Goodbye wallet, hello Smartphone?!
Factors that influence consumers’ intention to use mobiles in the
payment process
Thesis for obtaining the Master of Science in Marketing
By:
Anastasia Nanou
MSc. Marketing Management2
Goodbye wallet, hello Smartphone?! :
Factors that influence consumers’ intention to use mobiles in the
payment process
-Master thesis -
Author: Anastasia Nanou Oosterhamrikkade 105, 9713KC, Groningen Tel: +31 0647920653 Email: anastnanou@hotmail.com Student number: S3701301 MSc Marketing Management University of Groningen Faculty of Economics and Business1st Supervisor: Dr. Hans Berger
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Acknowledgements
The decision to take a Master’s degree was a great challenge for me, as it was my first real experience abroad. This year in RUG changed my life and helped me to grow up both academically and personally. Through the master, I learnt to approach things and issues in a different way, see everything from another point of view and be more critical. This educational “trip” helped me to become more confident about myself and my capabilities and to be able to work effectively and efficiently in an international environment. I am very proud of myself and of what I have already achieved and I am grateful to RUG for giving me this opportunity.
Furthermore, I want to thank my supervisor Dr. Hans Berger for the support and encouragement he gave me, when I was too stressed and concerned. Thank you for your positivity throughout the whole thesis procedure. This aspect has been really important and helpful.
I want to thank my thesis group, as well, for the positive and constructive atmosphere we had during the meetings. The questions, the insights and the experiences we shared was extremely important and useful during this procedure.
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ABSTRACT
Given the widespread use of mobile phones and customers’ need for convenient transactions, mobile payment is a key for the growth of in-app payments and proximity transactions. Even though Asia/Pacific and Africa have already adopted mobile payments in a great extent, Europe seems to have low adoption rates for such technology. The current research is trying to explain this acute contrast taking into consideration the Netherlands as a great example. The aim of the research is to find out the factors that motivate or hinder consumers’ intention to use mobile payments. The proposed model for this study is a development of the Technology Acceptance Model (TAM), in order to make it more precise and compatible with mobile payment field answering the calls for its improvement. Word-of mouth, Technology readiness dimensions and privacy concerns taken into account for the research. An online questionnaire was created where 100 participants, mainly students, answered a series of questions related to m-payment and personality traits. Through Partial Least Square regression (SmartPLS), we found out that WOM has an effect on intention to use, when people perceive m-payments as useful and helpful (full mediation). Moreover, we observed that some of the personality dimensions of technology readiness affect consumers’ intention, while others not. The most unexpected one was the positive effect of discomfort on intention to use m-payments. Last but not least, we discovered that privacy concerns does not have an effect on intention to use mobile payment services, which is a result of the “privacy paradox”. From a managerial perspective, this research gives insights on how to better embolden Dutch consumers in order to adopt mobile payments. Furthermore, this research can be a source for further research in this direction.
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Table of Content
1. Introduction.………...………...8
2. Theoretical framework………...….……….…………...….….12
2.1 Literature review on mobile payments...12
2.2 TAM model review ...12
2.3 Research hypotheses………...……….……….…15 2.3.1 Word-of-mouth (WOM)…………... ……….15 2.3.2 Technology readiness (TR)………...….………16 2.3.2.1 Optimism………...……….………..16 2.3.2.2 Innovativeness…...……….………..17 2.3.2.3 Discomfort……….………..17 2.3.2.4 Insecurity…………...……….…….……….18
2.3.3 Perceived usefulness (PU)………....……….19
2.3.4 Perceived ease of use (PEOU)………...………..….….19
2.3.5 Privacy concerns………...………20
2.3.6 Control variables………...20
2.3.6.1 Gender………20
2.3.6.2 Early- late adopters……….21
2.4 Proposed conceptual framework…...………22
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4.1 Sample characteristics………27
4.2 Analysis………..27
4.2.1 The measurement model………..…27
4.2.2 The structural model………...….29
4.2.2.1 Hypotheses testing……….…..……..….30
5. Discussion………34
6. Conclusion……….………..37
7. Limitations and future research………...39
Reference list………..………..………...40
Appendices………..51
Appendix 1 - Questionnaire………...…...……...………51
Appendix 2 - Loadings...……...……..……….53
Appendix 3 - Inner VIF scores ………...………...………….………….54
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1. Introduction
The business environment has become dynamic throughout the years. A significant number of technological innovations had and still have an essential impact on the world economy. Rapid technological development brought many possibilities for changes that have an effect on our everyday lives and offer a new shopping experience. One of them is online shopping, which has exploded the last years, providing new digital marketplaces rather than traditional ones (Gummesson, 2008). Mobile commerce came, also, into the foreground offering attractive and advantageous digital services such as mobile payment. Mobile payment (m-payment) is defined as “any payment where a mobile device is used to initiate, authorize and confirm an exchange of financial value in return for goods and services” without any limitation on time or space (Au and Kauffman, 2008). The primal vision of m-payment is to surmount the spatiality and temporality of conventional web payment and cash-centric payment (Lu et al., 2011), utilizing innovative wireless communication technologies such as Short Messaging Service (SMS), Near Field Communication (NFC), QR codes (Bangdao & Roscoe, 2011). Since mobile phones offer a wide range of functionalities, they can support several financial transactions and services such as bill payments, account transfers, epoint of sales payment, and person-to-person transfers as well as remote payments for acquiring goods and services (Oliveira et al., 2016). According to Forrester Research, mobile payments can be divided into three categories: in-person mobile payments, such as proximity (contactless) payments; remote mobile payments (via an app or mobile website, the purchase does not happen physically in a store); and peer-to-peer mobile payments (eMarketer, 2017). The benefits of m-payment are considerable for both consumers and businesses, as they offer convenience and speed (Teo et al. 2015), high performance and transfer of secure information among devices (Leong et al. 2013). This decreases the operation time significantly, leading to clear productivity gains.
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million users in 2021 (Statista 2016). Looking specifically at various continents, the adoption intention of m-payment services among European users is 13% in 2016, which is low compared to other regions such as Asia/Pacific with 37% or Africa with 19% (Nielsen 2016). China and India are the leaders of the mobile payment market, and their population’s willingness to use mobile payments for their purchases exceed global average with 46% and 45% respectively (Nielsen, 2016). However, the number of users in Europe is anticipated to increase from 300.000 in 2016 to 6.1 million in 2021 (Statista 2016a), mainly due to mobile wallets from Apple, Samsung, and Google. Gartner (2015) corroborates this trend, foreseeing that by 2018 almost half of the consumers in mature markets will use smartphones or wearable devices for mobile payments.
Looking specifically in the Netherlands, we can forecast that mobile payment transactions will increase in the near future. Netherlands’ population is a frontrunner in the adoption of alternative (i.e., non-cards) payments (European Payments Council, 2019). The Netherlands has higher smartphone penetration globally with a percentage of 93% in 2017 (Deloitte, 2017). Dutch often use their smartphones for practical and financial matters, and this is noticeable in 2018, where 36% of online purchases were made using mobile phones compared to 30% in 2017 (Gfk.com, 2019). The most dominant payment method in the Netherlands is i-Deal, which is a platform made from a consortium of Dutch banks that offers inter-bank transactions for e-commerce and mcommerce payments (BCG, 2017). I-Deal has reached 63% of all online payments transaction in the Netherlands in 2018 (Statista, 2019) and as the year progresses, it is expected that almost 8 million Dutch mobile users will be able to make i-Deal payments through scanning QR codes quickly and safely (Ideal.nl, 2019). However, many other payment methods are available such as PayPal, Samsung Pay, and Google Pay, with the last option not fully supported on in-store contact payments, but only on remote payments via mobile applications.
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and prefer mostly contactless transactions, they have not adapted m-payment to the expected extent. Mobile transactions represent approximately 10% of the overall online payments (Gfk, 2017), but it is likely to rise in the upcoming years.
This acute contrast between the sustainable growth of the mobile payment market and the low stickiness in Europe raises a thought-provoking question: which factors
influence consumers’ intention to use mobile payments?. This lack of dissemination
of the service with a considerable potential demonstrates that successful cases from other countries and the main obstacles for discouraging adoption have not been investigated deeply enough to be clearly understood and allow implementation strategies. Hence, it is essential to examine further the factors for approval to have more insights and a better understanding. From a managerial aspect, this research will guide merchants and businesses on how to encourage users better to adopt mobile payments. It can also be used as an instrument to develop and take the relationship between companies and consumers to a new level (Shankar et al. 2010). By understanding those factors that influence consumers’ usage intentions, m-payment will bring significant benefits to both parties, in terms of shopping experience improvement for consumers and higher brand performance and revenue drivers as well as higher product and brand awareness for companies.
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factors that motivate or hinder its use. To be more specific, there are previous researches available that have used Technology Readiness as a variable, but either they have not investigated it on in its full dimensionality or they have used it in a general technology field rather than m-payment. Many of those researches have already used it as one constructor and they haven't taken into account all of its four dimensions. In our case, we want to investigate the effect of each of the four dimensions on mobile payment rather than the aggregate construct itself. Furthermore, the Netherlands is a country with high smartphone usage levels, but mobile payment services are still in a developing stage. This makes it an exciting and challenging example to use in our research. Although there are researches available about the topic, they are inferred mostly in countries such as China, South Arabia, India, and Hong Kong, in which the adoption intention rates are higher. In Europe, the availability is limited as the research is still in progress; thus, the need to explore the inhibitors of such adoption is intriguing and necessary for such purposes.
Therefore, the main focus of this research is:
“What is the impact of personality traits and written/verbal recommendations in consumers’ intention to use m-payment?”
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2. Theoretical framework
2.1 Literature review on mobile payments
Mobile payment research, in general, is still in a nascent stage in comparison with other related fields such as mobile commerce, internet banking or mobile banking (Oliveira et al. 2016). Nevertheless, there is a significant increase in the number of studies during the last years, as between 2007 and 2015, 188 new articles were promulgated (Dahlberg et al. 2015). This highlights the increasing importance and relevance of m-payment, which authors ascribe to its potential to transform the payment market (Hedman & Henningsson 2015), its success in Asian countries (Miao & Jayakar 2016) and its promising future as a technological innovation generally (Oliveira et al. 2016). There are some notable studies, published in top tier journals that have emphasized the value of understanding the needs and preferences of consumers, in order to be able to provide a service that creates unique value for consumers as well as for merchants (Lai & Chuah 2010). Leong et al. (2013) utilized constructs from psychological science and technology acceptance model - TAM (Davis 1989) to examine the intention to use mobile payment. Slade et al. (2013) suggested that further analysis is needed and they took into consideration trust and perceived risk; the last one dimension was recently explored by Yang et al. (2015). Furthermore, there are some authors, who scrutinize that the widespread adoption of mobile payment services depends on a secure and reliable payment system that is convenient and easy to use (Chang et al. 2010). However, Dahlberg et al. (2008) acknowledge in their literature review that m-payment field needs to be enriched with new interesting research variables such as consumer involvement in the development of mobile payment services or other psychological and behavioral traits, as a mobile payment service is perceived as a “means to behave.”
2.2 TAM model review
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Technology (CIT), there is a variety of user acceptance models available. The technology acceptance model (TAM) has been applied widely in many contexts, and it has been confirmed, extended, and improved. TAM2, TAM3 (Venkatesh and Davis, 2000) are direct descendants of TAM. The unified theory of technology acceptance (UTAUT) is also based on elements of TAM.
However, the most popular and frequently applied is the TAM model, which is used as a framework for investigating intentions to adopt new technology (Aboelmaged and Gebba, 2013). TAM was firstly proposed by Davis in 1983 and then redefined in 1989. Basically it is a combination of earlier behavioral psychology studies such as the Theory of Reasoned Action (TRA) and the Theory of Planned Behavior (TPB), which suggest that “people base their behavior on the attitude they hold towards that behavior, the perceived control they have over that behaviour and the subjective norm” (Fishbein, Ajzen 1975). The primary difference with Theory of Planned Behavior (TPB is that perceived usefulness (PU) and perceived ease of use (PEOU) influence an individual's attitudes toward their intention to use new technology and the intention serves as a mediator to the actual use of that technology.
According to Davis (1989), the Technology Acceptance Model developed a relationship among ‘‘belief”, ‘‘attitude”, and intention”, and behavior that explores the reasons behind the acceptance or rejection of computer technology in an organization, considering the given benefits of computer systems (Davis et al. 1989). Thus, TAM used us to predict users’ intention towards new technologies. The model has two main variables, perceived usefulness and perceived ease of use, respectively, which have a significant impact on people's attitudes towards new technologies.
This is illustrated in figure 2.2.1.
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The model has been applied in several studies with a different context, as it is recognized as one of the most influential frameworks in the available literature to analyze customers’ adoption intention. More specifically, it is a basis in many fields such as mobile banking, mobile commerce, RFID, augmented reality, electronic labels, healthcare information systems, e-financial services, and NFC (Gao and Waechter, 2017).
However, the TAM model has been criticized a lot due to its focus on the adoption of technology rather than the behavior or the benefits that are tried to be achieved (Bagozzi, 2007). The model focalizes on the individual user, excluding economic and social factors. Thus, many mobile banking adoption researches extend or readapt the original TAM model by introducing additional variables, such as social norm, relative advantage and personal innovativeness (Chitungo and Munongo, 2013), perceived risk, compatibility with lifestyle (Hanafizadeh et al., 2014), and perceived security (Hsu et al., 2011).
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trustworthiness, and trust are low. Hence, a positive or negative suggestion can have a significant impact on consumer's choices towards mobile payment services.
In the next chapter, all the variables mentioned above will be explained and analyzed, concluding into our main hypotheses and our conceptual model.
2.3 Research hypotheses
2.3.1 Word-of-mouth (WOM)Kotler et al. (2008) defined WOM as “personal communication about a product between target buyers and neighbors, friends, family members, and associates" emphasizing the interpersonal aspect of WOM. Many types of research have studied the effect of WOM and they considered it as “one of the most influential resources of information transmission since the beginning of society" (Lee et al., 2008). Word-of-mouth is based on consumer's experience through the use of product or service and reflects the customer's perspective. Moreover, WOM has a robust persuasive impact on consumer's purchase intention, which is associated with credibility” (Lee et al., 2008). Current literature reports that people perceive recommendations and suggestions from their peers as more reliable and convincing than formal advertisements and other sources of influence (Bickart & Schindler, 2001). More specifically, consumers ask for information, as a way to reduce the perceived risk, while they are dealing with high-risk purchases (Velázquez et al., 2015), in the pre-purchase phase. This happens mostly in the context of services (Jalilvand and Samiei, 2012), as they are experience-based “products,” thus, consumers betake and trust WOM information more. In the post-purchase phase, WOM serves as a way to make denunciations and complains or reduce dissonance (Velázquez et al., 2015), but mainly to prevent errors and help others (Laughlin and MacDonald, 2010) in the purchasing process. Research acknowledges that WOM has a significantly higher impact on consumer behavior than advertising or promotion does. According to Hogan et al. (2004), we can understand the value of WOM, as it can triple advertising's effectiveness. Thus, we conclude to the following hypotheses:
Hypothesis 1a. Word of Mouth has a positive effect on the intention to use mobile
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Hypothesis 1b. The relationship between Word of Mouth and intention to use mobile
payments is mediated by perceived usefulness.
Hypothesis 1c. The relationship between Word of Mouth and intention to use mobile
payments is mediated by perceived ease of use. 2.3.2 Technology readiness (TR)
Technology readiness is defined as an “individual's propensity to embrace and use new technologies” (Parasuraman, 2000). Technology readiness describes perceptions, feelings, and beliefs an individual holds towards high-tech products and services. Existing literature indicates that an individual can have favorable and unfavorable technological attitudes in parallel. In this “equation,” the balance between the two beliefs designate which one is the most dominant and leads to specific behavior – accept or reject new technologies (Rosenbaum and Wong, 2015). Technologies can induce feelings of anxiety (Meuter et al., 2003) and fun (Agarwal and Karahanna, 2000), that influence customers' beliefs and behavior subliminally or supraliminal towards technologies. After extensive empirical researches, Rogers (1995) suggested four dimensions of TR: optimism, innovativeness, discomfort, and insecurity. Optimism and innovativeness are considered to act as facilitators that increase an individual's intention to use new technology, while insecurity and discomfort act as inhibitors, leading to resistance.
2.3.2.1 Optimism
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Hypothesis 2a. Personal optimism about technology leads to higher perceived
usefulness of mobile payments.
Hypothesis 2b. Personal optimism about technology leads to higher perceived ease of
use of mobile payments.
2.3.2.2 Innovativeness
Innovativeness refers to the ‘‘willingness of an individual to try out any new information technology’’ (Flynn & Goldsmith, 1993). By accepting the new technology, the customer turns into a pioneer or opinion leader. Generally, an innovative person actively seeks new ideas and he can cope better with high levels of uncertainty. Even if an individual does not have much knowledge or experience about new technology, curiosity acts as a motivator to use it (Flynn & Goldsmith, 1993). This boosts their confidence and heightens their perception that they can handle adequately new technology. As innovative people are more likely to take risks, we expect them to show more positive intentions towards mobile payments. Therefore, we propose:
Hypothesis 2c. Innovativeness has a direct positive impact on perceived usefulness of
mobile payments.
Hypothesis 2d. Innovativeness has a direct positive impact on perceived ease of use of
mobile payments.
2.3.2.3 Discomfort
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Hypothesis 2e. Personal discomfort about technology leads to lower perceived
usefulness of mobile payments.
Hypothesis 2f. Personal discomfort about technology leads to lower perceived ease of
use of mobile payments.
2.3.2.4 Insecurity
Insecurity is related to the lack of trust and skepticism about new technology based on security reasons (Parasuraman, 2000). Insecurity is perceived as an essential factor that inhibits usage and contributes to the low adoption levels of e-payment (Hoffman et al., 1999). Customers with a sense of insecurity are suspicious about new technologies and they have a feeling of discomfort, which leads to anxiety, avoidance, and resistance. According to Meuter et al. (2003), technology anxiety causes unfavorable beliefs about new technologies and tries to reduce the amount of time spent using such technology. As a consequence, customers become suspicious of new technological functions and overcome acceptance and usage trials. Thus, we suppose that:
Hypothesis 2g. Personal insecurity about technology leads to lower perceived
usefulness of mobile payments.
Hypothesis 2h. Personal insecurity about technology leads to lower perceived ease of
use of mobile payments.
Erdoğmuş and Esen (2011) found out that optimism and innovativeness affect usefulness and ease of use significantly, while discomfort and insecurity do not affect them. Moreover, Lin and Chang (2011) discovered that technology readiness affects adoption intention not only indirectly through PU and PEOU, but also directly. Taking the above into consideration, we hypothesize that:
Hypothesis 2i. Optimism has a positive direct effect on intention to use mobile
payments.
Hypothesis 2ii. Innovativeness has positive a direct effect on intention to use mobile
payments.
Hypothesis 2iii. Discomfort has a negative direct effect on intention to use mobile
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Hypothesis 2iv. Insecurity has a negative direct effect on intention to use mobile
payments.
2.3.3 Perceived usefulness (PU)
Various studies have identified the importance of perceived usefulness (Guriting & Ndubisi, 2006). Perceived usefulness is the extent to which consumers believe that using a particular technology can help them to achieve their goals. In our research, perceived usefulness is expected to affect and improve consumers’ intention to use m-payment services.
According to TAM, the perceived usefulness is “the extent to which a person believes that utilizing a particular method or technique would enhance his or her job performance or routine responsibility.”(Davis, 1993). Numerous studies have evidenced that perceived usefulness has a direct effect on the intention to use (Liébana-Cabanillas, Sánchez-Fernández, & Muñoz-Leiva, 2014). In the current study, we consider that the perceived usefulness of the payment system will affect the intention to use mobile payment. Based on the previous thoughts, we propose the following hypothesis:
Hypothesis 3. Perceived usefulness has a positive effect on the intention to use
m-payments.
2.3.4 Perceived ease of use (PEOU)
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Hypothesis 4. Perceived Ease of use has a positive effect on the intention to use
m-payments.
2.3.5 Privacy concerns
Bauer (1960) signalizes that risk is an essential factor that influences consumer behavior. Privacy risk occurs when an individual has a lack of control over his personal information when he/ she gives information to other parties for completing a specific transaction (Featherman & Pavlou, 2003). Privacy concerns refer to the individual’s concerns to control the collection and use of information acquired through online transactions (Castañeda & Montoro, 2007). With the development of e-commerce applications and technologies, several entities can exploit personal data to provide targeted advertisements, even without the consumer's knowledge. This raises several privacy concerns about the collected amount of personal information and acts as an inhibitor of the intention to use (Gadzheva, 2007). Consequently, companies are endeavoring to change this perception, in order to enhance the sense of security and lessen the negative impact. Many studies found out that privacy concerns are one of the main factors that impede the implementation of new electronic payment systems (Lee, 2009). Thus, it is of utmost importance to settle new security mechanisms for new electronic payment services, in order to increase transaction security and cultivate a feeling of trust, assurance, and confidence, hence improving attitude and intention. Then, we suggest the following research hypothesis:
Hypothesis 5. Privacy concerns have a negative direct effect on the intention to use m-
payments.
2.3.6 Control variables
The main research focuses on the impact of tech readiness, PEOU and PU on the intention to use, and the model is controlled for additional factors, such as gender and early/late adopters. Previous academic research has already proven that the abovementioned variables have an influence on both intention to use and on behavioral traits, therefore, there is the need to be controlled.
2.3.6.1 Gender
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compared to females (Gilroy and Desai, 1986). Moreover, Tsikriktsis (2004) mentioned that males are more willing to adopt new technological devices and they have higher self-confidence levels in using such new technologies than women (Elliot and Hall, 2005). Studies about online purchasing habits found out that males and females have different levels of adoption, where males seem to be the dominant online shoppers (Rodgers & Harris, 2003). This attributed to the fact that women are less risk-taking, less experienced being online and more anxious with new technologies. Considering the differences between the two sexes, we presume that males will be more positively directed to use m-payments comparing to females.
2.3.6.2 Early-late adopters
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2.4 Proposed conceptual framework
Based on the literature review and the developed hypotheses, we conclude in the following conceptual model:
Figure 2. Conceptual model
Control variables:
Gender
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3. Research method
3.1 Research design
In order to meet the object of the current study, a personal cross-sectional survey was conducted, in order to examine the given model and its hypotheses. In the present study, we used the form of self-completion questionnaires via computer or mobile phones, as a method to collect the primary data. The structured computer questionnaires simplify the survey and make the data more reliable, as fixed-response questions provide reduced variability (Malhotra, 2010). The items used to measure the constructs were adapted or modified from the existing literature by using established scales that fit in the context of mobile payment. We used already established measures, in order to assure the validity of the instrument. The questionnaire was structured in three sections and seven-point Likert scales were used, with scales ranging from one (strongly disagree) to seven (strongly agree). As 7-point Likert scales are more accurate in measuring consumers’ evaluations (Finstad K., 2010), we chose to use such scales in all of our variables and rescale them when it is needed.
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questionnaire, we included several demographic questions in order to form the participants’ background. Gender, age, educational level, occupation, and income level have been taken into account for this purpose.
3.1.1 Measurements
Based on the existing literature review and models, we adapted scales from previously validated studies, in order to measure the identified factors that have an impact on adoption intention of m-payment. More specifically, technology readiness variables were taken from Rosenbaum & Wong (2015) and measure optimism, innovativeness, discomfort, and insecurity in a 3-item scale per variable. A 3-item scale from Mehrad & Mohammadi (2016) was used to measure Word-of-Mouth. The 4-item scale for perceived ease of use and perceived usefulness was developed fromVenkatesh &Davis (2000) and Tan et al. (2014) studies respectively. The measurement for privacy concerns consisted of three items adapted from Dinev & Hart (2005). Finally, behavioral intentions towards mobile payment were measured using three items adapted from Venkatesh, Thong & Xu (2012). Appendix 1 depicts more extensively all the above-mentioned information.
3.1.2 Sample
A random sample would be an ideal sample for such a survey. However, the limited resources and the scale of the research make it unattainable to acquire a completely random sample. Therefore, convenience sampling has been chosen as a way to access respondents easily (Malhotra, 2010). Convenience sampling is “a type of non-probability or non-random sampling, where members of the target population that meet specific practical criteria, such as easy accessibility, geographical proximity, availability at a given time, or the willingness to participate are included for the purpose of the study (Dörnyei, 2007). Most of the respondents will be situated spatially, near to the personal network of the researcher, meaning that a large part of the population sample will include students between 20 and 30 years old. For the collection of the primary data, an online structured questionnaire via a computer or mobile phone will be used and will be completed by the sample.
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for mobile banking (Wang et al., 2012) and not for mobile payments in the retail sector. This fact comes in line with the limited research on the topic and indicates that research zooming in the Netherlands is essential. Such research will help to gain knowledge about consumers' behavior in terms of m-payment usage and to provide solutions for change and improvement. Thus, our sample will be mainly Dutch people.
3.2 Statistical technique
In the current research, structural equation modeling (SEM) was employed, in order to evaluate the proposed research model and hypotheses with the Smart Partial Least Squares (PLS) software (Chin 1998). Structural equation modeling contains multivariate techniques that integrate features of factor as well as regression analysis, allowing the researcher to evaluate relationships among "measured and latent variables- assessment of measurement model, and between latent variables- assessment of the structural model simultaneously. Measurement model (outer model) examines the relationships between a construct and its observed indicators, while the structural model (inner model) examines the relationships between constructs. Both of the models are sets of linear equations that form the PLS path model. Furthermore, SEM assesses the evaluation of the relationships between exogenous and endogenous variables and encourages the development of an assessment about how a combination of different causal variables can generate a specific outcome (Ordanini et al., 2014; Carrión et al., 2016). PLS supports the measurement of two types of constructs- reflective and formative. Formative constructs are “instances in which the indicators cause the construct,” while reflective constructs are instances where indicators are “caused by the construct” (Hair et al., 2014). In our model specifically, all of the constructs are perceived as reflective and thus, the arrows go from the latent variable to the indicator variables. This happens as the indicators are representative and they reflect the measured latent variable.
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PLS-SEM constitutes an algorithm, which is a sequence of regressions using weight composites (Henseler et al., 2009). These weight composites that are acquired in convergence comply with the fixed point equations. More specifically, the PLS algorithm consists of three main stages (SmartPLS, 2018): a repetitious assessment of latent variable scores that is reiterated until we obtain convergence; an evaluation of outer weights-loadings and path coefficients; an estimate of the parameters. By using weighted composites of indicator variables, the account for measurement error is facilitated, making PLS-SEM as a superior model compared with those of multiple regression using sum scores (Henseler et al., 2014).
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4. Results
4.1 Sample characteristics
A total of 100 valid responses were obtained. As we wanted only those who use mobile phones for their purchases, six respondents of the sample where excluded, thus the final sample was 94 respondents. The respondents in the sample were 39 males and 55 females. The vast majority of the sample was people aged between 22 and 27 years and the majority of their employment status was students. This can be justified from the fact that we used a convenience sampling method. All of the respondents were Dutch, as our research focuses on the Dutch market. A large part of the sample had obtained a bachelor degree 60.6% and a 26.6% of the sample has a Master’s degree.
Moreover, we wanted to distinguish our sample between early and late adopters. The results showed that 71.27% of the respondents perceives themselves as “early adopters” (n=67), 20. 21% perceives themselves as “late adopters” (n=19), while a percentage of 8.5% believes that is equally behaving both as “early and late adopters”.
4.2 Analysis
PLS-SEM analysis uses a two-step process (Hair et al., 2010). The first step contains the assessment of the measurement model, also called the outer model, which estimates the relationships between the latent constructs and their indicators. Measuring the reliability and validity of the latent constructs’ is necessary before the assessment of the structural model. If the measurement model is sufficient, the assessment of the structural model begins (step two). The structural model, also called the inner model, is an estimation of the relationships between the latent constructs.
4.2.1 The measurement model
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Firstly, we examined the indicator reliability. According to the theory, all indicator loadings should be above 0.70, in order to have acceptable convergent validity. Outer loadings between 0.40 and 0.70, should be removed from the scale, when deleting an indicator increases the composite reliability or the average variance extracted (AVE). However, indicators with outer loadings below 0.40 should be eliminated from the model (Bagozzi, Yi, & Philipps, 1991; Hair et al., 2011). The table in appendix 2 is showing the indicator loadings. Since early_adopters_3, early_adopters_4, late_adopters_1 have outer loadings below 0.4, were removed from the model. Some indicators of technology readiness (dis1, innovativ1, insec2, optimism2) have loadings approximately 0.6, but we retained them as they met the validity and reliability criteria and also, their removal will impact the dimensionality of the construct. Even though the outer loadings of PEOU_1, late_adopters_2, late_adopters_3, early_adopters_2 and wom1 are below the valid threshold, we decided to retain them as a not significant increase in Composite reliability or AVE was achieved after deleting (Hair et al., 2014). Then, we have to examine the Construct reliability of the model. While Cronbach’s alpha is traditionally used to measure internal consistency, Composite reliability is the preferred alternative as it leads to higher estimates of actual reliability. A value of 0.70 or greater shows sufficient reliability (Nunnally and Bernstein, 1994). The table 1 below, verifies that all coefficients are 0.70 or higher.
Variables Cronbach’s alpha Composite reliability Average Variance Extracted (AVE) WOM 0.652 0.801 0.585
Perceived ease of use 0.875 0.916 0.738
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Table 1: Cronbach’s alpha, Composite reliability & AVE of the constructs.
Furthermore, discriminant validity is examined by comparing “the square root of the AVE values with the latent variable correlations and the other latent constructs” (Fornell and Larcker, 1981). The square root of the AVE should be higher between the constructs and the other constructs, for each latent construct. The model meets these criteria, which reflects Discriminant validity. Table 2 shows the square root of AVE of the first-order constructs.
Table 2: Square root of AVE of the first-order constructs.
Since the measurement model is sufficiently strong, we can continue with the interpretation of structural model.
4.2.2 The structural model
30 4.2.2.1 Hypotheses testing
As the overall model is sufficient, hypotheses must be examined. Considering the path coefficients, the figure 4 in the appendix 4 depicts a graphical description of the results, including path coefficients and p-value, while the table 3 below shows the relationships among the constructs presenting their betas, standard error and p-value.
Independent Dependent Beta Standard
error P- value
H1a:WOMIntention 0.116 0.089 0.208
H2a:OptimismPerceived Usefulness 0.124 0.094 0.192
H2b:OptimismPerceived Ease Of Use 0.059 0.098 0.553
H2i:OptimismIntention -0.145 0.107 0.208
H2c:InnovativenessPerceived Usefulness 0.093 0.099 0.358
H2d:Innovativeness Perceived Ease Of Use 0.202 0.103 0.051*
H2ii:InnovativenessIntention 0.086 0.126 0.495
H2e:DiscomfortPerceived Usefulness 0.143 0.110 0.195
H2f:Discomfort Perceived Ease Of Use -0.118 0.124 0.341
H2iii:DiscomfortIntention 0.172 0.091 0.058*
H2g:Insecurity Perceived Usefulness -0.181 0.105 0.085*
H2h:Insecurity Perceived Ease Of Use -0.095 0.097 0.330
H2iv:InsecurityIntention -0.127 0.074 0.084*
H3: Perceived Usefulness Intention 0.476 0.094 0.0***
H4: Perceived Ease Of Use Intention 0.121 0.113 0.288
H5:Privacy risksIntention -0.033 0.095 0.726
Note: ns = not significant, ***p < .01, **p < .05, *p<0.1
Table 3: Standardized path coefficients
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variables, but only sex variable found to be significant. It seems that females are more intended to use the mobile payment services.
Since the mediation effect is present in our model, we have to estimate both indirect effects and total effects, besides the direct effect mentioned above. Specifically, Word-of-mouth is the independent variable and Intention is the dependent, while PEOU and PU act as mediators. In order to examine if there are mediation effects, we need to estimate the effect of both mediators (multiple mediation); thus, we ran the bootstrapping method with 500 subsamples and two-tailed type. If the indirect effect for both of the mediators is significant, then mediation exists in our model. In order to measure the total effect of the model, we need to take into account the indirect effects of each mediator. This can be calculated by adding in the direct effect the indirect effects of each mediator (Hair et al., 2014). Table 4 below presents the direct, indirect and total effects of our model.
Relationships Path coefficient
95% confidence
intervals T-stat P-value Direct effect WOMIntention to use 0.116 [-0.028,0.270] 1.261 0.208 Indirect effects H1b:WOMIntention to use (M=Perceived Usefulness) 0.165 [0.064, 0.273] 2.600 0.010*** H1c:WOMIntention to use
(M=Perceived Ease Of Use) 0.038 [-0.028,0.108] 0.903 0.367
Total effect (with both mediators)
WOMIntention to use 0.319 [0.141,0.472] 3.140 0.002**
Note: ns = not significant, ***p < .01, **p < .05, *p<0.1
Table 4: Mediation effect (direct, indirect & total effect)
32 Figure 3.Hypotheses significance and non-significance
Hypothesis 1a,1b and 1c
To test H1a, H1b, and H1c, we need to consider the total effect, indirect effect, and direct effect. Looking at table 4, we can see that the direct effect of WOM to the Intention to use mobile payment is not significant, so H1a is rejected. H1b and H1c are based on the mediation which appears in the model. As the direct effect is not significant, but the indirect effect of WOM to intention to use through PU is, we conclude that the mediation effect is present. More specifically we have full mediation, thus H1b is accepted. However, H1c is rejected, because both direct and indirect effects are not significant. Hence, the relationship between WOM and Intention to use mobile payments is not mediated by PEOU, which is an unexpected finding.
Hypothesis 2
Hypotheses 2a-2iv measure the effect of the four dimensions of Technology readiness on PEOU, PU and Intention to use mobile payment.
Regarding to Optimism, we find out that there is no significant effect between the PEOU (0.059, p= 0.553), PU (0.124, p=0.192) and Intention to use (-0145, p=0.208). Thus, H2a, H2b, and H2i are rejected.
Innovativeness seems to be correlated only with PEOU (0.202, p=0.051), although there is no correlation with PU (0.093, p=0.358) and Intention to use (0.086, p=0.495). Therefore, we accept only H2d and we reject H2c and H2ii.
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Considering Discomfort, we infer from the table 3 that is has a positive direct effect on Intention to use (0.172, p=0.058), while it is not correlated with PEOU (-0.118, p=0.341) and PU (0.143, p=0.195). The positive effect on the intention to use is an unexpected outcome, as it is opposed to the current literature. Concluding, we reject both H2e and H2f and H2iii.
Taking into account Insecurity, we observe that it is negatively correlated with PU (-0.181, p=0.085) and Intention to use (-0.127, p=0.084), but it has no effect on PEOU (-0.095, p=0.330). The results support the findings from previous researches leading to the rejection of H2h and acceptance of H2g and H2iv.
Hypothesis 3 & 4
Based on the results, we ascertain that PU has a positive effect on Intention to use (0.476, p=0.000), which comes in line with the literature. However, it seems that PEOU does not have any effect on Intention to use (0.121, p=0.288), which is opposed to the literature findings. Thus, H3 is accepted and H4 is rejected.
Hypothesis 5
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5. Discussion
The current study aimed to evaluate the factors that affect consumers’ intention to use mobile payment. To answer this call, we took into consideration the TAM model and we extended it, in order to make it more precise and compatible with the mobile payment field. We empirically examined the impact of WOM, as well as the four dimensions of Technology readiness, as we wanted to detect the factors that motivate or hinder the use of mobile payment services.
Regarding our results, we derived that Word-of-mouth does not affect directly the intention to use mobile payment. This outcome differs from our assumption, as Word-of-mouth is employed usually for the evaluation of intangible products that are difficult to evaluate prior to use (Huete-Alcocer N., 2017). A possible reason can be the fact that Word-of-mouth develops and affects an individual’s attitudes and those favorable or unfavorable attitudes determine the intention to use (Smith & Vogt, 1995). However, Word-of-mouth has an effect on the Intention to use, when Perceived usefulness is present. This means that people who perceive mobile payment as useful and helpful have more positive thoughts about it and they are more willing to transmit it, leading to the intention to use. When “perceived ease of use” is present, Word-of-mouth has no effect on the intention to use mobile payment. Even when people believe that mobile payment services are easy to use, they do not communicate it, so there is no influence on intention to use. A possible explanation in our research is the presence of people aged between 22 and 27 years, who are more experienced in new technologies and they can easily use them; thus they do not focus on the ease of use as they take it for granted.
Furthermore, our findings reveal that some of the personality dimensions of technology readiness affect consumers’ intentions, while others not. Starting with optimism, results showed that this personality trait does not have an effect at all, which is an unexpected outcome. According to theory, an optimistic person will find new technology, in general, as more useful, easier to use and will be more willing to use it (Godoe & Johansen, 2012). In our research, it seems that whether an individual is optimistic or not does not determine the intention to try the new technology.
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This result indicates that it is easier for innovative people to use mobile payments, as they are more open to learn new technologies and to understand and use them, which increases their technology acceptance rate (Turan, Tunc, & Zehir, 2015). Unexpectedly, however, the relationship between innovativeness and perceived usefulness, as well as the relationship with the intention to use, were not significant. This result contradicts those of previous researches, where innovativeness obtained significant positive effects on both perceived usefulness and intention to use m-payments (Ward, Chitty, & Graham, 2007). Nevertheless, Walczuch, Lemmink, and Streukens (2007) discovered the same outcome as the present study. Based on such results, a question arises: “How innovativeness can be positively related to perceived usefulness and at the same time does not correlate at all with perceived ease of use and intention?” A possible explanation might be that innovative individuals are more carping towards new technologies and they want to identify if the latest developments and possibilities fulfill their higher demands. Therefore, they would easily waver to use mobile payment services.
The most unanticipated result of this research was those of discomfort. We discerned that the personality trait of discomfort does not associate with perceived usefulness and perceived ease of use, which comes in line with previous studies of Godoe and Johansen (2012) and Kuo et al. (2013). However, a positive effect of discomfort on the intention to use is observed. Since people have high levels of discomfort, they find mobile payment as complicated and not useful to use. Nevertheless, a possible justification of the positive effect on the intention to use can be that individuals may not be concerned about the discomfort that comes with the use of the mobile payment, as long as they recognize the benefits. The main aim of users might be to maximize the benefit out of using mobile payment services; thus, they ignore or deal efficiently with the discomfort they face with the use.
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technological insecurity might worry a lot about the lack of security in the mobile payment system; thus, the usefulness of the service might not be their priority.
Furthermore, our analysis revealed that intention to use was directly affected by perceived usefulness, but not by perceived ease of use. These results come in line with many previous researches, proving that out of the two cognitive dimensions of TAM only perceived usefulness was the main contribution to mobile payment intention usage (King & He, 2006; Legris et al., 2003; Schepers & Wetzels, 2007). Given the high level of learning capability and cognitive ability, since the majority of the sample has obtained either a Bachelor or a Master’s degree, individuals may still intend to use mobile payment services despite their complexity.
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6. Conclusion
The main purpose of the current study was to investigate the factors that affect consumers’ intention to use mobile payments. The theoretical contributions of it were two-fold. First of all, this study provides a useful theoretical framework in the literature on mobile payments. To be more specific, through this research, we proposed a new research model based on TAM. Due to its focus mainly on the adoption of technology rather than in the individual’s behavior, it has received a lot of criticism. Therefore, the need to extend it and make it more detailed and precise was of great importance. Since the mobile payment field is more customer oriented, there was the need to include behavioral constructs to identify their influence on the use of those services. Hence, we decided to include the four dimensions of Technology readiness in order to focalize into the individual’s behavioral aspects. Moreover, we applied Word of mouth as another variable in our model based on its huge impact on consumer intentions and behavior.
As purchases through mobile phones perceived as “high risk,” we assumed that a positive or negative suggestion could have a significant impact on an individual’s intention to use m-payments. The second contribution of this study was its focus on the Netherlands. Mobile payments in the Netherlands and Europe generally are still in progress, even though smartphone penetration is high. This fact, combined with limited available research on the m-payment field made the exploration of the inhibitors of such adoption intriguing.
Previous studies, including the Technology readiness index, propose that an individual’s technology inclination influence the adoption of new technologies (Lin et al., 2007; Walczuch et al., 2007). However, the results of this study among Dutch consumers do not support the previous findings, as some technology readiness traits influence the intention to use m-payments, while other hypothesized relationships proved to be statistically not significant. This equivocal outcome may be based on the limited and not diverse sample.
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found to be positively correlated with the intention to use. Such an opposing outcome shows that Dutch peoples’ goal is probably to reap the benefits from such technology; thus, they do not care about the discomfort that is accompanied by. Furthermore, this study confirmed the negative correlation between insecurity and perceived usefulness, as well as the intention to use. Another surprising result was the lack of correlation between privacy risks and intention adoption of mobile payment services. It seems that Dutch consumers are not worried about the use and the process of their private information and as such, this does not have any impact on their intention to use their smartphones for purchases. Last but not least, the causal relationship of perceived usefulness and behavior intention was confirmed as well.
The managerial contribution of this research is to give insights on how to better encourage Dutch consumers in order to adopt mobile payments. Taking into consideration all the above-mentioned findings, managers need to understand that Word-of-mouth plays a vital role in behavioral intention. Through written and/or verbal means both online and offline, they should promote the use of mobile payments, aiming to target and change consumers’ attitudes about mobile purchases. If they want to focus specifically on innovative people, they should present how easily a purchase can be accomplished through the use of a smartphone. Moreover, managers need to find ways to reverse the presence of insecurity, because higher levels of insecurity act as inhibitors of intention to use. By promoting that transactions through mobile are similar to all the other kinds of transactions in the sense of implementation and effectiveness, they will manage to reduce the real feelings of insecurity.
To conclude, it is essential to pay attention to users and their attitudes toward an upcoming implementation of technology, especially when it is difficult to test the system before its adoption. However, much emphasis should be placed on the initial implementation of the technology, when we have obtained all the useful information about users if we want to be successful.
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7. Limitations and further research
Despite the contribution of the research in the literature, several limitations should be noted offering opportunities for future research. First of all, due to time constraints and budget limitations, the respondents of this research were mainly students (convenient sample). If the sample obtained was a diverse group regarding age, education, and employment, this might have given us a completely different research outcome. In this case, we cannot generalize our results as they cannot be representative of the whole country. Therefore, future research with a more extensive and more diverse sample is necessary, as it can yield different results with more significant relationships.
Second, in the current research, we used quantitative methods in order to obtain our results. The use of field experiments and secondary data might be interesting for future research because it allows examining the extent to which the TAM framework and technology readiness affects adoption intention of mobile payment services.
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Reference list
Aboelmaged, M., Gebba, T.R., (2013), Mobile banking adoption: an examination of the technology acceptance model and the theory of planned behavior. Int. J. Bus.
Acquisti, A., & Gross, R. (2006). Imagined communities: Awareness, information sharing, and privacy on Facebook. Lecture Notes in Computer Science, 4258, 36–58.
Agarwal, R., Karahanna, E., (2000), Time flies when you’re having fun: cognitive absorption and beliefs about information technology usage. MIS Quarterly 24 (4), 665–694
Albers S. (2010) PLS and Success Factor Studies in Marketing. In: Esposito Vinzi V., Chin W., Henseler J., Wang H. (eds) Handbook of Partial Least Squares. Springer Handbooks of Computational Statistics. Springer, Berlin, Heidelberg.
Au, Y. A., & Kauffman, R. J. (2008). The economics of mobile payments: Understanding stakeholder issues for an emerging financial technology application. Electronic Commerce Research and Applications, 7, 141–164.
Bagozzi, R. (2007). The Legacy of the Technology Acceptance Model and a Proposal for a Paradigm Shift. Journal of the Association for Information Systems, 8(4), pp.244-254.
Bagozzi, R. P., Yi, Y ., & Philipps, L. W . (1991). Assessing construct validity in organizational research. Administrative Science Quarterly, 36, 421–458.
Bailey, A., Pentina, I., Mishra, A. and Ben Mimoun, M. (2017). Mobile payments adoption by US consumers: an extended TAM. International Journal of Retail & Distribution Management, 45(6), pp.626-640.
Bangdao, C., Roscoe, A. W., Kainda, R., & Nguyen, L. H. (2010). The missing Link: Human interactive security protocols in mobile payment. In Proceedings of the 5th international
workshop on security (pp. 94e109). Retrieved from
http://www.cs.ox.ac.uk/publications/publication4044-abstract.html.
Bauer, R. A. (1960). Consumer behavior as risk-taking. In R. Hancock (Ed.), Dynamic marketing for a changing world, Proceedings of 43rd Ed. (pp. 389–398). Chicago, IL: American Marketing Association.
41
Bickart, B., & Schindler, R. M. (2001). Internet Forums as Influential Sources of Consumer Information. Journal of Interactive Marketing, 153, 31-40.
Carrión, G.C., Henseler, J., Ringle, C.M., Roldán, J.L., (2016), Prediction-oriented modeling in business research by means of PLS path modeling: introduction to a JBR special section. J. Bus. Res. 69 (10), 4545–4551.
Castañeda, Alberto & Ríos, Francisco. (2007). The effect of Internet general privacy concern on customer behavior. Electronic Commerce Research. 7.
Chang, W. L., Yuan, S. T., & Carol, W. (2010). Creating the experience economy in e-commerce. Communications of the AMC, 53(7), 122e127ς
Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In Vis, B., 2012. The comparative advantages of fsQCA and regression analysis for moderately large-N analyzes. Social. Methods Res. 41 (1), 168–198.
Chitungo, S.K., Munongo, S. (2013), Extending the technology acceptance model to mobile banking adoption in rural Zimbabwe. J. Bus. Adm. Educ. 3 (1), 51–79.
Dahlberg, T, N Mallat, J Ondrus, and A Zmijewska (2008), “Past, present and future of mobile payments research: A literature review.” Electronic Commerce Research and Applications 7 (2): 165-181.
Davis F. D. (1986), Technology acceptance model for empirical testing new end-user information systems theory and results, Unpublished Doctoral Dissertation, MIT.
Davis F. D., Bagozzi R. P. and Warshaw P. R. (1989), User acceptance of computer technology: A comparison of two theoretical models, Management Science, vol. 35, no. 8, pp. pp. 9821003. Davis F.D. (1989), Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q., 13(3):319–340. doi: 10.2307/249008.
Davis, F. D. (1993). User acceptance of information technology: System characteristics, user perception, and behavior impacts. International Journal of Man-Machine Studies, 38(3), 475– 487
Deloitte (2017). [Available at:
https://www2.deloitte.com/content/dam/Deloitte/nl/Documents/technology-media-telecommunications/2017%20GMCS%20Dutch%20Edition.pdf
42
Dörnyei, Z. (2007). Research methods in applied linguistics. New York: Oxford University Press.
Duncombe, Richard; Boateng, Richard (2009). Mobile Phones and Financial Services in Developing Countries: A Review Of Concepts, Methods, Issues, Evidence And Future Research Directions, Third World Quarterly, 30 (7): 1237-1258.
Elliot, K.M. and Hall, M.C. (2005), “Assessing consumers’ propensity to embrace self-service technologies: are there gender differences?”, Marketing Management Journal, Vol. 15 No. 2, pp. 98-107.
eMarketer (2017). Personal Mobile Payments on the Rise in Europe.
eMarketer.https://www.emarketer.com/Article/Personal-Mobile-Payments-on-Rise-Europe/1015592.
Erdoğmuş, Nihat & Esen, Murat. (2011), An Investigation of the Effects of Technology Readiness on Technology Acceptance in e-HRM. Procedia - Social and Behavioral Sciences. 24. 487-495.
European Payments Council. (2019), The Dutch payment landscape: One of the most cashless in Europe. [online] Available at: https://www.europeanpaymentscouncil.eu/news-insights/insight/dutch-payment-landscape-one-most-cashless-europe.
Falk, R.F. and Miller, N.B. (1992), A Primer for Soft Modeling. University of Akron Press, Akron.
Featherman, M., & Pavlou, P. (2003), Predicting e-services adoption: A perceived risk facets perspective. International Journal of Human-Computer Studies, 59(4), 451–474. doi:10.1016/S1071-5819(03)001113.
Finstad K. (2010), Response interpolation and scale sensitivity: Evidence against 5-point scales. Journal of Usability Studies;5(3):104-110
Fishbein, M. and Ajzen I., (1975), Belief, attitude, intention and behavior: An introduction to theory and research.
43
Forrester (2016), Forrester Forecast: Mobile Payments to Reach $90B By 2017. Forrester.https://www.forrester.com/Forrester+Forecast+Mobile+Payments+To+Reach+90B+ By+2017/-/E-PRE4544.
Gadzheva, M. (2007), Privacy concerns pertaining to location-based services. International Journal of Intercultural Information Management, 1(1), 49–57. doi:10.1504/IJIIM.2007.014370.
Gao, L., and Waechter, K. A. (2017), “Examining the role of initial trust in user adoption of mobile payment services: an empirical investigation,” Information Systems Frontiers (19:3), pp. 525-548.
Gao, L., Bai, X. (2014), A unified perspective on the factors influencing consumer acceptance of internet of things technology. Asia Pac. J. Mark. Logist. 26 (2), 211-231.
Garaus, M., Wolfsteiner, E., Wagner, U. (2016), Shoppers' acceptance and perceptions of electronic shelf labels. J. Bus. Res. 69 (9), 3687–3692.
Garrett, J. L., Rodermund, R., Anderson, N., Berkowitz, S., and Robb, C. A. (2014), “Adoption of mobile payment technology by consumers,” Family and Consumer Sciences Research Journal (42:4), pp. 358-368..
Garson, G. D. (2016), Partial Least Squares: Regression and Structural Equation Models. Asheboro, NC: Statistical Associates Publishers.
Gartner (2015), Gartner Says By 2018, 50 Percent of Consumers in Mature Markets Will Use Smartphones or Wearables for Mobile Payments. Gartner. http://www.gartner.com/newsroom/id/3178217.
Gatignon H. and Robertson T.( 1985), Journal of Consumer Research, vol. 11, issue 4, 849-67.
Gfk.com. (2019). Smartphone steeds vaker portemonnee, minder telefoon. [online] Available at: https://www.gfk.com/insights/press-release/smartphone-steeds-vaker-portemonnee-minder-telefoon/.
Gilroy, D.F. and Desai, H.B. (1986), “Computer anxiety, sex, race and age”, International Journal of Man-Machine Studies, Vol. 25 No. 6, pp. 711-719
44
Gummesson, E. (2008). Customer centricity: reality or a wild goose chase?, European Business Review, 20(4), pp.315-330.
Guriting, P., & Ndubisi, N. (2006). Borneo online banking: Evaluating customer perceptions and behavioral intention. Management Research News, 29, 6–15.
Gutek, B.A. and Bikson, T.K. (1985), “Differential experiences of men and women in computerized offices”, Sex Roles, Vol. 13 Nos 3/4, pp. 123-136.
Hair, J. F., Black, W . C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis. Englewood Cliffs, NJ: Prentice Hall.
Hair, J. F., Hult, G. T. M., Ringle, C. M., and Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), 2^nd^ Ed., Sage: Thousand Oaks.
Hair, J. F.; Ringle, C. M.; & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet.
Hair, J.F.; Hult, T.M.; Ringle, C.M. e Sarstedt, M (2014), A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Los Angeles: SAGE
Hanafizadeh, P., Behboudi, M., Khoshksaray, A., Shirkhani Tabar, M. (2014), Mobile-banking adoption by Iranian bank clients. Telematics Inform. 31, 62–78.
Hedman, J., & Henningsson, S. (2015), The new normal: Market cooperation in the mobile payments ecosystem. Electronic Commerce Research And Applications, 14 (5), 305-318. Henseler, J., Dijkstra, T. K., Sarstedt, M., Ringle, C. M., Diamantopoulos, A., Straub, D. W ., et al. (2014). Common beliefs and reality about partial least squares: Comments on Rönkkö & Evermann (2013). Organizational Research Methods, 17, 182–209.
Henseler, J., Ringle, C. M., and Sinkovics, R. R. (2009). The Use of Partial Least Squares Path Modeling in International Marketing, in Advances in International Marketing, R. R. Sinkovics and P. N. Ghauri (eds.), Emerald: Bingley, pp. 277-320.
Hoffman, D.L., Novak, T.P., Peralta, M. (1999), Building consumer trust online: how merchants can win back lost consumer trust in the interest of e-commerce sales. Communications of the ACM 42 (4), 80–85.
Hogan, J.E., Lemon, K.N., Libai, B. (2004), Quantifying the ripple: word of mouth and adverting effectiveness. J. Advert. Res. 44 (3), 271–280.
45
Hubert Gatignon and Thomas S Robertson, (1985), A Propositional Inventory for New Diffusion Research, Journal of Consumer Research, 11, (4), 849-67
Huete-Alcocer N. (2017), A Literature Review of Word of Mouth and Electronic Word of Mouth: Implications for Consumer Behavior. Frontiers in psychology, 8, 1256. doi:10.3389/fpsyg.2017.01256
Ideal.nl. (2019). iDEAL | iDEAL leading payment method on mobiles. [online] Available at: https://www.ideal.nl/en/actueel/nieuws/ideal-leading-payment-method-on-mobiles/.
Im, I., Hong, S., Kang, M.S., (2011), An international comparison of technology adoption: testing the UTAUT model. Inf. Manage. 48 (1), 1–8.
Jalilvand, M.R., Samiei, N. (2012), Perceived risks in traveling to the Islamic Republic of Iran. J. Islamic Market. 3 (2), 175–189
Joreskog, K.G.,& Wold, H.O.(Eds). (1982), The ML and PLS technique for modeling with latent variables: Historical and comparative aspects. In: Systems under indirect observation, Part I (pp. 263–270). Amsterdam: North-Holland.
Kapoor, K. K., Dwivedi, Y. K., and Williams, M. D. (2014), “Examining the role of three sets of innovation attributes for determining adoption of the interbank mobile payment service,” Information Systems Frontiers (17:5), pp. 1039-1056.
King, W. R., & He, J. (2006), A meta-analysis of the technology acceptance model. Information & Management, 43, 740-755. doi:10.1016/j.im.2006.05.003.
Kotler, P., Amstrong, G., Wong, V., & Saunders, J. (2008), Principles of Marketing (Fifth European Edition ed.). Pearson.
Kuo, K. M., Liu, C. F., & Ma, C. C. (2013). An investigation of the effect of nurses’ technology readiness on the acceptance of mobile electronic medical record systems. BMC Medical Informatics and Decision Making, 13(1), 88–101.
Lai, P.M. & Chuah, K. (2010). Developing an Analytical Framework for Mobile Payments Adoption in Retailing: A Supply-Side Perspective. Proceedings - 2010 International Conference on Management of e-Commerce and e-Government, ICMeCG 2010. 356 - 361. 10.1109/ICMeCG.2010.79.
