Abstract
Purpose
This study examines the drivers of consumers’ intentions to adopt mobile wallets in Pakistan’s hospitality industry. Specifically, it proposes and tests a model of non-user consumer intention to adopt mobile wallets for hospitality in Pakistan.
Design/methodology/approach
A conceptual framework grounded in the mobile technology acceptance model (MTAM) integrating personal innovativeness in IT, mobile perceived compatibility, perceived critical mass, perceived enjoyment, mobile perceived risk and mobile perceived wireless trust was used as a theoretical model of the study. Using structural equation modeling, we tested the research model and its relevant hypotheses on a sample of 310 mobile wallet nonusers.
Findings
Findings from the expanded model demonstrate that only four of the suggested hypotheses were insignificant in this study and require additional examination. Overall, the modified model explained 63% of the variance in the behavioral intention to adopt mobile wallets. This paper concludes with key implications and directions for future work concerning the limitations of this study.
Originality/value
This study contributes to a theoretical understanding of the factors that explain nonusers’ behavioral intention to use a mobile wallet in the hospitality context.
Keywords
Citation
Khan, S. and Zhang, Q. (2025), "Consumer acceptance of mobile wallet in the hospitality industry", International Hospitality Review, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IHR-07-2023-0038
Publisher
:Emerald Publishing Limited
Copyright © 2024, Salman Khan and Qingyu Zhang
License
Published in International Hospitality Review. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
1. Introduction
In the last two decades, smartphones have become an integral part of everyday life (Shin & Lee, 2021). This cutting-edge technology is not limited to communication but is utilized for additional information sharing, mobile payments, and mobile commerce. Researchers have extensively studied mobile payment systems for end-users and retailers, which are considered standard payment solutions and affect behavior and technology usage (de Luna, Liébana-Cabanillas, Sánchez-Fernández, & Muñoz-Leiva, 2019; Slade, Dwivedi, Piercy, & Williams, 2015). Consumers demand rapid, easy, and realistic technologies for unified platforms. In this context, mobile payment has created new potential for restaurants, public transit, wireless communication, and more (Liébana-Cabanillas, Singh, Kalinic, & Carvajal-Trujillo, 2021). Mobile payment (MP) includes any payment platform accessible via mobile phones. Numerous forms of accessible mobile payment systems exist, including mobile and in-person payments (de Luna et al., 2019). First, several points of sale facilities can be used, including NFC or a sound wave-based system, which is the platform for consumer bank credit/debit card transfers to consumers via a secure platform (Liébana-Cabanillas, Marinkovic, de Luna, & Kalinic, 2018). Second, mobile wallets (m-wallet) and QR codes are accepted in stores, as well as wireless payment technology (Liébana-Cabanillas, Ramos de Luna, & Montoro-Ríos, 2015). M-wallet is a mobile technology that empowers consumers to deposit money and perform online purchases remotely from the wallet. In contrast, QR codes operate with a limited number of banking applications and allow shop applications to incorporate debit/credit card information (Madan & Yadav, 2016).
Owing to increasing smartphone proliferation, Internet penetration, and awareness among users regarding mobile payment options, the global wallet industry is witnessing exponential growth. With these technological developments, mobile wallets have revolutionized transactions globally. Mobile wallets, a new program that substitutes physical payments and processes private information such as credit card information and personal accounts, have grown rapidly in recent years (Sharma, Mangla, Luthra, & Al-Salti, 2018). It can be used to pay across several platforms, from person to person, buyer to business, and buyer to online shopping platforms (Shin, 2009). The emergence of m-wallets is expected to help overcome traditional payment issues. The m-wallet provides easy communication setup, ease of use, mobility, and low power usage (Singh, Sinha, & Liébana-Cabanillas, 2020). M-wallets are supposed to play a critical role in facilitating shifts in customer behavior from using cash to adopting electronic transactions. Recently, the use of smartphones and m-wallets has been gradually increasing in the hospitality industry (Okumus, Ali, Bilgihan, & Ozturk, 2018); this trend may be ascribed to mass digital transformations, such as Airbnb, Grab, and other mobile services. Today, Pakistan has several m-wallet technology players such as Easy Paisa, Pay Max, Jazz Cash, Keenu Wallet, and Sim Sim Wallet. Despite their benefits, m-wallets are seldom used in Pakistan, where customers continue to pay cash or debit cards. Considering the significance of the restaurant industry to travelers and its contribution to the Pakistani economy, this study focuses on the restaurant sector.
To fill the gap highlighted and recommended by Gupta and Arora (2020), this study focused on investigating emerging contributors to m-wallet technology adoption in the hospitality and tourism industry. Ali, Raza, Hakim, Puah, and Chaw (2022) argued that mobile payment trend passes through a crescent stage in Pakistan, whereas, this system has become a life style in majority of the nations like, US, Germany, and China etc. To cope with the challenges in payment structures, particularly in the hospitality industry of Pakistan, this study attempts to follow the recommendations of existing research (Twum, Kosiba, Hinson, Gabrah, & Assabil, 2023) by conducting this research in Pakistan’s hospitality industry to remain competitive and meet the challenging needs of customers.
To that end, viewing the existing literature, recent years have witnessed a series of theoretical models for technology adoption including “theory of reasoned action (TRA)” [theory of reasoned action (TRA)] (Fishbein & Ajzen, 1977), “theory of planned behavior” (TPB) (Ajzen, 1991), “task technology fit” (Goodhue & Thompson, 1995), “unified theory of acceptance and use of technology” (UTAUT) (Venkatesh, Morris, Davis, & Davis, 2003) and “technology acceptance model” (TAM) (Davis, Bagozzi, & Warshaw, 1989). To differentiate this research from previous studies, we used MTAM to examine non-user behavioral intention (BI) and the actual use of technology (Ooi & Tan, 2016). Numerous studies on the acceptance of financial payments have included different variables in the MTAM. This, in turn, has evolved in a variety of different variables and a tremendous number of expanded MTAM frameworks in the context of financial-payment acceptance (Zhang, Khan, Cao, & Khan, 2023). These variables often change across studies, depending on the setting and individuals. Consequently, a comprehensive modified MTAM model is required for acceptance of mobile wallets. The present study incorporates three MTAM dimensions: mobile ease of use (MEOU), mobile usefulness (MU), and consumer-perspective factors such as personal innovativeness in IT (PIIT), mobile perceived compatibility (MPC), perceived critical mass (PCM), perceived enjoyment (PE), mobile perceived risk (MPR), and mobile perceived wireless trust (MPT) for a better understanding of technology adoption behavior. It is anticipated that such an integrated approach will be innovative in accepting mobile wallets, particularly in the context of non-users. This study contributes to the literature in several ways. First, it examines the behavioral intentions of mobile wallet technology adoption among non-users in Pakistan. Second, the results contribute to the literature on m-wall technology acceptance (Kim, Radic, Chua, Koo, & Han, 2022; Twum et al., 2023), which has primarily been conducted in developed economies. Third, after reviewing the most recent literature in this field, this study provides a comprehensive model. Finally, this study contributes to marketing practitioners by guiding the adoption and use of m-wallets in Pakistan’s hospitality context.
2. Literature review and hypotheses development
2.1 Theoretical foundation
Ooi and Tan (2016) developed MTAM to address the shortcomings of the original TAM (Davis, 1989). The original TAM is one of the most generally accepted and regularly used models for evaluating antecedents that influence the intention to adopt a new technology (Kao & Huang, 2023). However, this model has certain limitations. These limitations include the original definitions of perceived usefulness (PU) and perceived ease of use (PEOU). PU refers to an individual’s opinion that implementing a specific system enhances work performance, whereas PEOU refers to an individual’s conviction that implementing a particular system is easy (Davis, 1989). Both PU and PEOU are described in such a way that they are contextualized within an organizational framework. This is a cause of concern because technology adoption outside the working environment differs in many areas, including job types and complexities (Alsyouf et al., 2023). In addition, many researchers have argued that different factors are taken from other studies of technological mobile adoption (Zhang et al., 2023). Therefore, this model was developed using two constructs: MU and MEOU. MU refers to how efficiency is enhanced via mobile devices, whereas MEOU refers to how effortless payment is through mobile devices (Ooi & Tan, 2016). These two constructs were modified to accurately represent the AR environment and provide a more comprehensive view. The mobile TAM has been used in various fields, including mobile social media marketing (Wong, Tan, Hew, Ooi, & Leong, 2022), cyberbullying (Ooi, Lee, Hew, & Lin, 2019), fashion shopping (Ng, Yap, Tan, Lo, & Ooi, 2022), and mobile social learning (Loh, Lee, Tan, Hew, & Ooi, 2019). MTAM’s fundamental variables of MTAM (i.e. MU and MEOU) can be used across many facets of mobile technology. Thus, MTAM is well suited for examining the intention to adopt an M-wallet. However, MTAM offers only two constructs; thus, it overlooks other factors deemed critical in influencing new mobile technology adoption. Consequently, an expanded MTAM was used in this study to investigate the relationship between M-wallets and the intention to use. This approach is consistent with the recommendation of other scholars to include other factors to comprehensively understand the antecedents influencing the adoption of new technologies, particularly mobile services (Yan, Tan, Loh, Hew, & Ooi, 2021). This approach also entails considering non-technological variables that influence consumers’ decision-making processes. This study uses MTAM, which incorporates other factors such as personal innovativeness in IT (PIIT), mobile perceived compatibility (MPC), perceived critical mass (PCM), perceived enjoyment (PE), mobile perceived risk (MPR), and mobile perceived wireless trust (MPT) in light of their importance in examining the adoption of mobile wallets by non-users in Pakistan.
2.1.1 Mobile usefulness (MU)
Mobile usefulness refers to the perceived advantage gained from adopting mobile devices (Ooi & Tan, 2016). Numerous studies have explored the influence of usefulness on m-payment acceptance from different perspectives and settings (Aslam, Awan, & Fatima, 2021). Lew, Tan, Loh, Hew, and Ooi (2020) found that mobile usefulness is a predictor of the intention to use m-wallets. Zhang et al. (2023) examined the variables affecting BI’s usage of MP services in Pakistan, and perceived usefulness was found to be a significant influencer of consumers’ intention to adopt mobile payment services. Further, Dewasiri, Karunarathna, Rathnasiri, Sood, and Saini (2023) determined the impact of health-related views on mobile payment adoption in Sri Lanka and found perceived usefulness to be a significant influencer of consumers’ intention to adopt mobile payment services. In addition, Joshi and Chawla (2023) found that, in India, PU is significantly associated with BI to accept m-wallets. Accordingly, we hypothesize the following:
MU is positively related to BI when adopting an m-wallet.
2.1.2 Mobile ease of use (MEOU)
MEOU corresponds to perceived ease of understanding and employing m-technologies or services (Ooi & Tan, 2016). Within the framework of this study, mobile technology or services correspond to mobile wallets. Previous research has shown that MEOU is a dynamic variable that increases BI to embrace m-payments. Yang, Yang, and Chang (2023) investigated factors that influence the behavioral intentions of older adults to use mobile payments in Taiwan found that the most important explanatory factor of consumers’ intentions is ease of use. Additionally, Haritha (2023) distributed a questionnaire survey to customers in India to explore FinTech and its dynamic changes in the banking sector. Perceived ease of use was found to be a driving factor that enabled software designers to encourage accessible processes and device advantages for customers. In addition, Shankar and Datta (2018) studied factors affecting the BI of MP distributed to prospective users in India via a web-based and offline survey. Based on the above findings, MEOU substantially influences customers’ BI to embrace MP. Furthermore, (Lew et al., 2020) revealed that MEOU needs to be constructive and substantially associated with adopting an m-wallet. Accordingly, we hypothesize the following:
MEOU is positively related to BI when an m-wallet is adopted.
2.2 Expanding MTAM
2.2.1 Perceived critical mass (PCM)
PCM denotes “the minimum number of adopters of an interactive innovation for the future adoption rate to be self-sustaining” (Mahler & Rogers, 1999). The PCM idea is identical to the network externality (Koohikamali, Gerhart, & Mousavizadeh, 2015) and bandwagon influence (Leibenstein, 1950). This theory suggests that extrinsic incentives arise if a critical mass of adopters is reached, thus enticing additional customers to participate (Marcus, 1990). When a sufficient number of consumers accept a particular IT/IS, prospective consumers perceive that the system is significant and merits consideration (Illia, Lawson-Body, Lee, & Akalin, 2023). PCM is widely used in technology adoption research because it affects the interaction of BI with different users (Lyu, Guo, & Chen, 2023). For instance, PCM has been revealed to be significantly associated with BI’s adoption of virtual social worlds through the Habbo Hotel (Mäntymäki & Salo, 2013). Similarly, Saif, Hussin, Husin, Alwadain, and Chakraborty (2022) found that PCM has a strong positive correlation with the acceptance of digital banking in Malaysian mobile hotel reservations. Furthermore, PCM was found to significantly affect perceived enjoyment (PEJ) (Zhang et al., 2023). Zhou, Li, and Liu (2015) highlighted the relationship between a network’s referent size and PEJ in a mobile study and found a positive association between network size and PEJ. This finding demonstrates the value of exploring the impact of PCM on PEJ mutual interactions with regard to emerging technologies. Accordingly, we hypothesize the following:
PCM is positively related to BI when an m-wallet is adopted.
PCM are significantly and positively related to PEJ.
2.2.2 Perceived enjoyment (PEJ)
PEJ is the level at which the interaction of an approach is deemed pleasant despite anticipated results (Venkatesh & Davis, 2000). They are intrinsically motivated or hedonically attracted (Bagdi & Bulsara, 2023). Moreover, it comprises pleasure, leisure, entertainment, and cheerfulness, and it is essential to use modern applications and systems for customer BI (Nguyen & Llosa, 2023; Won, Chiu, & Byun, 2023). Furthermore, Joe, Kim, and Zemke (2022) investigated the impact of perceived enjoyment on the usage of self-service technology and found that PEJ positively influences user behavioral intention. The impact of PEJ on BI’s usage of online payment systems in Kuwait was examined. Furthermore, Chin and Ahmad (2015) investigated the association between PEJ and Malaysian users’ BI to utilize e-payments. All these studies have revealed that the PEJ plays a valuable role in implementing m-payment services. Accordingly, we hypothesize the following:
PEJ is significantly and positively associated with BI.
2.2.3 Mobile perceived risk (MPR)
Munikrishnan, Huang, Mamun, and Hayat (2023) defined perceived risk as a person’s belief that an unpredictable activity is likely to occur, which causes a decline in trust, particularly when making decisions. M-technology is more frequently correlated with advanced susceptibility to cyber threats and encroachment than wired systems because of the system’s lean details (Kumar, Singh, Kumar, Khan, & Corvello, 2023). Previous studies on mobile technology have developed essential links between PR and BI (de Blanes Sebastián, Antonovica, & Guede, 2023; Putri, Widagdo, & Setiawan, 2023). In the context of tourism and hospitality research, Wang and Wang (2010) opined that customers are typically cautious about adopting mobile technology for hotel reservations because of concerns about the advancement of nascent mobile applications, which could lead to financial data exposure. Peng, Xiong, and Yang (2012) studied BI in the context of introducing MP systems for tourism in China, where residents regarded the platform as unsafe for customers. Finally, Ozturk, Nusair, Okumus, and Hua (2016) considered that customers’ reluctance to reserve hotel rooms via m-devices rather than e-commerce or travel companies is consistent with PR. Accordingly, we hypothesize the following:
MPR is negatively related to BI when adopting the m-wallet.
2.2.4 Mobile perceived wireless trust (MPT)
Prior studies have documented mobile perceived trust (MPT) as a crucial factor affecting customers’ intention to adopt technology-based products or services (Jayashankar, Nilakanta, Johnston, Gill, & Burres, 2018; Lian & Li, 2021). The MPT stimulates the cognitive response associated with a user’s assessment of the extent of their advantages and costs. This finding is significant because no personal association exists between clients and employees (Kumar et al., 2023). Furthermore, TR significantly influences BI in the context of mobile banking services (Parayil Iqbal, Jose, & Tahir, 2023). In the hospitality industry, most users hesitate to trust small businesses (Cobanoglu, Yang, Shatskikh, & Agarwal, 2015). Morosan (2014) states that mobile devices also carry personal details from a single user (e.g. photos, addresses, and messages). The association between MPT and BI has been supported by numerous tourism studies, such as tourism and destination (Quan, Moon, Kim, & Han, 2023), green behavioral intention in eco-friendly hotels (Hashish, Abdou, Mohamed, Elenain, & Salama, 2022), and self-ordering kiosk technology in Malaysian quick-service restaurants (Baba, Hanafiah, Mohd Shahril, & Zulkifly, 2023). Accordingly, we hypothesize the following:
MPT is positively related with BI to adopt m-wallet
2.2.5 Personal innovativeness in IT (PIIT)
PIIT is defined as “the willingness of an individual to try out any new information technology” (Agarwal & Prasad, 1998). Agarwal and Prasad (1998) proposed that individuals with higher PIIT need fewer positive perceptions about technology than less innovative individuals, demonstrating the moderating effect on antecedents of technology adoption. Sia, Saidin, and Iskandar (2023) found that PIIT is a significant determinant influencing the adoption of smart mobile tourism apps (SMTA). Previous studies have concluded that the level of personal innovativeness is an important predictor of individual readiness for innovative technology use (Okumus et al., 2018). Furthermore, when customers have high PIIT, they establish a more desirable attitude toward MU and MEOU (Leong, Hew, Tan, & Ooi, 2013; Tan, Ooi, Leong, & Lin, 2014; Twum, Ofori, Keney, & Korang-Yeboah, 2022). Thus, the following hypothesis was established:
PIIT is significantly and positively related to MU.
PIIT is significantly and positively related to MEOU.
PIIT is positively related to BI when an m-wallet is adopted.
2.2.6 Mobile perceived compatibility (MPC)
Mobile perceived compatibility relates to the extent to which current mobile advancements are compatible with prospective customers’ current values, attitudes, requirements, and behavioral patterns (Mayer, Davis, & Schoorman, 1995). Compatibility can also describe how novel technology fits in consumers’ existing lifestyle and past experience (Labrecque, Wood, Neal, & Harrington, 2017). Additionally, in the digital platform context, prior studies demonstrated that the higher the perceived compatibility of a platform, the more confidence users have when adapting it or performing activities with that platform (Chau, Deng, & Tay, 2020; Nguyen & Ha, 2022). In tourism research, MPC has been acknowledged as a positive indicator of the acceptance of mobile payments in China’s tourism industry (Peng et al., 2012). Each of these researchers agreed that because m-devices have been used with further mobile services, users’ confidence and BI have increased. Furthermore, MPC was an enhancement of EE studies to accept m-devices for hotel reservations on mobile sites (Ozturk, Bilgihan, Nusair, & Okumus, 2016). Furthermore, MPC results in a higher PE, as demonstrated empirically in research on electronic scooter-sharing systems extending a modified TAM model (Samadzad, Nosratzadeh, Karami, & Karami, 2023). The researchers added the perceived compatibility factor as a predictor of PEOU, PU, PE, and BI. Some studies were conducted to explore mobile phone users’ acceptance of MLA in Taiwan (Al-Bashayreh, Almajali, Altamimi, Masa’deh, & Al-Okaily, 2022; Cheng, 2015). The findings confirm that perceived compatibility was a major predictor of PU, PEOU, and BI. In another study, an extended TAM was implemented to explore university students’ acceptance of the MLA in Jordan. They confirmed that perceived compatibility is a major predictor of BI (Almaiah & Al Mulhem, 2019). Thus, we hypothesize the following:
MPC is significantly and positively related to MU.
MPC is significantly and positively related to MEOU.
MPC is positively related to BI when m-wallet is adopted.
3. Methodology
3.1 Proposed research model and survey development
The model was established by extending the MTAM to integrate additional variables (PCM, PE, MPR, MPT, PIIT, and MPC) as predictors of MU, MEOU, and BI to investigate consumers’ BI to accept m-wallets in Pakistan. Figure 1 illustrates the model based on these hypotheses. This study adopts a comprehensive methodology using a sample questionnaire to gather data—measures and scales from previous studies. The survey questions were rated on a 7-point Likert scale (strongly agree = 1 to strongly disagree = 7) (Churchill & Iacobucci, 2006). The MU, MEOU, and BI scales were taken from Ooi and Tan (2016), Tan, Ooi, Chong, and Hew (2014). The PCM scale was adopted (Tan & Ooi, 2018). The PE scale was adopted from Nysveen, Pedersen, and Thorbjørnsen (2005). The MPC scale was adopted from Ooi and Tan (2016). We used the PIIT scale from Tan, Ooi, Leong et al. (2014). The MPT items were borrowed from Ooi and Tan (2016). Finally, the MPR scale was adopted (Leong et al., 2013; Tan, Ooi, Chong et al., 2014). Table 1 details the instruments, items, and sources of the scale.
3.2 Data collection process
This study aimed to identify the drivers influencing non-users’ intentions to use m-wallet services in the hospitality context. The target population of the study consisted of individuals who had never used mobile wallet services and were targeted as the unit of analysis. The distributed questionnaire included two exit questions to confirm that the respondents met the specified criteria. A short explanation of m-wallet technology was included on the first page, and respondents were asked if they used m-wallet technology. A total of 450 questionnaires were distributed online to collect data. Convenience and purposive sampling were used to select respondents. Specifically, only respondents who did not use mobile wallets were included in this study. After collecting data over two months, 310 responses were used for the analysis (see Table 2). An empirical dataset was collected to verify the established theoretical framework and test the hypotheses based on these responses. Table 2 summarizes their demographic characteristics.
4. Data analysis and findings
4.1 Common method bias (CMB)
The data of the study were cross-sectional, and according to Podsakoff, MacKenzie, Lee, and Podsakoff (2003), cross-sectional data might encounter a common method bias (CMB) issue. Therefore, we ran Harmon’s one-factor test to detect CMBs. The test results show a 20.64% variance in the first factor, which is less than the standard value of 50% recommended by Podsakoff et al. (2003). This indicates that CMB was not a concern in this study.
SEM in AMOS was used to analyze the data. According to Bollen (1989), SEM is a powerful multivariate second-generation approach for evaluating theoretical frameworks. SEM in AMOS first generates a model fit and then a structural model. Model fit indicates the constructs’ reliability and validity. The structural model evaluates the relationships between the constructs.
4.2 Measurement model
CFA was employed to investigate the factors’ convergent validity, discriminant validity (DV), and internal consistency (Slade et al., 2015). This study adopted Anderson and Gerbing (1988) three ad hoc tests, standardized FL, CR, and AVE, to evaluate the convergent validity of the latent variables. The standardized factor loadings varied between 0.54 and 0.88, thus meeting the mandatory 0.50 cut-off value (Gefen, Straub, & Boudreau, 2000). Furthermore, the CR values showed the latent constructs’ internal consistency, with values exceeding the 0.70 criterion (Hair, Anderson, Tatham, & Black, 1992; Nunnally & Bernstein, 1978). Finally, the results presented in Table 3 signify convergent validity (average variance extracted [AVE]). The values were more significant than 0.50, thus meeting the suggested standard level of AVE (Fornell & Larcker, 1981). The latent construct succeeded in the convergent validity test when its values exceeded the predefined threshold of the three tests (Anderson & Gerbing, 1988).
The general fit of the measurement model was evaluated using the following five metrics: CMIN/DF, AGFI, CFI, RMSEA, TLI, GFI, NFI, and RMR. The results for all model fit indices were above the required levels, indicating a satisfactory fit to the data: chi-square/df = 2.628, GFI = 0.81, AGFI = 0.80, CFI = 0.83, TLI = 0.81, NFI = 0.81, RMR = 0.026, and RMSEA = 0.073 (Gefen et al., 2000; Hair, Black, Babin, Anderson, & Tatham, 2006). The DV was obtained by calculating the square root of convergent validity, and the results were above 0.70 for all constructs, thus meeting the recommended standard levels (Fornell & Larcker, 1981). Table 4 presents the descriptive statistics and correlations between the constructs.
4.3 Structural model
AMOS 21.0 was applied to calculate the path coefficient of the research model. We evaluated the structural model to measure the relationships between latent variables, as highlighted in Figure 2. Before conducting a path analysis of the proposed model, an appropriate model fit index must be developed for the structural model. Table 5 demonstrates the fitness of the model; for example, chi-square/df, GFI value, AGFI, TLI, and NFI, to be within the acceptable ranges (Hu & Bentler, 1999; Deligianni, Dimitratos, Petrou, & Aharoni, 2016) recommended that the values should be beyond 0.90 for GFI, NFI, and CFI, for a stronger model fit. The structural model fit index estimation criteria yielded acceptable results. CMIN/DF 3.025, GFI = 0.81, AGFI = 0.79, CFI = 0.89, TLI = 0.85, NFI = 0.71, RMR = 0.057, and RMSEA = 0.081 were within the anticipated thresholds. All indicators show a good fitness value, except NFI (0.71), which is marginally small. Nonetheless, studies have deemed that NFI values that exceed 0.85 are sufficient if other metrics fit within the range (Devaraj, Krajewski, & Wei, 2007; Li, Huang, & Tsai, 2009; Oruç & Tatar, 2017). Structural model analyses demonstrated a good model fit.
To obtain sufficient fit indices for the structural model, path analysis was appropriate. Table 6 and Figure 2 display the results of the hypothesis testing. The results indicate that MU (β = 0.260, p < 0.001), PCM (β = 0.317, p < 0.001) on BI, PCM (β = 0.191, p < 0.05) on PEJ, MPT (β = 0.186, p < 0.05), PIIT (β = 0.397, p < 0.001) on MU, PIIT (β = 0.324, p < 0.001) on MEOU, MPC (β = 0.243, p < 0.001) on MU, MPC (β = 0.139, p < 0.001) on MU, MPC (β = 0.299, p < 0.05) on MEOU are significant. However, MEOU (β = 0.043, p > 0.05) on BI, MPC (β = 0.030, p > 0.05) on BI, PEJ (β = 0.040, p > 0.05) on BI, and MPR (β = 0.045, p > 0.05) on BI are insignificant.
4.4 Evaluating the mediation effects
H1, H2, and H3 posit that MEOU, MU, and PE mediate PIIT, PC, PCM, and BI, respectively. We shadowed Preacher and Hayes (2008) to test for mediation effects using the bootstrapping method. Table 7 suggests that the mediation effect of MU on the relationships between PIIT and BI (0.0456, 0.1615) and between MPC and BI (0.0664, 0.1808) is significant with a 95% confidence interval. The indirect effects of MEOU on PIT, BI (0.0057, 0.0883), MPC, and BI (0.0107, 0.0822) were also significant. However, the indirect effects of PEJ between PCM and BI (−.0002, 0.0415) are insignificant.
5. Discussion and implications
These findings suggest that MU is significantly associated with BI. The findings were corroborated empirically (Lew et al., 2020) by a study on m-wall technology. These findings indicate that consumers in the hospitality sector embrace m-wallets because of their usefulness in everyday life. However, MU was significantly associated with BI. The results agree with those of previous research by Tan, Ooi, Chong et al. (2014), who showed that the greater the advantages of mobile payment adoption, the greater the IU. In previous mobile research, MU was shown to be a determinant of IU (Tan, Ooi, Sim, & Phusavat, 2012), m-learning (Wong, Tan, Tan, & Ooi, 2015), m-marketing (Pan, Chew, Cheah, Wong, & Tan, 2015), and m-television (Wong, Tan, Loke, & Ooi, 2014). This implies that if consumers consider mobile wallet adoption useful and improve their living standards, they are more likely to continue using the service.
MEOU was an insignificant predictor of BI. The results contrast with previous research on mobile wallet acceptance, which found that customers generally embrace innovation if the system is effortless. Gefen and Straub (1997) stated that the significance of ease of use increases when an online user purchases a virtual service or product, in contrast to collecting information about a service or product. Wu and Wang (2005) reported that ease of use has an insignificant effect on intentions regarding acceptance of mobile commerce.
Our findings show that PCM positively influences BI. This finding gained empirical support from Lew et al. (2020) and Tan and Ooi (2018) and Duygan, Fischer, and Ingold (2023). The findings suggest that an individual’s use of m-wallets is affected by whether their peers in the group also utilize the same technology. If users believe that other members of their group use an m-wallet application, they will be more inclined to adopt it. Furthermore, the PCM has a significant relationship with the PEJ. PEJ had an insignificant association with BI. These results are in contrast with those of previous studies, such as Tan and Ooi (2018), who argued that users have a particular incentive or hedonic motivation during the entire process of utilizing new technology. One possible reason is that Pakistani consumers are more interested in innovation usefulness than in enjoyment.
MPR was not significantly associated with BI, which is consistent with previous findings (Tan & Ooi, 2018). One possible reason for this is that the mobile wallet is thought to have superior encryption, unlike other conventional payment systems (Zupanovic, 2015). As a result, customers trust that their private information will not be stolen throughout the payment process and therefore do not see MPR as critical. Furthermore, the MPT was found to be a significant determinant of the BI to adopt mobile wallets. Similar results have been observed by Ooi and Tan (2016), who determined that MPT was the leading persuasive factor in increasing BI to accept m-payments. This outcome suggests that the regulatory aspects of the hospitality industry play a crucial role in building trust. This highlights the need for businesses to take action to enhance trust in the wireless mobile environment by devising policies and regulations that specifically address the mobile segment of the Internet. Additionally, providing proactive management and guidance to mobile users can strengthen trust perceptions. Furthermore, PIIT had a statistically positive effect on the MU, MEU, and BI scores. This study corroborates previous research on mobile payment services (Mew & Millan, 2021; Yang, Lu, Gupta, Cao, & Zhang, 2012). The results of Ooi and Tan (2016) on SSC in Malaysia showed that MPC is favorably associated with MU and MEOU.
This study shows that when a mobile wallet is compatible with a customer’s lifestyle, the consumer finds it helpful. For instance, if customers are already using a convenient payment method, such as mobile peer-to-peer, a change to a more robust payment method, such as proximity payment, would create the impression that a mobile wallet is more convenient throughout the payment process. This is because customers may examine the usefulness of both mobile payment methods. However, PCM was not significantly related to BI, and this finding is consistent with the results of Balachandran and Tan (2015), where MPC was discovered to have a significant impact on BI. This may be related to customer familiarity with the m-devices used for m-commerce objectives (Wong, Tan, Ooi, & Lin, 2015). Furthermore, a strong connection between MPC and MEU was discovered. Thus, consumers find mobile wallets simple to use if the innovation is compatible with their existing habits, requirements, values, and experiences.
5.1 Theoretical implications
Based on the results of this study, theoretical insights are provided into the dynamics of consumer acceptance of mobile wallets in the hospitality industry. First, the upheld hypotheses regarding Mobile Ease of Use and Mobile Usability confirm the ongoing relevance of established technology acceptance models, particularly the Technology Acceptance Model (TAM). These findings suggest that perceived ease of use and perceived usefulness are important factors in shaping consumers' intentions to use mobile wallets. Additionally, Perceived Critical Mass' significant influence on adoption decisions emphasizes the importance of social influence and network effects, suggesting that peer recommendations and social norms have a significant impact on consumers' perceptions. The significant role of Perceived Enjoyment in technology adoption further emphasizes the importance of hedonic factors, suggesting that consumers' enjoyment of using mobile wallets may have a positive impact on their adoption intentions. Although Mobile Perceived Risk did not significantly affect Behavioral Intentions, the significant impact of Mobile Perceived Wireless Trust indicates that trust in wireless infrastructure may play a more critical role in influencing adoption decisions. The development of future theoretical models should further explore the factors influencing trust and risk perception as they relate to the adoption of mobile wallets, taking into account the unique features of wireless technology and the implications for consumer behavior. In addition, the importance of Personal Innovativeness in IT underscores that individual characteristics play a significant role in shaping consumers' attitudes towards the adoption of technology, which suggests that personal innovativeness should be considered when predicting consumer perceptions and intentions. Finally, the upheld significance of Mobile Perceived Compatibility and Perceived Critical Mass illustrates the importance of perceived fit with existing habits and the influence of social norms on technology adoption. It is suggested that future theoretical models explore how compatibility with user needs and preferences affects behavioral intentions by influencing perceptions of adoption by others. The theoretical implications provide researchers with insight into consumer behavior while refining existing theoretical models and advancing our understanding of the hospitality industry’s mobile wallet adoption.
5.2 Practical implications
The practical implications resulting from this study can be beneficial for businesses and stakeholders in the hospitality industry who are seeking to increase consumer acceptance and adoption of mobile wallets. First, the confirmed significance of factors such as Mobile Ease of Use and Mobile Usefulness further underscores the significance of prioritizing user experience and functionality in the design and implementation of mobile wallet platforms. Mobile wallets can be more attractive and foster positive attitudes towards adoption if they are intuitive and provide tangible benefits to users. In addition, the observed influence of Perceived Critical Mass highlights the potential benefits of leveraging social influence and network effects in the promotion of mobile wallet adoption. By taking advantage of peer recommendations and social norms, businesses can foster a sense of community and trust around the use of mobile wallets, thereby facilitating consumer adoption. Additionally, the fact that Perceived Enjoyment appears to be a significant factor indicates that mobile wallets should incorporate elements of enjoyment and satisfaction into their experiences. Businesses should strive to provide mobile wallet users with engaging and enjoyable interfaces and features that enhance their overall experience and encourage them to continue using the wallet. Furthermore, even though Mobile Perceived Risk did not have a significant impact on adoption intention, the significance of Mobile Perceived Wireless Trust underscores how important it is to build and maintain trust in the reliability and security of wireless technology infrastructure supporting mobile wallet transactions. Businesses should prioritize implementing robust security measures and transparent communication in order to alleviate consumer concerns and foster confidence in mobile wallets. Furthermore, Personal Innovativeness in IT and Mobile Perceived Compatibility findings suggest that businesses need to design mobile wallets according to individual preferences and habits as well as societal norms and expectations. As a result of addressing these practical implications, businesses can improve the appeal and acceptance of mobile wallet technology among consumers, ultimately increasing the adoption and usage of the technology across all hospitality sectors.
6. Limitations and future research direction
While this study offers significant insight into the acceptance and usage of mobile wallets, it is hampered by several limitations. First, the most notable of these is its inability to broaden its findings. Because the data collection procedure is limited to Pakistanis, the results may not be representative of the behavior and receptivity to mobile wallets in other economic environments. This restriction stems from enormous differences across nations in terms of cultural influences, levels of development, and a variety of other factors that might impact mobile technology adoption. As a result, future research efforts may explore a comparative study, increasing the study’s application by integrating data from many other countries. Second, we did not include any moderating factors. However, future studies should consider variables such as customer engagement, personal innovativeness, and consumers’ privacy and security concerns. This might be investigated to determine how the relationship between important success factors and consumer experience varies across different degrees of moderating variables. Third, the same study may be conducted across numerous fields, such as e-banking, m-commerce, and mobile shopping. It would be interesting to see whether the given paradigm holds true in other technological sectors. We also encourage the use of the model in other mobile usage contexts, such as m-learning, m-banking, m-health, and m-gaming.
Figures
Questionnaire source and items
Constructs | No. of items | Source |
---|---|---|
Mobile usefulness | 4 | Ooi and Tan (2016) |
Mobile ease of use | 4 | Ooi and Tan (2016) |
Perceived critical mass | 5 | Tan and Ooi (2018) |
Perceived enjoyment | 3 | Nysveen et al. (2005) |
Mobile perceived risk | 4 | Leong et al. (2013), Tan, Ooi, Chong et al. (2014) |
Mobile perceived wireless trust | 4 | Ooi and Tan (2016) |
Personal innovativeness in IT | 4 | Tan, Ooi, Leong et al. (2014) |
Mobile perceived compatibility | 4 | Ooi and Tan (2016) |
Behavioral intention | 3 | Tan, Ooi, Chong et al. (2014) |
Source(s): Table by authors
Descriptive statistics
Variable | Group | N | (%) |
---|---|---|---|
Gender | Male | 117 | 37.7 |
Female | 193 | 62.3 | |
Age | 15–19 | 32 | 10.3 |
20–24 | 65 | 21 | |
25–29 | 110 | 35.5 | |
30–34 | 45 | 14.5 | |
35–39 | 35 | 11.3 | |
40 and above | 23 | 7.4 | |
Education level | High School | 150 | 48.3 |
Undergraduate | 82 | 26.5 | |
Graduate | 40 | 12.9 | |
Doctorate | 38 | 12.3 | |
Marital status | Single | 220 | 71 |
Married | 90 | 29 | |
Profession | Unemployed | 15 | 4.8 |
Working professional | 60 | 19.4 | |
Self-employed | 95 | 30.6 | |
Private employed | 80 | 25.8 | |
Student | 60 | 19.4 | |
Monthly income | Below or equal to 30,000 Pkr | 50 | 16.1 |
31,000–40,000 | 59 | 19.0 | |
41,000–50,000 | 125 | 40.3 | |
51,000–60,000 | 40 | 13.0 | |
Above 60,000 | 36 | 11.6 |
Source(s): Table by authors
Construct validity
Constructs | FL | CR | AVE |
---|---|---|---|
Mobile usefulness | 0.81 | 0.52 | |
MU1 | 0.74 | ||
MU2 | 0.82 | ||
MU3 | 0.69 | ||
MU4 | 0.61 | ||
Mobile ease of use | 0.85 | 0.59 | |
MEOU 1 | 0.86 | ||
MEOU2 | 0.71 | ||
MEOU3 | 0.87 | ||
MEOU4 | 0.61 | ||
Perceived critical mass | 0.81 | 0.55 | |
PCM1 | 0.78 | ||
PCM2 | 0.62 | ||
PCM3 | 0.75 | ||
PCM4 | 0.61 | ||
PCM5 | 0.62 | ||
Perceived enjoyment | 0.83 | 0.56 | |
PEJ1 | 0.87 | ||
PEJ2 | 0.66 | ||
PEJ3 | 0.85 | ||
Mobile perceived risk | 0.78 | 0.56 | |
MPR1 | 0.74 | ||
MPR2 | 0.60 | ||
MPR3 | 0.79 | ||
MPR4 | 0.58 | ||
Personal innovativeness in IT | 0.78 | 0.57 | |
PIIT1 | 0.72 | ||
PIIT2 | 0.58 | ||
PIIT3 | 0.78 | ||
PIIT4 | 0.66 | ||
Mobile perceived compatibility | 0.85 | 0.53 | |
MPC1 | 0.88 | ||
MPC2 | 0.58 | ||
MPC3 | 0.86 | ||
MPC4 | 0.72 | ||
Mobile perceived wireless trust | 0.81 | 0.55 | |
MPT1 | 0.68 | ||
MPT2 | 0.87 | ||
MPT3 | 0.59 | ||
MPT4 | 0.65 | ||
Behavioral intention | 0.73 | 0.56 | |
BI1 | 0.66 | ||
BI2 | 0.72 | ||
BI3 | 0.68 |
Note(s): AVE = Average Variance Extracted, CR = Composite Reliability, FL = Factor Loading
Source(s): Table by authors
Discriminant validity
Variables | PU | PEOU | PCM | PE | MPR | PIIT | MPC | MPT | BI |
---|---|---|---|---|---|---|---|---|---|
MU | 1 | ||||||||
MEU | 0.449** | 1 | |||||||
PCM | 0.374** | 0.299** | 1 | ||||||
PEJ | 0.210** | 0.231** | 0.154** | 1 | |||||
MPR | 0.284** | 0.170** | 0.297** | 0.247** | 1 | ||||
PIIT | 0.264** | 0.149** | 0.288** | 0.087 | 0.195** | 1 | |||
MPC | 0.323** | 0.157** | 0.248** | 0.503** | 0.471** | 0.208** | 1 | ||
MPT | 0.085 | 0.012 | 0.215** | 0.085 | 0.248** | 0.021 | 0.217** | 1 | |
BI | 0.419** | 0.319** | 0.531** | 0.189** | 0.319** | 0.411** | 0.309** | 0.185** | 1 |
Source(s): Table by authors
Model fit
Models | Chi-square/Df | GFI | AGFI | CFI | TLI | NFI | RMR | RMSEA |
---|---|---|---|---|---|---|---|---|
Measurement model | 2.628 | 0.81 | 0.80 | 0.83 | 0.81 | 0.81 | 0.026 | 0.073 |
Structure model | 3.025 | 0.81 | 0.79 | 0.89 | 0.85 | 0.71 | 0.057 | 0.081 |
Acceptable range* | 1–3 | >0.80 | >0.80 | >0.90a | >0.90 | >0.80 | <0.09 | <0.08b |
Note(s): * The goodness of fit has no set value; however, research has shown that the acceptable level for GFI, AGFI, CFI, and NFI should be near 1, and RMR and RMSEA should be near 0.05 (Hair et al., 2006; Hu & Bentler, 1999). The thresholds enumerated for recognized range on the table, a CFI ≥0.95 great; >0.90 traditional; >0.80 sometimes acceptable. bRMSEA ≤0.05 good fit; 0.05–0.10 moderate; >0.10 poor
Source(s): Table by authors
Hypotheses testing results
Hypotheses | Paths | Estimate | SE | CR | p | Results | Decision |
---|---|---|---|---|---|---|---|
HI | MU → BI | 0.260 | 0.058 | 4.478 | *** | Significant | Supported |
H2 | MEOU → BI | 0.043 | 0.038 | 1.127 | 0.260 | Insignificant | Not supported |
H3 | PCM → BI | 0.317 | 0.058 | 5.481 | *** | Significant | Supported |
H4 | PCM → PEJ | 0.191 | 0.090 | 2.114 | * | Significant | Supported |
H5 | PEJ → BI | 0.040 | 0.035 | 1.129 | 0.259 | Insignificant | Not Supported |
H6 | MPR → BI | 0.045 | 0.047 | 0.948 | 0.343 | Insignificant | Not supported |
H7 | MPT → BI | 0.186 | 0.083 | 2.241 | * | Significant | Supported |
H8 | PIIT → MU | 0.397 | 0.093 | 4.262 | *** | Significant | Supported |
H9 | PIIT → MEOU | 0.324 | 0.099 | 3.283 | *** | Significant | Supported |
H10 | MPC → MU | 0.243 | 0.059 | 4.138 | *** | Significant | Supported |
H11 | MPC → MEOU | 0.139 | 0.063 | 2.197 | * | Significant | Supported |
H12 | PIIT → BI | 0.299 | 0.070 | 4.275 | *** | Significant | Supported |
H13 | MPC → BI | 0.030 | 0.039 | 0.780 | 0.435 | Insignificant | Not Supported |
Note(s): ***p < 0.001, **p < 0.01, NS p > 0.05
Source(s): Table by authors
Mediation results
95% bootstrap confidence intervals for an indirect effect | |||
---|---|---|---|
MU | |||
Effect | SE | CIs | |
PIIT | 0.0955 | 0.0294 | (0.0456, 0.1615) |
MPC | 0.1163 | 0.0288 | (0.0664, 0.1808) |
MEOU | |||
PIIT | 0.0397 | 0.0211 | (0.0057, 0.0883) |
MPC | 0.0410 | 0.0185 | (0.0107, 0.0822) |
PEJ | |||
PCM | 0.0151 | 0.0110 | (−0.0002, 0.0415) |
Source(s): Table by authors
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Acknowledgements
This research was supported by Key Project of National Social Science Foundation of China (21AGL014), Shenzhen Science and Technology Program (JCYJ20210324093208022), Shenzhen University Humanities and Social Sciences High-level Innovation Team Project for Leading Scholars (24LJXZ06) and General Project for Humanities and Social Sciences Research of Ministry of Education of China (No. 20YJA630098).