Evaluating the FinTech success factors model to achieve a sustainable financial technology business: An empirical study in Vietnam

Figures & data

Table 1. The board of experts and their detail

Expert	Responsibility	Years of experience	Education level	Major	Association
E1	Company Owner	13	Master	Business Administration	Member of Board
E2	Senior Executive	15	Master	Financial Management	Vice President
E3	Professional FinTech expert	16	Master	IT Management and Business Administration	Member of Board
E4	Company Owner	18	Master	Business Administration	Member of Board
E5	Senior Executive	20	Master	Financial Management	Vice President
E6	Professional FinTech expert	19	Master	IT Management and Business Administration	Exc. Com. Board
E7	Senior Executive	20	Master	Financial Management	Exc. Com. Board
E8	Senior Executive	19	Master	Financial Management	Vice President
E9	Company Owner	16	Master	Economy	Exc. Com. Board
E10	Company Owner	24	Master	Financial Management	Exc. Com. Board

Table 2. Fintech industry evaluation criteria

Criteria	Description
Low margin (C1)	A low profit margin is a key characteristic in the initial phase of a fintech business (Lee & Teo, 2015). According to Accenture, even successful fintech companies take 8 to 14 years to become profitable. Many spend a significant amount of time ‘waiting in the lobby’ which implies a significant probability of the company going out of business. However, if the initial growth phase remains sufficiently high and volatile over time, there is a real possibility that the company can achieve operational success (Schwartz & Moon, 2000). It is centred around the idea of obtaining a large mass of users while attaining a high burn rate and high customer demand.
Asset light (C2)	Asset-light businesses can innovate and scale without incurring large fixed costs on assets. This will allow relatively low marginal costs which reinforces the first principle of ‘low profit margin’. One can add on to an existing system (such as the mobile phone) that depreciates quickly but offers an alternative revenue source (such as an internet phone messaging service) at low marginal costs. By riding on existing infrastructure, fixed costs and initial setup costs can be minimised. Yang and Liu (2012) synthesised the work of researchers and presented a description of agility in an enterprise context. They describe asset-light enterprises as ‘a complex, multidimensional, and context-specific concept comprised of the ability to sense environmental change and quickly respond to unpredicted change by flexibly assembling resources, processes, knowledge, and capabilities’ (Yang & Liu, 2012, p. 1023).
Security (C3)	Security management is the identification of an organisation’s assets, followed by the development, documentation, and implementation of policies and procedures for protecting these assets (Bernardo, 2017). A security management procedure is used within an organisation as an informal classification, risk assessment, and risk analysis to identify threats, categorise assets, and rate the vulnerability of the company’s systems so that the most effective controls can be implemented. Such controls are presented by Focardi and Martinelli (1999) in an approach to analysing various security properties (Bernardo, 2017, p. 169). The general idea is that the security property should be satisfied even in a hostile environment. A set of general conditions were presented that permits one to check a property only against a most powerful intruder. The approach is applicable to several existing security properties (Bernardo, 2017).
Innovative (C4)	Successful fintech companies need to be innovative in multiple aspects of their businesses: products, processes, organisation, and business models (Bernardo, 2017). With the emergence of technology and digitalisation, the majority of innovation in the fintech space can be performed through technology such as mobility, Big Data, analytics, the Internet of Things (IoT), social networks, cloud computing, and AI. According to Drucker et al. (2013), innovation is the specific function of entrepreneurship. Regardless of whether it is in an existing business, a public service institution, or a new venture started by individuals. It is how an entrepreneur creates new wealth-producing resources or endows existing resources with the enhanced potential to create new wealth; in some literature, this can also be distinguished as incremental and radical innovation (or entrepreneurship). There is still no proper definition of entrepreneurship; the ambiguity varies from small businesses to new businesses. However, many well-established companies engage in successful entrepreneurship; therefore, the term does not refer to the size or age of a business, but the type of activity performed (Drucker et al., 2013).
Compliance easy (C5)	Compliance means conforming to requirements such as specifications, policies, standards, or laws. Compliance regarding legal financial regulation is mandatory, and every nation has its own individual financial regulation based on culture, financial system, and historically based experiences (Bernardo, 2017). As fintech companies often conduct cross-border operations, they need to comply with multilevel regulations overseen by different regulators, which can be complex to manage. Due to the rise in the number of regulations and the need for operational transparency, organisations–including both fintech companies and incumbents–are increasingly adopting consolidated and harmonised sets of compliance controls. This approach can ensure that all necessary requirements are fulfilled without any unnecessary duplication of work (Bernardo, 2017).

Table 3. Linguistic terms for assessing the alternatives

Linguistic term	Abbreviation	IVIFNs
Absolutely more importance	AMI	〈[0.85,0.90],[0.05,0.10]〉
Relatively more importance	RMI	〈[0.80,0.85],[0.10,0.15]〉
Very high importance	VHI	〈[0.75,0.80],[0.15,0.20]〉
High Importance	HI	〈[0.65,0.75],[0.20,0.25]〉
Slightly more importance	SMI	〈[0.55,0.65],[0.30,0.35]〉
Equal importance	EI	〈[0.45,0.55],[0.35,0.40]〉
Slightly low importance	SLI	〈[0.30,0.40],[0.45,0.55]〉
Low importance	LI	〈[0.20,0.30],[0.55,0.65]〉
Very low importance	VLI	〈[0.10,0.20],[0.70,0.75]〉
Relatively less importance	RLI	〈[0.05,0.10],[0.85,0.90]〉

Figure 1. The framework of interval-valued intuitionistic fuzzy-combined compromise solution approach for fintech industry evaluation.

Table 4. Computational results: IVIFPV and decision weights (DEs)

DEs	IVIFPV ${ }_k$	${\tau }_k$	${\rho }_k$	${\varpi }_k$
E1	0.0236	0.9764	0.1003	0.0933
E2	0.0192	0.9808	0.1008	0.0946
E3	0.0240	0.9760	0.1003	0.0966
E4	0.0302	0.9698	0.0996	0.1065
E5	0.0304	0.9696	0.0996	0.1031
E6	0.0284	0.9716	0.0998	0.1047
E7	0.0264	0.9736	0.1000	0.1032
E8	0.0286	0.9714	0.0998	0.0955
E9	0.0276	0.9724	0.0999	0.1023
E10	0.0275	0.9725	0.0999	0.1002

Table 5. The criteria weights assessed by decision experts

	C1	C2	C3	C4	C5
Aggregated subjective weights	[0.499,0.6369,0.252,0.356]	[0.4539,0.5921,0.2845,0.3929]	[0.4269,0.5649,0.3018,0.4189]	[0.5091,0.6375,0.235,0.3429]	[0.3589,0.5059,0.3609,0.485]
Score value of aggregated subjective weights	0.3899	0.4050	0.4079	0.3839	0.4119
$\left(w_j^s\right)$	0.1949	0.2019	0.2109	0.1919	0.2059

Table 6. Overall aggregation and combined compromise ranking results of the alternative with $\gamma =0.5$

	${\mathfrak{O}}_i^{\left(2\right)}$	$\text{*}{\mathfrak{O}}_i^{\left(1\right)}$	$\text{*}{\mathfrak{O}}_i^{\left(2\right)}$	$\text{*}{\mathfrak{O}}_i^{\left(2\right)}$	${\mathit{\xi }}_{{\mathit{\alpha }}_{\mathit{i}}}$	${\mathit{\xi }}_{{\mathit{\beta }}_{\mathit{i}}}$	${\mathit{\xi }}_{{\mathit{\gamma }}_{\mathit{i}}}\left(\mathit{\vartheta }\right)$	${\mathit{\xi }}_{\mathit{i}}\left(\mathit{\vartheta }\right)$	Rank
A1	[0.6556,0.7426],[0.1853,0.2487]	[0.6528,0.7404],[0.1878,0.2509]	0.753	0.7504	0.0988	2.102	0.9472 (0.3)	1.6335 (0.3)	5
							0.9462 (0.5)	1.633 (0.5)
							0.9453 (0.9)	1.6324 (0.9)
A2	[0.6817,0.7583],[0.1641,0.2313]	[0.6746,0.7523],[0.1685,0.2359]	0.775	0.7693	0.1016	2.1591	0.9718 (0.3)	1.6773 (0.3)	3
							0.972 (0.5)	1.6773 (0.5)
							0.9721 (0.9)	1.6774 (0.9)
A3	[0.6596,0.7351],[0.1879,0.2537]	[0.654,0.7299],[0.1932,0.2577]	0.7503	0.745	0.0983	2.0907	0.9411 (0.3)	1.6241 (0.3)	8
							0.9411 (0.5)	1.6242 (0.5)
							0.9412 (0.9)	1.6242 (0.9)
A4	[0.6673,0.7448],[0.1812,0.2491]	[0.645,0.7263],[0.1972,0.2636]	0.7574	0.7388	0.0984	2.0919	0.9366 (0.3)	1.6224 (0.3)	6
							0.9417 (0.5)	1.6251 (0.5)
							0.9468 (0.9)	1.6278 (0.9)
A5	[0.7094,0.7817],[0.1499,0.2121]	[0.7037,0.7762],[0.1551,0.2169]	0.7963	0.7908	0.1044	2.2189	0.9989 (0.3)	1.7238 (0.3)	2
							0.9989 (0.5)	1.7238 (0.5)
							0.9989 (0.9)	1.7238 (0.9)
A6	[0.6697,0.7452],[0.1762,0.2417]	[0.6596,0.7365],[0.1841,0.2484]	0.7625	0.7537	0.0997	2.1199	0.9529 (0.3)	1.6468 (0.3)	1
							1.6461 (0.5)	0.9556 (0.5)
							0.9543 (0.9)	1.6476 (0.9)
A7	[0.6328,0.7134],[0.2079,0.2737]	[0.6214,0.7038],[0.2186,0.282]	0.7267	0.7159	0.0948	2.017	0.9058 (0.3)	1.5658 (0.3)	9
							0.908 (0.5)	1.567 (0.5)
							0.9102 (0.9)	1.5681 (0.9)
A8	[0.663,0.7398],[0.1904,0.2518]	[0.6617,0.7385],[0.192,0.2531]	0.7516	0.7501	0.0987	2.0996	0.9466 (0.3)	1.6319 (0.3)	7
							0.9452 (0.5)	1.6311 (0.5)
							0.9438 (0.9)	1.6304 (0.9)
A9	[0.6749,0.7522],[0.1765,0.2398]	[0.6705,0.7486],[0.1794,0.2427]	0.7652	0.7617	0.1004	2.1348	0.9617 (0.3)	1.6588 (0.3)	4
							0.961 (0.5)	1.6585 (0.5)
							0.9603 (0.9)	1.6581 (0.9)
A10	[0.6188,0.7055],[0.2141,0.2791]	[0.6116,0.6994],[0.2193,0.284]	0.7179	0.7117	0.0939	1.9989	0.8993 (0.3)	1.5526 (0.3)	10
							0.8998 (0.5)	1.5529 (0.5)
							0.9003 (0.9)	1.5531 (0.9)

Table 7. Degree of importance for criteria weights with aggregated weight mechanism coefficient $\gamma$ for sensitivity analysis

γ	C1	C2	C3	C4	C5
0.00	0.0486	0.3317	0.2905	0.2998	0.0283
0.10	0.0632	0.3189	0.2817	0.2892	0.0460
0.20	0.0777	0.3060	0.2730	0.2785	0.0636
0.30	0.0923	0.2932	0.2642	0.2678	0.0813
0.40	0.1069	0.2804	0.2555	0.2550	0.0990
0.50	0.1214	0.2676	0.2467	0.2466	0.1167
0.60	0.1360	0.2547	0.2379	0.2359	0.1343
0.70	0.1506	0.2419	0.2292	0.2253	0.1520
0.80	0.1652	0.2291	0.2204	0.2146	0.1697
0.90	0.1797	0.2162	0.2117	0.2040	0.1873
1.00	0.1943	0.2034	0.2040	0.1933	0.2050

Figure 2. Caption: Ranking order of criteria with the aggregated weight mechanism coefficient for sensitivity analysis.

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