Consumers
Acceptance towards
NFC Mobile
Payments
A research conducted by a Business Information System 2013-‐2014 student of the University of Amsterdam for his Master Thesis
Student: Shailin Mohan 100078754 Supervisor: Dick Heinhuis Second Assessor: Tom van Engers
………... ………... ………...
Date: 27th of August
1. Introduction
Mobile payments is a combination of payment systems with mobile devices and services to provide users with the ability to initiate, authorize and complete a financial transaction over mobile network or wireless communication technology (Chandra et al., 2010). The boundless invasion of mobile devices and its
closeness to the user, with the far-reaching specification of the devices, makes them applicable for many payment scenarios and even for carrying everything that would fit into a wallet. This can provide the mobile network operators the opportunity to develop a new business model and increase their revenues (Chen, 2008). Mobile payments consist of many types of payments, like payment via a mobile app (called MyOrder), bank transfer via a mobile app, payment by sms etc. However this research will focus on near field communication (NFC). NFC is a two-way, short-range communication method. This method facilitates the transaction between two devices, the mobile devices and the payment terminal, when in close range of each other. The transactions can provide service
providers information on the consumers’ preferences, which can be used to offer personalized discounts, coupons etcetera.
Nowadays large international companies also make use of NFC mobile payments (Slade et al., 2014). For example, Google has the GoogleWallet and MasterCard with Samsung’s Smart Ticket app. Also on a national level in the Netherlands companies are more focusing on NFC mobile payments: Vodafone recently introduced the SmartPass and Rabobank has the MyOrder Cashless payment. Despite the investment of the providers made, worldwide adoption of NFC has been very low (Gartner, 2013). This proposethat NFC mobile payments
providers need to better understand the stimulators of consumers acceptance of NFC mobile payments to adjust their strategies according to consumer needs (Schierz et al., 2010). In addition, foreign business models of mobile payment cannot directly be applicable to different cultural contexts due to the different market constraints in terms of economic, technology and social aspects. NFC mobile payment adoption in the context of the Netherlands, where to date no similar research has been undertaken, is also important.
NFC mobile payments have a number of advantages, because of the ubiquity of the device, there is no fuss about bankcards, receipts and tickets; everything is available on the mobile device. Also the payment itself will be quicker and the
extra information, such as account balance, will be easier to see. However, there are uncertainties and risks involved due to the vulnerability of both the devices and the network to hacker attacks (Zhou, 2014). For example, the public transportation card (in Dutch: OV-chipkaart) in the Netherlands also works with NFC technology. This card was hacked shortly after it’s release (the card could be upgraded for free). The above-mentioned advantages and risks can influence the acceptance of NFC mobile payments.
In search for the factors, which affects the acceptance of NFC payments, several acceptance models and theories are available for research. For example,
Diffusion of Innovation (Rogers, 1962 & 1995), Theory of Planned behavior (Ajzen, 1991), Technology Acceptance Model (Davis, 1989) and the Unified Theory of Acceptance and Use of Technology (Venkatesh et al., 2003 & 2012). Most of these acceptance models are focused on the employee acceptance towards technology in a workplace context. However the focus of this research is the consumer’s acceptance of the use of technology for mobile payments. For the consumers focus, the Unified Theory of Acceptance and Use of Technology will be well suited for this research, because the model focuseson the consumer acceptance of technology. This model consists of constructs from different models on acceptance of technology. Moreover the construct selection is based on the consumers perspective and needs of the user of today.
Taking the above in consideration, the following research question will be central in this research:
“What factors influence the intention to use NFC mobile payments by consumers in the Netherlands?”
The research question will be supported by the following sub questions: - Which models/theories are available for technology acceptance? - Which model/theory is well suited to determine the factors, which
influence the intention to use NFC mobile payments?
- Which factor is the strongest for acceptance of NFC mobile payments? The remainder of the research is organized as followed:
Chapter 2 will describe the technology acceptance models. First, the models that are related to the UTAUT. Secondly, the UTAUT model will be described. Finally, the UTAUT2 model, which is an extension of the UTAUT, will be described.
Chapter 3 will describe the methodology of this research. First, the hypothesis will be described. Secondly, the conducted survey will be described.
Chapter 4 will present the results of the survey and research. First, demographic information about the respondents will be described. Secondly, a factor analysis will be conducted. Thirdly, the reliability of the results will be tested. Fourthly, a regression analysis will be conducted. Finally, the multicollinearity will be checked amongst the variables to test whether they measure redundant information. Chapter 5 will present the discussion section where explanation will be discussed on topics.
Chapter 6 will present the conclusion with the future research.
2. Theory
2.1 Literature review
Studies on the user’s acceptance of technology mobile payments are quite extensively. There have been developed several models and theories for
technology acceptance: Diffusion of Innovation (DOI), Theory of Planned Behavior (TPB), Technology
Acceptance Model (TAM) and Unified Theory of Acceptance and Use of
Technology (UTAUT). These models are selected from literature of studies on mobile payments. The following section will describe the models and one will be suitable for investigate the factors that influence the intention to use NFC mobile payments. Table 1 gives an
overview of the relevant literature on mobile payments with their location and subject.
Table 1 - Overview of acceptance research of mobile payments.
S o u r c e T h e o r y A p p l i c a t i o n
L o c a t i o n Brown et al., 2003 DOI Mobile banking South Africa Chen, 2008 TAM Mobile
payment United- States of America Cheong et al., 2004 TAM Mobile payment Korea Dahlberg & Oorni, 2007 UTAUT Mobile payment Finland Goeke & Pousttchi , 2010 TAM Mobile payment Germany Hongxia et al., 2011 UTAUT Mobile payment China Kim et al.,
2010 UTAUT Mobile payment Korea Leong et al., 2013 UTAUT2 Mobile payment Malaysia Puschel et al., 2010 D-TPB Mobile banking Brazil Schierz et al. 2010 TAM Mobile payment Germany Shin, 2010 TAM Mobile
payment United- States of America Slade et al., 2014 UTAUT2 Mobile payment United Kingdom Suoranta, 2003 DOI Mobile banking Finland Wang & Yi,
2012
UTAUT Mobile payment
China Wu & Wang,
2003 TAM Mobile payment Taiwan Zmijewska et al., 2004 UTAUT Mobile payment Australi a
D i f f u s i o n o f I n n o v a t i o n ( D O I )
First, Rogers (1962,1995) developed the DOI, although this model was not designed for Information System (IS) research it has been used for explaining the acceptance of IS. The DOI suggests that when consumers
perceive the innovation to have a greater relative advantage,
observability, trialability and
compatibility, the rate of technology adoption will increase. Brown et al. (2003) and Suoranta (2003) are the only two quantitative studies in mobile
banking adoption, which have used the DOI as their core theory.
According to Slade et al. (2013) DOI was relative, in comparison to other studies, which applied different research models, unsuccessful due to its low percentage of variance in behavior intention. The DOI will be
insufficient for the acceptance of NFC mobile payments, due to the purpose of the model which is explaining the
acceptance of IS and not NFC mobile payments.
T h e o r y o f P l a n n e d B e h a v i o r ( T P B )
The Theory of Planned Behavior suggests that behavior is a direct function of behavioral intention which itself is driven by an
individual’s attitude, subjective norms and perceived behavioral control (Ajzen, 1991). The TPB is extended, also known as D-TPB, by decomposing the antecedents of
attitudinal beliefs. Puschel et al. (2010) is the only study which has used the D-TPB as the core theory. In this study there is a high percentage of variance in behavioral intention to adopt mobile banking. According to Slade et al. (2013) components such as subjective norm have been included by other research (Schierz et al, 2010; Sripalawat et al, 2011). The component subjective norm has also been used in the UTAUT2 model of Venkatesh et al.
(2012). This model will be
NFC mobile payments due to the context of the application of TPB, which is in the health care and not NFC mobile
payments.
T e c h n o l o g y A c c e p t a n c e M o d e l ( T A M )
The Technology Acceptance Model (TAM) is developed for IS by Davis (1989). According to TAM, usage is a direct function of behavioral intention, which itself is influenced by
attitudes towards the IS formulated from the innovation’s perceived usefulness and perceived ease of use (Davis, 1989). The model is
originally intended to predict
employee the acceptance and the usage of technology in organizational
context. Schepers & Wetzels (2007) are the first who applied the TAM to examine individual acceptance of technology in a consumer context, but have not
empirically validated their
research. According to Slade et al. (2013), TAM is the most used amongst all theories in research for mobile payments. Since the development of the TAM (Davis, 1989) studies have used the model with mobile payments.For example, Dahlberg, Mallat, & Öörni, (2003) is one of the first mobile
payments adoption study.They added in their study the factor ‘trust’ to the TAM to better describe consumer acceptance of mobile payment solutions. The studies (e.g. Dahlberg et al., 2003; Lee &
Warkentin, 2004) on the adoption of mobile payments before 2003 were mostly
qualitative or descriptive of nature. In 2004 quantitative
research on mobile payment adoption began to emerge. Cheong & Park,
(2004) was one of the first who did a quantitative research on mobile
payment adoption using the TAM, after Cheong & Park (2004) several studies (Chen, 2008; Goeke & Pousttchi, 2010; Shin, 2010) examining followed.
Wu & Wang (2003) used the TAM2
(Venkatesh & Davis, 2000) to model users acceptance of using mobile
payments. The TAM2 is an extension of the TAM, as you can see in figure 1. In this extension the social influences and cognitive instrumental processes are added. However, this model is also intended to predict employee acceptance of technology and the usage of
technology in organizational context.
U n i f i e d T h e o r y o f A c c e p t a n c e a n d U s e o f T e c h n o l o g y ( U T A U T )
After the TAM2 model Venkatesh et al. (2003) developed the Unified Theory of Acceptance and Usage of Technology (UTAUT). The UTAUT model is derived from several theories and models. From these models several key
constructs were derived:
• Performance expectancy was
derived from the TAM’s perceived
usefulness and from the DOI’s relative advantage;
• Effort expectancy was derived
from TAM’s perceived ease of use and
from DOI’s complexity;
• Social influence was derived
from the TPB’s subjective norm and
DOI’s image and
• Facilitating conditions was
derived from the DOI’s
compatibility and TPB’s perceived behavioral control (Venkatesh et al., 2003).
These constructs on behavioral intention of use behavior are
moderated by different combinations of gender, age, experience and
voluntariness of use. Hongxia et al. (2011) and Wang & Yi (2012) have empirically validated UTAUT in the mobile payment context, but excluded the UTAUT moderators. Several studies
(Dahlberg & Oorni, 2007; Hongxia, Xianhao, & Weidan, 2011; Kim,
Mirusmonov, & Lee, 2010; Zmijewska, Lawrence, & Steele, 2004) have used the UTAUT in mobile payment adoption.
The TAM, TAM2 and UTAUT models were
originally developed to explain employee technology acceptance within an
organizational context, for the consumer context Venkatesh et al (2012) developed the UTAUT2 model, which is an extension of the UTAUT model. This model is tailored to the consumer technology acceptance
context, which is also the context of this research and therefore will be used in this research. In the study of Leong, Hew, Tan, & Ooi (2013) the
UTAUT2 is used to determine the factors influencing the adoption of Near
Field Communication (NFC)-enabled mobile credit card in Malaysia. Another study by Slade et al. (2014) used the UTAUT2 to show that
performance expectancy is the
strongest predictor in their research on mobile payments in the UK.
According to Slade et al. (2013) mobile payment adoption research is still in its infancy with regard to the TAM, UTAUT and UTAUT2 models. The relevant studies, mentioned earlier, have taken place across different countries. The table gives an overview of the studies with the theories they have used for their research.
Moreover, it shows that there haven’t been conducted a research on mobile
payment adoption in the Netherlands context.
2.2. Unified Theory of
Acceptance and Use of
Technology (UTAUT)
Venkatesh et al. (2003) developed the Unified Theory of Acceptance and Use of Technology (UTAUT) model. This model is based on eight acceptance models, each with a different set of acceptance determinants. These eight models are the theory of
reasoned action (TRA), the
technology acceptance model (TAM), the motivational model (MM), the
theory of planned behavior (TPB), a model combining the technology
acceptance model, the theory of planned behavior, the model of PC
utilization (MPCU), the innovation diffusion theory (IDT) and the
social cognitive theory (SCT). These models are described in appendix A, with their core constructs and it’s definition.
Of all the constructs from the different models there are four constructs that will play a
significant behavioral role as direct determinants of intention and usage: performance expectancy, effort expectancy, social influence and facilitating conditions (see figure 2).
Figure 2 - The UTAUT model (Venkatesh et al., 2003)
P e r f o r m a n c e e x p e c t a n c y
According to Venkatesh et al. (2003), the determinant
performance expectancy can be
defined as the degree to which an individual believes that using the system will help him or her to attain gains in job performance. From the different models, five constructs relate to performance expectancy:
perceived usefulness from the TAM,
extrinsic motivation from the MM, job- fit from the MPCU, relative
advantage from the IDT and outcome expectation from the SCT. These
constructs are all related to
enhancing the job performance. As visible in the figure2, the
relationship link between performance expectancy and
gender and age. Based on research, men tend to be more task-oriented
(Minton & Schneider, 1980) and
therefore performance expectancy is strongly noticeable to men because the focus of performance expectancy is on task accomplishment (Venkatesh et al., 2003). Comparable to gender, age is to play a moderating role
according to research (Hall &
Mansfield, 1975; Porter, 1963).
E f f o r t e x p e c t a n c y
Effort expectancy is the degree of ease associated with the use of the system (Venkatesh et al., 2003). This determinant captures three constructs from the different
models of acceptance: perceived ease of use from the TAM, complexity from the MPCU and ease of use of the IDT. These constructs (see appendix A) have
similarities in the definitions; all of them are related to the usability of a system. Also effort expectancy has moderators, these are gender,
age and experience. Venkatesh & Morris (2000) suggests that effort
expectancy is more notable for women than for men. According to earlier research (Plude & Hoyer, 1985),
when men get older their ability to
process complex stimuli and allocating attention to information on the job tends to be difficult. These are necessary when using software systems.
The degree to which an individual perceives that important others
believes he or she should use the new system, is called social influence (Venkatesh et al., 2003). This
determinant captures three constructs from the different models of
acceptance: subjective norm from the TRA, social factors from the MPCU and image from the IDT. Similarity can be seen in the definitions of these constructs, all are associated with influence of the status of a person or group. This determinant, social influence, is the only one that has 4 moderators: gender, age,
experience and voluntariness of use. Venkatesh et al. (2000) suggests that women look to be more sensitive to others opinion and find social influence to be more notable when forming an intention to use new technology. According to Rhodes
(1983) that affiliation needs is to be increased with age. Older workers are more likely to place increased
importance on social influence with the effect of decrease with
experience.
F a c i l i t a t i n g c o n d i t i o n s
The last determinant is
facilitating conditions, this is the degree to which an individual believes that an organizational and technical infrastructure exists to support the use of the system.
Facilitating conditions is a
determinant consists of three
different constructs: perceived behavioral control from the TPB,
facilitating conditions from the MPCU and compatibility from the IDT. The similarity among these constructs is that, according to Venkatesh et al. (2003), they are used to include aspects of the technological and/or organizational environment that are designed to remove barriers to use the system. The influence of
facilitating conditions of usage will be moderated by age and experience (Venkatesh et al., 2003).
According to Venkatesh et al.
(2003), behavioral intention will have a positive influence on
technology usage. This is consistent with the underlying theory of all the intention models mentioned before. This is also visible in the model (see figure 2) by the relation between
behavioral intention and use behavior.
As mentioned before the UTAUT model is originally developed to explain employee technology acceptance and use. The
focus of this research is on the consumer use context, therefore the extension of the UTAUT by Venkatesh et al. (2012), also called UTAUT2, will be appropriate.
U T A U T 2
Venkatesh et al. (2012) developed an extension of the UTAUT, called the
consumer use and context. Venkatesh et al. (2012) added three new
constructs: hedonic motivation,
price value and habit. Various
constructs related to hedonic behavior (for example: enjoyment) are
important in consumer product and/or technology use (Brown & Venkatesh
2005; Holbrook & Hirschman 1982;
Nysveen et al. 2005; van der Heijden
2004). According to Venkatesh et al. (2012), hedonic motivation will
complement UTAUT’s strongest predictor that emphasizes utility.
Furthermore, contradictory to workers in an organization
consumers have to pay the costs and these can be of an influence to the consumer’s decisions (Brown & Venkatesh 2005; Chan et al. 2008; Coulter & Coulter
2007). By adding a construct that is related to price and costs will
complement UTAUT’s existing resource
considerations that focus only on time and effort. Finally, habit as
another critical predictor of technology use will complement the focus on intentionality as the
overarching mechanism and key driver of behavior (Venkatesh et al., 2012).
As regards to the moderators, the moderator voluntariness from the UTAUT model is dropped, because the most consumer behaviors are completely
voluntary (Venkatesh et al., 2012).
Figure 3 - UTAUT2 model (Venkatesh et al., 2012)
H e d o n i c m o t i v a t i o n
According to Venkatesh et al. (2012), hedonic motivation is defined as the fun or pleasure
derived from using a technology. Hedonic motivation plays an important role in determining technology
acceptance and use (Brown &
Venkatesh, 2005). Research on IS (van der Heijden 2004; Thong et al. 2006) showed that hedonic
motivation can be of an influence on the technology acceptance and use. Also in consumer context is hedonic motivation an important determinant of technology acceptance and use
P r i c e v a l u e
In an organizational use setting the employees do not have the costs of the technology, in contrary to consumers
who do have costs of use. According to Chan et al. (2008), cost and price may have a convincing impact on
consumers’ technology use. Venkatesh et al. (2012) define price value as
consumer’s cognitive tradeoff between the perceived benefits of the
applications and the monetary cost for using them. When the perks of using a technology are perceived to be greater than the monetary costs and such price value has a positive impact on
intention, then the price value can be marked as positive.
H a bi t
Habit is defined as the extent to which people tend to perform behaviors
automatically because of learning
(Limayem et al., 2007). According to Venkatesh et al (2012), habit is
operationalized in two ways: habit is viewed as prior behavior and is
measured as the extent to which an individual believes that behavior has to be automatic.
The models, DOI, TPB, TAM, UTAUT and UTAUT2 are models to investigate the technology acceptance. To determine which model is well suited to
investigate what factors influence the intention to use NFC mobile payments is it important to look at the focus of
this research and the models. The focus of this research is on the
consumers, the factors of the
intention of the consumers to use NFC mobile payments what is going to be
investigated. Therefor, The UTAUT2 model of Venkatesh et al. (2012) is the best model to use to investigate the factors of the intention to use NFC mobile payments for consumers in the Netherlands. The focus of this model is on the consumers whereas the focus of the other models is on the employee in a workplace, which implies that the context is also different.
Furthermore, the UTAUT2 model is
developed in 2012 and therefor is it more up-to-date then the other models, this will provide the research more representative results. This is also answers sub question one and two.
3 . Meth od ol og y
In this chapter the research design (see paragraph 3.1) and the survey design (see paragraph 3.2) will be described. In paragraph 3.1
hypotheses will be described. The hypotheses are each based on every construct of the UTAUT2 model. Paragraph 3.2 will describe the construction of the survey.
3.1 Research Design
Figure 4 presents the model for this research. It is derived from the UTAUT2 model of Venkatesh et al.
(2012). Every hypothesis corresponds to a construct or moderator. The
hypotheses are divided into two groups: hypotheses on the constructs that are directly connected to the intention to
use NFC mobile payments and hypotheses on the moderators. These hypotheses are tested with the help of a survey (see appendix B). The participants of the survey were recruited by using online networks such as Facebook, Twitter
and email. The questions in the survey were based on the hypotheses that are
described in the next paragraph. For example, in appendix B is the survey presented and the questions are coded like PE1, PE2, PE3, EE1, EE2,
EE3, EE4, etcetera. PE1, PE2 and PE3 correspond to the construct
performance expectancy and the same principle with the other. The survey will give quantitative results for the UTAUT2 model. According to Slade et al. (2014), a survey is a good way to
gather information for the model.
3.1.1 Hypotheses
The hypotheses were implemented from the constructs and moderators of the UTAUT2 model (Venkatesh et al.,
2012). In the paper of Venkatesh et al. (2012) the constructs were in the
context of mobile internet. In this research the context will be NFC mobile payments. Therefore, the hypotheses will be formulated in the context of mobile payments. Paragraph 3.1.1.1 will describe the hypotheses that are directly related to the constructs and paragraph 3.1.1.2 will describe the hypotheses that are related to the
moderators.
3.1.1.1 Hypotheses - Direct links
In the consumer’s context, performance expectancy is the degree to which using a technology will provide benefits to consumers in performing certain activities (Venkatesh et al.,
2012). In UTAUT, the original model, Venkatesh et al. (2003) found
performance expectancy to be the
strongest predictor of intention, this applies in the employee context. However, in the consumer context,
Venkatesh et al. (2012) found in the UTAUT2 model hedonic motivation and habit the strongest predictors of
behavior intention. Performance expectancy has been supported in the mobile payment context by studies of Hongxia et al. (2011) and Wang & Yi
(2012). As NFC mobile payment could lead to the end of carrying cash and