Examining the factors influencing fintech adoption behaviour of gen Y in India

Figures & data

Figure 1. Conceptual Model.

Figure 2. Flowchart of research methods.

Table 1. Demographic profile

Category	Type of respondents	No of respondents	Percentage
Gender	Male	182	52.14%
	Female	167	47.86%
Age	18–22	130	37.24%
	22–26	131	37.53%
	26–30	88	25.23%
Educational Background	Graduate	75	21.48%
	Post Graduate	90	25.80%
	Professional	184	52.72%
Income level	Less than 200,000	73	20.91%
	200000–500000	100	28.65%
	500000–1000000	93	26.64%
	More than 1,000,000	83	23.80%

Table 2. Variables and factors

Variables Codes	Description	Reference
Information Quality
IQ1	I have access to updated information.	Huang et al. (2012)
IQ2	It provides the information I need.
IQ3	I always get relevant information.
IQ4	It provides me with quick and easy access to information.
Attitude toward fintech adoption
ATT1	I feel delighted while using fintech.	Gholami et al. (2018); Hu et al. (2019)
ATT2	It is my prudent decision to use fintech.
ATT3	It brings confidence in me.
Social Norms
SN1	The use of fintech provides me with a distinctive position among my friends and colleagues.	Teo et al. (2012); Vicente Sales Melo et al. (2019)
SN2	I feel honoured among my peers while using fintech.
SN3	It makes me trendy among my peers.
SN4	I am acceptable in my peer group while using fintech.
Observed Behavioural Control
OBC1	I have the potential to use fintech effectively	Njanja et al. (2014); Thoradeniya et al. (2015)
OBC2	I can handle fintech transactions smoothly.
OBC3	I have the capabilities and understanding to control fintech.
OBC4	I can easily use fintech apps in my routine.
Adoption Intention
Adop_int1	I would advise others to use fintech	Dai and Van Hove (2017); Mikalef et al. (2013)
Adop_int2	I am strongly willing to use fintech
Adop_int3	I am favourable toward fintech
Adop_int4	I use positive words to others for fintech
Readiness to Pay for the Privileges
Pay_priv1	I am ready to pay more to get satisfaction.	Lestari and Nita (2021)
Pay_priv2	I am ready to pay more to get convenience.
Pay_priv3	I am ready to pay to enjoy social status
Actual Intention
Actu_Int1	I feel good while working on fintech.	Tsai et al. (2011)
Actu_Int2	My life is more life comfortable with fintech.
Actu_Int3	I feel more involved in fintech

Table 3. Convergent validity for the reflective measurement model

Constructs	Items	Outer Loading	CR	AVE
Information Quality	IQ1	0.793	0.890	0.670
	IQ2	0.853
	IQ3	0.829
	IQ4	0.798
Attitude	ATT1	0.841	0.909	0.769
	ATT2	0.909
	ATT3	0.879
Social Norms	SN1	0.851	0.930	0.769
	SN2	0.864
	SN3	0.926
	SN4	0.864
Observed Behavioural Control	OBC1	0.962	0.980	0.925
	OBC2	0.961
	OBC3	0.963
	OBC4	0.961
Adoption Intention	Adop_Int1	0.855	0.912	0.721
	Adop_Int2	0.786
	Adop_Int3	0.863
	Adop_Int4	0.890
Pay for Premium	Pay_Priv1	0.941	0.957	0.881
	Pay_Priv3	0.924
	Pay_Priv_2	0.950
Actual Intention	Actu_Int1	0.881	0.922	0.797
	Actu_Int2	0.910
	Actu_Int3	0.886

Table 4. Discriminant validity- HTMT ratio

	Actual Intention	Adoption Intention	Attitude	Information Quality	Observed Behavioural Control	Privilege Pay
Adoption Intention	0.718
Attitude	0.786	0.612
Information Quality	0.718	0.753	0.652
Observed Behavioural Control	0.591	0.536	0.521	0.412
Privilege Pay	0.715	0.678	0.656	0.527	0.729
Social Norms	0.758	0.819	0.765	0.797	0.470	0.567

Table 5. Fornell Larcker (FL) criteria

	Actual Intention	Adoption Intention	Attitude	Information Quality	Observed Behavioural Control	Privilege Pay	Social Norms
Actual Intention	0.893
Adoption Intention	0.801	0.849
Attitude	0.745	0.792	0.877
Information Quality	0.618	0.643	0.554	0.819
Observed Behavioural Control	0.545	0.496	0.477	0.379	0.822
Privilege Pay	0.645	0.612	0.587	0.469	0.695	0.838
Social Norms	0.673	0.726	0.672	0.695	0.441	0.519	0.877

Figure 3. Path coefficient model.

Table 6. Testing of Hypothesis

Structural Linkage	β value	SE	T value	p- values	VIF	F^2	Findings
Attitude -> Adoption Intention	0.521	0.061	8.511	0.000	1.967	0.464	Accepted
Social Norms -> Adoption Intention	0.332	0.071	4.695	0.000	1.886	0.197	Accepted
Observed Behavioural Control -> Adoption Intention	0.101	0.047	2.131	0.033	1.34	0.026	Rejected
Adoption Intention -> Actual Intention	0.657	0.051	12.823	0.000	1.698	0.797	Accepted
Information Quality -> Attitude	0.554	0.05	11.18	0.000	1.000	0.443	Accepted
Information Quality -> Social Norms	0.695	0.039	17.816	0.000	1.000	0.934	Accepted
Information Quality -> Observed Behavioural Control	0.379	0.054	7.02	0.000	1.000	0.168	Accepted
Privilege Pay -> Actual Intention	0.251	0.059	4.252	0.000	1.606	0.123	Accepted
Privilege Pay x Adoption Intention -> Actual Intention	0.02	0.029	0.681	0.496	-	-	Rejected

Table 7. Specific indirect effect

	Std Beta	Std. Error	t-value	p- values	Decision
Information Quality -> Social Norms -> Adoption Intention -> Actual Intention	0.152	0.035	4.334	0.000	Supported
Information Quality -> Attitude -> Adoption Intention	0.288	0.046	6.306	0.000	Supported
Information Quality -> Social Norms -> Adoption Intention	0.231	0.05	4.628	0.000	Supported
Information Quality -> Attitude -> Adoption Intention -> Actual Intention	0.19	0.035	5.461	0.000	Supported
Information Quality -> Observed Behavioural Control -> Adoption Intention -> Actual Intention	0.025	0.013	1.902	0.057	Not Supported
Attitude -> Adoption Intention -> Actual Intention	0.342	0.05	6.912	0.000	Supported
Observed Behavioural Control -> Adoption Intention -> Actual Intention	0.066	0.032	2.098	0.036	Supported
Social Norms -> Adoption Intention -> Actual Intention	0.218	0.05	4.372	0.000	Supported
Information Quality -> Observed Behavioural Control -> Adoption Intention	0.038	0.02	1.923	0.055	Supported

Table 8. Goodness of Fit and Predictive Relevance

	R^2	Q^2
Attitude	0.307	0.295
Social Norms	0.483	0.473
Observed Behavioural Control	0.144	0.135
Adoption Intention	0.703	0.396
Actual Intention	0.681	0.482

Figure 4. Consumer’s readiness to pay for a privilege as moderating variable.

Table 9. Predictive capacity of the model

Sr. No.	Range of the Variables	Model’s Predictive Ability
1	All the values of PLS RMSE > LM RMSE	No Predictive power
2	Majority of values of PLS RMSE > LM RMSE	Poor predictive power
3	Majority of values of PLS RMSE < LM RMSE	Medium predictive power
4	All the values of PLS RMSE < LM RMSE	High Predictive power

Table 10. The PLS predict

	PLS SEM RMSE	LM RMSE
Act 1	1.082	1.102
Act 2	1.319	1.317
Act 3	1.117	1.177

Figure 5. Importance performance map.

Huang, H., Stvilia, B., Jörgensen, C., & Bass, H. W. (2012). Prioritisation of Data Quality Dimensions and Skills Requirements in Genome Annotation Work. Journal of the American Society for Information Science and Technology, 63(1), 195–207. https://doi.org/10.1002/asi.21652

 Web of Science ®Google Scholar

Gholami, Z., Abdekhoda, M., & Zarea Gavgani, V. Determinant Factors in Adopting Mobile Technology-based Services by Academic Librarians. (2018). Journal of Library & Information Technology, 38(4), 271–277. July 2018. https://doi.org/10.14429/djlit.38.4.12676

 Web of Science ®Google Scholar

Hu, Z., Ding, S., Li, S., Chen, L., & Yang, S. (2019). Adoption Intention of fintech Services for Bank Users: An Empirical Examination with an Extended Technology Acceptance Model. Symmetry, 11(3), 340. https://doi.org/10.3390/sym11030340

 Google Scholar

Teo, A.C., Wei-Han Tan, G., Cheah, C.M., Ooi, K.B., & Yew, K.T. (2012). Can the Demographic and Subjective Norms Influence the Adoption of Mobile Banking? International Journal of Mobile Communications, 10(6), 578–597. https://doi.org/10.1504/IJMC.2012.049757

 Web of Science ®Google Scholar

Vicente Sales Melo, F., de Farias, S. A., & Trajano Barbosa, O. (2019). Dressing in white, a true Brazilian tradition: Social influence, values, and symbolic consumption. Revista de Administração da Universidade Federal de Santa Maria, 12(2), 215–232. https://doi.org/10.5902/1983465917984

 Google Scholar

Njanja, L., Ogutu, M., & Ogutu, R. O. (2014), “The Moderating Effect of Subjective Norms, Perceived Behavioural Control and Gender on the Relationship Between Attitude Towards Internet Advertising and Purchase Intention of University Students in Kenya,” available at: https://www.semanticscholar.org/paper/the-moderating-effect-of-subjective-norms%2c-control-njanja-ogutu/21d1bfec355e9d5bd5d65aa81717e2877788102e, Retrieved on October 23, 2022)

 Google Scholar

Thoradeniya, P., Lee, J., Tan, R., & Ferreira, A. (2015). Sustainability reporting and the theory of planned behavior. Accounting, Auditing & Accountability Journal, 28(7), 1099–1137. https://doi.org/10.1108/AAAJ-08-2013-1449

 Web of Science ®Google Scholar

Dai, M., & Van Hove, L. (2017). The impact of customer images on online purchase decisions: Evidence from a Chinese C2C Web site. First Monday, 22(10).

 Google Scholar

Mikalef, P., Giannakos, M., & Pateli, A. (2013). Shopping and Word-of-Mouth Intentions on Social Media. Journal of Theoretical and Applied Electronic Commerce Research, 8(1), 17–34. https://doi.org/10.4067/S0718-18762013000100003

 Google Scholar

Lestari, M. R., & Nita, A. (2021). The Influence of Sustainable Product’s Attributes Toward the Willingness to Pay for Sustainable Products. Malaysian Journal of Social Sciences and Humanities (MJSSH), 6(8), 542–551. https://doi.org/10.47405/mjssh.v6i8.981

 Google Scholar

Tsai, M. T., Cheng, N. C., & Chen, K. S. (2011). Understanding online group buying intention: The roles of sense of virtual community and technology acceptance factors. Total Quality Management & Business Excellence, 22(10), 1091–1104. https://doi.org/10.1080/14783363.2011.614870

 Web of Science ®Google Scholar