VALIDATION OF A SCALE FOR MEASURING PRECAUTIONS FOR SAFE USE OF ATM

Authors

  • Ajimon George Ph.D, Associate Professor & Research Guide, Marian College Kttikkanam Author
  • Ajay George Post Baccalaureate Diploma in Business Management (PG scholar), Cape Breton University, Sydney, Canada Author

DOI:

https://doi.org/10.61841/sd8ck073

Keywords:

Automated Teller Machine, Fraud, Personal Identification Number, Precautions

Abstract

ATM fraud is a big threat and it requires a coordinated and cooperative action on the part of the bank, customers and the law enforcement machinery. It not only causes financial loss to banks but also undermine customers' confidence in the use of ATMs. The specific objectives of the study are (a) to develop and validate a scale for measuring the precautions taken by ATM users for the safe use of ATM and (b) to examine the adequacy or otherwise of the precautions taken by ATM users. The study is empirical in nature and survey method has been employed for collecting data from 342 respondents. The response was collected using a structured questionnaire which was pilot tested with 25 respondents who are using ATM. Non-Probability convenience sampling technique was used to identify the respondents. Seventeen statements were used to elicit precautions taken by ATM users for the safe use of ATM. These are the statements that describe the practices ATM users should follow while using ATM’s. The reliability of the measures is assessed by examining the Cronbach’s Alpha Coefficient. Exploratory Factor Analysis is used in the current study to explore six underlying dimensions of the measured items and each dimension were suitably labeled based on the characteristics of the items converged in each dimension. These six dimensions are precautions related to PIN, precautions related to Machine, Precautions related to Card, Precautions related to Cash, Precautions related to bank accounts and precautions related to transactions. These dimensions were further validated through confirmatory factor analysis. The findings have managerial implications for banks, as they will help them to take necessary steps to safeguard their customers against the risks associated with the use of ATM. The mean of percentage score of all the constructs were found to be above 80 percent except precautions related to Machine which is 72.4 per cent. This indicates that ATM users take fairly enough precautions while using ATM and found that comparatively highest precautions are taken on transactions dimension and lowest precautions related to machine. If banks can take initiative in creating awareness among their users to take more required precautions, it will instill more confidence among ATM users so that they can do banking transactions without the fear of risk and fraud. The most important contribution of this study to the existing literature is the development and validation of a scale for measuring the precautions for safe use of ATM.

Downloads

Download data is not yet available.

References

1. Adepoju, A. A., & Alhassan, M. E. (2010). Challenges of Automated Teller Machine (ATM) usage and fraud occurrences in Nigeria: A case study of selected banks in Minna Metropolis. Journal of Internet Banking and Commerce, 15(2). Retrieved from http://www.arraydev.com/commerce/jibc/

2. Agarwal, R., Rastogi, S., & Mehrotra, A. (2009). Customers’ perspectives regarding e-banking in an emerging economy. Journal of Retailing and Consumer Services, 16, 340–351.

3. Bentler, P. M. (1989). EQS: Structural equations program manual (Version 3.0). BMDP Statistical Software, Los Angeles.

4. Bentler, P. M. (1992). On the fit of models to covariances and methodology. Psychological Bulletin, 112(3), 400–404.

5. Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94.

6. Brunner, A., Decressin, J., & Kudela, B. (2004). Germany’s three-pillar banking system: Cross-country perspectives in Europe. Occasional Paper, International Monetary Fund, Washington DC.

7. Darren, G., & Paul, M. (2011). SPSS for Windows step by step (10th ed.). Pearson, Delhi.

8. Demirbag, M., Koh, S. C. L., Tatoglu, E., & Zaim, S. (2006). TQM and market orientation’s impact on SMEs’ performance. Industrial Management & Data Systems, 106(8), 1206–1228.

9. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

10. Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51–90.

11. George, A. (2017). Precautions for safe use of internet banking: Scale development and validation. IIM Kozhikode Society & Management Review, 6(2), 186–195.

12. George, A. (2018). Perceptions of internet banking users: A structural equation modeling (SEM) approach. IIMB Management Review, 30(4), 357–368.

13. Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis (5th ed.). Prentice Hall, Upper Saddle River, NJ.

14. Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1992). Multivariate data analysis with readings (3rd ed.). Macmillan, New York.

15. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis: A global perspective (7th ed.). Pearson Education, Upper Saddle River, NJ.

16. Hinkin, T. R. (1995). A review of scale development practices in the study of organizations. Journal of Management, 21, 967–988.

17. Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis. Structural Equation Modeling, 6(1), 1–55.

18. Jain, S. (2017). ATM frauds – detection & prevention. International Journal of Advances in Electronics and Computer Science, 4(10), 82–89.

19. Kamakodi, N., & Ahmed Khan, M. B. (2008). Customer expectations and service level in e-banking era: An empirical study. The ICFAI University Journal of Bank Management, 7(4), 50–70.

20. Kumar, G. S. G., Bijoy, A. P., & George, A. (2012). Effect of service quality dimensions on adoption of internet banking. In Proceedings of the International Conference on Business, Finance and Geography (ICBFG 2012), Phuket, Thailand.

21. Kumar, R., & Sinha, A. B. (2009). An overview of e-banking in India. Professional Banker, October issue, 35–37.

22. Malviya, D. (2015). ATM card and safety chip: Embedded in human preventing ATM frauds. International Journal of Computer Science and Information Technologies, 6(5), 4469–4474.

23. Mohammed, S., & Shariq, S. (2011). A study of ATM usage in banks in Lucknow. International Journal of Engineering and Management Studies, 2(1), 47–53.

24. Mohan, K. (2006). Information technology in Indian banking. SCMS Journal of Indian Management, July–September issue, 18–24.

25. Nunnally, J. C. (1978). Psychometric theory. McGraw-Hill, New York.

26. Paur, S. (1991). Protect your customers and institutions against ATM crimes. Journal of Texas Banking, 80(10), 13–19.

27. Raghavan, R. S. (2006). Perception of Indian banks in 2020. The Chartered Accountant, October issue.

28. Seibert, P. (1994). Does your ATM security check out? Credit Union Management, 17(10), 33–36.

29. Srinivas, V. (2009). No more traditional banking, only virtual. Professional Banker, August issue, 41–43.

30. Shukla, R., & Shukla, P. (2011). E-banking: Problems and prospects. International Journal of Management & Business Studies, 1(1), 23–25.

31. Straub, D. W. (1989). Validating instruments in MIS research. MIS Quarterly, 13(2), 147–169.

32. Uppal, R. K., & Chawla, R. (2009). E-delivery channel-based banking services: An empirical study. The Icfaian Journal of Management Research, 8(7), 7–33.

Downloads

Published

13.07.2020

How to Cite

George, A., & George, A. (2020). VALIDATION OF A SCALE FOR MEASURING PRECAUTIONS FOR SAFE USE OF ATM. International Journal of Psychosocial Rehabilitation, 24(10), 3272-3284. https://doi.org/10.61841/sd8ck073