Technological readiness and its impact on mobile payment usage: A case study of go-pay

Abstract

This research was conducted to analyze the factors that influence the use of mobile payment Go-Pay in Indonesia. The modeling of mobile payment usage behavior is a combination of technological readiness theory, technological acceptance theory, valence framework, and usage pattern theory. Data were collected using online questionnaires, which were analyzed using partial least squares-structural equation modeling (PLS-SEM) method. Data analysis of 1,225 respondents showed that technology readiness influenced significantly positively perceived usefulness, perceived ease of use, and significantly negatively influenced perceived cost and perceived risk. However, out of these four factors, only perceived risk and perceived usefulness that significantly affected the use intention. Furthermore, the results also show that facilitating conditions and use intention significantly and positively affected the usage rate of Go-Pay’s basic function, whereas the frequency of usage rate of innovative function of Go-Pay was influenced by facilitating conditions and the frequency of users in using the basic features (usage rate of basic function) of Go-Pay. The results imply that mobile payment companies looking to improve their services might consider optimizing their application to accommodate technology readiness and acceptance factors, as well focus on delivering basic functions well to drive future use of innovative functions.

Keywords:

• behavioral intention
• mobile payment
• PLS-SEM
• survey
• technology readiness
• theory perception
• usage pattern

1. Introduction

In the era of globalization and full of mobility, one of the fastest growing technologies is mobile devices. Mobile devices are becoming one of the technologies that have more and more new functions, one of which is to support several financial services (Liébana-Cabanillas et al., 2021; Oliveira et al., 2016). Mobile devices play an important role in financial services in payment, banking, investment, and other transaction-based and security sensitive applications (Choi et al., 2020). The use of technology to provide financial services is called financial technology or FinTech.

FinTech is a fairly important topic because of the advancement in terms of technical development, opportunities for business or market innovation, cost savings, and customer demand (Gai et al., 2018; Senyo & Osabutey, 2020). This advancement is further fueled by many companies are developing businesses by utilizing technology and starting to engage in the FinTech field. According to Indonesian Financial Services Authority (OJK), there were 125 FinTech companies that registered with the OJK (2021). These companies provide different financial services. One of the financial technology products that has become a trend in providing financial services is a mobile payment. Mobile payment is a process in which at least one transaction phase is carried out using a mobile device (such as a mobile phone, a smartphone, or a wireless device) capable of securely processing financial transactions via cellular networks or various wireless technologies such as Near-Field Communication (NFC), Bluetooth, Radio-Frequency Identification, and others. Mobile payments are experiencing rapid adoption in the market as the result of the growth of mobile devices in the world (Kaur et al., 2020).

Mobile payment is also a research topic chosen by many researchers. Some researchers discuss mobile payment by looking at the technology acceptance side by users. Researchers such as Luarn and Lin (2005), Cheng et al. (2006), Djauhari et al. (2020), and Liébana-Cabanillas et al. (2021) and others use the Technology Acceptance Model (TAM) to determine the factors that influence a person’s intention to use mobile payment technology. There are also researchers who use the Unified Theory of Acceptance and Use of Technology (UTAUT2) theory to determine the acceptance of mobile payments by someone. Researchers who used the UTAUT2 theory included Baptista and Oliveira (2015), Oliveira et al. (2016), and others.

Lu et al. (2011) also conducted research related to technology acceptance where they used the valence framework as a research model. Valence framework is a theory that uses “cognitive thinking” to determine user behavior by considering positive and negative attributes. Furthermore, Cocosila and Trabelsi (2016) also conducted research that used positive and negative approaches to determine user adoption of mobile payments.

Based on these studies, most of the research conducted focuses on the context of technology acceptance. However, not many researchers have paid attention to how these factors affect actual usage behavior. Research conducted by Oliveira et al. (2016) has actually investigated further, not just the intention of use. However, the research looked at it from a different point of view, namely seeing how a person’s intention to adopt mobile payment is to recommend the technology.

In addition, Parasuraman (2000) developed a theory, namely Technology Readiness (TR), which refers to the tendency of people to embrace and use new technology to achieve goals in home life and at work. This study looked at a person’s readiness for a technology from four perspectives, namely insecurity, discomfort, optimism, and innovativeness. This research was conducted because according to Parasuraman (2000), although technology provides benefits to users, there is also evidence that technology has an effect on increasing one’s frustration. Lin et al. (2011) conducted a study that discussed the relationship between technology readiness and TAM. Their research showed that technology readiness improves technology acceptance, especially perceived usefulness and perceived ease of use to the user’s intention to use a technology. Judging from this research, one’s readiness to technology is also an important factor influencing the acceptance of a technology. Acceptance of a technology is an important factor because users’ reluctance to accept and use the available systems often becomes a barrier to performance achievement (Davis, 1989).

Currently, technological developments in Indonesia also continue to increase. Many companies with different types of industry have launched several mobile payment products. The mobile payment product also supports several other services such as m-commerce (S. Yang et al., 2012) and online transportation. In Indonesia, there are several mobile payment products, such as Go-Pay and Grab Pay that support online transportation services, Telkomsel which issues T-Cash for transactions at several merchants, Bank Mandiri issues e-cash, BCA with Sakuku, and other products such as XL Tunai, PayPro, BBM Money, Doku Wallet, OVO, and CIMB Niaga Mobile Account. Of several mobile payment products in Indonesia, Go-Pay is mobile application-based mobile payment platform with a large number of users that has been touted as a market leader. Go-Pay is a leading mobile payment issued by parent company Go-Jek as a transaction method that can be used when users use the Go-Jek application or other platforms of various services, which have integrated Go-Pay into their services outside the Go-Jek ecosystem.

The number of mobile payment emerging is also accompanied by increased functions or features developed for each mobile payment. Go-Pay has several features, both basic features and innovative features. Go-pay can be used to make payments for basic services provided within the Go-Jek application, such as paying for Go-Ride, Go-Car, Go-Food, Go-Pulsa, and Go-Bills. Besides being able to make Go-Pay payments, it also has other advanced features such as money transfers and withdrawals. The money transfer feature is a feature that allows each user to transfer a certain amount of balance to another user’s account without being charged a transfer fee. Withdrawal feature is a feature that allows users to withdraw their balance to the user’s registered bank account. We choose Go-Pay as the mobile payment to be studied as it is a leading mobile payment in Indonesia.

Judging from previous research and the phenomenon of mobile payments in Indonesia, it is an interesting topic to explore. In this study, we attempt to analyze how a person’s technology readiness for a mobile payment can affect the factors that influence the person to accept mobile payments. The factors influencing the mobile payment used are based on acceptance theory comprising positive and negative factors. From these factors, we seek to understand the influence of mobile payment acceptance on the intention to use the mobile payment. In addition, we attempt to analyze the effect of use intention combined with one of the factors in the User Acceptance of Information Technology 2 (UTAUT2) theory, namely the facilitating conditions on actual usage behavior of the basic and innovative features of mobile payment. This is done because there are still a few studies that discuss the relationships between technology readiness, technology acceptance theory and the actual usage behavior stage. Based on the background and issues described above, the objectives of this study are three-fold:

1. To analyze the relationship between technology readiness and technology acceptance factors which are formulated into positive and negative factors.

2. To analyze the relationship between factors of acceptance of a person on mobile payment, especially Go-Pay and the intention to use mobile payment (Go-Pay).

3. To analyze the relationship between intention to use mobile payment (Go-Pay) combined with one’s facility willingness and actual usage behavior of the basic and innovative features of Go-Pay.

The remainder of sections are described as follows. Section two will focus on the literature review, followed by section three on research method. Subsequently, section four will discuss the research results. Finally, section five will present the conclusions, limitations, and future research.

2. Literature review

In this section, we review related theories and previous research that serve as a theoretical background. In addition, we propose a research model as well as associated hypotheses.

2.1. Theoretical background and previous research

There have been many studies investigating acceptance of mobile payment phenomenon from various theoretical perspectives. Oliveira et al. (2016) studied the main determinants of intention to adopt and recommend mobile payment among Portuguese users. Based on the User Acceptance of Information Technology 2 (UTAUT2) and diffusion of Innovation (DOI) theories, their results indicate that user’s intention to adopt a mobile payment is influenced by performance expectancy, social influence, compatibility and innovativeness factors. Similarly, a study by Abrahão et al. (2016) in Brasil found that the most significant factors influencing user intention to use mobile payment are performance expectancy, effort expectancy and social influence. In addition, their study also found perceived risk to have a negative impact on a user’s behavioral intention.

Furthermore, using uses and gratification theory (UGT), Alhassan et al. (2020) found ease of use and usefulness to be significant factors that affect towards attitude to continue using mobile payment. Another study based on an extended Technology Acceptance Model (TAM) in United States by Bailey et al. (2017) found that user self-efficacy impacted perceived ease of use and perceived usefulness, which in turn influence intention to use tap-and-go mobile payment. This highlights the importance of self-efficacy or user readiness in relationship with mobile payment acceptance. The concept of technology readiness is a well-known one (Parasuraman, 2000) that has been applied in various contexts. While other studies have attempted to link the readiness concept with technology acceptance in enterprise systems context, for instance, in a study of e-HRM (Erdoggmu & Esen, 2011), it has not been much used to investigate mobile payment.

Aside from adapting acceptance theories, other previous studies have tried to adapt the valance framework, which is used to distinguishes positive and negative valence determinants related to certain user behavior. Lu et al. (2011) are among several researchers who adapted the valence framework to study mobile payments. Their results indicate that positive and negative valence factors, in combination with trust, influence the intention of mobile payment users. In that study, Lu et al. (2011) categorized perceived cost and perceived risk into negative valences, while relative advantage, compatibility, and image were categorized into positive valences. Their results are in line with a study by Ryu (2018) who suggested that the valence framework can be used to determine perceived benefit and risk influencing FinTech adoption. Similarly, using the valence framework, Ozturk et al., 2017) suggested that privacy concern is considered as negative valence while convenience as positive valence—both significantly influence intention to use NFC-based mobile payment. Their results are in agreement with those of Liébana-Cabanillas et al. (2018) that found that perceived usefulness and security risk to be highly ranked factors influencing intention to use NFC-based mobile payment. Diving deeper into risk aspect of mobile payment, several other studies such as those of Y. Yang et al. (2015) investigated further how determinants of perceived risk significantly negatively affect intention to use mobile payments. In addition to perceived risks, it is discovered that trust in combination with perceived risks may influence usage intention of mobile payments in China (Shao et al., 2019) and Ghana (Kailan et al., 2019).

Beyond the intention to use a particular system, the use of the system or its actual use is a critical factor in the explaining the effectiveness of the system. Huh and Kim (2008)) conducted research on usage behavior by investing system usage and proposed two categories of system functions: basic and innovative functions. Son and Han (2011) expanded upon this concept and conducted research related to actual use. Their research identified three types of usage patterns, namely the usage rate of basic functions, the usage rate of innovative functions, and the usage variety of innovative functions. Son and Han (2011) examined how technology readiness affects the usage patterns of a system. In their study, it was discovered that technology readiness affects user’s post-adoption behavior.

As discussed in this sub-section, previous studies have largely applied technology acceptance model, UTAUT2, DOI theory, valence framework, and UGT theory with a focus on acceptance or discovering factors that predict intention to use mobile payments. While the relevance of these theories cannot be understated, there have been a dearth of existing studies that attempt to link user technology readiness to acceptance within the valence framework and actual usage pattern of mobile payment. This proposed combination of technology readiness theory, the technology acceptance theory, the valence framework, and the usage pattern are expected to present a wider perspective on mobile payment usage phenomenon preceding acceptance (through technology readiness) and succeeding acceptance (through usage pattern). The following sub-section discusses the conceptual model built upon a combination of these theories and their corresponding hypotheses.

2.2. Conceptual model and hypotheses

The research model for this study is a result of combination of several previous theories with some adjustments. The theories used are the technology readiness theory, the technology acceptance theory, the valence framework, and the usage pattern.

The technology readiness theory that we adopt is a theory developed by Parasuraman (2000) which we formulate to be the technology readiness variable, comprising four constructs that are used to determine a person’s readiness for a system or technology. The four constructs are insecurity, discomfort, optimism, and innovativeness.

The acceptance factors that we adopt in this study are perceived ease of use, perceived usefulness, perceived cost, perceived risk, facilitating conditions, and use intention. These factors were adopted from the TAM and UTAUT theories. These factors are used to analyze the acceptance of mobile payments, which in this case by Go-Pay users. The acceptance factors of perceived ease of use, perceived usefulness, perceived cost, and perceived risk are divided into two valences, namely positive and negative.

With regard to usage pattern, usage rate of basic function and usage rate of innovative function are factors that are adopted from Huh and Kim (2008) as well as Son and Han (2011). We use these two factors to find out usage of available features on Go-Pay. All in all, there were nine variables tested in our study, namely technology readiness, perceived risk, perceived cost, perceived ease of use, perceived usefulness, facilitating conditions, use intention, usage rate of basic function, and usage rate of innovative function. The proposed model can be seen in Figure 1. In the following sub-sections, we will formulate the research hypotheses related to this model.

Figure 1. Proposed research model.

2.2.1. Effect of technology readiness on perceived risk, perceived cost, perceived ease of use, and perceived usefulness

According to Parasuraman (2000) technology readiness is the tendency for people to embrace and use new technology to achieve goals in home life and at work. Individuals with low technology readiness tend to think negatively about new emerging technologies. Individuals with this character will see more of the negative impacts they may have to face if they use this technology. Based on the explanation of mobile payments, where transactions are made via cellular networks such as the internet, the individual may think that using a mobile payment poses certain risks such personal data leakage, wiretapping, and other risks. Perceived risk is defined as the level at which consumers believe that they may get some risks, such as certain financial, social, psychological, physical or time risks (Abrahão et al., 2016). Hence, it can be assumed that technology readiness has an inversely proportional effect to perceived risk. To support the theory and phenomenon of technology acceptance, we formulate the following hypothesis:

H1: Technology readiness has a negative effect on users’ perceived risk of using mobile payments.

Furthermore, individuals who have low technology readiness may think that the costs associated with using mobile payments could be high (e.g., administrative costs, time to learn about new systems, etc.). This implies the user may perceive that using a mobile payment requires substantial costs. Perceived cost refers to the initial costs, subscriptions, transactions, and communications that must be paid (Abrahão et al., 2016; Lu et al., 2011). Thus, it can be assumed that someone with low technology readiness tends to have a high perceived cost. To support the theory and phenomenon of technology acceptance, we formulate the following hypothesis:

H2: Technology readiness has a negative effect on users’ perceived costs for using mobile payments.

Individuals with high technology readiness tend to have good attitudes towards technology. Individuals with this character will believe that mobile payment technology will be easy to use. Perceived ease of use is defined as the degree to which a person believes that they do not require effort in using a system (Alhassan et al., 2020; Davis, 1989). Thus, it can be assumed that technology readiness with perceived ease of use is directly proportional. Research conducted by Erdoggmu and Esen (2011) showed that the technology readiness element affects the perceived ease of use of users. To support the theory and phenomenon of technology acceptance, we formulate the following hypothesis:

H3: Technology readiness positively affects users’ feelings of ease of use of mobile payments (perceived ease of use) on the use of mobile payments.

Individuals who have high readiness will also think that new technology provides many benefits to improve performance in carrying out their duties. These individuals will use mobile payment because of its capability to be used anytime and anywhere and the presence of useful services, so that they will save more time and energy when they have to make a payment transaction. Perceived usefulness is defined as the degree to which a person believes that using a system can improve their job performance (Alhassan et al., 2020; Davis, 1989). Hence, it can be assumed that technology readiness is directly proportional to perceived usefulness. Someone with high technology readiness has a positive perception because of the benefits provided by mobile payment. To support the theory and phenomenon of technology acceptance, we formulate the following hypothesis:

H4: Technology readiness has a positive effect on perceived usefulness by users on the use of mobile payments.

2.2.2. Relationships of perceived risk, perceived cost, perceived ease of use, perceived usefulness, and facilitating conditions to use intention

Previous studies suggest that perceived cost, perceived ease of use, and perceived usefulness affect the use intention of mobile payment (Bailey et al., 2017; Lu et al., 2011; Luarn & Lin, 2005). To complete these factors, we adopted the facilitating conditions factor from the UTAUT2 theory as suggested by Oliveira et al. (2016). In this study, we hypothesize that perceived risk, perceived cost, perceived ease of use, perceived usefulness, and facilitating conditions have effects on Go-Pay use intention. According to Cheng et al. (2006), use intention refers to the user’s intention to use the system, as opposed to their actual use.

Risk is the potential to gain or lose something of value such as physical health, social status, emotional well-being, or financial wealth (Cline, 2004). In using mobile payments, the risks that must be faced by users are related to personal information and payment transactions. This is because user information and data are stored in the cloud and transmitted over the internet. Therefore, it is important for mobile payment service providers to prioritize data security. If the user feels that the risk they will face is high, the user’s intention to use mobile payment will be low and vice versa. Thus, it can be said that perceived risk affects the use intention of mobile payment users. This is supported by research conducted by Abrahão et al. (2016), Shao et al., 2019) and Kailan et al. (2019), which indicate that perceived risk significantly and negatively affects mobile payment intentions. Thus, we formulate the following hypothesis:

H5: Perceived risk negatively affects the use intention of Go-Pay by users.

The time and costs that must be incurred by users are also one of the factors determining user intentions for mobile payments. If using a mobile payment an individual has to pay more (such as administration fees, devices, transaction fees, and time to study mobile payments), then he tends to be reluctant to use the mobile payment. This is because individuals tend to see the benefits they might get by using mobile payments both from financial and non-financial perspectives. That way it can be said from the user’s perception, the higher the costs, the lower the user’s intention to use mobile payment. This is supported by the research conducted by Zhang et al. (2012), which states that perceived costs negatively affect user intentions. This research is in line with research conducted by Luarn and Lin (2005). So, we can assume that perceived cost affects use intention. To support this statement, we make a hypothesis to be tested as follows:

H6: Perceived costs negatively affect the use intention of Go-Pay by users.

To prevent system problems that are less desirable, the mobile payment system must be easy to learn and easy to use. If the use of mobile payments is easy to do and users no longer need to learn to use mobile payments, then they tend to want to use mobile payments. Perceived ease of use is defined as the degree to which a person believes that they do not require effort in using a system (Alhassan et al., 2020; Davis, 1989). So we assume the higher the perceived ease of use, the higher one’s use intention towards mobile payments. This statement is in line with research conducted by Luarn and Lin (2005), Teo (2011), Zhang et al. (2012), and Alhassan et al. (2020), that perceived ease of use significantly affects user intentions. Therefore, we formulate the following hypothesis:

H7: Perceived ease of use positively affects the use intention of Go-Pay by users.

One reason someone wants to use mobile payments is because they think mobile payments can provide many benefits. Mobile payments offer high mobility to users. Users can make transactions anywhere and anytime quickly, so that in carrying out their daily activities, user quality of life will further improve. The more benefits the user will get, the greater the user’s intention to use mobile payments. Perceived usefulness is defined as the degree to which a person believes that using a system can improve their job performance (Davis, 1989). So we assume, perceived usefulness affects use intention. This statement is supported by research conducted by Liébana-Cabanillas et al. (2018) who found perceived usefulness to have a direct influence on user intentions. Therefore, we formulate the following hypothesis:

H8: Perceived usefulness positively affects the use intention of Go-Pay by users.

Oliveira et al. (2016) proposed that facilitating conditions affect behavioral intentions to adopt. This study is also in line with research by Baptista and Oliveira (2015) and Im et al. (2011). Facilitating conditions refer to consumers’ perceptions of the resources and support available to carry out an activity (Venkatesh et al., 2012). Apart from the high ability of mobile payments, adequate resources are also an important factor in determining one’s intention to use mobile payments. If someone has sufficient resources (such as mobile phones, internet, or shops or merchants) to make mobile payment transactions, that person will tend to be motivated to adopt mobile payment services. This is because individuals can carry out transactions smoothly with the support of sufficient resources. That way, it can be assumed that the more precise the facilities or resources available, the higher the user’s use intention. Therefore, we formulate the following hypothesis:

H9: Facilitating conditions affect the use intention of Go-Pay by the user.

2.2.3. Relationships of use intention and facilitating conditions to usage rate of basic functions and usage rate of innovative functions

In research related to information systems, the effectiveness of a system can be represented by its actual use. System usage behavior that can be measured from the actual frequency of users using technology. In this study, we measure usage behavior by looking at the usage patterns as suggested by Huh and Kim’s (2008) and Son and Han’s research (2011). In the their research, the user’s usage pattern comprises the usage rate of basic functions and the usage rate of innovative functions. Usage rate of basic function represents the average usage of basic features by users. Usage rate of innovative function represents the average usage of innovation features by users. The basic feature referred to in the context of our study is a feature for making payments, while the innovative feature is the cash transfer and withdrawal feature from Go-Pay.

The use of basic features and innovative features of mobile payment is influenced by the resources owned by the user. The resources in question are the phone or tablet, the internet, and the user’s basic knowledge. Someone who has a cellphone with a capable operating system can run applications smoothly. Thus, he or she will use the mobile payment more often. If someone has sufficient basic knowledge to use mobile payments, then there will be no obstacles in using the mobile payment feature. If a mobile payment company launches a new feature, users are required to update the application. To be able to update, users must have the appropriate device. This means that resources are an important factor so that users can use these innovative features. Facilitating conditions refer to consumers’ perceptions of the resources and support available to carry out an activity (Venkatesh et al., 2012). Thus, it can be said that facilitating conditions affect the frequency of users using basic features and innovative features. Therefore, we formulate the following hypotheses:

H10: Facilitating conditions affect the frequency of using the basic features of Go-Pay (usage rate of basic functions).

H11: Facilitating conditions affect the frequency of use of Go-Pay’s innovative features (usage rate of innovative functions).

Apart from sufficient resources, user intention is also an important factor in the frequency of users using the mobile payment feature. A person who has a high intention of mobile payment means that he is interested in the mobile payment and has a good acceptance of mobile payments. With good acceptance, someone will try to use the available mobile payment features. This is because the use of mobile payments is based on a desire or intention to use them. If someone has a high intention of using mobile payment, he or she will be interested in the innovative features launched by the mobile payment company. Then he or she will try to use these innovative features. Thus, it can be said that use intention affects user use behavior as measured by the usage rate of basic functions and the usage rate of innovative functions. This argument is supported by research conducted by Zhang et al. (2012), where user intentions significantly influence actual behavior. Therefore, we formulate the following hypothesis:

H11: Use intention affects the frequency of use of Go-Pay’s basic features (usage rate of basic functions).

H13: Use intention affects the frequency of use of Go-Pay’s innovative features (usage rate of innovative functions).

2.2.4. Relationship of usage rate of basic function to usage rate of innovative function

According to Maslow (1943), humans have five needs that form levels or also called hierarchies from the most important to the less important ones and from the easy to the difficult ones to achieve or obtain. Human motivation is very much influenced by basic needs that need to be met. According to Maslow, a person must meet the most important needs first then increase to the higher level needs. In the realm of product development and usage, Huh and Kim (2008) proposed two categories of related to their user needs, namely basic and innovation functions. This is applicable for the mobile payment feature usage. The use of innovative features in an mobile payment application or system cannot be separated from the use of basic features. Innovative features on a system can appear and be adjusted by looking at user habits or behavior when using basic features. Usually, a company may propose an additional innovative feature after studying user’s initial or basic usage behavior. This is because user behavior in using basic features may describe or suggest the user’s needs for other advanced features. The more often users use basic features, they may feel the need for other features that can support their increasing needs. Thus, it can be said that the use of innovative features can be affected by the frequency of basic feature usage. To prove this assumption, we formulate the following hypothesis:

H14: Frequency of use of Go-Pay basic features (usage rate of basic functions) affects the frequency of use of Go-Pay’s innovative features (usage rate of innovative functions).

3. Research method

As a quantitative study, this section specifically explains the process of research instrument development, data collection, and data analysis process.

3.1. Measurement and data collection

To facilitate data collection, we start by formulating research instruments. At this stage, we compile questions and statements based on the model that is proposed in the previous section. These statements and questions are taken from previous theory and research, in which we translated the sentence from English into Indonesian without changing the meaning of each sentence. The questionnaire formed is presented on a Likert scale.

Subsequently after the questionnaire was formed, we conducted a readability test on the questionnaire. This is to ensure the feasibility of the questions and questionnaire statements in terms of the rules of writing, research, and the meaning of each sentence and work instructions. The readability test was conducted on 11 testers who had used Go-Pay with different educational background, ages and experiences. This selection of testers is intended to ensure that the questions and statements on the questionnaire can be understood by various types of Go-Pay users. The age range of the testers was around 17–25 years, with some of testers coming from the Faculty of Mathematics and Natural Sciences Universitas Indonesia (UI), Faculty of Psychology UI, Faculty of Computer Science UI, Faculty of Cultural Sciences UI, Faculty of Engineering UI, Faculty of Engineering Diponegoro University, Faculty of Communication Science Veteran National Development University Jakarta, and others are four private employees.

Having tested and revised the questionnaire, it was ready for data collection. In this study, data were obtained through sampling from existing populations. The sample is a small part or the number which is intended to represent the actual population. This study uses non-probability sampling technique that does not provide equal opportunities for each element or member of the population to be selected as samples (Galloway, 2005). The method used was convenience or accidental sampling, in which the sample selection is based on the availability of population members. The process of collecting data was by distributing questionnaires online to Go-Pay users. The questionnaire could be accessed by visiting a hyperlink: https://bit.ly/SurveiGOPAY. We distributed the questionnaire through several social media and communication platforms, namely LINE, WhatsApp, Instagram, Twitter, and Facebook. In distributing the questionnaire, we added information that participants could stand the chance to win prizes for 10 lucky respondents. This was conducted to attract Go-Pay users to fill out the questionnaire. According to J. F. Hair et al. (2011), the minimum sample size that must be met for a PLS-SEM study is at least 10 times the number of most predicted construct. The number of respondents in this study has met this requirement with a total of 1,253 responses against the minimum sample collected of 40 responses.

3.2. Data analysis

At this stage, data processing was carried out using the Structural Equation Model (SEM) technique with the Partial Least Square (PLS) method. According to J. F. Hair et al. (2011), PLS-SEM is used when the objective of the research is to predict the main constructs or for research that is exploratory or extends to existing theories. We used PLS-SEM since our proposed research model consists of reflective-formative and first-order second-order constructs. According to J. F. Hair et al. (2011) and Becker et al. (2012), if the research model consists of a complex construct and has a second-order construct, then the analysis of funding is more appropriate to do with PLS-SEM. The form of the research model is categorized as the type II formative-reflective model according to the types of models discussed by Becker et al. (2012). The first-order constructs that we propose consist of insecurity, discomfort, optimism, formative innovativeness towards technology readiness as second-order constructs. In completing the analysis, we used a repeated indicator approach. This is because repeated indicator approach is suitable for use in type II or IV models, where the research model proposed is included in the type II category. In addition, repeated indicator approach also has the ability to estimate all constructs simultaneously, so that the analysis results are more representative. We used the SmartPLS 3.2.7 application to perform data processing encompassing two stages, namely the measurement model (outer model) and structural model (inner model). After the data were processed, we analyzed the data by linking the results of calculations and hypotheses previously made.

The online data collection of Go-Pay users resulted in 1,253 responses. Of all the data, an examination was made of the possibility of duplicate data or the possibility of the respondent answering the same from the beginning to the end of the questionnaire. From this examination, there were 28 invalid responses, so that the total data that were used in this study are 1,225 responses. Please see, Table 1 for characteristics of respondents based on age, gender, occupation, earnings per month, the level of frequency of use and the duration of use of Go-Pay. To ensure privacy and confidentiality of the data collected, we restrict data access only to the authors of this paper for the sole purpose of answering the research questions. In addition, the survey instrument also allowed anonymous responses. Furthermore, we also ensure that no personally identifying information can be associated with a particular response unless a respondent chose to provide additional personal information to be considered for the lucky prizes.

Table 1. Summary of respondents’ demographics

Demographics	Frequency	Percentage
Domicile
Jabodetabek Area	793	64.73
Java Island (Non-Jabodetabek)	303	24.73
Outside of Java Island	129	10.53
Gender
Male	510	53.22
Female	765	46.78
Age
≤ 16 years	159	2.61
17–25 years	578	63.27
26–35 years	66	31.43
36–45 years	330	2.20
> 45 years	48	0.49
Educational background
High school diploma or below	796	33.55
Diploma 3	256	8.08
Bachelor’s degree	102	52.65
Master’s degree	60	5.71
Doctoral degree	61	0
Current occupation
Students	229	39.84
Public employees/civil servants	356	4.73
Private employees	327	39.92
Entrepreneurs	279	7.18
Others	84	8.33
Income per month
≤ Rp1.000.000	171	19.51
Rp1.000.001—Rp5.000.000	263	48.98
Rp5.000.001—Rp10.000.000	244	20.08
Rp10.000.001—Rp15.000.000	597	6.78
> Rp15.000.000	597	4.65
Duration of Use of Go-Pay
< 1 month	171	4.90
1–3 months	263	8.41
4–6 months	244	11.92
7–9 months	597	7.92
10–12 months	597	11.67
> 12 months	597	55.18

4. Results & discussion

In this section, we evaluate the results of the data analysis, specifically measurement model test, structural model test, and hypotheses test.

4.1. Measurement model results

Outer model (measurement model) is the first step in the analysis process using PLS-SEM. In the model that has been created, it can be seen that a relationship is formed between the latent variable or factor being tested and the manifest variable or indicator. This relationship shows that each indicator explains each latent variable that has a relationship with it. This first stage aims to determine the validity and reliability of each indicator.

The research model proposed contains a second-order variable, namely technology readiness (TR). The insecurity (INS), discomfort (DIS), innovativeness (INN), optimism (OPT) variables are the first-order variables of the technology readiness (TR) variable. Each indicator of each first-order relates reflectively to each of its variables. The first-order variable is formative to TR. Therefore, processing at the measurement model stage is carried out by looking at the validity and reliability in a reflective and formative manner.

One of the steps to measure the reliability of an indicator is by looking at the loading factors. The loading factor value is obtained by running the PLS Algorithm on the SmartPLS software. The loading factor value of each reflective indicator must be greater than 0.7. After discarding 11 indicators that did not meet the threshold value (i.e. DIS2, DIS3, DIS4, FC4, FC5, INN3, INN5, INS2, INS3, OPT1, PR1), the remaining indicators exceed the value of 0.7, suggesting that the model being tested meets the indicator reliability requirements.

Aside from the loading factors, we also need to examine internal consistency reliability of the indicators. Internal consistency reliability is a measure of how well the indicators of the same variables are. According to J. F. Hair et al. (2011), measuring internal consistency reliability can be done by looking at the composite reliability (CR) value of a reflective construct. J. F. Hair et al. (2011) also suggested that the CR value must be greater than 0.7 (in exploratory research). Table 2 shows that all variables meet the requirements for internal consistency reliability because they have CR values of more than 0.7.

Table 2. Composite reliability and average variance extracted results

Variables	Composite Reliability (CR)	Average Variance Extracted (AVE)
Discomfort (DIS)	0.766	0.62
Facilitating Conditions (FC)	0.903	0.757
Innovativenss (INN)	0.827	0.615
Insecurity (INS)	0.863	0.677
Optimism (OPT)	0.846	0.58
Perceived Cost (PC)	0.889	0.616
Perceived Ease of Use (PEOU)	0.934	0.74
Perceived Risk (PR)	0.874	0.635
Perceived Usefulness (PU)	0.909	0.667
Technology readiness (TR)	0.829	0.755
Use Intention (UI)	0.939	0.755
Usage Rate of Basic Functions (URBF)	0.961	0.756
Usage Rate of Innovative Functions (URIF)	0.828	0.708

Convergent validity of construct measures that should theoretically be related to one another are in fact observed to be related to one another. Convergent validity can be measured by looking at the Average Variance Extracted (AVE) value which must be greater than 0.50. According to J. F. Hair et al. (2011), a high AVE value equal to 0.5 indicates that the latent variable explains more than half of the variance of the indicator. Table 2 shows that the AVE values of all latent variables are greater than 0.50.

Discriminant validity is a measure of constructs that should theoretically be related to one another, in fact, be observed to be unrelated to one another. Discriminant validity is measured using cross loading test and Fornell–Larcker criterion (AVE value against other variables). Testing with cross loading is carried out by looking at the loading factor value of the indicator with the associated latent construct must be higher than the loading factor with all other constructs (J. F. Hair et al., 2011). Each indicator has a loading factor value with its construct higher than the other constructs. The TR construct is not included in checking discriminant validity, this is because TR is a first-order second-order construct.

The Fornell–Lacker criterion test is a condition where latent constructs share more variance with the set indicator compared to other latent variables in the structural model. The value of the square root AVE of each latent construct must be higher with the construct than the other latent constructs (Fornell & Larcker, 1981). Table 3 shows that the tested model fulfills the requirements of Fornell-Larcker creation. Each indicator has a square root value of AVE that is greater than itself compared to other constructs.

Table 3. Fornell-lacker creation test results

	DIS	FC	INN	INS	OPT	PC	PEOU	PR	PU	UI	URBF	URIF
DIS	0.788
FC	0.297	0.87
INN	0.235	0.437	0.784
INS	0.283	0.198	0.116	0.823
OPT	0.296	0.535	0.465	0.235	0.761
PC	−0.382	−0.424	−0.173	−0.378	−0.347	0.785
PEOU	0.258	0.655	0.416	0.182	0.478	−0.399	0.86
PR	−0.291	−0.222	−0.101	−0.633	−0.215	0.474	−0.22	0.797
PU	0.268	0.635	0.434	0.209	0.601	−0.407	−0.278	0.22	0.817
UI	0.262	0.578	0.406	0.247	0.53	−0.382	0.541	−0.278	0.668	0.869
URBF	0.184	0.217	0.119	0.1	0.244	−0.197	0.185	−0.116	0.22	0.292	0.869
URIF	−0.017	0.026	0.088	0.012	0.032	−0.011	0.013	−0.036	0.04	0.095	0.21	0.841

The research model proposed has a second-order construct, namely Technology Readiness (TR) which is composed of four first-order constructs. The four constructs that support TR are insecurity (INS), discomfort (DIS), innovativeness (INN), and optimism (OPT). At the formative measurement model stage, it is necessary to test the significance and multicollinearity on the four first-order constructs of the second-order constructs.

At this stage the we used bootstrapping on SmartPLS to assess significance on all constructs. The minimum number of bootstrap samples is 5,000. The t-statistics value for the two-tailed test is 1.65 (significance level = 10%), 1.96 (significance level = 5%) and 2.58 (significance level = 1%). When all indicator weights are significant, there will be empirical support to support all indicators. If weights and loadings are not significant, there is no empirical support for supporting indicators. In this study, we used a significance value of 5% when running the bootstrapping. Table 4 shows significant test results for second-order constructs.

Table 4. Results of the second order construct significance test

Second-order construct	First-order construct	Path coefficient	T-statistics
TR	DIS	0.197	16.318
FC	INN	0.327	20.586
INN	INS	0.352	15.686
INS	OPT	0.555	31.008

Table 4 also shows that all path coefficient values of the formative constructs are greater than 0.10 with a positive consistent sign. In addition, the t-statistics value of each first order construct is also greater than 1.96 (5% significance level). This shows that all indicators of the first order construct are the dimensions of the second construct.

The second step when performing a formative measurement model is to check the degree of multicollinearity of the first order. Indicators with a high degree of multicollinearity in a formative measurement model can cause the indicator to be insignificant (J. F. Hair et al., 2011). Multicollinearity can be measured by looking at the variance inflation factor (VIF) value. Each indicator must have a VIF value of less than 5. Table 5 shows that all first-order constructs have a VIF value below 5. This illustrates that first-order constructs have a formative relationship to second-order constructs.

Table 5. Results of the multicollinearity

Construct	VIF Technology Readiness
DIS	1.247
INN	1.365
INS	1.243
OPT	1.465

4.2. Structural model testing

Coefficient of determination measures the amount of variation taken into account in endogenous constructs by exogenous constructs (J. F. Hair et al., 2011). Coefficient of determination can be measured by looking at the r-square (R²) value. According to J. F. Hair et al. (2011), the R² values of 0.75; 0.50; and 0.25 respectively indicates that the constructs are strong, moderate, and weak.

Table 6 presents the results of coefficient of determination calculation. While the UI construct has moderate coefficient of determination, PC, PEOU, PR, PU, URBF, and URIF constructs have weak coefficient of determination. The r-square value of PC construct is weak, namely 0.228. This value indicates that 22.8% of the variation in PC data is influenced by TR and the remaining 77.2% is determined by other constructs not contained in the research model. The r-square value of perceived ease of use (PEOU) construct is weak, namely 0.275. This value indicates that 27.5% of the variation in PEOU data is influenced by TR and the remaining 72.5% is determined by other constructs not included in the research model. The r-square value of perceived risk (PR) construct is very weak, namely 0.194. This value indicates that 19.4% of the PR data variation is influenced by TR and the remaining 80.6% is determined by other constructs not contained in the research model. The r-square value of the moderate perceived usefulness (PU) construct is close to weak, namely 0.359. This value indicates that 35.9% of PU data variation is influenced by TR and the remaining 64.1% is determined by other constructs that are not included in the research model.

Table 6. R square (R²) values

Construct	R^2
PC	0.228
PEOU	0.275
PR	0.194
PU	0.359
UI	0.505
URBF	0.087
URIF	0.047

The r-square value of use intention (UI) construct is moderate, namely 0.505. This value indicates that 50.5% of UI data variation is influenced by PC, PEOU, PR, PU, and facilitating conditions (FC). The remaining 49.5% is determined by other constructs not included in the research model. The r-square value of perceived cost (URBF) construct is very weak, namely 0.087. This value indicates that only 8.7% of the URBF data variation is influenced by UI and FC. The data variation of 91.3% is determined by other constructs not included in the research model. The r-square value of perceived cost (URIF) construct is very weak, namely 0.047. This value shows that only 4.7% of the variation in the URIF data is influenced by UI, FC, and URBF, the remaining 95.3% is determined by other constructs not found in the research model.

In addition to coefficient determination, we also assess predictive relevance, which is a measure of the ability of the research model to predict. Predictive relevance can be measured by Stone-Geisser’s Q² value. The value of Q² illustrates how big the predictive relevance of the inner model that has been designed. If the Q2 value of the endogenous construct is greater than 0, it can be said that the endogenous construct has good predictive relevance (J. F. Hair et al., 2011). Table 7 shows that all constructs have a Q2 value that is greater than 0. This shows that all constructs have good predictive relevance.

Table 7. Stone-Geisser’s Q²

Construct	Q^2 (=1-SSE/SSO)
PC	0.126
PEOU	0.193
PR	0.115
PU	0.237
TR	0.287
UI	0.367
URBF	0.085
URIF	0.03

At this stage, we subsequently determine whether the hypotheses made in section 2 are accepted or not. The assessment of the hypotheses is seen from the level of significance of a construct to the intended construct. The process is done by using bootstrapping on the SmartPLS application. The minimum number of bootstrap samples is 5,000 (J. F. Hair et al., 2011). The high or low relationship (significance) between variables can be seen in the t-statistic value and also the p-value (Roca et al., 2009) and from the path coefficient value. The t-statistics value for the two-tailed test is 1.65 (significance level = 10%), 1.96 (significance level = 5%) and 2.58 (significance level = 1%). In this study, we used a significance value of 5% when running the bootstrapping. If the t-statistic is greater than 1.96 and the p-value is less than 0.05, then the path is significant.

Table 8 shows that there are three hypotheses that have a t-statistical value smaller than 1.96 and a p-value greater than 0.5, namely H6, H7, H11. There are 11 hypotheses that have a statistical t-value greater than 1.96 and a p-value smaller than 0.5, namely H1, H2, H3, H4, H5, H8, H9, H10, H12, H13, H14. TR construct has a significant influence on the PR, PC, PEOU and PU construct. PR and PU construct significantly influence UI construct. FC construct significantly affects URBF. UI construct significantly influences URBF and URIF construct. URBF construct significantly influences URIF construct.

Table 8. Hypotheses test results

Hyp.	Path	Path Coefficients	T-statistics	P-values	Notes
H1	TR -> PR	−0,433	14,22	0	Accepted
H2	TR -> PC	−0,458	17,647	0	Accepted
H3	TR -> PEOU	0,518	22,08	0	Accepted
H4	TR -> PU	0,603	29,001	0	Accepted
H5	PR -> UI	−0,102	4,201	0	Accepted
H6	PC -> UI	−0,037	1,381	0,167	Rejected
H7	PEOU -> UI	0,015	0,453	0,65	Rejected
H8	PU -> UI	0,4795	14,211	0	Accepted
H9	FC -> UI	0,228	6,806	0	Accepted
H10	FC -> URBF	0,073	2,079	0,038	Accepted
H11	FC -> URIF	−0,058	1,596	0,111	Rejected
H12	UI -> URBF	0,249	7,369	0	Accepted
H13	UI -> URIF	0,069	2,081	0,037	Accepted
H14	URBF -> URIF	0,203	7,364	0	Accepted

4.3. Discussion

From the results of data processing, analysis, and hypothesis testing carried out in previous sub-section, there were three rejected hypotheses and eleven accepted hypotheses. The hypothesis that is rejected is considered an insignificant hypothesis, in which the t-statistics value is less than 1.96 and the p-value is greater than 0.05. Further discussion regarding the effect of each construct is presented as follows.

4.3.1. Effect of technology readiness on perceived risk, perceived cost, perceived ease of use, and perceived usefulness

From the results of hypothesis testing, the results of technology readiness affect the acceptance of mobile payments. Of the four proposed mobile payment acceptance variables, all variables are influenced by technology readiness. The strongest relationship is the relationship between technology readiness and perceived usefulness which is supported by the path coefficient value of 0.603 and has a positive relationship. From these results, it can be said that someone who has high mobile payment technology readiness will have good perceived usefulness for Go-Pay. It can be said that individuals with high technology readiness tend to have good attitudes towards technology, so that they will feel that Go-Pay provides benefits that can improve their performance in carrying out their duties (transactions). These results are in line with research conducted by Erdoggmu and Esen (2011) which showed that the technology readiness element affected the user’s perceived usefulness.

Apart from perceived usefulness, technology readiness also has a significant and positive relationship to perceived ease of use. This relationship indicates that the higher a person’s technology readiness on mobile payments, the more he or she has a high perceived ease of use of mobile payments. It can be said that someone who has high readiness believes that Go-Pay will be easy to use. These findings are in line with research conducted by Erdoggmu and Esen (2011).

Apart from the positive effect, technology readiness negatively affects perceived risk and perceived cost which is supported by a negative path coefficient value. It can be said that mobile payment technology readiness affects a person’s perception in terms of risk and cost. Whenever a mobile payment is made, users will believe that they may get some risks, such as financial, social, psychological, physical or time risks and they believe that they will have to pay for using mobile payments (especially Go-Pay). The relationship between technology readiness and perceived risk and perceived cost have never been discussed in previous studies, so the results of this study are new findings.

With that said, it can be concluded that technology readiness affects technology acceptance of mobile payments, especially Go-Pay, which consists of perceived risk, perceived cost, perceived ease of use, perceived usefulness.

4.3.2. Relationship of perceived risk, perceived cost, perceived ease of use, perceived usefulness, and facilitating conditions to use intention

From the results of hypothesis testing, it was found that out of five variables tested, only three variables influenced use intention. Use intention is influenced by perceived risk, perceived usefulness, and facilitating conditions. Of the three factors, the strongest relationship is the relationship between use intention and perceived usefulness which is supported by a path coefficient value of 0.4795 and has a positive relationship. Users may think that Go-Pay can provide many benefits will be more interested in using Go-Pay. An example is Go-Pay which has many functions for various types of payments on the Go-Jek application, such as buying credit, paying for motorcycle taxis, making transfers, and others. This finding is in line with research conducted by Luarn and Lin (2005) Cheng et al. (2006), Bailey et al. (2017), and Alhassan et al. (2020).

Furthermore, perceived risk negatively affects use intention. This can be seen from the negative path coefficient value. From the results obtained, it can be said that users who feel they will face a big risk if using Go-Pay, then the user’s intention to use Go-Pay decreases. This is because users try not to take the risk of using Go-Pay. The results of this study are in line with research conducted by Abrahão et al. (2016), Zhang et al. (2012), Ryu (2018), and Liébana-Cabanillas et al. (2018), Shao et al., 2019), and Kailan et al. (2019).

Facilitating conditions also positively affect use intention. Facilitating conditions describe how available resources can influence user intention to use Go-Pay. From the results of the analysis, it can be said that a person has sufficient resources (such as cellphones, internet, and knowledge) to make transactions with Go-Pay, so that person will tend to be motivated to adopt Go-Pay services as a means of electronic payment. For instance, to use Go-Pay for purchasing a wide array of services, users do not need other applications, because Go-Pay is a payment tool that is already integrated to Go-Jek application to these various services. The results of this study are in line with research conducted by Miltgen et al. (2013), Yu (2012), and Zhou et al. (2010).

However, the results of the study show that there is no significant relationship between perceived cost and perceived ease-of-use on use intention. Perceived costs have an insignificant relationship with use intention. The results show that the time and costs that must be incurred by users are not the determining factors that encourage users to use Go-Pay. This is understandable since Go-Pay currently does not charge fees for new users. In fact, on the Go-Jek application, if someone makes payments with Go-Pay, the costs will be less as there are various cashback promotions offered to Go-Pay users. These results are supported by research conducted by Abrahão et al. (2016) and S. Yang et al. (2012). Both of these studies found that perceived cost did not significantly affect user use intention.

Apart from the cost aspect, the ease of using Go-Pay in this study is not a determining factor that encourages user intention to use Go-Pay. The results showed that perceived ease of use did not affect use intention. This means that even though Go-Pay is easy to use, users do not feel compelled to use Go-Pay. However, several previous studies have also shown that perceived ease of use does not affect use intention. The research was conducted by Cheng et al. (2006).

Hence, it can be concluded that the technology acceptance of mobile payment affects the intention to use Go-Pay. However, in this study, the factors that encourage users to use Go-Pay are perceived risk, perceived usefulness, and facilitating condition. Users tend to care more about or prioritize their perceptions in terms of the resources they have, the risks to be faced and the benefits of using Go-Pay than the costs or convenience of using it.

4.3.3. Relationship of use intention and facilitating conditions to usage rate of basic functions and usage rate of innovative functions

Usage rate of basic function and usage rate of innovative function in this study are used as part of the use behavior. The basic feature referred to in the context of this research is a feature for making payments, while the innovative feature is the cash transfer and withdrawal feature from Go-Pay. From the analysis, it was found that use intention and facilitating conditions significantly affected the usage rate of basic functions. However, facilitating conditions do not affect the usage rate of innovative functions, whereas use intention affects the usage rate of innovative functions. As the frequency of use of basic and innovative features are formed as separate variables in the research model, our results are positioned as unique findings in the literature.

From these results it can be said that someone who has high usage intentions will often use the use of basic features to make Go-Pay payment transactions (such as Go-Jek, Go-Car, Go-Tix and others) and are interested in using innovative features. This is because the use of Go-Pay is based on intention. In addition, if a person has sufficient facilities (resources), he tends to use Go-Pay more often. For example, to be able to access the system requires an internet network. Someone who has reliable internet resources will tend to use Go-Pay more often (especially basic features). This is because the user has a greater opportunity to access the Go-Pay system than people who do not have sufficient internet access. However, the frequency of use of innovative features such as transfer and cash withdrawal are not affected by facilitating conditions. This implies that a person with high facilitating conditions does not necessarily influence the frequency with which he or she uses innovative features.

4.3.4. Relationship of usage rate of basic function to usage rate of innovative function

This study shows that the usage rate of basic functions significantly affects the usage rate of innovative functions. The use of innovative features in an application or system cannot be separated from the use of basic features. Innovative features on a system can appear and be adjusted by looking at user habits or behavior when using basic features. The more users use the basic features, the more accustomed they are to use Go-Pay and feel the need for advanced features. That way, the frequency of users using innovative features is influenced by the frequency with which they use basic features. This is in line with the development of the mobile payment feature. The use of innovative features in an application or system cannot be separated from the use of basic features. This presents a unique finding to the literature on actual usage behavior of system, especially mobile payment. Thus, it can be concluded that the usage rate of basic functions is influenced by use intention and facilitating conditions, while the usage rate of innovative functions is influenced by use intention and the usage rate of basic functions.

4.4. Implications

This research provides several theoretical implications. In the previous research conducted by Son and Han (2011), technology readiness had a direct relationship with actual behavior. In other studies, technology readiness was associated with satisfaction. In this study we tried to link technology readiness with the theory of technology acceptance, valence framework, and usage behavior pattern. The factors that we adopt are perceived risk, perceived cost, perceived ease of use, and perceived usefulness. The result shows that technology readiness significantly influences these four factors. These results can be used as a new perspective for researchers in the future that technology readiness can be used as an object of mobile payment. In this study, it was found that there was a relationship between technology readiness and the inhibitor factor in acceptance (perceived risk and perceived cost). Previous studies only linked technology readiness with the driver factor in acceptance (perceived ease of use and perceived usefulness).

In addition, there are other findings related to actual behavior. Thus far the existing research only focuses on the user’s intention. In this study, the results explain the relationship between user intentions and usage behavior (consisting of the use of basic features and innovative features). This study also found that the use of innovative mobile payment features is influenced by basic features. These findings add new knowledge to the literature in the context of mobile payment.

The practical implications of this research include: 1) Mobile payment development companies, especially Go-Pay, can find out that the factors that influence the user’s intention to use Go-Pay i.e. perceived risk, perceived usefulness, and facilitating conditions. From these results, mobile payment companies can focus on deepening or improving the usability of Go-Pay and making the system easily accessible with any device. 2) Mobile payment development companies, especially Go-Pay, can improve services and adapt to the conditions of Indonesian people’s interest so that the services or features provided to mobile payment users, especially Go-Pay, are more in line with user acceptance. 3) Research results can also be used as a reference for mobile payment companies to create a good system. This can be seen from the facilitating conditions that significantly affect the usage rate of the basic function of the mobile payment. From these results, it is hoped that mobile payment development companies, especially Go-Pay, will continue to develop features that do not require a lot of resources to use. 4) From the research results, it was found that there is a relationship between the usage rate of basic functions and the usage rate of innovative functions. Mobile payment development companies, especially Go-Pay, can focus first on basic features. Companies can also study user behavior in using basic features and learn about user needs. 5) The results of this study can be used by other mobile payment development companies as a point of reference in developing and improving mobile payment services and features by through several theoretical lenses.

5. Conclusions, limitations, and future research

This study was conducted to analyze the factors that support technology readiness (insecurity, discomfort, optimism, and innovativeness) of mobile payment Go-Pay, the effect of mobile payment technology readiness on technology acceptance (perceived risk, perceived cost, perceived ease of use, and perceived usefulness) of Go-Pay, the influence of Go-Pay acceptance factors and facilitating conditions on its use intention, the effect of use intention and facilitating conditions on the frequency of users of basic features (usage rate of basic functions) and innovative features (usage rate of innovative function) of Go-Pay, and the influence of Go-Pay’s usage rate of basic function on usage rate of innovative function.

First, this study succeeded in proving that insecurity, discomfort, optimism, and innovativeness are the building blocks of technology readiness. Technology readiness has a significant positive effect on perceived usefulness, perceived ease of use and has a significant negative effect on perceived cost and perceived risk. Second, the results show that perceived risk has a significant and negative effect on use intention, while perceived usefulness has a significant and positive effect on use intention. Facilitating conditions have a significant effect on use intention. Perceived cost and perceived ease of use do not significantly affect use intention. Third, facilitating conditions affect significantly and positively the usage rate of basic function. Use intention has a significant and positive impact on the usage rate of basic functions and the usage rate of innovative functions. In addition, the frequency of use of Go-Pay’s innovative features is influenced by the frequency of users using the basic Go-Pay features.

However, this study also has several limitations, namely: 1) Respondents aged 35 and over were quite difficult to find because of the data collection through social media which is commonly used by people aged 17–25 years. 2) In the use behavior section, we only looked at the frequency of use of basic features and innovative features. 3) This research did not involve other factors such as variations in the use of innovative features which are a factor in the research conducted by Son and Han (2011). 4) The research only examined the use of behavior, not how someone will recommend the mobile payment or on satisfaction or retention.

Based on the research results obtained and the limitations that exist in the study, we suggest further research on other mobile payment platforms, not just limited to Go-Pay as a market leader. This is to determine the effect of technology readiness on any mobile payment. Subsequent research should also examine other important factors besides the factors that the authors have tested, such as habit, social influence, and performance expectancy. In addition, future research could explore more about the result of use behavior, such as how someone would recommend the mobile payment or on user satisfaction or user retention.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The authors received no direct funding for this research.

Notes on contributors

Untung Rahardja

Untung Rahardja was born on March 31^st, 1965. He received the B.S. degree in electrical engineering from University of California Berkeley, U.S.A. in 1989. Subsequently he received the master’s degree in information technology from Faculty of Computer Science, Universitas Indonesia in 2008 and a Ph.D in management science from Universitas Persada Indonesia (YAI) in 2018. From 1986-1989 he was a teaching assistant at UC Berkeley. He has since been a Chairman now an Associate Professor at Universitas Raharja, Indonesia, a university he founded in 1993. Prof. Rahardja is an author of 10 books and his articles have been widely published in Scopus-indexed international journals. He has been active in academic communities in Indonesia, including the Indonesian Association of Higher Education Informatics and Computer Science.

Intan Dwi Hapsari

Intan Dwi Hapsari received the B.S. in computer science from the Universitas Indonesia in 2018. She was a Research Assistant at the Faculty of Computer Science, Universitas Indonesia in 2018. Her research interests include financial technology, human-computer interaction, and information system. Ms. Sigalingging was a staff member at Startup Academy, Compfest in 2016 and worked as as a business analyst at Altrovis, in 2017.

P.O.HADI Putra

P.O.HADI Putra received the B.Sc. degree in information system with honours from Sunway University in Malaysia. He also received the M.Bus. degree with distinction from College of Business and Economics, the Australian National University and a Ph.D. degree in computer science from the Faculty of Computer Science, Universitas Indonesia. Before entering the academia, Dr. Putra is a seasoned entrepreneur having started companies in the field of information technology. From 2012 to 2015, he was a CEO at fast-growing digital agency in Indonesia, Fostrom. Since 2015, he has been faculty member and a researcher at the Faculty of Computer Science, Universitas Indonesia. In addition, currently he also manages the public relations department for the university. His interest includes e-commerce, e-learning, and user experience. In addition to being a member of IEEE, he has been active in other communities such as ACM-SIGCHI and the Indonesian Chamber of Commerce.

Achmad Nizar Hidayanto

Achmad Nizar Hidayanto received the B.S. degree in 1999 in computer science from the Faculty of Computer Science, Universitas Indonesia. He also received the M.S., and Ph.D. degrees all in computer science from Universitas Indonesia in 2002 and 2008 respectively. Since 2002, he has been a faculty member and a researcher at the Faculty of Computer Science, Universitas Indonesia, where he is now a Full Professor and Vice Dean for Resources, Venture, and General Administration. His research interest includes information systems, IT adoption, digital economy, and financial technology. Prof. Hidayanto has published more than 265 peer-reviewed scientific articles and an author of multiple books. Prof. Hidayanto has received many awards, including a top researcher award from the Indonesian Ministry of Research and Technology/National Research and Innovation Agency in 2020. He has been also recognized as a top reviewer in the academic community as per his Publons profile in September 2019.