Study on Changing Payment Preferences: A Comparative Analysis of UPI and Cash Usage
Authors
1. Vishesh Goyal
2. Harsh Dixit
3. Hema Lalwani
Introduction
One of the main reasons for the shift in consumer transaction behavior in India is the fast growth of digital payment systems, especially UPI. Even though cash has been the most used mode of payment, digital platforms are slowly but surely changing consumer preferences. Convenience, security, availability of rewards, and trust in technology are some of the key factors that influence people’s payment decisions. This research is focused on understanding the shift in the preference of payment modes i.e., between UPI and cash and figuring out the factors that affect consumer behavior.
Objectives
1. To understand the underlying variables influencing payment preference using Exploratory Factor Analysis (EFA).
2. To cluster respondents based on their payment behavior and preference patterns.
Data Collection
The research is built on primary data collected through a structured questionnaire administered via Google Forms.
The method of sampling: Convenience Sampling
Respondents: People who are using UPI and/or Cash
Scale:
1. 0-10 Linear Scale
2. 1-5 Likert Numeric Scale
Total No. of Responses: 30
Data Analysis
1. Factor Analysis
1.1 KMO and Barlett’s test
Interpretation:
The Kaiser, Meyer, Olkin (KMO) measured value of 0.660 indicates that the sampling adequacy is quite good since it is higher than the lowest limit of 0.6. This means that the data is very appropriate for factor analysis.
Bartletts Test of Sphericity produces a Chi, square value of 43.007 with 15 degrees of freedom and the level of significance p < 0.001, which is statistically significant. As the significance value is below 0.05, we reject the null hypothesis.
Thus, there is correlation amongst the variables and Factor Analysis can be performed on this dataset.
1.2 Total Variance explained
Interpretation
Two components have eigenvalues above 1 (2.612 and 1.256), therefore it was decided to keep two factors.
· Component 1 accounts for 43.535% of the variance in the data.
· Component 2 accounts for 20.936% of the variance in the data.
The two components together explain 64.471% of the total variance, which implies a quite satisfactory level of explanation of the dataset:
1.3
Component Matrix:
Interpretation:
The first component is based on:
· UPI Usage (0.837)
· Convenience (0.728)
· Overall Preference (0.650)
· Security (0.588)
It is also negatively related to Cash Usage (, 0.622). In other words, Component 1 is a representation of Digital Adoption & UPI Orientation.
The second component is based on:
· Future UPI Increase (0.688)
· Security (0.551)
In other words, Component 2 is a representation of Future Intent & Trust Dimension.
Therefore, two main factors are revealed which influence the payment preference:
· Current Digital Preference
· Future Digital Intention & Security
2.
Cluster Analysis
2.1 Number of Cases in Each Cluster
Interpretation:
Out of 30 respondents:
· 66.7% belong to Cluster 1
· 33.3% belong to Cluster 2
2.2 Iteration History
Interpretation:
The algorithm converged after 3 iterations as the change in cluster centers became zero. This indicates stable cluster formation.
2.3 Initial Cluster Centers
Interpretation:
These initial seeds clearly represented extreme digital vs extreme cash users.
2.4
Final Cluster Centers.
Interpretation:
Cluster 1 represents UPI-Dominant Users who:
· Use UPI more frequently.
· Prefer UPI overall (8/10)
· Have strong future intention (7)
· Experience fewer technical issues (2)
Cluster 2 represents Cash-Preferred or Balanced Users who:
· Use Cash more frequently.
· Have lower UPI preference (3)
· Moderate security and convenience perception
· Lower future digital adoption intention (4)
Conclusion
The research was set to find out if the payment preferences of consumers from UPI to Cash are changing and what factors are affecting consumer behavior. The Exploratory Factor Analysis results not only confirmed the dataset to be suitable for factor extraction but also showed a KMO value of 0.660 with a statistically significant Bartlett’s Test (Chi, square = 43.007, p < 0.001).
The analysis yielded two major factors that accounted for 64.471% of the total variance, which means that the identified factors have a strong explanatory power. The first factor is mostly a representation of Digital Usage Orientation since it has a high positive correlation with the UPI usage (0.837), convenience (0.728), overall payment preference (0.650), security perception (0.588), and a negative correlation with the cash usage (0.622).
Hence, it can be inferred that increased digital usage and convenience lead to the preference for UPI. The second factor is that of Future Intention and Trust Dimension, which has a strong correlation with future increase in UPI usage (0.688) and security perception (0.551). From this, it can be inferred that the future usage of UPI depends mostly on the perceived security and the level of trust in digital systems.
Cluster analysis identified two separate groups of respondents. First group, Cluster 1, is made up of 20 respondents (66.7%) and represents UPI, dominant users who have a higher UPI usage (mean = 7), a stronger overall preference toward UPI (8), and a higher intention to increase future usage (7). Cluster 2, made up of 10 respondents (33.3%), represents cash, centric users who have a higher cash usage (7), a lower overall digital preference (3), and a lower future intention toward UPI (4).
Overall, results reveal a progressive move towards digital payment systems, especially UPI, by most of the respondents. That said, cash still plays a key role for a notable group due to moderate trust and reliability perceptions. Hence, it is expected that while digital adoption is on the rise, the use of cash will not be totally phased out soon. Primarily, the shift to digital payments is driven by factors such as convenience, security perception, and future usage intention.