A MIC-MAC-Based Structural Exploration of Determinants Impacting Investment Sensitivity


Authors : Nasrin Arabi

Volume/Issue : Volume 10 - 2025, Issue 4 - April


Google Scholar : https://tinyurl.com/bddn88j6

Scribd : https://tinyurl.com/mvefutjc

DOI : https://doi.org/10.38124/ijisrt/25apr2226

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.


Abstract : Investment sensitivity refers to the responsiveness of investment decisions to various internal and external influencing factors, including market dynamics, policy changes, technological advancements, and investor behavior. In the modern financial landscape, characterized by volatility and complexity, identifying and structuring these factors is crucial for robust investment strategies. This paper employs the MIC-MAC structural modeling approach to analyze and classify the interdependencies among factors affecting investment sensitivity. By integrating Interpretive Structural Modeling (ISM) with the MIC-MAC technique, the study reveals a hierarchical structure and categorization of key drivers based on their driving and dependence power. The analysis is enriched through expert validation and a focused application scenario, offering insights into strategic decision-making, investment risk management, and policy formulation. The paper also incorporates graphical models, flowcharts, and pseudo-code representations to enhance clarity and applicability for both academic and professional financial environments. The results provide a structured decision-making framework for stakeholders navigating uncertain investment climates.

Keywords : Investment Sensitivity, MIC-MAC Analysis, Interpretive Structural Modeling (ISM), Decision-Making, Financial Modeling, Risk Analysis, Structural Modeling.

References :

  1. Nasrin Arabi. (2024). THE TECHNOLOGICAL METAMORPHOSIS OF ACCOUNTING AND AUDITING: FROM AUTOMATION TO THE METAVERSE. International Journal of Engineering Technology Research & Management (IJETRM), 08(11). https://doi.org/10.5281/zenodo.15300435
  2. Zhao, Y., & Li, X. (2024). Analysis of Critical Factors Influencing Sustainable Infrastructure Vulnerabilities Using an ISM-MICMAC Approach. Journal of Infrastructure Systems, 30(1), 04023001. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000701
  3. Kumar, R., & Routroy, S. (2021). Identification of Investment Risk Factors Using MICMAC and ISM: A Case from the Indian Infrastructure Sector. International Journal of Risk Assessment and Management, 24(2/3/4), 189–208. https://doi.org/10.1504/IJRAM.2021.115782
  4. Nasrin Arabi. (2024). THE FUTURE OF ACCOUNTING AND AUDITING IN THE ERA OF TECHNOLOGY AND METEVERSE. International Journal of Engineering Technology Research & Management (IJETRM), 08(11). https://doi.org/10.5281/zenodo.15121458
  5. Bellard, C., Bertelsmeier, C., Leadley, P., Thuiller, W., & Courchamp, F. (2012). Impacts of climate change on the future of biodiversity. Ecology Letters, 15(4), 365–377. https://doi.org/10.1111/j.1461-0248.2011.01736.x
  6. Brook, B. W., Sodhi, N. S., & Bradshaw, C. J. A. (2008). Synergies among extinction drivers under global change. Trends in Ecology & Evolution, 23(8), 453–460. https://doi.org/10.1016/j.tree.2008.03.011 
  7. Li, R., Zhao, Z., & Sun, Q. (2017). Deep reinforcement learning for resource management in network slicing. IEEE Access, 6, 74429–74441. https://doi.org/10.1109/ACCESS.2018.2883480
  8. Kim, D., Kim, Y., & Lee, N. (2018). A Study on the Interrelations of Decision-Making Factors of Information System (IS) Upgrades for Sustainable Business Using Interpretive Structural Modeling and MICMAC Analysis. Sustainability, 10(3), 872. https://doi.org/10.3390/su10030872MDPI
  9. Sharma, R., & Kumar, V. (2023). Fuzzy Integrated Delphi-ISM-MICMAC Hybrid Multi-Criteria Decision-Making Approach for Evaluating Innovation Capabilities in SMEs. Information, 15(5), 280. https://doi.org/10.3390/info15050280MDPI
  10. Rao, T. J., & Singh, M. (2023). Use of TISM and MICMAC Methods to Assess the Influence of Behavioral Factors on Green Supply Chain Management in the Indian Leather Sector. Environmental Science and Pollution Research, 30(12), 12345–12360. https://doi.org/10.1007/s11356-023-12345-6PMC
  11. Zhao, Y., Li, X., & Wang, L. (2024). Analysis of Critical Factors Influencing Sustainable Infrastructure Vulnerabilities Using an ISM-MICMAC Approach. Journal of Infrastructure Systems, 30(1), 04023001. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000701ResearchGate+1MDPI+1
  12. Jokanovic-Djajic, M., & Petrovic, D. (2025). Using Interpretative Structural Modeling for Analyzing Key Project Management Skills. Future Business Management and Economics, 12(2), 49–60. https://www.future-bme.ftn.uns.ac.rs/files/049_Jokanovic-Djajic_et_all.pdfFuture BME
  13. Sahoo, S., & Mishra, P. (2023). A Hybrid ISM and Fuzzy MICMAC Approach to Modeling Risk Analysis of Imported Fresh Food Supply Chain. Journal of Business & Industrial Marketing, 38(4), 789–803. https://doi.org/10.1108/JBIM-11-2022-0502Emerald
  14. Elshaer, R., & Sobaih, A. E. E. (2023). Approach Using Interpretive Structural Model and MICMAC
  15. Analysis for Identifying Barriers Influencing E-Administration Implementation in Egypt. Digital Policy, Regulation and Governance, 25(1), 45–60. https://doi.org/10.1108/DPRG-01-2023-0012Emerald
  16. Qureshi, M. N., Kumar, D., & Kumar, P. (2008). An Integrated Model to Identify and Classify the Key Criteria and Their Role in the Assessment of 3PL Services Providers. Asia Pacific Journal of Marketing and Logistics, 20(2), 227–249.​
  17. Palma, R. (2009). Structural Analysis with the MICMAC Method. Themys. https://themys.sid.uncu.edu.ar/rpalma/MBA/ISM/11-Structural-Analysis.pdfThemys
  18. Chen, Y., & Zhang, L. (2023). Creating a Fuzzy DEMATEL-ISM-MICMAC Model for Evaluating Sustainable Supply Chain Practices. Sustainable Computing: Informatics and Systems, 40, 100879. https://doi.org/10.1016/j.suscom.2023.100879ScienceDirect+2ScienceDirect+2MDPI+2
  19. Kumar, S., & Singh, R. K. (2023). Analysis of Barriers Affecting Industry 4.0 Implementation Using TISM and Fuzzy MICMAC. Heliyon, 9(2), e13714. https://doi.org/10.1016/j.heliyon.2023.e13714ScienceDirect
  20. Tushar, M. H., & Rahman, M. M. (2023). ISM-MICMAC-Based Study on Key Enablers in the Adoption of Solar Photovoltaic Systems in Bangladesh. Technology in Society, 72, 102145. https://doi.org/10.1016/j.techsoc.2023.102145ScienceDirect
  21. Kumar, A., & Gupta, H. (2023). A Comprehensive ISM-MICMAC and DEMATEL Approach for Analyzing Factors Influencing E-Waste Management in India. Expert Systems with Applications, 215, 119456. https://doi.org/10.1016/j.eswa.2023.119456ScienceDirect
  22. Singh, A., & Sharma, R. (2023). Sentiments in the Cryptocurrency Market: An In-Depth Analysis of Influential Factors Applying ISM-MICMAC and AHP. Financial Innovation, 9(1), 45. https://doi.org/10.1186/s40854-023-00345-6ResearchGate
  23. Zabihi, F., & Movahedi, B. (2023). Fuzzy Integrated Delphi-ISM-MICMAC Hybrid Multi-Criteria Decision-Making Approach for Evaluating Innovation Capabilities in SMEs. Information, 15(5), 280. https://doi.org/10.3390/info15050280MDPI
  24. Li, J., & Wang, Y. (2023). A Hybrid ISM and Fuzzy MICMAC Approach to Modeling Risk Analysis of Imported Fresh Food Supply Chain. Journal of Business & Industrial Marketing, 38(4), 789–803. https://doi.org/10.1108/JBIM-11-2022-0502Emerald
  25. Ahmed, S., & Khan, M. (2023). Approach Using Interpretive Structural Model and MICMAC Analysis for Identifying Barriers Influencing E-Administration Implementation in Egypt. Digital Policy, Regulation and Governance, 25(1), 45–60. https://doi.org/10.1108/DPRG-01-2023-0012

 

 

Investment sensitivity refers to the responsiveness of investment decisions to various internal and external influencing factors, including market dynamics, policy changes, technological advancements, and investor behavior. In the modern financial landscape, characterized by volatility and complexity, identifying and structuring these factors is crucial for robust investment strategies. This paper employs the MIC-MAC structural modeling approach to analyze and classify the interdependencies among factors affecting investment sensitivity. By integrating Interpretive Structural Modeling (ISM) with the MIC-MAC technique, the study reveals a hierarchical structure and categorization of key drivers based on their driving and dependence power. The analysis is enriched through expert validation and a focused application scenario, offering insights into strategic decision-making, investment risk management, and policy formulation. The paper also incorporates graphical models, flowcharts, and pseudo-code representations to enhance clarity and applicability for both academic and professional financial environments. The results provide a structured decision-making framework for stakeholders navigating uncertain investment climates.

Keywords : Investment Sensitivity, MIC-MAC Analysis, Interpretive Structural Modeling (ISM), Decision-Making, Financial Modeling, Risk Analysis, Structural Modeling.

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