Sustainable Development of Fintech: Focused on Uncertainty and Perceived Quality Issues
Abstract
:1. Introduction
2. Theoretical Background
2.1. Fintech Viewpoint
2.2. Trust and Perceived Risk
2.3. IT Quality
3. Research Model and Hypotheses
4. Research Methodologies
4.1. Measurement Development
4.2. Data Collection
5. Analysis and Results
5.1. Measurement Model
5.2. Structural Model
6. Discussions and Implications
6.1. Key Findings
6.2. Managerial Implications
6.3. Limitations and Future Research Directions
Author Contributions
Funding
Conflicts of Interest
Appendix A
Constructs | ID | Questionnaire | Reference |
---|---|---|---|
Perceived risk (PR) | PR1 | Using Fintech has many unexpected problems. | [16,30] |
PR2 | Using Fintech has high uncertainty in respect of legal issues | ||
PR3 | Overall, there is a higher potential for loss in using Fintech than using traditional financial services. | ||
Trust (TR) | TR1 | Fintech is secure in conducting its transaction. | [49,51] |
TR2 | Fintech is reliable in conducting its transactions. | ||
TR3 | Overall, Fintech is trustworthy. | ||
System quality (STQ) | STQ 1 | Fintech systems are easy to use. | [13,43] |
STQ 2 | Fintech systems can be accessed immediately. | ||
STQ 3 | Fintech systems enable me to accomplish my financial transactions. | ||
SYQ 4 | Fintech systems provide helpful functions for my financial transactions. | ||
Information quality (IFQ) | IFQ 1 | Information provided by Fintech systems is accurate. | [13,43] |
IFQ 2 | Information provided by Fintech systems is up to date. | ||
IFQ 3 | Information provided by Fintech systems is easy to understand. | ||
IFQ 4 | Information provided by Fintech systems meets my needs. | ||
Service quality (SVQ) | SVQ 1 | Fintech service quickly responds to my needs. | [13,43] |
SVQ 2 | Fintech service has the knowledge to answer my questions. | ||
SVQ 3 | Fintech service understands my specific needs. | ||
SVQ 4 | Fintech service is always willing to help me. | ||
Continuance intention (CI) | CI1 | I would positively consider Fintech in my choice set. | [48,51] |
CI2 | I would prefer Fintech. | ||
CI3 | I would intend to continue to use Fintech. | ||
CI4 | I will use Fintech in the future. |
References
- Deng, X.; Cheng, X. Can ESG indices improve the enterprises’ stock market performance? An empirical study from China. Sustainability 2019, 11, 4765. [Google Scholar] [CrossRef] [Green Version]
- Zavolokina, L.; Dolata, M.; Schwabe, G. Fintech-What’s in a Name? In Proceedings of the 37th International Conference on Information Systems (ICIS 2016), Dublin, Ireland, 11–14 December 2016; pp. 1–19. [Google Scholar]
- KPMG 2019. The Pulse of Fintech 2018. KPMG. Available online: https://assets.kpmg/content/dam/kpmg/xx/pdf/2018/07/h1–2018-pulse-of-fintech.pdf (accessed on 1 November 2019).
- Cortina, J.J.; Schmukler, S.L. The Fintech Revolution: A Threat to Global Banking; World Bank: Washington, DC, USA, 2018. [Google Scholar]
- Ryu, H.S. What makes users willing or hesitant to use Fintech?: The moderating effect of user type. Ind. Manag. Data Syst. 2018, 118, 541–569. [Google Scholar] [CrossRef]
- Sobehart, J.R. The FinTech revolution: Quantifying earnings uncertainty and credit risk in competitive business environments with disruptive technologies. J. Risk Manag. Financ. Inst. 2016, 9, 163–174. [Google Scholar]
- Namahoot, K.S.; Laohavichien, T. Assessing the intentions to use internet banking: The role of perceived risk and trust as mediating factors. Int. J. Bank Mark. 2018, 36, 256–276. [Google Scholar] [CrossRef]
- Nicolaou, A.I.; McKnight, D.H. Perceived information quality in data exchanges: Effects on risk, trust, and intention to use. Inf. Syst. Res. 2006, 17, 332–351. [Google Scholar] [CrossRef]
- Song, H.-L. Customer adoption of Internet banking: An integration of TAM with trust, perceived risk, and quality. In Proceedings of the 2010 International Conference on Multimedia Information Networking and Security, Nanjing, Jiangsu, China, 4–6 November 2010; pp. 264–268. [Google Scholar]
- Arner, D.W.; Barberis, J.N.; Buckley, R.P. The Evolution of Fintech: A New Post-Crisis Paradigm; University of Hong Kong: Hong Kong, China, 2015; pp. 1–46. [Google Scholar]
- Shin, J.S.; Choi, Y. Feasibility of the Fintech Industry as an Innovation Platform for sustainable economic growth in Korea. Sustainability 2019, 11, 5351. [Google Scholar] [CrossRef] [Green Version]
- Ernst and Young. Landscaping UK Fintech. UK Trade & Investment. 2015, 1, 1–20. [Google Scholar]
- Delone, W.H.; McLean, E.R. The DeLone and McLean model of information systems success: A ten-year update. J. Manag. Inf. Syst. 2003, 19, 9–30. [Google Scholar]
- Gomber, P.; Kauffman, R.J.; Parker, C.; Weber, B.W. On the fintech revolution: Interpreting the forces of innovation, disruption, and transformation in financial services. J. Manag. Inf. Syst. 2018, 35, 220–265. [Google Scholar] [CrossRef]
- Demirgüç-Kunt, A.; Kane, E.; Laeven, L. Deposit insurance around the world: A comprehensive analysis and database. J. Financ. Stab. 2015, 20, 155–183. [Google Scholar] [CrossRef]
- Kim, D.J.; Ferrin, D.L.; Rao, H.R. A trust-based consumer decision-making model in electronic commerce: The role of trust, perceived risk, and their antecedents. Dec. Supp. Syst. 2008, 44, 544–564. [Google Scholar] [CrossRef]
- Pratono, A.H. From social network to firm performance: The mediating effect of trust, selling capability and pricing capability. Manag. Res. Rev. 2018, 41, 680–700. [Google Scholar] [CrossRef]
- Wu, C.-C.; Huang, Y.; Hsu, C.-L. Benevolence trust: A key determinant of user continuance use of online social networks. Inf. Syst. e-Bus. Manag. 2014, 12, 189–211. [Google Scholar] [CrossRef]
- Marriott, H.R.; Williams, M.D. Exploring consumers perceived risk and trust for mobile shopping: A theoretical framework and empirical study. J. Retail. Consum. Serv. 2018, 42, 133–146. [Google Scholar] [CrossRef] [Green Version]
- Yang, S. Role of transfer-based and performance-based cues on initial trust in mobile shopping services: A cross-environment perspective. Inf. Syst. e-Bus. Manag. 2016, 14, 47–70. [Google Scholar] [CrossRef]
- Sharma, S.K.; Sharma, M. Examining the role of trust and quality dimensions in the actual usage of mobile banking services: An empirical investigation. Int. J. Inf. Manag. 2019, 44, 65–75. [Google Scholar] [CrossRef]
- Zhou, T. Understanding users’ initial trust in mobile banking: An elaboration likelihood perspective. Comput. Hum. Behav. 2012, 28, 1518–1525. [Google Scholar] [CrossRef]
- Gao, L.; Waechter, K.A. Examining the role of initial trust in user adoption of mobile payment services: An empirical investigation. Inf. Syst. Front. 2017, 19, 525–548. [Google Scholar] [CrossRef]
- Yan, H.; Yang, Z. Examining mobile payment user adoption from the perspective of trust. Int. J. u-and e-Serv. Sci. Tech. 2015, 8, 117–130. [Google Scholar] [CrossRef]
- Pavlou, P.A. Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. Int. J. Electron. Commer. 2003, 7, 101–134. [Google Scholar]
- Pavlou, P.A.; Gefen, D. Building effective online marketplaces with institution-based trust. Inf. Syst. Res. 2004, 15, 37–59. [Google Scholar] [CrossRef] [Green Version]
- Groß, M. Impediments to mobile shopping continued usage intention: A trust-risk-relationship. J. Retail. Consum. Serv. 2016, 33, 109–119. [Google Scholar] [CrossRef]
- Luo, X.; Li, H.; Zhang, J.; Shim, J.P. Examining multi-dimensional trust and multi-faceted risk in initial acceptance of emerging technologies: An empirical study of mobile banking services. Dec. Supp. Syst. 2010, 49, 222–234. [Google Scholar] [CrossRef]
- Slade, E.L.; Dwivedi, Y.K.; Piercy, N.C.; Williams, M.D. Modeling consumers’ adoption intentions of remote mobile payments in the United Kingdom: Extending UTAUT with innovativeness, risk, and trust. Psy. Mark. 2015, 32, 860–873. [Google Scholar] [CrossRef]
- Stewart, H.; Jürjens, J. Data security and consumer trust in FinTech innovation in Germany. Inf. Comput. Secur. 2018, 26, 109–128. [Google Scholar] [CrossRef]
- Wang, E.S.-T.; Lin, R.-L. Perceived quality factors of location-based apps on trust, perceived privacy risk, and continuous usage intention. Behav. Inf. Tech. 2017, 36, 2–10. [Google Scholar] [CrossRef]
- Mayer, R.C.; Davis, J.H.; Schoorman, F.D. An integrative model of organizational trust. Acad. Manag. Rev. 1995, 20, 709–734. [Google Scholar] [CrossRef]
- Hsieh, M.-T.; Tsao, W.-C. Reducing perceived online shopping risk to enhance loyalty: A website quality perspective. J. Risk Res. 2014, 17, 241–261. [Google Scholar] [CrossRef]
- Forsythe, S.; Liu, C.; Shannon, D.; Gardner, L.C. Development of a scale to measure the perceived benefits and risks of online shopping. J. Inter. Mark. 2006, 20, 55–75. [Google Scholar] [CrossRef]
- Forsythe, S.M.; Shi, B. Consumer patronage and risk perceptions in Internet shopping. J. Bus. Res. 2003, 56, 867–875. [Google Scholar] [CrossRef]
- Lim, N. Consumers’ perceived risk: Sources versus consequences. Electron. Commer. Res. Appl. 2003, 2, 216–228. [Google Scholar] [CrossRef]
- DeLone, W.H.; McLean, E.R. Information systems success: The quest for the dependent variable. Inf. Syst. Res. 1992, 3, 60–95. [Google Scholar] [CrossRef]
- Zhou, T. An empirical examination of continuance intention of mobile payment services. Dec. Supp. Syst. 2013, 54, 1085–1091. [Google Scholar] [CrossRef]
- Hsu, M.-H.; Chang, C.-M.; Chu, K.-K.; Lee, Y.-J. Determinants of repurchase intention in online group-buying: The perspectives of DeLone & McLean IS success model and trust. Comput. Hum. Behav. 2014, 36, 234–245. [Google Scholar]
- Wang, W.-T.; Wang, Y.-S.; Liu, E.-R. The stickiness intention of group-buying websites: The integration of the commitment–trust theory and e-commerce success model. Inf. Manag. 2016, 53, 625–642. [Google Scholar] [CrossRef]
- McKnight, D.H.; Lankton, N.K.; Nicolaou, A.; Price, J. Distinguishing the effects of B2B information quality, system quality, and service outcome quality on trust and distrust. J. Strateg. Info. Syst. 2017, 26, 118–141. [Google Scholar] [CrossRef]
- Zheng, Y.; Zhao, K.; Stylianou, A. The impacts of information quality and system quality on users’ continuance intention in information-exchange virtual communities: An empirical investigation. Dec. Supp. Syst. 2013, 56, 513–524. [Google Scholar] [CrossRef]
- Wang, Y.S. Assessing e-commerce systems success: A respecification and validation of the DeLone and McLean model of IS success. Inf. Syst. J. 2008, 18, 529–557. [Google Scholar] [CrossRef]
- McKnight, D.H.; Choudhury, V.; Kacmar, C. The impact of initial consumer trust on intentions to transact with a web site: A trust building model. J. Strateg. Inf. Syst. 2002, 11, 297–323. [Google Scholar] [CrossRef]
- Oliveira, T.; Faria, M.; Thomas, M.A.; Popovič, A. Extending the understanding of mobile banking adoption: When UTAUT meets TTF and ITM. Int. J. Inf. Manag. 2014, 34, 689–703. [Google Scholar] [CrossRef]
- Petter, S.; DeLone, W.; McLean, E. Measuring information systems success: Models, dimensions, measures, and interrelationships. Eur. J. Inf. Syst. 2008, 17, 236–263. [Google Scholar] [CrossRef]
- Lee, K.C.; Chung, N. Understanding factors affecting trust in and satisfaction with mobile banking in Korea: A modified DeLone and McLean’s model perspective. Interact. Comput. 2009, 21, 385–392. [Google Scholar] [CrossRef]
- Chen, C. Perceived risk, usage frequency of mobile banking services. Int. J. Manag. Serv. Qual. 2013, 23, 410–436. [Google Scholar] [CrossRef]
- Featherman, M.S.; Pavlou, P.A. Predicting e-services adoption: A perceived risk facets perspective. Int. J. Hum. Comput. Stud. 2003, 59, 451–474. [Google Scholar] [CrossRef] [Green Version]
- Bharati, P.; Chaudhury, A. An empirical investigation of decision-making satisfaction in web-based decision support systems. Dec. Supp. Syst. 2004, 37, 187–197. [Google Scholar] [CrossRef]
- Lee, M.C. Factors influencing the adoption of internet banking: An integration of TAM and TPB with perceived risk and perceived benefit. Electron. Commer. Res. Appl. 2009, 8, 130–141. [Google Scholar] [CrossRef]
- Abramova, S.; Böhme, R. Perceived benefit and risk as multidimensional determinants of Bitcoin use: A quantitative exploratory study. In Proceedings of the 37th International Conference on Information Systems (ICIS 2016), Dublin, Ireland, 11–14 December 2016; pp. 1–20. [Google Scholar]
- Lee, S.-G.; Chae, S.H.; Cho, K.M. Drivers and inhibitors of SaaS adoption in Korea. Int. J. Inf. Manag. 2013, 33, 429–440. [Google Scholar] [CrossRef]
- Dapp, T.F.; Slomka, L.; AG, D.B.; Hoffmann, R. Fintech–The digital (r) evolution in the financial sector. Deutsche Bank Res. 2014, 11, 1–37. [Google Scholar]
- Gefen, D.; Straub, D.W. Gender differences in the perception and use of e-mail: An extension to the technology acceptance model. MISQ 1997, 21, 389–400. [Google Scholar] [CrossRef] [Green Version]
- Venkatesh, V.; Morris, M.G.; Davis, G.B.; Davis, F.D. User acceptance of information technology: Toward a unified view. MISQ. 2003, 27, 425–478. [Google Scholar] [CrossRef] [Green Version]
- Kuo Chuen, D.L.; Teo, E.G. Emergence of FinTech and the LASIC principles. J. Financ. Perspect. 2015, 3, 24–36. [Google Scholar]
- Gulamhuseinwala, I.; Bull, T.; Lewis, S. FinTech is gaining traction and young, high-income users are the early adopters. J. Financ. Perspec. 2015, 3, 1–21. [Google Scholar]
- Chin, W.W. Commentary: Issues and opinion on structural equation modeling. MISQ 1998, 22, vii–xvi. [Google Scholar]
- Gefen, D.; Straub, D.; Boudreau, M.-C. Structural equation modeling and regression: Guidelines for research practice. Commun. Assoc. Inf. Syst. 2000, 4, 7. [Google Scholar] [CrossRef] [Green Version]
- Straub, D.; Boudreau, M.-C.; Gefen, D. Validation guidelines for IS positivist research. Commun. Assoc. Inf. Syst. 2004, 13, 24. [Google Scholar] [CrossRef]
- Fornell, C.; Larcker, D.F. Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 1981, 18, 39–50. [Google Scholar] [CrossRef]
- Podsakoff, P.M.; MacKenzie, S.B.; Lee, J.-Y.; Podsakoff, N.P. Common method biases in behavioral research: A critical review of the literature and recommended remedies. J. Appl. Psy. 2003, 88, 879–903. [Google Scholar] [CrossRef]
- Melewar, T.; Alwi, S.; Tingchi Liu, M.; Brock, J.L.; Cheng Shi, G.; Chu, R.; Tseng, T.-H. Perceived benefits, perceived risk, and trust: Influences on consumers’ group buying behaviour. Asia Pacific J. Mark. Logis. 2013, 25, 225–248. [Google Scholar]
- Sobel, M.E. Asymptotic confidence intervals for indirect effects in structural equation models. Soc. Method. 1982, 13, 290–312. [Google Scholar] [CrossRef]
- Xu, J.; Benbasat, I.; Cenfetelli, R.T. Integrating service quality with system and information quality: An empirical test in the e-service context. MIS Q. 2013, 37, 777–794. [Google Scholar] [CrossRef]
- Mitchell, V.-W. Consumer perceived risk: Conceptualisations and models. Eur. J. Mark. 1999, 33, 163–195. [Google Scholar] [CrossRef]
(a) Gender | (b) Fintech Type | ||||
---|---|---|---|---|---|
Gender | Frequency | Percent | Fintech Type | Frequency | Percent |
Male | 98 | 45.0% | Mobile payment | 63 | 28.9% |
Female | 120 | 55.0% | Mobile remittance | 58 | 26.6% |
P2P lending | 44 | 20.2% | |||
Crowdfunding | 53 | 24.3% | |||
Total | 218 | 100% | Total | 218 | 100% |
(c) Age | (d) Education | ||||
Range | Frequency | Percent | Range | Frequency | Percent |
Under 20 | 0 | 0% | Under high school | 1 | 0.5% |
20–29 | 48 | 22.0% | High school | 25 | 11.5% |
30–39 | 53 | 24.3% | College/associate | 37 | 17.0% |
40–49 | 66 | 30.3% | Bachelor | 129 | 59.2% |
50 over | 51 | 23.4% | Master | 24 | 11.0% |
PhD | 2 | 0.9% | |||
Total | 218 | 100% | Total | 218 | 100% |
(e) Period of Use | (f) Frequency of Use | ||||
Range | Frequency | Percent | Range | Frequency | Percent |
~3 months | 81 | 37.2% | Daily | 1 | 0.5% |
~6 months | 52 | 23.9% | Weekly | 60 | 27.5% |
~12 months | 34 | 15.6% | Monthly | 78 | 35.8% |
~18 months | 10 | 4.6% | Every 3 months | 40 | 18.3% |
~24 months | 15 | 6.9% | Every 6 months | 20 | 9.2% |
≥ 24 months | 26 | 11.9% | Once 1 year or less | 12 | 5.5% |
Once 2 year or less | 7 | 3.2% | |||
Total | 218 | 100% | Total | 218 | 100% |
Construct | Item | Cronbach’s Alpha | Composite Reliability | Average Variance Extracted | Loading | T-Statistic |
---|---|---|---|---|---|---|
Perceived risk | PR1 | 0.802 | 0.879 | 0.709 | 0.821 ** | 17.629 |
PR2 | 0.815 ** | 17.590 | ||||
PR3 | 0.887 ** | 56.008 | ||||
Trust | TR1 | 0.804 | 0.885 | 0.721 | 0.775 ** | 20.668 |
TR2 | 0.906 ** | 61.397 | ||||
TR3 | 0.861 ** | 24.920 | ||||
Perceived benefit | PB1 | 0.824 | 0.895 | 0.739 | 0.864 ** | 31.174 |
PB2 | 0.884 ** | 47.801 | ||||
PB3 | 0.831 ** | 27.837 | ||||
System quality | STQ1 | 0.881 | 0.919 | 0.740 | 0.910 ** | 78.111 |
STQ2 | 0.896 ** | 43.111 | ||||
STQ3 | 0.863 ** | 33.691 | ||||
STQ4 | 0.765 ** | 19.326 | ||||
Information quality | IFQ1 | 0.865 | 0.908 | 0.711 | 0.865 ** | 48.757 |
IFQ2 | 0.847 ** | 35.537 | ||||
IFQ3 | 0.812 ** | 14.724 | ||||
IFQ4 | 0.848 ** | 36.574 | ||||
Service quality | SVQ1 | 0.893 | 0.926 | 0.757 | 0.868 ** | 33.623 |
SVQ2 | 0.878 ** | 38.947 | ||||
SVQ3 | 0.872 ** | 33.095 | ||||
SVQ4 | 0.862 ** | 31.880 | ||||
Continuance intention | CI1 | 0.899 | 0.929 | 0.767 | 0.887 ** | 51.416 |
CI2 | 0.853 ** | 29.536 | ||||
CI3 | 0.896 ** | 50.815 | ||||
CI4 | 0.867 ** | 38.177 |
Construct | Mean (SD) | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
---|---|---|---|---|---|---|---|---|
1. Perceived risk | 3.754 (0.953) | 0.842 | ||||||
2. Trust | 4.344 (0.772) | −0.321 * | 0.849 | |||||
3. Perceived benefit | 5.372 (0.945) | −0.356 * | 0.301 * | 0.860 | ||||
4. System quality | 5.140 (0.922) | −0.415 * | 0.412 * | 0.626 ** | 0.860 | |||
5. Information quality | 4.891 (0.806) | −0.361 * | 0.555 * | 0.588 * | 0.769 * | 0.843 | ||
6. Service quality | 4.811 (0.839) | −0.420 * | 0.529 * | 0.482 * | 0.663 * | 0.785 * | 0.870 | |
7. Continuance intention | 4.687 (0.835) | −0.446 * | 0.587 * | 0.510 ** | 0.579 ** | 0.662 * | 0.714 * | 0.876 |
Mediated Path | Path Coefficient | Standard Error | z-Value | p-Value |
---|---|---|---|---|
System quality→Trust→Continuance intention | −0.057 0.341 | 0.096 0.057 | −0.591 | 0.555 |
System quality→Prisk→Continuance intention | −0.313 −0.169 | 0.098 0.059 | −2.132 ** | 0.033 |
Information quality→Trust→Continuance intention | 0.410 0.248 | 0.101 0.060 | 2.896 *** | 0.002 |
Information quality→Prisk→Continuance intention | 0.158 −0.180 | 0.122 0.059 | −1.192 | 0.233 |
Service quality→Trust→Continuance intention | 0.240 0.242 | 0.100 0.055 | 2.107 ** | 0.035 |
Service quality→Prisk→Continuance intention | −0.273 −0.123 | 0.092 0.053 | 1.828 * | 0.068 |
Path | Direct Effect | Indirect Effect | Total Effect |
---|---|---|---|
Prisk→Continuance intention | −0.213 *** | - | −0.213 *** |
Trust→Continuance intention | 0.398 *** | 0.029 | 0.427 *** |
Trust→Prisk | −0.138 *** | - | −0.138 *** |
System quality→Trust | −0.058 | - | −0.058 |
System quality→Prisk | −0.315 *** | 0.008 | −0.307 *** |
System quality→Continuance intention | - | 0.042 | 0.042 |
Information quality→Trust | 0.415 *** | - | 0.415 ** |
Information quality→Prisk | 0.153 | −0.057 | 0.096 |
Information quality→Continuance intention | - | 0.144 ** | 0.144 ** |
Service quality→Trust | 0.241 ** | - | 0.241 ** |
Service quality→Prisk | −0.273 *** | −0.033 | −0.305 ** |
Service quality→Continuance intention | - | 0.161 *** | 0.161 *** |
Mediated Path | Hypotheses | Type of Eediated Eath | Test Eesults |
---|---|---|---|
System quality→Trust→Continuance intention | H10 | (insignificant) | Not supported |
System quality→Prisk→Continuance intention | H11 | Fully mediated | Not supported |
Information quality→Trust→Continuance intention | H12 | Partially mediated | Supported |
Information quality→Prisk→Continuance intention | H13 | (insignificant) | Not supported |
Service quality→Trust→Continuance intention | H14 | Partially mediated | Supported |
Service quality→Prisk→Continuance intention | H15 | Partially mediated | Supported |
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Ryu, H.-S.; Ko, K.S. Sustainable Development of Fintech: Focused on Uncertainty and Perceived Quality Issues. Sustainability 2020, 12, 7669. https://doi.org/10.3390/su12187669
Ryu H-S, Ko KS. Sustainable Development of Fintech: Focused on Uncertainty and Perceived Quality Issues. Sustainability. 2020; 12(18):7669. https://doi.org/10.3390/su12187669
Chicago/Turabian StyleRyu, Hyun-Sun, and Kwang Sun Ko. 2020. "Sustainable Development of Fintech: Focused on Uncertainty and Perceived Quality Issues" Sustainability 12, no. 18: 7669. https://doi.org/10.3390/su12187669
APA StyleRyu, H. -S., & Ko, K. S. (2020). Sustainable Development of Fintech: Focused on Uncertainty and Perceived Quality Issues. Sustainability, 12(18), 7669. https://doi.org/10.3390/su12187669