Unveiling the future of Artificial Intelligence: Banking Customers's Age affects the Adoptability of Artificial Intelligence-driven Financial Assistant and Behavioural Intention
Nisha Rani1, Tilak Sethi2, Pardeep Gupta
1Research Scholar, Haryana School of Business, Guru Jambheshwar University of Science & Technology, Hisar
2Professor (Retired), Haryana School of Business,
Guru Jambheshwar University of Science & Technology, Hisar, Haryana, India.
3Professor, Haryana School of Business, Guru Jambheshwar University of Science & Technology, Hisar, India.
*Corresponding Author E-mail: nisha.garg1987@gmail.com, tilaksethi@hotmail.com, pardeephsb@gmail.com
ABSTRACT:
Financial assistants driven by artificial intelligence offer a continuously and readily available service that includes assistance on investment plans, understanding of spending trends, and even answers to questions about banking-related activities. The banking sector's adoption of AI-powered financial assistants is completely changing the way financial services are delivered by offering convenience, individualized offerings, and improved efficient performance. The use of AI-powered financial assistants for banking-related operations is the most recent technical advancement in the banking sector. The aim of the current study is to investigate how demographic variables, such as age, affect customers' adoption factors of AI-driven financial assistants, given that more and more consumers are leaning toward tech-based services. The study used an adapted questionnaire to gather primary data, which included the dependent variable Behavioural Intention to use AI-driven financial assistants and the six primary adoption factors of AI-driven financial assistants: Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Trust, and Personal Innovativeness. The data was collected from Haryana's banking customers via a survey. The results were determined using ANOVA. The study's findings demonstrate that, depending on the respondents' ages, customers' adoption factors of Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Trust, and Personal Innovativeness vary significantly. Additionally, behavioral intention to use AIFA differentiates groups according to the customers' ages. The results of this study will help banks and other financial institutions better understand their customers.
KEYWORDS: Artificial Intelligence (AI), Banks, AI-driven financial assistant, Customers adoption factors, Behavioral Intention to use AIFA.
1. INTRODUCTION:
One significant technical advancement that is completely changing the banking industry is the introduction of Financial Assistant services powered by Artificial Intelligence (AI). These organizations are more equipped to examine consumer data, which enables them to understand the specific needs and preferences of each individual. Because AI technologies can handle questions and offer round-the-clock help, this leads to better customer interactions and personalized product recommendations. Additionally, automating repetitive processes like account management and loan processing boosts productivity and lowers the possibility of human mistake, enabling up employees to focus on more complex customer issues.
In order to improve customer service, expedite operational procedures, and provide individualized banking experiences, financial institutions are progressively implementing AI-powered technologies2. The banking industry's use of AI-powered financial assistants is transforming the offering of financial services by providing a combination of enhanced operational performance, personalized services, and comfort15. This technology boosts operational effectiveness while minimizing chances for human error and manual intervention17. These AI assistants may effectively perform duties including processing transactions, offering financial advice, answering consumer concerns, and making personalized product recommendations by utilizing machine learning techniques, natural language processing, and data analytics18.
AI can also improve the security of financial transactions by spotting unusual activity and potential fraud, making banking services safer for customers20. Financial organizations may offer a more secure banking environment by utilizing AI's complex algorithms, which will better protect consumers' funds and personal data from fraudulent activity. AI will be a crucial instrument in protecting our financial transactions and boosting trust in the banking system as technology develops further22. By evaluating enormous volumes of financial data in real-time, AI-driven financial assistants can help with decision-making and generate accurate projections23.Overall, the use of AI in banking promotes a quicker and more responsive financial environment in addition to increasing client happiness.
2. OBJECTIVES OF THE STUDY:
• To examine the impact of Age on the Customer's adoption factors of AI-driven financial assistant.
• To examine the impact of Age on the Behavioural Intention to use AI-driven financial assistant.
3. LITERATURE REVIEW:
• Perceived privacy concerns decreased user satisfaction, although information, entertainment, media appeal, and social presence all positively predicted it. User satisfaction also had a beneficial effect on customer loyalty and continuing use of chatbot services3.
• In Nigeria, the intention to keep using e-banking services is strongly correlated with satisfaction, which is positively correlated with AI quality4.
• Due to their high regard for factors including time, performance, simplicity of use, security, and safety, customers' behavioral intentions toward new financial products are greatly impacted by performance expectations, effort expectations, and perceived credibility10.
• The adoption behavior of digital payment systems is positively impacted by technological know-how, privacy, security, and economic value13.
• Due to their convenience and time savings, chatbot advisors in financial services are influenced by a number of criteria, including perceived risk, perceived privacy, enjoyment, social impact, and perceived strength of control15.
• Since chatbot technology makes financial transactions with banks easier and more convenient, innovativeness, perceived usefulness, perceived simplicity of use, and attitude toward using the chatbot all had an impact on behavioral intention17.
• The client experience is impacted by the quality of the systems, data, and services21.
4. RESEARCH METHODOLOGY:
• Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Trust, and Personal Innovativeness to use AI-driven financial assistants are the six main adoption factors of AI-driven financial assistants. The questionnaire used to gather primary data for the study also includes the dependent variable, behavioral intention to use AI-driven financial assistants. The data was collected through a survey of 374 customers of banks in Haryana. Secondary data has been gathered from books, journals, periodicals, and websites to support the primary data.
• Statistical tools such as ANOVA was used to analyze the factors that influence customer acceptance of AI-based financial assistants and the influence of demographic variables on these adoption factors and behavioral intention to use AI-based financial assistants. The Levene’s test statistic was used to test the homogeneity of variance hypothesis and accordingly the F statistic and Welch test were used to see if there were significant differences between groups for different acceptance factors and behavioral intention to use AIFA.
5. HYPOTHESIS OF THE STUDY:
H1: There is significant difference between the Performance Expectancy of AIFA on the basis of Age of the respondents.
H2: There is significant difference between the Effort Expectancy of AIFA on the basis of Age of the respondents.
H3: There is significant difference between the Social Influence of AIFA on the basis of Age of the respondents.
H4: There is significant difference between the Facilitating Conditions of AIFA on the basis of Age of the respondents.
H5: There is significant difference between the Trust of AIFA on the basis of Age of the respondents.
H6: There is significant difference between the Personal Innovativeness of AIFA on the basis of Age of the respondents.
H7: There is significant difference between the Behavioural Intention to use AIFA on the basis of Age of the respondents.
6. RESULTS AND DISCUSSION:
A. Demographic Profile of Respondents:
This section contains the findings and interpretation of the survey conducted regarding customer adoption factors and Behavioral Intention to use AI-driven Financial Assistant as follows:
Table 1: Demographic Attributes of Respondents
|
Categories |
Frequency |
Percentage |
Cumulative Percentage |
|
|
Age (in Years)
|
18-30 |
96 |
25.7 |
25.7 |
|
31-45 |
108 |
28.9 |
54.5 |
|
|
46-60 |
98 |
26.2 |
80.7 |
|
|
Above60 |
72 |
19.3 |
100 |
|
|
|
Total |
374 |
100 |
|
Source: Field Survey Data
The above Table 1 explains the findings of the frequency and percentage analysis of demographic attributes of 374 respondents who responded to the study. The result of the study also found that a large percentage of customers belongs to the age group of 31-45 years (28.9%) followed by the age groups of 46-60 years (26.2%) and18-30 years (25.7%) and above 60 years (19.3%).
B. Comparison of the customer adoption factors and Behavioural Intention to use AIFA on the basis of Age
The results of the table 2 shows that the Levene’s Test statistics for factors as Performance Expectancy, Social Influence and Trust are found insignificant which means the assumption of homogeneity of variances is not violated, so ANOVA F statistics results has been considered to identify the significant difference among the groups across age. The Levene’s Test statistics for adoption factors i.e. Effort Expectancy, Facilitating Conditions, Personal Innovativeness found significant which means the assumption of homogeneity of variances is violated, so Welch test results are considered to find out the significant difference among the groups across age. Similarly, the Behavioural Intention to use AIFA also found significant which means the assumption of homogeneity of variances is violated, so Welch test results has been considered to identify the significant difference among the groups across Age.
The result of the F test shows that there is significant difference for the customer adoption factors as Performance Expectancy, Social Influence and Trust, so hypothesis H1, H3, H5 has been accepted and further Post-Hoc Analysis among different groups has been done with Tukey HSD Method.
Table 2: Analysis of variance across the Age of banking customers
|
Customer Adoption Factors |
Levene Statistic |
Sig. |
F value |
Sig. |
Welch |
Sig. |
Hypothesis (Accepted/ Rejected) |
|
Performance Expectancy |
0.865 |
0.459 |
3.503 |
0.016 |
NA |
Accepted |
|
|
Effort Expectancy |
4.000 |
0.008 |
NA |
8.640 |
0.000 |
Accepted |
|
|
Social Influence |
0.908 |
0.437 |
3.548 |
0.015 |
NA |
Accepted |
|
|
Facilitating Conditions |
4.600 |
0.004 |
NA |
3.861 |
0.010 |
Accepted |
|
|
Trust |
2.566 |
0.054 |
4.145 |
0.007 |
NA |
Accepted |
|
|
Personal Innovativeness |
9.797 |
0.000 |
NA |
10.040 |
0.000 |
Accepted |
|
|
Behavioural Intention to use AIFA |
8.479 |
0.000 |
NA |
13.935 |
0.000 |
Accepted |
|
|
Tukey HSD Statistics |
|||||
|
Customer Adoption Factors |
(I) Age (in Years) |
(J) Age (in Years) |
Mean Difference (I- J) |
Std. Error |
Sig. |
|
Performance Expectancy |
18-30 |
31-45 |
-0.06829 |
0.12511 |
0.948 |
|
46-60 |
0.30463 |
0.12808 |
0.083 |
||
|
Above60 |
0.16088 |
0.13905 |
0.654 |
||
|
31-45 |
18-30 |
0.06829 |
0.12511 |
0.948 |
|
|
46-60 |
.37292* |
0.12443 |
0.015 |
||
|
Above60 |
0.22917 |
0.1357 |
0.331 |
||
|
46-60 |
18-30 |
-0.30463 |
0.12808 |
0.083 |
|
|
31-45 |
-.37292* |
0.12443 |
0.015 |
||
|
Above60 |
-0.14375 |
0.13844 |
0.727 |
||
|
Above60 |
18-30 |
-0.16088 |
0.13905 |
0.654 |
|
|
31-45 |
-0.22917 |
0.1357 |
0.331 |
||
|
46-60 |
0.14375 |
0.13844 |
0.727 |
||
*The mean difference is significant at the 0.05 level.
Further, there is significant difference for the customer adoption factors as Effort Expectancy, Facilitating Conditions and Personal Innovativeness, hypothesis H2, H4, H6 has been accepted and further Post-Hoc Analysis among different groups has been done with Games Howell Method. Similarly, result of the Welch test for the dependent variable Behavioural Intention to use AIFA shows that there is significant difference in the different groups across Age, hence hypothesis H7 has been accepted and further Post-Hoc Analysis among different groups has been done with Games Howell Method.
The table 3 depicts that the Post hoc analysis of Tukey test results highlight that age does influence the Performance Expectancy and there is significant difference found between the 31-45 and 46-60 age groups. However, there are no significant differences found when comparing Performance Expectancy among the other age groups.
|
Tukey HSD Statistics |
|||||
|
Customer Adoption Factors |
(I) Age (in Years) |
(J) Ag (in Years) |
Mean Difference (I-J) |
Std. Error |
Sig. |
|
Social Influence |
18-30 |
31-45 |
-0.12639 |
0.12846 |
0.759 |
|
46-60 |
0.28155 |
0.13151 |
0.142 |
||
|
Above 60 |
0.00972 |
0.14278 |
1.000 |
||
|
31-45 |
18-30 |
0.12639 |
0.12846 |
0.759 |
|
|
46-60 |
0.40794* |
0.12777 |
0.008 |
||
|
Above 60 |
0.13611 |
0.13934 |
0.763 |
||
|
46-60 |
18-30 |
-0.28155 |
0.13151 |
0.142 |
|
|
31-45 |
-0.40794* |
0.12777 |
0.008 |
||
|
Above 60 |
-0.27183 |
0.14215 |
0.225 |
||
|
Above 60 |
18-30 |
-0.00972 |
0.14278 |
1.000 |
|
|
31-45 |
-0.13611 |
0.13934 |
0.763 |
||
|
46-60 |
0.27183 |
0.14215 |
0.225 |
||
*The mean difference is significant at the 0.05 level.
The results of the above table 4 demonstrate that the Post hoc analysis of Tukey HSD test results highlight that age does influence the Social Influence and there is significant difference found between the 31- 45 and 46-60 age groups. However, there are no significant differences found when comparing Social Influence among the other age groups.
|
Tukey HSD Statistics |
|||||
|
Customer Adoption Factors |
(I) Age (in Years) |
(J) Ag (in Years) |
Mean Difference (I-J) |
Std. Error |
Sig. |
|
Trust |
18-30 |
31-45 |
-0.114 |
0.13407 |
0.830 |
|
46-60 |
0.3277 |
0.13725 |
0.081 |
||
|
Above60 |
0.17766 |
0.14901 |
0.632 |
||
|
31-45 |
18-30 |
0.114 |
0.13407 |
0.830 |
|
|
46-60 |
0.44170* |
0.13334 |
0.006 |
||
|
Above60 |
0.29167 |
0.14542 |
0.188 |
||
|
46-60 |
18-30 |
-0.3277 |
0.13725 |
0.081 |
|
|
31-45 |
-0.44170* |
0.13334 |
0.006 |
||
|
Above60 |
-0.15004 |
0.14836 |
0.743 |
||
|
Above 60 |
18-30 |
-0.17766 |
0.14901 |
0.632 |
|
|
31-45 |
-0.29167 |
0.14542 |
0.188 |
||
|
46-60 |
0.15004 |
0.14836 |
0.743 |
||
*The mean difference is significant at the 0.05 level.
The table 5 shows that the Post hoc analysis of Tukey test results highlight that age does influence the Trust and there is significant difference found between the 31-45 and 46- 60 age groups. However, there are no significant differences found when comparing Trust among the other age groups.
|
Games-Howell Method Statistics |
|||||
|
Customer Adoption Factors |
(I) Age (in Years) |
(J) Ag (in Years) |
Mean Difference (I-J) |
Std. Error |
Sig. |
|
Effort Expectancy |
18-30 |
31-45 |
-0.20795 |
0.11474 |
0.271 |
|
46-60 |
0.35976* |
0.12982 |
0.031 |
||
|
Above60 |
0.15625 |
0.1477 |
0.716 |
||
|
31-45 |
18-30 |
0.20795 |
0.11474 |
0.271 |
|
|
46-60 |
0.56771* |
0.11474 |
0.000 |
||
|
Above60 |
0.36420* |
0.13464 |
0.039 |
||
|
46-60 |
18-30 |
-0.35976* |
0.12982 |
0.031 |
|
|
31-45 |
-0.56771* |
0.11474 |
0.000 |
||
|
Above60 |
-0.20351 |
0.1477 |
0.515 |
||
|
Above 60 |
18-30 |
-0.15625 |
0.1477 |
0.716 |
|
|
31-45 |
-0.36420* |
0.13464 |
0.039 |
||
|
46-60 |
0.20351 |
0.1477 |
0.515 |
||
*The mean difference is significant at the 0.05 level.
The table 6 shows that for Effort Expectancy, significant differences were found between the 18-30 and 46-60 age groups as well as between the 31-45 and 46-60 groups and 31-45 and above 60 age groups. However, no significant difference was observed between the other age groups.
|
Games-Howell Method Statistics |
|||||
|
Customer Adoption Factors |
(I) Age (in Years) |
(J) Ag (in Years) |
Mean Difference (I-J) |
Std. Error |
Sig. |
|
Facilitating Conditions |
18-30 |
31-45 |
-0.04948 |
0.12522 |
0.979 |
|
46-60 |
0.28173 |
0.13151 |
0.144 |
||
|
Above60 |
0.31163 |
0.1634 |
0.230 |
||
|
31-45 |
18-30 |
0.04948 |
0.12522 |
0.979 |
|
|
46-60 |
0.33121* |
0.1171 |
0.026 |
||
|
Above60 |
0.36111 |
0.15204 |
0.087 |
||
|
46-60 |
18-30 |
-0.28173 |
0.13151 |
0.144 |
|
|
31-45 |
-0.33121* |
0.1171 |
0.026 |
||
|
Above60 |
0.0299 |
0.15726 |
0.998 |
||
|
Above 60 |
18-30 |
-0.31163 |
0.1634 |
0.230 |
|
|
31-45 |
-0.36111 |
0.15204 |
0.087 |
||
|
46-60 |
-0.0299 |
0.15726 |
0.998 |
||
*The mean difference is significant at the 0.05 level.
The table 7 shows that the Post hoc analysis of Games-Howell test results highlight that age does influence the Facilitating Conditions and there is significant difference found between the 31-45 and 46- 60 age groups. However, there are no significant differences found when comparing Facilitating Conditions among the other age groups.
|
Games-Howell Method Statistics |
|||||
|
Customer Adoption Factors |
(I) Age (in Years) |
(J) Ag (in Years) |
Mean Difference (I-J) |
Std. Error |
Sig. |
|
Personal Innovativeness |
18-30 |
31-45 |
-0.34977* |
0.11813 |
0.018 |
|
46-60 |
0.20004 |
0.12588 |
0.387 |
||
|
Above60 |
-0.10347 |
0.15683 |
0.912 |
||
|
31-45 |
18-30 |
0.34977* |
0.11813 |
0.018 |
|
|
46-60 |
0.54981* |
0.102 |
0.000 |
||
|
Above60 |
0.2463 |
0.1384 |
0.289 |
||
|
46-60 |
18-30 |
-0.20004 |
0.12588 |
0.387 |
|
|
31-45 |
-0.54981* |
0.102 |
0.000 |
||
|
Above60 |
-0.30351 |
0.14507 |
0.161 |
||
|
Above 60 |
18-30 |
0.10347 |
0.15683 |
0.912 |
|
|
31-45 |
-0.2463 |
0.1384 |
0.289 |
||
|
46-60 |
0.30351 |
0.14507 |
0.161 |
||
*Themeandifferenceissignificantatthe0.05level.
The table 8 shows that Personal Innovativeness, significant differences were found between the 18-30 and 31-45 age groups, with the younger group (18-30) showing lower levels of personal innovativeness. Additionally, the 31-45 group is significantly different from the 46- 60 group. However, there were no significant differences found between other age groups.
|
Games-Howell Method Statistics |
|||||
|
Customer Adoption Factors |
(I) Age (in Years) |
(J) Ag (in Years) |
Mean Difference (I-J) |
Std. Error |
Sig. |
|
Behavioural Intention to use AIFA |
18-30 |
31-45 |
-0.33267* |
0.1183 |
0.028 |
|
46-60 |
0.35799* |
0.11618 |
0.013 |
||
|
Above 60 |
0.00595 |
0.15429 |
1.000 |
||
|
31-45 |
18-30 |
0.33267* |
0.1183 |
0.028 |
|
|
46-60 |
0.69067* |
0.10656 |
0.000 |
||
|
Above 60 |
0.33862 |
0.14718 |
0.103 |
||
|
46-60 |
18-30 |
-0.35799* |
0.11618 |
0.013 |
|
|
31-45 |
-0.69067* |
0.10656 |
0.000 |
||
|
Above 60 |
-0.35204 |
0.14548 |
0.079 |
||
|
Above 60 |
18-30 |
-0.00595 |
0.15429 |
1.000 |
|
|
31-45 |
-0.33862 |
0.14718 |
0.103 |
||
|
46-60 |
0.35204 |
0.14548 |
0.079 |
||
*Themeandifferenceissignificantatthe0.05level.
The table 9 shows that as for Behavioural Intention to use AIFA, there is significant differences were found between the 18-30 years and 31-45 years age group and 46-60 years groups. Additionally, the 31-45 group is significantly different from the 46-60 group. However, there were no significant differences being observed for comparisons among other age groups.
7. CONCLUSION:
The present study focuses on examining the impact of Age on the Customer's adoption factors of AI-driven financial assistant as well as on the Behavioural Intention to use AI-driven financial assistant. The overall result of the present study indicates that on the basis of Age, there is significant difference found in customer’s adoption factors as Performance Expectancy, Social Influence and Trust. Among All these three factors, the persons having the age groups of 31-45 years are more prone towards the better performance, influence among society as well as having more trust on AIFA as compared to the age group 46-60 years of age. The present study also demonstrate that on the basis of Age, there is significant difference found in customer’s adoption factors as Effort Expectancy, Facilitating Conditions and Personal Innovativeness. Among All these three factors, the persons having the age groups of 31-45 years are more prone towards the less efforts being required to use AIFA, better conditions to use AIFA and innovativeness towards new technology i.e. AIFA as compared to the other age groups.
In addition to this, behavioural intention to use AIFA also differentiates among different groups on the basis of Age i.e. the persons having the age groups of 31-45 years are more inclined towards the usage of AIFA for banking tasks as compared to other age groups. The study also depicts that group having 31-45 years Age group is more inclined towards AIFA as compared to other age groups in all the adoption factors of AIFA due to more innovativeness in their attitude and perceptions regarding use of latest advancements in the banking sector.
Given the above facts, banks can change their approach to target their customers better and improve their understanding of why they use AI Financial Assistant. The findings of this research on the services of AI-based financial assistants provide valuable information that banks can use to better understand their customers. Finally, in a constantly evolving financial environment, this technology helps banks stay competitive.
8. DIRECTIONS FOR FUTURE WORK:
Due to time constraints, the current study has been conducted by taking into account only one demographic variable i.e. age of the respondents. The other demographic variables can also be considered for further investigation. Haryana is the only target population of this study. Future research initiatives may be implemented in other states across the country. This study can also be categorized by the geographical region of the country, such as the North, South, West, and Eastern regions. Future research may consider additional factors related to customer adoption of AIFA.
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Received on 28.10.2025 Revised on 03.12.2025 Accepted on 01.01.2026 Published on 20.07.2026 Available online from July 30, 2026 Asian Journal of Management. 2026;17(3):245-250. DOI: 10.52711/2321-5763.2026.00038 ©AandV Publications All right reserved
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