Exploring the Factors Affecting Consumer Acceptance of
Proximity M-Payment Services
Apostolos Giovanis
1
, Androniki Kavoura
1
, Ioannis Rizomyliotis
2
, Sotiris Varelas
3
and
Aspasia Vlachvei
4
1
University of West Attica, Ag.Spiridonos, 12210, Athens, Greece
2
University of Brighton,Mithras House, Lewes Road, Brighton BN2 4AT, UK
3
Neapolis University of Pafos, 2 Danais Av., 8042, Paphos, Cyprus
4
Western Macedonia University of Applied Sciences, 52100, Kastoria, Greece
agiovanis@uniwa.gr
Abstract. The purpose of this study is to analyze the factors determining con-
sumers’ intentions to adopt NFC proximity mobile payment services (p-mps).
An extended version of the Unified Theory of Acceptance and Use of Technol-
ogy (UTAUT), a model that incorporates the most relevant theoretical ap-
proaches in the adoption literature was selected to investigate the consumers’
adoption process. To empirically test the proposed model, data were collected
from 513 consumers of mobile internet service providers in Greece and were
analyzed using PLS. The results indicated that p-mps features, expressed by
consumers’ expectations about service performance and required efforts, along
with the social context effects have the biggest impact on consumers’ intentions
to use the service, followed by channel characteristics, reflecting consumers’
beliefs about p-mps usage risk and trust. Potential customers’ characteristics
moderating analysis suggested that the effects of performance expectancy and
perceived trust on behavioral intentions are affected by gender, age and previ-
ous experience, while that of social influence only by potential customers’ pre-
vious experience. Theoretical and managerial implications, limitations and sug-
gestions for further research are provided at the end of the study.
Keywords: M-payment, NFC p-mps adoption, UTAUT, Risk, Trust, Individual
Differences, Marketing Financial Self-service Technologies.
1 Introduction
Mobile payment system (mps) is an emergent self-service technology (SST) offering
payment services through mobile devices without locational and temporal restrictions
[1], [2]. According to Gerpott and Meinert [3], there are two categories of mps: a)
Remote mps (hereafter r-mps), enabling payments through mobile telecommunication
or Wi-Fi networks, and allow the payments for digital content or online purchases
through SMS or mobile internet connection, b) Proximity mps (hereafter p-mps),
enabling payments through short-range communication technologies and allow for
payments for purchases such as ticketing, vending, and point-of-sale items, employing
2
a QRcode displayed on the smartphone, or a NFC (Near Field Communication) de-
vice or Bluetooth low energy (BLE) proximity sensing technology [3], [4]. This study
focuses on NFC-enabled p-mps that has become important part of consumers’ shop-
ping experience due to the continuous advancements of the technology [3].
The study of the p-mps adoption and usage process has attracted significant at-
tention from academics and practitioners over the last years. Yet consumers’ adoption
and usage of this m-service is characterized as a complex process due to interplay of
many factors that are under-researched so far [2]. According to Adapa and Roy [5]
and Frimpong et al. [6] there are technology-, social-, channel- and personal-related
factors affecting consumer behavior towards SSTs usage intentions. Although, there
are many studies that have used different well-known theoretical frameworks to in-
vestigate the adoption of p-mps, there aren’t studies that have examined the interplay
of all the aforementioned groups of factors in order to better understand the potential
customers’ decision making process, towards p-mps usage.
Thus, the purpose of this study is to identify the factors affecting the p-mps adop-
tion process and the degree of influence of each factor leading p-mps usage inten-
tions. Drawing on the studies of Adapa and Roy [5] and Frimpong et al. [6], four
groups of innovation adoption drivers (i.e. innovation features; social context; channel
credibility; and personal characteristics) mainly affect SST’s usage intentions. Thus,
the proposed modelling framework extends the UTAUT, proposed by Venkatesh et al.
[7] and its constructs express the effects of technology-related and social-related fac-
tors, with potential users’ perceived risk and trust, expressing the role of channel-
related factors, and potential users’ demographics in order to better predict the p-mps
adoption intentions.
2 Literature Review and Proposed Model
There are several theoretical models which have been used to explain the adoption of
SSTs. These models include the Technology Acceptance Model (TAM), the Theory
of Planned Behavior (TPB), the Innovation Diffusion Theory (IDT), and the Unified
Theory of Acceptance and Use of Technology Model (UTAUT) [4], [8], [10]. Focus-
ing on the p-mps context, the majority of the adoption studies use and support the
TAM [11] and its extensions as a theoretical framework [12], [13], [14], [15], [16],
[17]. Despite the fact that the TAM and its extensions provide a reliable and valid
modelling framework to investigate the technology adoption process, it has received a
lot of criticism about its predictive ability for two reasons: Firstly, it considering two
consumers’ salient beliefs related to innovation’s attributes (i.e. perceived usefulness
and perceived ease of use), and no other external factors that might affect consumers’
behavior; Secondly, it assuming that usage is volitional without constraints [7], [9].
To address these limitations Venkatesh et al. [7] proposed the UTAUT which
integrates eight well established innovation adoption theoretical frameworks. The
UTAUT includes three key constructs: performance expectancy (PE), effort expectan-
cy (EE), social influence (SI) to model behavioral intentions (BI). The latter along
with facilitating conditions (FC) predicts also usage behavior. These determinants are
3
defined as follows [7, p.447-453]: PE refers to “the degree to which a potential
adopter has the opinion that the innovation adoption will help him to improve the
performance of a task or work”. EE refers to “the degree of ease associated with use
of the innovation”. SI is defined “as the degree to which an individual perceives that
important others believe he or she should use the innovation” and reflects the norma-
tive drivers of the innovation adoption process. Finally, FC is defined as “the degree
to which an individual believes that an organizational and technical infrastructure
exists to support use of the system” (TPB, DTPB).
The original UTAUT considers that PE, EE, and SI are antecedents of BI, while
BI and FC are drivers of potential customers’ actual behavior. Hence, this model as-
sumes that the concept of “BI captures the motivational factors that affect the poten-
tial adopters’ behavior and reflects the effort that they are willing to undertake in
order to develop an action” [18]. This study does not consider the FC, as it investi-
gates only potential consumers’ intentions to adopt p-mps and not their actual behav-
ior. These three variables represent the technology- and social-related variables moti-
vating consumers to use an SST. Focusing in the context of p-mps there are three
studies using UTAUT to investigate the adoption process in Malaysia, UK and USA
[9], [19], [20]. All three studies support the significance of PE and BI, two of them
found a significant effect of SI on BI [9], [20], and only one confirms the significance
of EE on BI [19]. Lately, Slade et al. [8] [9] suggest the extension of UTAUT with
perceived risk (PR) and perceived trust (PT) in order to consider the effects of SSTs’
channel-related factors in the adoption process. PR in the context of SSTs expresses
“the potential for loss in the pursuit of a desired outcome of using the service” [21,
p.453]. PT, on the other hand, refers to potential customers’ subjective belief that a
service provider will fulfil its obligations [8]. Consumers often face spatial and tem-
poral separation from their online providers and this makes them more vulnerable to
greater risks as they do not have the full control of their action [8], [9]. Thus, trust in
p-mps is essential to mitigate the uncertainty of m-payments to motivate the consumer
to use it. Many previous studies in the field of m-services in general and mps in par-
ticular empirically validate the positive effect of PT and the negative effect of PR on
BI. The studies of Slade et al. [8], [9] for example, suggest PR and PT to be included
among the significant drivers of mps adoption in the UK. Moreover, the UTAUT
posits that the effects of these five constructs on BI are moderated by individual dif-
ferences such as gender, age, and technology experience [7], [8], [22]. Among the
three previous UTAUT studies in the context of p-mps, none of them considers the
moderating role of personal factors in the adoption process. Thus, the use of the com-
plete UTAUT is expected to provide a more comprehensive theoretical framework for
predicting p-mps usage intentions and, further, better support the development of a
differentiated marketing strategy towards the extension of p-mps adoption and usage.
Based on the above discussion, PE, EE, SI, PR, and PT are theorized to influence
behavioral intention to use a p-mps. Moreover, individual characteristics, such as
gender, age, and previous experience are theorized to moderate the five previous di-
rect relationships. Thus following the studies of Venkatesh et al. [7] and Slade et al.
[8] it is hypothesized that:
H1: PE directly and positively affects BI
4
H2: EE directly and positively affects BI to adopt p-mps
H3: SI directly and positively affects BI to adopt p-mps
H4: PR directly and negatively affects BI to adopt p-mps
H5: PR directly and positively affects BI to adopt p-mps
H6: a) Gender, b) age and c) experience moderate the positive effect of PE on BI
to adopt p-mps, such that the effect will be stronger for younger males with
high levels of technology experience
H7: a) Gender, b) age and c) experience moderate the positive effect of EE on BI
to adopt p-mps, such that the effect will be stronger for older females with
low levels of technology experience
H8: a) Gender, b) age and c) experience moderate the positive effect of SI on BI to
adopt p-mps, such that the effect will be stronger for older males with low
levels of technology experience
H9: a) Gender, b) age and c) experience moderate the positive effect of PT on BI
to adopt p-mps, such that the effect will be stronger for older females with
limited experience of the technology
H10: a) Gender, b) age and c) experience moderate the negative effect of PR on BI
to adopt p-mps, such that the effect will be stronger for older females with
limited experience of the technology
3 Research Methodology
To ensure the content validity of the scales used to measure the constructs of the pro-
posed models, validated scale items from prior studies were used. As such, the scales
proposed by Venkatesh et al. [22] were used to measure PE, EE, SI and BI, while
those include in the study of Slade et al. [9] were used to measure PR and PT. All
scale items were measured using a 7-point Likert scale with 1 corresponding to
“strongly disagree” and 7 to “strongly agree”. A conclusive research design was se-
lected in order to examine the relationships described in the conceptual framework. A
convenience sampling was employed and a questionnaire was developed and distrib-
uted to 600 individuals. This procedure resulted in 530 questionnaires. After eliminat-
ing those with unanswered items 513 questionnaires were coded for data analysis. The
method of partial least squares (PLS) path methodology [23], an implementation of
structural equation modeling (SEM) with Smart PLS 2.0 M3 [24], was used to exam-
ine the model and test the proposed hypotheses.
4 Results
Among the 513 survey participants 59.1% were male. In terms of age 29.6% were less
than 24 years old; 30.6% were in the 25–34 age group; 19.1% were in the 35–44 age
5
group and 20.7% were more than 45 years old. In terms of educational background
52% of the respondents have college degree or higher. The test of the measurement
model involves the estimation of reliability; convergent validity, and discriminant
validity of the extended UTAUT’s constructs, indicating the strength of measures
used to test the proposed model [23]. As shown in Table 1, all measures present high
item reliability as all corresponding loadings values exceeds the cut-off value of 0.70.
Composite Reliability (CR) values of all measures included in the study exceed 0.93
suggesting that all measures were good indicators of their respective components.
Average Variance Extracted (AVE) values for all constructs exceed 0.77, higher than
the recommended cut-off value of 0.50 [23] suggesting satisfactory convergent validi-
ty. Finally, the square roots of AVE for all first-order constructs, provided in the di-
agonal of the table, are higher than their shared variances providing strong evidence
of discriminant validity among all first order constructs [23].
Table 1. Measurement model assessment.
LV
Loadings
AVE
CR
PE
EE
SI
PR
PT
ITU
PE
[0.85-0.90]
0.77
0.91
0.88
EE
[0.86-0.97]
0.80
0.95
0.57
0.89
SI
[0.93-0.97]
0.91
0.97
0.45
0.30
0.95
PR
[0.88-0.93]
0.81
0.95
-0.35
-0.32
-0.25
0.90
PT
[0.83-0.96]
0.82
0.93
0.51
0.41
0.47
-0.49
0.91
ITU
[0.97-0.98]
0.95
0.98
0.66
0.56
0.52
-0.48
0.59
0.97
The PLS-PM method was also used to confirm the hypothesized relationships be-
tween the constructs in the proposed model. The significance of the paths included
into the proposed model was tested using a bootstrap resample procedure. In assessing
the PLS model, the squared multiple correlations (R
2
) of the endogenous latent varia-
ble was initially examined and the significance of the structural paths was evaluated
[23], [24]. The data analysis for the main effects model, depicted in Table 2(a), indi-
cates that all six hypotheses concerning the direct effects were confirmed. Significant
positive relationships were yielded between PE and BI (confirming H1: β = 0.31), EE
and BI (confirming H2: β = 0.20), SI and BI (confirming H3: β = 0.20), and PT and
BI (confirming H5: β = 0.17). Significant negative relationships were observed be-
tween PR and BI (confirming H4: β = -0.17). The five significant constructs ex-
plained 60% of variance in BI. Multi-group analysis [25] was used to investigate the
moderating effects of individual differences on the relationships between adoption
drivers and potential customer BI. As such, the pool sample separated in two groups
of respondents according to their gender (male vs. females), age (young: ≤ 30 yrs. vs.
old: > 30 yrs.) and declared familiarity with m-commerce (high vs. low). As shown in
Tables 2b,c,d the results suggest that gender, age and experience moderate the rela-
tionships between PE and BI (confirming H6a, H6b, H6c) and between PT and BI
(confirming H9a, H9b, H9c), while experience also moderates the relationship be-
tween SI and BI (confirming H8c).
