User Acceptance and Mobile Payment Security

International Journal of E-Services and Mobile Applications, 6(2), 37-66, April-June 2014 37

User Acceptance and Mobile Payment Security Florian Urmetzer, University of Cambridge, Cambridge, UK Isabelle Walinski, Thomas Cook AG, Oberursel, Germany

ABSTRACT There have been multiple studies detailing mobile payment and its market potential. There is a gap in the literature when it comes to the study of acceptance factors focusing on security and trust. The researchers asked which qualities of security have an influence on the acceptance of a mobile payment service provider. Therefore this study will focus on distinguishing security in two dimensions: objective and subjective security. Objective security represents the user’s perception of existing technical safety mechanisms. Subjective security is intangible, based on the user’s feelings and perception towards security (trust). The Technology Acceptance Model (TAM) was the theoretical model used in the study. About three hundred responses were collected using an online questionnaire. The study showed that despite the financial crisis banks are still the preferred providers for mobile payment services, where over 80% of the respondents would like to receive the service from a bank. In contrast, only 20% would like to receive such a service from a mobile phone producer. Additionally objective security does not substantially increase subjective security; hence the user trusts the provider rather than the technology itself. Keywords:

Consumer Acceptance, Mobile Payment, Security, Technology Acceptance Model (TAM), User Adoption

1. INTRODUCTION Using the mobile phone for payment has been described in the literature as a market with large growth potential, predicting transaction volumes of more than $37 billion by 2008 (Chen 2008). The total U.S. market potential can be regarded as the $3.7 trillion that Americans charged to their debit and credit cards

in 2010 (The Economist 2011). Market insiders have even been describing the market possibilities as so large as to predict that credit cards may not exist anymore within the next five years and calling mobile payment the next internet revolution (Spiegel Online 2011). Mobile payment has been in development since the 1990s (Andreoli 2008). The first commercial vendor

DOI: 10.4018/ijesma.2014040104 Copyright © 2014, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

38 International Journal of E-Services and Mobile Applications, 6(2), 37-66, April-June 2014

offering services within the market was the startup company Paybox. The company failed in most countries, with the notable exception of Austria; reasons for the failure are said to have been high costs and cumbersome use (Georgi & Pinkl 2005). It seems that since then mobile payment has only been a matter of analysis for industry specialists and scientists; high growth rates were forecasted but never materialized (Bregulla 2011). Gartner lowered their forecast for mobile payment users and the number of transactions by 2014 (Shen 2011) in their latest study. In 2009, mobile payment accounted for only 0.05% of all noncash payment transactions (Capgemini 2010). Therefore mobile payment still has not reached any significant market penetration (Bussmann 2010). However, there are new pull factors from the market, as technological conditions continue to change. Specifically mobile couponing combined with smartphones and their fast mobile networks, enabling data collection after a transaction, show growth potential (Georgi & Pinkl 2005). What also helps is that the smartphone technology has become cheaper and gained in market share in combination with affordable mobile internet flat rates for the devices. Thousands of apps for mobile phone customers are being made available on the market so that practical functionality is offered to the customer, and companies can sell wherever the customer is. With this, mobile payment has a technological grounding to sustain itself on the market. In the past it has been argued that enhanced availability of mobile technology, independence of location and time

and a complementary relationship with traditional payment services (e.g. cash payment) would be a key enabler for mobile payment (Mallat 2007). There has been a lot of research on mobile payment in general; however, less in the area of adoption of mobile payment by end-users. There is certainly a gap looking at user adoption specifically when considering a combination of security and trust. Hence researchers asked which aspects of security have an influence on the acceptance of a mobile payment service provider. The first part of this paper provides a background to the study conducted including a definition of the term mobile payment. The second part will focus on the methods used to establish user acceptance. The third part will detail an online survey of around 300 people that was conducted in order to investigate the security preferences of the consumers. Finally the results of the survey will be discussed and conclusions will be drawn.

2. BACKGROUND The following section will define some terms about payment in general, before defining and giving a short overview of the application of mobile payment. The term payment is used to describe the settlement of a receivable of purchased goods and services, which happens by transferring the payment currency. So, the payment currency becomes a means of barter with a certain value. Payment currency can be cash, book money or digital money (Pousttchi 2008). Multiple forms of a transaction of payment currency are available, e.g. cash over the

Copyright © 2014, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.


counter, direct debit, check, cards in the form of credit or debit cards, third party payment providers like Paypal and as well mobile payment. Some countries are developing towards a cashless society, both for economic reasons and payment simplicity (Judt, 2006; Godschalk, 2006). Cashless transactions exist as well outside of bank environments in the form of prepaid cards such as gift cards, phone cards (Pousttchi, 2005) or prepaid payment means in the internet such as Facebook credits and Bitcoins. Trust is seen by the authors as the most important indicator of the level of confidence in a service. In this context it is the belief by the first party that a specific service offered by the other party functions within a specified context and dependably for a certain time (Olmedilla 2005). Security is widely established as the factor that enables an organization to achieve all of its objectives by implementing systems with the aim to reduce IT-related risks to the organization, its partners and customers (Stoneburner, G. 2001; Linck, K. 2006). Examples for risk in this paper’s context are the

theft of information or the execution of fraudulent payments. 2.1. Mobile Payment Definition Mobile payment belongs to the category of cashless transactions. There is no single definition within the industry itself and prepaid and credit payments are possible via different types of mobile services. When reviewing the different transaction mechanisms available via mobile phones, two major approaches are identified: Payment Centric or Mobile Centric (See Figure 1). Mobile Centric describes all forms of mobile commerce where payment can only be seen as a side effect to a wider process. Mobile Centric forms two subgroups: One, Mobile Purchase; where the user accesses a webpage using his mobile phone browser. The web page specially caters to mobile phones, hence like an app has a special navigation and human computer interface catering to the use via a mobile phone. This is often also described as mobile commerce (Linck 2006). In contrast to an app, all content and functionality is stored online rather than on the mobile phone. The web-page

Figure 1. Categorization of mobile payment types



is then used to search for products or access product reviews and to compare features of similar products. The main purpose would be to have the availability of information for comparison as well, as the option to make a purchase. However, the purchase is just one option in a longer process. Similarly option two, an App Centric purchase, is based on an “App”, which is a small application installed on a mobile phone similar to the web page accessed through the mobile phone, the app also has the option to use mobile phone specific features like the definition of a location through the mobile phone or information that is stored within the mobile phone. Examples may be a train ticketing app or a hotel booking app (Burkard, 2012). Neither has payment as their main purpose; hence they are not payment centric, but the payment is embedded in a longer process. Payment Centric is defined as use of the mobile phone primarily for payment. Hence, the mobile phone is used as a replacement of a medium like a credit card. There are two sub categories identified which are Mobile Banking related and Contactless Payment. Both have the main purpose of payment. For example, for Mobile Banking the transfer of money would be the central purpose (Luarn, P, et al., 2005). As well, Contactless Payment is centrally used for a money transaction usually using Near-Field-Communication (NFC) as a technology to enable communication between the mobile phone and a physical point-of-sale (POS). In this study, when referring to mobile payment, the Contactless Payment will be the considered

technology. This can also be described as M-Payment (Linck, 2006). 2.2. Prerequisites for Mobile Payment Success The prerequisites for mobile payment success are a mix of technical and nontechnical factors. The most important success factors are: (Pfirsching, 2005) •

•

•

Customer acceptance with an intuitive and easy to use system and a high subjective security with a wellknown and strong brand. Cooperation of companies and organizations in the market to reach an industry standard for mobile payment. Reaching a critical mass of participants with added values of an industry standard, lots of points of acceptance and no additional costs.

Technical prerequisites are easy to measure as they are tangible. Those are e.g. the installed payment network, existence of an industry standard, the market coverage of smartphones and merchant readers. However, no matter how wide the coverage of mobile phones is in a given market, if people are not using them for mobile payment, then there will not be a big mobile payment success story, even if all technical prerequisites are met. Two-thirds of the population in Germany do not feel well informed about the possibilities of mobile payment; part of it is the uncertainty of how data and payment information are protected (Kre-



imer, 2010). This leads to the conclusion that one reason for the slow adoption of mobile payment services could also be a failure in communicating a clear benefit to potential users (Schierz, 2010). Other studies differentiate between essential and commensurate conditions (Pousttchi, 2003). Hence, meeting all essential conditions (making it technically available) enables a user to accept mobile payment as a method of payment, however does not make him use it (Pousttchi, 2003). This means that even though all essential conditions are fulfilled, it only removes obstacles but it still does not guarantee the usage. In the finding of Pousttchi’s study a hierarchy of important criteria is defined allowing the acceptance. The first was confidentiality of data followed by direct costs (Pousttchi, 2003). Hence, security was seen as a major factor for acceptance here as well. As seen, there must be other factors than only technical prerequisites that are important for success of mobile payment e.g. Lyytinen, K. et al. (2002) describe inter-organizational problems. The authors see these as intangible prerequisites, which are the objective and subjective security that will be examined in this study. Therefore, the next section will review methods to study and establish user acceptance followed by the definition on how the data was gathered.

3. THEORETICAL BACKGROUND-USER ACCEPTANCE “User acceptance is often the pivotal factor determining the success or failure

of an information system project” (Davis, 1993). It does not help to have the fanciest gadget on the information technology market: if the potential customer does not accept it and hence does not want to use it, the product will be a failure. There have been many empirical studies done on user acceptance and hence there are well established frameworks available (e.g. Beaudry et al., 2010; Jingjun et al., 2013; Kim et al., 2009; Wixom et al., 2005). For the background of this paper the authors chose to only give a short overview of four the most prominent frameworks in the literature. 3.1. Theoretical Models of User Acceptance in Information Technology and Mobile Payment 3.1.1. TAM – Model In an early field study about user acceptance of information technology, Fred Davis developed the Technology Acceptance Model (TAM), first in 1989, later with further amendments, initially to explain user acceptance in a business context (see Figure 2). Davis identified two major user acceptance criteria: “Perceived usefulness” (the degree to which a person believes that using a particular system would enhance his or her job performance) and “Perceived ease of use” (the degree to which a person believes that using a particular system would be free from effort) (Davis, 1993). E.g. with two smartphones that have an identical set of functions, a user will find the one that is more intuitive more useful. The TAM evolved, got amended throughout the years and more variables were added and removed again, in order



Figure 2. TAM-Model (Davis 1993)

to be able to predict the actual usage behaviour (Davis 1993). The TAM, as the most established model, was the basis for many later studies on acceptance of information technology (e.g. Brown et al., 2012; Mathieson, 1991; Wu et al., 2009) and on acceptance of mobile payment (e.g. Selvan et al., 2011; Ghobakhloo et al., 2013).

age, experience and whether or not use is voluntary (Venkatesh, 2003). The UTAUT model explains about 70 per cent of the variance in intention to use technology and approaches the practical limit to explain individual acceptance and usage decisions (Venkatesh, 2003; Esteva-Armida, 2012; Sykes, 2009; Alotaibi, 2013).

3.1.2. UTAUT - Model

3.1.3. Studying User Acceptance of Mobile Payment Services

Viswanath Venkatesh et al. (2003) identified eight prominent theoretical models in their review in 2003, amongst them the TAM Model, and discussed their similarities and differences. The aim was to develop a “unified theory of individual acceptance of technology” (UTAUT) (Venkatesh, 2003) (see Figure 3). He examined in detail new variables of user acceptance and usage behaviour, such as performance expectancy, effort expectancy, social influence and facilitating conditions, which all contribute to usage behaviour either directly or through behavioural intentions. His model includes factors such as gender,

In 2009, years after mobile payment should have already been on the market and in use by everybody, scientists were still wondering why it had not been adopted so far and what the reasons were. In his empirical study, Schierz researched acceptance factors and developed a TAM-based conceptual model that was extended with additional constructs that were important to mobile payment acceptance (see Figure 4). His model identified numerous positive relationships between varieties of acceptance factors (Schierz, 2010).



Figure 3. UTAUT Model (Venkatesh 2003)

Figure 4. Conceptual model (Schierz, 2010)



The results showed three main mobile payment acceptance factors: perceived compatibility, which has the greatest impact on the use (how m-payment fits into existing values, behavioural patterns and experiences in daily life), individual mobility (how mobile the individual’s lifestyle is) and subjective norm (what the social environment thinks of m-payment) (Schierz, 2010). Social influence on acceptance has already been examined (Venkatesh et al., 2012; Venkatesh, 2003). If the social environment is in favour of m-payment, the adoption rate of non-users might rise and kick-start the viral marketing process (Schierz, 2010). 3.2. Overview of the Concepts Used This next section will give an overview of the concepts used and their definition for this study. The important ones are security, objective security and subjective security.

Security as an essential condition is one of the most important acceptance factors; it could even be seen as a hygiene factor for trust and acceptance (Cimiotti & Martin, 2008). Without security, mobile payment would be unacceptable, with security; mobile payment is just acceptable, but not necessarily desirable. Security can be split up into two dimensions: objective and subjective security (Linck, 2006). Objective security is a tangible technical characteristic, where a technological solution exists, however it is difficult for users to judge whether a system is technically secure or not (Linck, 2006) (see Table 1). Subjective security is rather intangible. It is the perceived feeling that the potential user has about the security of the payment and when this feeling tells him that there might be a security issue, the potential user will not become an actual user, even though he could do so from a technological perspective. Hence, one of the main drivers to accept

Table 1. Types of objective security (Merz, 2002) Objective Security

Definition

Technical solution

Confidentiality

Ensures that transaction information cannot be viewed by unauthorized persons

Encryption

Authentication

Ensures that transaction information actually originates from the presumed transaction partner

Possession of a token, knowledge of a PIN, a biometric characteristic

Integrity

Ensures that transaction information remains intact during transmission and cannot be altered

Digital signature

Authorization

Ensures that parties involved must be able to verify if everyone involved in a transaction is allowed to make the transaction

Digital certificate

Non-repudiation

Ensures that nobody can claim that transactions on his behalf were made without his knowledge

Digital signature



the idea to use mobile payment must be subjective security. Security issues have also been researched by Dahlberg (2003) and by Linck (2006). There was only little empirical research about the concept of subjective security regarding mobile commerce - especially mobile payment (Linck, 2006) - with no technical solution to this problem (Pousttchi, 2003). Dahlberg et al. identified security and customer trust as key influencing factors for mobile payment acceptance, thus enhancing the original TAM with trust (Dahlberg, 2003; Linck, 2006) confirmed that mobile payment service providers must meet security requirements; otherwise customers will ignore the payment option completely. But security is only an essential condition, so the mobile payment service provider also has to fulfill the commensurate conditions, in the form of added value for the customer (Linck, 2006). Market conditions have changed since those surveys were conducted. Over 82% in Germany and 53% world wide of mobile phone users currently use a smartphone and the market share is growing daily. Mobile internet for mobile shopping and for social networking is the main driver for the smartphone market. The extensive use of social networking sites like Facebook could imply that the users are less sensitive to security concerns and thus have a higher subjective security in this context. These are very different market conditions and this is why it would be desirable to validate some of the security answers again in the new market environment.

The intention of the survey conducted for this study is to examine the drivers of subjective security when using mobile payment. For that purpose, a research model was developed and operationalized with formative and reflective constructs and validated by using the Partial Least Squares Regression (PLS) method.

4. METHODS 4.1. Research Model Many surveys and studies have already been conducted on the subject of mobile payment acceptance, identifying many acceptance factors for technology and mobile payment. First Davis with his TAM model explained that intention to use was all about “perceived usefulness” and “perceived ease of use”, the later models added more factors such as performance expectancy, effort expectancy, social influence and facilitating conditions (Venkatesh, 2003, pp 447). Then, Pousttchi (2003) identified security and cost as important criteria. Finally Schierz (2010) researched perceived compatibility, individual mobility and subjective norm as being important acceptance factors. The survey developed was based on the survey by Linck (2006), however, much more focused on the aspects of security. Linck’s study examines more open questions and defined categories such as confidentiality, authentication and authorization, transparency and traceability, but also trust in the mobile payment service provider, broad



acceptance, convenience and ease of use (Linck, 2006). The most frequently named categories were the ones being bundled together under the title technical security. Therefore, the construct technical security will be used in this research model, as well, hypothesizing that there is a positive correlation between technical security and subjective security and trust in the mobile payment service provider. H1: Technical security has a positive effect on subjective security H2: Technical security has a positive effect on trust in the mobile payment service provider The next frequent category in the Linck et al. security survey was trust in the mobile payment service provider. Trust in the mobile payment service provider as an important factor was also mentioned in a later study (Pousttchi & Goeke, 2010). Therefore, this will also be integrated into the research model of this study, forming the third hypothesis: there is a positive correlation between trust in mobile payment service provider and subjective security. H3: Trust in the mobile payment service provider has a positive effect on subjective security Social influence on acceptance of new technology should not be underestimated. It has been examined for example in two research studies: Fishbein and Ajzen, defined it as a “person’s percep-

tion that most people who are important to the person think he should or should not perform the behavior in question” (Fishbein & Ajzen, 1975). Another study defined it as “the degree to which an individual perceives that important others believe he or she should use the new system” (Venkatesh, 2003, pp 451). A person would rather accept new technologies because the social environment is doing so at the moment, maybe even hoping for a potential social status gain (Venkatesh, 2003). If the social network is in favour of mobile payment, the adoption rate of non-users might rise and initiate the viral marketing process (Schierz, 2010). This theory would seem to be supported by the perpetual smartphone and social media hype. Smartphone coverage has been rising and people who are in the decision-making process of buying a new mobile phone buy a smartphone rather than a regular mobile phone. As for social media, this phenomenon can be seen especially in the generation of teenagers and young adults who spend hours on social media because their peers do it, as well. The main reason for this is the fear of becoming socially alienated (Watkins, 2009). An interesting point to investigate would be whether this ‘peer pressure’ would also be confirmed in trusting the friends’ opinions and hence, to also feel secure about mobile payment and trust the mobile payment service provider, simply because the others do. Hence, the last two hypotheses are that there is a positive correlation between subjective norm and subjective



security and trust in the mobile payment service provider. H4: Subjective norm has a positive effect on subjective security H5: Subjective norm has a positive effect on trust in the mobile payment service provider The proposed research model and the hypotheses are summarized below: Operationalization of the model the research model was developed and operationalized with formative and reflective constructs and validated by using the Partial Least Squares (PLS) method with the help of the software SmartPLS (Ringle, 2005). PLS is a method of modelling a causal network of latent variables (see Figure 5). Latent variables can be operationalized in two ways, into a reflective or formative measurement

model. In the reflective model, the latent variable itself influences the reflective indicators (Fassot & Eggert, 2005). The model used here is built with four constructs formed by formative indicators: technical security, trust in mobile payment service provider, subjective norm and subjective security. The indicators are partially based on the answers given to open questions posed in the relevant surveys (Linck, 2006; Pousttchi, 2010) and on research conducted by the authors. The aim of extending the research done by other authors was to allow comparability within the discussion. Hence, newly phrased questions were used only where needed. For example, for the construct of Subjective Norm, there was not enough input given by the literature. The construct technical security is operationalized by ten formative indica-

Figure 5. Research model



tors with questions grouped into the five categories of objective security: confidentiality (TEC1-TEC3), authentication (TEC4), integrity (TEC5-TEC6), authorization (TEC7) and non-repudiation (TEC8-TEC10). The construct trust in mobile payment service provider is operationalized by five formative indicators (TMPSP1-5) with questions each describing the different factors of trust towards a mobile payment service provider. The construct subjective norm is operationalized by nine formative indicators (SN1-9) with questions targeting behaviour within a social group (See Figure 5 and compare to Table 2). The construct subjective security is operationalized by reflective indicators as the indicators reflect the same aspect of the construct (see Table 3). Furthermore, questions about mobile internet usage and demographics of the respondents were posed which are not reflected in the above model. 4.2. Research Method and Questionnaire Based on the developed model an online questionnaire was developed for the research and conducted via SurveyMonkey ®.(2009) Some questions were adopted from other surveys as indicated in the source column of Table 2 or are based on the answers of the study by Linck (2006). The questions were formulated in English and in German with a branching logic in order to maximize the responses among international respondents. A pre-test was performed in order to check the distinctness of the questions

and after the feedback the questionnaire was slightly amended. The questionnaire was composed of three main parts. First, some general behavioural questions about the use of smartphones and mobile internet were given, where proposed answers could be chosen with radio buttons, but where participants could also add their own comments. There also was a branching logic put in place to skip questions about the way of using mobile internet, if the participant had already stated that they did not have internet access over their mobile device. Then in a row of opinion questions about objective security, trust in mobile payment provider and influences of the social network, the respondents were asked to express their agreement/ disagreement with statements on a fourpoint Likert-type scale ranging from “strongly agree” to “strongly disagree”. The four-point scale was chosen to force the participants to make a clear pro or contra decision and not to have too many neutral answers as would have been the case with a five-point scale. On one hand this is easier to evaluate later in the model; on the other hand, the quality of the data might have been affected by the forced answers. Finally, the participants were asked classic demographic questions forming the sample. By the end of the deadline 364 participants had started the questionnaire; however, 66 did not complete the survey. Most of those opted out after the third or fourth question. After eliminating the incomplete response data files, 298 valid responses formed the final sample,



Table 2. Formative constructs and their indicators Construct

Item

Formative Indicator

Source

TEC1

Data protection is important to me.

Linck et al.

TEC2

I want data transfer of my data to be encrypted.

Linck et al.

TEC3

My personal information should be dealt with confidentially.

Goeke/ Pousttchi

TEC4

I want to enter my PIN (personal identification number) for each transaction.

Linck et al.

TEC5

I want my personal data to be protected from unauthorized access.

Linck et al.

TEC6

I want to be sure my payment arrives at the payee.

Linck et al.

TEC7

I want to receive a payment confirmation immediately after each transaction.

Goeke/ Pousttchi i

TEC8

I want to be able to check my payment history at any time

Goeke/ Pousttchi

TEC9

I want the option to cancel a payment that I have already made.

Linck et al.

TEC10

I want the purchase process to be performed without transmitting my personal data.

Goeke/ Pousttchi

TMPSP1

I trust the mobile payment service provider if I feel that the technology is secure.

Walinski/Urmetzer

TMPSP2

I trust the mobile payment service provider if he states that his technology is secure.

Walinski/Urmetzer

TMPSP3

The mobile payment service provider must be well-known.

Linck et al.

TMPSP4

The mobile payment service provider must give a reliable impression to me.

Linck et al.

TMPSP5

The mobile payment service provider should be approved by an independent institution.

Goeke/ Pousttchi

Construct

Item

Formative Indicator

Source

Subjective norm

SN1

If people who are important to me would recommend using mobile payment I would trust them.

Gerpott/ Kornmeier

SN2

If people who are important to me would find using mobile payment beneficial, I would find it beneficial, too.

Schierz et al.

SN3

If the media recommends using mobile payment, I would follow their recommendation.

Goeke/ Pousttchi

SN4

If people who are important to me think mobile payment is secure, I think so, too.

Walinski/Urmetzer

SN5

The use of mobile payment services is a sign of technological savvy in my social network.

Gerpott/ Kornmeier

SN6

I want to use mobile payment when I see that it is being used in my social network.

Walinski/Urmetzer

SN7

If I see people in my social network use mobile payment I assume it must be secure.

Walinski/Urmetzer

SN8

If people who are important to me say I can trust a certain mobile payment service provider, then I do.

Walinski/Urmetzer

SN9

I trust people who are important to me when it comes to judging the security of mobile payment.

Walinski/Urmetzer

Technical security

Trust in mobile payment service provider



Table 3. Reflective constructs and their indicators Construct Subjective security

Item

Reflective Indicator

Source

SEC1

I feel comfortable making purchases over the internet using my mobile device.

Walinski/Urmetzer

SEC2

Mobile payment services are secure

Walinski/Urmetzer

which is a quota of 83%. The questionnaire was sent out via social networks focusing on news groups and on interest groups around technology. Specifically the platforms Xing and Linked In were used for distribution purpose. 4.3. Results 4.3.1. The Sample The sample consists of male (58%) and female (42%) - mainly students and business people, both groups being rather technophile, from six countries: Germany (n = 244), France (n = 15), USA (n = 29) and others (n=10). Nearly half (47%) is between 20-29 years, which confirms the high participation of students, followed by the 30-somethings (25%) and the 40-somethings (20%). Only 0.7% is below 20 years and 6.7% over 50 years (see Figure 6). The educational background is high (see Figure 7) - 63% hold a university degree (Bachelor or Masters), which is reflected in the monthly disposable household income as seen in Figure 8: 30% have $3,000-$6,000 and 25% over $6,000. Over 70% of the sample claim to have internet access over their mobile device, thereof 51% over a smartphone (see Figure 9).

Looking at this particular question separately between the answers from Europe and from the USA, the percentage of smartphone holders alone was almost 70% across the Atlantic, whereas it was only around 43% in Germany. Considering who has internet access over their mobile device, the analysis shows a similar overall age distribution as of the participants. The sample is split up in 3 groups - 57% are between 20-29 years old, the next group down at 26% is between 30-39 years old and the last group with 14% is between 40-49 years old. The frequency of mobile internet usage differs between countries (see Figure 10). On average, 60% use the internet over their mobile device on a daily basis, whereas this applies to 86% in the USA but only to 55% in Germany. These differences are less pronounced when considering the reasons of usage, +/- 90% for email, +/- 70% for social networking and +/- 30% for electronic tickets (see Figure 11). A little difference can be seen between the age groups. The older the participant, the less the smartphone is used for social networks, but rather for email, electronic tickets or surfing. Participants who answered “other” are using mobile internet e.g. for weather forecast, news etc. However, when it comes down to



Figure 6. Table of demographics – age distribution

Figure 7. Table of demographics – educational background

Figure 8. Table of demographics – disposable household income in USD



Figure 9. Internet access over their mobile device

Figure 10. Frequency of accessing internet over their mobile device

Figure 11. Reasons for using mobile internet



mobile shopping, the participants from abroad are more likely to buy things using their smart phone: 30% use it to make purchases, but only half of that in Germany. 4.3.2. Sample Behavior Asking participants “how often they have made purchases over the mobile phone” was the transitional question before going into detail with more trust and security questions (see Figure 12). Only less than 5% have made mobile purchases on a regular basis. Over 50% of the participants have never bought anything using their mobile device. The rest is divided up between “at least monthly” and “less than monthly”. The answers to the questions focusing on security and trust were not surprising. The majority of the respondents strongly agreed when it came to data protection (81%) and encryption (90%) of their personal data, or to confidentiality (95%) and protection from unauthorized access (96%) (see Figure 13). There were no major differences between the continents. However, a slight difference could be seen between the age groups: the younger the participant was, the less concerned they were about data protection. To the question “I feel comfortable making purchases over the internet using my mobile device” as seen in Figure 14 only 9% of the overall participants from Germany strongly agreed. When looking at the age groups, only 5% of the 30-39 year olds strongly agreed, the 20-29 year olds strongly agreed twice as often and 22% of the participants from

the USA strongly agreed to that question. A clear affinity towards mobile payment can be seen in the younger generation in Germany and in the USA. Concerning trust preferences as seen in Figure 14, over 70% of the respondents agreed or strongly agreed that they would trust the mobile payment service provider if they had the feeling that the technology was secure. Split up into the responses from the continents, only 30% of the Germans agreed, whereas twice as many participants from the USA agreed to that. The same percentages apply to the statement “mobile payment is secure”. Even higher agreement was expressed that the “mobile payment service provider needs to be an independent institution” (90%), “make a reliable impression” (93%) and “needs to be well-known” (79%). Looking at the different markets, people in the USA had higher trust in mobile payment than Europeans (see Figure 14). As for whom the participants would trust the most as a mobile payment provider, the answers showed a clear winner seen in Figure 15: of all respondents 85% would only trust a bank and 66% would trust a credit card company. Despite the financial crisis, this has not changed since Dahlberg et al.’s survey in 2001. Banks were the most trusted providers of mobile payment services at that time, as well (Dahlberg, 2003). A third party provider such as PayPal has a higher trust ratio in the USA (74%) than in Germany (58%). Overall it is 61%, but that might have had the “PayPal effect”, i.e., the brand PayPal is very well known. Without having



Figure 12. Making purchases over the mobile phone

Figure 13. Statements “strongly agree” reflecting security preferences

proposed PayPal as an example for a third party provider the answers might have looked different. 21% would trust a phone maker such as Apple or Nokia. This number also differs between the

continents. In Germany, only 16% would trust a phone maker, whereas in the USA it is 41%. The same applies for the telecommunication provider as a mobile payment service provider; in Germany,



Figure 14. Statements “strongly agree and agree” reflecting trust preferences

Figure 15. Trust in mobile payment provider

only 25% would trust a telecommunication provider whereas in the USA it is 41%. Participants who checked “other” stated mostly that they would not trust anyone. The last questions in the survey focused on the influences of the social network. As seen in Figure 16 around 60% of the respondents would trust their friends if they recommended the use of mobile payments and if they thought that mobile payment was secure, they would adopt that opinion. Otherwise, the friends’ opinions are important, but they do not dictate the behaviour of oth-

ers. The answers are pretty homogenous between the markets. However, one statement brought up very different ratings: “If people who are important to me would find using mobile payment beneficial, I would find it beneficial, too”; only 20% of the respondents from Germany agreed, whereas 55% of the respondents from the USA agreed. The rule the younger the respondent, the bigger influence of the social network does not seem to be confirmed here. Only the 30-somethings seem to be more influenced by friends, but the 20-29 year olds have the same ratings as the 40-49 year olds.



Figure 16. Statements “strongly agree” and “agree” reflecting social network

5. DISCUSSION As the emphasis of the survey was on security issues, trust and social influences, the questions on those topics were asked in a way to be applicable to the previously described research model. As explained in 4.1 a PLS algorithm was performed on all items to evaluate the validity of the items and the underlying constructs in the research model. Bootstrapping with 500 samples and 300 cases in PLS, a t-test was performed and the significance of the items was established. To be considered significant, items have a t-value of 1.923 and higher (Ringle &

Spreen, 2007). The hypotheses about the relationships between the constructs in the model were tested through the significance of the structural coefficients. In order to ensure that the results regarding the structural relationships were based on validated measurement instruments, additional statistical tests needed to be performed for the reflective and for the formative indicators according to commonly accepted quality criteria for PLS analyses. Considering that the reflective construct of subjective security is the most critical determinant of the adoption of mobile payment, the validity of the



results of the sample opinion questions can be confirmed by testing the internal consistency reliability (ICR) and average variance extracted (AVE) that were calculated for subjective security. To be considered adequate, these two measures should have values of at least 0.7 and 0.5, respectively (Krafft, 2005). Based on the results shown in the Table 4, both values for internal consistency reliability and convergent and discriminant validity are above the suggested thresholds and are therefore valid. After reflective constructs, also formative constructs were analysed according to commonly accepted quality criteria. Some items measuring the constructs came out with low or negative factor loadings. All items measuring the construct technical security were not significant because of being below the suggested threshold of 1.923, leading to the result that no statement could be made about technical security. Each factor loading, its t-value and the significance are presented in the Table 5. Apart from H1 and H2 all other hypotheses were significant. Table 6 shows the hypotheses with their structural path coefficients and the results. According to the data both hypotheses H1 and H2 (technical security on subjective security and on trust in mobile payment service provider) could not be

supported. Although in the sample behaviour questions participants regarded technical security as one of the most important aspects with mostly “strongly agreeing”, the influence on subjective security could not be proved in the analysis of the sample opinion questions. As already mentioned in 3.2 it is difficult for consumers to judge whether a system is technically secure or not. This confirms that security is a hygiene factor and therefore an essential condition for the general acceptance of mobile payment. Hence, technical security is not a commensurate condition leading to an increase in subjective security. Figure 17 shows the PLS path coefficients for the research model generated by smartPLS 2.0 using the survey data of the 298 respondents. Path coefficients in PLS are similar to standardized beta weights in regression analysis. For each path it also reports the statistical significance. H3 claimed that trust in the mobile payment service provider had a positive effect on subjective security. The construct trust in mobile payment service provider was operationalized with five items. Although two out of five items were not significant (TMPSP4-5), the other three (TMPSP1-3) were highly significant with high ratings. In the sample behaviour questions participants regard-

Table 4. Reflective indicator loadings Construct Subjective security

ICR 0.875

AVE 0.777

Indicator

Factor loading

SEC1

0.858

SEC2

0.905



Table 5. Formative indicator loadings Item

Formative Indicator

Factor loading

t-value (cases 300, samples 500)

Significance * t > 1,923

TEC1

Data protection is important to me.

-0.220

0.768

Not Significant

TEC2

I want data transfer of my data to be encrypted.

-0.288

0.790

Not Significant

TEC3

My personal information should be dealt with confidentially.

0.423

1.183

Not Significant.

TEC4

I want to enter my PIN (personal identification number) for each transaction.

-0.369

0.995

Not Significant

TEC5

I want my personal data to be protected from unauthorized access.

-0.281

0.814

Not Significant

TEC6

I want to be sure my payment arrives at the payee.

0.871

1.557

Not Significant

TEC7

I want to receive a payment confirmation immediately after each transaction.

0.135

0.559

Not Significant

TEC8

I want to be able to check my payment history at any time

-0.409

0.997

Not Significant

TEC9

I want the option to cancel a payment that I have already made.

0.315

1.086

Not Significant

TEC10

I want the purchase process to be performed without transmitting my personal data.

-0.527

1.222

Not Significant

TMPSP1

I trust the mobile payment service provider if I feel that the technology is secure.

0.574

7.028

Significant

TMPSP2

I trust the mobile payment service provider if he states that his technology is secure.

0.502

5.934

Significant

TMPSP3

The mobile payment service provider must be well-known.

0.244

2.674

Significant

TMPSP4

The mobile payment service provider must give a reliable impression to me.

0.009

0.087

Not Significant

TMPSP5

The mobile payment service provider should be approved by an independent institution.

-0.074

0.651

Not Significant

SN1

If people who are important to me would recommend using mobile payment I would trust them.

0.388

3.239

Significant

SN2

If people who are important to me would find using mobile payment beneficial, I would find it beneficial, too.

0.036

0.300

Not Significant

SN3

If the media recommends using mobile payment, I would follow their recommendation.

0.276

2.583

Significant

SN4

If people who are important to me think mobile payment is secure, I think so, too.

0.238

2.080

Significant

SN5

The use of mobile payment services is a sign of technological savvy in my social network.

0.247

2.011

Significant

SN6

I want to use mobile payment when I see that it is being used in my social network.

-0.086

0.647

Not Significant

SN7

If I see people in my social network use mobile payment I assume it must be secure.

0.024

0.203

Not Significant

SN8

If people who are important to me say I can trust a certain mobile payment service provider, then I do.

0.047

0.319

Not Significant

SN9

I trust people who are important to me when it comes to judging the security of mobile payment.

0.125

0.943

Not Significant

ed trust in the mobile payment service provider as an important prerequisite for trust in mobile payment, especially that “the mobile payment service provider

must give a reliable impression” and that “the mobile payment service provider should be approved by an independent institution. However, in the analysis of



Table 6. Model hypothesis-testing results Hypotheses

Structural Path

Structural Coefficient ß

Result

H1

TEC - SEC

0.127

not supported

H2

TEC - TMPSP

0.135

not supported

H3

TMPSP - SEC

0.450

Supported

H4

SN - SEC

0.250

Supported

H5

SN - TMPSP

0.496

Supported

Figure 17. Structural model results

the sample opinion questions these influences could not be proved. But, high ratings were measured for developing trust, when the mobile payment service provider’s technology is secure, when it states that the technology is secure and when the mobile payment service provider is well-known. H3 showed the second highest influence with a path coefficient of ß>0.5 which is substantial. Thus, the hypothesis H3 was supported. Trust in the mobile

payment service provider is an important acceptance factor and should not be underestimated as it has a high influence on subjective security. With the last two hypotheses (H4H5) the influences between subjective norm, subjective security and trust in mobile payment service provider were examined; and also whether the social network influences the acceptance of new technology. The construct social norm was operationalized with nine



items. Although five out of nine items were not significant (SN2; SN6-9), the other four (SN1; SN3-5) were highly significant with high ratings. According to the data the social network greatly influences the trust level in the mobile payment provider and with that the subjective security. As using mobile payment is perceived to being technologically savvy, people would believe their friends if they said that mobile payment is secure. The same applies for media; if media recommended mobile payment, people would follow the advice. What is interesting to see is that people, whose friends recommend mobile payment would trust their advice, however, they would not trust the claim that mobile payment is beneficial, which is basically the same question. Further research might be necessary and a rephrasing of the questions to investigate this topic again. The hypotheses H4 (subjective norm has a positive effect on subjective security) and H5 (subjective norm has a positive effect on trust in the mobile payment service provider) are supported. Subjective norm has the greatest impact on subjective security. Trust in mobile payment service provider is strongly influencing it, even more so than technical security. Thus, the influence of the social network is greatly influencing the mobile behaviour. An increasingly mobile society will adopt mobile services and with that mobile payment services, either because of wanting to ‘keep up with the Jonses’ and be perceived as technologically savvy or because it simply makes some part of life easier.

This is considered an important finding and it confirms earlier research studies about the social influence performed by Fishbein et al. (1975), Venkatesh (2003) and Schierz et al. (2010) To gain trust in the mobile payment system, the mobile payment service provider must be trustworthy and well-known, and the social environment must trust the mobile payment provider, as well.

6. CONCLUSION AND SUGGESTIONS FOR FUTURE RESEARCH This study was designed to explore user acceptance of mobile payment specifically viewing security, which is seen as major factor for the adoption of mobile payment. The research question was; which aspects of security have an influence on the acceptance of a mobile payment service provider. The authors distinguished security in two dimensions: objective and subjective security. Objective security represents the user’s perception of existing technical safety mechanisms. Subjective security is intangible, based on the user’s feelings and perception towards security (trust). The security aspects were formalized in parts on basis of examples taken from existing literature as well as on the knowledge of the authors. The data was gathered using electronic questionnaires delivered through distribution lists aiming to gather technology affine people, with smartphones, who would be able to use such a service. For the majority of the respondents, data protection and other technical



security issues were very important. However, a clear result was: the younger the participant the less concerned they were about data protection. A clear affinity towards mobile services and mobile payment could be seen in the younger generation. In review of the research questions, there are two factors which can be seen as strong influencers for subjective security. First, the trust factor was established by the mobile payment service provider being an independent institution, which makes a reliable impression and is well known. For 85% of participants this was a bank (in spite of the financial crisis); for 66% it was a credit card company and for 61% a third party provider, such as PayPal. Second, the influence of the social network was very important as well, as 60% would trust friends if they recommended mobile payment and thought that mobile payment were secure. The influence of the social network was important, however, it did not dictate the behaviour entirely. In the analysis of the sample opinion questions, three out of five hypotheses could be supported. The constructs subjective norm and trust in mobile payment service provider could be stated as strong influencers of subjective security. In contrast, the weaker influencer of subjective security is surprisingly technical security. In detail the hypotheses of technical security and trust in mobile payment service provider influencing subjective security could not be supported. This confirms that technical security does not lead to an increase in subjective security. Hence, users feel secure about mobile payment when they

trust the mobile payment provider’s technology. However, they cannot necessarily judge the security of the technology; the claim that provider’s technology is secure helps increase the subjective security. If the mobile payment service provider is well-known, the trust level increases, as well. Subjective security as an essential precondition for mobile payment acceptance is greatly influenced by the social network through trust in a mobile payment service provider. This is an important finding which results in the need to build up strong trusted brands; hence more marketing effort is needed on top of the technology effort. A possible method to provide subjective security is a marketing campaign in order to make the customers aware that this form of payment even exists and to provide information about how it works; also critical is creating a recognizable brand that would give the feeling of security. User acceptance can be reached with an intuitive and easy to use system coupled with a high subjective security, a well-known and strongly branded mobile payment provider and an added value. Reliable information, awareness advertisement and communication of a clear benefit to potential users – marketing in general will be indeed needed. With more awareness and increased market penetration people will begin to encounter mobile services on a daily basis and the trust level will rise, all leading to a higher acceptance level of mobile payment. However, the key driver might be the added value that could go along



with mobile payment, for example in the form of mobile couponing. The authors see that there is further need to understand the end-users’ reality of technical security in more detail. Hence, it would be good to understand in depth whether there are indicators besides brands and trust which allow people to understand technical mechanisms in place. The limitation of this study is clearly that it is based mainly

around the central European market and has small sample from the United States included. It would be interesting to conduct a study specifically looking at separate countries as well comparing new economies and established economies. In addition it would be interesting to follow up the study and have a longitudinal study resulting in understanding of the factors which are shifting trust.



REFERENCES Alotaibi, M. B. (2013). Determinants of mobile service acceptance in Saudi Arabia: A revised UTAUT model. International Journal of E-Services and Mobile Applications, 5(3), 43–61. doi:10.4018/ jesma.2013070103 Andreoli, G. (2008). Mobile payments - reloaded, New regulation opens doors. Ericsson Business Review, No., 3/2008, 52–56. Beaudry, A., & Pinsonneault, A. (2010). The other side of acceptance: Studying the direct and indirect effects of emotions on information technology use. Management Information Systems Quarterly, 34(4), 689–A3. Bregulla, C. (2011), Zahlen per Handy: Ein rascher Durchbruch ist nicht in Sicht. cards Karten cartes, 1/2011, p. 25–27. Brown, S. A., Venkatesh, V., & Goyal, S. (2012). Expectation confirmation in technology use. Information Systems Research, 23(2), 474–487. doi:10.1287/ isre.1110.0357 Burkard, C. et al. (2012). NFC payment (pp. 142–146). Trends in der IT.

Cimiotti, G. et al. (2008). Das Kartengeschäft, Zwischen Convenience, Effizienz und Profitabilität. Taschenbücher für Geld, Bank, Börse (p. 3). Frankfurt am Main, Germany: Knapp. Dahlberg, T., et al. (2003). Consumer acceptance of mobile payment solutions – Ease of use, usefulness and trust. In Froeschl, K. (Ed.), mBusiness 2003 The 2nd International Conference on Mobile Business (ICMB) (pp. 211–218). Österreichische Computer-Gesellschaft. Davis, F. (1993). User acceptance of information technology: system characteristics, user perceptions and behavioral impacts. International Journal of Machine Studies, 38/1993, 475–487. Der Kontakter. (2011). Smartphone wird Geldbörse. Mobiles Bezahlen. Der Kontakter, 5/2011, 20–21. Esteva-Armida, E., & Rubio-Sanchez, A. (2012). Adoption process for VoIP: The UTAUT model. International Journal of E-Services and Mobile Applications, 4(4), 15–31. doi:10.4018/ jesma.2012100102

Capgemini (2010). World payments report 2010.

Fassot, G., et al. (2005), Zur Verwendung formativer und reflektiver Indikatoren in Strukturgleichungsmodellen. Bestandsaufnahme und Anwendungsempfehlungen. In Bliemel, F. (Ed.), Handbuch PLS-Pfadmodellierung. Methoden, Anwendung, Praxisbeispiele (pp. 31–47). Stuttgart, Germany: Schäffer-Poeschel.

Chen, L.-D. (2008). A model of consumer acceptance of mobile payment. International Journal of Mobile Communications, 6(1/2008), 32–52.

Fishbein, M. et al. (1975). Attributes, attitude, intention, and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.

Bussmann, J., et al. (2010), Mobile payment. Markt mit Wachstumsperspektive, die bank, 10/2010, 68–72.



Georgi, F., et al. (2005). Der zweite Anlauf. Mobile Banking in Deutschland, die bank, 03/2005, p. 56–61. Ghobakhloo, M. et al. (2013). Adoption of mobile commerce: The impact of end user satisfaction on system acceptance. International Journal of E-Services and Mobile Applications, 5(1), 26–50. doi:10.4018/jesma.2013010102 Godschalk, H. (2006). Zahlungsverhalten am point of sale in Deutschland - aktuelle Entwicklungen. In T. Lammer (Ed.), Handbuch e-money & m-payment (pp. 35–42). Heidelberg, Germany: Physica-Verlag. doi:10.1007/3-79081652-3_4 Google. (2011). Google wallet. Available at www.google.com Jingjun (David), X., Benbasat, I., & Cenfetelli, R. T. (2013). Integrating service quality with system and information quality: An empirical test in the e-service context. Management Information Systems Quarterly, 37(3), 777–A9. Judt, E. (2006). Zahlungsverkehrinnovationen im Wandel der Zeit und ihre Vermarktung. In T. Lammer (Ed.), Handbuch e-money, e-payment & mpayment (pp. 19–34). Heidelberg, Germany: Physica-Verlag. doi:10.1007/37908-1652-3_3 Kim, H., & Kankanhalli, A. (2009). Investigating user resistance to information systems implementation: A status quo bias perspective. Management Information Systems Quarterly, 33(3), 567–582.

Krafft, M., et al. (2005), Die Validierung von Strukturgleichungsmodellen mit Hilfe des partial-least-squares (PLS)Ansatzes. In F. Bliemel (Ed.), Handbuch PLS-Pfadmodellierung. Methoden, Anwendung, Praxisbeispiele. Stuttgart, Germany: Schäffer-Poeschel. Kreimer, T. et al. (2010). Mobile payment. Anforderungen, Barrieren, Chancen. KPMG. Linck, K. et al. (2006), Security issues in mobile payment from the customer viewpoint. In Proceedings of the 14th European Conference on Information Systems. Luarn, P., & Lin, H.-H. (2005, November). Toward an understanding of the behavioral intention to use mobile banking. Computers in Human Behavior, 21(6), 873–891. doi:10.1016/j. chb.2004.03.003 Lyytinen, K., & Youngjin, Y. (2002). Research commentary: The next wave of nomadic computing. Information Systems Research, 13(4), 377–388. doi:10.1287/isre.13.4.377.75 Mallat, N. (2007). Exploring consumer adoption of mobile payments – A qualitative study. The Journal of Strategic Information Systems, 16(4), 413–432. doi:10.1016/j.jsis.2007.08.001 MasterCard. (2010). Mastercard PayPass. Available at www.mastercard.us



Mathieson, K. (1991). Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior. Information Systems Research, 2(3), 173–191. doi:10.1287/ isre.2.3.173 Merz, M. (2002). e-Commerce und EBusiness, Marktmodelle, Anwendungen und Technologien (2nd ed.). Heidelberg, dpunkt-Verlag. Olmedilla, D., Rana, O., Matthews, B., & Nejdl, W. (2005). Security and trust issues in semantic grids. In Proceedings of the Dagsthul Seminar, Semantic Grid: The Convergence of Technologies (vol. 05271). Pfirsching, V. (2005), Marktentwicklung und kritische Erfolgsfaktoren für Mobile Payment. In F. Lehner (Ed.), Mobile Anwendungen. Heidelberg, Germany: dpunkt-Verlag. Pousttchi, K. (2003). Conditions for acceptance and usage of mobile Payment Procedures. In K. Froeschl (Ed.), mBusiness 2003- The 2nd International Conference on Mobile Business (ICMB). Österreichische Computer-Gesellschaft. Pousttchi, K. (2005). Mobile payment in Deutschland, Szenarienübergreifendes Referenzmodell für mobile Bezahlvorgänge. Dt. Universitäts-Verlag. doi:10.1007/978-3-322-81904-8 Pousttchi, K. (2008). A modeling approach and reference models for the analysis of mobile payment use cases. Electronic Commerce Research and Applications, 7(2), 182–201. doi:10.1016/j. elerap.2007.07.001

Pousttchi, K., et al. (2010). A scenariobased analysis of mobile payment acceptance (pp. 371-378). Athens, Greece. Ringle, C. (2005). SmartPLS 2.0 (M3). Ringle, C. et al. (2007). Beurteilung der Ergebnisse von PLS-Pfadanalysen. WISU, 2/2007, 211–216. Schierz, P., et al. (2010), Understanding consumer acceptance of mobile payment services. An empirical analysis. Electronic Commerce Research and Applications, 9(3/2010), 209–216. Selvan, N. T., Arasu, B. S., & Sivagnanasundaram, M. (2011). Behavioral intention towards mobile banking in India: The case of state bank of India (SBI). International Journal of E-Services and Mobile Applications, 3(4), 37–56. doi:10.4018/jesma.2011100103 Shen, S. (2011). Forecast: Mobile payments worldwide 2008-2015. Gartner Inc. ID Number: G00212782. Spiegel Online. (2011). Zahlung per Handy, Mobilfunk-Manager prophezeit Kreditkarten das Aus. Available at www. spiegel.de Stoneburner, G. (2001). SP 800-33. Underlying technical models for information technology security. Gaithersburg, MD: Technical Report. NIST. SurveyMonkey 2.0. (2009). Available at www.surveymonkey.com



Sykes, T., Venkatesh, V., & Gosain, S. (2009, June). 2009 model of acceptance with peer support: A social network perspective to understand employees’ system use. Management Information Systems Quarterly, 33(2), 371–393. The Economist. (2011). Money? There’s an app for that. The Economist, 8735/2011, 71. Venkatesh, V. (2003). User acceptance of information technology: Toward a unified view. Management Information Systems Quarterly, 3/2003, 425–478. Venkatesh, V. et al. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. Management Information Systems Quarterly, 36(1), 157–178.

Watkins, C. (2009). Young and the digital: What the migration to social network sites, games and anytime media means to our future. Beacon Press. Wixom, B. H., & Todd, P. A. (2005). A theoretical integration of user satisfaction and technology acceptance. Information Systems Research, 16(1), 85–102. doi:10.1287/isre.1050.0042 Wu, J., & Lederer, A. (2009). A metaanalysis of the role of environment-based voluntariness in information technology acceptance. Management Information Systems Quarterly, 33(2), 419–A-9.
