Next Article in Journal
Technical and Economic Analysis of Solar PV/Diesel Generator Smart Hybrid Power Plant Using Different Battery Storage Technologies for SRM IST, Delhi-NCR Campus
Previous Article in Journal
Study on Temporal and Spatial Variation in Soil Temperature in Artificial Ground Freezing of Subway Cross Passage
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 15
Issue 4
10.3390/su15043664
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Factors Determining Consumer Acceptance of NFC Mobile Payment: An Extended Mobile Technology Acceptance Model

by
Qingyu Zhang
1,
Salman Khan
1,*,
Mei Cao
2 and
Safeer Ullah Khan
3
1
Research Institute of Business Analytics and Supply Chain Management, College of Management, Shenzhen University, Shenzhen 518060, China
2
School of Business & Economics, University of Wisconsin-Superior, Superior, WI 54880, USA
3
Department of Business Administration, Gomal University, Dera Ismail Khan 29050, Pakistan
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(4), 3664; https://doi.org/10.3390/su15043664
Submission received: 29 December 2022 / Revised: 30 January 2023 / Accepted: 7 February 2023 / Published: 16 February 2023
(This article belongs to the Section Sustainable Management)
Browse Figures
Review Reports Versions Notes

Abstract

:
The demand for mobile payments using smartphones to substitute the need for cash, credit cards, or checks is swiftly increasing in Pakistan. This study investigates the factors determining consumers’ behavioral intention to adopt near-field communication mobile payment from a developing country’s viewpoint. A conceptual framework was adopted based on the mobile technology acceptance model (MTAM), integrating self-efficacy theory, critical mass theory, flow theory, and system and service quality to elucidate the behavioral intention. Data were collected through a self-administered questionnaire applied to 310 nonusers of near-field communication mobile payment in Pakistan. The analysis was performed using SmartPLS3.0. The results demonstrated that other independent variables are the main predictors of the intention to adopt mobile payment besides technology self-efficacy, perceived critical mass, and mobile ease of use. The study concludes with key implications and future work directions concerning the limitation of this study.

1. Introduction

Before the Internet was developed in the late 1990s, people used to shop or order via telephone [1]. Technological advancements have altered customers’ purchasing habits and payment methods over the last decade; it is no surprise that mobile payment adoption and usage are increasing [2]. Moreover, mobile payment is anticipated to be a prevalent payment method in the future for various financial transactions [3]. This is due to mobile payment performance enhancement and error minimization [4]. To achieve sustainable and comprehensive development in sustainable mobile payment system, it is essential to develop an advanced electronic payment system to maintain the strength and efficiency of the national payment system. This research focuses on financial technology; customer acceptance of financial technology, i.e., near-field communication (NFC) mobile payment, is generally not well known in Pakistan.
Smartphones, nowadays, are at the core of our daily lives. This cutting-edge technology is no longer restricted to communication; it is also being utilized for increased information sharing, mobile payments, and m-commerce which could play a significant role in developing a sustainable mobile system. In this regard, NFC technology has provided new business opportunities for restaurants, public transportation, wireless communication, and so on [5]. According to the Mobile Economy report from the GSMA [6], in 2017, there were over 5 billion active users globally and a 66% penetration rate for mobile services. The total population of Pakistan is 225.2 million; between them, 194 million are active mobile users. NFC is a pivotal m-payment technology [5], and it has two main parts: (a) an initiator to establish and manage the communication exchange and (b) a target responding to requests. This technology is more useful because it can work even when the device is not turned on [7,8]. NFC transmits data to a customer’s bank through a microchip, SIM card, or memory card. The wide range of license-free NFC applications, such as paying bills, using public transportation tickets, and making in-store purchases, can be executed in all main terminals integrated with a dedicated non-contact communication chip, with a greater user value addition and enhanced security.
Additionally, NFC may be utilized to send and receive digital money transactions while enhancing the financial system’s sustainability [9]. This technology transfers payment information from a user’s mobile phone to the point-of-sale terminal using radio waves [10]. This process has resulted in considerable developments in the domain of consumer behavior [11]. All the benefits mentioned earlier are significant in light of the aim to investigate the NFC technology adoption by nonusers [12,13]. On the contrary, the effect of the COVID-19 epidemic encourages and demands everyone to uphold a “new normal” mobile payment; in particular, everyone is urged to use alternate payment mechanisms to prevent physical interaction with currency [13,14]. The latest research has indicated changes in global consumer preferences from Africa and the Middle East to the United States and Latin America [15].
Thus, in differentiating this paper from previous research, we carried out the following; firstly, we build on the mobile TAM (MTAM) [16] as the fundamental model advocating continuous resting and refinement by extending the range of theories supplementing with self-efficacy theory [17,18], critical mass theory [19], and flow theory [20]. Secondly, we extended the research by including service quality; this provides a better understanding of nonusers’ acceptance MP system as an indicator of NFC mobile payment success. It is believed that such an integrated model is novel in the context of mobile payment adoption especially in the context of nonuser context. Thus, in this area of research, we put forward number of contributions. First, this research examines the behavioral intentions of NFC-mobile technology adoption among nonuser’s consumers in Pakistan. Second, the results contribute to the literature on NFC mobile technology acceptance [5], which has mostly been performed in more developed economies. Third, this study proposes a holistic model after a review of the most current literature in this area of research. Finally, this research adds to marketing practitioners by guiding the adoption and use of NFC mobile payment in Pakistan.
Given the practical implication, the advancement of mobile payment has created many possibilities for companies to improve their operational processes. Moreover, the level at which m-payment may change the payment industry in Pakistan has not yet been entirely determined. The reason is that a considerable amount of potential for future uses remains unexplored. However, the insufficient willingness of Pakistanis to utilize m-payments at the moment is a paramount concern. Thus, this research offers valuable insights and results to various stakeholders in the business sector. Contributions to theory include bridging and understanding the gap in nonusers’ critical variables affecting m-payment acceptance. Additionally, this study is the first to examine the nonuser’s behavioral intention to accept NFC m-payment through a novel extended MTAM in the context of Pakistan. Overall, this distinctively developed model and its results advance the current development of literature on mobile technology adoption.

2. Literature Review

2.1. MTAM

Ref. [16] developed MTAM to address the shortcomings of the original TAM [21]. The original TAM is one of the most generally accepted and regularly used models in evaluating the antecedents influencing the intention to adopt new technology [22]. However, it still has limitations. These limitations include the original definitions of perceived usefulness (PU) and perceived ease of use (PEOU). In particular, PU relates to an individual’s opinion that implementing a specific system enhances his/her work performance, whereas PEOU refers to an individual’s conviction that implementing a particular system is easy [21]. Both PU and PEOU are described in such a way that they are contextualized within an organizational framework. This matter is a cause of concern because technology adoption outside the working environment differs in many areas, including the kinds and complexities of jobs [23]. In addition, many researchers argued that different factors are taken from other technological studies for use in mobile adoption studies [16]. Therefore, this model has been developed using two constructs, namely, MU and MEOU. In particular, MU refers to how efficiency is enhanced via mobile devices, whereas MEOU is about how effortless paying through mobile devices is [16]. Given the mobility provided by m-payment, an individual may perform numerous payments at any time and location. These two constructs have been modified to represent the mobile environment accurately and provide a comprehensive view. MTAM has been used in various study fields, including mobile social media marketing [24], cyberbullying [25], and fashion shopping [26], and mobile social learning [27]. MTAM’s fundamental variables (i.e., MU and MEOU) can be used across many facets of mobile technology. Thus, MTAM is well suited for examining m-payment adoption intention. However, MTAM offers just two constructs; thus, it misses other factors that would be deemed critical in influencing new mobile technology adoption. As a result, an expanded MTAM was used in the present research to investigate m-payment adoption intention. This approach is consistent with the recommendation of other scholars to include other factors to understand the antecedents comprehensively influencing the adoption of new technologies, particularly mobile services [28,29]. This approach also entails considering nontechnological variables that influence consumers’ decision-making processes. This study used an expanded MTAM that incorporates other factors, such as self-efficacy theory, critical mass theory, flow theory, and system and service quality, in light of their importance in examining the adoption of NFC mobile payment to provide a comprehensive view.

2.2. Self-Efficacy Theory

The self-efficacy theory developed by [17,18] postulates that individuals who feel a common sense of self-efficacy in performing a task might consequently avoid it, but those who believe that they are capable would readily perform. The idea of self-efficacy also argues that people’s self-perception is the most important predictor of their behavior in particular circumstances [30,31]. Since its conception, the function of self-efficacy has been investigated across various study areas [31], including the adoption and acceptance of new technologies [32]. In the present study, self-efficacy is stated as an “Individual’s personal belief about his or her ability to initiate, persist in, and be successful in behavior” (p. 141) [33]. If people understand how to use technology, they can adapt to and accept new technologies [34]. Self-efficacy theory is concerned with individual beliefs. Thus, it may complement MTAM, which is mainly concerned with technological factors. In recent years, various studies have examined the interaction between consumers and technology using the self-efficacy theory in the hospitality industry [35,36]. Therefore, self-efficacy theory is incorporated into the present research with the modest MTAM. Following [37], we operationalized the theory of self-efficacy as two separate constructs: mobile self-efficacy (MSE) and technology self-efficacy (TSE).

2.3. Critical Mass Theory

The critical mass theory suggested by [19] tries to forecast the efficiency of group action directed toward a common goal. Concerning technology adoption, the critical mass theory may be used to analyze the influence of others on the use of technology within an organization [38]. In particular, the number of people who embrace technology may influence the favorable views of prospective adopters because critical mass theory claims that individuals’ decisions rely on their participation in groups, for example, organizations [39]. Ref. [40] showed that pull factors significantly influenced a critical mass in e-payment in the context of the COVID-19 pandemic. Thus, [37] stated that an individual’s perception of a technology’s critical mass (PCM) would influence their future adoption behavior. Similarly, previous research showed that PCM influences the acceptance and adopt technology [30]. Given that the PCM reflects an individual’s subjective opinions of others’ technology usage, its incorporation with the austere MTAM is considered to have complimented the technical elements included inside MTAM.

2.4. Flow Theory

Ref. [20] defined flow as an enjoyable experience of giving complete attention to a job while performing it. When experiencing flow, individuals become highly focused and can filter out extraneous thoughts, perceptions, and ideas that may otherwise arise. Their awareness progressively shrinks from the periphery, reacting solely to precise objectives and rapid feedback [41]. Understanding the role of flow in mobile payment adoption is essential because m-payment takes only a short time to conclude a transaction, to a positive reception [30]. Recent studies have examined the interaction between consumers and technology using flow theory [42,43] in the hospitality and tourism industries [44]. Flow has been regarded as a desirable intrinsic benefit associated with engaging in an activity [45]; therefore, it serves as a motivating factor in the adoption of technology [46] in the hospitality sector [47,48]. With this in mind, the theory concerns intrinsic motivation or hedonic motivation experienced by consumers when using technology [49]. It is included in the minimalist MTAM to enhance its technological beliefs, which focus only on the features of mobile technology. Similar to the findings of [50], this research operationalizes flow theory as perceived enjoyment (PEJ) to assess the degree of intrinsic enjoyment or hedonic motivation associated with paying through m-payment.

3. Hypotheses Development

3.1. Mobile Usefulness (MU)

MU is stated as consumers’ perceptions of how their performance is enhanced when they use mobile technology or service [51,52]. In particular, mobile technologies or services approximate this research’s NFC- financial technology. Extensive studies from various locations have shown the effect of usefulness on m-payment acceptance, such as Malaysia [29], China [53], and India [54,55]. Additionally, usefulness had a substantial influence on consumers’ behavioral intention (BI) to use SMS, NFC, and QR code payment systems [56]. All this research indicated that the usefulness of m-payment systems has a substantial impact on the BI of customers. Furthermore, Ref. [16] found that MU is strongly and positively associated with BI in Malaysia’s adoption of m-payment. Henceforward, the following is hypothesized:
H1: 
MU positively influences BI to adopt NFC m-payment.

3.2. Mobile Ease of Use (MEOU)

MEOU is highlighted as the extent of effortlessness consumers perceive learning and using mobile technology or service [21,57]. In this research, mobile technology or service relates contextually to the NFC mobile payment. Previous research has shown that MEOU is vital in persuading the BI to embrace m-payment. Ref. [58] investigated consumer attitudes towards using m-payment systems in in India. Similarly, Ref. [59] usefulness substantially influenced consumers’ behavioral intention (BI) to use a mobile wallet in the hospitality industry. Additionally, Ref. [60] integrated the UTAUT 2 with Innovation Resistance Theory to accommodate pertinent constructs for consumers adoption of mobile payment context. In addition, Ref. [61] studied factors affecting the user acceptance of QR code MPS and identified the causal relationship between the factor affecting the acceptance. The above investigations showed that MEOU substantially influences customers’ BI to adopt m-payment. Particularly, Ref. [62] found MEOU to be favorably and substantially associated with the BI’s adoption of m-payment. The convenience and easiness with which proximity m-payments may be used directly affect an individual’s BI to use m-payments. Accordingly, we hypothesized the following:
H2: 
MEOU positively influences BI to adopt NFC m-payment.

3.3. Perceived Critical Mass (PCM)

PCM denotes “the minimum number of adopters of an interactive innovation for the future adoption rate to be self-sustaining” [63,64]. The PCM concept is identical to network externality [65] and bandwagon influence [66]. The concept suggested that if a critical mass of adopters is reached, extrinsic incentives arise, enticing additional customers to participate [67]. When a sufficient number of consumers accept a particular IT/IS, a warning is sent to prospective consumers, indicating that the system is important and worth following [68]. PCM is widely used in tourism research because it affects the BI to interact with different users [69]. For instance, PCM is considerably associated with the BI’s adoption of wearable medical devices [70]. Similarly, [24,71] uncovered that PCM strongly correlates positively with mobile social media marketing. Furthermore, PCM considerably affects PEJ [63]. Ref. [72] examined the association between the referent size of the network and PEJ in a mobile study and found a positive association between the network size and PEJ. This finding shows the value of exploring the influence of PCM on PEJ mutual interactions concerning emerging technology. Thus, we hypothesized the following:
H3: 
PCM positively influences BI to adopt NFC m-payment.
H4: 
PCM is positively related to PEJ.

3.4. Perceived Enjoyment (PEJ)

Perceived enjoyment is the level to which the interaction of the approach is deemed pleasant despite the anticipated results [73]. It is intrinsically motivated or hedonic [74,75], composed of pleasure, leisure, entertainment, and cheerfulness, and it is essential to the use of modern applications and systems concerning customers BI [73]. The influence of PEJ on BI’s adoption of mobile shopping adoption during the COVID-19 lockdown in Malaysia [76]. Furthermore, [43] investigated the association between PEJ and BI to utilize mobile applications for teaching usage. All these studies showed that PEJ would play a valuable role in implementing m-payment services. Accordingly, we hypothesized the following:
H5: 
PEJ positively influences BI to adopt NFC m-payment.

3.5. Mobile Self-Efficacy (MSE)

MSE denotes consumers’ confidence in their desire to learn and utilize particular purchasing technology [27,77]. Self-efficacy is a vital construct for evaluating the use of novel technology, particularly in the m-payment adoption context. Self-efficacy has a statistically substantial positive effect on the BI’s adoption of mobile-assessed language in Taiwan [78]. Furthermore, self-efficacy positively influences usefulness and ease of use [52,79]. Given that Pakistan’s cash and debit cards are substantially different from m-payment, MSE has a critical role in their acceptance. Accordingly, we hypothesized the following:
H6: 
MSE positively influences BI to adopt NFC m-payment.
H7: 
MSE is positively related to MU.
H8: 
MSE is positively related to MEOU.

3.6. Technology Self-Efficacy (TSE)

A substantial MSE level may not invariably lead to a high TSE level [63,80], which is characterized as the individuals’ conviction that they have a sufficient degree of expertise to perform technology-related tasks effectively [81]. Generally, a high degree of TSE indicates a vital willingness to use technology-based services [82]. The association between TSE and intention is proved in the framework of mobile technology studies [83]. Ref. [84] also determined that customers with a high degree of TSE strongly prefer easy-to-use mobile technology. Furthermore, TSE positively affects MU and MEOU [85]. Accordingly, we hypothesized the following:
H9: 
TSE positively influences BI to adopt NFC m-payment.
H10: 
TSE is positively related to MU.
H11: 
TSE is positively related to MEOU.

3.7. System and Service Quality (SSQ)

Since its inception, system and service quality (SSQ) has been widely used to describe the consumer experience and usability of new systems and services [86,87]. SSQ corresponds to consumers’ overall satisfaction with service and system performance, closely related to consumers’ attitudes toward technology. SSQ is a term that corresponds to the total degree of satisfaction with a service or system’s effectiveness, which is closely related to user perception of technology [52]. SSQ is a critical contextual element affecting the user’s cognitive and affective responses regarding the system through which mobile payment services are delivered [88]. In particular, m-payment is a component of the system and service that enables consumers to pay using their mobile devices. If the system is not working as anticipated (e.g., delay or disconnection), service delivery and user adoption may be affected [89,90]. The NFC mobile payment system and payment service are inextricably connected; thus, the present research analyzes them as one construct. This concept is expected to considerably influence an individual’s attitude toward BI and utilizing NFC mobile payment services. Earlier research has shown that SSQ has a considerable influence on BI [91,92], whereas others have discovered that it is a critical predictor of PU and PEOU [93]. Generally, when an NFC m-payment system provides services that align with the service’s purpose, the payment method is seen as beneficial and takes minimal mental effort to understand. Consequently, this scenario results in a considerable willingness to adopt NFC m-payment. Accordingly, we hypothesized the following (Figure 1):
H12: 
SSQ positively influences BI to adopt NFC m-payment.
H13: 
SSQ is positively related to MU.
H14: 
SSQ is positively related to MEOU.

4. Research Methodology

As the subject matter of this study heavily involves smartphone use, the respondents were required to be smartphone users. Non-probability sampling technique in the form of purposive sampling and J [94] criteria was utilized in determining sample size. They established a minimum sample size of 5–8 times of total indicators. Based on 33 total numbers of indicators used in the current study, a minimum (165–264) sample is required. Thus, the sample of 310 valid responses used for analysis meets the criteria. Surveys were administrated face-to-face to ensure consistency in data collection. The questionnaire was divided into three sections. A short description of NFC mobile technology was included on the first page, and respondents were asked if they used it. Only respondents that were nonusers of NFC technology were considered in the sample for this study. The following section asked participants about NFC mobile technology attitude and their behavior. The usage of NFC mobile technology followed this. Finally, the questionnaire collected demographic variables and thanked them for their participation. The questionnaire was developed in English and translated into Urdu by a native speaker, and a Pakistani colleague back-translated the item into English [95,96].
Furthermore, the questionnaire was pretested with 17 graduate students who responded to it. Their comments on the questionnaire lead to minor wording modifications. A total of 400 questionnaires were distributed, and only 310 responses were eligible for data analysis. This translates to a response rate of 77.5%. Therefore, the sample size was more than sufficient for the analysis. The Appendix A provides the details of the instrument, measurement items, and sources of the scales. Table 1 present the sample characteristics.

5. Analysis of Data and Findings

The PLS-SEM was applied to evaluate and validate the construct and assess the hypothesized model. PLS-SEM is an integrated modeling technique that enables researchers to determine the relationships among variables and the reliability and validity of any research framework [97]. In the context of mobile payment literature, PLS-SEM has gained considerable research interest [52]. Furthermore, PLS-SEM is a powerful technique that can anticipate a complex model without the need for distribution assumptions [98]. Given the advantages of PLS, the current study investigated the variables that influence NFC mobile payment acceptance by consumers employing PLS; these variables are deemed appropriate to assess the association in any structure model, particularly in the IS setting [99]. We utilized the Smart-PLS 3 software (SmartPLSGmbH, Boenningstedt, Germany) in this study.

5.1. Common Method Bias (CMB)

Although the questionnaire was assembled by applying a self-reporting technique, Common Method Bias (CMB) may become a problem to the findings’ validity. The study by [100] indicated that such data might encounter the CMB problem. Following CMB in Partial Least Square Structural Equation Modeling (PLS-SEM): A Full Collinearity Evaluation Approach, we checked the CMB issue in the current study using PLS-SEM. We calculated the VIF inner values of all constructs by generating random variables. Table 2 shows the VIF inner values of all constructs, which are smaller than the recommended level of 3.3, indicating that CMB is not an issue in the study (CM in PLS-SEM: A Full Collinearity Evaluation Technique).

5.2. Measurement Model

The proposed model was evaluated by employing confirmatory factor analysis (CFA). We measured the proposed model in terms of composite validity, average variance extracted (AVE), and Cronbach’s alpha. The partial least square (PLS) algorithm was performed to estimate the outer loads for each construct. Table 2 describes the overall descriptive statistics of mean/s.e. of the specific questions. Table 3 highlights the results of composite validity; Cronbach’s alpha constructs’ loadings crossed the recommended threshold of 0.7 [101,102], and the average variance extracted (AVE) variance outperformed the 0.5 thresholds [103]. CFA results disclosed that each item loading factor is more remarkable than 0.7. As shown in Table 2, CFA results meet the cut-off value of CA, CR, and AVE, which are more than 0.7, 0.7, and 0.5, respectively, indicating good convergent validity [102,104].
Discriminant validity, which shows that one variable’s measurements are distinct from the others, was assessed using three techniques [105]. First, we analyzed the correlations among variables to see how well they agreed with the AVE of all the hypotheses, as contended by [104]. Next, we evaluated discriminant validity by adopting the heterotrait–monotrait ratio (HTMT) approach, all the values are less than 0.85, the standard level in the HTMT approach, signifying that the variables have good discriminant validity. Table 4 demonstrates that the AVE square root is greater than the correlation values for all constructs, indicating good discriminant validity.
Third, we evaluated discriminant validity by adopting the Heterotrait-Monotrait ratio (HTMT) approach. As shown in Table 4, all the values are less than 0.85, the standard level in the HTMT approach, signifying that the variables have good discriminant validity. Table 5 shows the cross-loadings of all items.

5.3. Structural Model Results

After assessing the validity of the measurement model, the study tested the hypotheses using the structural model. The research model’s explained variance (R2) value was 0.416, indicating that the model has the ability to explain 41.0 percent of the variance in BI associated with mobile payment adoption. In addition to assessing the R2 values, effect size (f2) is used to examine whether a specific independent variable has a substantive impact on a dependent variable. Based upon [106] guideline, the results show that the f2 for the supported hypotheses was acceptable (Table 6). Predicted relevance (Q2) value was also evaluated by running the blindfolding procedure and calculated using the cross-validated redundancy approach. Ref. [107] suggested that a Q2 value bigger than zero indicates that the model has predictive relevance. This study’s dataset exhibits satisfactory predictive relevance, as the Q2 value was 0.258 (i.e., above zero). We established the good fit of the model by measuring the standardized (SRMR). The value is 0.069, following the threshold value of 0.08 [108].
The hypothesized associations between the variables were tested with a standardized path examination using the bootstrapping method with 2000 samples [109]. The direct and indirect effects of dependent on the independent variable were analyzed. The path coefficients are given in Table 6. We estimated the significance level [110] by performing the bootstrap method with a resampling of 2000 times, providing the most desired results with zero change [103]. Table 6 and Figure 2 show that out of 14 hypotheses, only nine hypotheses were supported; in particular, HI (β = 0.172, p < 0.001), H4 (β = 0.251, p < 0.001), H5 (β = 0.142, p < 0.01), H6 (β = 0.265, p < 0.001), H7 (β = 0.19, p < 0.01), H10 (β = 0.211, p < 0.001), H12 (β = 0.288, p < 0.001), H13 (β = 0.272, p < 0.001), and H14 (β = 0.257, p < 0.01) significantly support NFC mobile payment adoption. Similarly, H2 (β = 0.078, p > 0.05), H3 (β = 0.052, p > 0.05), H8 (β = 0.1, p > 0.05), H9 (β = 0.069, p > 0.05), and H11 (β = 0.063, p > 0.05) are insignificant.

6. Discussion and Implications

The results demonstrate that MU has a positive association with BI. The result validates previous empirical research [52,63]. The more advantages experienced by adopters of NFC mobile payment are, the more favorable NFC mobile payment is to customers’ BI. Alternatively, convenience, time savings, and increased productivity associated with NFC mobile payment are major motivations for consumers to adopt it. In previous mobile research, MU is a determinant of IU [111], mobile learning [112], mobile marketing [113], and mobile television [114]. Compared with offline payment, NFC mobile payment provides time savings, convenience, and security features, which are important for customers. Conversely, MEOU is an insignificant predictor of BI. The results contrast previous research on NFC mobile devices, which found that customers generally embrace innovation if the system is seamless for users [1,115,116]. Additionally, the result contradicts prior research on mobile shopping and learning studies [117,118]. One possibility is that the number of steps required to complete an NFC mobile payment transaction is fewer than those required for other mobile payment systems. Thus, most customers viewed NFC mobile payment as simple, requiring little effort to understand. However, the findings indicate that H3 is not supported because PCM has no significant association with BI. This result is unexpected, but it is consistent with that of [63]. One plausible reason is that the sample size is small, and most of the participants are highly educated, implying that their choices are mainly based on pure logical thinking rather than mass-market impact. Although the present level of PCM is insufficient to encourage adoption, it is sufficient to induce PEJ in this research. Similarly, PCM indicates a significant positive association with PEJ. As such H4 is supported, and the results enhance the work of [63] in their research on mobile wallet adoption in Pakistan. Furthermore, a significant association between PEJ and BI is discovered, corroborating H5. This result implies that the intrinsic or hedonic motive experienced by individuals utilizing NFC mobile payment fuels their intention to use. Additionally, MSE has a positive association with BI, thereby confirming H6. The findings were corroborated empirically [63] on the study of m-wallet technology. In general, using an NFC mobile payment entails the individuals’ assurance in their ability to do so. Thus, individuals with high levels of MSE have a great proclivity to utilize NFC mobile payment. Conversely, the study shows a positive association between MSE and MU, confirming H7. For instance, the more an individual trusts in performing NFC m-payment, the greater the apparent performance improvement (usefulness) is. On the contrary, our findings indicate that MSE is not a major determinant of MEOU. Thus, H8 is statistically not supported. The results contradict the previous work conducted on NFC m-payment, where individuals uncover advantages related to the usage of NFC mobile payment [52]. Additionally, the result contradicts the previous research on the intention of m-wallets [63]. TSE is not significantly related to BI, which is similar to the findings obtained by [63]. The results also agree with the findings by [119], who found a similar result in the background of mobile payments. The insignificant path may be caused by the fact that NFC mobile payment was previously useful and easy to use in performing fundamental functions (i.e., making payments) without needing a considerable measure of additional technical knowledge (e.g., verification of account). By contrast, TSE has a direct and positive association with MU. Hence, H10 is supported in this research. The results are consistent with the work of [52]. On the contrary, our findings indicate that TSE has no direct and significant association with MEOU. Hence, H11 is not supported in this study. The finding is unexpected and is not in agreement with the work on NFC mobile payment [52] and m-wallet [63]. In addition, H12 indicates a statistically significant association among SSQ and BI. The reason is that users are hesitant to utilize the new technology or innovation when they have flaws and failures. This scenario may manifest itself in the form of frequent responding delays and weak security. This finding is likewise true in the case of SSQ with MU and MEOU, where both H13 and H14 are supported. The results are similar with the research of [52]. This finding demonstrates that when promises are kept via the performance of necessary tasks, SSQ results in a favorable impression of the “usefulness” of NFC m-payment. Additionally, consumers rate SSQ on its ease of use and flawless integration with NFC m-payment for transaction processing.

6.1. Theoretical Contributions

Regarding the theoretical implications, the suggested model in this research incorporates nontechnological adoption factors into the minimalist MTAM, centered on three theories: explicitly self-efficacy theory [17,18], critical mass theory [19], and flow theory [20]. Several researchers [120,121] argued for an expanded MTAM, arguing that it should contain additional factors to demonstrate the adoption of new technologies, particularly in mobile services [122]. Additionally, other scholars advocated for including non-technological factors in future mobile technology studies [123,124]. Compared with the minimalistic MTAM, which focuses only on technological factors, the expanded MTAM contributes to theoretical understanding by generating complete results. Additionally, service providers should strive to provide a simple service for customers to comprehend and use. This approach may be accomplished in various ways, most notably via the user experience of the NFC m-payment. The primary focus should be navigation, effortless interoperability, and an easy-to-use payment procedure. Thus, customers can learn how to utilize NFC mobile payment rapidly, thereby increasing its acceptance.

6.2. Practical Contributions

Service providers can consider improving the usefulness of NFC mobile payment concerning the practical implications. It must be capable of enhancing the quality of life of users. Thus, they must first comprehend the expectations of prospective consumers throughout the research and commercialization phases. For instance, NFC m-payment should enable customers to finish their transactions relatively conveniently through incentives, efficiency, and convenience. Moreover, MEOU, TSE, and PCM should not be overlooked or neglected, even though they have no direct effect on BI. The reason is that these structures can have an indirect effect on BI. Mobile payment service providers may investigate how it may be enhanced to be increasingly essential in this context.

7. Limitations and Recommendations

This study has shortcomings, the first of which is its inability to generalize its results. The data collection is limited to Pakistanis; thus, the findings may not represent the BI or willingness to adopt NFC m-payment in other economies. The reason is that countries vary considerably in terms of culture, degree of development, and other factors that may affect the acceptance of mobile technology. Thus, future research may perform a comparative study to expand the scope of the study by gathering data from other countries. Second, although our study offers an investigation of mobile payment, contrasting our findings with other m-payment systems, for example, QR or biometrics is an exciting extension of our research. Third, given the possibility of substantial BI variation in mobile payment use, our research did not include other factors associated with technology adoption, for instance, perceived risk, perceived trust, etc. However, in this study, we targeted only the m-payment users, whereas, in practice, users of a service/technology deny risk brazenly compared to non-users. Similarly, the trust level of m-payment users is deemed higher compared to non-users or nascent users of m-payments. Future research should be conducted on the subject matter while focusing on the associated factors such as perceived risk and trust level of existing and potential m-payment users. Future research may refine the model by removing insignificant factors and expanding it to incorporate new variables. Fourth, the COVID-19 epidemic has been generating considerable changes in consumer behavior. In particular, e-commerce, digitization, and technologies that enable customers to minimize physical contact are encouraged; m-payment methods do not need physical contact. During the post-COVID-19 stabilization and regularization epoch, the actual and long-lasting impact of the pandemic on the usage of these new technologies must be examined. Conversely, future research may include mediating and/or moderating factors to obtain complete results. We also encourage using the model in other contexts of mobile usage, such as m-learning, m-banking, m-Health, and m-gaming.

Author Contributions

Methodology, S.K., M.C. and S.U.K.; Data curation, S.K.; Writing—original draft, S.K. and Q.Z.; Writing—Review & Editing, Q.Z., S.K. and M.C.; Project administration, Q.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by Key Project of National Social Science Foundation of China (21AGL014); Natural Science Foundation of Guangdong—Guangdong Basic and Applied Basic Research Foundation (2021A1515011894); Guangdong 13th-Five-Year-Plan Philosophical and Social Science Fund (GD20CGL28); Shenzhen Science and Technology Program (JCYJ20210324093208022).

Data Availability Statement

Data sharing is not applicable to this article.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

ConstructsMeasurement ItemSource
Mobile Usefulness MU1: Using NFC mobile payment system will increase my performance. [16]
MU2: Using NFC mobile payment system will enhance my effectiveness in day-to-day life.
MU3: Using NFC mobile payment makes the payment easy.
MU4: Overall, I will find NFC mobile payment system to be advantageous.
Mobile Ease of Use MEOU1: Learning to use NFC mobile payment will be easy for me. [16]
MEOU2: Using NFC mobile payment does not need a lot of mental effort.
MEOU3: Becoming skillful when using NFC mobile payment will be easy for me.
MEOU4: NFC technology uses my mobile device; hence, NFC mobile payment is easy to use.
System and Service Quality SSQ1: I do not have any limitations or problems when using NFC mobile payment system. [28,71,87]
SSQ2: NFC mobile payment system offers services that fully meet my needs.
SSQ3: NFC mobile payment provides precise services that are aligned with the purpose of the service.
SSQ4: NFC mobile payment provides convenient access.
SSQ5: NFC mobile payment is easy to use.
Mobile Self-EfficacyMSE1: I feel confident when using NFC mobile payment. [77]
MSE2: I can complete a transaction using NFC mobile payment in a short time.
MSE3: I feel confident to use NFC mobile payment even if no one guides me.
Technology Self-Efficacy TSE1: I feel confident in my ability to figure out what to do when a feature does not work in the NFC mobile payment. [124]
TSE2: I feel confident turning to an online discussion group in the NFC mobile payment.
TSE3: I feel confident understanding the terms or words that are needed to use the NFC mobile payment.
TSE4: I feel confident learning advanced features in the NFC mobile payment.
Perceived Critical Mass PCM1: Most of my colleagues frequently use NFC mobile payment for paying. [37]
PCM2: Most of the people I communicate with use NFC mobile payment for paying.
PCM3: Most people in my group use NFC mobile payment.
PCM4: Many people I communicate with regularly use NFC mobile payment.
PCM5: Most of my friends frequently use NFC mobile payment for paying.
Perceived Enjoyment PE1: I find using NFC mobile payment for paying is fun. [122]
PE2: I find using NFC mobile payment for paying pleasant.
PE3: I find using NFC mobile payment for paying exciting.
PE4: I find using NFC mobile payment for paying entertaining.
Behavioral Intention BI1: I am likely to increase my use of NFC mobile payments in the future. [16]
BI2: I am willing to use NFC mobile payment in the future.
BI3: I intend to use NFC mobile payment system when an opportunity arises.
B4: Given the opportunity, I will use NFC mobile payment.

References

  1. Balachandran, D.; Tan, G.-W.-H. Regression modelling of predicting NFC mobile payment adoption in Malaysia. Int. J. Model. Oper. Manag. 2015, 5, 100–116. [Google Scholar] [CrossRef]
  2. Martinez, B.M.; McAndrews, L.E. Do you take...? The effect of mobile payment solutions on use intention: An application of UTAUT2. J. Mark. Anal. 2022, 1–12. [Google Scholar] [CrossRef]
  3. Aydin, G.; Burnaz, S. Adoption of mobile payment systems: A study on mobile wallets. J. Bus. Econ. Financ. 2016, 5, 73–92. [Google Scholar] [CrossRef]
  4. Busu, S.; Karim, N.; Haron, H. Factors of adoption intention for near field communication mobile payment. Indones. J. Electr. Eng. Comput. Sci. 2018, 11, 98–104. [Google Scholar] [CrossRef]
  5. Liébana-Cabanillas, F.; Singh, N.; Kalinic, Z.; Carvajal-Trujillo, E. Examining the determinants of continuance intention to use and the moderating effect of the gender and age of users of NFC mobile payments: A multi-analytical approach. Inf. Technol. Manag. 2021, 22, 133–161. [Google Scholar] [CrossRef]
  6. Mobile, G. The Mobile Economy. 2018. Available online: https://www.gsma.com/newsroom/press-release/number-of-global-mobile-subscribers-to-surpass-five-billion-this-year/ (accessed on 1 February 2023).
  7. Al-Qudah, A.A.; Al-Okaily, M.; Alqudah, G.; Ghazlat, A. Mobile payment adoption in the time of the COVID-19 pandemic. Electron. Commer. Res. 2022, 1–25. [Google Scholar] [CrossRef]
  8. Sun, S.; Zhang, F.; Liao, K.; Chang, V. Determine factors of NFC mobile payment continuous adoption in shopping malls: Evidence from Indonesia. Int. J. Bus. Intell. Res. 2021, 12, 1–20. [Google Scholar] [CrossRef]
  9. Mu, H.-L.; Lee, Y.-C. How Inclusive Digital Financial Services Impact User Behavior: A Case of Proximity Mobile Payment in Korea. Sustainability 2021, 13, 9567. [Google Scholar]
  10. Triggs, R. What is NFC & How Does It Work? 2014. Available online: https://www.androidauthority.com/what-is-nfc-270730/ (accessed on 16 December 2022).
  11. Khalilzadeh, J.; Ozturk, A.B.; Bilgihan, A. Security-related factors in extended UTAUT model for NFC based mobile payment in the restaurant industry. Comput. Hum. Behav. 2017, 70, 460–474. [Google Scholar] [CrossRef]
  12. Hsu, C.-L.; Lin, J.C.-C. An empirical examination of consumer adoption of Internet of Things services: Network externalities and concern for information privacy perspectives. Comput. Hum. Behav. 2016, 62, 516–527. [Google Scholar] [CrossRef]
  13. Upadhyay, N.; Upadhyay, S.; Abed, S.S.; Dwivedi, Y.K. Consumer adoption of mobile payment services during COVID-19: Extending meta-UTAUT with perceived severity and self-efficacy. Int. J. Bank Mark. 2022, 40, 960–991. [Google Scholar] [CrossRef]
  14. Hart, C.W. Spiritual Lessons from the Coronavirus Pandemic; Springer: Berlin/Heidelberg, Germany, 2020. [Google Scholar]
  15. Flavian, C.; Guinaliu, M.; Lu, Y. Mobile payments adoption–introducing mindfulness to better understand consumer behavior. Int. J. Bank Mark. 2020, 38, 1575–1599. [Google Scholar] [CrossRef]
  16. Ooi, K.-B.; Tan, G.W.-H. Mobile technology acceptance model: An investigation using mobile users to explore smartphone credit card. Expert Syst. Appl. 2016, 59, 33–46. [Google Scholar] [CrossRef]
  17. Bandura, A. Self-efficacy: Toward a unifying theory of behavioral change. Psychol. Rev. 1977, 84, 191. [Google Scholar] [CrossRef] [PubMed]
  18. Bandura, A. Self-efficacy mechanism in human agency. Am. Psychol. 1982, 37, 122. [Google Scholar] [CrossRef]
  19. Oliver, P.; Marwell, G.; Teixeira, R. A theory of the critical mass. I. Interdependence, group heterogeneity, and the production of collective action. Am. J. Sociol. 1985, 91, 522–556. [Google Scholar] [CrossRef] [Green Version]
  20. Getzels, J.W.; Csikszentmihalyi, M. From problem solving to problem finding. In Perspectives in Creativity; Routledge: London, UK, 2017; pp. 90–116. [Google Scholar]
  21. Davis, F.D. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 1989, 13, 319–340. [Google Scholar] [CrossRef] [Green Version]
  22. Villa, E.; Ruiz, L.; Valencia, A.; Picón, E. Electronic commerce: Factors involved in its adoption from a bibliometric analysis. J. Theor. Appl. Electron. Commer. Res. 2018, 13, 39–70. [Google Scholar] [CrossRef] [Green Version]
  23. Brown, S.A.; Venkatesh, V.; Bala, H. Household technology use: Integrating household life cycle and the model of adoption of technology in households. Inf. Soc. 2006, 22, 205–218. [Google Scholar] [CrossRef]
  24. Wong, L.W.; Tan, G.W.-H.; Hew, J.-J.; Ooi, K.-B.; Leong, L.-Y. Mobile social media marketing: A new marketing channel among digital natives in higher education? J. Mark. High. Educ. 2022, 32, 113–137. [Google Scholar] [CrossRef]
  25. Ooi, K.-B.; Lee, V.-H.; Hew, J.-J.; Lin, B. Mobile social cyberbullying: Why are keyboard warriors raging? J. Comput. Inf. Syst. 2019, 61, 371–382. [Google Scholar] [CrossRef]
  26. Ng, F.Z.-X.; Yap, H.-Y.; Tan, G.W.-H.; Lo, P.-S.; Ooi, K.-B. Fashion shopping on the go: A Dual-stage predictive-analytics SEM-ANN analysis on usage behaviour, experience response and cross-category usage. J. Retail. Consum. Serv. 2022, 65, 102851. [Google Scholar] [CrossRef]
  27. Loh, X.-M.; Lee, V.-H.; Tan, G.W.-H.; Hew, J.-J.; Ooi, K.-B. Towards a cashless society: The imminent role of wearable technology. J. Comput. Inf. Syst. 2019, 62, 39–49. [Google Scholar] [CrossRef]
  28. Park, E.; Kim, K.J. An integrated adoption model of mobile cloud services: Exploration of key determinants and extension of technology acceptance model. Telemat. Inform. 2014, 31, 376–385. [Google Scholar] [CrossRef]
  29. Yan, L.-Y.; Tan, G.W.-H.; Loh, X.-M.; Hew, J.-J.; Ooi, K.-B. QR code and mobile payment: The disruptive forces in retail. J. Retail. Consum. Serv. 2021, 58, 102300. [Google Scholar] [CrossRef]
  30. Al-Maroof, R.S.; Alhumaid, K.; Akour, I.; Salloum, S. Factors that affect e-learning platforms after the spread of COVID-19: Post acceptance study. Data 2021, 6, 49. [Google Scholar] [CrossRef]
  31. Schunk, D.H. Self-efficacy and academic motivation. Educ. Psychol. 1991, 26, 207–231. [Google Scholar] [CrossRef] [Green Version]
  32. Sharma, S.; Saini, J.R. On the Role of Teachers’ Acceptance, Continuance Intention and Self-Efficacy in the Use of Digital Technologies in Teaching Practices. J. Furth. High. Educ. 2022, 46, 721–736. [Google Scholar] [CrossRef]
  33. Kwon, Y. Effects of Organizational Climates on the Self-Efficacy of Practitioners in Continuing Higher Education in Korea. Perform. Improv. Q. 2018, 31, 141–163. [Google Scholar] [CrossRef]
  34. Hooks, D.; Davis, Z.; Agrawal, V.; Li, Z. Exploring factors influencing technology adoption rate at the macro level: A predictive model. Technol. Soc. 2022, 68, 101826. [Google Scholar] [CrossRef]
  35. Abuelhassan, A.E.; AlGassim, A. How organizational justice in the hospitality industry influences proactive customer service performance through general self-efficacy. Int. J. Contemp. Hosp. Manag. 2022; ahead of print. [Google Scholar] [CrossRef]
  36. Bolar, K.; Mallya, J.; Roy, P.; Payini, V.; Thirugnanasambantham, K. Determinants of hospitality students’ perceived learning during COVID 19 pandemic: Role of interactions and self-efficacy. J. Hosp. Leis. Sport Tour. Educ. 2022, 30, 100335. [Google Scholar]
  37. Tan, G.W.-H.; Lee, V.-H.; Hew, J.-J.; Ooi, K.-B.; Wong, L.-W. The interactive mobile social media advertising: An imminent approach to advertise tourism products and services? Telemat. Inform. 2018, 35, 2270–2288. [Google Scholar] [CrossRef]
  38. Wicker, P.; Feiler, S.; Breuer, C. Board gender diversity, critical masses, and organizational problems of non-profit sport clubs. Eur. Sport Manag. Q. 2022, 22, 251–271. [Google Scholar] [CrossRef]
  39. Van Slyke, C.; Ilie, V.; Lou, H.; Stafford, T. Perceived critical mass and the adoption of a communication technology. Eur. J. Inf. Syst. 2007, 16, 270–283. [Google Scholar] [CrossRef]
  40. Purwandari, B.; Suriazdin, S.A.; Hidayanto, A.N.; Setiawan, S.; Phusavat, K.; Maulida, M. Factors Affecting Switching Intention from Cash on Delivery to E-Payment Services in C2C E-Commerce Transactions: COVID-19, Transaction, and Technology Perspectives. Emerg. Sci. J. 2022, 6, 136–150. [Google Scholar] [CrossRef]
  41. Chen, Y.-M.; Hsu, T.-H.; Lu, Y.-J. Impact of flow on mobile shopping intention. J. Retail. Consum. Serv. 2018, 41, 281–287. [Google Scholar] [CrossRef]
  42. Almarzouqi, A.; Aburayya, A.; Salloum, S.A. Determinants of intention to use medical smartwatch-based dual-stage SEM-ANN analysis. Inform. Med. Unlocked 2022, 28, 100859. [Google Scholar] [CrossRef]
  43. Ateş, H.; Garzón, J. Drivers of teachers’ intentions to use mobile applications to teach science. Educ. Inf. Technol. 2022, 27, 2521–2542. [Google Scholar] [CrossRef]
  44. Ahn, J.; Lee, C.-K.; Back, K.-J.; Schmitt, A. Brand experiential value for creating integrated resort customers’ co-creation behavior. Int. J. Hosp. Manag. 2019, 81, 104–112. [Google Scholar] [CrossRef]
  45. Ha, I.; Yoon, Y.; Choi, M. Determinants of adoption of mobile games under mobile broadband wireless access environment. Inf. Manag. 2007, 44, 276–286. [Google Scholar] [CrossRef]
  46. Rauschnabel, P.A.; Rossmann, A.; tom Dieck, M.C. An adoption framework for mobile augmented reality games: The case of Pokémon Go. Comput. Hum. Behav. 2017, 76, 276–286. [Google Scholar] [CrossRef] [Green Version]
  47. Kim, B.; Yoo, M.; Yang, W. Online engagement among restaurant customers: The importance of enhancing flow for social media users. J. Hosp. Tour. Res. 2020, 44, 252–277. [Google Scholar] [CrossRef]
  48. Nusair, K.; Parsa, H. Introducing flow theory to explain the interactive online shopping experience in a travel context. Int. J. Hosp. Tour. Adm. 2011, 12, 1–20. [Google Scholar] [CrossRef]
  49. Joe, S.; Kim, J.; Zemke, D.M.V. Effects of social influence and perceived enjoyment on Kiosk acceptance: A moderating role of gender. Int. J. Hosp. Tour. Adm. 2022, 23, 289–316. [Google Scholar] [CrossRef]
  50. Koufaris, M. Applying the technology acceptance model and flow theory to online consumer behavior. Inf. Syst. Res. 2002, 13, 205–223. [Google Scholar] [CrossRef] [Green Version]
  51. Almajali, D.; Al-Okaily, M.; Al-Daoud, K.; Weshah, S.; Shaikh, A.A. Go Cashless! Mobile Payment Apps Acceptance in Developing Countries: The Jordanian Context Perspective. Sustainability 2022, 14, 13524. [Google Scholar] [CrossRef]
  52. Tew, H.-T.; Tan, G.W.-H.; Loh, X.-M.; Lee, V.-H.; Lim, W.-L.; Ooi, K.-B. Tapping the next purchase: Embracing the wave of mobile payment. J. Comput. Inf. Syst. 2021, 62, 527–535. [Google Scholar] [CrossRef]
  53. Wang, J.; Xu, C.; Liu, W. Understanding the adoption of mobile social payment: From the cognitive behavioural perspective. Int. J. Mob. Commun. 2022, 20, 483–506. [Google Scholar] [CrossRef]
  54. Chakraborty, D.; Siddiqui, A.; Siddiqui, M.; Rana, N.P.; Dash, G. Mobile payment apps filling value gaps: Integrating consumption values with initial trust and customer involvement. J. Retail. Consum. Serv. 2022, 66, 102946. [Google Scholar] [CrossRef]
  55. Koenig-Lewis, N.; Marquet, M.; Palmer, A.; Zhao, A.L. Enjoyment and social influence: Predicting mobile payment adoption. Serv. Ind. J. 2015, 35, 537–554. [Google Scholar] [CrossRef]
  56. Liébana-Cabanillas, F.; Kalinic, Z.; de Luna, I.R.; Marinkovic, V. A holistic analysis of near field communication mobile payments: An empirical analysis. Int. J. Mob. Commun. 2022, 20, 703–726. [Google Scholar] [CrossRef]
  57. Lau, A.J.; Tan, G.W.-H.; Loh, X.-M.; Leong, L.-Y.; Lee, V.-H.; Ooi, K.-B. On the way: Hailing a taxi with a smartphone? A hybrid SEM-neural network approach. Mach. Learn. Appl. 2021, 4, 100034. [Google Scholar] [CrossRef]
  58. Manrai, R.; Gupta, K.P. A study on factors influencing mobile payment adoption using theory of diffusion of innovation. Int. J. Bus. Inf. Syst. 2022, 39, 219–240. [Google Scholar] [CrossRef]
  59. Li, J.; Wang, J.; Wangh, S.; Zhou, Y. Mobile payment with alipay: An application of extended technology acceptance model. IEEE Access 2019, 7, 50380–50387. [Google Scholar] [CrossRef]
  60. Migliore, G.; Wagner, R.; Cechella, F.S.; Liébana-Cabanillas, F. Antecedents to the adoption of mobile payment in China and Italy: An integration of UTAUT2 and innovation resistance theory. Inf. Syst. Front. 2022, 24, 2099–2122. [Google Scholar] [CrossRef]
  61. Türker, C.; Altay, B.C.; Okumuş, A. Understanding user acceptance of QR code mobile payment systems in Turkey: An extended TAM. Technol. Forecast. Soc. Change 2022, 184, 121968. [Google Scholar] [CrossRef]
  62. Tan, G.W.-H.; Ooi, K.-B.; Chong, S.-C.; Hew, T.-S. NFC mobile credit card: The next frontier of mobile payment? Telemat. Inform. 2014, 31, 292–307. [Google Scholar] [CrossRef]
  63. Lew, S.; Tan, G.W.-H.; Loh, X.-M.; Hew, J.-J.; Ooi, K.-B. The disruptive mobile wallet in the hospitality industry: An extended mobile technology acceptance model. Technol. Soc. 2020, 63, 101430. [Google Scholar] [CrossRef]
  64. Mahler, A.; Rogers, E.M. The diffusion of interactive communication innovations and the critical mass: The adoption of telecommunications services by German banks. Telecommun. Policy 1999, 23, 719–740. [Google Scholar] [CrossRef]
  65. Koohikamali, M.; Gerhart, N.; Mousavizadeh, M. Location disclosure on LB-SNAs: The role of incentives on sharing behavior. Decis. Support Syst. 2015, 71, 78–87. [Google Scholar] [CrossRef]
  66. Leibenstein, H. Bandwagon, snob, and Veblen effects in the theory of consumers’ demand. Q. J. Econ. 1950, 64, 183–207. [Google Scholar] [CrossRef] [Green Version]
  67. Marcus, M. Toward a “critical mass” theory of interactive media. In Organizations and Communication Technology; Sage Publications: London, UK, 1990; pp. 194–218. [Google Scholar]
  68. Chang, I.-C.; Liu, C.-C.; Chen, K. The effects of hedonic/utilitarian expectations and social influence on continuance intention to play online games. Internet Res. 2014, 24, 21–45. [Google Scholar] [CrossRef] [Green Version]
  69. Lin, K.-Y.; Lu, H.-P. Why people use social networking sites: An empirical study integrating network externalities and motivation theory. Comput. Hum. Behav. 2011, 27, 1152–1161. [Google Scholar] [CrossRef]
  70. Yang, Q.; Al Mamun, A.; Hayat, N.; Salleh, M.F.M.; Jingzu, G.; Zainol, N.R. Modelling the mass adoption potential of wearable medical devices. PLoS ONE 2022, 17, e0269256. [Google Scholar] [CrossRef]
  71. Kwon, S.J.; Park, E.; Kim, K.J. What drives successful social networking services? A comparative analysis of user acceptance of Facebook and Twitter. Soc. Sci. J. 2014, 51, 534–544. [Google Scholar] [CrossRef]
  72. Zhou, T.; Li, H.; Liu, Y. Understanding mobile IM continuance usage from the perspectives of network externality and switching costs. Int. J. Mob. Commun. 2015, 13, 188–203. [Google Scholar] [CrossRef] [Green Version]
  73. Venkatesh, V.; Davis, F.D. A theoretical extension of the technology acceptance model: Four longitudinal field studies. Manag. Sci. 2000, 46, 186–204. [Google Scholar] [CrossRef] [Green Version]
  74. Baabdullah, A.M. Consumer adoption of Mobile Social Network Games (M-SNGs) in Saudi Arabia: The role of social influence, hedonic motivation and trust. Technol. Soc. 2018, 53, 91–102. [Google Scholar] [CrossRef]
  75. Yen, Y.-S. Channel integration affects usage intention in food delivery platform services: The mediating effect of perceived value. Asia Pac. J. Mark. Logist. 2022, 35, 54–73. [Google Scholar] [CrossRef]
  76. Chan, X.Y.; Rahman, M.K.; Mamun, A.A.; ASalameh, A.; Wan Hussain, W.M.H.; Alam, S.S. Predicting the Intention and Adoption of Mobile Shopping During the COVID-19 Lockdown in Malaysia. SAGE Open 2022, 12, 21582440221095012. [Google Scholar] [CrossRef]
  77. Mahat, J.; Ayub, A.F.M.; Luan, S. An assessment of students’ mobile self-efficacy, readiness and personal innovativeness towards mobile learning in higher education in Malaysia. Procedia-Soc. Behav. Sci. 2012, 64, 284–290. [Google Scholar]
  78. Hsu, H.T.; Lin, C.C. Extending the technology acceptance model of college learners’ mobile-assisted language learning by incorporating psychological constructs. Br. J. Educ. Technol. 2022, 53, 286–306. [Google Scholar]
  79. Molina-Castillo, F.-J.; Rodriguez-Guirao, A.; Lopez-Nicolas, C.; Bouwman, H. Analysis of mobile pre-payment (pay in advance) and post-payment (pay later) services. Int. J. Mob. Commun. 2016, 14, 499–517. [Google Scholar] [CrossRef]
  80. Keith, M.J.; Babb, J.S.; Lowry, P.B.; Furner, C.P.; Abdullat, A. The role of mobile-computing self-efficacy in consumer information disclosure. Inf. Syst. J. 2015, 25, 637–667. [Google Scholar] [CrossRef]
  81. Ozturk, A.B.; Bilgihan, A.; Nusair, K.; Okumus, F. What keeps the mobile hotel booking users loyal? Investigating the roles of self-efficacy, compatibility, perceived ease of use and perceived convenience. Int. J. Inf. Manag. 2016, 36, 1350–1359. [Google Scholar]
  82. Makki, A.M.; Ozturk, A.B.; Singh, D. Role of risk, self-efficacy, and innovativeness on behavioral intentions for mobile payment systems in the restaurant industry. J. Foodserv. Bus. Res. 2016, 19, 454–473. [Google Scholar] [CrossRef]
  83. Arvidsson, N. Consumer attitudes on mobile payment services–results from a proof of concept test. Int. J. Bank Mark. 2014, 32, 150–170. [Google Scholar] [CrossRef]
  84. Yang, K. The effects of technology self-efficacy and innovativeness on consumer mobile data service adoption between American and Korean consumers. J. Int. Consum. Mark. 2010, 22, 117–127. [Google Scholar] [CrossRef]
  85. Tan, G.W.-H.; Ooi, K.-B. Gender and age: Do they really moderate mobile tourism shopping behavior? Telemat. Inform. 2018, 35, 1617–1642. [Google Scholar] [CrossRef]
  86. DeLone, W.H.; McLean, E.R. The DeLone and McLean model of information systems success: A ten-year update. J. Manag. Inf. Syst. 2003, 19, 9–30. [Google Scholar]
  87. Lee, K.C.; Chung, N. Understanding factors affecting trust in and satisfaction with mobile banking in Korea: A modified DeLone and McLean’s model perspective. Interact. Comput. 2009, 21, 385–392. [Google Scholar] [CrossRef]
  88. Hew, J.-J.; Leong, L.-Y.; Tan, G.W.-H.; Lee, V.-H.; Ooi, K.-B. Mobile social tourism shopping: A dual-stage analysis of a multi-mediation model. Tour. Manag. 2018, 66, 121–139. [Google Scholar] [CrossRef]
  89. De Luna, I.R.; Liébana-Cabanillas, F.; Sánchez-Fernández, J.; Muñoz-Leiva, F. Mobile payment is not all the same: The adoption of mobile payment systems depending on the technology applied. Technol. Forecast. Soc. Chang. 2019, 146, 931–944. [Google Scholar]
  90. Ram, J.; Corkindale, D.; Wu, M.-L. Enterprise resource planning adoption: Structural equation modeling analysis of antecdants. J. Comput. Inf. Syst. 2013, 54, 53–65. [Google Scholar] [CrossRef] [Green Version]
  91. AL-Nuaimi, M.N.; Al Sawafi, O.S.; Malik, S.I.; Al-Emran, M.; Selim, Y.F. Evaluating the actual use of learning management systems during the COVID-19 pandemic: An integrated theoretical model. Interact. Learn. Environ. 2022, 1–26. [Google Scholar] [CrossRef]
  92. Vickers, N.J. Animal communication: When I’m calling you, will you answer too? Curr. Biol. 2017, 27, R713–R715. [Google Scholar] [CrossRef]
  93. Nan, D.; Lee, H.; Kim, Y.; Kim, J.H. My video game console is so cool! A coolness theory-based model for intention to use video game consoles. Technol. Forecast. Soc. Change 2022, 176, 121451. [Google Scholar] [CrossRef]
  94. Hair, J.; Black, W.C.; Babin, B.J.; Anderson, R.E.; Tatham, R.L. Pearson new international edition. In Multivariate Data Analysis, 7th ed.; Pearson Education Limited: London, UK, 2014. [Google Scholar]
  95. Masuda, H.; Han, S.H.; Lee, J. Impacts of influencer attributes on purchase intentions in social media influencer marketing: Mediating roles of characterizations. Technol. Forecast. Soc. Change 2022, 174, 121246. [Google Scholar]
  96. Mullen, M.R. Diagnosing measurement equivalence in cross-national research. J. Int. Bus. Stud. 1995, 26, 573–596. [Google Scholar] [CrossRef]
  97. Gudergan, S.P.; Ringle, C.M.; Wende, S.; Will, A. Confirmatory tetrad analysis in PLS path modeling. J. Bus. Res. 2008, 61, 1238–1249. [Google Scholar] [CrossRef]
  98. Hair Sarstedt, M.; Ringle, C.M.; Mena, J.A. An assessment of the use of partial least squares structural equation modeling in marketing research. J. Acad. Mark. Sci. 2012, 40, 414–433. [Google Scholar] [CrossRef]
  99. Khan, I.U.; Hameed, Z.; Yu, Y.; Islam, T.; Sheikh, Z.; Khan, S.U. Predicting the acceptance of MOOCs in a developing country: Application of task-technology fit model, social motivation, and self-determination theory. Telemat. Inform. 2018, 35, 964–978. [Google Scholar] [CrossRef]
  100. Zhou, H.; Long, L. Statistical remedies for common method biases. Adv. Cogn. Psychol. 2004, 12, s942. [Google Scholar]
  101. Hair Black, W.C.; Babin, B.J.; Anderson, R.E. Canonical correlation: A supplement to multivariate data analysis. In Multivariate Data Analysis: A Global Perspective, 7th ed.; Pearson Prentice Hall Publishing: Upper Saddle River, NJ, USA, 2010. [Google Scholar]
  102. Nunnally, J.C.; Bernstein, I. The role of university in the development of entrepreneurial vocations: A Spanish study. In Psychometric Theory; McGraw-Hill: New York, NY, USA, 1978. [Google Scholar]
  103. Hair Hult, G.T.M.; Ringle, C.M.; Sarstedt, M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM); Sage Publications: Thousand Oaks, CA, USA, 2021. [Google Scholar]
  104. Fornell, C.; Larcker, D.F. Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 1981, 18, 39–50. [Google Scholar] [CrossRef]
  105. Henseler, J.; Ringle, C.M.; Sarstedt, M. A new criterion for assessing discriminant validity in variance-based structural equation modeling. J. Acad. Mark. Sci. 2015, 43, 115–135. [Google Scholar] [CrossRef] [Green Version]
  106. Cohen, J. Statistical Power Analysis for the Behavioral Sciences; Lawrence Eribaum Associates: New York, NY, USA, 1988. [Google Scholar]
  107. Chin, W.W.; Marcolin, B.L.; Newsted, P.R. A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. Inf. Syst. Res. 2003, 14, 189–217. [Google Scholar] [CrossRef] [Green Version]
  108. Henseler, J.; Dijkstra, T.K.; Sarstedt, M.; Ringle, C.M.; Diamantopoulos, A.; Straub, D.W.; Calantone, R.J. Common beliefs and reality about PLS: Comments on Rönkkö and Evermann (2013). Organ. Res. Methods 2014, 17, 182–209. [Google Scholar] [CrossRef] [Green Version]
  109. Henseler, J.; Ringle, C.M.; Sinkovics, R.R. The use of partial least squares path modeling in international marketing. In New Challenges to International Marketing; Emerald Group Publishing Limited: Bingley, UK, 2009; pp. 277–319. [Google Scholar]
  110. Ringle, C.M.; Wende, S.; Becker, J.-M. SmartPLS 3; SmartPLS GmbH: Boenningstedt, Germany, 2015. [Google Scholar]
  111. Tan, G.W.-H.; Ooi, K.-B.; Sim, J.-J.; Phusavat, K. Determinants of mobile learning adoption: An empirical analysis. J. Comput. Inf. Syst. 2012, 52, 82–91. [Google Scholar]
  112. Wong, C.-H.; Tan, G.W.-H.; Loke, S.-P.; Ooi, K.-B. Adoption of mobile social networking sites for learning? Online Inf. Rev. 2015, 39, 762–778. [Google Scholar] [CrossRef]
  113. Pan, V.-Q.; Chew, P.-Q.; Cheah, A.S.-G.; Wong, C.-H.; Tan, G.W.-H. Mobile marketing in the 21st century: A partial least squares structural equation modelling approach. Int. J. Model. Oper. Manag. 2015, 5, 83–99. [Google Scholar] [CrossRef]
  114. Wong, C.-H.; Tan, G.W.-H.; Loke, S.-P.; Ooi, K.-B. Mobile TV: A new form of entertainment? Ind. Manag. Data Syst. 2014, 114, 1050–1067. [Google Scholar] [CrossRef]
  115. Dutot, V. Factors influencing near field communication (NFC) adoption: An extended TAM approach. J. High Technol. Manag. Res. 2015, 26, 45–57. [Google Scholar] [CrossRef]
  116. Leong, L.-Y.; Hew, T.-S.; Tan, G.W.-H.; Ooi, K.-B. Predicting the determinants of the NFC-enabled mobile credit card acceptance: A neural networks approach. Expert Syst. Appl. 2013, 40, 5604–5620. [Google Scholar] [CrossRef]
  117. Wong, C.-H.; Tan, G.W.-H.; Tan, B.-I.; Ooi, K.-B. Mobile advertising: The changing landscape of the advertising industry. Telemat. Inform. 2015, 32, 720–734. [Google Scholar] [CrossRef]
  118. Wong, C.H.; Lee, H.S.; Chua, B.H.; Chai, B.H.; Tan Han, G.W. Predicting the consumers’ intention to adopt mobile shopping: An emerging market perspective. Int. J. Netw. Mob. Technol. 2012, online. [Google Scholar]
  119. Shin, D.-H. Towards an understanding of the consumer acceptance of mobile wallet. Comput. Hum. Behav. 2009, 25, 1343–1354. [Google Scholar] [CrossRef]
  120. Benbasat, I.; Barki, H. Quo vadis TAM? J. Assoc. Inf. Syst. 2007, 8, 7. [Google Scholar] [CrossRef] [Green Version]
  121. Phan, K.; Daim, T.U. Exploring technology acceptance for mobile services. J. Ind. Eng. Manag. 2011, 4, 339–360. [Google Scholar] [CrossRef]
  122. Nysveen, H.; Pedersen, P.E.; Thorbjørnsen, H. Explaining intention to use mobile chat services: Moderating effects of gender. J. Consum. Mark. 2005, 22, 247–256. [Google Scholar] [CrossRef]
  123. Chen, L.-D. A model of consumer acceptance of mobile payment. Int. J. Mob. Commun. 2008, 6, 32–52. [Google Scholar] [CrossRef]
  124. Kim, C.; Mirusmonov, M.; Lee, I. An empirical examination of factors influencing the intention to use mobile payment. Comput. Hum. Behav. 2010, 26, 310–322. [Google Scholar] [CrossRef]
Sustainability 15 03664 g001 550
Figure 1. Depicts the conceptual framework for this research from the hypotheses.
Figure 1. Depicts the conceptual framework for this research from the hypotheses.
Sustainability 15 03664 g001
Sustainability 15 03664 g002 550
Figure 2. Structural Model Testing.
Figure 2. Structural Model Testing.
Sustainability 15 03664 g002
Table
Table 1. The demographic characteristics of the respondents. (N = 310).
Table 1. The demographic characteristics of the respondents. (N = 310).
Demographics CharacteristicsGroupFrequencyPercentage
Gender Male11737.7%
Female19362.3%
Age15–193210.3%
20–246521%
25–2911035.5%
30–344514.5%
35–393511.3%
40 and above237.4%
Education LevelHigh School15048.3%
Undergraduate8226.5%
Graduate4012.9%
Doctorate3812.3%
Monthly IncomeBelow or equal to 30,000 Pkr5016.1%
31,000–40,0005919.0%
41,000–50,00012540.3%
51,000–60,0004013.0%
Above 60,0003611.6%
Table
Table 2. Descriptive Statistics.
Table 2. Descriptive Statistics.
NMinimumMaximumMeanStd. ErrorStd. Deviation
BI310−3.313.220.00020.056891.00171
MEU310−3.962.81−0.00020.056891.00163
MSE310−3.223.170.00010.056881.00145
MU310−3.792.440.00000.056891.00167
PCM310−3.273.480.00000.056891.00168
PE310−3.411.990.00010.056891.00168
SSQ310−4.732.51−0.00010.056881.00152
TSE310−3.043.090.00000.056881.00154
Valid N (listwise)310
Table
Table 3. Loading, Composite Reliability, Cronbach’s alpha, and Average Variance Extracted.
Table 3. Loading, Composite Reliability, Cronbach’s alpha, and Average Variance Extracted.
ConstructItemsFactor LoadingαCRAVEVIF Inner
Behavioral intention (BI)BI10.7540.8340.890.6691.824
BI20.87
BI30.845
BI40.799
Mobile ease of Use (MEOU)MEOU10.8310.8670.9090.7141.303
MEOU 20.869
MEOU 30.856
MEOU 40.824
Mobile Usefulness (MU)MU10.8440.8480.8980.6881.57
MU 20.876
MU 30.837
MU 40.756
Perceived Critical Mass (PCM)PCM10.7350.7890.8550.5431.346
PCM20.737
PCM30.793
PCM40.681
PCM50.733
Perceived Enjoyment (PE)PE10.7010.7610.8430.5741.161
PE20.777
PE30.842
PE40.701
System and Service Quality (SSQ)SSQ10.8140.8350.8820.61.501
SSQ20.806
SSQ30.746
SSQ40.775
SSQ50.729
Mobile Self-Efficacy (MSE)MSE10.7960.7620.8620.6751.327
MSE20.845
MSE30.823
Technology Self-Efficacy (TSE)TSE10.8630.8010.8720.6321.395
TSE20.715
TSE30.896
TSE40.686
Table
Table 4. Discriminant Validity.
Table 4. Discriminant Validity.
ConstructAVECABIMEOUMSEMUPCMPESSQTSE
BI0.6690.8340.818
MEOU0.7140.8670.3150.845
MSE0.6750.7620.4510.1920.822
MU0.6880.8480.4550.4470.3250.83
PCM0.5430.7890.3130.1720.1680.2880.737
PE0.5740.7610.2590.0140.0130.1060.2510.757
SSQ0.60.8350.5250.3060.30.3860.3040.2220.775
TSE0.6320.8010.3440.1610.2560.3350.4430.2410.2770.795
Heterotrait–Monotrait Ratio (HTMT)
BI
MEU 0.372
MSE 0.5660.222
MU 0.5380.5230.383
PCM 0.3860.2070.2170.347
PE 0.3060.0690.1190.120.293
SSQ 0.6150.350.3650.4420.3660.258
TES 0.4180.1920.3230.4070.5650.2990.324
Note: BI = Behavioral Intention; MEOU = Mobile Ease of Use; MU; Mobile Usefulness; PCM = Perceived Critical Mass; PEJ = Perceived Enjoyment; MSE = Mobile Self-Efficacy; TSE = Technology Self-Efficacy; SSQ = System and Service Quality.
Table
Table 5. Cross-loading.
Table 5. Cross-loading.
Constructs BIMEOUMSEMUPCMPESSQTSE
BI10.7540.2370.340.4090.2630.2060.3770.268
BI20.870.2630.4160.3670.2660.220.4420.289
BI30.8450.2730.3570.3610.2480.230.4420.316
BI40.7990.2570.3610.3560.250.1890.4520.252
MEU10.2780.8310.1560.4170.1060.0280.2230.126
MEU20.2480.8690.1460.3880.1750.0090.2860.132
MEU30.2880.8560.2440.3960.109−0.0080.2160.121
MEU40.2560.8240.1060.3180.1840.0190.3030.162
MSE10.3750.0420.7960.1330.1260.080.2550.204
MSE30.4030.1540.8450.3210.1440.0310.2750.202
MSE40.3370.2480.8230.3120.142−0.060.2120.225
MU10.3720.4080.2820.8440.2440.1040.3430.279
MU20.4840.3830.3050.8760.2690.0950.3710.281
MU30.3170.3860.2750.8370.2380.0840.2920.24
MU40.3190.3020.210.7560.20.0650.2660.314
PCM10.2190.0560.1390.1730.7350.1180.2930.313
PCM20.2740.1850.1770.2430.7370.1460.2370.345
PCM30.2230.1010.1360.2160.7930.2610.2550.336
PCM40.2080.1660.1220.1840.6810.1450.1320.333
PCM50.2280.1220.0520.2340.7330.2260.2020.308
PE10.2390.0550.0610.1610.1950.7010.1610.177
PE20.1520.005−0.0790.0260.130.7770.130.12
PE30.219−0.0340.0420.0530.2620.8420.2170.225
PE40.1330.024−0.0390.0560.1190.7010.1360.187
SSQ10.4250.2450.2310.3070.2260.1590.8140.156
SSQ20.4580.2770.2970.3620.2490.1950.8060.27
SSQ30.4010.2090.150.2320.1930.2130.7460.187
SSQ40.4310.2510.2770.3490.3090.220.7750.295
SSQ50.2770.1820.1710.2030.1740.0310.7290.126
TSE10.2640.160.230.2970.3340.1760.2180.863
TSE20.3150.0720.210.2780.3640.2110.2490.715
TSE30.2940.1560.2240.2590.3390.2070.2270.896
TSE40.2090.1220.1360.2240.380.170.1780.686
Table
Table 6. Hypotheses testing results.
Table 6. Hypotheses testing results.
Hypotheses (1 to14)Path CoefficientStandard ErrorT-Valuepf2Support
H1: MU → BI0.1720.0523.283***0.098Yes
H2: MEOU → BI0.0780.0731.0710.2840.021No
H3: PCM → BI0.0520.0570.9240.3560.012No
H4: PCM → PEJ0.2510.0574.394***0.073Yes
H5: PEJ → BI0.1420.0542.6350.008 **0.033Yes
H6: MSE → BI0.2650.0763.499***0.016Yes
H7: MSE → MU0.190.0633.0050.003 **0.054Yes
H8: MSE → MEOU0.10.0731.3780.1680.013No
H9: TSE → BI0.0690.0611.1230.2620.019No
H10: TSE → MU0.2110.0514.095***0.05Yes
H11: TSE → MEOU0.0630.0541.1640.2450.003tNo
H12: SSQ → BI0.2880.0883.273***0.081Yes
H13: SSQ → MU0.2720.0654.161***0.084Yes
H14: SSQ → MEOU0.2570.0862.9810.003 **0.062Yes
Note: BI = Behavioral intention; MEOU = Mobile Ease of Use; MU; Mobile Usefulness; PCM = Perceived Critical Mass; PEJ = Perceived Enjoyment; MSE = Mobile Self-Efficacy; TSE = Technology Self-Efficacy; SSQ = System and Service Quality. *** p < 0.001, ** p < 0.01.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Zhang, Q.; Khan, S.; Cao, M.; Khan, S.U. Factors Determining Consumer Acceptance of NFC Mobile Payment: An Extended Mobile Technology Acceptance Model. Sustainability 2023, 15, 3664. https://doi.org/10.3390/su15043664

AMA Style

Zhang Q, Khan S, Cao M, Khan SU. Factors Determining Consumer Acceptance of NFC Mobile Payment: An Extended Mobile Technology Acceptance Model. Sustainability. 2023; 15(4):3664. https://doi.org/10.3390/su15043664

Chicago/Turabian Style

Zhang, Qingyu, Salman Khan, Mei Cao, and Safeer Ullah Khan. 2023. "Factors Determining Consumer Acceptance of NFC Mobile Payment: An Extended Mobile Technology Acceptance Model" Sustainability 15, no. 4: 3664. https://doi.org/10.3390/su15043664

APA Style

Zhang, Q., Khan, S., Cao, M., & Khan, S. U. (2023). Factors Determining Consumer Acceptance of NFC Mobile Payment: An Extended Mobile Technology Acceptance Model. Sustainability, 15(4), 3664. https://doi.org/10.3390/su15043664

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop