop to parse through columns For i = 1 To n //n is the total number of elements //start loop to parse through row of specific column for lower triangular matrix For j = i To n //search for first non-zero row cell whose level is greater than the level of that Lastly, a macro was written to draw a MICMAC diagram. The basic input for this diagram was the dependence and driving powers of variables from FRM. This was the longest macro as it required many shape objects such as Line, Connector, Rectangle, Oval, and Textbox. However, it didn't require any special algorithm to be used. Nevertheless, logic to initiate, aggregate, and draw different objects based on number of variables, and dependence and driving powers in a specified space, required careful arrangement.

Results
This section presents the scoping review answers to the questions described in the research methodology section with respect to domain of ISM applications, variables of study, composition of decision makers, decision making, and data collection techniques, as summarized in Table 2. Furthermore, the results of the assessment of ISM technique using SmartISM on the selected 77 papers are summarized in Table 3.

Results
This section presents the scoping review answers to the questions described in the research methodology section with respect to domain of ISM applications, variables of study, composition of decision makers, decision making, and data collection techniques, as summarized in Table 2. Furthermore, the results of the assessment of ISM technique using SmartISM on the selected 77 papers are summarized in Table 3.
Added extra edge from variable 4 to variable 5 2 [15] Some edges are missed such as between variable 9 and variable 1 4 Transitivity not incorporated though mentioned in the methodology 7 Wrong calculation for transitivity, only two transitive links but shown more 13 * Wrong calculation for transitivity and for generalized transitivity; all will be having same dependence and driving powers with max value

Domain of Study
ISM is being applied in numerous fields such as sustainability, social sciences, management, engineering, and information technology. The results show 21 different domains, with highest studies in sustainability (32), supply chain and logistics (13), information technology (9), energy (5), human resource (3), marketing (3), and operations (3) ( Table 2). Within sustainability, the highest studies are in the area of green supply chain management (GSCM) and sustainable supply chain management (SSCM) with seven studies each followed by two studies in construction [40,58] and several other areas such as e-waste recycling [16], healthcare waste [13], recycling 3D printing waste [47], green IT/IS [11], among others. In the area of supply chain and logistics, studies have focused on supplier relationship [9] and selection [44], food SCM [24,25], e-procurement [72], and reverse logistics [14,62,84], among others. In the field of information technology studies are conducted in the areas of building information modeling [21,93], cloud computing [37,52], e-commerce security [19], m-commerce [96], enterprise resource planning [45], supply chain management [94], and social networking sites [95]. Energy domain studies were in the area of bio-diesel [68], smart grid technologies [60], and solar energy [79]. For the human resource domain, two studies were in the area of occupational health and safety [83,89] and one in team performance [88]. The studies in the marketing area focused on motivation [39], retail brand [39], and app-based retailing [97]. Furthermore, the articles in the area of operations focused on maintenance [35,36] and lean manufacturing [12]. Some of the innovative areas were landfill communities [31], emission trading system [92], tour value [66], and quality of passenger interaction process [51].

Variables of Study
Most studies focus on enablers or drivers, challenges or barriers, critical success factors, and influencing or significant elements in the domain of research (Table 2). Other studies have tried to explore different sets of variables. For example, article [84] has studied seven attributes of third party reverse logistics; article [39] has studied 17 motivational factors in the marketing area; article [44] has used 9 corporate social responsibility factors in the area supplier selection; article [85] has studied 25 SSCM practices; article [35] interrelated maintenance tools and technique; article [11] explored 13 psychological drivers of motivation in the area of green IT/IS; article [95] studied the interrelationships between 12 factors for abandoning social networking sites; article [29] studied capabilities and drivers for new product development; and article [30] studied 13 strategies for renewable energy. The number of variables being studied ranged from 5 [58] to 32 [20]. Additionally, some studies explored two types of factors such as 10 barriers and 10 enablers [15] and 14 barriers and 15 benefits [72]. One study gave variables in two applications such as 8 CSFs for roads and bridges and 10 CSFs for embarkment [40]. These variables are identified mostly through literature review, experts' opinions, and/or survey. Some of the unique approaches used to identify variables are thematic analysis [58], upper echelon theory [11], contingency theory [59], content analysis [52], best worst method [28], SWOT (strength, weakness, opportunity, and threat) analysis [30], idea engineering workshop [40,60], and Delphi technique [37,61].

Domain Experts or Decision Makers
This is the most crucial step as it provides the input for further steps. There are two important aspects, namely, selection of decision makers (DM) and method of information gathering from them. There are three different sets of DMs in the sample studies participants from industry, academia, and government. Participants varied from 120 through survey [64] to 2 [62,63] through group discussion and consultation. The median value of the total number of participants was 10. Only four studies [16,30,41,65] had taken participants from all three sectors: industry, academia, and government. While 56 studies had DMs from industry including others and two studies [51,72] had only academic DMs, 17 studies didn't mention the number of DMs.

Assessment of Application of ISM Technique Using SmartISM
The SSIM matrices from all the 77 articles were entered into the developed SmartISM software and resulted in 77 excel files. Thereafter, for each article results were reproduced in the SmartISM software buy running the macros in 77 excel files. Variations between the reported results in the articles and corresponding SmartISM reproduced results were studied. Due to differences in transitivity incorporation FRM was checked for firstly nonincorporation of transitivity, followed by two-hop transitivity (second-order) and lastly generalized transitivity (all levels). In some cases, second order and generalized transitivity could be same. Furthermore, in case of inconsistency the digraph was manually built and transitivity was traced before reporting the results. Similarly, the complete process was analyzed to identify the reasons for the errors in any of the steps. As ISM technique is sequential, error in one step will cause subsequent steps to be erroneous specially if error exists in the steps of RM, FRM, and level partitioning.
The results of the assessments are summarized in Table 3 where 'Y' means the articles' calculations match with that of SmartISM and 'N' means different results. For each article SSIM, RM, FRM, level partitioning, digraph, final model, edges in the final model, and MICMAC diagrams are verified. Three studies [64,92,94] didn't report standard SSIM and two studies [15,66] had no SSIM, therefore results were not reproduced for them. Out of 72 remaining studies, 29 studies came out correct on all the parameters and their serial numbers (S. No.) are marked with stars '*'. Of these 29 studies, 25 had either second level (two-hop or second-order transitivity) or all levels (generalized transitivity), and four [70,71,88,97] had no transitivity calculations.
In the documentation of application of ISM, only eight studies [12,20,27,35,65,[68][69][70] reported digraph. MICMAC analysis has been used by all studies except five [19,39,47,71,72] to explain grouping of the variables. Five studies [27,29,59,65,93] have explicitly mentioned X and one study [52] has mentioned 1 in the SSIM for variables to represent mutual self-relation, although it is a basic assumption in ISM therefore, other studies have not mentioned it.

Discussion
The operationalization of the ISM is best to be conducted through software, as there are tedious calculations such as transitivity, level partitioning, and graphical displays of digraph, final ISM model, and MICMAC diagram. Moreover, these calculations and displays need to be iterated until the high confidence model is approved by the experts. Therefore, this study has explained the methodology to develop MS Excel and VBA based, end-to-end software, SmartISM for ISM and MICMAC with the help of pseudo codes. For incorporating transitivity in FRM, the Warshall algorithm has been used, and a new algorithm RCM has been introduced for removing edges from variables' second lower level onwards without affecting reachability and digraph structure. Further, the demonstration video of SmartISM has been added as a Supplementary Material, and this tool has been extensively tested on the existing studies and applied successfully in some of the studies [98][99][100].
Furthermore, the scoping review shows that the ISM and MICMAC techniques are being applied in different domains of social sciences, management, engineering, and technology such as sustainability, SCM, operations, manufacturing, human resource, information technology, and many other innovative areas. This technique is also employed in different multi criteria decision making techniques such as AHP, ANP, TOPSIS, DEMATEL, etc. There are four important issues that need to be addressed such as variables and their context, decision makers' experience and numbers, decision making and data collection techniques, and utilization of software tools. The nature of variables has been enablers or drivers, challenges or barriers, critical success factors, strategies, capabilities and drivers, and influencing or significant elements in the area of study. Their numbers have varied from 5 to 32 and they have been identified through domain specific literature review, experts' opinions, and/or survey. Furthermore, techniques such as thematic analysis, upper echelon theory, contingency theory, content analysis, best worst method, SWOT analysis, idea engineering workshop, and Delphi technique are used for variables' identification. Similarly, the variables have been explained well to establish the contextual meaning.
Another important aspect is the experts or decision makers, as the whole analysis is dependent upon their knowledge and experience. There should be representation from all stakeholders of the domain being studied. In the best-case, experts from academia, industry, government and regulatory bodies should be selected in the panel of DMs. There are very few studies such as four in the sample of articles that have had DMs from all the stakeholders. The number of DMs varied from 2 to 120, whereas in most of the studies they were 11. Two approaches have been utilized for extracting information from DMs namely discourse and surveys. The discourse techniques are idea engineering workshops, telephonic enquiries, group decision making, personal interview, brainstorming, laddering interview, direct meetings, semi-structured and structured interview, Delphi technique, and focus group discussion. Survey techniques have used individual and consensus questionnaires, Likert scale questionnaire, email questionnaire, library survey method, and self-administered questionnaire.
The SmartISM reproduced results, on the existing studies selected in scoping review, show that only 29 out of 77 studies had correct calculations with varied transitivity incorporation such as no transitivity, second order transitivity, or generalized transitivity. Wrong transitivity calculation has been the most frequent reason for incorrect ISM results followed by variations in drawing edges in the final model that affects the reachability of the variables.
Lastly, five studies didn't report standard SSIM, which is essential to reproduce the calculations. Therefore, as a standard practice some minimum outputs must be reported namely SSIM, FRM, level partitioning (final after all iterations), and final ISM model. Similarly, MICMAC analysis is also an important and indispensable part of ISM, as all studies except five have used it for classifying variables into one of the four groups, namely, dependent, independent, linkage, and autonomous.

Conclusions
Human decisions play a very important role in any social or technical system development. Domain experts have intricate knowledge on the system and can predict the contextual interrelationships between the variables of interest in the particular domain. The interpretive structural modelling technique can assemble their tacit knowledge into a tangible hierarchical model leading to an enhanced understanding of the subject. This study has developed a software tool such as SmartISM to implement ISM in an error-free, user-friendly, and graphical style. In addition to automation of existing routines of ISM, the Warshall algorithm is used for transitivity calculations and a new algorithm, reduced conical matrix, has been introduced to convert the digraph into a final model with lesser edges while retaining the digraph structure and reachability of variables. Furthermore, the scoping review of this research will guide practitioners, policy makers, and academicians in applying this technique in different disciplines in an informed way. It will help in managing ISM configuration settings such as variables' selection, composition of decision makers, decision making, and data collection techniques. The poor results of assessment of application of ISM technique in the previous studies necessitate the utilization of an end-to-end software, such as SmartISM, to produce a high confidence final model, explaining the interrelationships between important constructs in the applied domain. To limit the number of articles in the review process only the ScienceDirect database was used, and for the last four years articles were randomly selected; therefore, results should be interpreted accordingly. The future studies will focus on the development of software tools to apply ISM in conjunction with other MCDM techniques such as AHP, ANP, TOPSIS, and DEMATEL.

Data Availability Statement:
The data presented in this study are available on request from the corresponding author.