Research on Hong Kong People’s Intentions and Behaviors of the Use of Online Loan Systems by Financial Institutions: Security, Privacy, Convenience, and Trust

This study explores how security, privacy, convenience, and trust influence the intentions and behaviors related to the use of online loan systems. It employs a ‘Triangulation Analysis Method’ that combines qualitative and quantitative research approaches. Qualitative data was collected through focus groups and literature discussions, while quantitative data was gathered using both small and large questionnaires. After reviewing the small questionnaire through channel analysis, the large questionnaire was collected using a Snowball Sampling Method. There are 268 questionnaires in total. This study utilized the SPSS system to analyze the collected data, and the results indicate that all factors influence consumers’ intentions and behaviors. Furthermore, there is a positive correlation among the various groups of variables. It is recommended that Financial Institutions take a proactive defense approach; the government needs to strengthen the regulation of online loan systems; Consumers should also raise their awareness of security and privacy. This will enhance consumer usage intention and behavior, fostering industry growth and innovation.  

Introduction

Fintech is driving ongoing innovation in global financial services, enabling consumers to manage bank accounts, make payments, borrow, and invest through the Internet and mobile apps. While fintech provides considerable convenience and new investment opportunities, it also introduces various risks and challenges. To foster industry growth and innovation, financial institutions should address these risks and develop effective strategies.

Development of Fintech Credit

Fintech Credit is rapidly evolving across many economies, transforming credit markets worldwide. It started with digital lending models and peer-to-peer (P2P) online platforms and has now advanced to big-tech credit, partnering directly with financial institutions to offer loans (Cornelliet al., 2023). Today, the United States leads the way with the highest number of top fintech companies and belongs to a leading country in financial technology (CNBC, 2023).

Status of Fintech Credit in Hong Kong

Hong Kong established its first P2P lending platform in 2014, but it remained relatively niche, accounting for only about 1% of total loans by 2020 (IFEC, 2020).

That year, virtual banks officially began operations in Hong Kong, providing financial and banking services through digital channels, with all services conducted online. This offered more flexible lending options, shorter approval times, and an online document submission process. As a result, the share of online loans in total new private loans in Hong Kong increased from about 1% in 2020 to approximately 7% in 2022, reaching around 9% by June 2023 (TU, 2023).

The market has seen significant growth, indicating that Hong Kong residents are increasingly accepting loan services offered through digital channels. This trend benefits financial innovation and creates new opportunities for financial institutions. However, the financial industry is particularly vulnerable to cyber threats, which can impact users.

This study primarily explores the factors affecting Hong Kong consumers’ intention and behavior in using online loan systems, including security, privacy, convenience, and trust (see Fig. 1).

Fig. 1. Proposed theoretical model.

The research results can be used as a reference for financial institutions, the Hong Kong government, and consumers to enhance users’ intention when using online loan systems.

Literature Review

Fintech Credit

Fintech lending is defined as all credit activities facilitated by electronic or online platforms operated by non-commercial banks. This definition encompasses all credit activities facilitated by platforms that match borrowers with lenders (investors). These platforms are referred to as “peer-to-peer (P2P) lenders,” “loan-based crowdfunding organizations,” or “marketplace lenders.” Credit activities provided by technology companies can also be included in this category (Claessenset al., 2018). Fintech lending platforms can directly offer online credit contracts without imposing strict credit standards on borrowers, thereby alleviating credit rationing (Junarsinet al., 2023).

In recent years, several prominent ‘big tech companies’ have entered the lending market, offering a new type of credit through online platforms and big data credit assessments. They collaborate directly with financial institutions or banks to provide loans to customers. ‘Big tech companies’ possess excellent monitoring and screening capabilities and can operate within regulatory frameworks that differ from those of traditional banks, allowing them to offer credit services under various conditions (Cornelliet al., 2023).

Security

In the application of fintech, customers and service providers do not need to interact face-to-face, making security and privacy issues a significant concern for customers (Nguyenet al., 2022).

Cybercrime is a major issue and threat in the world of cybersecurity. Every network system must have reliable and trustworthy security to maintain customer information trust (Ananthioet al., 2023). When transmitting sensitive data over online channels, security is one of the key barriers for consumers, as the data may be exposed or stolen by hackers (Merhiet al., 2019).

Privacy

Privacy refers to the ability to control the collection and use of both digital and non-digital information and is also defined as the right to prevent the unauthorized disclosure of personal information (Merhiet al., 2019).

In the digital age, the risks to privacy include online data sharing and the tracking and monitoring of online activities. Users may need to disclose personal information when using network systems, which can be vulnerable to attacks. Therefore, a balance must be struck between sharing information and protecting privacy (Kyytsonenet al., 2024).

Convenience

With the strong development of the internet and mobile applications, consumers expect the convenience of accessing information about products and services without restrictions (Duarteet al., 2018). Consumers want to spend the least amount of time and effort to enjoy financial services (Shankar & Jebarajakirthy, 2020).

Online convenience refers to the ability to use self-service technology, where the convenience of a website can reduce the time customers spend on processing tasks. The convenience of e-commerce is defined as customers perceiving the website as simple and user-friendly (Salehiet al., 2012).

Trust

Trust plays a particularly important role in financial services sector. Absolute transparency in communication with consumers can strengthen the trust relationship between institutions and customers (Moinet al., 2015). If a website can create a safer and more trustworthy environment, it will enhance the likelihood of customers using the site (Shariffuddinet al., 2023).

Intention

Intention is defined not only as the specific actions or behavioral tendencies that consumers may take toward a product or service provider but also includes the willingness of consumers to recommend the service provider to others and to spread favorable information about the provider. Additionally, there is an intention to make future purchases from the service provider (Zhang & Liu, 2013).

Behavior

Consumer behavior encompasses the actions and reactions of individuals in the realm of consumption (Krestyanpol, 2023). Usage behavior involves users thinking systematically and rationally based on the information they receive before taking action. The effectiveness of website services depends on customers repeatedly using them, as providers prefer ongoing engagement rather than one-time use. Consistent usage is crucial for the website’s sustainable growth (Chenet al., 2008).

Hypothesized Relationships of Factors

Relationship between Security and Intention

Hoet al. (2021) emphasize the importance of online device security to keep personal information safe from external access. (Kapooret al., 2022) found that when users trust that transactions will go as planned and their personal information will remain secure, it significantly encourages them to use the system.

• H1: Security has a positive effect on Intention.

Relationship between Privacy and Intention

In online systems, users worry about how their personal information is handled by internet service providers. They see privacy protection as part of the overall service. Improving data privacy security will encourage users to use online services (Esfahbodiet al., 2022).

• H2: Privacy has a positive effect on Intention.

Relationship between Convenience and Intention

Mudjahidin and Aristio (2022) found that online service systems help consumers complete transactions more efficiently and conveniently. Their ease of use boosts consumers’ willingness to use and recommend them to others.

• H3: Convenience has a positive effect on Intention.

Relationship between Trust and Intention

Talwaret al. (2020) research concluded that trust is important because it can increase the adoption rate of a product or service by enhancing usage intentions.

• H4: Trust has a positive effect on Intention.

Relationship between Intention and Behavior

Sheeran (2002) noted that intention can predict behavior, as it reflects a person’s motivation to act. Intention is the closest predictor of behavior. Alkhowaiter (2022) believes that potential users’ intentions regarding mobile networks can lead to actual usage.

• H5: Intention has a positive effect on Behavior.

Research Methods

Data Collection

The data collection for the research is mainly divided into two parts. The first part is the collection of secondary data; literature theories will be referenced on a qualitative and quantitative basis.

The second part is the collection of primary data. Firstly, discussed through focus groups, face-to-face individual interviews, and questionnaire design as a qualitative research method. After reviewing the small-scale questionnaires, distributing the large-scale questionnaires survey to obtain data and information as quantitative research methods.

Sampling Objects

The interviewees are Hong Kong consumers aged 18 and above who are familiar with credit services (such as credit cards and revolving loans) and understand the operation of online lending systems. The sample size is 268. Using the snowball sampling method, data will be collected through interviews and questionnaire surveys.

Questionnaire Design

The five-point Likert scale (Chenget al., 2021) was used for the design of the questionnaire. The questionnaire content of the interviewees can be seen in Table I.

Table I. Questionnaire Design

Variable type	Variable	Keywords	References	Measurement method
Independent	Security (S)	1 Ease of use
		2 Protects information	Abdennebi (2023) Hoet al. (2021)	5-point Likert Scale (Chenget al., 2021)
		3 Not share information with other sites
		4 Security of IT devices
		5 Take action to improve the security of IT devices
Independent	Privacy (P)	1 Protect the privacy	Mikuletic et al . (2023)
		2 Respect the privacy	Bajunaied et al . (2023)
		3 Respect the right to privacy
		4 Giving personal information
		5 Clearly explains how information is used
Independent	Convenience (C)	1 Not restricted by business opening hours	Xu et al . (2019)
		2 Not restricted by location	Berry et al . (2002)
		3 Procedure is relatively simple
		4 Quickly
		5 Easily
Independent	Trust (T)	1 Trustworthy	Abdennebi (2023)
		2 Keeps its promises	Cheah et al . (2022)
		3 Reliable
		4 Products are dependable
		5 Service are dependable
Independent dependent	Intention (I)	1 Willing to use	Xu et al . (2019)
		2 Will continue to use
		3 Willing to recommend other people to use
Dependent	Behavior (B)	1 Often use	Xu et al . (2019)
		2 Often use repeatedly
		3 Often recommend to other people
Consumer characteristic	Gender	Male | Female	Hong Kong Census and Statistics Department (2021)
	Age	18–25 | 26–35| 36–45 | 46–55 | 56–65 | 65 or above
	Education level	Primary school or below | Middle school or College | Undergraduate or above	Wu (2003)
	Income (HK$)	10,000 or below | 10,001−20,000 | 20,001−30,000 | 30,001−40,000 | 40,001 or above
	Favorite application methods	Online | Telephone | Salesperson | Physical store | Others

Results

Descriptive Analysis

Descriptive analysis serves to characterize the collected data, offering an overview of both the data and sample characteristics. In this study, the descriptive analysis is divided into two parts: the first focuses on the qualitative data gathered from the focus group, while the second examines the quantitative data derived from the questionnaire.

Focus Groups

In this study, a focus group of 6 people was established, with members from different industries, including finance, securities, regulatory staff, healthcare, civil servants, and information technology. Discuss the factors that affecting Hong Kong people’s intention and behavior to use online loan systems. Five of the six respondents believed that “security” was a factor affecting usage intention, amounting to 83.3% of the total number of respondents; All six respondents believed that “privacy” was a factor affecting usage intention, amounting to 100% of the total number of respondents; all six respondents believed that “convenience” was a factor affecting usage intention, amounting to 100% of the total number of respondents; four of the six respondents believed that “trust” was a factor affecting usage intention, amounting to 66.7% of the total number of respondents.

Questionnaire

The age, gender, education level, income, and favorite application methods of the respondents are in the questionnaire survey (Table II).

Table II. Summary of Respondent Characteristics (N = 268)

Respondent characteristic	Category	N	%
Age	18–25 years old	25	9.3%
	26–35 years old	60	22.4%
	36–45 years old	1054	39.2%
	46–55 years old	47	17.5%
	56–65 years old	25	9.3%
	65 years old or above	6	2.2%
Gender	Male	141	52.6%
	Female	127	47.4%
Education level	Middle school or below	97	36.2%
	College	72	26.9%
	Undergraduate or above	99	36.9%
Income (HK$)	10,000 or less	11	4.1%
	10,001–20,000	59	22.0%
	20,001–30,000	84	31.3%
	30,001–40,000	54	20.2%
	40,001 or above	60	22.4
Favorite application methods	Online	103	38.4%
	Telephone	54	20.2%
	Salesperson	47	17.5%
	Physical store	48	17.9%
	Others	16	6.0%

Reliability Analysis

In this study, Cronbach’s coefficient alpha of at least 0.7 is considered acceptable reliability (Praveen & Donald, 1997). The Cronbach’s alpha coefficients are collected by small-scale questionnaires and large-scale questionnaires, 0.943 and 0.951, respectively, in this study, which are greater than the typical coefficient of 0.7. The results in Table III show that all variables have values between 0.705 and 0.931. The reliability of this study was very high indicating that the internal consistency was “acceptable” to “excellent.”

Table III. Reliability Detection Coefficient Values

Variables	Cronbach’s alpha
Security (S)	0.705
Privacy (P)	0.874
Convenience (C)	0.857
Trust (T)	0.931
Intention (I)	0.892
Behavior	0.853
Data collection methods	Collection quantity	Cronbach’s alpha
Small-scale questionnaires	40	0.943
Large-scale questionnaires	268	0.951

Correlation Analysis

After carrying out a correlation analysis, it is concluded that the Pearson coefficient value of the relationship between security and intention, the relationship between privacy and intention, the relationship between convenience and intention, the relationship between trust and intention, and the coefficient value of the relationship between intention and behavior are 0.656, 0.652, 0.562, 0.676, and 0.698, respectively (Table IV).

Table IV. Pearson Correlations between Variables (N = 268)

Hypothesis	Variables	Correlation (r)	Strength of correlation
H1	SI	0.656**	Moderate
H2	BI	0.652**	Moderate
H3	CI	0.562**	Moderate
H4	TI	0.676**	Moderate
H5	IB	0.698**	Moderate

The analysis results show that the relationship between each group of variables is positively correlated. The Pearson coefficient value is in the range from 0.562 to 0.698, which shows a moderate positive correlation. The P values between the five variables of security, privacy, convenience, trust, behavior, and “intention” are all below the significance level of 0.01. The confidence level indicating the correlation is above 99%.

Analysis Results

According to the descriptive analysis in this chapter, it is concluded that security, privacy, convenience, and trust are all important factors affecting usage intention. The reliability analysis shows that the internal consistency of all variables is very high.

In the correlation analysis, all the hypothesized relationships among the factors set in this study were confirmed. There are significant positive relationships between security, privacy, convenience, trust, and behavior with intention (see Fig. 2).

Fig. 2. Theoretical model with analysis results. Note: ^** = p < 0.001 for correlations analysis.

Conclusions and Recommendations

Conclusions

This study conducted focus group discussions, where the members believed that security, privacy, convenience, and trust were important factors affecting usage intention. The results of the reliability analysis showed that the values of all variables were between 0.705 to 0.931, with an overall reliability of 0.951 (Table IV), indicating high reliability for this study, with internal consistency reaching levels from ‘acceptable’ to ‘excellent.’ The Pearson correlation coefficients among the factors in this study indicate significant positive relationships between each group of variables.

Recommendations

Suggestions for Measures to Enhance the Security of the Loan System

Through the correlation analysis, security has a significant impact on usage intention. Financial institutions should adopt a proactive security method called Zero Trust Architecture (Roseet al., 2020). This approach continuously verifies users before granting access to applications and data. It allows for repeated checks when customers open accounts or apply for loans, reducing the risk of false information being sent to backend servers and enhancing customer data security.

Additionally, along with large banks, all licensed lenders with online application systems should follow the Cybersecurity Fortification Initiative (CFI) (HKMA, 2020) guidelines. These institutions must strictly comply with these requirements, ensuring they have robust cybersecurity systems, improve defenses against cyber-attacks, and raise security standards to protect customer data.

It is recommended that consumers also enhance the security of their devices by regularly changing passwords and downloading reliable antivirus software (HKCERT, 2023).

Suggestions for Establishing User Privacy Protection Measures

Through the correlation analysis, privacy has a significant impact on usage intention. When applying for a loan, consumers must provide sensitive personal and financial information. If this data is stolen, it can lead to attacks on their financial accounts, resulting in losses. This risk may deter potential customers who are interested in loans but unsure about using the system. To improve this, financial institutions should verify basic information through the loan system before customers submit documents. This allows customers to simulate an application and receive preliminary approval, enhancing their experience and encouraging more to try the loan system (Marriottet al., 2015).

However, human error is a major cause of data loss. Consumers are advised to actively protect their personal data and photos following the guidelines from the Office of the Privacy Commissioner for Personal Data in Hong Kong (PCPD, 2023).

Suggestions for Optimizing the Loan Process and Enhancing Convenience

The correlation analysis found that respondents believe convenience has a significant impact on usage intention. The rapid growth of fintech and e-commerce has transformed how consumers manage their finances and conduct transactions. As a result, website functionality and easy access to information are essential (Almarashdehet al., 2019). The loan application process, however, requires applicants to provide extensive personal information and follow multiple steps. Financial institutions should clearly list the required documents and steps before applicants start the process to prevent delays. For verified customers or those who have used the service before, offering pre-approved loan amounts and interest rates would allow for easy withdrawals. Additionally, implementing a loan simulation system would help both new and existing customers familiarize themselves with the loan process.

Suggestions for Increasing User Trust in the Loan System

The correlation analysis also found that respondents believe trust has a significant impact on usage intention. Consumer trust is essential in online activities, especially in financial services. Many consumers prefer traditional banks for loan applications because of their long-term reliability. However, there is a growing trend toward using mobile services (Chenget al., 2022). Financial institutions should enhance their security and privacy measures. The Hong Kong government should also implement strict cybersecurity guidelines for lenders and establish relevant legislation to build consumer trust in fintech. When consumers view online loan systems as trustworthy and reliable, they are more likely to use these services (Gao & Waechter, 2017).

Conflict of Interest

The authors declare that they do not have any conflict of interest.