Unveiling the Path to Mobile Payment Adoption: Insights from Thai Consumers

Abstract

Mobile payment, replacing traditional methods like cash and cards, offers users convenience and accessibility, benefiting individuals, businesses, and governments. However, most research on mobile payment adoption has primarily focused on developed countries, leaving a gap in understanding the adoption factors in developing nations. This study addresses this gap by investigating the determinants of mobile payment adoption in Thailand, an emerging economy experiencing significant smartphone adoption and e-commerce growth. Through a quantitative approach and a survey of 475 Thai consumers, this research applies an extended Unified Theory of Acceptance and Use of Technology (UTAUT) model as a theoretical foundation to examine Thai consumers’ mobile payment adoption. Data analysis using SPSS 28.0 and AMOS 28.0 identifies key factors influencing Thai consumers to adopt mobile payment. By offering a comprehensive research model and considering evolving smartphone technology, this study aims to guide policymakers and stakeholders in promoting mobile payment adoption, ultimately enhancing Thailand’s economic development and tourism industry.

1. Introduction

In the era of the 4.0 Industrial Revolution, advancements in information and communications technology, along with wireless Internet, have integrated functionalities into smartphones, making them indispensable in daily life worldwide (Arniati 2023; Nguyen et al. 2023). This evolution has significantly influenced the growth of e-commerce and mobile commerce (Changchit et al. 2023b; Loh et al. 2023). Mobile commerce, encompassing various services like mobile advertising, mobile games, and mobile payment, has become a cornerstone of modern transactions (Changchit et al. 2023b).

Mobile payment, particularly, serves as a crucial platform supporting other mobile commerce applications (Tew et al. 2022). Defined as the use of wireless Internet-connected devices to conduct financial transactions, mobile payment enables convenient, anytime, and anywhere purchases and services (Bailey et al. 2022). The concept of a cashless society has garnered growing interest due to the widespread adoption of cashless transactions and the increasing circulation of digital currency (Chang et al. 2024). It is gradually replacing traditional forms of payment, offering benefits such as expanded consumer reach for businesses and improved convenience for consumers (Belanche et al. 2022).

The growth of mobile payment extends beyond developed countries, with significant adoption in developing and newly emerging economies (Chang et al. 2023). Southeast Asia, for instance, has seen a rapid increase in mobile payment transactions, driven by modernizing telecommunications infrastructure and increasing smartphone penetration (Arniati 2023).

Despite the growing importance of mobile payments, research has predominantly focused on developed countries, leaving a gap in the understanding of the adoption factors in developing or newly emerging countries (Bui et al. 2022). The emphasis on developed countries in previous studies may be attributed to their advanced telecommunications infrastructure and higher income levels. This study aims to fill this gap by examining the factors influencing mobile payment intention in Thailand, a newly emerging economy with a high smartphone adoption rate and a booming tourism industry (Chinnapakjarusiri et al. 2024).

This research aims to propose a comprehensive model to understand mobile payment adoption factors, considering the evolving landscape of mobile payment and its implications for developing economies like Thailand. The findings are expected to inform policymakers and businesses on strategies to promote mobile payment, potentially attracting more international business and tourism to Thailand.

Overall, this paper contributes to the understanding of mobile payment adoption in emerging economies and provides insights that can drive its further growth and development. The subsequent sections of this paper detail the characteristics of mobile payment, the adoption landscape in Thailand, the research methodology, results, and implications for theory and practice, followed by conclusions, limitations, and future research directions.

2. Literature Review

2.1. Previous Studies on Mobile Payment

Mobile payment is considered as an evolutionary form of online payment (Hanafiah et al. 2024). Mobile payment is conducted via a mobile network (Ghosh 2024). In other words, mobile payment is regarded as a process in which at least one stage of a financial transaction is performed through a handheld mobile device connected to the wireless Internet, for example, a smartphone, tablet, or personal digital assistant, capable of processing a financial transaction through a mobile network (Chang et al. 2024).

Revolutionary advances in information and communications technology, along with wireless Internet, are fundamentally transforming the mobile payment sector (Mollick et al. 2023). Mobile payment is predicted to become the most popular payment method in the future, superseding traditional forms such as cash, checks, debit cards, and credit cards (Changchit et al. 2023b).

Numerous studies have been conducted to identify the factors influencing the intention to use mobile payment (Ling et al. 2024; Wen et al. 2023). These studies draw on theories/models of behaviors, attitudes, and technology acceptance (Momani 2020). The theory of reasoned action (TRA) by Fishbien and Ajzen (1975) explains individual behaviors, focusing on attitudes toward the behavioral and subjective norms. The theory of planned behavior (TPB) by Ajzen (1991) suggests that beliefs can determine intended and actual behaviors.

The technology acceptance model (TAM) by Davis et al. (1989) examines information technology acceptance, with perceived ease of use and usefulness as key variables. Some studies augment these with factors like perceived trust, transaction convenience, speed, security, privacy, and perceived compatibility, influencing mobile commerce intentions (Mollick et al. 2023).

A study by Sun et al. (2020) emphasizes perceived compatibility, portability, and subjective norms, while Handarkho (2021) focuses on behavioral beliefs, social influences, and personal characteristics. Phan et al. (2020) and Belanche et al. (2022) highlight similar factors, with Singh (2020) adding social influences and performance expectancy. Chen and Lai (2023) note that perceived risk and cost hinder mobile payment intention.

Most research on mobile payment has been on developed countries, with limited studies in Southeast Asia and even fewer in Thailand despite its high smartphone adoption rate. Prior studies often consider factors in isolation. Therefore, a comprehensive and systematic research model that integrates various factors is needed to enhance the explanatory power of mobile payment intention, especially given the evolving technological landscape in newly emerging countries like Thailand. This study addresses these gaps.

2.2. Current Situation on Mobile Payment in Thailand

Thailand is a newly emerging country with a population of 71 million people (Worldometers 2023). The country boasts enormous potential for economic growth and development, situated in a strategic position in Southeast Asia that attracts significant foreign direct investment. As a result, more multinational companies and their supply chains are establishing business operations in Thailand.

The success of the Thai economy can be attributed to economic reform and transformation policies, which aim to attract foreign companies to establish manufacturing and processing plants in the country. Additionally, Thailand’s tourism industry is flourishing, supported by its beautiful landscapes and the hospitality of its people (Chinnapakjarusiri et al. 2024). The country’s Internet infrastructure and information and telecommunications technology are also increasingly modernized, with over 90% of the population using smartphones (Boonsiritomachai and Sud-On 2023) and approximately 49% participating in e-commerce.

Thailand’s e-commerce sales hit $31 billion in 2023, accounting for about 10% of the country’s total retail sales of consumer goods (Vu and Nguyen 2024). Thailand is seen as having significant potential to further develop mobile payment. Popular forms of mobile payment in the world are present in Thailand. Statistics show that within the next 5 years, the growth rate of mobile payment in Thailand will reach an average of 7.51% a year (Ponsree 2024).

Despite the benefits of mobile payment, it is surprising that not all Thai citizens use mobile payment. This could be due to approximately 48 percent of the Thai population living in rural areas in Thailand that have limited telecommunications infrastructure (Telecom Review 2024; Worldometers 2024). Previous studies have identified factors that influence mobile payment usage. However, these studies are mostly conducted in developed countries. A notable feature of these studies is that they examine factors in isolation and discretely, not as constituent components of an integrated research model. Furthermore, the evolution of mobile payment requires adding new factors to the research model to increase its explanatory power for intention to use mobile payment. These factors include perceived security, perceived privacy, perceived compatibility, and technology competency.

Given the fact that Thailand can be a representative of developing or newly emerging countries, it is necessary to choose a comprehensive research model to examine intention to use mobile payment. The model chosen in this study is the Unified Theory of Acceptance and Use of Technology (UTAUT). The reason for choosing this model is that it is built on the outstanding features of other technology acceptance theories and models. To increase its explanatory power for the intention to use mobile payment, variables added to the UTAUT include technology competency, perceived compatibility, perceived privacy, perceived security, and attitude toward mobile payment.

Thailand is increasingly attracting foreign direct investment, multinational companies and their supply chains are moving to Thailand, and Thailand’s tourism industry has potential for further growth. In addition, the Thai government is working to improve the telecommunications infrastructure in the rural parts of Thailand (Telecom Review 2024), opening even more opportunities to expand mobile payment in Thailand. Therefore, identifying factors that influence intention to use mobile payment in Thailand will help Thailand develop strategies and policies to promote the acceptance and use of mobile payment, which, in turn, further support economic growth and development in Thailand.

3. Theoretical Background, Research Model, and Hypotheses

Based on the review of outstanding features of technology acceptance models and theories, Venkatesh (2022) developed the Unified Theory of Acceptance and Use of Technology (UTAUT). Four basic factors make up the UTAUT, including performance expectancy, effort expectancy, social influence, and facilitating conditions. The UTAUT has been used in many studies on the acceptance of a new technology in different contexts.

Consistent with studies using the UTAUT, this study also focuses on four basic factors: performance expectancy, effort expectancy, social influence, and facilitating conditions to examine the intention to use mobile payment in Thailand. It is worth noting that mobile payment is an evolutionary form of online payment. In the mobile payment environment, transactions and exchanges are conducted anytime and anywhere. As a form of technology use in payment, an individual’s technology competence plays an important role in influencing the intention to use mobile payment.

Perceived compatibility is also an important factor, because individuals will be willing to participate in activities that are compatible with their lifestyle and social image. In the mobile payment environment, transactions and exchanges take place without limitations of space and time, so risks related to privacy and security are considered higher than in other payment contexts. Therefore, perceived privacy, perceived security, perceived compatibility, technology competency, and attitude toward mobile payment, from a study by Changchit et al. (2023a), were integrated into the UTAUT. These factors are expected to influence the intention to use mobile payment. This is consistent with the studies of Changchit et al. (2023a). Figure 1 shows the integrated research model utilized to examine consumers’ intentions to use mobile payment in Thailand.

3.1. Perceived Privacy and Security

Accepting and using a new technology involves risks (Bland et al. 2024; Katini et al. 2023). Since mobile payment can be conducted anywhere and anytime, consumers face difficulties in evaluating its reliability (Abrahim Sleiman et al. 2023). In other words, consumers are concerned about security and privacy issues. If consumers are assured that their personal and financial information is respected, not used for illegal purposes, and not transferred to third parties without their consent, their expectations about the outcomes of using mobile payment will be enhanced. Empirical studies also show that perceived privacy and perceived security are positively related to performance expectancy (Changchit et al. 2023b). Consistent with these studies, the following hypotheses are proposed:

H1. 

A positive relationship exists between perceived privacy and performance expectancy.

H2. 

A positive relationship exists between perceived security and performance expectancy.

3.2. Technology Competency

Technology competency is seen as the degree to which consumers can use technologies in different contexts (Alzeaideen et al. 2024). Consumers with high technology competency seem to be motivated and encouraged to use technologies to complete tasks at work, at home, or in social interactions and communications (Ling et al. 2024). Consumers with high levels of technology competency will feel it is easy to learn how to use mobile payment (Changchit et al. 2024). Furthermore, in the context of mobile payment, consumers with good technology competency tend to be confident that using mobile payment does not require significant efforts in terms of time and cost (Al-Okaily et al. 2024). Consistent with previous studies, the following hypothesis is proposed:

H3. 

A positive relationship exists between technology competency and effort expectancy.

3.3. Perceived Compatibility

Perceived compatibility in the mobile payment environment can be understood as the extent to which the characteristics of mobile payment align with the intrinsic characteristics of mobile payment consumers (Ayoungman et al. 2021). These intrinsic characteristics may include lifestyles, values, social images, or experiences (Changchit et al. 2024). The intrinsic characteristics of consumers are considered crucial in determining their preference for mobile payment over other forms of payment. Empirical studies in the contexts of online banking, online payment, e-commerce, and m-commerce have demonstrated that perceived compatibility significantly influences attitudes toward the use of technology or innovation (Pushpa et al. 2022). Thus, the following hypothesis is proposed:

H4. 

A positive relationship exists between perceived compatibility and attitude toward mobile payment.

3.4. Social Influence

Social influence is understood as the extent to which consumers perceive that individuals significant to them, such as friends, co-workers, or family members, believe they should or should not adopt a technology or innovation (Venkatesh et al. 2012). It is worth noting that in many situations, the attitudes and behaviors of these individuals influence consumers’ attitudes toward technology adoption (Geng et al. 2023). In the context of mobile payment, if social influence is substantial, consumers’ attitudes toward mobile payment adoption will be positively influenced. Consistent with previous studies, the following hypothesis is proposed:

H5. 

A positive relationship exists between social influence and attitudes toward mobile payment.

3.5. Facilitating Conditions

Facilitating conditions are the degree to which a consumer believes that the technological, organizational, and human infrastructures exist to support the adoption or use of a technology or innovation (Chawla and Joshi 2023). These enabling conditions include technological, organizational, and human factors or attributes that act to remove the barriers related to the acceptance or use of a technology or innovation (Man et al. 2022). Consistent with previous studies (e.g., Man et al. 2022; Roh et al. 2023), the following hypothesis is proposed:

H6. 

A positive relationship exists between facilitating conditions and attitudes towards mobile payment.

3.6. Performance Expectancy

Performance expectancy demonstrates the usefulness of a technology or innovation (Purohit et al. 2022). The expected benefits generated by using a technology can influence the intention to use that technology. Consumers’ perception that using mobile payment can bring convenience and benefits will lead to the intention to use or accept mobile payment (Widyanto et al. 2020). Consistent with previous studies, the following hypothesis is proposed:

H7. 

A positive relationship exists between performance expectancy and the intention to use mobile payment.

3.7. Effort Expectancy

Effort expectancy is understood as the degree to which using a technology is difficult or easy (Wei et al. 2021). Effort expectancy serves as an important factor influencing technology acceptance (Saprikis et al. 2022). In the mobile payment environment, if consumers perceive that it is simple to use mobile payment or that using mobile payment does not require significant efforts, they will tend to use mobile payment. Consistent with previous studies, the following hypothesis is proposed:

H8. 

A positive relationship exists between effort expectancy and the intention to use mobile payment.

3.8. Attitude toward Mobile Payment

In the e-commerce or mobile commerce environment, a technology is considered successful if consumers are able and willing to accept and use that technology (Upadhyay et al. 2022). Consumers with positive attitudes are more likely to accept technology than other consumers (Lu and Ahn 2023). In the context of mobile payment, consumers will have a positive attitude towards mobile payment if they can achieve their goals or expected benefits from using mobile payment (Changchit et al. 2023b). Positive attitudes will boost the intention to use mobile payment. Consistent with previous studies, the following hypothesis is proposed:

H9. 

A positive relationship exists between attitude toward mobile payment and the intention to use mobile payment.

4. Research Methodology

4.1. Measurement Instrument

The survey instrument for this study was developed based on an adaptation of the UTAUT scales developed by Venkatesh (2022) and a study by Changchit et al. (2023). The questionnaire includes scales, namely the intention to use mobile payment, performance expectancy, effort expectancy, attitude towards mobile payment, perceived privacy, perceived security, technology competency, perceived compatibility, social influence, and facilitating conditions.

A mobile payment expert, fluent in both English and Thai, translated the questionnaire into Thai. Another mobile payment expert, proficient in both English and Thai, back-translated the Thai version into English to evaluate the level of consistency between the Thai and English versions. Two mobile payment researchers fluent in both Thai and English independently reviewed the Thai and English versions of the survey. The results indicated that both the Thai and English versions were consistent and accurate. Evaluations to validate the appropriateness and reliability of the items constituting the scales in the measurement model were performed.

The survey questionnaire consists of two parts: The first part aimed to collect data on consumers’ perceptions about mobile payment and their intention to use mobile payment. The second part aimed to collect demographic information. To enhance the reliability and validity of the questionnaire, three professors and ten mobile payment consumers filled out the questionnaire and provided feedback. Some adjustments were made based on the feedback to improve the questionnaire.

Appendix A summarizes the constructs and measurement items designed to assess consumer perception levels about mobile payment and their intention to use mobile payment. All items use a 5-level Likert scale (1 = strongly disagree and 5 = strongly agree).

4.2. Data Collection and Subjects’ Demographics

Questionnaires were developed using Google Forms and distributed online via Facebook pages, using a snowball method for data collection. The survey began with a qualifying question asking if the potential participant was a Thai citizen currently living in Thailand and at least 18 years old. If the potential participant did not meet these criteria, they were thanked for their willingness to participate but informed that they did not qualify, as only participants with Thai citizenship living in Thailand were being studied. Four hundred and seventy-five (475) subjects qualified and participated in this study. Each question was set with validation to require a response, ensuring that all surveys were fully completed. According to Hair et al. (2009), the sample size should be at least ten times the number of variables; thus, the sample size for this study is considered sufficient and appropriate. The demographics of the respondents are shown in

Table 1. Subjects’ demographics (n = 475).

Gender
Male: 166 (34.95%)		Female: 309 (65.05%)
Age (in years)
18–25	26–35	36–45	46–55	56–65
371 (78.11%)	65 (13.68%)	18 (3.79%)	15 (3.16%)	6 (1.26%)
Employment
Full time: 127 (26.74%)		Part time: 39 (8.21%)		Not employed: 309 (65.05%)
Online orders last month
0	1–2	3–5	6–9	>10
49 (10.32%)	93 (19.58%)	152 (32%)	65 (13.68%)	116 (24.42%)
Online orders last year
0	1–2	3–5	6–9	10–20	>20
0 (0%)	27 (5.68%)	37 (7.79%)	57 (12%)	102 (21.47%)	252 (53.05%)
Having credit or debit card
Yes: 398 (83.79%)		No: 77 (16.21%)

.

5. Data Analysis

SPSS 28.0 and AMOS 28.0 were used to analyze the data. This section describes the data analysis.

5.1. Reliability Test

A reliability test was conducted to assess the internal consistency of the survey instrument’s constructs. The reliability of each construct in the research model was calculated, and the results are presented in

Table 2. Reliability test *.

Constructs	Measurement Items	Cronbach’s α
Performance expectancy	PE2, PE3, PE4, PE5	0.840
Effort expectancy	EE1, EE2, EE3, EE4	0.857
Perceived privacy	PP1, PP2, PP3, PP4, PP5	0.952
Perceive security	PS1, PS2, PS3, PS4, PS5	0.901
Perceived compatibility	PC1, PC2, PC3, PC4	0.891
Social influence	SI1, SI3, SI4	0.851
Technology competency	TC1, TC3, TC5	0.824
Facilitating conditions	FC1, FC2, FC3, FC5	0.869
Attitude toward mobile payment	ATT1, ATT2, ATT3, ATT4	0.872

* Items with factor loadings <0.6 were removed.

. All reliability test results exceed the recommended value of 0.70 (Nunnally 1978), indicating that the internal consistency of the constructs is satisfactory.

5.2. KMO and Bartlett’s Test

The Kaiser–Meyer–Olkin (KMO) and Bartlett’s tests were conducted to assess the unidimensionality of the scales (refer to

Table 3. KMO and Bartlett’s Test.

KMO and Bartlett’s Test
KMO Sampling Adequacy Measurement.		0.959
Sphericity test	Approx. Chi-square	13,980.765
	Degree of freedom	741
	Significance	0.000

). The sphericity test yielded a significant p-value of 0.000. Additionally, the sampling adequacy was confirmed by a value of 0.959.

5.3. Common Method Bias

Harman’s single-factor test was employed to verify the absence of common method bias in the model. The analysis was performed using SPSS, conducting an unrotated, single-factor constraint factor analysis. As depicted in

Table 4. Total variance explained.

Total Variance Explained
Components	Initial Eigenvalues			Extraction Sums of Squared Loadings
	Total	Variance %	Cumulative %	Total	Variance %	Cumulative %
1	16.339	41.896	41.896	16.339	41.896	41.896
2	4.472	11.466	53.362	4.472	11.466	53.362
3	1.733	4.442	57.805	1.733	4.442	57.805
4	1.427	3.658	61.463	1.427	3.658	61.463
5	1.341	3.437	64.900	1.341	3.437	64.900
6	1.053	2.701	67.601	1.053	2.701	67.601
7	1.014	2.601	70.201	1.014	2.601	70.201
8	0.949	2.432	72.634	0.949	2.432	72.634
9	0.775	1.987	74.621	0.775	1.987	74.621
10	0.689	1.767	76.388
11	0.546	1.400	77.788
12	0.524	1.344	79.131
13	0.491	1.259	80.391
14	0.478	1.226	81.617
15	0.453	1.161	82.777
16	0.442	1.133	83.910
17	0.423	1.084	84.994
18	0.398	1.021	86.015
19	0.379	0.971	86.986
20	0.371	0.952	87.938
21	0.361	0.926	88.864
22	0.348	0.892	89.755
23	0.344	0.883	90.639
24	0.320	0.821	91.460
25	0.310	0.796	92.256
26	0.300	0.770	93.026
27	0.298	0.765	93.791
28	0.272	0.697	94.488
29	0.259	0.664	95.153
30	0.240	0.615	95.768
31	0.232	0.595	96.364
32	0.224	0.576	96.939
33	0.210	0.539	97.478
34	0.200	0.513	97.991
35	0.195	0.501	98.492
36	0.178	0.456	98.948
37	0.151	0.387	99.335
38	0.137	0.351	99.686
39	0.123	0.314	100.000

, the highest variance explained by one factor was 41.896%, suggesting that there are no significant concerns regarding common method bias.

5.4. Analysis of Factor Loadings

To assess the convergent validity of the factors, factor loadings were examined to ensure that each survey item loaded appropriately onto its corresponding factor (refer to

Table 5. Factor analysis *.

Constructs	Component
	1	2	3	4	5	6	7	8	9	10
PE2	0.067	0.036	0.209	0.647	0.394	0.125	0.087	0.047	−0.014	0.275
PE3	0.032	0.044	0.182	0.793	0.161	0.128	0.162	0.113	0.083	−0.027
PE4	0.036	0.106	0.243	0.754	−0.053	0.075	0.186	0.120	0.144	0.033
PE5	0.051	0.015	0.149	0.681	0.137	0.243	0.210	0.066	0.123	0.337
EE1	0.236	0.100	0.061	0.173	0.200	0.728	0.131	0.094	0.199	0.123
EE2	0.142	0.185	0.075	0.026	0.044	0.794	0.139	0.113	0.153	0.085
EE3	0.088	0.248	0.170	0.274	0.167	0.671	0.099	0.156	0.116	0.117
EE4	0.183	0.157	0.294	0.163	0.251	0.648	0.077	0.132	0.136	0.103
PP1	0.821	0.291	0.058	0.040	0.126	0.127	0.056	0.142	0.054	−0.002
PP2	0.884	0.200	0.087	0.023	0.039	0.118	0.040	0.117	0.061	0.054
PP3	0.867	0.225	0.012	0.046	0.065	0.084	0.093	0.120	0.070	0.135
PP4	0.876	0.236	0.101	0.058	0.083	0.100	0.104	0.107	0.043	0.048
PP5	0.776	0.314	0.120	0.032	0.058	0.127	0.160	0.099	0.097	0.038
PS1	0.329	0.705	0.038	0.042	0.161	0.201	0.126	0.194	0.047	0.098
PS2	0.395	0.712	0.132	0.072	0.185	0.136	0.069	0.081	0.130	0.081
PS3	0.398	0.719	0.001	0.003	0.001	0.183	0.051	0.071	0.122	0.072
PS4	0.439	0.665	0.082	0.095	0.075	0.107	0.041	0.166	0.108	0.050
PS5	0.430	0.674	0.078	0.087	0.114	0.144	0.195	0.084	0.065	0.125
PC1	0.215	0.120	0.261	0.256	0.273	0.196	0.613	0.132	0.148	0.207
PC2	0.188	0.081	0.219	0.201	0.251	0.156	0.677	0.132	0.159	0.243
PC3	0.078	0.175	0.228	0.248	0.110	0.079	0.677	0.216	0.155	0.116
PC4	0.165	0.115	0.275	0.228	0.242	0.194	0.663	0.174	0.151	0.245
SI1	0.170	0.119	0.116	0.164	0.209	0.177	0.152	0.677	0.213	0.176
SI3	0.203	0.175	0.104	0.087	0.095	0.085	0.180	0.806	0.123	0.121
SI4	0.222	0.147	0.162	0.109	0.122	0.179	0.119	0.776	0.131	0.136
TC1	0.055	0.206	0.083	0.050	0.259	0.276	0.132	0.139	0.731	−0.002
TC3	0.086	0.105	0.181	0.091	0.200	0.176	0.126	0.143	0.766	0.116
TC5	0.158	0.046	0.158	0.199	0.130	0.099	0.152	0.161	0.737	0.224
FC1	0.112	0.220	0.217	0.123	0.689	0.184	0.154	0.165	0.175	0.163
FC2	0.101	0.180	0.203	0.130	0.627	0.237	0.224	0.108	0.323	−0.030
FC3	0.080	0.100	0.195	0.177	0.679	0.165	0.237	0.118	0.216	0.173
FC5	0.150	0.042	0.424	0.123	0.658	0.113	0.131	0.157	0.151	0.160
ATT1	0.150	0.046	0.689	0.173	0.306	0.171	0.290	0.101	0.143	0.106
ATT2	0.100	0.003	0.705	0.247	0.298	0.141	0.196	0.114	0.150	0.161
ATT3	0.092	0.057	0.679	0.360	0.171	0.038	0.121	0.101	0.138	0.151
ATT4	0.055	0.171	0.681	0.190	0.141	0.220	0.225	0.139	0.099	0.186
INT3	0.113	0.208	0.322	0.157	0.210	0.141	0.260	0.232	0.152	0.629
INT4	0.152	0.131	0.237	0.188	0.199	0.207	0.291	0.232	0.192	0.673
INT5	0.098	0.154	0.251	0.219	0.147	0.184	0.295	0.228	0.155	0.622

* Rotation method: varimax with Kaiser normalization. The items with factor loadings less than 0.6 were removed from data analysis.

). The findings indicate that the thirty-nine survey items loaded onto ten factors, explaining 74.621% of the total variance. Items with factor loadings below the recommended threshold of 0.6 (Hair et al. 2009) were eliminated during the data analysis process.

5.5. Multicollinearity Test

To address the potential adverse effects of multicollinearity, an assessment was conducted within the research model (Cenfetelli and Bassellier 2009). As shown in

Table 6. Multicollinearity test.

Coefficients a
Model	Unstandardized Coefficients		Standardized Coefficients	t	Sig.	Collinearity Statistics
	B	Std. Error	Beta			Tolerance	VIF
(Constant)	−0.304	0.186		−1.630	0.104
PE	0.099	0.050	0.074	1.972	0.049	0.507	1.974
EE	0.067	0.039	0.066	1.736	0.083	0.498	2.009
PP	−0.048	0.033	−0.059	−1.454	0.147	0.442	2.264
PS	0.082	0.039	0.092	2.138	0.033	0.388	2.578
PC	0.375	0.047	0.364	8.050	<0.001	0.354	2.828
SI	0.188	0.035	0.192	5.335	<0.001	0.556	1.799
TC	0.041	0.039	0.039	1.052	0.293	0.529	1.891
FC	0.047	0.051	0.041	0.924	0.356	0.371	2.698
ATT	0.221	0.053	0.186	4.169	<0.001	0.363	2.753

^a Dependent variable: INT.

below, the Variance Inflation Factor (VIF) ranged from 1.799 to 2.828, all below the threshold of 10. This indicates that multicollinearity is not a significant concern in this dataset.

5.6. Structural Equation Model (SEM)

SPSS AMOS 28.0 was utilized to analyze the research model. Seven structural equation modeling (SEM) fit measures were evaluated to determine the overall goodness of fit of the model. All goodness of fit indices were found to be within acceptable ranges (refer to

Table 7. Fit indices for the models.

Indices of Fit	Value Recommended	Model Value
df/Chi-square	≤3.00	2.289
Goodness of fit	≥0.90	0.993
Adjusted goodness of fit	≥0.80	0.949
Root mean square error of approximation	≤0.06	0.052
Comparative fit index	≥0.93	0.996
Tucker-Lewis index	≥0.90	0.980
Normed fit index	≥0.90	0.994

), suggesting that the model exhibited a strong fit with the data (Bentler and Bonett 1980; Hu and Bentler 1999; Tucker and Lewis 1973).

5.7. Hypothesis Testing

Table 8. Hypothesis testing.

H#	Hypothesis Testing			Standardized Estimate (β)	Critical Ratio	p-Value
1	Perceived privacy	→	Performance expectancy	−0.767	−4.233	***
2	Perceived security	→	Performance expectancy	1.364	5.093	***
3	Technology competency	→	Effort expectancy	1.096	14.130	***
4	Perceived compatibility	→	Attitude toward mobile payment	0.358	9.726	***
5	Social influence	→	Attitude toward mobile payment	0.032	1.074	0.283
6	Facilitating conditions	→	Attitude toward mobile payment	0.392	9.895	***
7	Performance expectancy	→	Intention to use mobile payment	0.093	1.052	0.293
8	Effort expectancy	→	Intention to use mobile payment	0.700	4.423	***
9	Attitude toward mobile payment	→	Intention to use mobile payment	0.332	1.599	0.110

*** indicates significance level <0.001.

provides the results of the hypothesis testing. Figure 2 illustrates the properties of the causal paths, including standardized path coefficients.

6. Results and Discussion

The research model proposed in this study received partial support, with the data validating six out of nine hypotheses. Specifically, H1, which posited a significant relationship between perceived privacy and performance expectancy, was supported (β = −0.767, p-value < 0.001). Contrary to the expected positive relationship, a negative correlation was found, consistent with Alanzi et al. (2023). While this aligns with the significance noted by Abd-Alrazaq et al. (2020), it contrasts with their positive correlations, possibly due to Thai consumers perceiving that enhanced privacy protections in mobile payment systems may detract from system performance.

The findings corroborate H2, providing evidence for a positive relationship between perceived security and performance expectancy (β = 1.364, p < 0.001). Given the direct linkage of mobile payments to consumers’ banking or credit card accounts, the security of these transactions emerges as a crucial part of the anticipated performance from mobile payment technologies. The likelihood of consumers attributing a higher performance to mobile payment technology increases when they perceive their financial information as safeguarded. The significant finding for H2 agree with the results reported in mobile payment studies by Katini et al. (2023) and Abrahim Sleiman et al. (2023).

The current study supports hypothesis H3, indicating a significant positive relationship between technology competency and effort expectancy (β = 1.096, p-value < 0.001). The rationale posited that higher technology competency reduces the effort required to use mobile payment technology. This finding aligns with Nguyen (2023) but contradicts Bailey et al. (2022), who found no significant relationship. The discrepancy may be attributed to the generally simplistic design of mobile payment technology, which minimizes the effort needed across all levels of technology competency.

The data analysis revealed support (β = 0.358, p-value < 0.001) for the proposed positive relationship between perceived compatibility and attitude toward mobile payment (H4). This relationship essentially looks at how mobile payment fits into the lifestyles of consumers. The better the fit that mobile payment provides with the consumer’s lifestyle, the more likely the consumer is to have a positive attitude towards mobile payment. This finding agrees with the findings reported in studies by Nguyen (2023) and Alzaidi (2022). This could be explained by the ubiquity of mobile devices in Thailand. Those who possess a smart mobile device are likely to have that device with them for most of their waking hours. Thus, it is perceived as compatible with their lifestyles, resulting in a more favorable attitude toward mobile payment.

The data analysis results did not support H5, which proposed a positive relationship between social influence and attitude toward mobile payment (β = 0.032, p-value = 0.283). This finding is consistent with studies by Lu and Lu (2020) on Chinese Millennials’ mobile payment adoption and Sharma et al. (2020). However, it contradicts Geng et al. (2023), who reported a significant relationship. The discrepancy may be attributed to Thailand’s collectivist culture, where individuals have large social networks with diverse opinions on mobile payment. Consequently, Thai consumers may prioritize perceived benefits over social influence when deciding to adopt mobile payment.

Support was found for H6, which proposed a positive relationship between facilitating conditions and attitude toward mobile payment (β = 0.392, p-value < 0.001). Facilitating conditions, such as having a compatible device, the ability to use it, and a wireless Internet connection, are essential for utilizing mobile payment (Chawla and Joshi 2023). This finding aligns with studies by Chawla and Joshi (2023) and Roh et al. (2023) but contrasts with Abd-Alrazaq et al. (2020). Since the introduction of the Thailand 4.0 Strategy in 2016, the Thai government has promoted digital economy initiatives, enhancing mobile device usage and broadband connectivity. Collaborative efforts with the Central Bank of Thailand have further supported mobile payment adoption, fostering a positive attitude toward mobile payment among Thai consumers (Mastercard 2024).

The data analysis results did not support H7, which proposed a positive relationship between performance expectancy and the intention to use mobile payment (β = 0.093, p-value = 0.293). This finding aligns with the results of de Blanes Sebastián et al. (2023) and Purohit et al. (2022) but diverges from studies by Bailey et al. (2022), Linge et al. (2023), and Widyanto et al. (2022). The absence of a significant link between performance expectancy and intention may be due to Thailand’s longstanding preference for cash transactions, which diminishes the perceived advantages of mobile payments. Additionally, the maturity of mobile payment technology in Thailand may lead consumers to base their decision on other factors.

The analysis confirmed the hypothesized positive relationship between effort expectancy and the intention to use mobile payment (H8), with significant support (β = 0.700, p < 0.001). This aligns with findings from Linge et al. (2023) and Purohit et al. (2022) while contrasting with studies by Bailey et al. (2022) and de Blanes Sebastián et al. (2023), which found no significant correlation. The current study’s findings may be attributed to the simplicity of mobile payment systems, which require minimal effort to use, and the technological competence of consumers who find these systems easy to adopt, thereby increasing their intention to use mobile payment.

The data analysis results did not support H9, which proposed a positive relationship between attitude toward mobile payment and the intention to use mobile payment (β = 0.332, p-value = 0.110). This finding is quite interesting and contradicts reports found in many prior studies (e.g., Lu and Ahn 2023; Yang et al. 2023). Several factors may contribute to this unexpected result.

First, it is possible that external factors, such as social influence and facilitating conditions, play a more dominant role in shaping the intention to use mobile payments in Thailand. In a collectivist society like Thailand, the opinions and behaviors of peers, family, and social networks can have a stronger impact on individual decisions compared to personal attitudes.

Second, the widespread adoption of mobile payments in Thailand may have reached a point where the intention to use is driven more by practical considerations and convenience than by personal attitudes. With the Thai government’s strong push for digital economy initiatives and the collaboration with the Central Bank of Thailand, mobile payment infrastructure and support have become robust, possibly reducing the influence of personal attitudes on usage intention.

Third, the novelty effect of mobile payment technology might have worn off, making attitudes less predictive of usage intentions. As mobile payments become a standard and routine part of everyday transactions, users’ intentions might be influenced more by habitual behaviors and established routines than by their attitudes toward the technology.

7. Study Implications

7.1. Theoretical Implications

The current study significantly expands upon the Unified Theory of Acceptance and Use of Technology (UTAUT) by incorporating several crucial variables specific to the mobile payment context within Thailand’s dynamic market landscape. By integrating perceived privacy, perceived security, technological competency, perceived compatibility, and attitude toward mobile payment, this research enriches our understanding of the intricate factors influencing mobile payment adoption in developing regions.

This advancement is particularly noteworthy, as it sheds light on the unique considerations and challenges faced by consumers in emerging economies like Thailand. Unlike findings from more advanced economies, the factors driving technology adoption in developing contexts can vary significantly due to cultural, economic, and infrastructural differences. Therefore, this study provides valuable insights that cannot be simply extrapolated from research conducted in developed nations.

Moreover, this research endeavor addresses a notable gap in the existing literature, as identified by Bui et al. (2022), by offering a comprehensive examination of mobile payment adoption within developing economies. By delving deeply into this relatively underexplored area, the study contributes not only to academic knowledge but also to practical understanding and application in real-world settings.

Furthermore, the dynamic nature of technological advancements, particularly in the realm of mobile payment, necessitates continuous investigation and adaptation. As new technologies emerge and consumer preferences evolve, previously significant factors may become obsolete or require re-evaluation. Thus, ongoing research efforts are crucial for staying abreast of these developments and ensuring the relevance and applicability of findings over time.

Lastly, the proposed research model serves as a robust framework that can guide future studies on mobile payment adoption in developing economies. By providing a comprehensive structure and incorporating the key variables relevant to these contexts, this model lays the groundwork for further exploration and refinement in this field. It not only facilitates a deeper understanding of mobile payment dynamics but also offers practical implications for policymakers, businesses, and other stakeholders striving to promote technological innovation and economic development in developing nations.

7.2. Practical Implications

The findings of this study provide actionable insights for mobile payment service providers, offering guidance on how to develop systems that better align with user preferences. For instance, the research highlights the paramount importance of privacy and security for Thai consumers, indicating that providers should prioritize implementing robust measures in their systems to ensure the safety and confidentiality of users’ information. Additionally, the study emphasizes the significance of aligning mobile payment systems with the needs and lifestyles of Thai consumers. This is exemplified by the observed relationship between perceived compatibility and attitudes toward mobile payment, suggesting that systems tailored to fit seamlessly into users’ lives are more likely to be embraced.

Moreover, the study’s focus on facilitating conditions resonates with the Thai government’s Thailand 4.0 Strategy, which seeks to enhance Internet connectivity and advance the digital economy. Enhancing the telecommunications infrastructure to better serve the rural areas of Thailand (see Telecom Review 2024) is only part of the solution; the Thai government should also consider implementing educational programs to boost the digital literacy of residents in the rural areas. This could be accomplished through public–private partnerships to improve the financial inclusion among the Thai population. By emphasizing the importance of user experience, the research suggests that providers should concentrate on creating intuitive user interfaces, simplifying registration and authentication processes, and refining transaction pathways. These efforts are essential for enhancing the overall user experience and fostering a greater adoption of mobile payment solutions in Thailand.

8. Conclusions, Limitations, and Future Research Directions

This study applied an extended UTAUT model as a theoretical foundation to examine Thai consumers’ mobile payment adoption. The proposed research model accounted for approximately 74 percent of the variance on Thai consumers’ behavioral intentions towards mobile payment adoption. This study takes a step towards filling a gap in the literature by addressing the adoption of mobile payment in the developing economy of Thailand.

By expanding the Unified Theory of Acceptance and Use of Technology (UTAUT) framework to include constructs such as perceived privacy, perceived security, technological competency, perceived compatibility, and attitude toward mobile payment, this investigation explains the impact of certain factors on the willingness of individuals to adopt mobile payments. Highlighting the pivotal role of privacy and security, this study underscores consumer apprehensions regarding the safeguarding of their financial dealings within mobile payment frameworks. Furthermore, it provides evidence of the significance of technology competency and the perceived compatibility with individual consumer lifestyles and their attitude towards mobile payments, subsequently influencing their intent to adopt.

This study provides practical insights for businesses offering mobile payment services, advocating for a concerted emphasis on bolstering privacy, security, and overall user experience. Through the alignment of mobile payment frameworks with consumer preferences and the assurance of stringent security protocols, service providers are positioned to stimulate broader acceptance among Thai consumers. Moreover, the findings furnish critical implications for policy architects, highlighting the instrumental role of enabling conditions, such as expansive broadband wireless Internet access and conducive digital economic policies, in the enablement of mobile payment adoption.

Overall, this study addresses a gap within the existing literature by delivering an exhaustive dissection of the determinants influencing mobile payment adoption in the context of developing economies. It not only corroborates the expanded UTAUT model within the Thai milieu but also offers actionable items for stakeholders endeavoring to bolster financial inclusivity and economic advancement via mobile payment technologies. Future inquiries may delve into the enduring effects of these determinants on mobile payment acceptance and investigate the transformative potential of emerging technologies in refining consumer dispositions and behaviors towards mobile payment.

Empirical research inherently has its limitations, and this study is no exception. The focus was on Thai consumers, with data collected via an online survey from self-selected participants, introducing self-selection bias as a limitation. Participants who chose to participate may hold different perspectives compared to those who did not, potentially limiting the representativeness of the sample for the Thai population. However, given the constraints of empirical research, the researchers consider the sample sufficient.

Furthermore, this study solely concentrated on participants from Thailand, despite the availability of mobile payment in numerous other developing economies. Future research could explore a more diverse sample across multiple developing countries and compare mobile payment perspectives between developed and developing economies, offering insights into cultural and economic influences. A comparative study across developing economies with diverse cultures could also be an intriguing area for future research. Additionally, exploring the impact of financial risk on mobile payment adoption in Thailand is another promising avenue for future investigation.

The investigation into mobile payment system acceptance in Thailand represents an advancement in understanding technology acceptance within developing economies. This research offers insights that can be instrumental in similar economies grappling with comparable technological adoption challenges and opportunities. Understanding the dynamics of technology adoption among consumers in these contexts is vital for businesses and policymakers aiming to drive innovation and economic progress. This study contributes to the scholarly dialogue on technology acceptance, laying the groundwork for future research endeavors in this domain and offering valuable implications for academia and industry alike.