iBusiness, 2013, 5, 77-85
http://dx.doi.org/10.4236/ib.2013.53010 Published Online September 2013 (http://www.scirp.org/journal/ib)
77
Empirical Study on Usability Impact Factors of Electronic
Wallet-One Card Solution within College Students*
Jiangping Wan1,2, Ming Zeng1, Lianyu Liang1
1School of Business Administration, South China University of Technology, Guangzhou, China; 2Institute of Emerging Industriali-
zation Development, South China University of Technology, Guangzhou, China.
Received June 30th, 2013; revised July 25th, 2013; accepted August 4th, 2013
Copyright © 2013 Jiangping Wan et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
ABSTRACT
Empirical study on usability impact factors of electronic wallet-one card solution within college students includes the
analysis of the current situation within the electronic wallet-one card solution for college students, the analysis of be-
havior habit within using the electronic wallet-one card solution for college students, and sets up a preliminary usability
evaluation indicator system of electronic wallet-one card solution for college students, and the factor analysis for us-
ability evaluation indicator system through the questionnaire. According the result of factor analysis, we set up an im-
proved usability evaluation indicator system of electronic wallet-one card solution for college students. The purpose of
the study is to improve the usability of electronic wallet-one card solution.
Keywords: Electronic Wallet; One Card Solution; Usability; Factor Analysis; Empirical Study
1. Introduction
The definition of electronic wallet has narrow and broad
sense. Electronic wallet in the narrow sense refers to a
kind of payment tool commonly used in activities of daily
living and shopping, and it is suitable for small shopping
[1]. Electronic purse generalization is no longer confined
to a stored-value card, it also includes digital cash from
the bank or the family of electronic wallet and other kinds
of electronic money, it can also be consumed correctly in
the POS machine, be carried out by online consumer and
internet consumer. Electronic money is stored in the elec-
tronic wallet, such as electronic cash, electronic money,
Alipay, TenPay, baifubao, new pay, Yee Pay, China mo-
bile phone payment [2]. With low transaction costs, con-
venient and fast, high penetration rate as the carrier of
electronic purse (intelligent mobile phone), low interfer-
ence, electronic wallet gets more and more groups favor.
One Card Solution is to realize the intelligent man-
agement of various functions in the same card. Its core
content is to bring about the standardization and automa-
tion of the information resource management from the
generation, collection, transmission to the summary
analysis of business data by using the particular physical
media of the card. From the perspective of industry ap-
plication, one card solution can be divided into: campus
one card solution, business one card solution, park one
card solution, city one card solution (bus one card solu-
tion, super highway one card solution, social security etc.
IC collection fee, all that can be seen as a city card) [3].
This paper is organized in the following: Section 2 is a
literature review, which focuses on the theory of usability,
the theory of usability and user experience, and the in-
troduction of tools and methods of electronic wallet-one
card solution for college students. Section 3 is setting up
a preliminary usability evaluation indicator system of
electronic wallet-one card solution for college students.
Section 4 is analyzing the usability evaluation indicator
system using a factor analysis method. Section 6 is the
conclusion.
2. Literature Review
International organization for standard ISO9241-11
shows the definition of usability is “a product can be
used by specified users with effectiveness, efficiency and
satisfaction in a specified environment” [4]. Effective-
ness refers to the degree of correctness and completeness
of the user to fulfill a particular task and achieve the spe-
cific goal, namely whether users can use products to do
what they want; Efficiency refers to the ratio of effec-
*This research was supported by Key Project of Guangdong Province
Education Office (06JDXM63002), NSF of China (70471091), and
QualiPSo (IST-FP6-IP-034763).

Empirical Study on Usability Impact Factors of Electronic Wallet-One Card Solution within College Students 78
tiveness with the use of resources (e.g., time); Satisfac-
tion refers to users’ subjective satisfaction and accep-
tance in the process of using products. Hartson believes
usability has two meanings: usefulness and ease of use.
Usefulness means whether the product can implement a
series of functions. Ease of use refers to the interaction
efficiency between the user and the interface, the learn-
ability, and user’s satisfaction [5]. Jacob Nielsen, the
American guru the Web ease of use, believes that usabil-
ity is not a unit attribute of the user interface, the product
will have high usability only when each element achieve
good level. Usability has many components, and it has
association with the following five dimensions of usabil-
ity traditionally [6]: 1) Learnability: whether the product
is easy to learn, and the user can begin to complete the
task with the system in a short time; 2) The interaction
efficiency: the efficiency for the user to use the product
to complete specific tasks; 3) Memorability: the usage of
the product is easy to remember, which means the user
who does not use the product frequently can still re-
member how to operate it after a period of time, without
learning from scratch; 4) The error rate and severity: us-
ers can make least error in the process of using the prod-
uct, or it can avoid serious disasters caused by the mis-
take; 5) Customer satisfaction: users can feel satisfied or
even delighted after using the product.
There are four criteria of usability engineering indica-
tor (Table 1).
For the user, user experience is the subjective feeling
of the user to the product, and is the sum of the contents
of what the user feels and obtains in the process of using
the product [7]. Usability is a kind of overall experience,
and is an important product of interactive IT product/
system. For the user, usability means whether the product
is effective, easy to learn, efficient, easy to remember,
little fault, and how is the efficiency and subjective feel-
ing when he finish the task with the product. It involves
three important usability information [8]: the intended
use, the environmental conditions and the usability met-
rics (Figure 1).
Factor analysis method is a technology to simplify
multivariate, with the purpose to decompose the original
variables, and to generalize the potential “category”. The
indicators with strong correlation will be classified as the
same category. Each kind of variable represents a general
factor, and there are little correlations between different
categories. Thus the several original correlative indica-
tors can be combined into a few independent indicators
which can fully reflect the overall information, and it can
solve the multi-collinearity problem between the vari-
ables on the premise of not losing the main information
[10]. Factor analysis generally can be divided into the
following five steps [11]: 1) confirm whether it is suit-
able for factor analysis; 2) extract common factors, and
confirm the number of factors and the method to calcu-
Table 1. Usability engineering standards.
Standards
Usability Factors
ISO9241-11 ISO9241-10 Jakob Nielsen Human-computer Interaction
Effectiveness √ √
Efficiency √ √ √
Satisfaction √ √ √
Memorability √ √
Suitability for learning √ √ √
Error tolerance √ √ √
Suitability for task √ √
Consistency √ √
Suitability for individualization √ √
Controllability √ √
Self-descriptiveness √ √
Utility √
Compatibility √
Proximity √
Legibility or audibility √
Identifiable √
Feedback √
Minimized memory load √
Shortcut √

Empirical Study on Usability Impact Factors of Electronic Wallet-One Card Solution within College Students 79
late the factors; 3) make the factors with more named
interpretability; 4) determine and name factors; 5) calcu-
late factor score of each sample. The research of this pa-
per is based on an empirical study on usability impact
factors of electronic wallet-one card solution within col-
lege students.
3. The Preliminary Usability Evaluation
Indicator System of the Electronic
Wallet-One Card Solution
3.1. The Analysis of the Current Situation for the
Electronic Wallet
Electronic wallet is an important part of electronic trad-
ing, the transaction size of electronic trading scale in
China reached 7 trillion in 2011, increased by 46.4%
(note: data from iResearch: the release of the E-com-
merce core data of Q4 and annual 2011 in China). Today,
the most widely used field of the electronic wallet is the
electronic ticket system of city public transportation.
They are convenient for citizens to go out, and have been
extended gradually in all cities of China. The characteris-
tics of electronic wallets, which are secure, convenient,
efficient and fast, can satisfy the requirements of secure
payment for today’s electronic commerce, especially for
the small-amount shopping. After two expansions of
university enrollment, college students have become a
special large consumer group, and they have both real
consumption ability and consumption potential in the
future. With the deepening of college informatization
degree, the use of electronic wallet increase every day, as
well as the use size of college students, such as bus IC
card, campus-one card solution (eating, shopping, cam-
pus consumption), mobile phone card, and pay treasure.
The college students’ consumption is getting more and
more attention. It was reported that, in 2004, college stu-
dents’ average consumption amount had exceeded the
national average annual disposable amount [12]. The fac-
tors affect students’ use of electronic wallet include col-
lege students’ cognition degree of electronic wallet, the
intention to use, the affordability of terminal equipment
price, the concentration degree, after-sales service to-
gether with students’ cognitive preferences, habits, ide-
ology, safety performance.
3.2. The Analysis of User Behavior
E.S. Lewis (1898) proposed the traditional consumer
behavior model: attention to products, interested, gener-
ate purchase desire, get memories, and purchase action
(ADIMA mode) (Figure 2). But with the arrival of in-
formation age, the behavior model has translated into:
attention to products, interested, information search,
purchase action and information sharing. Dentsu pro-
posed the AISAS model later, aiming at consumers life-
style change in the Internet and wireless applications era
(Figure 3).
Electronic wallet users’ behavior pattern has and simi-
larities with that of the information age users, namely
they understand the electronic wallet by friends or
through the Internet, thereby become interested in the
electronic wallet and generate using behavior, then the
satisfaction or realization of the function and demand
will be produced during the process of use, which
prompts the next action, and the introduction of the elec-
tronic wallet, realizing the information sharing (Figure
4).
Through the market research reports of the electronic
wallet and the actual results of related interviews, three
factors, the usage terminal and scenario, the main behav-
Product
User
Task
Equipment
Environment
Anticipated goalGoals
Usability: the effectiveness, efficiency and subjective
if ihiifil
Effectiveness
Efficiency
Subjective
Interaction
Usability evaluation
Figure 1. The relationship between the usability and user experience [9].
Attention Interest Desire Memory Action
AIDMA Model
Figure 2. AIDMA model [13].

Empirical Study on Usability Impact Factors of Electronic Wallet-One Card Solution within College Students 80
Attention Interest Search Action Share
AISAS Model
Figure 3. AISAS model [13].
Friends/Ad Interest
Action
PC behavior
Satisfaction
Share
Figure 4. The real consumer behavior model of the elec-
tronic wallet.
ior modes and the factors impact using the electronic
wallet, were used to analyze the user behavior in this
paper. In terms of the usage terminal and scenario, the
scene of using the electronic wallet-one card solution for
most electronic wallet users can be divided into four
categories: eating in the dining hall, consuming in the
supermarket, taking a bus, taking the subway. Function is
the priority for user when using the electronic wallet, so
the user may not consider the fast operation speed of.
Generally, the user needs the adaptation time after their
first use of an electronic wallet, for the inadaptability in
changing habits. But after the first use, the user will get
satisfaction, and it will promote the use of next time. The
vast majority of the user’s behavior patterns accord with
the consumption pattern mentioned above (Figure 3). As
the factors that affect users’ using of the electronic wallet,
users will be affected by the function and safety of the
electronic wallet-one card solution, and it will also have
influence whether it can give users the “aesthetic feeling”,
as well as the user’s feelings and emotions. The forma-
tion process of the user behavior habit of the electronic
wallet was concluded (Figure 5).
3.3. The Determination of Usability Evaluation
Indicator
According to the above analysis, the usability evaluation
indicator system of the electronic wallet-one card solu-
tion (Table 2) were built, which were certificated in four
dimensions, cognition, functionality, ease of use, and
satisfaction respectively. Specifically, cognition factor
includes three indicators: the target users, environmental
conditions, and the future prospects. Different users, dif-
ferent environmental conditions and the views towards
the prospects of the electronic wallets have effect on the
user’s cognition of the electronic wallet-one card solution;
Functionality factor includes 5 indicators: effectiveness,
compatibility, security, extensibility and practicability.
Whether the electronic wallets can finish the task that
users need or not, and whether it have compatibility, well
security, and extensibility will influence its function;
Ease of use factor includes 4 indicators: learnability,
memorability, controllability, and operational efficiency,
which directly affect its usability; Satisfaction factor in-
cludes 5 indicators: dependence degree, professional de-
gree, tangibles, the same degree, and reactivity.
4. Factor Analysis for Usability Evaluation
Indicator System
4.1. The Design of the Questionnaire
4.1.1. The Aim of the Questionnaire
Through related literature reference and the typical user
interviews, we preliminary determine usability evalua-
tion indicator system of electronic wallet-one card solu-
tion for college students, and design the questionnaire on
the basis of the preliminary usability evaluation indicator
system of electronic wallet-one card solution for college
students, we improve the indicator system according to
the feedback and suggestions of the questionnaire, the
weight of each indicator will be supported by the results
of the questionnaire analysis.
4.1.2. The Principles of Questionnaire Design
The results of questionnaire survey will determine the
structure of usability evaluation indicator system of elec-
tronic wallet-one card solution for college students and
the weight of each indicator, questionnaire design is a
prerequisite condition whether it can get a scientific sur-
vey result, therefore questionnaire design must follow: 1)
rationality: rationality refers to the questionnaire must be
closely related with the investigation subject, otherwise,
it may appear different results; 2) generality: generality
refers to the setting of issue should be of universal sig-
nificance, avoid excessive details or too many special
case problems; 3) logicality: logicality refers to ques-
tionnaire design should have associative perception,
namely problems should have logicality between each
other, can’t have logical errors, the questionnaire should
be a small system of relatively perfect; 4) clarity: clarity
refers to the setting of problems must be standardized,
clear, easy to answer; 5) non-inducible: non-inducible
refers to the setting of problems should be at a neutral
position, no prompt or subjective assumption, can’t re-
strict the independence and objectivity of is the respon-

Empirical Study on Usability Impact Factors of Electronic Wallet-One Card Solution within College Students 81
Function of
electronic wallet
Scope of influence,
applicability
Objective
environmental,
social and cultural
background
Objective condition
Visual
ex
p
erience
Interaction
experience
Similar
operating
experience
Subjective experience
Usability
impression
Figure 5. The formation process of the user behavior habit.
Table 2. The usability evaluation indicator system of the
electronic wallet-one card solution.
Research object First grade
indicator Second grade indicator
X1: The target users
X2: The environmental
conditions
Cognition
X3: The future prospects
X4: Effectiveness
X5: Compatibility
X6: Security
X7: Extensibility
Functionality
X8: Practicability
X9: Learnability
X10: Memorability
X11: Controllability
Usability
X12: Operational efficiency
X13: Reliability
X14: Assurance
X15: Tangibles
X16: Empathy
Empirical study on
usability impact
factors of electronic
wallet-one card
solution within
college students
Satisfaction
X17: Responsiveness
Dents; 6) facilitate the collation and analysis: Good ques-
tionnaire should consider not only the combination of
survey subjects and convenient collection of information,
but also consider the results of the survey are easier to
obtain and have a great convincingness. So it must take
into account the collation and analysis work after ques-
tionnaire survey.
4.1.3. The Questionnaire Design
We design the usability evaluation indicator system of
electronic wallet-one card solution for college students
according to the above principles, its basic structure is as
follows: the first part explains the purpose of the investi-
gation. The second part is the questionnaire survey in-
structions, explaining the significance of the score rating
and the score to the respondents. Investigation uses the
10 scale, scores from the 1 - 10, the importance increase
successively. The third part is the survey respondents
give the rating scale of indicator. According to the above,
this paper selects 17 indicators, in order to avoid ambigu-
ity, we have explanation to part of indicator in the design
of the questionnaire. The fourth part is the personal in-
formation of the respondents.
4.1.4. Dat a Col l ecti on
This paper-empirical study on usability impact factors of
electronic wallet-one card solution within college stu-
dents, positions the investigation object in college stu-
dents, especially sophomore, junior, senior students, de-
signs the questionnaire using a written form, collects the
questionnaires filled by the students, eliminates some
questionnaires which do not meet the requirements, col-
lates and counts up the questionnaire results using the list
form, prepares for the using of factor analysis.
4.2. Correlation Analysis of Evaluation Indicator
When we are using the factor analysis, if the original
variables are independent between each other, related
degree is very low, there is no information overlap, there
is no common factor among all the variables, then it
would not comprehensive and concentrated, so there is
no need to carry on the factor analysis, so it must have a
set of analysis for statistical data to determine whether
there is a strong correlation between factors, whether it is
suitable for factor analysis, SPSS provides the KMO test
and Bartlett test of sphericity for us to confirm the corre-
lation factor. We operate the data of the questionnaire by
using SPSS and get the results (Tables 3 and 4).
Through the test results of Tables 3 and 4, from the
correlation coefficient matrix of the initial variables, we
know the correlation coefficient of X1 and X2 (0.956), X1
and X3 (0.980) etc. are large, and the corresponding Sig
value is small, it indicates they have more obvious corre-
lation between these variables, at the same time the
KMO value of the sample is 0.704, the P value of
Bartley’s test of spherical is 0, so it is suitable for apply-
ing the factor analysis.
4.3. Determination of Main Factors
In the factor analysis, we extract and colligate factor
based on the sample data using the principal component
analysis method. We get the results by operating related
operations using SPSS (Tables 5 and 6).
4.3.1. Extract Main Factors
We extract the principal factors using the principal com-
ponents analysis method, the number of principal factors
is determined by the Kaiser standard (namely eigenvalue
greater than 1). According to Table 5, the corresponding
eigenvalue of 4 factors are greater than 1, respectively

Empirical Study on Usability Impact Factors of Electronic Wallet-One Card Solution within College Students 82
Table 3. Correlation matrix.
Correlatio n Matrix
X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 X16 X17
X1 1.000 0.676 0.847 0.091 0.052 0.1430.0240.1710.051−0.090 0.094 −0.0390.065 0.008 0.442 0.0090.085
X2 0.676 1.000 0.649 −0.147 −0.194 0.018−0.170 0.065−0.008−0.2350.091 −0.015 −0.016 −0.016 0.681 0.0140.002
X3 0.847 0.649 1.000 −0.032 −0.075 −0.015 −0.094−0.0240.0460.0270.1610.0410.031 0.044 0.401 0.0910.053
X4 0.091 −0.147 −0.032 1.000 0.404 0.6850.8200.4910.0730.0700.1400.0750.146 0.107 0.042 0.1410.153
X5 0.052 −0.194 −0.075 0.404 1.000 0.3560.3540.252−0.1950.0280.145 −0.0860.086 0.146 −0.044 0.1150.031
X6 0.143 0.018 −0.015 0.685 0.356 1.0000.6100.589−0.075 0.029 −0.026 −0.056−0.068 −0.053 0.024 −0.110 −0.077
X7 0.024 −0.170 −0.094 0.820 0.354 0.6101.0000.5040.2340.0540.0680.0540.038 0.063 0.010 0.0930.068
X8 0.171 0.065 −0.024 0.491 0.252 0.5890.5041.000−0.004 −0.071 −0.038 −0.194−0.086 −0.039 0.092 −0.130 −0.084
X9 0.051 −0.008 0.046 0.073 −0.195 −0.075 0.234 −0.0041.0000.213−0.0270.3580.143 0.054 −0.067 0.1180.061
X10 −0.090 −0.235 0.027 0.070 0.028 0.0290.054−0.0710.2131.0000.1810.2760.332 0.174 −0.213 0.1380.132
X11 0.094 0.091 0.161 0.140 0.145 −0.0260.068 −0.038 −0.0270.1811.000 −0.0210.679 0.858 0.225 0.8080.800
X12 −0.039 −0.015 0.041 0.075 −0.086 −0.056 0.054 −0.1940.3580.276 −0.0211.0000.090 −0.077 −0.109 −0.0300.071
X13 0.065 −0.016 0.031 0.146 0.086 −0.0680.038 −0.0860.1430.3320.6790.0901.000 0.622 0.149 0.6500.824
X14 0.008 −0.016 0.044 0.107 0.146 −0.0530.063 −0.0390.0540.1740.858 −0.0770.622 1.000 0.139 0.8040.732
X15 0.442 0.681 0.401 0.042 −0.044 0.0240.0100.092−0.067 −0.213 0.225−0.1090.149 0.139 1.000 0.2330.211
X16 0.009 0.014 0.091 0.141 0.115 −0.1100.093 −0.130 0.1180.1380.808 −0.0300.650 0.804 0.233 1.0000.732
correlation
X17 0.085 0.002 0.053 0.153 0.031 −0.0770.068 −0.0840.0610.1320.8000.0710.824 0.732 0.211 0.7321.000
X1 0.000 0.000 0.228 0.334 0.1180.4230.0790.3380.2290.2190.3760.297 0.473 0.000 0.4690.243
X2 0.000 0.000 0.112 0.054 0.4420.0790.2960.4740.0250.2270.4520.447 0.449 0.000 0.4550.492
X3 0.000 0.000 0.397 0.268 0.4500.2180.4230.3520.4120.0910.3690.398 0.359 0.000 0.2260.331
X4 0.228 0.112 0.397 0.000 0.0000.0000.0000.2740.2830.1240.2700.114 0.189 0.364 0.1220.104
X5 0.334 0.054 0.268 0.000 0.0010.0010.0180.0530.4090.1160.2390.241 0.114 0.360 0.1720.399
X6 0.118 0.442 0.450 0.000 0.001 0.0000.0000.2700.4060.4150.3220.288 0.330 0.423 0.1830.263
X7 0.423 0.079 0.218 0.000 0.001 0.000 0.0000.0260.3290.2890.3290.378 0.303 0.467 0.2220.288
X8 0.079 0.296 0.423 0.000 0.018 0.0000.000 0.4860.2800.3780.0530.240 0.374 0.224 0.1420.244
X9 0.338 0.474 0.352 0.274 0.053 0.2700.0260.486 0.0390.4130.0010.120 0.330 0.290 0.1650.307
X10 0.229 0.025 0.412 0.283 0.409 0.4060.3290.2800.039 0.0670.0100.003 0.075 0.038 0.1280.139
X11 0.219 0.227 0.091 0.124 0.116 0.4150.2890.3780.4130.067 0.4320.000 0.000 0.030 0.0000.000
X12 0.376 0.452 0.369 0.270 0.239 0.3220.3290.0530.0010.0100.432 0.229 0.263 0.185 0.4010.279
X13 0.297 0.447 0.398 0.114 0.241 0.2880.3780.2400.1200.0030.0000.229 0.000 0.109 0.0000.000
X14 0.473 0.449 0.359 0.189 0.114 0.3300.3030.3740.3300.0750.0000.2630.000 0.125 0.0000.000
X15 0.000 0.000 0.000 0.364 0.360 0.4230.4670.2240.2900.0380.0300.1850.109 0.125 0.026 0.040
X16 0.469 0.455 0.226 0.122 0.172 0.1830.2220.1420.1650.1280.0000.4010.000 0.000 0.026 0.000
Sig.
(one-tailed)
X17 0.243 0.492 0.331 0.104 0.399 0.2630.2880.2440.3070.1390.0000.2790.000 0.000 0.040 0.000
Table 4. KMO and Bartlett test.
KMO and Bartlett test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.704
Approx. Chi-Square 751.439
df 136
Barlett’s Test of Sphericity
Sig. 0.000
are 4.249, 3.109, 2.893, 1.681, the accumulated variance
contribution rate is 70.194%, so the 4 factors reflect the
information content of more than 70%, therefore, it can
choose the 4 factors as the main factors, named F1, F2, F3,
F4 in order. According to Table 6, we get the rotating
load matrix of 4 main factors after using maximum vari-
ance and orthogonal rotating, can get the name of each
main factor by the rotating load matrix.
1) The main factor F1 has the maximum load coeffi-
cient (component matrix) in the following factors: X11
controllability, X13 reliability, X14 assurance, X16 empa-
thy, X17 responsiveness, so we name the main factor F1
as “satisfaction” level.
2) The main factor F2 has the maximum load coeffi-
cient (component matrix) in the following factors: X4
effectiveness, X5 compatibility, X6 safety, X7 scalability,
X8 practicability, so we name the main factor F2 as “sat-
isfaction” level.
3) The main factor F3 has the maximum load coeffi-
cient (component matrix) in the following factors: X1

Empirical Study on Usability Impact Factors of Electronic Wallet-One Card Solution within College Students 83
Table 5. Eigenvalue and variance contribution rate.
Total variance explained
Initial eigenvalues Extraction sums of squared loadings Rotation sums of squared loadings
Component
Total % of Variance Cumulative %Total % of VarianceCumulative %Total % of Variance Cumulative %
1 4.249 24.996 24.996 4.249 24.996 24.996 4.131 24.301 24.301
2 3.109 18.290 43.287 3.109 18.290 43.287 3.124 18.378 42.679
3 2.893 17.018 60.305 2.893 17.018 60.305 2.928 17.226 59.905
4 1.681 9.889 70.194 1.681 9.889 70.194 1.749 10.289 70.194
5 0.991 5.830 76.025
6 0.763 4.486 80.510
7 0.679 3.997 84.507
8 0.544 3.197 87.705
9 0.491 2.891 90.596
10 0.463 2.726 93.322
11 0.326 1.917 95.239
12 0.191 1.123 96.362
13 0.175 1.030 97.392
14 0.145 0.852 98.244
15 0.120 0.707 98.951
16 0.103 0.608 99.559
17 0.075 0.441 100.000
Extraction method: principal component analysis
Table 6. Factor load matrix after rotating.
Rotated component matrixa
Component
1 2 3 4
X1 0.007 0.131 0.887 0.040
X2 −0.027 −0.110 0.898 −0.094
X3 0.041 −0.058 0.861 0.125
X4 0.134 0.886 −0.018 0.147
X5 0.154 0.552 −0.137 −0.219
X6 −0.105 0.846 0.066 −0.034
X7 0.052 0.860 −0.063 0.210
X8 −0.119 0.734 0.121 −0.143
X9 0.024 0.022 0.060 0.752
X10 0.221 0.021 −0.183 0.584
X11 0.924 0.046 0.113 −0.032
X12 −0.038 −0.067 −0.003 0.762
X13 0.830 0.006 0.019 0.211
X14 0.900 0.032 −0.005 −0.038
X15 0.210 0.031 0.704 −0.217
X16 0.895 0.003 0.046 0.024
X17 0.900 0.004 0.059 0.080
Extraction method: principal component analysis
target users, X2 environmental conditions, X3 future pros-
pects, X15 tangibles, so we name the main factor F3 as
“satisfaction” level.
4) The main factor F4 has the maximum load coeffi-
cient (component matrix) in the following factors: X9
learnability, X10 memorability, X12 operational efficiency,
so we name the main factor F4 as “satisfaction” level.
4.3.2. The Correlation and Reliability Analysis of
Main Factors
We perform relevant operation for the questionnaire data
using SPSS and get the results as follows (Tables 7 and
8): Ta ble 7 shows that 6 main factors are completely or-
thogonal selected by factor covariance matrix, namely
the 6 main factors are highly uncorrelated. They reflect
independently 6 party of usability evaluation indicator
system. At the same time, we can see from Table 8, the
Cronbach’s Alpha’s α coefficient value of 4 main factors
are 0.851 (F3), 0.843 (F4), 0.838 (F2), 0.937 (F1). The
Cronbach’s Alpha’s α coefficient value of 4 main factors
are greater than 0.8, so we can think the factor structure
has great consistency and validity.
4.3.3. Sol vi ng the Weight of Factors
1) The weight of the main factors
Factor variance contribution refers to the main factors
provides the variance contribution sum for all variables,
it is an indicator which measures the relative importance
of the main factors, the variance contribution is greater,

Empirical Study on Usability Impact Factors of Electronic Wallet-One Card Solution within College Students 84
the main factor is more important, so it can use the vari-
ance contribution rate to calculate the weight of main
factors, we can get the weight computational formula
which each main factor for the target:
1
i
in
i
i
c
W
c



(1)
Notes: Wi means the weight of main factor i to general
objective; ci means the variance contribution rate of main
factor i; n means the number of the main factors.
We can see from Table 5, the variance contribution
rate of 4 main factors selected respectively is 4.249,
3.109, 2.893, 1.681, we put these data into the formula 1,
then we can draw the weight of each factor to the total
target (Table 9).
2) The weight of the two level indicators to the main
factors
We make a little change to the formula above, where n
reflects the number of a group of interclass variables, Wi
reflects the factor i of interclass, ci reflects the score of
the factor i to corresponding main factor. So we can cal-
culate the weight of each tow level indicator to the cor-
responding main factor (Table 10).
4.4. The Revise and Determination of Usability
Evaluation Indicator System of Electronic
Wallet-One Card Solution for College
Students
In the Section 3.3, based on the literature study and in-
terview situation, we build the usability evaluation indi-
cator system of electronic wallet-one card solution for
college students (Table 2). After analyzing using factor
analysis, we find the indicator system is feasible basi-
cally, the only difference is that we classify the two indi-
cator of “controllable” into a “subjective satisfaction in-
dicator”, it means controllable has a close relationship
with dependence degree, professional degree, empathy,
responsiveness, they act on the subjective satisfaction
together; also we classify the the two indicators “tangi-
bles” into one class indicator “cognitive”, it means tangi-
bles has a close relationship with target user, use envi-
Table 7. Component score covariance matrix.
Component Score Covariance Matrix
Component 1 2 3 4
1 1.000 0.000 0.000 0.000
2 0.000 1.000 0.000 0.000
3 0.000 0.000 1.000 0.000
4 0.000 0.000 0.000 1.000
Extraction method: principal component analysis.
Rotation method: varimax with Kaiser normalization.
Component scores.
Table 8. The analysis result of internal consistency.
VARIABLES = X1 X2 X3 X15
Case Processing Summary
N %
Valid 70 100.0
Excludeda 0 0.0
Cases
Total 70 100.0
aListwise deletion based on all variables in the procedure
Reliability Statistics
Cronbach’s AlphaCronbachs Alpha based
on Standard items N of Items
0.851 0.865 4
VARIABLES = X4 X5 X6 X7 X8
Case Processing Summary
N %
Valid 70 100.0
Excludeda 0 0.0
Cases
Total 70 100.0
aListwise deletion based on all variables in the procedure
Reliability Statistics
Cronbach’s AlphaCronbachs Alpha based
on Standard items N of Items
0.838 0.837 5
VARIABLES = X9 X10 X12
Case Processing Summary
aListwise deletion based on all variables in the procedure
Reliability Statistics
Cronbach’s AlphaCronbachs Alpha based
on Standard items N of Items
0.843 0.841 3
VARIABLES = X11 X13 X14 X16 X17
Case Processing Summary
N %
Valid 70 100.0
Excludeda 0 0.0
Cases
Total 70 100.0
aListwise deletion based on all variables in the procedure
Reliability Statistics
Cronbach’s AlphaCronbachs Alpha based
on Standard items N of Items
0.937 0.938 5
Table 9. The main factors weight for general goal.
Main factors F1 F
2 F 3 F 4
Weight 0.356 0.261 0.242 0.141
ronment, future prospects, the act on the cognitive factor
together. From a practical perspective, it is also in line
with people’s habit of thinking. In general, the indicator
system after revising is more reasonable, more in line
with the actual (Figure 6).

Empirical Study on Usability Impact Factors of Electronic Wallet-One Card Solution within College Students 85
Empirical study on usability impact factors of electronic wallet-one card solution within college students
Cognition Functionalit
y
Satisfaction
Usability
Learnability
Memora bilit y
Operational
efficiency
Compatibility
Security
extensibility
Effectiveness Relia bility
Assurance
Empathy
Tangibles
Practicability Resp onsiven ess
Target users
Environmental conditions
Future prospects
controllability
Figure 6. The usability evaluation indicator system of the electronic wallet-one card solution after revising.
Table 10. The weight coefficient of each to level indicator to the corresponding main factor.
Main factors F1 F
2 F 3 F 4
The two class indicator X11 X
13 X 14 X 16 X 17 X4X5X6X7X8X1X2X3 X 15 X 9 X 10 X12
The weight coefficient 0.208 0.186 0.203 0.201 0.2020.2280.1420.2180.2220.190.265 0.2680.257 0.21 0.358 0.2780.364
5. Conclusion [5] H. R. Hartson, “Human-Computer Interaction: Interdisci-
plinary Roots and Trends,” The Journal of System and
Software, Vol. 43, No. 2, 1998, pp. 103-118.
doi:10.1016/S0164-1212(98)10026-2
Behavior habits within using the electronic wallet-one
card solution for college students are analyzed. We build
up a preliminary usability evaluation indicator system
and design the questionnaire on the basis of the usability
evaluation indicator system. We find out the main factors
and corresponding weight using factor analysis methods
and set up an improved usability evaluation indicator
system of electronic wallet-one card solution for college
students. Of course, we just do singleton studies about
affecting factors, process and meaning of usability influ-
ence problems. Whether the usability evaluation indica-
tor system has universal applicability remains to be fur-
ther researched and discussed.
[6] J. Nielson, “Usability Engineering,” Z. J. Liu, et al.,
Translate, China Machine Press, Beijing, 2004.
[7] B. Wei, “User Experience in the Internet,” Art and De-
sign, No. 2, 2008, pp. 20-22.
[8] H. T. Zhang, “The Usability Evaluation and Improvement
of ATM Products,” Dalian University of Technology, No.
6, 2005, pp. 4-5.
[9] Y. Z. Wu, “Case Study on User Experience of Digital
Product for Farmers,” Dalian University of Technology,
No. 5, 2008, pp. 15-16.
[10] Y. J. Fu and H. N. Huang, “The Management Perform-
ance Evaluation of Listed Company Based on Factor
Analysis Model,” Statistics and Decision, No. 12, 2006,
pp. 167-168.
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