Introduction

The rapid adoption of mobile phones worldwide has been one of the reasons for development in society. With the emerging trends in technology, mobile phones are now not restricted as communication devices. People use mobile phones for multiple reasons—one of them is online payments. With an increase in mobile phone usage, technology companies are concentrating on develo** new services. One of those services includes online mobile payments (m-payments). Most mobile payment types include browser-based payments, in-app payments, mobile/wireless credit cards, and mobile wallets. As explained by Akhtar et al. (2019) and Farzin et al. (2021), mobile payment is a method that includes payment services that are performed through a mobile device.

To overcome the fears related to risks in m-payments, various academic researchers have confirmed that updated and regular privacy guidelines, user-friendly apps, and governmental support could help increase mobile payment usage (Anderson (2016). Although mentioned studies have revealed a positive image of m-payments, some studies (Ranmpton 2016) have highlighted that the growth of m-payment in develo** countries is still slow. Thus, mobile payment adoption in Pakistan is still lower (Aslam et al. 2017). One of the reasons could be the superstitious nature of users from 3rd world countries when it comes to technology associated with money which could further develop stress while doing transactions (Aslam et al. 2017).

Pakistan, a develo** country, has still launched various m-payment apps for economic development. As m-payment is gaining success in developed countries, Pakistan is also adapting the technology as e-commerce is growing every day. The success of cellular networks in Pakistan, payment through an online platform was introduced in 2009 in Pakistan. With this rise in m-payments, this technology has become a branchless banking sector in Pakistan. Thus, with this boom in technology, m-payment is supported by various financial institutions and—providers.

M-payment supports traditional banking by enhancing its image, service, and quality. Hence, every bank in Pakistan now offers an independent online banking app. M-payment in Pakistan could provide various economic facilities that are the elements of socioeconomic development. M-payment services offer several chances for financial inclusion. The most famous and used m-payment apps include Mobile Banking, EasyPaisa, JazzCash, Upaisa, Payoneer, PayPak, MoneyGram, Western Union, and HBL-Konnect. Thus, the research aims to discuss the barriers, facilitators, and theoretical drivers of the intention to adopt M-payment in the context of Pakistan. It further highlights the impacts of intention to adopt M-payment on the usage behavior of customers.

Previous studies have discovered low awareness of m-payment in develo** nations. Several works of literature have been conducted in the context of both developed and develo** nations that are already using m-payment or will adopt the technology. In developed countries, the study of Pham and Ho (2015) has confirmed the importance and economic advantage of m-payment that highlights both the system and user-related features. Most of the mentioned studies have used system or user-related models to measure the intention and usage of mobile payments. According to the authors' knowledge, limited studies were found that argued the intention to adopt m-payment and its impact on user behavior in the Pakistani context. Moreover, no studies were found that reflected and discussed the impact of barriers, theoretical drivers, and facilitators on m-payment.

Therefore, the current study contributes to the literature by evaluating the barriers, theoretical drivers, and facilitators that have been individually discussed limitedly in Pakistan. Following the introduction, the second chapter, the literature review, discusses the analysis of the research question with an emphasis on the theoretical foundations of the paper's hypothesis. Further, the methodology's third chapter illustrates the techniques used to investigate the analysis. The fourth chapter discusses the data collection and the outcomes that are gathered. Finally, the research is concluded in the last section, which reflects the research outcomes, future suggestions, and the weaknesses of the paper for future studies.

Literature review

Theoretical background

In the past, many researches are available highlighting the intention to adopt mobile payment and its impact on user behavior. The available literature proves that there are a set of other variables that influence this intention. These variables are independent: barriers, facilitators, and theoretical drivers. The existing literature on the intention to adopt m-payment is based on the theory of reasoned action (TRA), Technology Acceptance Model (TAM), Diffusion of Innovations (DOI), and Unified Theory of Acceptance and Use of Technology (UTAUT). Thus, the theory TAM, DOI, and UTAUT are used to develop a conceptual model. The model was established by the variables from existing models and adding new ones relatable to m-payments.

According to (Islam et al. 2020), m-payment is a technology used for paying bills and various digital services using a mobile phone. In their research, Aslam et al. (2017) defined it as a payment method that utilizes a mobile device for commercial transactions. Moreover, Pham and Ho (2015) explained the whole process as any transaction conducted through a mobile device or network that leads to the transaction of values in exchange for information, services, and types of merchandise.

Technology acceptance model (TAM)

TAM is an information system theory that highlights how users come to receive and practice a specific theory. Various researchers have previously studied the theory of TAM. Fred Davis and Richard Bagozzi developed it. The theory explains the perceived facilitators mentioned in the conceptual model and introduces the perceived usefulness and perceived ease of use (Davis 1989). TAM is also an intention-based model that explains the intention to adopt specific technology as a useful predictor. The study of Di Pietro et al. (2015) has enlightened the explanatory power of TAM, maintaining reliable results. According to Di Pietro et al. 2015), TAM is beneficial in calculating the acceptance of technological systems.

Diffusion of innovation (DOI)

DOI is a theory explaining how, why, and to what degree a technology grows publicly. Everett Rogers, in 1962 proposed the theory of DOI (Rogers 1995). According to Rogers, diffusion is a method by which an innovation is linked among people. Innovation is an idea or practice that is perceived to be new and has to be adopted. The diffusion of innovation theory proposes five characteristics that affect the acceptance and adoption of any innovation. These characteristics involve; relative advantage, compatibility, complexity, observability, and trialability. The innovation involving these characteristics is more likely to be readily adopted than other innovations (Di Pietro et al. 2015).

The unified theory of acceptance and use of technology (UTAUT)

UTAUT is a model of technology acceptance that Venkatesh introduced. The theory explains the intention of users to utilize a new information system and subsequent usage behavior (Venkatesh et al. 2003). The theory has four key characteristics: performance expectancy, effort expectancy, social influence, behavioral intention, and facilitating conditions. UTAUT captures the essential characteristics of eight previously proposed models and theories used to study technology usage behavior. The theory has been previously used by (Di Pietro et al. 2015) to study the usage behavior and intention to adopt technology in a broader context.

Why mobile payment in emerging economies?

Mobile payment has become an area of interest for business practitioners, financial mediators, and researchers among various mobile phone services. According to research by Rehman and Esichaikul, (2011), m-payment has played a significant role in economically develo** the world. Thus, it is an advanced tool for the development of develo** countries in the South Asian region (Kar 2021; Arjun et al. 2021; Grover and Kar 2020; Chhonker et al. 2018; Rana and Dwivedi 2022; Asrani and Kar 2022; Hoh et al. 2022; Tew et al. 2022; Rui et al. 2022; Lew et al. 2020; Hew et al. 2019; Raza et al. 2018; Ali et al. 2021). In Pakistan, various commercialized platforms provide mobile payment services. Moreover, the banks in Pakistan now offer the facility of branchless banking. People in Pakistan still prefer to pay through cash by avoiding such digital payment tools. Similarly, in the UAE, the Covid-19 pandemic forced financial institutions to adopt mobile payment to digitize the economic system (Al-Qudah 2022). The reason behind the adoption decision of mobile payment mentioned by various researchers is the issues associated with trust and security. Additionally, Afshan and Sharif (2016) claimed that another reason is a lower awareness rate of digital/mobile payments. In collaboration with banks, the Pakistani government is effortlessly working on promoting cashless transactions.

Hypothesis development

Impact of facilitators on intention to adopt mobile payment (INT)

Perceived innovativeness

As Karjaluoto et al. (2014) mentioned, innovativeness is the readiness of a person to try new technology. A previous study by Yang et al. (2012) has confirmed that adopting new technology is founded on the Diffusion of Innovation model. The m-payment service is still a new technology for develo** countries; innovativeness has an integral role in evaluating the intention to adopt new technology. The interest of consumers toward a new technology shows the significance of innovativeness on intention to adopt. Liébana-Cabanillas et al. (2015), in their study, have explained the role of innovativeness toward the acceptance of m-payment. Likewise, de Luna et al. (2017) have argued the impact of innovativeness on the intention to adopt. Based on the context mentioned above, the proposed hypothesis is generated:

H1a:

Perceived Innovativeness has an impact on INT.

Perceived trust

Mortimer et al. (2015) literature on e-commerce, information technology, and m-payment has highlighted the significance of trust concerning the adoption intentions of new technology. Research in marketing has studied the importance of trust. According to Liébana-Cabanillas et al. (2021), trust is the faith that an authority's promise is reliable and will fulfill its relational exchange commitments. Trust determines the accomplishment of the adoption of new technologies, such as in the context of m-payments. Mobile payment is more than just a modern technological innovation; it is also a tool to manage consumers' money and personal data. Since the transfers are connected to their digital accounts and wallets, users should trust the m-payment service provider and network with their money (Morawczynski and Miscione 2008). People hesitate to use confidential stock information, bank account information, and debit/credit card numbers on untrustworthy payment applications (Lawrence and Tar 2010). As a result, in developed countries, confidence will play an essential role in promoting mobile payment adoption. Based on the context mentioned above, the proposed hypothesis is generated:

H1b:

Perceived Trust has an impact on INT.

Perceived ease of use

According to Sampat (2016), perceived ease of use is related to an individual's perception that consuming a specific technology or system needs less struggle and is easy to use. This variable is also considered the most impactful in adopting new technology. Further, Kleivene (2018) claims that perceived ease of use impacts self-efficacy and instrumentality simultaneously and affects the intention to adopt, and efficacy has been considered a motivation factor. Thus, this motivation directly impacts the intention to adopt, though perceived ease of use could also be individually assessed as an instrument to influence the intention. This way, intention further improves the behavior of the individual. Singh et al. (2020) studied the similar impact of variables, and the proposed hypothesis is generated:

H1c:

Perceived Ease of Use has an impact on INT.

Perceived usefulness

Various researchers have studied perceived usefulness. According to de Luna et al. (2019), perceived usefulness is the possibility of users using technology to progress their routine in an organizational context. It is considered a relative advantage that has been studied on numerous occasions. For this reason, Singh et al. (2020) reflects it as a degree to which an innovation is measured better than similar existing technologies. In the current study, m-payment offers various rewards over other payment methods. According to the Technology Acceptance Model, the effectiveness of a payment method directly affects the intention to use technology, and this relationship has been studied by Lara-Rubio et al. (2020). Based on the context mentioned above, the proposed hypothesis is generated:

H1d:

Perceived Usefulness has an impact on INT.

Perceived satisfaction

Perceived satisfaction is particularly relevant while evaluating a customer's experience when purchasing, receiving or using a service or product, which would be critical to the customer's long-term reactions promoting continuation purpose and commitment, wherever appropriate. Various research has focused on consumers' perceptions and experiences regarding technology use. Satisfaction is the level of desire resulting from individual performance. According to Zhou (2013), in the context of m-payment, satisfaction is a psychological reaction to the process of purchase or payment. Moreover, Liébana-Cabanillas et al. (2020) claim that this feeling is created during the interaction with a particular service provider.

On the other hand, perceived satisfaction is also an indicator of consumers' future technology usage. Few studies have examined how people have used mobile payments since they were adopted (Yuan et al. 2020). How a person plans to continue using application software (Hsu and Lin 2015) or, in either situation, the payment system in question, satisfaction forecasts a person's user acceptance (Shang and Wu 2017). Based on the context mentioned earlier, the proposed hypothesis is generated:

H1e:

Perceived Satisfaction has an impact on INT.

Impact of theoretical drivers on intention to adopt mobile payment

The theoretical drivers are adapted from the Diffusion of Innovation by Everett Rogers. The DOI theory proposes five features that affect any innovation or technology adoption. These characteristics involve; relative advantage, compatibility, complexity, observability, and Trialability. Ali and Puah (2017) argued that DOI theory is a useful framework that could use technology adoption studies.

Relative advantage

According to Rogers (2003), the relative advantage is how an invention is supposed to be better than the idea it succeeds. Various researchers have used this specific attribute. Some of those researches have been exemplified for a better understanding. For example, a study conducted by Hsu, Lu, and Hsu (2007) claims that accepting mobile internet has found a lead to influence the adoption intention. Moreover, Kim and Garrison (2009) evaluated that mobile wireless technology is significant for adoption and use. Thus, m-payment is more influential in develo** countries. Based on the context mentioned above, the proposed hypothesis is generated:

H2a:

Relative Advantage has an impact on INT.

Compatibility

Compatibility is how an invention is perceived in terms of consistency, value, experiences, and potential adopters (Rogers 2003). Schierz et al. (2010) found that compatibly impacts consumers' usage intention. Moreover, Koenig‐Lewis, Palmer, and Moll (2010) found compatibility as a predictor of intent to adopt and use in their research. Another study found that compatibility has an impact on the intention to use; hence, the proposed hypothesis is generated:

H2b:

Compatibility has an impact on INT.

Complexity

Complexity is the state of being complex or intricate. According to Rogers (2003), it highlights an innovation's level of difficulty and complexity. Frambach et al. (1995), Reviewing the adoption is a new service, displayed that complexity negatively impacts the intention to adopt. Another study by Lean et al. (2009) highlights that complexity negatively impacts the intention to adopt and use. The study by Brumec (2006) depicted that complexity had a negative impact on the intentions to adopt and use. Based on the context mentioned earlier, the proposed hypothesis is generated:

H2c:

Complexity has an impact on INT.

Observability

Observability is the degree to which the results or benefits of using new technology are noticeable to adopters. Research conducted by Rogers (2003) found that observability impacts the action of adoption. Furthermore, Ajili et al. (2012) examined the approach and intention to adopt and use; they also investigated that observability directly impacts the intention to adopt. Moreover, the study on the adoption of precision agricultural technology by Rezaei-Moghaddam and Salehi (2010) highlighted that observability impacts the intention to adopt new technology. Based on the context mentioned above, the proposed hypothesis is generated:

H2d:

Observability has an impact on INT.

Trialability

Trialability is the ease that allows the customers to try and test a new product or service. According to Rogers (2003), it is a degree with the option of experimentation for a limited time. The study by Lee et al. (2011) found that Trialability has influenced individuals to adopt e-learning systems. A Nigerian study by Odumeru (2012) explored that Trialability significantly impacts online recruiting technology, and Trialability affects the adoption of e-MBA in the survey of Rezaei-Moghaddam and Salehi (2010). Based on the context mentioned above, the proposed hypothesis is generated:

H2e: Trialability has an impact on INT.

Impact of barriers on intention to adopt mobile payment (INT)

Operational constraints

According to Rogers (2003), when individuals find a new technology relatively challenging to use, they experience operational constraints. In the context of m-payment, the size of mobile phones could be difficult and troublesome to use. The interface and functional features also serve as obstacles to operating in low-income countries due to the high rates of technological illiteracy (Sheth and Ram 1989). According to discussions, the architecture of mobile devices is a significant factor in their unavailability to the vulnerable (Duncombe 2012). Including disturbingly poor technology literacy figures worldwide (Pal et al. 2020), the greatest obstacle to cell phone use is consumers' failure to use the technology effectively (Rouvinen 2006). In addition, users will have difficulty using payment apps due to recent interface formats requiring complex mobile handset features. Based on the context mentioned above, the proposed hypothesis is generated:

H3a:

Operational constraints have an impact on INT.

Perceived risk

Since the effects of consumer behavior cannot be predicted with precision, all consumer behavior carries some risk. The key reasons why m-payment schemes are riskier than other online payments, according to Ozturk et al. (2017) and Liébana-Cabanillas et al. (2019), include payment processing irregularities, a lack of transaction history and payment documents, and financial fraud. In a recent study of the critical inhibitors of m-payments adoption, Kalini et al. (2020) identified PR as the most significant downside from the purchaser's viewpoint, confirming the value of this partnership. On the other side, Shin (2010) documented user adoption behavior toward social networking sites. The study highlighted the importance of perceived security and privacy, which possessed distinctive features. In our study context, perceived risk may also be associated with perceived security and privacy. Later, Shin et al. (2022) extended the concept of security and privacy risk when users intend to disclose their personal information using online platforms. Additionally, the user's assumption that a digital financial transaction is subject to volatility is referred to as perceived risk, a recurring concept in m-payment. Consumers all over the world have been plagued by news of digital payment theft for years. Based on the context mentioned above, the proposed hypothesis is generated:

H3b:

Perceived Risk has an impact on INT.

Stress

Some customers may have a negative nature and consume anxiousness or worried while using new technology. This feeling may develop resistance toward accepting and adopting new technology Swilley, E. (2010). Venkatesh, Thong, and Xu (2012) measured the feeling of stress toward new technologies; they highlighted that anxiety and nervousness strongly resist recent changes and profoundly affect the acceptance and adoption of new payment services. Such customers or new users feel that new technology is risky to use, thus strongly impacting the adoption of m-payment. Based on the context mentioned above, the proposed hypothesis is generated:

H3c:

Stress has an impact on INT.

Unavailability of facilitating condition

The degree to which consumers know how to access the technology when supported by infrastructure is referred to as facilitating conditions (Venkatesh et al. 2012). It has often been mentioned in previous m-payment papers (Pal et al. 2018), mainly in developed nations where illiteracy has prevented people from benefiting. Facilitating conditions have been a significant influencer of attitudes toward adoption in surveys concentrating on consumers from poor communities (Raleting and Nel 2011).

Convenient conditions have recently been established as a factor in m-payment applications in studies conducted in develo** countries. Most electronic payments necessitate using a handset with Internet access for software installation and use and the users' working knowledge of the app and its compliance with other elements such as bank accounts or debit cards. These situations, on the other hand, are not every day in developed countries. As a result, we anticipate that the lack of enabling factors will play a significant role in developed countries. Based on the context mentioned above, the proposed hypothesis is generated:

H3d:

Unavailability of facilitating condition has an impact on INT.

Impact of intention to adopt M-payment on usage behavior

Usage behavior is the continuous commitment toward any product. It is dependent on the level of use and the level of the initial adoption of the product. The usage behavior is highly impacted by the intention to adopt any technology. According to Park (1998), customers' usage behavior is high if the product has fewer barriers and many facilities. Moreover, the study of Kim et al. (2010) highlights that customers are more interested in how the technology works and are more willing to develop an increase in the uses for that technology. Based on the context mentioned above, the proposed hypothesis is generated:

H4:

INT has an impact on Usage Behavior.

Methodology

Research model

Figure 1 demonstrates the model of the study. This model portrays the Facilitators, Theoretical drivers, and Barriers with their impact on the intention to adopt M-payment. Furthermore, the impact of the intention to adopt M-payment on Usage Behavior is also studied (Fig. 2).

Fig. 1
figure 1

Conceptual model

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Fig. 2
figure 2

Results of path analysis

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Data collection and measurement of variables

This research is based on the quantitative data collected through questionnaires. The data collection tool was established using a five-point Likert scale. The study targeted the general public for the survey. Survey questionnaires were distributed online in Karachi, Pakistan. In total, 421 respondents participated, and after data screening, 13 responses were deleted because of incomplete or missing values. The final sample size used in the study was 408. The sample size designated for the data was built on the rules offered by Raza and Hanif (2013), Comrey and Lee (2013), Raza et al. (2020a) Raza et al. (2020b).

The data collection instrument was developed using the items adapted from prior studies. For instance: Perceived Innovativeness, Perceived Trust, Perceived Ease of Use, Perceived Usefulness, and Perceived Satisfaction were adapted from previous studies (Liébana-Cabanillas et al. (2021). The items of Relative Advantage, Compatibility, Complexity, Observability, and Trialability were adapted from (Kapoor et al. (2015) and Makanyeza (2017). The Operational Constraints, Perceived Risk, Stress, and Unavailability of facilitating conditions were adapted from the study of Liébana-Cabanillas et al. (2020). The following studies have been used for the items of intention to adopt M-payment, i.e., (Baptista and Oliveira (2015) and Kim et al. (2010). Lastly, items of Usage Behavior were adapted from Liébana-Cabanillas et al. (2018).

Moreover, the research questionnaire comprised six sections. Section A includes fourteen items measuring Intention to Adopt Mobile Payment, and section B contains fifteen items measuring Barriers. Section C consists of three items measuring Facilitators, and section D comprise six items measuring Theoretical Drivers. Further, section E includes five items of the usage behavior. At last, section F consists of demographic items such as gender, age, and education.

Demographics

The statistics of the participants are in Table 1. Referring to the demographic features, examining the gender ratio, the percentage of males is 67.9 percent, while the share of females is 32.1 percent. In terms of age, 31.6 percent of respondents are between the 20–26 age group, 38.7 percent of respondents are between the 27–33 age group, 15.0 percent of respondents are between the 34–40 age group, 7.6 percent respondents fall in the age bracket of 41–47, and remaining 7.1 percent are 48 and above. Regarding education, 32.1 percent of respondents were undergraduate, 38.5 percent were graduate, 27.5 percent were postgraduate, and 2.0 percent were at the option of others. Table 1 highlights the respondent profile of this research.

Table 1 Respondent profile
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Data analysis

Structural equation modeling and the support of statistical facts are used to validate the theory (Ringle et al. 2005). For the variance-based method, PLS-SEM is used to process the hypothetical model. Additionally, bootstrap resampling is used according to the criteria of Hair et al. (2011), supported by Amin et al. (2022) and Raza et al. (2017a, 2017b).

Furthermore, the study compared variance and covariance-based techniques and concluded that the method has the least restrictions in sample size and residual distributions. Estimations are based on Anderson and Gerbing's (1998) two-step approach, i.e., measurement model and structural model.

Measurement model

Construct reliability, individual item reliability, convergent validity, and discriminant validity are in the measurement model to evaluate the proficiency of the model. Cronbach's Alpha, Composite reliability, and Average Variance Extract (AVE) are used to assess the results. The criteria of Straub (1989) are highlighted in the variables of Cronbach's alpha and composite reliability mentioned in Table 2. The criteria of Fornell and Larcker (1981) are used by average variance extracted (AVE) to calculate the convergent validity. Table 2 illustrates the findings for measurement model results.

Table 2 Measurement model results
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Cross-loading analysis, the Fornell and Larcker criterion, and the heterotrait–monotrait ratio of correlations (HTMT) are used to measure the discriminant validity. Table 3 signifies the square root of AVE that is presented diagonally. Also, to explain the discriminant validity of adequacy, Table 4 displays loadings, and cross-loadings. This follows the criteria of (Gefen and Straub 2005). Moreover, the heterotrait–monotrait ratio of correlations (HTMT) is displayed in Table 5. Therefore, Tables 3, 4, and 5 demonstrate the results for the Fornell–Larcker criterion, cross-loading, and HTMT results to further strengthen the measurement model.

Table 3 Fornell–Larcker criterion
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Table 4 Loadings and cross-loadings
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Table 5 Heterotrait–monotrait ratio (HTMT)
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The measurement model approves convergent and discriminant validity and considers variables unique. Therefore, it is useful to inspect the structural model.

Structural model

In the structural model, hypotheses are verified, and the relationship between proposed constructs is inspected. The structural model is determined based on regression. Additionally, the standard significance can be seen in Table 6, there are 15 hypotheses. 10 depict the significant association from hypotheses, but the remaining 5 hypotheses display an insignificant relationship between the proposed variables. Hence, Table 6 shows the results of the path analysis.

Table 6 Results of path analysis
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Discussion

The path analysis H1a is related to Perceived Innovativeness and Intention to adopt M-payment. It is discovered that they have a positive but insignificant relationship (β = 0.016, p > 0.1), thus rejecting the hypothesis. The results are different from the generated hypothesis. According to Liébana-Cabanillas et al. (2015), with differing results, for develo** nations, m-payment services are still a new technology; innovativeness is critical in determining whether to accept new technology. Consumer demand in a new technology demonstrates the impact of innovation on adoption intentions. Another counter-intuitive finding in de Luna et al. 's (2017) study explains the importance of innovation in m-payment acceptability.

The path analysis H1b is related to Perceived Trust and Intention to adopt M-payment. It is discovered that they have a relationship of positive and significant (β = 0.305, p < 0.01), thus accepting the hypothesis. Similar results are shared by (Morawczynski and Miscione 2008). According to their study, Mobile payment is more than a new technological innovation; it is also a service that handles customers' money and sensitive data. Because the transactions are connected to their bank accounts, and the payment wallets hold some cash, customers should trust the digital payment provider and platform with their money.

The path analysis H1c is related to Perceived Ease of Use and Intention to adopt M-payment. It is discovered that they have a relationship of positive and significant (β = 0.234, p < 0.01), thus accepting the hypothesis. Similar results are found in the study of Singh et al. (2020). According to similar research, perceived ease of use influences self-efficacy, instrumentality, and the intention to adopt. On the other hand, perceived ease of use might be used as an individual tool to affect intent. In this approach, the individual's conduct is improved even more.

The path analysis H1d is related to Perceived Usefulness and Intention to adopt M-payment. It is discovered that they have a positive but insignificant relationship (β = 0.006, p > 0.1 thus rejecting the hypothesis. The results are different from the generated hypothesis and previous literature. In contrast, Lara-Rubio, Villarejo-Ramos, and Liébana-Cabanillas (2020) define it as a measure of how superior an invention is to its predecessor. According to the current research, m-payment has several benefits over traditional payment systems.

The path analysis H1e is related to Perceived Satisfaction and Intention to adopt M-payment. It is discovered that they have a relationship of positive but significant (β = 0.215, p < 0.01), thus accepting the hypothesis. Similar significant results are shared by Yuan et al. (2020); accordingly, satisfaction in the context of m-payment is a psychological reaction to the process of purchase or payment since satisfaction is the amount of desire arising from individual performance. Furthermore, according to Hsu and Lin (2015), this emotion develops through the contact between that particular bank and its benefits form-payment adoption.

The path analysis H2a relates to Relative Advantage and Intention to adopt M-payment. It is discovered that they have a relationship of found positive and significant (β  = 0.357, p < 0.01), thus accepting the hypothesis, according to the findings of a study by (Hsu, Lu, and Hsu (2007), adopting m-payment has discovered a relative benefit in influencing adoption intention. Furthermore, (Kim and Garrison (2009) concluded in their study that mobile wireless technology is essential for adoption and use, and m-payment is increasingly popular in underdeveloped nations.

The path analysis H2b is related to Compatibility and Intention to adopt M-payment. It is discovered that they have a positive but insignificant relationship (β  = 0.033, p > 0.1), thus rejecting the hypothesis. Contrasting results are found as the generated hypothesis has been rejected. Lean et al. (2009) and (Brumec 2006) discovered that compatibility considerably influenced customer usage intentions.

The path analysis of H2c is related to Complexity and Intention to adopt M-payment. It is discovered that they have a negative and insignificant relationship (β  = -0.029, p > 0.1), thus rejecting the hypothesis. Similar results are found for the generated hypothesis. Pal et al.' s (2020) research on adopting a new service found that complexity negatively influenced adoption intentions. Another study by Sheth and Ram (1989) found that complexity negatively influenced adoption and usage intentions. According to Rouvinen's (2006) research, complexity negatively influences adoption and usage intentions.

The path analysis H2d is related to Observability and Intention to adopt M-payment. It is discovered that they have a relationship of positive and significant (β  = 0.031, p < 0.1), thus accepting the hypothesis. Similar results are found in the prior literature. For example, research conducted previously discovered that observability affects adoption behavior. Furthermore, Ajili et al. (2012) evaluated the attitude and intention to adopt and use and the influence of observability on the intention to adopt. Furthermore, according to research on the adoption of precision agriculture technology, observability influences the desire to embrace new technology (Rezaei-Moghaddam and Salehi 2010).

The path analysis H2e is related to Trialability and Intention to adopt M-payment. It is discovered that they have a positive but insignificant relationship (β  = 0.025, p > 0.1), thus rejecting the hypothesis. However, the generated hypothesis was rejected, and we found contrasting results. Trialability has impacted individuals to embrace e-learning systems. According to research by Odumeru (2012), Trialability has a considerable influence on online recruiting technologies, according to Rezaei-Moghaddam and Salehi (2010). Furthermore, trialability had a significant influence on the research of e-MBA uptake.

The path analysis H3a is related to Operational Constraints and the intention to adopt M-payment. It is discovered that they have a negative and insignificant relationship (β  = -0.005, p > 0.1), thus rejecting the hypothesis. The contrasting results of Pal et al. 2020) highlight that the design and operational aspects frequently function as hurdles to utilization in low-income nations because of the high prevalence of digital illiteracy. According to debates, the design of mobile devices is a significant factor in their inaccessibility to the poor (Duncombe 2012).

The path analysis H3b is related to Perceived Risk and Intention to adopt M-payment. It is discovered that they have a positive but insignificant relationship (β  = 0.000, p > 0.1), thus rejecting the hypothesis. The study of Ozturk et al. (2017) and Liébana-Cabanillas et al. (2019) found PR as a significant variable. This highlights that Perceived risk is a term used frequently in mobile banking to describe a user's opinion that a digital financial transaction is subject to danger (Zhou 2019). Users throughout the world have been concerned by news about digital payment fraud for decades.

The path analysis H3c is related to Stress and Intention to adopt M-payment. It is discovered that they have a relationship of negative but significant (β  = -0.148, p < 0.01), thus accepting the hypothesis. Venkatesh, Thong, and Xu (2012) measured the feeling of stress toward new technologies; they found that stress and nervousness strongly resist new changes and profoundly affect the acceptance and adoption of new payment services. Such customers or new users feel that new technology is risky to use, thus strongly impacting the adoption of m-payment.

The path analysis H3d is related to the unavailability of facilitating conditions and the intention to adopt M-payment. It is discovered that they have a relationship of negative but significant (β  = -0.034, p < 0.1), thus accepting the hypothesis. Venkatesh et al. (2012) have found a significant association between the unavailability of facilitating conditions and the intention to adopt. It emphasizes that digital resources necessary to run technology, such as facilitating infrastructure and digital literacy rates, are typically low in develo** nations. Therefore, the lack of enabling conditions is critical (Pal et al. 2018).

The path analysis H4 is related to the impact of the intention to adopt M-payment on Usage Behavior. It is discovered that they have a relationship of positive but significant (β  = 0.864, p < 0.01), thus accepting the hypothesis. The results are similar to Kim et al. (2010) study. It highlights that customers are more interested in how the technology works and are more willing to increase that technology.

Theoretical implications

The research findings contribute to a greater understanding of the factors that influence consumer decision-making and, as a result, the adoption intention of mobile payment in Pakistan. Instead of relying on a single paradigm, the performed study presents an inclusive strategy for researchers by merging three recognized theories connected to technological acceptance. TAM, DOI, and UTAUT are the theories that established the conceptual model. As a result, the literature focuses on how people perceive technology. The current study fills a gap in the literature by integrating theoretical causes, impediments, and facilitators. This means that while looking at the factors that influence m-payment acceptance, we should concentrate on the variables based on the chosen theories.

Practical implications

The main problem with m-payment in Pakistan is that it is inactive. As a result, a strong sense of m–payment acceptance and active user involvement is required. The research has demonstrated the importance of theoretical drivers, impediments, and facilitators in resolving the m-payment issue in Pakistan. The findings may be utilized to create well-known marketing strategies, business models, swiftly processed plans, awareness programs, and updated initiatives aimed solely at Pakistan's lower-income and less-educated citizens. The findings aid financial institutions and mobile service providers in formulating new plans at the national level to reduce uncertainty connected with impartial facilitators for mobile payments. This might eventually benefit Pakistan's new m-payment consumers.

Facilitators influence customers, and the performance of m-payments is influenced by facilitators. In addition to facilitators, drivers such as relative advantage, compatibility, complexity, observability, and trialability have a role. This emphasizes the importance of concentrating on the challenging aspects of mobile phones, such as complex input, tiny screens, design difficulties, and easy-to-use interfaces.

As m-payment usage in Pakistan is still growing, there is a need to launch efficient marketing initiatives to encourage mobile payment. Consumers can influence customers decisions to use m-payments by raising awareness, which will have various implications on user behavior. This might be accomplished by stressing the benefits and ensuring that the m-payment service improves user experience, provides rapid development, minimizes risks, and provides wider accessible opportunities.

M-payment is now getting much attention in Pakistan. However, the major hindrance is its intention of adoption, which is majorly related to the service's privacy, safety, and security. The local service providers and banks should consider this aspect and ensure that trust and ethics are maintained. This needs the process of communication with the user at regular intervals. Moreover, this includes renewing client protection policies in banks and other m-payment service providers. Furthermore, the Intention barriers to adopting m-payment concluded that banks should facilitate their customer 24/7 with highly qualified representatives. Thus, by focusing on the provided facilitators.

The research can be useful for banking firms and their communications managers regarding practical ramifications. The study recommends that marketing and operations managers improve expectations of ease of use and create confidence in conventional cash-mode payment customers. To summarize, the most crucial factors that consumers consider when promoting m-payment are ease of use and trust. It is also beneficial for the state and related agencies to ensure transparency in financial and banking transactions through mobile payment systems, which can assist developed countries in preventing corruption and financial fraud.

As a result, this study is mainly helpful for banks and financial institutions. Bank executives may develop practical approaches by informing people around the country that m-payments are simple to use and increase the bank's confidence among Pakistani customers. In addition, tech engineers and service providers should create cutting-edge software and services for banking operations, especially in Pakistan. This forward-thinking initiative would aid in the introduction of mobile banking. Due to their various infancy in Pakistan, micro-financial institutions can also offer new applications and simple services for m-banking.

Conclusion

The study aimed to investigate the impact of theoretical drivers, facilitators, and barriers to adopting m-payment. It further examines the impact of the intention to adopt m-payment on usage behavior. Furthermore, the facilitators included Perceived Innovativeness, Perceived Trust, Perceived Ease of Use, Perceived Usefulness, and Perceived Satisfaction. Theoretical drivers included Relative Advantage, Compatibility, Complexity, Observability, and Trialability. Lastly, barriers studied included Operational Constraints, Perceived Risk, Stress, and Unavailability of facilitating condition.

This research is limited to a selection of participants in Karachi, Pakistan. The survey should be conducted nationally to obtain more comprehensive results; it could also be beneficial to determine customer preferences toward mobile payment systems in other nations. A cross-cultural analysis will be also helpful in obtaining more generally applicable customer behavior data on mobile payment services. Another thing worth noting is that the group mainly comprises young, skilled traditional technology consumers.

This type of sample may significantly impact the study's findings. Future studies could also look at the perspectives of non-users of mobile apps and see what affects their actions. First and foremost, the current research is constrained by its small sample size. Since the time available to complete this experiment was minimal, the report recommends combining a similar framework with a greater sample size. Second, the current study used urban data; a potential suggestion may be to compare the behavioral and technical facets of m-banking acceptance in rural and urban areas. Finally, we carried out this study in Pakistan, where m-banking is increasingly rising but still in its infancy. Our findings may apply to similar situations but cannot be applied to countries where digital marketing is more developed.