SmartISM: Implementation and Assessment of Interpretive Structural Modeling

Abstract

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 Croisés Multiplication Appliquée 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.

Assessing the challenges to medical waste management during the COVID-19 pandemic: Implications for the environmental sustainability in the emerging economies

“…Interpretive Structural Modeling (ISM) is an analytical and directional structuring technique that offers a systematic approach to dealing with complicated real-world problems [ 64 ]. It provides a graphic depiction of the scenario (or problem) that may be utilized to get fresh insights and come up with new alternative solutions.…”

“…This technique is usually applied to calculate the driving power and dependence power of the factors under analysis, which is calculated by adding the entries of interaction possibilities in its row and column, respectively. Based on their dependency and driving power levels, which are shown on the horizontal and vertical axes, variables are mapped onto a two-dimensional grid grouping into four clusters [ 64 ]. In Cluster 1 (Autonomous cluster), challenges have both poor driving and dependence power, and they play a neutral role in the MWM implementation process.…”

Barriers to adopt industry 4.0 in supply chains using interpretive structural modeling

“…1 was constructed based on the final results of level partitions. It was conducted following Ahmad and Qahmash (2021). It indicates that two barriers, i.e., barrier 3 and barrier 6 are at the top of the model in level 1, and the other barriers, i.e., 1, 2, 4, 5, 7, 8, 9, and 10, are in the second level.…”

Assessing the Barriers of Green Innovation Implementation: Evidence from the Vietnamese Manufacturing Sector

“…Thus, the authors employed the interpretive structural modeling (ISM) technique to address these research objectives. The ISM analysis has particular characteristics and advantages to deal with the research requirements [36], more importantly, clarifying the interactions among the green innovation variables [37]. Thanks to the expert's opinion, they have suggested the essential factors, which as a basis for making decisions related to defining cross-relationships among them, including direct and indirect.…”

“…Noticeably, the literature review has witnessed the various multi-criteria decision-making methods to deal with the cross relationships among elements, such as AHP [13], TOPSIS [12,39], and DEMATEL [40]. However, these techniques have some limitations regarding internal consistency and assessing indirect relationships [36,41]. Thus, the authors believe in the suggestion of several authors such as Movahedipour et al, (2017), Gupta and Barua (2021), and Singh and Gupta (2021).…”