Factors influencing the adoption of Cryptocurrency in Jordan: An application of the extended TRA model

Figures & data

Figure 1. Cryptocurrency users in Jordan.

Figure 2. Research model.

Table 1. Measurements of factors

Factor	Code	Measurement Dimensions of Factors
Subjective Norm (SN)Walton and Johnston (2018); Mazambani and Mutambara (2019); and verified by Abbasi et al. (2021).	SN1	My significant others influence me to utilize Cryptocurrency in the purchase or sell of products, as a good way of trading.
	SN2	My significant others influence me to give Cryptocurrency a try.
	SN3	My significant others influence me to show a positive sentiment in involving myself in Cryptocurrency use.
	SN4	My significant others influence me in deciding my purchases using Cryptocurrency.
	SN5	My significant others encourage me in my potential utilization of Cryptocurrency.
Perceived Risk (PR)Sohaib et al. (2019), and verified by Nadeem et al. (2021).	PR1	Others could misuse my Cryptocurrency payment transactions information.
	PR2	I think it is unsafe to give personal information over Cryptocurrency payments.
	PR3	Cryptocurrency offers secure money transfer.
	PR4	I have more control over my money with Cryptocurrency.
	PR5	I think that Cryptocurrency is very costly.
	PR6	Cryptocurrency cannot be converted to conventional currencies, and even so, the price will be unreasonable.
Perceived Usefulness (PU)Won-Jun (2018); Arias-Oliva et al. (2019); and verified by Sohaib et al. (2019).	PU1	I think that my purchase will be faster with Cryptocurrency.
	PU2	I think Cryptocurrency makes my purchase easier.
	PU3	I think Cryptocurrency will make my purchase more effective.
	PU4	I think Cryptocurrency will make my purchase more efficient.
	PU5	I can make better purchase decision with Cryptocurrency.
Perceived Enjoyment (PE)Sohaib et al. (2019); Abbasi et al. (2021); and verified by Nadeem et al. (2021).	PE1	I think it is fun to use Cryptocurrency.
	PE2	I think Cryptocurrency is pleasant to use.
	PE3	I find Cryptocurrency use enjoyable.
	PE4	Cryptocurrency is flexible.
	PE5	I am not creative in my Cryptocurrency use.
Perceived Ease of Use (PEOU)Davis (1989).	PEOU1	I am confident that learning the technology will be difficult.
	PEOU2	I think that Cryptocurrency will easily do what I want it to do.
	PEOU3	I think that Cryptocurrency will be easy to use.
	PEOU4	Cryptocurrency contact, in my opinion, is made more flexible by technology.
Trust (TR)Slade et al. (2015).	TR1	I am sure that Cryptocurrency is reliable.
	TR2	I am sure that Cryptocurrency is secure.
	TR3	I am sure that Cryptocurrency is trustworthy.
	TR4	I am confident in Cryptocurrencies.
Facilitating Condition (FC)Beza et al. (2018).	FC1	I have the resources that I need in Cryptocurrency use.
	FC2	I have the knowledge that I need in Cryptocurrency use.
	FC3	Cryptocurrency is well-matched with other technologies that I use.
	FC4	I can find help from others when facing problems in my use of Cryptocurrency.
Attitude (ATT)Walton and Johnston (2018); Mazambani and Mutambara (2019); and verified by Abbasi et al. (2021).	ATT1	I am confident that Cryptocurrency purchase is a good idea.
	ATT2	I am confident that Cryptocurrency usage in financial transactions is a smart idea.
	ATT3	I think it is good to use Cryptocurrency as a currency.
	ATT4	The use of Cryptocurrency makes me feel good.
	ATT5	The idea of Cryptocurrency usage excites me.
Intention to Use Cryptocurrency (IUC)Won-Jun (2018); Arias-Oliva et al. (2019); Mazambani and Mutambara (2019); and verified by Nadeem et al. (2021).	IUC1	Cryptocurrency will be my other currency source in my future product purchase and sell.
	IUC2	I am confident that Cryptocurrency use will greatly help me in fulfilling my obligations on time.
	IUC3	I plan to regularly use Cryptocurrency.
	IUC4	I will promote Cryptocurrency use to others, as an exchange method.
	IUC5	I think Cryptocurrency is more appropriate for gaming.

Table 2. Characteristics of the respondents. (N = 600)

Characteristics	Number	Percentage
Gender
Male	399	66.5%
Female	201	33.5%
Total	600	100%
Age
Less than 20	48	8%
20–29	528	88%
31–40	24	4%
Total	600	100%
Education level
High school or less	76	12.7%
College degree	200	33.3%
Bachelor’s degree	324	54%
Total	600	100%
Income per month JD
350–400 JD	217	36.2%
401–450JD	198	33%
More than 450 JD	185	30.8%
Total	600	100%
Online experience
Less than 1 year	52	8.6%
1–4 years	60	10%
More than 4 years	488	81.4%
Total	600	100%

Table 3. Measurement of model fit indices

Model	CMIN	Df	P	CMIN /df	GFI	AGFI	NFI	CFI	RMSEA
Initial measurement model	1420.111	276	0.00	5.14	0.79	0.77	0.70	0.75	0.151
Modified measurement model	413.122	267	0.00	1.54	0.92	0.81	0.94	0.91	0.04

Minimum recommended value: CMIN/DF ≤3.000, GFI ≥0.90, AGFI ≥0.80, NFI ≥0.90, CFI≥0.90, RMSEA ≤0.08.

Table 4. Properties of the final measurement model

Constructs andIndicators	FactorLoadings	Std.Error	SquareMultipleCorrelation	ErrorVariance	Cronbach’sAlpha	CompositeReliability	AVE
Subjective Norm (SN)					0.906	0.71	0.76
SN1	0.552	0.022	0.531	0.643
SN2	0.631	0.012	0.540	0.513
SN3	0.605	0.061	0.722	0.625
SN4	0.633	0.056	0.620	0.534
Perceived Risk (PR)					0.871	0.66	0.89
PR1	0.610	0.122	0.653	0.522
PR2	0.511	0.021	0.655	0.344
PR3	0.591	0.036	0.788	0.251
PR4	0.635	0.025	0.764	0.123
PR5	0.541	0.017	0.678	0.244
PR6	0.714	0.023	0.549	0.420
Perceived Usefulness (PU)					0.931	0.75	0.79
PU1	0.722	0.031	0.542	0.645
PU2	0.635	0.034	0.643	0.512
PU3	0.551	0.051	0.571	0.648
PU5	0.564	0.022	0.638	0.231
Perceived Enjoyment (PE)					0.945	0.75	0.79
PE1	0.541	0.010	0.629	0.472
PE2	0.570	0.040	0.651	0.321
PE3	0.636	0.025	0.733	0.612
PE4	0.527	0.041	0.672	0.321
Perceived Ease of Use (PEOU)					0.868	0.80	0.83
PEOU1	0.536	0.011	0.655	0.158
PEOU2	0.630	0.022	0.510	0.417
PEOU3	0.644	0.044	0.560	0.310
PEOU4	0.555	0.051	0.691	0.558
Trust (TR)					0.914	0.72	0.76
TR1	0.537	0.060	0.516	0.518
TR2	0.601	0.071	0.530	0.511
TR3	0.620	0.062	0.548	0.574
TR4	0.609	0.033	0.521	0.581
Facilitating Condition (FC)					0.961	0.83	0.86
FC1	0.681	0.041	0.513	0.344
FC2	0.720	0.321	0.533	0.362
FC3	0.666	0.375	0.540	0.391
FC4	0.541	0.041	0.502	0.325
Attitude (ATT)					0.922	0.80	0.83
ATT1	0.525	0.133	0.631	0.310
ATT2	0.520	0.114	0.526	0.420
ATT3	0.539	0.201	0.577	0.110
ATT5	0.638	0.145	0.522	0.399
Intention to Use Cryptocurrency (IUC)					0.881	0.81	0.84
IUC1	0.559		0.567	0.233
IUC3	0.623		0.548	0.394
IUC4	0.746		0.623	0.381
IUC5	0.580		0.590	0.451

Table 5. Correlations of constructs

Constructs	SN	PR	PU	PE	PEOU	TR	FC	ATT	IUC
SN	0.88
PR	0.441	0.87
PU	0.635	0.334	0.95
PE	0.327	0.311	0.370	0.89
PEOU	0.634	0.344	0.549	0.630	0.83
TR	0.680	0.341	0.534	0.216	0.410	0.84
FC	0.651	0.522	0.311	0.531	0.506	0.322	0.81
ATT	0.688	0.420	0.551	0.303	0.539	0.317	0.351	0.90
IUC	0.514	0.433	0.378	0.501	0.482	0.344	0.367	0.412	0.85

Note: Diagonal elements form the square roots of the average variance extracted for the entire nine constructs. Off-diagonal elements denote the correlations between constructs.

Table 6. Summary of proposed results for the theoretical model

Proposed Research Paths	CoefficientValue β	t-value	p-value	EmpiricalEvidence
H1: SN→ IUC	0.144	1.122	0.001	Supported
H2: ATT → IUC	0.332	3.413	0.021	Supported
H3: PR → ATT	0.153	3.221	0.005	Supported
H4: PR → IUC	0.520	5.220	0.030	Supported
H5: PU → ATT	0.211	2.123	0.002	Supported
H6: PU → IUC	0.401	4.662	0.011	Supported
H7: PE → ATT	0.513	3.752	0.033	Supported
H8: PE → IUC	0.501	4.721	0.055	Supported
H9: PEOU→ ATT	0.070	3.332	0.003	Supported
H10: PEOU→ IUC	0.051	2.112	0.001	Supported
H11: TR → IUC	0.613	4.111	0.006	Supported
H12: FC→ IUC	0.071	2.715	0.210	Not Supported

Table 7. Results of mediating hypotheses

Hypothesis	From	Mediation	To	Directeffect	Indirecteffect	Mediation
H13	PR	ATT	IUC	0.040	0.122	Mediation
H14	PU	ATT	IUC	0.001	0.176	Mediation
H15	PE	ATT	IUC	0.003	0.155	Mediation
H16	PEOU	ATT	IUC	0.040	0.144	Mediation

Walton, A., & Johnston, K. (2018). Exploring perceptions of bitcoin adoption: The South African virtual community perspective. Interdisciplinary Journal of Information, Knowledge, and Management, 13, 165–182.

 Google Scholar

Mazambani, L., & Mutambara, E. (2019). Predicting FinTech innovation adoption in South Africa: The case of cryptocurrency. African Journal of Economic and Management Studies, 11(1), 30–50. https://doi.org/10.1108/AJEMS-04-2019-0152

 Web of Science ®Google Scholar

Abbasi, G. A., Tiew, L. Y., Tang, J., Goh, Y. N., & Thurasamy, R. (2021). The adoption of cryptocurrency as a disruptive force: Deep learning-based dual stage structural equation modelling and artificial neural network analysis. Plos One, 16(3), e0247582. https://doi.org/10.1371/journal.pone.0247582

 PubMed Web of Science ®Google Scholar

Sohaib, O., Hussain, W., Asif, M., Ahmad, M., & Mazzara, M. (2019). A PLS-SEM neural network approach for understanding cryptocurrency adoption. IEEE Access, 8, 13138–13150. https://doi.org/10.1109/ACCESS.2019.2960083

 Web of Science ®Google Scholar

Nadeem, M. A., Liu, Z., Pitafi, A. H., Younis, A., & Xu, Y. (2021). Investigating the adoption factors of cryptocurrencies – A case of Bitcoin: Empirical evidence from China. SAGE Open, 11(1), 1–15. https://doi.org/10.1177/2158244021998704

 Web of Science ®Google Scholar

Won-Jun, L. (2018). Understanding consumer acceptance of Fintech service: An extension of the TAM model to understand Bitcoin. IOSR Journal of Business and Management, 20(7), 34–37. https://www.semanticscholar.org/paper/Understanding-Consumer-Acceptance-of-Fintech-%3A-An-Wonjun/eb522c342fdff71a4a3cf76df03a3f043b5014c4

 Google Scholar

Arias-Oliva, M., Pelegrín-Borondo, J., & Matías-Clavero, G. (2019). Variables influencing cryptocurrency use: A technology acceptance model in Spain. Frontiers in Psychology, 10(MAR), 1–13.

 PubMed Web of Science ®Google Scholar

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.

 Web of Science ®Google Scholar

Slade, E. L., Dwivedi, Y. K., Piercy, N. C., & Williams, M. D. (2015). Modeling consumers’ adoption intentions of remote mobile payments in the United Kingdom: Extending UTAUT with innovativeness, risk, and trust. Psychology & Marketing, 32(8), 860–873. https://doi.org/10.1002/mar.20823

 Web of Science ®Google Scholar

Beza, E., Reidsma, P., Poortvliet, P. M., Belay, M. M., Bijen, B. S., & Kooistra, L. (2018). Exploring farmers’ intentions to adopt mobile short message service (SMS) for citizen science in agriculture. Computers and Electronics in Agriculture, 151, 295–310. https://doi.org/10.1016/j.compag.2018.06.015

 Web of Science ®Google Scholar