Skip to main content
Log in

Exploring the Domain of Interpretive Structural Modelling (ISM) for Sustainable Future Panorama: A Bibliometric and Content Analysis

  • Review article
  • Published:
Archives of Computational Methods in Engineering Aims and scope Submit manuscript

Abstract

Interpretive Structural Modelling (ISM) is one of the most widely used techniques in identifying the complex structural relationship between various elements. It is commonly used in multiple disciplines but hardly explored its all-encompassing scientific productivities. Hence, this paper has endeavoured to scrutinize 1480 documents using the ISM technique from 2000 to 2020 in the Scopus database. A systematic two-tier approach comprising bibliometric analysis and visualization review has been made with VOSviewer and Biblioshiny software. Extensive data mining has been done to collect required information with certain filters containing document type, language, author, subject, publication status, source title, affiliation, country, and source type. The study has generated information regarding ISM documents, their types, publications, citations, and predictions. The citation analysis is used to ascertain the most prolific and dominant authors, sources, articles, countries, and organizations. The author-keywords, index-keywords, and text data content analysis is conducted to find ISM's hotspots and progress trends. The study has found a rapid exponential pace in annual publications using the ISM technique since 2000. The most prolific and dominant articles based on total citation include Diabat and Govindan, 2011; the top source is the Journal of Cleaner Production. The chief author is Shankar R., the leading organization IIT New Delhi, India, and the leading country is India. The study has explored many research hotspots and less explored areas using ISM techniques. The present research is the first paper that has utilized bibliometric analysis to analyze the ISM publications widely. This bibliometric review has further contributed to the ISM technique, usability, and exploitation areas and future scope for scholars working in this area through its research hotspots and valuable findings.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Source Citation Overlay Visualization based on 30 Citations and Five Documents

Fig. 5

Source Dynamics

Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

References

  1. Mandic K, Bobar V and Delibašić B (2015) Modeling interactions among criteria in MCDM methods: a review. in International Conference on Decision Support System Technology Springer

  2. Warfield JN An assault on complexity. Battelle Monograph 1973, Battelle Memorial Inst., Columbus, Ohio: Battelle, Office of Corporate Communications

  3. Attri R, Dev N, Sharma V (2013) Interpretive structural modelling (ISM) approach: an overview. Res J Manag Sci 2319(2):1171

    Google Scholar 

  4. Mathiyazhagan K et al (2013) An ISM approach for the barrier analysis in implementing green supply chain management. J Clean Prod 47:283–297

    Article  Google Scholar 

  5. Raj T, Shankar R, Suhaib M (2008) An ISM approach for modelling the enablers of flexible manufacturing system: the case for India. Int J Prod Res 46(24):6883–6912

    Article  Google Scholar 

  6. Tavakolan M, Etemadinia H (2017) Fuzzy weighted interpretive structural modeling: improved method for identification of risk interactions in construction projects. J Constr Eng Manag 143(11):04017084

    Article  Google Scholar 

  7. Kannan G et al (2008) Analysis and selection of green suppliers using interpretative structural modelling and analytic hierarchy process. Int J Manag Decision Mak 9(2):163–182

    Google Scholar 

  8. Kishore R et al., Eco-efficiency and business performance evaluation—lean and green manufacturing approach, in International Conference on Intelligent Manufacturing and Energy Sustainability, ICIMES 2020, AN Reddy, et al., Editors. 2021, Springer Science and Business Media Deutschland GmbH p 779–789

  9. Tham TT et al (2020) An integrated approach of ISM and fuzzy TOPSIS for supplier selection. Int J Procure Manag 13(5):701–735

    MathSciNet  Google Scholar 

  10. Iqbal M et al (2021) Promoting sustainable construction through energy-efficient technologies: an analysis of promotional strategies using interpretive structural modeling Int J Environ Sci Technol 18: 3479–3502

  11. Khan M et al (2020) Applying interpretive structural modeling and micmac analysis to evaluate inhibitors to transparency in humanitarian logistics. Utopia y Praxis Latinoamericana 25(Extra2):325–337

    Google Scholar 

  12. Kim I, Watada J (2009) Decision making with an interpretive structural modeling method using a DNA-based algorithm. IEEE Trans Nanobiosci 8(2):181–191

    Article  Google Scholar 

  13. Sankar H and Suresh M (2018) Modelling the factors of workplace spirituality in healthcare organization Int J Eng Technol (UAE) 7(2.33 Special Issue 33) 786–790

  14. Luthra S et al (2011) Barriers to implement green supply chain management in automobile industry using interpretive structural modeling technique-an Indian perspective. J Indus Eng Manag 4(2):231–257

    Google Scholar 

  15. Faisal MN, Banwet DK, Shankar R (2006) Supply chain risk mitigation: modeling the enablers. Bus Process Manag J 12(4):535–552

    Article  Google Scholar 

  16. Gao H, Xu Y, Zhu Q (2016) Spatial interpretive structural model identification and AHP-based multimodule fusion for alarm root-cause diagnosis in chemical processes. Ind Eng Chem Res 55(12):3641–3658

    Article  Google Scholar 

  17. Zadeh MA, Aleagha MM, and Nia AB (2018) The development of a cleaner production model and applied management solutions for the pharmaceutical industry. Eurasian J Anal Chem 13(3): 1–9

  18. Jharkharia S, Shankar R (2005) IT-enablement of supply chains: understanding the barriers. J Enterp Inf Manag 18(1):11–27

    Article  Google Scholar 

  19. Yang JL et al (2008) Vendor selection by integrated fuzzy MCDM techniques with independent and interdependent relationships. Inf Sci 178(21):4166–4183

    Article  MATH  Google Scholar 

  20. Hu JL, Tang XW, Qiu JN (2016) Assessment of seismic liquefaction potential based on Bayesian network constructed from domain knowledge and history data. Soil Dyn Earthq Eng 89:49–60

    Article  Google Scholar 

  21. Balaji M, Arshinder K (2016) Modeling the causes of food wastage in Indian perishable food supply chain. Resour Conserv Recycl 114:153–167

    Article  Google Scholar 

  22. Kumar A, Dixit G (2018) An analysis of barriers affecting the implementation of e-waste management practices in India: A novel ISM-DEMATEL approach. Sustain Prod Consump 14:36–52

    Article  Google Scholar 

  23. Sindhu S, Nehra V, Luthra S (2016) Identification and analysis of barriers in implementation of solar energy in Indian rural sector using integrated ISM and fuzzy MICMAC approach. Renew Sustain Energy Rev 62:70–88

    Article  Google Scholar 

  24. Haleem A et al (2012) Analysis of critical success factors of world-class manufacturing practices: an application of interpretative structural modelling and interpretative ranking process. Prod Plan Control 23(10–11):722–734

    Article  Google Scholar 

  25. Diabat A, Kannan D, Mathiyazhagan K (2014) Analysis of enablers for implementation of sustainable supply chain management - a textile case. J Clean Prod 83:391–403

    Article  Google Scholar 

  26. Dubey R et al (2017) Sustainable supply chain management: framework and further research directions. J Clean Prod 142:1119–1130

    Article  Google Scholar 

  27. Shimizu H et al (2021) Analysis of factors inhibiting the dissemination of telemedicine in Japan: using the interpretive structural modeling. Telemed e-Health 27(5):575–582

    Article  Google Scholar 

  28. Ahmad M et al (2019) Interpretive structural modeling and MICMAC analysis for identifying and benchmarking significant factors of seismic soil liquefaction. Appl Sci (Switzerland) 9(2):233

    Google Scholar 

  29. Dwivedi YK et al (2017) Driving innovation through big open linked data (BOLD): exploring antecedents using interpretive structural modelling. Inf Syst Front 19(2):197–212

    Article  Google Scholar 

  30. Wasuja S, Sagar M, and Sushil (2012) Cognitive bias in salespersons in specialty drug selling of pharmaceutical industry. Int J Pharm Healthcare Market 6(4): 310–335

  31. Yang T, Li YL and Su JF (2019) Research on influence factors of product configuration rebuilt design with demand preferences of customers. in 2nd International Conference on Computer Information Science and Application Technology, CISAT 2019 Institute of Physics Publishing

  32. Shankar R, Pathak DK, Choudhary D (2019) Decarbonizing freight transportation: an integrated EFA-TISM approach to model enablers of dedicated freight corridors. Technol Forecast Soc Chang 143:85–100

    Article  Google Scholar 

  33. Pfohl HC, Gallus P, Thomas D (2011) Interpretive structural modeling of supply chain risks. Int J Phys Distrib Logist Manag 41(9):839–859

    Article  Google Scholar 

  34. Ravi V, Shankar R (2005) Analysis of interactions among the barriers of reverse logistics. Technol Forecast Soc Chang 72(8):1011–1029

    Article  Google Scholar 

  35. Yenradee P, Dangton R (2000) Implementation sequence of engineering and management techniques for enhancing the effectiveness of production and inventory control system. Int J Prod Res 38(12):2689–2707

    Article  Google Scholar 

  36. Kanungo S, Bhatnagar VV (2002) Beyond generic models for information system quality: the use of interpretive structural modeling (ISM). Syst Res Behav Sci 19(6):531–549

    Article  Google Scholar 

  37. Singh RS et al (2003) An interpretive structural modeling of knowledge management in engineering industries. J Adv Manag Res 1(1):28–40

    Article  Google Scholar 

  38. Singh AK and Sushil (2013) Modeling enablers of TQM to improve airline performance. Int J Prod Perform Manag 62(3): 250–275

  39. Kumar D (2018) India’s rural healthcare systems: structural modeling. Int J Health Care Qual Assur 31(7):757–774

    Article  Google Scholar 

  40. Guan L, Abbasi A, Ryan MJ (2020) Analyzing green building project risk interdependencies using interpretive structural modeling. J Clean Prod 256:120372

    Article  Google Scholar 

  41. Hamidazada M, Cruz AM, Yokomatsu M (2019) Vulnerability factors of Afghan rural women to disasters. Int J Disaster Risk Sci 10(4):573–590

    Article  Google Scholar 

  42. Govindan K, Khodaverdi R, Vafadarnikjoo A (2015) Intuitionistic fuzzy based DEMATEL method for developing green practices and performances in a green supply chain. Expert Syst Appl 42:7207–7220

    Article  Google Scholar 

  43. Menon S, Suresh M (2021) Enablers of workforce agility in engineering educational institutions. J Appl Res High Edu 13(2):504–539

    Article  Google Scholar 

  44. Ben Mabrouk N (2020) Interpretive structural modeling of critical factors for buyer-supplier partnerships in supply chain management. Uncertain Supply Chain Manag 8(3):613–626

  45. Diabat A, Govindan K (2011) An analysis of the drivers affecting the implementation of green supply chain management. Resour Conserv Recycl 55(6):659–667

    Article  Google Scholar 

  46. Inoko K, Matsumoto H, Kuroda C (2011) Knowledge-based environments for instructors’ decision making in chemical process laboratory. Intell Decis Technol 5:47–63

    Article  Google Scholar 

  47. Tsai FM et al (2020) A performance assessment approach for integrated solid waste management using a sustainable balanced scorecard approach. J Clean Prod 251:119740

    Article  Google Scholar 

  48. Ahmad N, Hoda N, Alahmari F (2020) Developing a cloud-based mobile learning adoption model to promote sustainable education. Sustainability 12(8):3126

    Article  Google Scholar 

  49. Raut RD et al (2020) Analysing green human resource management indicators of automotive service sector. Int J Manpow 41(7):925–944

    Article  Google Scholar 

  50. Sharma M, Joshi S (2020) Digital supplier selection reinforcing supply chain quality management systems to enhance firm's performance. TQM J

  51. Sharma SK et al (2021) Challenges common service centers (CSCs) face in delivering e-government services in rural India. Govern Information Q 38(2):101573

    Article  Google Scholar 

  52. Pai SP, Gaonkar RSP (2020) Using interpretive structural modelling, fuzzy analytical network process, and evidential reasoning to estimate fire risk onboard ships. Int J Perform Eng 16(9):1321–1331

    Article  Google Scholar 

  53. Li Y, Wang X (2019) Using fuzzy analytic network process and ism methods for risk assessment of public-private partnership: a china perspective. J Civ Eng Manag 25(2):168–183

    Article  Google Scholar 

  54. Digalwar A et al (2020) Evaluation of critical constructs for measurement of sustainable supply chain practices in lean-agile firms of Indian origin: a hybrid ISM-ANP approach. Bus Strateg Environ 29(3):1575–1596

    Article  Google Scholar 

  55. Narwane VS et al (2021) Sustainable development challenges of the biofuel industry in India based on integrated MCDM approach. Renew Energy 164:298–309

    Article  Google Scholar 

  56. Hassan IU, Asghar S (2021) A framework of software project scope definition elements: an ism-dematel approach. IEEE Access 9:26839–26870

    Article  Google Scholar 

  57. Duleba S (2019) An AHP-ISM approach for considering public preferences in a public transport development decision. Transport 34(6):662–671

    Article  Google Scholar 

  58. Jain V, Raj T (2021) Study of issues related to constraints in FMS by ISM, fuzzy ISM and TISM. Int J Ind Syst Eng 37(2):197–221

    Google Scholar 

  59. Anantatmula VS (2015) Strategies for enhancing project performance. J Manag Eng 31(6):04015013

    Article  Google Scholar 

  60. Gardas BB, Raut RD, Narkhede BE (2017) A state-of the-art survey of interpretive structural modelling methodologies and applications. Int J Bus Excell 11(4):505–560

    Article  Google Scholar 

  61. Cherrafi A et al (2017) Barriers in Green Lean implementation: a combined systematic literature review and interpretive structural modelling approach. Prod Plan Control 28(10):829–842

    Article  Google Scholar 

  62. Chen Y, Xiao L and Mi C (2017) Opinion mining from online reviews: consumer satisfaction analysis with b&b hotels. in 21st Pacific Asia Conference on Information Systems: Societal Transformation Through IS/IT, PACIS 2017 Association for Information Systems

  63. Soda S, Sachdeva A, Garg RK (2017) Barriers analysis for green supply chain management implementation in power industry using ISM. Int J Logist Syst Manag 27(2):225–259

    Google Scholar 

  64. Attri R (2017) Interpretive structural modelling: a comprehensive literature review on applications. Int J Six Sigma Compet Adv 10(3–4):258–331

    Google Scholar 

  65. Gusdini N et al (2017) Water governance model in small city: review at distric Bekasi - Indonesia. Theor Empir Res Urban Manag 12(1):38–52

    Google Scholar 

  66. Azevedo SG et al (2019) Biomass-related sustainability: A review of the literature and interpretive structural modeling. Energy 171:1107–1125

    Article  Google Scholar 

  67. Wuni IY, Shen GQP (2019) Holistic review and conceptual framework for the drivers of offsite construction: a total interpretive structural modelling approach. Buildings 9(5):117

    Article  Google Scholar 

  68. Sangwan KS, Mittal VK (2015) A bibliometric analysis of green manufacturing and similar frameworks. Manag Environ Q Int J 26(4):566–587

    Google Scholar 

  69. Zhu J, Hua W (2017) Visualizing the knowledge domain of sustainable development research between 1987 and 2015: a bibliometric analysis. Scientometrics 110(2):893–914

    Article  Google Scholar 

  70. Li Y et al (2021) A comprehensive review on green buildings research: bibliometric analysis during 1998–2018. Environ Sci Pollution Res 28:46196–46214

  71. Bigliardi B, Casella G, Bottani E (2021) Industry 4.0 in the logistics field: a bibliometric analysis. IET Collabo Intell Manuf 3(1):4–12

    Article  Google Scholar 

  72. Garcia-Buendia N et al (2021) 22 Years of lean supply chain management: a science mapping-based bibliometric analysis. Int J Prod Res 59(6):1901–1921

    Article  Google Scholar 

  73. Tavares-Lehmann, AT and Varum C (2021) Industry 4. 0 and sustainability: a bibliometric literature review 13(6):3493

  74. Wang J, Cheng R and Liao PC (2021) Trends of multimodal neural engineering study: a bibliometric review. Arch Comput Methods Eng, 1–15

  75. Karimi S and Iordanova I (2021) Integration of BIM and GIS for construction automation, a systematic literature review (SLR) combining bibliometric and qualitative analysis. Arch Comput Methods Eng, 1–22

  76. Issaoui Y et al (2021) Toward smart logistics: engineering insights and emerging trends. Arch Comput Methods Eng 28(4):3183–3210

    Article  Google Scholar 

  77. Hire S, Sandbhor S and Ruikar K (2021) Bibliometric survey for adoption of building information modeling (BIM) in construction industry– a safety perspective Arch Comput Methods in Eng

  78. van Eck NJ, Waltman L (2010) Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 84:523–538

    Article  Google Scholar 

  79. Aria M (2017) bibliometrix: an R-tool for comprehensive science mapping analysis. J Informet 11:959–975

    Article  Google Scholar 

  80. van Eck NJ, Waltman L (2010) Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 84(2):523–538

    Article  Google Scholar 

  81. Faisal MN, Banwet DK, Shankar R (2006) Supply chain risk mitigation: modeling the enablers. Bus Process Manag J. 12(4):535–552

    Article  Google Scholar 

  82. Kannan G, Pokharel S, Sasi Kumar P (2009) A hybrid approach using ISM and fuzzy TOPSIS for the selection of reverse logistics provider. Resour Conserv Recycl 54(1):28–36

    Article  Google Scholar 

  83. Agarwal A, Shankar R, Tiwari MK (2007) Modeling agility of supply chain. Ind Mark Manag 36(4):443–457

    Article  Google Scholar 

  84. Gallardo LA, Meju MA (2003) Characterization of heterogeneous near-surface materials by joint 2D inversion of dc resistivity and seismic data. 30(13):1-4

  85. Luthra S et al (2011) Barriers to implement green supply chain management in automobile industry using interpretive structural modeling technique: an Indian perspective. J Indus Eng Manag 4(2):27

    Google Scholar 

  86. Govindan K et al (2012) Analysis of third party reverse logistics provider using interpretive structural modeling. Int J Prod Econ 140(1):204–211

    Article  Google Scholar 

  87. Sushil (2012) Interpreting the Interpretive Structural Model. Glob J Flex Syst Manag 13(2): 87–106

  88. Lotka AJ (1926) The frequency distribution of scientific productivity. J Wash Acad Sci 16(12):317–323

    Google Scholar 

  89. Egghe L (2005) Relations between the continuous and the discrete Lotka power function. J Am Soc Information Sci Technol 56(7):664–668

    Article  Google Scholar 

  90. Van Eck NJW (2011) Text mining and visualization using VOSviewer. ISSI Newsletter 7(3):50–54

    Google Scholar 

  91. Riehmann P, Hanfler M, Froehlich B (2005) Interactive Sankey diagrams. in IEEE Symposium on Information Visualization, 2005. INFOVIS 2005

  92. Aria M, Cuccurullo C (2017) bibliometrix: An R-tool for comprehensive science mapping analysis. J Informet 11(4):959–975

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Raman Kumar.

Ethics declarations

Conflict of interest

There is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kumar, R., Goel, P. Exploring the Domain of Interpretive Structural Modelling (ISM) for Sustainable Future Panorama: A Bibliometric and Content Analysis. Arch Computat Methods Eng 29, 2781–2810 (2022). https://doi.org/10.1007/s11831-021-09675-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11831-021-09675-7

Navigation