Use of mobile wallet among consumers: underlining the role of task-technology fit and network externalities

Abstract

Research on mobile payments is emerging as a separate research
area in information systems. Previous literature indicates that existing theoretical models do not extend the empirical inquiry into technology fitment domain to understand the user behaviour better. Integration of users’ requirement of technology, with network externalities, risk, trust, and promotion, was done to check mobile wallet usage of Gujarat’s users. To empirically test the hypothesised model, a total of 500 samples were collected using online and offline structured survey. Structural equation modelling technique was performed. The findings of the study indicate that all the hypotheses were supported and it has theoretical and managerial implications.

Full text

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nt. J. Business Information Systems, Vol. 37, No. 4, 2021
Copyright © 2021 Inderscience Enterprises Ltd.
Use of mobile wallet among consumers:
underlining the role of task-technology fit
and network externalities
Chinmay Baxi*
Faculty of Management Studies,
V.M. Patel Institute of Management,
Ganpat University,
Email: chinmay.baxi@gmail.com
*Corresponding author
Jayesh D. Patel
V.M. Patel Institute of Management,
Ganpat University, India
Email: Jayesh.patel@ganpatuniversity.ac.in
Email: jayesh.jd@gmail.com
Abstract: Research on mobile payments is emerging as a separate research
area in information systems. Previous literature indicates that existing
theoretical models do not extend the empirical inquiry into technology fitment
domain to understand the user behaviour better. Integration of users’
requirement of technology, with network externalities, risk, trust, and
promotion, was done to check mobile wallet usage of Gujarat’s users. To
empirically test the hypothesised model, a total of 500 samples were collected
using online and offline structured survey. Structural equation modelling
technique was performed. The findings of the study indicate that all the
hypotheses were supported and it has theoretical and managerial implications.
Keywords: mobile wallet; network externality; structural equation modelling;
SEM; task-technology fit; TTF.
Reference to this paper should be made as follows: Baxi, C. and Patel, J.D.
(2021) ‘Use of mobile wallet among consumers: underlining the role of
task-technology fit and network externalities’, Int. J. Business Information
Systems, Vol. 37, No. 4, pp.544–563.
Biographical notes: Chinmay Baxi is a Research scholar at Ganpat University,
V.M. Patel Institute of Management Studies, Faculty of Management Studies.
He is an Assistant Professor at V.M. Patel College of Management Studies,
Gujarat India. His research domain is information system and technology
adoption.
Jayesh D. Patel is an Associate Professor at Ganpat University, V.M. Patel
Institute of Management, MBA Department, Gujarat, India. He has published
research papers in the Journal of Cleaner Production, Marketing Intelligence
and Planning, and other reputed journal in the domain of marketing. He is a
recipient of ‘Journal of Service Management: 2015 Highly Commended
Award’ by Emerald Literati Network. He is an ad-hoc reviewer of JBR, JJRCs,
MIP, IJoEM, JCP, and APJML (Emerald Publishing).

Use of mobile wallet among consumers 545
1 Introduction
In the recent past, the payment mechanism has changed from coins to paper money to
plastic cards (Shah et al., 2016). The digital payment system is making the big wave for
the retail market and especially the mobile wallet has gained attention more in all
economies (Kumar and Venkatesan, 2019). Mobile wallet is a technology which resides
in the mobile phone or PDA of a user, which enable users to make the payment as an
alternative to the conventional payment method (Kumar et al., 2019). Penetration of
mobile phone has increased the availability of ‘mobile wallet’ among users. Such
penetration has increased the number of players who can provide mobile wallet services
to consumers.
As per the report from CIOL.com, “Wallet365.com was considered to the first mobile
wallet of India.” Wallet365.com was launch in the year 2006. As on today, approximately
more than 50 mobile wallet service providers are available in India. Increased number of
mobile wallet service providers is one of the indicators, which shows that the adoption of
a mobile wallet is increasing and mobile wallet industry is on the path of achieving its
‘critical mass’ becoming cashless (Venkatraman, 2008). Despite the growing pace of
mobile wallet adoption, does adoption has boosted the usage of the mobile wallet or what
are the factors which drive the usage of mobile wallet are the key research questions.
In the literature of technology adoption from an information systems perspective,
three theories are widely used to check the adoption drivers. These theories are TAM,
UTAUT and IS success. These theories emphasised in adoption, not on the use of
technology. Mobile wallet is a facility which falls under voluntary user, in a similar
context UTAUT was empirically tested and affirm the applicability of UTAUT in the
voluntary environment (Maity et al., 2019). In order to check the use of technology, a
task-technology fit (TTF)-based theoretical model is used along with other constructs
from different theoretical models (Klopping and McKinney, 2004; Shih and Chen, 2011;
Parkes, 2013). Mobile wallet adoption is in the process of reaching its critical mass,
therefore it is important to integrate and theorised network externalities construct with
TTF.
Use behaviour is the central point of research across information systems researchers.
Studied conducted to check the behaviour intention which influences the use behaviour
indicated that, behaviour intention should lead to use behaviour (Fishman et al., 2012).
Earlier studies of technology adoption largely check the behaviour intention to adopt
technology, as there are intended toward knowing driving factors for technology adoption
(David, 1989). Simply, behaviour intention can be a driving factor toward that
individual’s actual behaviour (Shin, 2009). It is argued that the intention to use a system
can largely explain a portion of a user’s actual system usage (Venkatesh et al., 2003).
Due to the growing popularity of mobile wallet, it is very interesting to understand
the effect of user networks using network externalities (Lin and Bhattacherjee, 2008) on
the use behaviour. Considering IS adoption behaviour, user network plays a critical role
in driving the adoption forward. However, the extent that IS researches has paid the
attention to network externalities and its size is under investigation (Sarkar and Khare,
2018; Grover and Kar, 2018). Therefore, the purpose of the study is to investigate the
extent of influence of TTF and network externalities on mobile wallet use behaviour.
The remaining part of the paper organised as follows. The research background,
theoretical foundation, and hypothesis development is presented in Section 2, and

546 C. Baxi and J.D. Patel
research methodology is presented in Section 3 followed by data analysis using structural
equation modelling (SEM) (Section 4). Discussion, implications, and limitations are
presented in Section 5 and Section 6.
2 Research background and theoretical foundation
With an increased number of technologies available to perform the task create the
confusion and may not provide desired results to the individual. Aligning the technology
with the task required ‘fitment’ between technology and task, which generate desired
output. Existing literature provided empirical validation of how such fitment affects the
performance of the individual (Lu and Yang, 2014; Tam and Oliveira, 2016).
2.1 Task-technology fit
TTF is referred to as a match between users’ task needs of technology and the available
functionality of the technology. TTF application focuses on the outcome of individual
performance which attributed to the actual use of technology. Goodhue and Thompson
(1995) opined that users will use that technology which suffices their requirement to
carry out their task efficiently and generate the desired output. Actual use of information
system greatly depends on the fitment between task and technology (Goodhue and
Thompson, 1995; Oliveira et al., 2014). On dimensionality perspective, Parkes (2013)
explained that TTF considers two dimensions: task characteristics and technical
characteristics which create task-technology fitment having an impact on utilisation and
performance of individuals.
According to Osang (2015), fitment between TTF is one of the key success factors
which predict the use behaviour. “Utilization is a complex outcome, based on many other
factors besides fit (such as habit, social norms, and other situational factors), the fit model
can benefit from the addition of this richer understanding of utilization and its impact on
performance” [Goodhue and Thompson, (1995), p.5]. Limited view of TTF restricted the
predictive ability of theoretical models which explain the use of technology (Lu et al.,
2014).
TTF was tested for e-commerce adoption among undergraduate students of the USA
to increase the explanatory power of the model which has a specific impact on the ease
of use (Klopping and McKinney, 2004). TTF influenced the use of location-based
information system, and also the performance of users (Junglas and Watson, 2008).
Balance fit between the user’s task and technology has a positive impact on performance
and imbalance fit between task and technology will have a negative impact on
performance (Parkes, 2013). TTF was integrated with TAM to check the m-commerce,
the study found that TTF positively influences the behaviour intention to adopt
m-commerce (Alqatan et al., 2017) is one of the in mobile application context, Alqatan
et al. (2019) integrated TTF with acceptance of m-commerce application used by tourism
firms. Similarly, it was believed that performance expectancy of mobile wallet use is
significantly influenced by TTF. Based on this, it is hypothesised that:
H1 TTF influence use behaviour of mobile wallet consumers.

Use of mobile wallet among consumers 547
2.2 Network externalities
Theory of network externalities proposed by Katz and Shapiro (1985) was originally
rooted in economics and social sciences. This variable was tested widely in marketing
and information systems literature. Network externalities capture how consumers get an
advantage when the adoption of product or behaviour under consideration is increased
from a few users to masses. Considering the wide penetration potential, companies are
more motivated to provide new features, functionality and value-added services. Mobile
wallet is one such service or behaviour which is in the process of reaching the mass of
people, and therefore this variable received great attention to include in understanding
behavioural formation.
Kats and Shapiro (1985) operationalised network externalities as a combination of
referent network size and perceived complementary of that product and services,
capturing direct and indirect mode of measurement. Direct network externalities derived
value form the number of users who consume the same product or service. Indirect
network externalities derived the value to consumer from the adoption of compatible
product or services by the consumer. Srinivasan et al. (2004) have said that network
externality plays an increasingly important role in the economy, and it has significant
implications on the firm’s marketing strategies. The finding of study contributes to
building the new dimension of in theory of marketing strategy. The literature of
marketing consistently found that network externality can alter the consumer behaviour
(Economides, 1996; Strader et al., 2007; Zho and Lu, 2011; Qasim and Abu-Shanab,
2016).
The literature further indicated that network externalities impact market structure
(Frels et al., 2003). The literature on information systems has empirically tested
network externalities to examine behaviour intention and user behaviour. An empirical
investigation of inter-organisational blogs argued that network externalities affect user
adoption and use of technology (Wattal et al., 2010). A study conducted to check the
instant messaging behaviour used network externalities as a core construct and found
significant predictability in behaviour intention of users (Lin and Bhattacherjee, 2008).
Denmark-based Carton et al. (2012) study concluded that network externalities play a
major role for users to adopt online payment. Strader et al. (2007) examined the use of
behaviour of consumer for electronic communication. A recent study conducted to check
the user satisfaction for using a mobile application and found that network externalities
have a significant influence on over the user’s perceived benefit (Hong et al., 2017).
Research conducted in China measuring the usage intention and user satisfaction for
mobile internet messaging has found that network externalities influence the usage
intention significantly (Kim et al., 2017). Based on the above mention literature, we are
proposing:
H2 Network externalities influence the use behaviour of mobile wallet consumers.
2.3 Trust
In the literature, trust has been defined from multiple perspectives (Lee and Kim, 1999).
A general understanding of trust is when people show faith or belief in others and adopt
trusting stand toward others (McKnight and Chervany, 2001). Disposition of trust is
people’s general tendency to trust someone is an indication of his/her personality trait

548 C. Baxi and J.D. Patel
(Hsiao et al., 2010) when they have higher confidence, reliability, and integrity among
themselves (Morgan and Hunt, 1994). Trust also played a central role in exchanging
relationships involving unknown risks (Jarvenpaa et al., 1999; Gefen et al., 2003).
According to Alzaidi and Qamar (2018), trust is a significant factor for mobile banking
adoption.
In the domain of information systems, trust has been widely studied construct (Mayer
et al., 1995). Trust is significantly associated with the consumer’s intention to adopt
mobile banking and mobile commerce (Lee and Dawes,, 2005; Kim and Jones, 2009). A
study conducted to check the behaviour intention to adopt e-commerce in the B2C
segment has found that the trust holds 34%–37% variance of consumer intention to
purchase a product online (Gefen et al., 2003). Trust became an inherent factor for
mobile banking users as mobile banking has an inherent element of risk (Kim and
Hwang, 2012; Nor and Pearson, 2007).
Trust was further investigated by various researchers from various contexts (Teo
et al., 2012; Chandra et al., 2010). Perceived trust in the payment systems critically
impacts the behaviour intention to adopt mobile payment (Teo et al., 2015; Yang et al.,
2015). For mobile payment services, trust was the most important predictor of behaviour
intention (Thatcher and Patel, 2011; Zhou, 2014).
A study conducted to check Vietnamese consumer’s behavioural intention to use
mobile payment has to use two most prominent theoretical modal TAM and TPB. The
researcher has added the trust construct into the conceptual model to check weather trust
influence the behaviour intention or not. The study has found that perceived trust is the
strongest influence on behaviour intention to adopt mobile payment (Cao et al., 2016;
Shaw, 2014; Chatterjee and Bolar, 2019; Chopra, 2019). In fact, Matemba and Li (2018)
showed that trust was a key predictor of willingness and use of consumers for WeChat
Wallet – people to people services. Based on the above mention literature, we are
proposing that:
H3 Trust influences the use of behaviour of mobile wallet consumers.
2.4 Risk
Risk measures the beliefs of the uncertainty regarding possible negative consequences.
Risk in online transaction context is feeling insecurity in making payment. It is a
multi-dimensional construct, there are five types of risk which are identified on the
domain mobile banking context (Chen, 2013). The risk associated with a product or
service has gained significance in consumer research on innovations (Mitchell, 1999;
Lim, 2003; Schierz et al., 2010). Security-related concern is one of the factors which
negatively effect the adoption of mobile banking (Alzaidi and Qamar, 2018). Privacy
related concerns are one of the major factors among users of electronic users. Electronic
services are characteristically more challenging to use thus perceived as more risky
(Mitchell, 1999; Gefen et al., 2003). Mobile payment is involved in the financial
transaction so consumers feel the risk of making a loss or losing money while making a
digital transaction (Bauer et al., 2005). A study conducted by Lu et al. (2011) found that
risk has a negative impact on user behaviour. Similar findings were reported in various
studies conducted in China, Brazil and Bangkok (Cruz et al., 2010; Sripalawat
et al., 2011; Yao and Zhong, 2011) reported by Thakur and Srivastava (2014). Specific to

Use of mobile wallet among consumers 549
m-wallet, the risk was also found to be negatively related to use behaviour (Singh et al.,
In press). Based on the above mention literature, we are proposing:
H4 Risk negatively influences use behaviour of mobile wallet consumers.
2.5 Promotion
Over the last few years, promotion construct is being studied extensively, which suggests
that promotion has a positive impact on behaviour intention to use the product or service.
Kotler (2009, p.661) defined promotion as “a diverse collection of incentive tools,
mostly, short-term, designed to stimulated the quicker and greater purchase of particular
product/service by consumers.” In marketing literature, cash-based promotion and
product-base promotion were the main approaches to stimulate behaviour (Chen et al.,
2012). The mobile wallet provides the cash-based promotional offer as it is related to the
financial transaction of the consumer. A study conducted to understand consumer
behaviour in web-based commerce found that promotion plays an influential role in
consumer behaviour (Koufaris et al., 2001). The similar study argued that consumer
purchases a product because of promotional offers on that product (Koufaris et al., 2011).
The promotion also influenced the buying behaviour of consumer across all gender and
all age group (Chen et al., 2012). Based on the above mention literature, we are
proposing:
H5 Promotion influence use behaviour of mobile wallet consumers.
Based on the above discussion, a research model was proposed consisting use behaviour
as the dependent variable and TTF, network externalities, trust, risk and promotion as
independent variables (Figure 1).
Figure 1 Research model
H
5
H
4
H
3
H
2
H
1
Use
behaviour
Task-technology
fit
Network
externalities
Risk
Trust
Promotion

550 C. Baxi and J.D. Patel
3 Research methodology
3.1 Instrument development process
Ideal sample for this study was consumers who have installed the mobile wallet. The
scale items for the construct TTF was adopted from TTF construct we adopted from
Klopping and McKinney (2004), and Larsen et al. (2009). The scale of user behaviour
was adopted from studies of Venkatesh and Bala (2008) and Venkatesh et al. (2012).
Scale for network externalities were adopted from Lin and Lu (2011) and Kim et al.
(2017). Items from the trust construct were adopted from Gefen et al. (2003) and Wang et
al. (2015) studies. All items of risk were adopted from Featherman and Pavlou (2003)
and Martins et al. (2014). All items for promotion construct were adopted from Yoo et al.
(2000). Sequence and wording of items were changed to capture contextually of study.
All items were measured on five-point Likert-type scale ranging from 1 to 5, where 1 is
‘strongly disagree’ and 5 ‘strongly agreeing’. There were four demographic questions
(age, gender, education and occupation) included in this structured questionnaire. Age
was measured in years. Gender was measured on a nominal scale where 1 represented
male and 2 represent female. Behaviour intention responses were captured on an actual
basis. A pilot study was carried out to among 20 users to get the input from the
respondents. Changes sought after the pilot study was amended in the instrument for the
analysis sample.
Table 1 Sample characteristics (n = 450)
Variable Frequency Percentage (%)
Gender
Male 257 57
Female 193 43
Age (years)
18–24 299 66
26–34 93 21
35–40 35 8
Above 41 23 5
Education
Up to schooling 135 30
Graduation 161 36
Postgraduation and above 154 34
Occupation
Student 311 69
Government service 28 6
Private service 62 14
Business 26 6
Self-employed 20 4
Pensioner and others 3 1

Use of mobile wallet among consumers 551
3.2 Sample selection
Data were collected through an online and offline survey in the state of Gujarat. After
removing the missing values from the respondents using series mean analysis, a total of
450 usable samples were obtained for further analysis. The overall response rate was well
within the acceptance range for both techniques (Zikmund et al., 2017). Table 1
demonstrated the sample composition used in this study. There were 435 female
respondents and 57 were male respondents. Age wise, 66% of the respondents (n = 299)
were between the age group of 18–24 years, and 21% were between the age group of 26–
34 years. Overall, all the participants were between 18 and 56 years of age. Considering
the education of respondent, 30% of respondents opted to school education, 36% of
respondents were graduates, and the remaining 34% of the respondents were
postgraduates. Occupation wise, 69% respondents were students, 14% of respondents
working in the private sector, 6% were government employee, 6% had their own business
and remaining and 5% were self-employed.
4 Data analysis
According to Oliveria et al. (2016), SEM is a technique for estimating causal relations
applying a combination of statistical data and qualitative causal hypothesis. Earlier
researchers have recognised the potential of SEM in distinguishing measurement and
structural models, and taking measurement error into consideration (Henseler et al.,
2009). AMOS version 20 was used to perform SEM as it gives multiple indicators of
latent variables (Schierz et al., 2010). The statistical tool SPSS 22.0 was used to evaluate
the reliability and validity of the study construct. By using the confirmatory factor
analysis (CFA), effective questionnaire items were chosen for their strong reliability and
validity in this study.
4.1 Reliability and validity
To assess the reliability of the scale, it was important to compute Cronbach’s coefficient
alpha and the computed values were all above 0.7, showing the reliable measurement of
scales in the instrument. Further to assess the validity of the construct, CFA was carried
out on TTF, network externalities, promotion, and trust, risk and use behaviour to check
convergent and discriminant validity. Table 2 indicated the result of reliability and
convergent validity. Standardised loading of all the variables was significant (p < 0.05)
and above 0.5. The average variance extracted values of each construct were above 0.5
and composite reliabilities were above 0.7 that indicates the establishment of construct
validity (Fornell and Larcker, 1981). Table 3 indicates that the square root of AVE values
shown in diagonal were more than inter-construct correlations. This was evidence
showing discriminant validity was established (Fornell and Larcker, 1981).
CFA was carried out to check the validity of the measurement model. In each stage,
the maximum likelihood estimation (MLE) method was employed (Byrne, 2001). All the
indicators of model were having acceptable values (χ2 = 1,214.60, χ2 / df = 2.525,
df = 481, p < 0.001, GFI = 0.861, AGFI = 0.838, TLI = 0.907, CFI = 0.915 and RMSEA
= 0.058) (Han et al., 2011). We established quality and adequacy of measurement

552 C. Baxi and J.D. Patel
through CFA by ensuring reliability, convergent and discriminant validity, the causal
relationships in the structural model were further tested through path modelling.
Table 2 Scale reliabilities
Variables Items Standardised estimates
Cronbach
α
CR AVE
Task-technology
fit
TTF5 0.73 0.89 0.90 0.56
TTF6 0.69
TTF7 0.74
TTF8 0.81
TTF9 0.81
TTF10 0.77
TTF11 0.67
Network
externalities
NE2 0.73 0.80 0.80 0.51
NE3 0.75
NE4 0.70
NE5 0.67
Promotion PRO1 0.71 0.88 0.88 0.60
PRO2 0.86
PRO3 0.87
PRO4 0.82
PRO5 0.59
Trust TR1 0.80 0.83 0.89 0.57
TR2 0.84
TR3 0.87
TR4 0.73
TR5 0.67
TR6 0.54
Risk RI1 0.71 0.86 0.91 0.59
RI2 0.80
RI3 0.80
RI4 0.81
RI5 0.79
RI6 0.69
RI7 0.70
User behaviour UB1 0.76 0.89 0.89 0.66
UB2 0.84
UB3 0.86
UB4 0.79

Use of mobile wallet among consumers 553
Table 3 Discriminant validity of scales
Construct UB PRO TTF TRUS NET_EX RISK
Use behaviour (UB) 0.815
Promotion (PRO) 0.269 0.777
Task-technology fit (TTF) 0.527 0.411 0.749
Trust (TURS) 0.494 0.343 0.612 0.753
Network externalities (NET_Ex) 0.428 0.360 0.501 0.473 0.712
Risk (RISK) –0.306 –0.070 –0.346 –0.349 –0.219 0.766
4.2 Testing of the structural model
The structural model was estimated on all independent variables (performance
expectancy, effort expectancy, facilitating condition, social influence, hedonic
motivation, TTF and cost) with use behaviour. Proposed hypothesis depicted in the
structural model (Figure 2) was tested using SEM technique in the AMOS. MLE was
used to estimate the coefficients. Standardised path coefficients were measured using
model fit indices. Model fit indices were χ2 = 1,062.62, χ2 / df = 2.237, p < 0.001,
df = 475, GFI = 0.877, AGFI = 0.855, TLI = 0.925, CFI = 0.932 and RMSEA = 0.052,
meeting the acceptable limits. This indicates that all the hypothesised structured model
has an acceptable fit (Han et al., 2011).
Figure 2 Structural model
R
2
= 0.36
NS
–0.10*
0.20*
0.17*
0.29*
Use
behaviour
Task-technology
fit
Network
externalities
Risk
Trust
Promotion
Notes: *p < 0.01.
: Significant.
: Not significant.

554 C. Baxi and J.D. Patel
The model’s explanatory power was 36% indicating that five independent variables were
able to explain 36% variation in use behaviour. TTF, network externalities, trust, and risk
were found to be statistically significant in explaining use behaviour. Table 4 outlined the
standardised coefficient estimates and concerned probability values. Findings pointed out
that relationship between TTF and use behaviour (
β
= 0.29, t = 4.545, p < 0.01), between
trust and use behaviour (
β
= 0.20, t = 3.108, p < 0.01), network externalities and use
behaviour (
β
= 0.17, t = 2.82, p < 0.01) was found positive and significant. Moreover,
the relationship between risk and use behaviour (
β
= –0.1, t = –1.964, p < 0.01) was
found negative and statistically significant. While the promotion was found to be a
non-significant predictor of use behaviour (p > 0.05).
Table 4 Standardised coefficients and t-value of model
Paths Coefficients (
β
) t-value Hypothesis supported
TTF → UB (+) 0.29 4.454* Yes
Network externalities → UB (+) 0.17 2.822* Yes
Trust → UB (+) 0.20 3.108* Yes
Risk → UB (–) –0.10 –1.964* Yes
Promotion → UB (+) 0.00 –0.062 No
Note: *p < 0.05.
5 Discussion and implications of the study
This study aimed to develop the theoretical model to understand the use behaviour of
mobile wallet consumers. As mentioned by Maruping et al. (2017), theory building
process in the domain of information system required new direction by incorporating
theories from the different domain. As a part of the model development process, the TTF
model was extended by integrating network externalities, promotion, trust and risk. The
study attempted to test two dominant theories from two different domains empirically.
We have added theory of externalities which is the dominant theory of economics with
TTF by extending the studies such as Liu et al. (2014) and Yuan et al. (2019). The model
was well supported by data. The results of the study found that all these constructs (TTF,
network externalities, trust and risk) predict use behaviour for mobile wallet consumers
except promotion.
One of the major contributions of the study to IS research is extending the TTF by
providing a theoretical model which exclusively predicts use behaviour directly. Earlier
findings are restricted toward to predict the behaviour intention (citation). The second
contribution is to integrating two most important construct into the TTF and provided
modified TTF which predict the use behaviour instead of behaviour intention. The third
contribution of this paper responds to the call for a new direction for information research
(Venkatesh et al., 2016a). A general contribution is that this paper served an example of
integrating construct of other dominant streams of theories, and contributes to the
development of theory in the domain of information system. General contribution, the
model will provide a new direction to expand the TTF model as a future model to
measure the use of behaviour and it can become the dominant model in IS research.

Use of mobile wallet among consumers 555
In the domain of information system research TAM, UTAUT-based research model
has reached to its peak, considering the trend, new direction of research is required as
suggested by Venkatesh et al. (2016b), the author argues that there are eight new
dimensions in which theory building in information systems reach should focus. One of
the dimensions is to find a new construct which explains adoption and use of technology.
The second dimension was to integrate theories of other domain to expand the body of
knowledge of information system. Considering the above-mentioned argument, we have
provided a new theoretical model which provides gives direction to information systems
theory building.
The current study is consistent with the results of Tam and Oliveira (2016), who
established the relationship with TTF and use behaviour have found that it plays a
positive and important role in improving the performance of the individual. Our study
finds that mobile wallet providers need to works on understanding the task related
requirement which improve the performance of the individual (Khan et al., 2018). The
government initiative of digital India has understood the needs of consumers and
provided the needed support by launching a mobile wallet like BHIM. Users will not
use those technologies which will not improve the performance or effectiveness of
technology (Yadegaridehkordi et al., 2016).
For example Paytm and phone pay, both are the dominant player of mobile wallet,
phone pay have integrated QR code which provides convince to user to pay directly from
the users bank account, so that users do not need to worry about keeping money in the
mobile wallet and while in the case of Paytm these feature is not integrated fully.
Trust was another construct which shows a positive impact on user behaviour, similar
to earlier studies in IS research (Shaw, 2014; Chatterjee and Bolar, 2019; Chopra, 2019).
If consumers do not have any feeling of insecurity, they can trust the mobile wallet
services providers more. M-wallet companies can enhance the trust perception by
providing the correct information about their transaction on time, which increase
continuous usage of the mobile wallet.
In the current study, the risk is negatively influencing the use behaviour, as trust is
already built so the feeling of insecurity is gone. Similar findings are reported by Shaw
and Sergueeva (2019) and Dwivedi et al. (2017). Mobile wallet captures the financial,
personal and location-specific information might increase the vulnerabilities to users,
which has created a negative impact on mobile wallet use.
Network externality is one of the strongest predictors of mobile wallet adoption, it
indicates that network externalities exist in the mobile wallet use, and users can avail
more benefit of it. These findings are consistent with Kim et al. (2017), Xiao et al. (2018)
and Lepoutre and Oguntoye (2018). The direct influence of network externalities on use
behaviour can be enhanced through the perception of the availability of various options
or services. The government also can introduce many wallets to increase availability. In
fact, the size perception of network externality is enhanced by offering complementary
services through m-wallet. Recently, we can observe the impact of network among all the
mobile wallet services provider, all the service provider has adopted common QR code,
and value-added services like booking of bus, train, hotel, other services and loyalty
program. Transferring cash back benefit to existing users for adding more users to the
same mobile wallet service provider network is another example of network externalities.
Promotion construct was failed to influence the mobile wallet usage which shows
that, consumer use the mobile wallet as there is financial or product-based rewards as the

556 C. Baxi and J.D. Patel
main purpose for using mobile wallet is financial and fund transfer transaction. A
significance level of promotion construct is not within the statistical expected level but it
provides proof that promotional scheme from the service provider of mobile wallet has
not played an important role, to influence use behaviour of mobile wallet users. In other
word, mobile wallet service provider needs to make necessary changes in their existing
promotion scheme.
Result of the study provides insight into the mobile service provider in formulating
their marketing strategy which will improve the use of mobile wallet among consumer.
As the number of mobile wallet service provider increased, they can provide they can
switch from basic services to value-added services like banking and financial services,
and other services like online shopping (shopping mall from Paytm) such initiative,
which will drive the use of the mobile wallet.
6 Limitations and future scope of the study
Apart from trust and risk, very important construct called promotion and two dominant
theory which are added to have the future direction to develop the TTF model to the next
level and developed the fully comprehensive model which predict the use behaviour of
consumers rather than the behaviour intention. The first limitation of the study is the
model does not contain any moderator. The second study is restricted towards the users
of Gujarat so, the generality of the study to another context of research is an issue of
external validity. The model might have omitted a few constructs from the use behaviour
perspective. Important link to the future research is this model can become the base
model for the future research in the domain of IS which check use behaviour which links
the task and technology and adds other collaborative constructs. Future research can be
carried out by adding moderators such as demographics, habit, etc. which will improve
the explanatory power of the model.
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Appendix
Promotion
• Cash back is provided by mobile wallet.
• The discount scheme is offered by mobile wallet.
• Promotion and gift schemes are offered by mobile wallet.
• Special offers are provided by mobile wallet.
• Rewards are offered by mobile wallet.
Task-technology fit
• I can quickly make payment at any time using a mobile wallet.
• Mobile wallet services are real-time (immediate).
• The mobile wallet gives a message of payment.
• The mobile wallet gives a message of balance.
• I can pay for mobile recharge using a mobile wallet.
• I can book a movie ticket using the mobile wallet.
• I can shop from mobile wallet.
• I can pay for electricity, LPG, etc. bill from mobile wallet.
• I can transfer money to friends/relatives using a mobile wallet.
• I can transfer money from bank account to mobile wallet.
• I can transfer money from mobile wallet to my bank account.
Trust
• Mobile wallet provides correct information.
• I can trust the mobile wallet.
• Mobile wallet is reliable.
• Mobile wallet is secure.
• Mobile wallet is created to help the consumer.
• I have installed well known the mobile wallet.
• I have installed the well reputed mobile wallet.

Use of mobile wallet among consumers 563
Risk
• It would be risky to use a mobile wallet.
• I may lose my money If I use a mobile wallet.
• I may lose my personal information If I use the mobile wallet.
• I may lose my banks account details if I use the mobile wallet.
• Chances of fraud will increase if I use the mobile wallet.
• I might be overcharged if I use the mobile wallet.
• In case of any error occurred, I may lose my money.
• I am not using mobile wallet because of the risk associated with it.
Network externalities (more and more people will use)
• Many people are using the mobile wallet.
• In the future, more and more people will use the mobile wallet.
• More than one mobile wallets are available.
• In the future, few more companies will provide mobile wallet.
• Currently, most of my friends have a mobile wallet.
• In the future, more friends and family members will have a mobile wallet.
Use behaviour
• Currently, I am using mobile wallet.
• I will keep using the mobile wallet in future also.
• I will suggest my friends to use the mobile wallet.
• I will suggest my family members to use the mobile wallet.