Exploring Tourists’ Behavioural Intentions Towards Use of Select Mobile Wallets for Digital Payments

Abstract

The present study goal is to identify assorted factors and also to investigate their influence on tourists’ behavioural intentions about their usage patterns with respect to M-wallet payment. A conceptual model has been anticipated and authenticated and 600 questionnaires were distributed and 482 were usable. Structural equation modelling was employed to assess research hypotheses. The result shows that the behavioural intention is significantly influenced by perceived value, trust, compatibility and social influence while tourists’ is less optimistic when it comes to use M-wallet with regard to perceived enjoyment. The study also showed that trust followed by compatibility has a more powerful influence on the behavioural intention of tourists in the context of M-payment. The present study is limited to only six M-wallet and that too limited to the certain age category, namely 18–25 in Gwalior city of India. Understanding the different dimensions of behavioural intentions can help M-wallet players to win the trust of tourists and enhance the frequency to use M-wallet for M-payment. The results indicate that M-wallet service providers should consider and manage all affecting factors proactively as mechanisms for intention to use M-wallet. It will establish a behavioural intention model of M-wallet users that can help organizations to manage the formation of behavioural intentions of their users.

Keywords

Consumer behavioural intentions M-wallet M-payment India

Introduction

At present, India is one of the most cash-intensive economies of the world and an omnipresent payment system has not been achieved in India till now. Besides this, it has near to 400 million internet users around the world. At present, there are 135 million e-wallet users in India’s M-wallet market. It is expected that 80 per cent of E-Commerce transactions will happen via M-wallet in 2020.

M-payment is a sub-set of mobile commerce (M-commerce) which confers a method for accomplishing micro payments and to facilitate commercial transactions on mobile devices. M-payment is any payment when a cellular phone is used to initiate, authorize and confirm a viable transaction (Rao & Troshani, 2007; Zhang et al., 2012). M-payment among tourists is vibrant and fastest growing in India as it is an alternative payment method for goods & services. M-payments can be grouped into two categories (a) payments for purchases which includes M-payment and complement cash, checks and debit cards too and (b) payments of bills/invoices are account-based payments like money transfer in account or online banking payments (Karnouskos & Fokus, 2004) Tourists used M-payment for recharge, flight tickets, parking fees and transportation fares.

M-wallet is a smartphone software application that allows a person to make electronic transactions such as purchasing an online item or something at a store (Rao & Troshani, 2007). M-wallet includes three things, namely (a) online setup, (b) operating software and (c) cell-phone. M-wallet helps not only in financial transactions but also authenticates the user’s identifications and secure user’s past payment information.

As India is growing in digital payment, simultaneously tourists and other stakeholders are facing several difficulties in M-Payment. India is home to the largest number of unbanked families and has huge scope for merchandising with multichannel payment services. But many of the potential users in India are less aware, less technology savvy and also feel worried about the safety and other consequences of their digital payment (Bashir & Madhavaiah, 2015). Hence, there is a need to create awareness amongst all stakeholders and make them tech savvy. Moreover, it is feared that big M-wallet players may show door to small players and either force their closure or make them to shift to green pastures.

Considering the potential of M-wallets, this industry has received wider interest and attention of numerous M-wallet players who made their inroads in India including both non-banking and banking sectors. Hence, it is essential to examine tourists’ behavioural intention towards adoption of mobile payment services through M-wallet. It is also imperative to investigate the influence of identified factors on tourists’ behavioural intentions to use M-wallet.

The present study portrays the six leading M-wallet players in the digital payment domain. For this study, researchers had chosen two players from each category, namely Mobile Network Operators (MNOs), Independent players and Banks. Selected players are (a) Airtel Money, Vodafone M-Pesa, from MNOs, that is, telecom-based wallets, (b) PhonePe, Paytm from web-based companies, that is, independent wallets and (c) State Bank Buddy, HDFC PayZapp from banks. Selecting Gwalior city is based on the assumption that Gwalior city characterizes a rich populated consumer base using M-wallet for payments.

Theoretical Background and Hypotheses Development

Technology Adoption Theories

Technological innovations and its acceptance process search started in the 1990s with inclusion of variables such as expectations, individual and environmental influences (Agarwal & Prasad, 1997; Bagozzi & Lee, 1999; Davis et al., 1989). Afterwards, work related to internet and mobile phones gained more attention (Bitner, 2001; Garfield, 2005; Im et al., 2011; Jarvenpaa & Lang, 2005; Oye et al., 2014) which lead to development of several theoretical frameworks regarding users’ adoption of new technologies such as Technology Acceptance Model (Davis, 1989); Theory of Reasoned Action (TRA; Fishbein & Ajzen, 1975); Theory of Planned Behaviour (TPB) (Ajzen, 1991); Unified Theory of Acceptance and Use of Technology (UTAUT/UTAUT2; Fishbein & Ajzen, 1975). The proposed research model includes variables from four models of technology adoption. These are:

Technology Acceptance Model (TAM) is the most influential model to explain users’ IT adoption behaviour (Davis et al., 1989). TAM employed to know the adoption behaviour of tourists with the help of two components, namely ‘perceived usefulness’ and ‘perceived ease of use’ (Davis, 1989; Davis et al., 1989).

Perceived usefulness is defined as the degree to which a person perceives that adopting the system will boost his/her job performance.

Perceived ease of use is defined as the degree to which a person believes that adopting the system will be free of effort.

Furthermore extended TAM (i.e., TAM-2) assessed the external variables effect on system usage (Venkatesh & Davis, 2000). TAM-2 includes social and organizational variables such as subjective norm, image, job relevance and result demonstrability.

Theory of Reasoned Action (TRA) determines human behaviour which specifies that a person’s performance is jointly evaluated by (a) person’s belief, that is, positive or negative evaluations of performing a behaviour and (b) subjective norms, that is, perceived influences that others may have (Fishbein & Ajzen, 1975). Persons’ belief intends to show a certain behaviour may be positive or negative while evaluating and determining the consequences of performing the behaviour. Subjective norms are all about the person acceptance or rejection of the behaviour in certain circumstances. It is affected by surroundings (such as spouse, reference group and family members) as he or she considers what others will think about his activities.

Theory of Planned Behaviour (TPB) is an extension of the well-known Theory of Reasoned Action (Ajzen, 1991) which assists in determination of behavioural intention (Fishbein & Ajzen, 1975). There are three components associated with TPB, namely attitude, subjective norm and perceived behavioural control (Ajzen, 1985, 1988, p. 134). Briefly, (a) attitude, that is, an individual’s positive or negative evaluation of behaviour, is seen to reflect beliefs about the likely consequences of performing a behaviour, whilst (b) subjective norm is an individual’s perception of social pressure and thus reflects the beliefs about the normative expectations of others (Ajzen, 2002; Ajzen & Fishbein, 1975). Additional component, namely (c) Perceived Behavioural Control (PBC) includes two components: (a) ‘facilitating conditions’ (Triandis, 1979), which show the availability of resources (time, money, etc.) required to connect with behaviour followed by (b) self-efficacy, which is all about individual’s self-confidence in his/her ability to perform a behaviour (Ajzen, 1991).

Unified Theory of Acceptance and Use of Technology (UTAUT) framework assists in prediction of the users’ adoption of information technologies (Venkatesh et al., 2003). UTAUT and its extended theoretical frameworks UTAUT-2 are very popular and widely used to predict behavioural intention for the adoption of technology. UTAUT components are derived from Technology Acceptance Model (TAM), Theory of Reasoned Action (TRA) and Theory of Planned Behaviour (TPB). UTAUT proposes four factors (performance expectancy, effort expectancy, social influence and facilitating conditions) which are direct determinants of usage intention and adoption behaviour. First, Performance expectancy provided evidence on performance and usefulness of technology influences on intention to adopt technology. Second, Effort expectancy emphasized that user’s attitude and ease of use (less effort) determines the acceptance of the technology. It is expected that the services will make users’ life easy due to user-friendly interface and quick payment setups. Third, social influence (like reference groups and society) decides the users’ behavioural intention with respect to technology adoption. Fourth, facilitating conditions is all about determining the degree to which one believes that a system can systematically and technically support the use of a new system.

All these theories are widely accepted in several applied research of new technologies adoption among users (Liu et al., 2011). Furthermore, these theoretical models have been adapted to different contexts such as acceptance of E-commerce (Koufaris, 2002) and mobile commerce (Koivumaki et al., 2006). Modification in existing technology acceptance theories may present a good theoretical foundation for understanding m-payment adoption (Shin, 2009). Hence, TAM, TPB, UTAUT and TRA models select variables have been used in this research.

A number of studies have focused on the adoption factors of m-payment primarily based on the TAM, TRA, TPB and UTUAT which includes security, trust, mobility, convenience, speed of transaction, facilitating condition, privacy and technology anxiety (Chen & Adams, 2005; Cheong et al., 2004).

Hypotheses Development

TAM, TRA, TPB and UTUAT models are commonly adopted in technology domain adoption-related studies (Arvidsson, 2014; Duane et al., 2012; Keramati et al., 2012; Kim et al., 2009; Yan et al., 2009). In this study, researchers used factors from these theoretical base models to hypothesize and examine an integrated model in Indian context due to non-availability of specified factors in the arena of M-wallet for digital payment (Kim et al., 2009). The proposed research model constructs, namely perceived enjoyment, compatibility, perceived value, social influence and trust have been added as the antecedents towards adoption of M-wallet, while M-wallet adoption for m-payment is considered as the dependent variable.

Perceived Enjoyment (PERE) and Intentions to Use M-Wallet

Perceived enjoyment or Hedonic motivation is not all about adopting new technologies to enhance the performance but also as a source of enjoyment. It brings ‘fun to user’ which comes from technology adoption (Venkatesh et al., 2012). PERE may be derived from tourists’ novelty-seeking in the context of intention to use M-wallet services (Csikszentmihalyi, 1990). PERE motivates user for intention to use new technology which develops its tolerability among users (Celik, 2008). Several studies in IT domain have analysed the influence of perceived enjoyment on intention to use (Mitra et al., 2003; Moon & Kim, 2001). PERE noticed to be a significant indicator of technology adoption from the tourist perspective (Venkatesh et al., 2012), and an appraise component of online browsing (Cox et al., 2005). Researchers revealed that PERE is having positive role in determining intention to adopt, although few research results remain mixed (Gefen, 2000). Besides, this, Higher degree of enjoyment in M-wallet adoption could lead to reduction in difficulty, worry or nervousness among users. In the light of these discussions, researchers propose that PERE can act as an antecedent of technology adoption in the domain of M-wallet (Agarwal & Karahanna, 2000; van der Heijden, 2004). Therefore, it is proposed that

H₁: Perceived enjoyment has a positive influence on intentions to use M-wallet.

Compatibility (COMP) and Intentions to Use M-wallet

COMP—Compatibility as a research construct has primarily been discussed in the marketing and tourist behaviour literature and it refers to persons’ controllability and friendliness (Berry et al., 2002; Jih, 2007; Ng-Kruelle et al., 2002). COMP with M-wallet leads to possibility of trying out this service in India (Ding et al., 2004; Mallat et al., 2004; Venkatesh et al., 2012). Earlier research has indicated that compatibility has been shown to be the most significant indicators of adoption (Mallat et al., 2004). Besides this, compatibility also has significant determinants like shaping intentions to use M-banking, M-commerce, etc. (Amoroso & Watanabe (2012); Chong, 2013). Madan and Yadav (2016) revealed that social behaviour compatibility, perceived risk & value, are significant factors to use mobile wallet. Researchers, such as Lee et al. (2011), Shin (2009), Schierz et al. (2010), Kim et al. (2007), Wang and Yi (2012), Amoroso and Watanabe (2011), Slade et al. (2015) and Yan and Yang (2015), have used this construct and found that COMP is one of the constructs that promote M-wallet adoption hence, it is proposed that

H₂: Compatibility has a positive influence on intentions to use M-wallet.

Perceived Value (PERV) and Intentions to Use M-wallet

PERV—Perceived value refer to values which were received by the tourists in against of the paid price for any product or service (Zeithaml, 1988). It is identified from the earlier research that this construct has considerable influence on behavioural intention (Deng et al., 2014). Venkatesh et al. (2012) revealed that PERV could affect behavioural intentions positively towards M-wallet adoption using unified theory of acceptance and use of technology (UTAUT2) model. Hence, it is proposed that

H₃: Perceived value has a positive influence on intention to use M-wallet.

Social Influence (SOCI) and Intentions to Use M-Wallet

SOCI—Mobile devices commonly used in community where user monitor other behaviour in terms of adoption (Nysveen et al., 2005). ‘To use or not to use’ M-wallet services are generally influenced by social surroundings such as reference group, family members and society (Riquelme & Rios, 2010). SOCI factors included in TPB and TRA models although TAM model ignored ‘social influence’ with reference to technology adoption (Shin, 2009) still SOCI significance cannot be ignored among users of M-wallet. Further, extended TAM (TAM2) has inclusion of social influence (Venkatesh & Davis, 2000). SOCI is one of the determining factors of M-wallet intentions to use (Lee et al., 2011; Schierz et al., 2010; Yang et al., 2012). SOCI has been identified as significant construct by using frameworks, that is, TAM-2 and UTAUT-2 (Venkatesh, 2000; Venkatesh et al., 2012). Lee et al. (2011) revealed that factors which influence individual adoptions of mobile payment technology are technological and social perspectives. More recently researches examining the role of SOCI in the field of M-commerce which also included M-wallet have shown significant influence on usage (Fang, 1998; Lee et al., 2009). Beside this mobile internet and online game communities also have direct influence on technology adoption (Hsu & Lu, 2007; Kim et al., 2007). Furthermore, the work of Venkatesh and Morris (2000) and Chong (2013) revealed that SOCI has significant influence on intention to use M-wallet. Consequently, in the context of M-payment, a positive relationship between social influence and intention to use M-wallet is expected. Hence, it is hypothesized that

H₄: Social influence has a positive influence on intention to use M-wallet.

Trust (TRUS) and Intentions to Use M-Wallet

TRUS—Trust is about tourists’ faith in M-payment services which plays an important role in acceptance of technology (Kim & Prabhakar, 2004). M-payment services often have issues with regard to security, retreat and reliability (Yoon, 2002) as it is one of the relevant factors in determining adoption intentions of users (Chong, 2013). Earlier studies on mobile wallet also identified TRUS construct as major determinants for adoption of technology (Shin, 2009; Xin and Liu, 2013). Kreyer et al. (2003) revealed that customers’ acceptance of mobile payment mainly depends on the issues of cost, security and convenience. Suki (2011) found that professed risk affects user behaviour in terms mobile banking adoption. Besides this, Adebiyi et al. (2013) revealed that all commercial banks have online facilities to their customers with M-payment services. Besides this, there are huge prospects of e-commerce but there is a need to have more focus on issues such as telecom network, infrastructure and security. The present study tries to study the risk factors that can affect a tourist’s decision to adopt digital wallet as a mode of online payment. Hence, it is considered that tourists’ trust must be engaged to measure behavioural intentions toward mobile wallet. The findings of earlier research overview clearly demonstrate the relevance as well as inadequacy of trust inclusion towards M-payment. In view of this, it is proposed that

H₅: Trust has a positive influence on intention to use M-wallet.

Research Gap

There are emerging mobile payments systems set off in all over the world, still scholarly and practical aspects in all countries are omitted. An important geographic gap is evidence in Asian countries. In addition, research on M-wallet payment in Indian context that too specifically in Gwalior region is unripe, attracting researchers’ interest.

Previously, most of the studies have included perceived risk, ease of use social risk, privacy, etc. (Rathore, 2016). Although, strategic framework in the online sphere with respect to M-wallet is still in emerging stage; empirical researches that aligned consumer intention with digital payment is limited in the phenomena of new technology adoption domain.

Literature reveals that there are numerous theoretical frameworks in information technology domain such as technology acceptance model (TAM) (Davis, 1989), the technology-organization and environment (TOE) framework (Tornatzky et al., 1990), the theory of planned behaviour (TPB) (Ajzen, 1991) and the unified theory of acceptance and use of technology (UTAUT2) (Venkatesh & Bala, 2008). These frameworks have employed on large extent in origin or modified form by the researchers time to time.

Furthermore, TAM framework has extensively employed worldwide in the area of M-wallets. Modified TAM includes two new constructs, namely social influence (SI) and cognitive process factors for understanding the adoption intentions (Venkatesh & Davis, 2000). It considers that without inclusion of individual characteristics TAM provides inefficient results (Agarwal & Prasad, 1997). UTAUT model has also employed to examine the usage in IT domain. It consists of performance expectancy, effort expectancy, facilitating conditions and social influence (Amoroso & Watanabe, 2012). Vanketash et al. (2012) modified model, namely UTAUT2 which includes seven constructs including facilitating conditions, performance expectancy, effort expectancy, social influence, perceived value, hedonic motivation and habit. Still the published literature related to mobile payment showed absence of the trust and perceived enjoyment factors. Several deductive studies took ‘trust & perceived enjoyment’ as general research variables without specific attention.

Even with the wealth of currently available research involving the TAM, TRA, TPB and UTAUT models continued to explore and improving in new research (Luarn & Lin, 2005). The existed models can be extended to investigate users’ intention to use m-payment, as mobile payment systems are a type of new information technology. In this research, studies assessing the acceptance of new technologies with different user populations are clearly required.

Besides this, there was no inclusion of all the five factors, namely perceived value, compatibility, perceived enjoyment, social influence and trust in information technology domain which leads to intention to use M-wallet for payment from the perspective of tourists.

Research Framework

In order to clarify the effect of predictors on dependent variable, namely BI, the proposed linkages among constructs the proposed research framework is shown in Figure 1. The model predictor variables are perceived value, compatibility, perceived enjoyment, social influence and trust while continuance behavioural intentions (BI) of consumer is presented as the outcome variable. The proposed research framework is shown in Figure 1.

Research Methods

Research Design

Considering the nature of research objectives, mixed-method approach has been adopted for this study. Study involving both quantitative and qualitative components can provide rich insights. In Phase 1, focus group interview followed by Phase 2, namely structured interview.

Generation of Scale Items

Statements of perceived enjoyment were taken out from Venkatesh and Bala (2008). Compatibility items were drawn from Slade et al. (2015) and Yang et al. (2012); perceived value was adapted from Venkatesh et al. (2002); social influence construct items were extracted from Yang et al. (2007) and Bearden et al. (1989); trust construct statements were adapted from Parasuraman and Grewal (2000) and Suh and Han (2002) while behavioural intention items were adapted from Matos et al. (2009). Demographic categories were adapted from Choudrie and Dwivedi (2006).

A self-administered structured questionnaire comprised of close-ended 5-point Likert type in nature is used. The set of questions were comprised of both positive and negative statements for ensuring the validity and consistency. Negative statements were reversed in SPSS before data processing.

Pretesting of Generated Items and Source of Data Collection

Items relevancy ‘to include or not to include’ in questionnaire were ensured. 30 M-wallet subscribers were approached via convenient mode. Preliminary set of questions were 48 which comprised of under five factors, namely PERE, COMP, PERV, SOCI, TRUS and BIs. Primary data was gathered from participants. Collected information were further consolidated, tabulated and analysed to draw logical inferences. The use of secondary data may be made by depending on the requirements of the study.

Sampling

Double stage sampling is the best suited which includes cluster followed by systematic sampling. Further, Inclusion of all the elements is not possible; hence, 600 tourists who are aware about M-wallet services were approached to make this study representative. Out of 600 tourists, 482 responses were found fit for data analysis.

Validity and Reliability

Convergent validity was established as all factors (average variance extraction) AVE loadings are more than 0.50 and less than composite reliability (CR) (Table 1). Discriminant validity was established as low correlation was revealed amongst diverse constructs.

Moreover, Cronbach’s alpha was used to evaluate the reliability of the scales (see Table 1). Values within range of 0.67 and 0.99 thus, signifying reliability of data. More than 0.60 values are in the permissible limit for internal consistency (Hair et al., 2010; Hasan, 2017). Discriminant validity of all factors shows in Table 2.

Table 1.

Reliability Statistics of All Dimensions

FACTORS	CR	AVE	MSV
SOCI	0.997	0.989	0.028
COMP	0.919	0.657	0.254
BIs	0.840	0.637	0.091
PERE	0.930	0.625	0.254
PERV	0.904	0.614	0.190
TRUS	0.988	0.964	0.028

Source: Authors’ own model.

Table 2.

Discriminant Validity of All Dimensions (Correlation Matrix)

	SOCI	COMP	BIs	PERE	PERV	TRUS
SOCI	0.995
COMP	0.131	0.810
BIs	0.091	0.301	0.798
PERE	0.168	0.504	0.229	0.791
PERV	0.138	0.411	0.271	0.436	0.783
TRUS	-0.026	0.018	0.104	0.168	0.100	0.982

Source: Authors’ own model.

Note: Bold number shows AVE while other numbers are MSV. Here Table 2 shows that AVE is more than MSV which is required for the discriminant validity and model fit.

Outliers

Using AMOS 20, Mahalanobis-Dsquared distance (D²) it found that 12 outlier cases values are not meeting the desirable criteria, namely 0.001 (see Table 3). Although there was large sample size and these 12 outlier cases would not be affect the results, hence retained (Tabachnick & Fidell, 2007).

Table 3.

Multivariate Outlier (Mahalanobis Distance)

Observation Number	Mahalanobis D²	p1	p2
169	92.332	0.00	0.00
50	74.470	0.00	0.00
357	88.416	0.00	0.00
118	54.762	0.00	0.00
379	74.277	0.00	0.00
94	84.127	0.00	0.00
359	64.065	0.00	0.00
140	74.020	0.00	0.00
70	72.670	0.00	0.00
198	71.648	0.00	0.00
208	70.260	0.00	0.00
13	57.572	0.00	0.00

Source: Authors’ own model.

Normality of the Data

Uni-variate normality of each variable was checked through skewness–kurtosis approach (Hair et al., 2010). It found that all analysed values were within their respective levels, namely skewness values were <3 and kurtosis values were <8 (Kline, 2005).

Respondents Profile

Youngsters within age group of 18–25 were approached due to their update knowledge in technology, this generation is tech friendly too (Davis, 1989; Yadav et al., 2016). Hence, the selected sample was considered for the study.

Descriptive Analysis

Researchers calculated mean values of all five factors (Table 4). It shows that PERE -F1 is the leading construct followed by PERV -F3 which leads towards intentions to use M-wallet.

Table 4.

Descriptive Statistics

Dimensions	Mean	SD
PERE—F1	4.12	0.66
COMP—F2	4.00	0.68
PERV—F3	4.08	0.64
SOCI—F4	3.58	1.00
TRUS—F5	3.95	0.78

Source: Authors’ own model.

Exploratory Factor Analysis

Before performing structural equation modelling, an exploratory factor analysis (EFA) was employed on independent items to check sampling adequacy and to identify representation of factors while EFA was not employed on dependent factor, namely behavioural intentions. Bartlett’s test of Sphericity is significant with p < 0.001 while Kaiser–Meyer Oklin’s (KMO) is 0.888, which shows adequacy too. Varimax technique was used for factor rotation, more than 1.0 Eigenvalue factors were retained (Field, 2009), and these five factors shows 76.858 per cent of the total variance. For every items having more than 0.5 SRWs value, common and representative name was used as per Indian context. The configured EFA results of identified factors are presented in Table 5.

Confirmatory Factor Analysis (CFA)

CFA was run on identified six factors, these are PERE, COMP, PERV, SOCI, TRUS and BIs.

All measures of CFA such as Chi-square (x²), GFI, AGFI, RMSR & RMSEA were appropriate as per the standard norms (see Table 6).

Table 5.

Factor Analysis

Independent Variable	PERE—F1	COMP—F2	PERV—F3	SOCI—F4	TRUS—F5
Q1.12Q1.14Q1.16Q1.25Q1.27Q1.30Q1.31Q1.33	0.7270.7560.8700.8100.6860.7990.7940.789
Q1.1Q1.3Q1.6Q1.9Q1.22Q1.38		0.8150.7980.7690.8390.8130.818
Q1.7Q1.10Q1.15Q1.18Q1.23Q1.28			0.8440.7940.8310.6760.8160.774
Q1.8Q1.20Q1.24Q1.34				0.9890.9890.9280.987
Q1.4Q1.5Q1.37					0.9820.8800.978
EigenvaluesCumulative %	8.81932.662	3.80014.074	3.08511.424	2.76010.223	2.2888.475

Source: Authors’ own model.

Notes: Extraction Method: Principal Component Analysis; Rotation; Method: Varimax with Kaiser Normalization.

Table 6.

Reliability and Validity of CFA

Construct	Chi-square	GFI	AGFI	CFI	RMR	RMSEA
F1	2.026	0.818	0.868	0.862	0.035	0.058
F2	3.170	0.790	0.795	0.865	0.049	0.034
F3	2.720	0.982	0.962	0.990	0.013	0.060
F4	2.143	0.962	0.808	0.993	0.001	0.026
F5	3.425	0.825	0.827	0.886	0.057	0.051
BI	1.093	0.998	0.989	0.966	0.010	0.014

Source: Authors’ own model.

Results

After performing CFA, two-stage SEM approach was applied for testing research hypotheses. First, evaluating measurement model; second, estimating the structural model (Byrne, 2013). The maximum likelihood estimation procedure was employed by the researchers.

Measurement Fit Indices

Initial fit indices of the measurement model fit are CMIN/DF-3.702; GFI-0.78; AGFI-0.73; NFI-0.82; CFI-0.83; RMSEA-0.08. These figures show poor fit model when compared with fit model (Byrne, 2013). Consequently, researchers eliminated five items (one item from PERE; two items from SOCI and two items from BIs) having less than 0.50 value. Besides this, PERV one item also deleted due to unaccepted residual value. Modified measurement values, that is, CMIN/DF-2.750; GFI-0.828; AGFI-0.797; NFI-0.914, CFI-0.935; RMSEA-0.07 are within acceptable limit.

Structural Model Analyses

Structural model was framed and analysed with their fit indices which were in the acceptable level. The effect of perceived enjoyment (PERE) towards tourists’ behavioural intention is insignificant (β1 = 0.087, p >0.05), hence rejected. Second hypothesis, namely compatibility (COMP) have a significant effect on tourists’ behavioural intention (β2 = 0.143, p < 0.05); thus, H₂ is supported. The analysis also suggests that there is a significant effect of perceived value (PERV) on tourists’ behavioural intention (β3 = 0.088, p < 0.05). Thus, H₃ is supported. The analysis suggests that social influence (SOCI) effect behavioural intention of tourists (β4=0.094, p < 0.05). Thus, H₄ is accepted. The analysis suggests that trust (TRUS) has a strong impact on behavioural intentions to use M-wallet (β5 = 0.443, p < 0.05), thus supporting H₅. Based on the hypotheses results it found that except perceived value (PERE) all other determinants were significant towards intention to use M-wallet.

On the other hand, fit indices of the structural model were fit as CFI-0.928; TLI-0.977; GFI-0.924; AGFI-0.946; RMSR-0.032 and RMSEA-0.038 were within the acceptable limit (see Figure 2).

Discussions

Current work authenticated insignificant relationship of perceived enjoyment with tourists’ behavioural intentions to use M-wallet for M-payment, findings are consistent with Lee et al. (2007) and Chong (2013). Hence, it revealed that tourists would not use the M-wallet even if they enjoyed it in their daily life.

Thereafter, another factor, namely compatibility with M-wallet has significant influence on BI. Crabbe et al. (2009), Yang (2007) and Chong (2013) also concluded that the compatibility with M-wallet would attract more tourists to use them. Tourists’ compatibility with M-wallet would directly influence by the user friendliness with M-wallet.

Besides this, perceived values positively affect BI (Venkatesh et al., 2012). If a tourist perceives that he is getting financial, social and product-associated values, he or she would have the intention for online transaction. Besides this, social influence is one of the determinants of behavioural intentions to use M-wallet (Chong, 2013; Riquelme & Rios, 2010). People use M-wallet in the case of their social status affect or may have recommendation from their friends or relatives (Schierz et al., 2010; Venkatesh et al., 2012). Trust positively influences M-payment behavioural intention which has identified in past research too. Therefore, tourists’ positive perception is required with respect to security, privacy policies, transactions and promises (Shin, 2009; Wu & Wang, 2005; Xin and Liu, 2013). Further, cell phones have personal information, that is, pictures, contacts, etc., and at the time of making any transaction, user considers risks of security and privacy. Hence, trust is one of the essential components leading towards constructive behavioural intention of tourists.

Implications of the Study

Tourists’ digital payment becomes easier after adoption of Mobile wallet, it eliminated all hurdles such as holding money, plastic cards, bank visit and physical transfer of money. As an outcome, tourists consider that M-wallet is the best substitute of hard cash payment mode.

Besides this, understanding the diverse factors of behavioural intentions can facilitate M-wallet players in enhancing the adoption of digital payment. Effective strategies can be design by M-wallet after having information about tourists. Hence, the framework was proposed based on TAM, UTUAT and UTUAT2 for the establishment of good amount of variance.

The exploration of influencing factors in the study provides further insights into why tourists take different decisions and choose different M-wallet application. Besides this, pragmatic researches that include tourists’ attitude towards Mobile payment are limited. This information could be used for the development of a theoretical model towards understanding of tourists’ behavioural intention with respect to M-wallet. In this study, researchers investigate the influence of each dimension on behavioural intention. Hence, Structural Equation Modelling was run through AMOS 20 to analyse the effect of all five factors. It revealed that out of five factors, four factors, namely COMP, PERV, SOCI and TRUS influenced behavioural intention of tourists. While PERE is not significantly affecting BIs while using M-wallet for M-payment.

Furthermore, the findings could be employed by M-wallet players to provide opportunities to develop and examine new construct. The result reveals intention to use dimensions that tourists view as important and eliminate the need for mere assumption by management. Furthermore, the results provide important information to M-wallet players in developing suitable strategies at their respective levels.

Limitations and Future Research

First, it confines to only M-wallet players although there are other channel of digital payment, hence future studies may include internet banking, telebanking, etc. Second, other factors such as culture, personality and psychological traits have not been taken into consideration, future studies may comprised these dimensions. Third, this was a quantitative analysis; further research can be pursued in qualitative way to uncover the major factors which influence uses of M-wallet. Fourth, respondents encompassed of only active M-wallet users, future studies can include non-users. Fifth, results may not be generalized to other age groups due to focused on youth tourists between the age group of 18 and 25, diverse age group can be include in future research.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

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