Validation and Modeling of Drivers and Barriers for Multivendor ATM Technology in India from the Perspectives of Banks

  • Jyotiranjan Hota
  • Saboohi Nasim


The purpose of this chapter is to apply total interpretive structural modeling (TISM) model used to develop a hierarchy among the key drivers and barriers to multivendor ATM technology adoption in India from the perspectives of banks. This approach is an extension of Warfield’s (IEEE Transactions: System, Man & Cybernetics 4:405–17, 1974) interpretive structural modeling (ISM) approach. Based on the literature, drivers and barriers for adoption of multivendor ATM technology are identified. TISM is used to develop a hierarchical model which states the interpretation of relationship among these drivers and barriers. Hierarchies of all relevant drivers and barriers were developed, and significant interrelationship was found out. Implications for both the researchers and industry practitioners are highlighted. For practitioners, a list of relevant barriers and drivers to adoption of this technology in India are indications to take a decision to adopt this technology in their respective banks. For researchers, TISM methodology facilitates to further carry out exploratory studies by identifying the factors in technology adoption domain and focus their interactions through hierarchical structures. The proposed model developed through TISM technique has been accomplished from the perspectives of banks in India in the domain of multivendor ATM technology for the first time in ATM banking as a contribution to the literature.


  1. ATMMarketplace. (2014). ATM software trends and analysis. Retrieved January 2, 2015, from
  2. Cluckey, S. (2013). As banking channels converge, the multi-vendor ATM software market expands. Retrieved September 14, 2013, from
  3. Ghosh. (2013). The rise of Indian telecom tower companies. Retrieved January 1, 2015, from
  4. Greengard. (2009). NCR registers gain through analytics. BaselineMag, pp. 30–31.Google Scholar
  5. Hota, J. (2012). Windows based and Web enabled ATMs: Issues and scopes. IUP Journal of Information Technology, 8(4), 52–59.Google Scholar
  6. Hota, J., & Nasim, S. (2015). Drivers and barriers in adopting multivendor ATM technology for banks in India. Retrieved from SSRN 2712659.Google Scholar
  7. Jetley. (2014). Solar ATMs changing the face of banking in India. Retrieved January 11, 2015, from
  8. Kadir, H. A., Rahmani, N., & Masinaei, R. (2011). Impacts of service quality on customer satisfaction: Study of online banking and ATM services in Malaysia. International Journal of Trade, Economics and Finance, 2(1), 1–9.CrossRefGoogle Scholar
  9. Kal. (2010). The future of ATM software. Retrieved December 20, 2011, from
  10. Kal. (2011). ATM software trend and analysis. Retrieved April 9, 2012, from
  11. Kumar. (2013). ATM challenges in India: Power, environment and security. Retrieved April 11, 2014, from
  12. Nasim, S. (2011). Total interpretive structural modelling of continuity and change forces in e-government. Journal of Enterprise Transformation, 1(2), 147–168.CrossRefGoogle Scholar
  13. Prasad, U. C., & Suri, R. K. (2011). Modelling of continuity and change forces in private higher technical education using total interpretive structural modelling (TISM). Global Journal of Flexible Systems Management, 12(3/4), 31.CrossRefGoogle Scholar
  14. Race. (2010). Multivendor- multiple choice. Banking Automation Bulletin, pp. 8–9.Google Scholar
  15. Race. (2011). Delivering the optimum customer experience. Banking Automation Bulletin, pp. 10–11.Google Scholar
  16. Retail Banking Research. (2010). Future of ATM software lies in integrated solutions. Retrieved December 11, 2011, from
  17. Saxena, J. P., Sushil, & Vrat, P. (2006). Policy and strategy formulation: An application of flexible systems methodology. GIFT Publication.Google Scholar
  18. Slawsky, R. (2011). Engaging the ATM customer with intelligent personalization. Retrieved December 16, 2011, from
  19. Slawsky. (2013). ATM software trends and analysis. NetWorld Alliance LLC, Sponsored by KAL, 1–63.Google Scholar
  20. Sushil. (2005a). Interpretive matrix: A tool to aid interpretation of management in social research. Global Journal of Flexible System Management, 6(2), 27–30.Google Scholar
  21. Sushil. (2005b). A flexible strategy framework for managing continuity and change. International Journal of Global Business and Competitiveness, 1(1), 22–32.Google Scholar
  22. Sushil. (2009). Interpreting the interpretive structural model: Organization research methods. Working paper, IIT, Delhi.Google Scholar
  23. Sushil. (2012). Interpreting the interpretive structural model, organization research methods. Global Journal of Flexible System Management, 13(12), 87–106.CrossRefGoogle Scholar
  24. Warfield, J. N. (1974). Towards interpretation of complex structural models. IEEE Transactions: System, Man & Cybernetics, 4(5), 405–417.Google Scholar
  25. Wollenhaupt. (2010). ATM software trends and analysis. NetWorld Alliance LLC, 1–38.Google Scholar
  26. Yili, Z. (2011). ATM software delivers business edge for China Everbright Bank. Presentation to ATMIA China, Deputy General Manager of E- Banking Department, China Everbright Bank. Retrieved September 1, 2011, from

Copyright information

© The Author(s) 2020

Authors and Affiliations

  • Jyotiranjan Hota
    • 1
  • Saboohi Nasim
    • 2
  1. 1.KIIT School of ManagementKIIT Deemed to be UniversityBhubaneswarIndia
  2. 2.Faculty of Management Studies and ResearchAligarh Muslim UniversityAligarhIndia

Personalised recommendations