Modelling passenger interaction process (PIP) framework using ISM and MICMAC approach

Kadam, Sanjeev; Bandyopadhyay, Probir K.

doi:10.1016/j.jrtpm.2019.100171

Cited by 11 publications

(9 citation statements)

References 18 publications

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“…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.…”

Section: Reduced Conical Matrix (Rcm) and Final Ism Modelmentioning

confidence: 99%

“…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].…”

Section: Domain Of Studymentioning

confidence: 99%

“…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.…”

Section: Domain Experts or Decision Makersmentioning

confidence: 99%

“…Finally, 12 studies had accurate hierarchical structures of variables in the final ISM model but variations in the edges that distorted the reachability of variables. Of these 12 studies, eight studies [11,14,[43][44][45][46][47][48] had some extra edges and four studies [49][50][51][52] had some missing edges in the final model.…”

Section: Assessment Of Application Of Ism Technique Using Smartismmentioning

confidence: 99%

“…Implementation of this technique and conduction of brainstorming sessions with experts in previous studies [17,18] led to identification of some key challenges such as variables' identification, selection of decision makers and method of decision making, and unavailability of end-to-end software for ISM and MICMAC. Furthermore, the literature shows erroneous applications of steps of ISM such as wrong reachability matrix [19][20][21][22], wrong transitivity calculations [9,13,16,[23][24][25][26][27][28][29][30][31][32][33][34][35][36][37], incorrect level partitioning and wrong structure of the model [31,[38][39][40][41][42], and incorrect addition [11,14,[43][44][45][46][47][48] or reduction [49][50][51][52] of edges affecting the reachability of variables. An error in an earlier step generally leads to an error in subsequent steps.…”

Section: Introductionmentioning

confidence: 99%

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SmartISM: Implementation and Assessment of Interpretive Structural Modeling

Ahmad

Qahmash

2021

Sustainability

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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.

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