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Modeling the Factors Affecting Crime Against Women: Using ISM Technique

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Advances in Computing and Intelligent Systems

Part of the book series: Algorithms for Intelligent Systems ((AIS))

Abstract

Crime or violence against women acts like a big hurdle in the development of any country or state. The main objective of this research paper is to propose an idea for developing a conceptual model by using interpretive structural modeling technique (ISM). ISM is the step-by-step mathematical procedure and has been proposed to be applied on items or factors, which affect the big societal issue that is crime against women. Various studies and research works have been done to control and prevent crime against women but the idea of developing a conceptual model through ISM will be helping various criminal and crime organizations to make decisions. Mainly, this approach focuses on the brainstorming sessions and the expert opinion and a few items and factors are the basic input to construct the final model.

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References

  1. Agarwal, A., Shankar, R., & Tiwari, M. K. (2007). Modelling agility of supply chain. Industrial Marketing Management, 36(4), 443–457.

    Article  Google Scholar 

  2. Agarwal, A., & Vrat, P. (2015). A TISM based bionic model of organizational excellence. Global Journal of Flexible Systems Management, 16(4), 361–376.

    Article  Google Scholar 

  3. Agarwal, A., & Vrat, P. (2015). Line and staff functions in organizations revisited: A bionic system analogy using ISM. Vision, 19(2), 89–103.

    Article  Google Scholar 

  4. Anantatmula, V. S., & Kanungo, S. (2010). Modelling enablers for successful KM implementation. Journal of Knowledge Management, 14(1), 100–113.

    Article  Google Scholar 

  5. Bolanos, R., Fontela, E., Nenclares, A., & Pastor, P. (2005). Using interpretive structural modelling in strategic decision-making groups. Management Decision, 43(6), 877–895.

    Article  Google Scholar 

  6. Burke, J. D., Loeber, R., Lahey, B. B., & Rathouz, P. J. (2005). Developmental transitions among affective and behavioral disorders in adolescent boys. Journal of Child Psychology and Psychiatry, 46(11), 1200–1210.

    Google Scholar 

  7. Bailey, W. C., & Peterson, R. D. (1995). Gender inequality and violence against women. Crime Inequality, 174–205.

    Google Scholar 

  8. Bharadwaj, A. (2014). Is poverty the mother of crime? empirical evidence of the impact of socioeconomic factors on crime in India. Atlantic Review of Economics, 1.

    Google Scholar 

  9. Chawla, N., & Solinas-Saunders, M. (2011). Supporting military parent and child adjustment to deployments and separations with filial therapy. The American Journal of Family Therapy, 39(3), 179–192.

    Article  Google Scholar 

  10. Centers for Disease Control and Prevention (CDC). (2008). Smoking-attributable mortality, years of potential life lost, and productivity losses-United States, 2000–2004. MMWR Morb Mortal Wkly Rep, 57, 1226–1228.

    Google Scholar 

  11. Christoffersen, M. N., Francis, B., & Soothill, K. (2003). An upbringing to violence? identifying the likelihood of violent crime among the 1966 birth cohort in Denmark. Journal of Forensic Psychiatry & Psychology, 14(2), 367–381.

    Article  Google Scholar 

  12. Chabaya, O., Rembe, S., & Wadesango, N. (2009). The persistence of gender inequality in Zimbabwe: Factors that impede the advancement of women into leadership positions in primary schools. South African Journal of Education, 29(2).

    Google Scholar 

  13. Cardoso, L. F., Gupta, J., Shuman, S., Cole, H., Kpebo, D., & Falb, K. L. (2016). What factors contribute to intimate partner violence against women in urban, conflict-affected settings? qualitative findings from Abidjan Côte d’Ivoire. Journal of Urban Health, 93(2), 364–378.

    Article  Google Scholar 

  14. Devries, K. M., Mak, J. Y., García-Moreno, C., Petzold, M., Child, J. C., Falder, G., et al. (2013). The global prevalence of intimate partner violence against women. Science, 340(6140), 1527–1528.

    Article  Google Scholar 

  15. Ending violence against Women and Girls: Programming essentials. (2013). http://www.endvawnow.org/uploads/modules/pdf/1372349234.pdf.

  16. Garcia-Moreno, C., Jansen, H. A., Ellsberg, M., Heise, L., & Watts, C. H. (2006). Prevalence of intimate partner violence: Findings from the WHO multi-country study on women’s health and domestic violence. The Lancet, 368(9543), 1260–1269.

    Article  Google Scholar 

  17. Haleem, A., Sushil Qadri, M.A., & Kumar, S. (2012). Analysis of critical success factors of world-class manufacturing practices: An application of interpretative structural modelling and interpretative ranking process. Production Planning & Control, 23(10–11), 722–734.

    Google Scholar 

  18. Hidron, A. I., Edwards, J. R., Patel, J., Horan, T. C., Sievert, D. M., Pollock, D. A., et al. (2008). Antimicrobial-resistant pathogens associated with healthcare-associated infections: Annual summary of data reported to the national healthcare safety network at the centers for disease control and prevention, 2006–2007. Infection Control & Hospital Epidemiology, 29(11), 996–1011.

    Article  Google Scholar 

  19. Heilman, B., Hebert, L., & Paul-Gera, N. (2014). The making of sexual violence. How does a boy grow up to commit rape? Evidence from five IMAGES countries.

    Google Scholar 

  20. Hill, K. G., Lui, C., & Hawkins, J. D. (2001). Early precursors of gang membership: A study of seattle youth. Washington, DC: US Department of Justice, Office of Justice Programs, Office of Juvenile Justice and Delinquency Prevention

    Google Scholar 

  21. Heilman, M. E., & Wallen, A. S. (2010). Wimpy and undeserving of respect: Penalties for men’s gender-inconsistent success. Journal of Experimental Social Psychology, 46(4), 664–667.

    Article  Google Scholar 

  22. Ingoldsby, E. M., & Shaw, D. S. (2002). Neighborhood contextual factors and early-starting antisocial pathways. Clinical Child and Family Psychology Review, 5(1), 21–55.

    Article  Google Scholar 

  23. Iyer, L., & Topalova, P. B. (2014). Poverty and crime: Evidence from rainfall and trade shocks in India. Harvard Business School BGIE Unit Working Paper (14–67).

    Google Scholar 

  24. Janes, F. R. (1988). Interpretive structural modelling: A methodology for structuring complex issues. Transactions of the Institute of Measurement and Control, 10(3), 145–154.

    Article  Google Scholar 

  25. Kiss, L., Schraiber, L. B., Heise, L., Zimmerman, C., Gouveia, N., & Watts, C. (2012). Gender-based violence and socioeconomic inequalities: Does living in more deprived neighbourhoods increase women’s risk of intimate partner violence? Social Science & Medicine, 74(8), 1172–1179.

    Article  Google Scholar 

  26. Kaur, I., Shri, C., & Mital, K. M. (2016). Modelling enhancement of team emotional intelligence. Vision, 20(3), 184–198.

    Article  Google Scholar 

  27. Kaur, B., Ahuja, L., & Kumar, V. (2018). Factors affecting crime against women using regression and K-means clustering techniques. In Proceedings of the International Conference on Industry Interactive Innovations in Science, Engineering and Technology (pp. 149–162). Singapore: Springer.

    Google Scholar 

  28. Kismödi, E., Cottingham, J., Gruskin, S., & Miller, A. M. (2015). Advancing sexual health through human rights: The role of the law. Global Public Health, 10(2), 252–267.

    Article  Google Scholar 

  29. Loeber, R., Pardini, D., Homish, D. L., Wei, E. H., Crawford, A. M., Farrington, D. P., et al. (2005). The prediction of violence and homicide in young men. Journal of Consulting and Clinical Psychology, 73(6), 1074.

    Article  Google Scholar 

  30. Lipsey, M. W., & Derzon, J. H. (1998). Predictors of violent or serious delinquency in adolescence and early adulthood: A synthesis of longitudinal research.

    Google Scholar 

  31. Mandal, A., & Deshmukh, S. G. (1994). Vendor selection using interpretive structural modelling (ISM). International Journal of Operations & Production Management, 14(6), 52–59.

    Article  Google Scholar 

  32. Malone, D. W. (1975). An introduction to the application of interpretive structural modelling. In Proceedings of the IEEE, 63(3), 397–404.

    Google Scholar 

  33. Muduli, K., Govindan, K., Barve, A., Kannan, D., & Geng, Y. (2013). Role of behavioural factors in green supply chain management implementation in Indian mining industries. Resources, Conservation and Recycling, 76, 50–60.

    Article  Google Scholar 

  34. Nishat Faisal, M., Banwet, D. K., & Shankar, R. (2006). Supply chain risk mitigation: Modelling the enablers. Business Process Management Journal, 12(4), 535–552.

    Article  Google Scholar 

  35. Nwabunike, C., & Tenkorang, E. Y. (2017). Domestic and marital violence among three ethnic groups in Nigeria. Journal of Interpersonal Violence, 32(18), 2751–2776.

    Article  Google Scholar 

  36. National Research Council. (1996). Causes and consequences of violence against women. In Understanding violence against women. Washington, DC: National Academy Press.

    Google Scholar 

  37. National crime record bureau report. (2015–2016), chapter-5 Crime against women, http://ncrb.nic.in/StatPublications/CII/CII2015/FILES/Compendium-15.11.16.pdf.

  38. Larivière, V., Ni, C., Gingras, Y., Cronin, B., & Sugimoto, C. R. (2013). Bibliometrics: Global gender disparities in science. Nature News, 504(7479), 211.

    Article  Google Scholar 

  39. Raj, T., Shankar, R., & Suhaib, M. (2008). An ISM approach for modelling the enablers of flexible manufacturing system: The case for India. International Journal of Production Research, 46(24), 6883–6912.

    Article  Google Scholar 

  40. Roess, A. A., & Aranda, E. L. (2013). Justification of intimate partner violence in Egypt.

    Google Scholar 

  41. Raj, T., Attri, R., & Jain, V. (2012). Modelling the factors affecting flexibility in FMS. International Journal of Industrial and Systems Engineering, 11(4), 350–374.

    Article  Google Scholar 

  42. Raj, T., & Attri, R. (2011). Identification and modelling of barriers in the implementation of TQM. International Journal of Productivity and Quality Management, 8(2), 153–179.

    Article  Google Scholar 

  43. Ramesh, A., Banwet, D. K., & Shankar, R. (2008). Modelling the enablers of supply chain collaboration. International Journal of Logistics Systems and Management, 4(6), 617–633.

    Article  Google Scholar 

  44. Ravi, V., & Shankar, R. (2005). Analysis of interactions among the barriers of reverse logistics. Technological Forecasting and Social Change, 72(8), 1011–1029.

    Article  Google Scholar 

  45. Sage, A. P. (1977). Methodology for large-scale systems.

    Google Scholar 

  46. Stanko, S. (2017). Assault on men: Masculinity and male victimization. In Crime, Criminal Justice and Masculinities (pp. 133–148). Routledge.

    Google Scholar 

  47. Saxena, J. P., & Vrat, P. (1990). Impact of indirect relationships in classification of variables—a micmac analysis for energy conservation. Systems Research and Behavioral Science, 7(4), 245–253.

    Article  Google Scholar 

  48. Sachdeva, N., Singh, O., & Kapur, P. K. (2015). Modelling critical success factors for adoption of big data analytics project: An ISM-MICMAC based analysis. Communications in Dependability and Quality Management, 18(4), 93–110.

    Google Scholar 

  49. Saxena, J. P., Sushil, & Vrat, P. (2006). Policy and strategy formulation: An application of flexible systems methodology. GIFT Pub.

    Google Scholar 

  50. Sharma, B. P, Singh, M. D., & Kumar A. (2012). Knowledge sharing barriers: An integrated approach of ISM and AHP. In Proceedings of the International Conference on Information and Knowledge Management (ICIKM 2012) (Vol. 45, pp. 227–232).

    Google Scholar 

  51. Singh, M. D., & Kant, R. (2008). Knowledge management barriers: An interpretive structural modelling approach. International Journal of Management Science and Engineering Management, 3(2), 141–150.

    Article  Google Scholar 

  52. Solinas-Saunders, M. (2007). Male intimate partner abuse: Drawing upon three theoretical perspectives (Doctoral dissertation, University of Pittsburgh).

    Google Scholar 

  53. Singh, M. D., Shankar, R., Narain, R., & Agarwal, A. (2003). An interpretive structural modelling of knowledge management in engineering industries. Journal of Advances in Management Research, 1(1), 28–40.

    Article  Google Scholar 

  54. Sushil, S. (2012). Interpreting the interpretive structural model. Global Journal of Flexible Systems Management, 13(2), 87–106.

    Article  Google Scholar 

  55. Singh, A. K., Sushil. (2013). Modelling enablers of TQM to improve airline performance. International Journal of Productivity and Performance Management, 62(3), 250–275.

    Google Scholar 

  56. Soler, H., Vinayak, P., & Quadagno, D. (2000). Biosocial aspects of domestic violence. Psychoneuroendocrinology, 25(7), 721–739.

    Article  Google Scholar 

  57. Toufique, M. M. K., & Razzaque, M. A. (2007). Domestic violence against women: Its determinants and implications for gender resource allocation (No. 2007/80). Research Paper, UNU-WIDER, United Nations University (UNU).

    Google Scholar 

  58. Toriizuka, T. (2001). Application of performance shaping factor (PSF) for work improvement in industrial plant maintenance tasks. International Journal of Industrial Ergonomics, 28(3–4), 225–236.

    Article  Google Scholar 

  59. Tolan, P. H., Gorman-Smith, D., & Henry, D. B. (2003). The developmental ecology of urban males’ youth violence. Developmental Psychology, 39(2), 274.

    Article  Google Scholar 

  60. Tenkorang, E. Y., Owusu, A. Y., Yeboah, E. H., & Bannerman, R. (2013). Factors influencing domestic and marital violence against women in Ghana. Journal of Family Violence, 28(8), 771–781.

    Article  Google Scholar 

  61. Warfield, J. (1974). Developing interconnected matrices in structural modelling. IEEE Transactions on Systems Men and Cybernetics, 4(1), 51–81.

    MathSciNet  Google Scholar 

  62. World Health Organization. (2013). Prevalence & health effects of intimate partner violence and non-partner sexual violence. Italy: WHO publication. World Health Organization Report.

    Google Scholar 

  63. Webster-Stratton, C., & Taylor, T. (2001). Nipping early risk factors in the bud: Preventing substance abuse, delinquency, and violence in adolescence through interventions targeted at young children (0–8 years). Prevention Science, 2(3), 165–192.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. AIIT, Amity University, Noida, UP, India

    Bhajneet Kaur & Laxmi Ahuja

  2. Ex Scientist & Professor, Vice Chairman (Cum Chairman Elec), CSI Delhi Chapter, Delhi, India

    Vinay Kumar

Authors
  1. Bhajneet Kaur
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  2. Laxmi Ahuja
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  3. Vinay Kumar
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Corresponding author

Correspondence to Bhajneet Kaur .

Editor information

Editors and Affiliations

  1. Rajasthan Technical University, Kota, Rajasthan, India

    Harish Sharma

  2. University of Southern Denmark, Odense, Denmark

    Kannan Govindan

  3. Amity University, Jaipur, Rajasthan, India

    Ramesh C. Poonia

  4. Amity University, Jaipur, Rajasthan, India

    Sandeep Kumar

  5. University of Bahrain, Zallaq, Bahrain

    Wael M. El-Medany

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Kaur, B., Ahuja, L., Kumar, V. (2020). Modeling the Factors Affecting Crime Against Women: Using ISM Technique. In: Sharma, H., Govindan, K., Poonia, R., Kumar, S., El-Medany, W. (eds) Advances in Computing and Intelligent Systems. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-0222-4_44

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