Research on user satisfaction of third-party mobile payment under the IoT environment—

Abstract

This paper establishes a theoretical model of third-party mobile payment user satisfaction based on the ASCI model and proposes research hypotheses. Then, the questionnaire survey method is used to collect sample data and carry out reliability and validity test. After the test is passed, structural equation modeling (SEM) is used to verify the research model and hypothesis. Finally, the analysis concludes: perceived quality and perceived value will have a positive impact on user satisfaction; user expectations will affect user satisfaction through perceived quality and perceived value; the impact of perceived risk on user satisfaction is not obvious. Propose reasonable suggestions for third-party mobile payment companies based on the research conclusions.

Keywords

Third-party mobile payment internet of things structural equation model (SEM)

1 Introduction

According to the 44th “Statistical Report on China’s Internet Development Status” released by CINNC, as of June 2019, the number of mobile Internet users in China reached 847 million, and the number of mobile payment users reached 621 million. The huge scale of netizens and the rapidly developing mobile e-commerce provide a huge market for third-party mobile payments. According to the “Overall Operation of the Payment System in 2019” issued by the Central Bank, in 2019, non-bank payment institutions had 7199.98 billion online payment transactions with an amount of 249.88 trillion yuan, a year-on-year increase of 35.69% and 20.10% respectively [1]. Although the development of third-party mobile payment is obvious to all, and it has immeasurable potential. However, people still have some concerns about this new payment method. In recent years, security issues such as hacking, telecommunications fraud, and user information leakage have caused users to question: Is third-party mobile payment really safe [2]? From the perspective of the enterprise, if these problems are not effectively resolved, it will inevitably affect user satisfaction greatly. Therefore, this research focuses on exploring the factors that affect the satisfaction of third-party mobile payment users, which is convenient for companies to improve the services and functions of existing products, and not only protects users’ own rights and interests but also helps companies to get ahead in the future development.

1.1 Related concepts of third-party mobile payment

At present, the academic circles explain the concept of “third-party payment” in a relatively general manner. Summarizing the existing research results, third-party payment generally refers to the way that users connect with bank payment and settlement interfaces through an independent platform with a certain scale and high reputation guarantee, so as to realize the payment method of capital transactions. Third-party payment has established a bridge between online sellers, consumers and banks, and has ensured the safety of funds to a certain extent, which is equivalent to acting as a “guarantor” [3]. As the name suggests, mobile payment is the way users use portable mobile devices (such as mobile phones, electronic watches, etc.) to make fund payments. When a user uses a mobile device to make a payment, he will directly or indirectly send instructions to the bank to realize the “instant transfer” of funds and successfully complete the payment. Third-party mobile payment belongs to both third-party payment and mobile payment, which is a combination of the two. The relationship between the three is shown in Fig. 1.

Fig. 1

The relationship between third-party payment, mobile payment and third-party mobile payment.

1.2 Internet of things

Since the concept of the Internet of Things (IoT) was proposed in 1999, the Internet of Things has received extensive attention from industry and academia. The Internet of Things in a narrow sense refers to the fact that by embedding information sensing devices such as radio frequency identification (RFID) in objects, through wireless communication technology, objects in the real world are connected to the Internet to form an intelligent network [4, 5].

The Internet of Things in a broad sense refers to the expansion of the connection between people in the Internet era to between people, things and things, and people and things through wireless networks, telecommunication networks and the Internet [6, 7].

The architecture of the Internet of Things is divided into three layers, namely: Internet of Things perception layer, Internet of Things network layer and Internet of the Things application layer. The specific working path is as follows: the sensor forms a wireless sensor local area network through wireless communication technology, and the sensor network is connected to the Internet through wireless or wired communication technology [8]. Individuals at the other end of the Internet can access and control the wireless sensor network and its devices through personal computers or mobile phones and other terminal devices to realize the connection between people and things [9]. With the popularization of sensor applications, IoT technology has been widely used in industrial manufacturing, logistics, smart transportation, medical and health, environmental monitoring, security, smart home and other fields.

The Internet of Things technology has promoted the development of informatization in various fields such as industry, military, and civilian use, and has set off the fourth wave of revolution in the information industry. The data types that can be used in the Internet of Things are also extended from static web pages, documents, audio, and video in the traditional Internet to web pages, documents, audio, video, and dynamic multi-dimensional data of people and things [10, 11]

2 Research model and hypothesis

2.1 Construction of research model

After consulting relevant materials, it is known that the satisfaction model widely used by scholars is the American Customer Satisfaction Index (ACSI) model established in 1994. The model has six structural variables, namely, Customer Expectations, Perceived Quality, Perceived Value, Customer Satisfaction, Customer Complaints, Customer Loyalty. Among them, “customer expectations", “perceived quality", and “perceived value” directly affect the level of customer satisfaction, and they are called conditional variables; the latter two are the results presented by customers based on satisfaction and are part of the model [12]. Outcome variables.

Due to the rapid development of third-party mobile payment technology, there are still certain loopholes in the protection of user safety. Users are always worried that some risks will threaten their own rights and interests. Therefore, for payment users, the influencing factor of “perceived risk” cannot be ignored. Based on the above analysis, this paper selected four factors that affect customer satisfaction, customer expectations, perceived quality, perceived value, and perceived risk. Since “customers” are usually called “users” in the network environment, this study replaces all “customers” in the above influencing factors with “users". The research model drawn based on the above research is shown in Fig. 2.

Fig. 2

Model of influencing factors of third-party mobile payment user satisfaction.

2.2 The research hypothesis

1) Perceived Risk

While the third-party mobile payment is booming, the risks have also risen. Security issues such as online fraud, account and password leaks, virus infringements, and privacy leaks have emerged one after another, revealing the hidden dangers of Internet finance and becoming a major obstacle in the development of third-party mobile payments. A large number of studies have shown that the higher the perceived risk, the lower user satisfaction with the product or service [13, 14]. Based on this, the research hypothesis is as follows:

H7: Perceived risk negatively affects perceived value

H8: Perceived risk negatively affects user satisfaction through perceived value

2) User Expectations

Compared with the previous payment methods, third-party mobile payment has broken through the limitations of time and space. Users will have great expectations for the quality of their services. When they are officially used, users will bring these expectations into the satisfaction of third-party mobile payments. That is, user expectations will directly or indirectly affect user satisfaction through perceived quality and perceived value [15 –17]. The research hypotheses are as follows:

H1: User expectations have a positive impact on perceived quality.

H2: User expectations positively affect the perceived value.

H3: User expectations positively affect user satisfaction.

H4: Users expect to positively affect user satisfaction through perceived quality.

H5: Users expect to positively influence user satisfaction through perceived value.

3) Perceived Quality

Compared with other payment products, third-party mobile payment has quality advantages such as simple operation, high payment efficiency, comprehensive functions, high reliability and fast response speed. Users will feel these advantages after using them, and then recognize and continue to use them [18, 19]. Therefore, the research hypotheses are as follows:

H6: Perceived quality positively affects user satisfaction.

4) Perceived Value

Although the use of third-party mobile payments requires time, energy, data costs, and the unpredictability of the consequences, the benefits or bill reductions obtained during payment, the improvement of the convenience of daily life, and the satisfaction of personalized needs still keep users Loyalty to third-party mobile payments, thus continuing to use [20, 21]. Therefore, perceived value will positively affect user satisfaction, and the research hypotheses are as follows:

H9: Perceived value positively affects user satisfaction.

2.3 Variable design

On the basis of sorting out the views of other scholars, this article determines to measure five variables such as user expectations, perceived quality, perceived value, perceived risk, and user satisfaction. The measurement standard uses the Likert Scale, the numbers 1–5 represent the different satisfaction levels of the survey respondents to the situation described in the item. As shown in Table 1:

Table 1
Item design of third-party mobile payment user satisfaction questionnaire

Variable No. Measurement Question Problem Origin

Perceived Risk (PR) PR1 1. Using third-party mobile payment may cause property damage. Featherman M, Pavlou P [5], Pavlou P, Gefen D. [6]

PR2 2. The use of third-party mobile payment may lead to the disclosure of private information.

PR3 3. The use of third-party mobile payment may have security issues, such as transaction information being tampered with, mobile phone poisoning, etc.

PR4 4. Using third-party mobile payment may feel a melancholy or upset variable.

User Expectation (UE) UE1 5. Before using third-party mobile payment, I have high expectations for it. Zeithaml V A, Berry L L, Parasuraman A. [7] Na Xie [8]

UE2 6. Before using third-party mobile payment, I think it has high payment efficiency and can quickly complete payment operations

UE3 7. Before using third-party mobile payment, I think it is easy to operate and the payment process is fast.

UE4 8. Before using third-party mobile payment, I think it can cover many life scenarios and a wide range of services

Perceived Quality (PQ) PQ1 9. In the process of using, I think it can quickly respond to transactions such as transfers. Fornell C, Johnson M D, Anderson E W, et al. [9] Xueping Tang [10]

PQ2 10. In the process of using, I think the operation of third-party mobile payment is simple.

PQ3 11. In the course of use, I think its services cover many scenarios, involving all aspects of daily life.

PQ4 12. In the process of using, I think its interface design and function settings are simple, clear and beautiful.

Perceived Value (PV) PV1 13. Although it costs some traffic, but using it can get some discounts or benefits. Yudan Bao [11]

PV2 14. Although I have paid some time and energy, using it still facilitates my daily life.

PV3 15. Using it is a trend, I want to keep up with the times.

User Satisfaction(US) US1 16. In general, I am satisfied with using third-party mobile payment. Fornell C. [12]

US2 17. Compared with my expectations, using third-party mobile payment makes me satisfied.

US3 18. Compared with other payment methods (cash, bank card), I think the third-party mobile payment is better.

Variable	No.	Measurement Question	Problem Origin
Perceived Risk (PR)	PR1	1. Using third-party mobile payment may cause property damage.	Featherman M, Pavlou P [5], Pavlou P, Gefen D. [6]
	PR2	2. The use of third-party mobile payment may lead to the disclosure of private information.
	PR3	3. The use of third-party mobile payment may have security issues, such as transaction information being tampered with, mobile phone poisoning, etc.
	PR4	4. Using third-party mobile payment may feel a melancholy or upset variable.
User Expectation (UE)	UE1	5. Before using third-party mobile payment, I have high expectations for it.	Zeithaml V A, Berry L L, Parasuraman A. [7] Na Xie [8]
	UE2	6. Before using third-party mobile payment, I think it has high payment efficiency and can quickly complete payment operations
	UE3	7. Before using third-party mobile payment, I think it is easy to operate and the payment process is fast.
	UE4	8. Before using third-party mobile payment, I think it can cover many life scenarios and a wide range of services
Perceived Quality (PQ)	PQ1	9. In the process of using, I think it can quickly respond to transactions such as transfers.	Fornell C, Johnson M D, Anderson E W, et al. [9] Xueping Tang [10]
	PQ2	10. In the process of using, I think the operation of third-party mobile payment is simple.
	PQ3	11. In the course of use, I think its services cover many scenarios, involving all aspects of daily life.
	PQ4	12. In the process of using, I think its interface design and function settings are simple, clear and beautiful.
Perceived Value (PV)	PV1	13. Although it costs some traffic, but using it can get some discounts or benefits.	Yudan Bao [11]
	PV2	14. Although I have paid some time and energy, using it still facilitates my daily life.
	PV3	15. Using it is a trend, I want to keep up with the times.
User Satisfaction(US)	US1	16. In general, I am satisfied with using third-party mobile payment.	Fornell C. [12]
	US2	17. Compared with my expectations, using third-party mobile payment makes me satisfied.
	US3	18. Compared with other payment methods (cash, bank card), I think the third-party mobile payment is better.

3 Data collection and inspection

3.1 Data sources and overview

The questionnaire in this article is conducted in the form of an online survey. The questionnaires are distributed through QQ, WeChat, Moments, Weibo and other channels, covering different groups of people in various regions of the country. A total of 201 questionnaires are collected. After deleting invalid questionnaires, finally, 166 valid questionnaires were obtained. The specific sample profile is as follows:

In terms of gender: males accounted for 48.19%, females accounted for 51.81%, and the ratio of men to women was basically the same. The number of men surveyed was slightly less than that of women.

In terms of age: respondents aged 18–25 accounted for the largest proportion of the sample, reaching 50%; followed by 26–30 years old, accounting for 28.92%; 18–30 years old accounted for 78.92%, showing that the respondents were young people Mostly; people with a lower age distribution are those aged 31–40 and those over 40, accounting for 7.83% and 4.22% respectively.

In terms of academic qualifications: Bachelor’s degree accounted for the highest proportion, reaching 48.8%; followed by a college degree, accounting for 26.51%; master’s degree and above accounted for 5.42%; high school / technical secondary school degree accounted for 13.86%.

3.2 Reliability and validity analysis

Reliability analysis is used to test the stability and reliability of the questionnaire. This article uses the most commonly used Cronbach’s coefficient (Cronbach’s α) for reliability analysis. According to Cronbach’s α coefficient measurement standard, when the coefficient is greater than 0.7, the reliability is acceptable; when the coefficient is greater than 0.8, the reliability is high, and the measurement results have high internal consistency.

Use SPSS 26.0 software to analyze the reliability of the overall scale and the scale of each variable. The results are shown in Table 2. It can be seen that the Cronbach’s α coefficient of each variable is above 0.7, and the reliability is good. The overall Cronbach’s α coefficient is 0.858 greater than 0.8, indicating that the questionnaire data results are highly reliable and can be analyzed in the next step.

Table 2
Cronbach’s α coefficient of the research variables

Variable Number of Cronbach’s α measurement items

Perceived Risk 4 0.836

User Expectation 4 0.889

Perceived Quality 4 0.779

Perceived Value 3 0.798

User Satisfaction 3 0.830

Total 18 0.858

Variable	Number of	Cronbach’s α measurement items
Perceived Risk	4	0.836
User Expectation	4	0.889
Perceived Quality	4	0.779
Perceived Value	3	0.798
User Satisfaction	3	0.830
Total	18	0.858

Validity is used to measure the degree of agreement between the measurement index and the expected results of the investigation. The more consistent the measurement results and the expected contents of the investigation, the higher the validity; otherwise, the lower. This article first uses the KMO value and Bartlett sphere test to test whether the sample data is suitable for factor analysis. The KMO value is between 0–1, and the closer the value is to 1, the stronger the correlation between the items. According to the academic definition, usually, when the KMO value is greater than 0.7 and the p value of the significance probability of Bartlett’s sphere test is less than 0.01, it is suitable for factor analysis.

The results in Table 3 show that the KMO value is 0.829, which is far beyond the reference standard of 0.7; and the p value is 0.000, which is less than 0.01. Therefore, it can be seen that the sample data collected in this article are highly correlated and suitable for the next step of factor analysis.

Table 3

KMO value and Bartlett spherical test

Kaiser.Meyer.Olkin(KMO) measure of sampling adequacy		0.829
Bartlett spherical test	Approximate chi-square	1528.668
	df	153
	Sig.	0.000

4 Experimental design and analysis

4.1 Appropriate test of structural equation model

The degree of fit between the theoretical model and the sample data is usually measured by the degree of the fit index, which includes the absolute fit index, the value-added fit index, and the simple fit index.

AMOS is used to obtain the degree of adaptation of the structural model to the sample data. The specific standards of the adaptation index and the model adaptation index are shown in Table 4. It can be seen from the table that the model as a whole has not reached the ideal state, but it is at an acceptable level, which can be further analyzed.

Table 4
Statistical table of model fitness index

Statistical test volume Adaptation standard and critical value Fit result Fitness

χ ² The smaller the better 251.789 Acceptable

χ²/df 1–3 1.967 Good

RMSEA <0.08 0.077 Good

GFI >0.90 0.844 Acceptable

AGFI >0.90 0.791 Acceptable

NFI >0.90 0.842 Acceptable

TLI >0.90 0.897 Acceptable

IFI >0.90 0.916 Good

CFI >0.90 0.914 Good

RFI >0.90 0.811 Acceptable

PGFI >0.50 0.631 Good

PNFI >0.50 0.705 Good

Statistical test volume	Adaptation standard and critical value	Fit result	Fitness
χ ²	The smaller the better	251.789	Acceptable
χ²/df	1–3	1.967	Good
RMSEA	<0.08	0.077	Good
GFI	>0.90	0.844	Acceptable
AGFI	>0.90	0.791	Acceptable
NFI	>0.90	0.842	Acceptable
TLI	>0.90	0.897	Acceptable
IFI	>0.90	0.916	Good
CFI	>0.90	0.914	Good
RFI	>0.90	0.811	Acceptable
PGFI	>0.50	0.631	Good
PNFI	>0.50	0.705	Good

4.2 Path analysis and hypothesis testing

1) Path test of structural equation model Import the questionnaire data in AMOS to run, and the model execution result is shown in Fig. 3.

Fig. 3

Diagram of the execution result of the structural equation model.

Among them, The paths that user expectation to perceived quality (β=0.482, CR = 4.818, P < 0.001) and user expectation to perceived value (β=0.626, CR = 6.757, P < 0.001) are both significant; The path that perceived quality to user satisfaction (β=0.222, CR = 2.152, P = 0.031 < 0.05) is significant, and the path that perceived value to user satisfaction (β=0.499, CR = 3.887, P < 0.001) is also significant; while the path that users expectation to user satisfaction (β=–0.019, CR=–0.153, P = 0.878 > 0.05) and the path that perceived risk to perceived value (β=–0.019, CR=–0.215, P = 0.830 > 0.05) are not significant. It can be seen that the assumptions H1, H2, H6, and H9 are all supported, while the assumptions of H3 and H7 are rejected. The remaining H4, H5, and H8 will be further verified in the mediation effect test. The detailed result parameters of each path are shown in Table 5.

Table 5

Parameter table of the execution result of the initial model of structural equation

path	Estimate	S.E.	C.R.	P
PQ< —UE	0.482	0.083	4.818	***
PV< —UE	0.626	0.079	6.757	***
PV< —PR	-0.019	0.077	–0.215	0.830
US< —UE	–0.019	0.093	–0.153	0.878
US< —PQ	0.222	0.095	2.152	0.031
US< —PV	0.499	0.115	3.887	***

Note: ***means p < 0.001.

2) Test of the intermediary effect of structural equation model

A mediator is an important statistical concept. If the independent variable X affects the dependent variable Y by influencing the variable M, then M is called the mediator variable. Common test methods for intermediary effects include stepwise test, coefficient product method, and Bootstrap method. The Bootstrap method is recognized so far, which is also the method recommended by most scholars. Therefore, this article will use the Bootstrap method to test the mediation effect.

The mediation effect of H4, H5, and H8 is tested by AMOS 26 software, and the results are shown in the above table: within the confidence interval after deviation correction, user expectations affect user satisfaction through perceived quality, and user expectations affect user satisfaction through perceived value. Both paths do not include 0, and the standardized effect values are 0.107 and 0.312, respectively, indicating that the mediating effect is significant. Combining the path test in the previous section, we can know that the direct impact of user expectations on user satisfaction is not significant, that is, user expectations have a positive and indirect impact on user satisfaction through perceived value and perceived quality, and there is a completely mediating effect. The range where perceived risk affects user satisfaction through perceived value includes 0, indicating that the mediation effect is not significant.

4.3 Result analysis

The structural equations are constructed by AMOS to test the fit and path of the sample data. 3 of the 9 hypotheses proposed in this study are rejected, and the remaining 6 hypotheses are supported. The specific hypothesis test results are shown in Table 7:

Table 6
Standardized Bootstrap Intermediary Effect Test

Path SE Effect Size Bias-corrected 90% CI

Lower Upper

UE⟶PQ⟶US 0.065 0.107 0.022 0.204

UE⟶PV⟶US 0.107 0.312 0.165 0.514

PR⟶PV⟶US 0.053 –0.009 –0.104 0.065

Path	SE	Effect Size	Bias-corrected 90% CI
UE⟶PQ⟶US	0.065	0.107	0.022	0.204
UE⟶PV⟶US	0.107	0.312	0.165	0.514
PR⟶PV⟶US	0.053	–0.009	–0.104	0.065

Table 7

Hypothesis Test Results

Research hypothesis	Results
H1: User expectations positively affect the perceived quality	Support
H2: User expectations positively affect the perceived value	Support
H3: User expectations positively affect user satisfaction	Reject
H4: Users expectations positively affect user satisfaction through perceived quality	Support
H5: Users expectations positively influence user satisfaction through perceived value	Support
H6: Perceived quality positively affects user satisfaction	Support
H7: Perceived risk negatively affects perceived value	Reject
H8: Perceived risk negatively affects user satisfaction through perceived value	Reject
H9: Perceived value positively affects user satisfaction	Support

1) The impact of user expectations on perceived quality, perceived value and user satisfaction It can be seen from the results that the assumptions of H1 and H2 are both valid. The path coefficient from user expectations to perceived quality is 0.482 (P < 0.001), and the path coefficient from user expectations to perceived value is 0.616 (P < 0.001). It shows that user expectations have a significant positive impact on perceived value, but relatively low impact on perceived quality. In other words, users make subjective evaluations of product quality and service experience based on their expectations before use, and this evaluation will have a positive impact on the quality and value perception of third-party mobile payments. However, the positive influence from user expectations to user satisfaction is rejected, and its path coefficient is –0.019 (P = 0.878 > 0.05), indicating that user expectations do not have a direct impact on user satisfaction. But combined with the intermediary variables, the assumption that user expectations positively affect user satisfaction through perceived quality and perceived value is supported, indicating that user expectations have a completely intermediary effect on user satisfaction, that is, In the process of using third-party mobile payment, the higher the user’s expectations of the product, the more satisfied the product is not necessarily, but it will significantly affect user satisfaction only after the quality and value of the product are perceived during the subsequent use.

2) The impact from the perceived quality and perceived value to user satisfaction.

It can be seen from Table 5 that both assumptions H6 and H9 are valid. The path coefficient from perceived quality to user satisfaction is 0.222 (P = 0.031 < 0.05), indicating that the perceived quality directly affects user satisfaction significantly. The path coefficient from perceived value to user satisfaction is 0.499 (P < 0.001), indicating that perceived value has a significant positive impact on user satisfaction and deeper than perceived quality.

3) The impact from perceived risk to perceived value and user satisfaction.

Regarding the hypothesis of perceived risk, the path coefficient from perceived risk to perceived value is –0.019 (P = 0.830 > 0.05). The hypothesis is rejected, indicating that perceived risk does not affect perceived value. Similarly, in the test of intermediary effect, the path that perceived risk affects user satisfaction through perceived value is rejected. This collectively shows that the possible security issues of third-party mobile payment have not hindered users from recognizing a series of values brought by third-party mobile payment, nor will it affect user satisfaction through perceived value.

In summary, the impact of perceived quality and perceived value on user satisfaction is significant; user expectations do not directly affect satisfaction but affect satisfaction through perceived quality and perceived value. The impact of perceived risk on perceived value and user satisfaction is negligible.

5 Conclusion

1) Perceived quality and perceived value have a significant impact on user satisfaction, of which perceived value has the greatest impact on satisfaction. The path coefficients of perceived quality and perceived value are 0.222 and 0.499, respectively. When users perceive the better the quality and service of third-party mobile payment products, the higher their satisfaction. Improving service quality and innovative product functions can increase user satisfaction. At the same time, perceived value is also an important factor in user satisfaction. After measuring costs and benefits, users feel that the greater the value gained from using third-party mobile payments, the higher their satisfaction. Therefore, to help users create revenue, improve their work and life efficiency, and let users experience tangible benefits, satisfaction will increase.

2) User expectations are indirect influencing factors. It cannot directly affect user satisfaction, but positively affect user satisfaction through perceived quality and perceived value. In other words, the higher the user’s expectations of third-party mobile payment, the higher the perceived actual quality and value, and the higher satisfaction. Therefore, companies should deeply understand the real needs of users and launch more personalized and differentiated services to improve users’ expectations before use, and ultimately improve satisfaction.

3) Perceived risk has a negative impact on user satisfaction, but it is not significant. It can be seen that after years of development of third-party mobile payment, companies have accumulated enough experience to improve their products and services, and users’ trust in third-party mobile payment has been greatly improved. With the improvement and promulgation of relevant laws and regulations, some security risk issues are being resolved. However, it is still necessary to take precautions, improve safety technology, continuously improve market mechanisms and legal supervision systems, and minimize perceived risks.

Footnotes

Acknowledgments

This work is support by “The Second Batch of Young and Middle-aged Research Scientists” of Nantong Institute of Technology (Grant No. ZQNGG206), and the Philosophy and Social Science Fund of Jiangsu Provincial Department of Education (Grant No. 2018SJA1287), and the 13th Five-Year Plan of Jiangsu Province “Key Construction Discipline Project of Business Administration Level 1” (SJY201609).

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Research on user satisfaction of third-party mobile payment under the IoT environment——based on the structural equation mode