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Antecedents of mobile app usage among smartphone users

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Although mobile apps are already an influential medium in the new media industry as a whole, these apps have received little academic attention within the communication and marketing literature. This study develops and tests a hypothesized model to explain antecedents affecting app usage among smartphone users. The analysis of the structural equation model determined a final model with four significant factors (perceived informative and entertaining usefulness, perceived ease of use, and user review). Cost-effectiveness, a key variable of this study due to the particularity of 99-cent app price, had no influence on app usage. This study not only includes marketing implications but also offers insight into various theoretical applications to the field of mobile communication research by suggesting a conceptual model for the acceptance of mobile apps.
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Antecedents of mobile app usage
among smartphone users
Sang Chon Kima, Doyle Yoonb & Eun Kyoung Hanc
a Gaylord College of Journalism & Mass Communication, University
of Oklahoma, 395 W. Lindsey, Suite 3140, Norman, OK 73019, USA
b Gaylord College of Journalism & Mass Communication,
University of Oklahoma, 395 W. Lindsey, Suite 3515, Norman, OK
73019, USA
c Department of Journalism & Mass Communication,
SungKyunKwan University, Seoul, Republic of Korea
Published online: 22 Aug 2014.
To cite this article: Sang Chon Kim, Doyle Yoon & Eun Kyoung Han (2014): Antecedents of
mobile app usage among smartphone users, Journal of Marketing Communications, DOI:
10.1080/13527266.2014.951065
To link to this article: http://dx.doi.org/10.1080/13527266.2014.951065
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Antecedents of mobile app usage among smartphone users
Sang Chon Kim
a
*, Doyle Yoon
b1
and Eun Kyoung Han
c2
a
Gaylord College of Journalism & Mass Communication, University of Oklahoma, 395 W. Lindsey,
Suite 3140, Norman, OK 73019, USA;
b
Gaylord College of Journalism & Mass Communication,
University of Oklahoma, 395 W. Lindsey, Suite 3515, Norman, OK 73019, USA;
c
Department of
Journalism & Mass Communication, SungKyunKwan University, Seoul, Republic of Korea
Although mobile apps are already an influential medium in the new media industry as a
whole, these apps have received little academic attention within the communication
and marketing literature. This study develops and tests a hypothesized model to explain
antecedents affecting app usage among smartphone users. The analysis of the structural
equation model determined a final model with four significant factors (perceived
informative and entertaining usefulness, perceived ease of use, and user review). Cost-
effectiveness, a key variable of this study due to the particularity of 99-cent app price,
had no influence on app usage. This study not only includes marketing implications but
also offers insight into various theoretical applications to the field of mobile
communication research by suggesting a conceptual model for the acceptance of
mobile apps.
Keywords: smartphone apps; technology acceptance; perceived usefulness; perceived
ease of use; user review; cost-effectiveness
As information technology continues to evolve, mobile phones get smarter and smarter.
Technological advancements have enabled mobile devices to add more advanced
computing ability and broader data access by wireless services, such as Wi-Fi, 3G, and 4G,
and have led the advent of current so-called ‘smartphones’ (Middleton 2010).
In particular, 3G represents the third generation of wireless service, which offers
always-on Internet connectivity (Okazaki and Barwise 2011). More recently, 4G based on
the long-term evolution system is an upgraded successor of the 3G standards (Patel 2011).
This mobile ultra-broadband Internet access is considered one of the features that
distinguish smartphones from previous feature phones. Moreover, a unique characteristic
of smartphones is their ability to download and run tens of thousands of applications
(apps), which vary from informative to entertaining (Middleton 2010). According to a
report from a mobile analytics firm (Perez 2014), more than 450,000 mobile apps have
been released across all major devices, including Android, iPhone, Blackberry, and
Windows phone. The report also finds that smartphone users in the USA spend most time
using mobile apps while spending 2 hours and 42 minutes a day on mobile services as of
March 2014. Specifically, users spend 2 hours and 19 minutes using mobile apps while
spending only 22 minutes a day using the mobile web.
Why do people use mobile apps? Why are people willing to accept this new
technology although there are small but technological and cognitive hurdles for them to
overcome? These challenges include locating (i.e., searching and downloading) a
particular app and learning how to use it. Previous studies on other mobile services e.g.,
use of mobile advertising (Zhang and Mao 2008) and use of the mobile Internet
q2014 Taylor & Francis
*Corresponding author. Email: sckim@ou.edu
Journal of Marketing Communications, 2014
http://dx.doi.org/10.1080/13527266.2014.951065
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(Fogelgren-Pedersen, Anderson, and Jelbo 2003) have already discussed factors
affecting acceptance or use of mobile technology. This study may be differentiated from
prior research in twofold. First, mobile apps are not only a medium, but are also a retail
product. Therefore, antecedents of mobile app usage may vary depending on the function.
The second gap is that, in comparison with other mobile services, the popularity of mobile
apps is growing at a very fast pace due to the dramatically rapid increase in smartphone
ownership. A comScore study reported that 62% of mobile phone users in the USA as of
September 2013 owned a smartphone, reaching to a total of 147.9 million current
subscribers (Lella 2013). Because mobile apps are an integral part of the smartphone
experience, the growing base of smartphone users leads to more apps being developed to
serve a wider and wider range of consumer needs. From a marketing perspective, this
growth of smartphone ownership and mobile app consumption may create new mobile
marketing opportunities beyond the traditional mobile marketing strategies (e.g., SMS
advertising). A recent study by Juniper Research proves the potential market value of
mobile apps, by reporting that spending on in-app advertising reached $3.5 billion in 2013
(Grant 2014).
Although smartphone apps already represent an important part of mobile marketing
strategies, these apps have received little academic attention within the communication
and marketing literature. The purpose of this study is (1) to identify a variety of factors
affecting smartphone app usage from behavioral theories, uses and gratification theory,
previous empirical studies, and the contexts of this study, and (2) to test a hypothesized
model extended from the technology acceptance model (TAM), which predicts how the
factors are associated with the usage of technology (i.e., the usage of smartphone apps).
This study has practical implications for mobile marketing practitioners, including app
developers, app advertisers, and app providers. This study also offers insight into various
theoretical applications to the field of mobile communication research by suggesting a
conceptual model for the acceptance of mobile apps. The model may show the possibility
of mobile apps as alternative media to gratify individuals’ various needs and the
importance of social influence in a networked audience environment.
Theoretical background: TAM
According to Fishbein and Ajzen (1975), behavioral intention is predicted based on
multiattribute models. Davis’s (1989) TAM predicts users’ attitudes toward using a piece
of technology and their intentions to use it, based on two key determinants perceived
usefulness and perceived ease of use (PEOU). Although this study expects more possible
determinants, TAM would be suitable as a theoretical framework for the aim of this study,
which attempts to find multiple factors affecting the acceptance of a new media
technology.
Perceived usefulness is defined in TAM as ‘the degree to which a person believes that
using a particular system would enhance his or her job performance’ (Davis 1989, 320).
Also, PEOU refers to ‘the degree to which a person believes that using a particular system
would be free of effort’ (320). Higher perceived usefulness or PEOU leads to more
positive attitudes toward using a piece of technology. According to TAM, these attitudes
affect behavioral intentions which in turn influence actual behavior. In comparison with
other behavioral theories such as the theory of reasoned action (TRA; Fishbein and Ajzen
1975) and the theory of planned behavior (TPB; Ajzen 1991), Gentry and Calantone
(2002) argue that TAM seems to have better explained variance in behavioral intention,
particularly in the context of technology usage.
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A recent study from the mobile marketing field (Okazaki and Barwise 2011) that
reviewed the literature on mobile advertising from 1993 to 2010 showed that TAM has
been the model most frequently used in recent studies. TAM explained the use of the
mobile Internet (Fogelgren-Pedersen, Anderson, and Jelbo 2003), acceptance of mobile
SMS advertising (Zhang and Mao 2008), and behavioral intention to use mobile services
(Nysveen, Pedersen, and Thorbjørnsen 2005).
In this study, the intention to use mobile apps is defined as the intention for smartphone
users to use mobile apps downloaded on their devices. In accordance with TAM, the
perceived usefulness of mobile apps may increase users’ intentions to use them because it
would enhance users’ job performance or help them achieve goals (Davis 1989). Also, the
PEOU of mobile apps may increase users’ intentions to use them because technology users
are reluctant to make their behavioral effort in using new technology (Venkatesh 2000).
Proposed model and concept explication
The traditional TAM has been recognized to have weaknesses including the inability to explain
other possible factors besides usefulness and ease of use (Mathieson 1991; Moon and Kim
2001;Venkatesh2000). In the context of this study, TAM is too simple (1) to reflect the various
needs of diverse individuals, (2) to predict the information systems of the networked society,
and (3) to account for the influence of practical marketing components.
As a medium, mobile services are highly personalized (Pagani 2004). This implies that
determinants affecting the use of mobile services may vary across different individuals.
Non-traditional factors, such as fashion (Nysveen, Pedersen, and Thorbjørnsen 2005) and
sociality (Zhang and Mao 2008), may affect use of personalized mobile services because
different individuals have different motivations and attitudes. Especially when it comes to
using mobile apps, users are likely to be influenced by a variety of factors. Users select
apps individually from the app store installed on their mobile device. Users probably have
particular reasons or needs for each app they download and use. Users are fragmented, but
share information about apps through the networked information systems (e.g., user
review sections in the app store). Users’ intentions to download and use apps are likely
influenced by the cost of each app, as well.
The traditional TAM is still predictive, but is not detailed enough to explain all the
factors that affect app users’ behavior. Therefore, the proposed model for this study is
TAM-related and adds app user reviews and cost-effectiveness to the traditional TAM
factors of perceived usefulness and PEOU (see Figure 1). In addition, uses and
gratification theory is used to further specify the TAM variable of perceived usefulness.
The concept of user reviews is derived from social psychology, while the cost-
effectiveness factor comes from the marketing perspective.
Perceived informative, entertaining, and social usefulness
As expected in the traditional TAM, PEOU is still a predictive variable in this study.
However, perceived usefulness is revised to reflect individual needs of app users and of the
various functions of apps (e.g., game, music, news, navigation, and social networking).
Other potential motivations for using technology have been often derived through the
application of uses and gratification theory. This approach proposes that the purpose of
audiences’ media use is to gratify their specific needs (Katz, Haas, and Gurevitch 1973).
According to Katz, Haas, and Gurevitch (1973), audiences use media to fulfill (1) ‘needs
related to strengthening information, knowledge and understanding’, (2) ‘needs related to
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strengthening aesthetic, pleasurable and emotional experience’, and (3) ‘needs related to
strengthening contact with family, friends, and the world’ (166 167). Many studies have
extended TAM by adding these constructs perceived information, perceived
entertainment, and perceived sociality (Bauer et al. 2005; Nysveen, Pedersen, and
Thorbjørnsen 2005; Zhang and Mao 2008). In the studies by Bauer et al. (2005) and Zhang
and Mao (2008), these three constructs were defined as another antecedent affecting
perceived usefulness. However, Nysveen, Pedersen, and Thorbjørnsen (2005) categorized
the three constructs into utilitarian versus hedonic motives, which influence attitude toward
use and intention to use. More specifically, information was viewed as a utilitarian motive
related to usefulness; thus, perceived information was integrated into the construct of
perceived usefulness. The two other concepts of entertainment and sociality were defined as
perceived enjoyment, a hedonic motive (Nysveen, Pedersen, and Thorbjørnsen 2005).
This study attempts to operationally define the abstract, inclusive construct of perceived
usefulness as three specific, independent dimensions perceived informative usefulness
(PIU), perceived entertaining usefulness (PEU), and perceived social usefulness (PSU). Each
construct refers to some of the key motivations to use the Web, such as research,
communicating, and entertainment (Yoon, Cropp, and Cameron 2002). This implies that each
construct could not be integrated into one construct of perceived usefulness because each one is
uniquely important, independently influencing attitude and behavioral intention. Based on
TAM and the uses and gratification approach, therefore, four hypotheses are suggested:
H1: The PIU of mobile apps positively affects attitudes toward using mobile apps.
H2: The PEU of mobile apps positively affects attitudes toward using mobile apps.
H3: The PSU of mobile apps positively affects attitudes toward using mobile apps.
H4: The PEOU of mobile apps positively affects attitudes toward using mobile apps.
As mentioned earlier, the basic assumption of TAM is that antecedents predict
attitude toward behavior and, in turn, attitude predicts behavioral intention. That is, the
above-mentioned four antecedents would lead to the overall positive attitude toward
Perceived
Informative
Usefulness
Perceived
Entertaining
Usefulness
Perceived
Social
Usefulness
Attitude
Toward
App Usage
Behavioral
Intention to Use
Mobile Apps
User Review
H1
H5
H6
Perceived
Easy of Use
Perceived Cost-
Effectiveness
H2
H3
H4
H7
Figure 1. The hypothesized research model.
4S.C. Kim et al.
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mobile app usage that would have a positive effect on the intention to use mobile apps.
Thus, an additional following hypothesis is suggested:
H5: Attitude toward using mobile apps positively affects intention to use mobile apps.
Product reviews by app users
The potential of product reviews by app users as another factor affecting behavioral
intention is theoretically explained by the TRA. This theory basically proposes that
behavior is determined by behavioral intention, which is predicted by people’s attitude
toward that behavior and subjective norms (Fishbein and Ajzen 1975). Fishbein and Ajzen
(1975) defined subjective norm as ‘the person’s perception that most people who are
important to him think he should or should not perform the behavior in question’ (302).
In research on mobile service use, TRA has been used mainly as a way to extend TAM and
include normative influences (Hung, Ku, and Chang 2003; Teo and Pok 2003). Because of
the importance of social influences on media use, TRA posits that subjective norms have a
direct effect on behavioral intention (Fishbein and Ajzen 1975). Subjective norms in this
study, accordingly, can be defined as user reviews about mobile apps.
In general, user reviews refer to original, first-hand opinions written by users about the
quality of products based on personal experiences with the products (Benlian, Titah, and Hess
2012). Little research has been conducted on user reviews regarding mobile apps; however,
the positive role of consumer reviews in the context of Internet-based electronic commerce
has been investigated in a number of studies. Basically, consumers perceive product reviews
to be very helpful in performing their shopping tasks (Pan and Zhang 2011)andhavemore
affective trust in consumer-generated product reviews than in system-filtered recommen-
dations by product providers (Benlian, Titah, and Hess 2012). Such positive perceptions
toward product reviews are likely to enhance consideration about product, quality of choice
(Gupta and Harris 2009), and consumer purchasing intention (Park, Lee, and Han 2007).
Many studies have found that favorable consumer reviews even lead to actual purchases
(Chevalier and Mayzlin 2006; Duan, Gu, and Whinston 2008;ZhuandZhang2010).
Moreover, Clemons, Gao, and Hitt (2006) argued that consumer reviews could make new
product introductions more successful. If so, user reviews could be especially important in the
mobile app market with hundreds of new apps being introduced daily.
This study defines ‘user review’ more broadly than other studies have defined it (e.g.,
Benlian, Titah, and Hess 2012). In this study, specified acquaintances and unspecified
users are included in the meaning of ‘user’. Accordingly, personal recommendations and
anonymous written reviews are both treated as ‘user reviews’. Based on previous studies,
user reviews are likely to function as subjective norms affecting app purchasing/using
behavior (either apps as an item of retail product or as a type of media). User reviews may
be more important in the online-networked audience environment. Therefore, the
following hypothesis is postulated:
H6: Positive user reviews of mobile apps have a direct positive influence on intention to
use mobile apps.
Cost-effectiveness of mobile apps
Another influential factor on mobile app usage is cost-effectiveness. Price is a traditional
primary variable that has an influence on decision-making (Zeithaml 1988). The price of
mobile apps has a very narrow price barrier, starting at 99 cents. Its distribution is likely to
be very positively skewed. A recent report has shown that app prices average 2.43 dollars
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in North America and 3.86 dollars in Europe (Lendino 2010, March). Because the price of
downloading a mobile app is typically low, price is less likely to constrain behavioral
intention or actual behavior. A study on adoption of mobile TV services found a significant
inverse relationship between adoption and cost (Pagani 2011). The TPB, a successor of
TRA, provides a rationale for why the price variable is relevant to this study.
According to TPB, behavioral intentions are affected by three primary determinants
attitude, subjective norms, and perceived behavioral control (Ajzen 2008). In other words,
TPB extends TRA by perceived behavioral control as another factor of behavioral
intention. Ajzen (1991) defined perceived behavioral control as ‘people’s perception of the
ease or difficulty of performing the behavior of interest’ (183). Perceived behavioral
control is also referred to as ‘self-efficacy in relation to the behavior’ (Ajzen 2008, 537).
If individuals have sufficient control over a behavior, they are expected to act on their
behavioral intentions when the opportunity occurs. On the other hand, when control over
the behavior is limited by individuals’ economic situation, personal experience, or skill
level, their behavioral intentions may decline. Therefore, perceived behavioral control
would directly influence behavioral intentions only if there are no behavioral limitations.
In the context of this study, low price of mobile apps theoretically refers to higher level
of perceived behavioral control. Individuals should have little difficulty performing the
behavior (i.e., app downloading). However, this study focuses on cost-effectiveness
instead of cost as an antecedent of behavioral intention. Cost-effectiveness is a better
reflection of the way behavioral intent varies when the price differences are small (Pagani
2004). Cost-effectiveness, which refers to what consumers conclude when evaluating
benefits against costs, should be considered as an alternative antecedent of TAM, which
directly influences behavioral intention (Pagani 2004). From a marketing perspective, no
matter how new technology is perceived in terms of usefulness or ease of use, the
technology will not be accepted if the costs outweigh the benefits. Therefore, TAM’s core
constructs of perceived usefulness and PEOU are insufficient to explain purchasing
behavior from a marketing viewpoint. In the case of mobile apps, users are likely to
download and use apps only if they believe the apps are worth the money. Thus, this study
suggests the following hypothesis:
H7: The perceived cost-effectiveness of mobile apps has a direct positive influence on
intention to use mobile apps.
In summary, based on these hypotheses, a proposed model extended from TAM is
formulated (see Figure 1). The four integrated constructs from TAM and uses and
gratification perceived informative (H1), entertaining (H2), social usefulness (H3), and
PEOU (H4) predict more positive attitudes toward using mobile apps, which mediates
intention to use mobile apps (H5). User reviews (H6) and cost-effectiveness (H7) directly
affect intention to use mobile apps.
Methods
Procedures
Using an e-mail list including the whole enrolled students, data were collected online from
257 undergraduates in a large southern university on a volunteer basis. College students
were deemed appropriate for this study because they represent a significant segment of the
mobile population with new media skills (Yang, Zhou, and Liu 2010). All respondents
were current smartphone users, who were classified by the investigator’s prior question.
A survey was conducted. Participants in the study were given the website address for an
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online survey (https://www.surveymonkey.com/s/appresearch). Participants were directed
to log onto the first page of the website where they read general instructions. The
participants were asked to fill out the questionnaire about their perceptions of mobile app
experience, attitudes toward mobile app usage, and behavioral intentions to use mobile
apps.
Measurement
Each of the model’s eight constructs PIU, PEU, PSU, PEOU, user reviews, perceived
cost-effectiveness, attitudes toward app usage, and intentions to use apps consisted of
three items measured with 7-point Likert scales, ranging from 1 (strongly disagree) to 7
(strongly agree). Intention to use apps was measured as a dependent variable, and attitude
toward app usage was measured as a mediating variable. The rest were measured as
independent variables. Some of the survey items were adapted from previous studies
intention from Bauer et al. (2005); perceived usefulness and ease of use from Davis
(1989); attitude from Nysveen, Pedersen, and Thorbjørnsen (2005) and others were self-
created user review based on the TRA and cost-effectiveness based on the TPB.
As for measurement of all variables, factor analysis and Cronbach’s
a
showed that
three items of each variable created an internally consistent scale. One component was
extracted from all three items for each variable. The statements of item, mean, standard
deviation, and Cronbach’s
a
are displayed in Table 1.
Analysis
Structural equation modeling (SEM) with AMOS 5 was employed to test the hypotheses in
the proposed model. To assess the fit of the proposed model, this study used (1) chi-square
statistic, specifically the ratio of chi-square value to degree of freedom, (2) the goodness-
of-fit index (GFI), (3) the adjusted GFI (AGFI), (4) the normed fit index (NFI), (5) the
comparative fit index (CFI), and (6) the root mean square error of approximation
(RMSEA).
Results
Data screening
Multivariate outliers were checked with Mahalanobis distance, which is ‘the distance of
the case from the centroid of the remaining cases where the centroid is the point created by
the means of all the variables’ (Tabachnik and Fidell 2001, 67). The cases with p,.05, a
total of 52 cases, were deleted from the samples. After multivariate outliers were checked,
the normality and linearity of the data were checked and confirmed.
Descriptive statistics
After data screening, 257 participants were valid for the data analysis out of 309 responses
from the survey. Of these 257 participants, 200 were female (77.8%) and 57 were male
(22.2%). A t-test was conducted to confirm a possible bias from the unbalanced gender
distribution. There was no significant difference. The average age of the participants was
20.38 years (SD ¼2.029) and ranged from 14 to 34 years. With respect to the use of
mobile apps, participants used mobile apps ranging from 1 to 18 hours per day, with an
average of 3.67 hours (SD ¼3.070). The numbers of mobile apps the participants
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downloaded was 27.87 on average (SD ¼23.135), ranging from 2 to 150. The duration for
using the mobile apps was 27.68 months (SD ¼19.142), and ranged from 1 to 84 months.
Analysis of SEM
Prior to model testing, analyses were undertaken to evaluate whether the scales attained
satisfactory levels of reliability and validity, as well as whether factor loadings were
significantly associated with their corresponding constructs. First, the measurement model
with the latent constructs and their observed variables were analyzed. Second, the structural
model with hypothesized relationships was tested and modified to more accurately account
for the data. This concluded with the development of a final, refined model.
Measurement model evaluation
A confirmatory factor analysis of the full measurement model was performed. This
verified that each of the indicators significantly loaded on their correspondent latent
construct ( p,.01). Scale items for measuring latent variables used in this study were
adapted from the literature. Thus, the measured variables were expected to load on only
one factor and no covariance was expected among the error terms of observed variables.
Table 2 describes correlations of the latent constructs in the measurement model. The
Table 1. Summary of measures.
Construct Measures
Mean/SD
Cronbach’s
a
PIU Using mobile apps improves my information-seeking performance.
Using mobile apps makes it easier to seek information.
I find mobile apps useful in seeking information.
6.41/0.878
.912
PEU Using mobile apps improves my entertaining performance.
Using mobile apps makes me playful.
I find mobile apps useful in having fun.
5.68/1.096
.792
PSU Using mobile apps improves my social performance.
Using mobile apps makes it easier to communicate with people.
I find mobile apps useful in having social relationships.
5.30/1.434
.865
PEOU Learning to download mobile apps is easy for me.
My interaction with mobile apps is clear and understandable.
I find mobile apps easy to use.
6.36/0.938
.909
User review I am interested in mobile apps used by people close to me.
What mobile apps to download is affected by users’ reviews of the
apps.
I am willing to use mobile apps recommended by people close to
me.
5.57/1.052
.734
Cost-
effectiveness
I find the overall price of mobile apps inexpensive.
Mobile apps deserve the current price range (starting from 99
cents).
I find mobile apps cost-effective.
4.94/1.212
.857
Attitude Overall I find using mobile apps positive.
Overall I feel favorable toward mobile apps.
Overall I am satisfied with mobile apps provided by my
smartphone.
6.02/1.012
.920
Intention My general intention to use mobile apps is high.
I will continue to search mobile apps that I am interested in.
I will continue to use mobile apps in the future.
6.02/1.084
.922
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highest correlation coefficient among these variables was .775 between attitudes toward
the mobile apps and intention to download. All other correlation coefficients were below
the recommended threshold of .70 (Tabachnik and Fidell 2001), which suggest no
significant multicollinearity issues. Overall, the results suggested that the scales reflected
what they were intended to measure and confirmed whether they were reliable.
Structural model evaluation
The structural model was assessed using maximum likelihood. Five out of seven
hypothesized relationships among eight latent variables were statistically significant in the
directions hypothesized. In examining the effects of the four latent constructs of TAM on
attitude, PIU, PEU, and PEOU were positively associated with attitudes toward the mobile
apps, lending support for H1, H2, and H4. However, no significant association was found
between PSU and attitudes toward the mobile apps, not supporting for H3. As expected,
both attitudes toward the mobile apps and user reviews were positively associated with
usage intention, confirming H5 and H6. However, no statistically significant association
was found between cost effectiveness and usage intention. Thus, H7 was not supported.
The parameter estimates for the proposed structural model are reported in Table 3.
The GFIs suggest that the model did not fit the data well;
x
2
¼1153.036, df ¼245,
p,.001,
x
2
/df ratio ¼4.7; GFI ¼.693; AGFI ¼.624; NFI ¼.777; CFI ¼.814;
RMSEA ¼.120. In order to improve the model and be more parsimonious, model
revision was conducted as follows. First, the non-significant relationships were removed
from the model. Chi-square values of revised models were re-estimated and compared
with the proposed model. The paths (1) from PSU to attitudes towards the mobile apps and
(2) cost to usage intention did not significantly change chi-square of the model fit: (1)
x
2
difference
¼1.1962, df ¼1, p..05; (2)
x
2
difference
¼1.933, df ¼1, p..05. As a result,
both paths, which failed to improve the model fit, were removed. Accordingly, the two
latent constructs were eliminated. Then, based on modification indices (MIs) from
statistical analysis, several paths which were not included in the initial model were added.
MIs suggested the relationships among five constructs in the model [PIU, PEU, PEOU,
User review, and Usage intention]. This modified model (see Figure 2), which is relatively
more parsimonious, was adopted as the final model. Overall, the modified model reflected
the data better than the initial model:
x
2
¼393.53, df ¼119, p¼,.001; GFI ¼.861;
AGFI ¼.800; NFI ¼903; CFI ¼.930; RMSEA ¼.095. Even though all indices were
improved from the initial proposed model, a couple of indices such as GFI and RMSEA
did not meet their threshold level. However, the chi-square difference tests showed that
Table 2. Correlation for latent constructs.
12345678
1. PIU 1.00
2. PEU .445 1.00
3. PSU .353 .518 1.00
4. PEOU .655 .453 .319 1.00
5. REVIEWS .457 .403 .303 .445 1.00
6. COST .405 .359 .278 .470 .353 1.00
7. ATTITUDE .647 .507 .292 .697 .434 .493 1.00
8. INTENTION .672 .515 .367 .670 .542 .480 .775 1.00
Note: All correlation coefficients are statistically significant at p,.001.
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final model is significantly better than the proposed model. Table 3 also reports parameter
estimates for the revised model.
Discussion
This study was designed to examine the antecedents of mobile app usage among smart
phone users. An extended TAM, which included the additional factors of user review and
cost-effectiveness, was applied to predict people’s intention to use mobile apps. Overall,
the hypothesized research model did a fairly good job explaining significant associations
between the independent variables and the dependent variable. The analysis result offers
three primary implications in using mobile apps: (1) the theoretical support for TAM (i.e.,
usefulness and easiness of mobile apps), (2) the importance of app user reviews, and (3)
implications of 99 cents.
Table 3. Analysis of structural models.
Relationship Proposed model Final model
From !To Unstd. Std. Unstd. Std.
H1: PIU !ATTITUDE .397*** .408*** .278*** .229***
H2: PEU !ATTITUDE .238*** .318*** .199*** .235***
H3: PSU !ATTITUDE 2.038 2.067 – –
H4: PEOU !ATTITUDE .511*** .522*** .551*** .499***
H6: REVIEWS !INTENTION .302*** .335*** .355*** .300***
H7: COST !INTENTION .066 .080
H5: ATTITUDE !INTENTION .798*** .723*** .760*** .656***
GFIs
x
2
(df) 1153.03 (245) 393.54 (119)
GFI .693 .861
AGFI .624 .800
NFI .777 .903
CFI .814 .930
RMSEA .120 .095
Note: ***p,.001.
Perceived
Informative
Usefulness
Perceived
Entertaining
Usefuless
Perceived
Easy of Use
Attitude
Toward
App Usage
Behavioral
Intention to Use
Mobile Apps
User Review
.23
.23
.50
.66
.30
Figure 2. The final research model.
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The theoretical support for TAM
As expected, both informative and entertaining usefulness positively affected smartphone
users’ attitudes toward mobile apps usage. Let us compare information seeking from apps
and from the Web. Much like using search engines on the Web, smartphone users can type
keywords into their mobile devices to search for certain kinds of apps. Then, users can see
lists of apps related to their keywords and read descriptions of each app. For example,
users who are interested in the stock market can find numerous stock-related apps. Without
sitting at the computer desk, these apps enable users to get specific information in the palm
of their hands about stocks they are interested in or what stocks are hot this week. Users
can check today’s weather by using weather-related apps. Users can strengthen knowledge
about major league baseball players from baseball-related apps. Users can understand their
proximity to useful local places, such as the nearest gas stations, parks or pharmacies, with
navigation apps. This is a very personalized informative function of mobile apps.
In addition to informative usefulness, there are many apps that gratify entertaining
usefulness. Users can play almost all kinds of game apps (e.g., sports, puzzle, and shooting
games), can listen to music wherever and whenever they want, and can watch TV
programs. In terms of PEOU, users are likely to find it easy to search for, download, and
use mobile apps. As expected, users’ attitude toward mobile apps was positively related to
their behavioral intention to use mobile apps. Based on the analysis, TAM seems to be
theoretically supported in the contexts of mobile apps.
Contrary to expectations, the social usefulness of mobile apps was not meaningful to
users. There are two possible reasons for this finding. First, mobile devices already offer
text messaging and e-mail services without requiring users to download additional apps.
Text messaging has been available on mobile devices since the age of feature phone and is
extremely popular. As the technology of newer phones has evolved, with developments
such as Wi-Fi, 3G, and 4G, as well as larger screen sizes, e-mail has become more and
more popular, too. Furthermore, social networking services such as Facebook and Twitter
started on the Web. Therefore, even if respondents use these services on their mobile
devices, they may not think of them as mobile apps.
The second reason social usefulness was not meaningful to users may be that most
users think of mobile devices in personalized and individual contexts rather than social
contexts. The link between entertaining and informative usefulness and mobile app usage
may be evidence of egocentric tendencies among mobile users. A study on mobile service
usage suggested the concept of self-expressiveness as a potential variable to motivate
users, reporting that users sometimes perceive mobile services as a tool for expressing
their image, emotion, status, and even fashion (Nysveen, Pedersen, and Thorbjørnsen
2005). In other words, people may use mobile services as a mediator of self-satisfaction
through self-expressiveness rather than as a channel for social interactions. Unlike
traditional mobile services, such as short message service (SMS), examined in Nysveen,
Pedersen, and Thorbjørnsen’s (2005) study, mobile app services are likely to be a much
more personalized medium for egocentric self-expressiveness due to users’ thorough
selective exposure.
In contrast to this study, Zhang and Mao (2008) did find the variable of sociality to be
an influential factor on acceptance of mobile SMS advertising among Chinese consumers.
The different findings may be due to the somewhat different media in each study.
However, the differences may also be the result of different cultural contexts (i.e., higher
individualism in the USA vs. higher collectivism in China). Future research could
compare motivations for app usage across cultures.
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Why user reviews are influential
Consistent with the assumption of TRA, the analysis showed that user reviews had an
influence on users’ app usage. Two points of view may explain this finding: (1) an
information processing perspective at the psychological level and (2) an information
system perspective at the sociological level. First, the human information processing
theory assumes that people have limited cognitive capacity; thus, they tend to satisfy the
minimum requirements when they process information and make decisions (Simon 1955).
The theory argues that there are two stages to reducing cognitive burdens in the decision-
making process. The first stage is to reduce the set of products to a manageable level and
the second stage is to evaluate the reduced set of products in detail (Simon 1955). In the
context of this study, when app users search for apps they want, users can see lists of
numerous apps related to their interests. For example, when a user searches for a GPS app,
she or he encounters hundreds of GPS apps. This information overload may cause
problems in choosing which app to use. However, user reviews may help users narrow
their choices to just the best GPS apps available. Consequently, user reviews may enable
app users to cope with app information overload by reducing their search costs and freeing
them to evaluate a reduced set of apps more elaborately. This, in turn, may lead to more
satisfaction with the decision made. If users follow this process, it is easy to see why user
reviews would affect their intention to download apps.
Second, in terms of a sociological perspective, Rogers’s (2003) argument explains
how information, especially related to innovation, is systematically diffused in the social
network. He pointed out ‘interpersonal diffusion networks are mostly homophilous’ (307).
Therefore, people who are relatively less innovative tend to seek information and advice
about innovation from near peers who are more innovative (Rogers 2003). App users also
may be networked through this systematic diffusion process and successively share
information about new apps. Successors would follow app reviews of innovators and
earlier successors, and the successors’ successors would be able to acquire greater
numbers of, and more organized user reviews. This networked group created in the virtual
space would reduce social distance toward each other (Simmel 1964) and feel
homophilous (Rogers 2003). Consequently, the systematically accumulated reviews
would be a subjective norm, which affects app usage based on credibility.
Implications of 99 cents
Price is a traditional factor affecting decision-making in marketing, and fits with TPB as an
antecedent of behavioral intention. The context of this study (i.e., mobile apps fixed at low
price mostly) and the reinterpretation of marketing-oriented TAM both encouraged to
investigate cost-effectiveness rather than price, itself. Contrary to the hypothesis, cost-
effectiveness had no significant association with mobile app usage. Despite no significant
result, more elaborated discussions are demanded because of the particularity of app cost.
Four possible rationales are considered: (1) price resistance, (2) price consciousness, (3)
willingness to pay, and (4) free apps.
Price resistance
There are two economics terms that have been used mainly in the trade market. Price
resistance refers to ‘the price level at which selling is thought to be strong enough to
prevent the price from rising further’, whereas price support is defined as ‘the price level at
which demand is thought to be strong enough to prevent the price from declining further’
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(Thomsett 2003). Since the emergence of mobile apps, most apps have cost 99 cents. This
price of 99 cents may have created a Maginot line of price resistance, keeping prices from
rising much higher. Thomsett (2003) explains that the longer the time, the stronger the
resistance. Thus, to the resistors 99 cents is a not-so-cheap price. This price will not be
cost-effective for resistors, either mathematically or psychologically.
Price consciousness
The economic meaning of 99 cents may vary according to app users’ price consciousness,
or ‘the degree to which the consumer focuses exclusively on paying low prices’
(Lichtenstein, Ridgway, and Netemeyer 1993). In other words, every single person has
different price-related cognitions. Therefore, consumers who have high price
consciousness tend to purchase products at low prices (Lichtenstein, Ridgway, and
Netemeyer 1993). These consumers might perceive a 99-cent app as not-so-
cheap. Regardless of quality of the app, the app to them is not worth 99 cents due to
their natural tendency to seek lower prices. Cost-effectiveness can be more exactly
measured by dividing subjects into two groups high and low price consciousness group.
Willingness to pay
The meaning of 99 cents may also vary according to app users’ willingness to pay, which
refers to the maximum cost a consumer is willing to pay for the product (Cameron and
James 1987). Consumers tend to evaluate the price offered by product providers according
to their personal situations; thus, validity of the price will be different for different
consumers (Cameron and James 1987). App users who place a value of 99 cents on an app
may perceive more cost-effectiveness than those who estimate it to be worth a lower price.
The between-group comparison will be more effective for exact measurement of cost-
effectiveness.
Free apps
Despite the low price of mobile apps, users may hesitate to download paid apps because
free versions of apps are provided. The free versions are divided into free apps with paid
items and free sample versions of paid apps. Both formats require users to view some type
of in-app advertising. In these cases, the service provider offers an incentive the free
app as a reward for viewing an advertisement (e.g., a full-screen video ad, a banner ad,
or a skip ad). Users may be willing to accept irritating advertising in order to avoid paying
for apps. Several studies on the effectiveness of incentives in accepting mobile advertising
have been conducted already (Barwise and Strong 2002; Hanley, Becker, and Martinsen
2006; Nittala 2011). However, free apps may be an obstacle against downloading paid
apps if users want to save 99 cents and are satisfied with free apps despite their limitations.
In other words, free apps may negatively affect the cost-effectiveness of paid apps.
Conclusion
It is reported that smartphone users in the USA spend more time using mobile apps than
other mobile services, even more than using the Web on a computer. This implies the
dramatically rapid growth of the mobile app market. Mobile apps are already an influential
medium not just in the mobile industry, but also in the new media industry as a whole.
Nonetheless, these apps have received little academic attention within the communication
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and marketing literature. This study, based on TAM, proposed a hypothesized model with
six factors affecting attitude toward app usage and intention to use apps (perceived
informative, entertaining, and social usefulness, PEOU, user review, cost-effectiveness).
The analysis of the structural equation model determined a final model with four
significant factors (perceived informative and entertaining usefulness, PEOU, and user
review).
Following the basic assumptions of TAM, the final model identified both perceived
usefulness and PEOU for antecedents of app usage. In terms of perceived usefulness, the
model showed that app users tended to use apps mainly with informative and entertaining
needs while they did not use apps as a communication tool (i.e., social usefulness). User
reviews, a social psychological factor, played a very important role in using apps. The
hypothesized model expected an influence of cost-effectiveness on app usage, but the final
model did not reveal it. Due to the particularity of app price fixed at 99 cents mostly, this
study highlighted cost-effectiveness as a differential variable from other technology
acceptance studies. However, the perceptions about app price were likely to vary (1)
according to individual economic tendencies and (2) because of free apps. These
individual differences were not likely to lead an exact measurement of cost-effectiveness.
For mobile app practitioners, the findings imply that the appropriate combination of
information and entertainment (e.g., information-related apps with entertaining
components or vice versa) is likely to be the best fit for app users’ needs. Apps that
satisfy these different types of needs may be the most successful. Of course, these apps
should be easy to use. Also, mobile app practitioners may need to pay closer attention to
user reviews. Positive user reviews should be highlighted to capture the attention of
potential users.
Of the many mobile app practitioners, brand communicators (i.e., brand marketers
using mobile apps) will find the current results to be of particular interest. Mobile apps
may play a role as a mediator of communication between brands and consumers.
According to a recent report by Ipsos OTX (2013) on branded mobile app usage, more than
half of worldwide smartphone users regularly used brand apps, product apps, and store
apps. Of those who experienced at least one branded mobile app, 52% (42% of US
respondents) responded that the app facilitated their interest in buying from that brand
(cited from eMarketer). This may be indicative of a correlation between the use of a
branded app and higher purchase intent. Although this study did not specifically focus on
branded apps, its findings may be applied to the context of branded app usage. The report
by Ipsos OTX revealed that 43% of branded app users used the app to keep informed about
the brand, product or store, which is in line with the findings of this study. Based on the
current findings, branded app marketers may need to include fun and interesting content in
their apps, which fulfills users’ hedonic needs. Such entertaining components are likely to
make users stay longer at the app and increase their return visits. For example, the sports
brand Nike provides a variety of free branded apps, such as Nike training club (the app that
offers information about effective weight training programs and introduces related
products) and Nike basketball (the app including information about NBA players and a
free basketball game). Branded app marketers may need to pay attention to two types of
user reviews: user reviews about the branded app itself listed in the search engine of
mobile devices and user reviews about the brands and products included within the
branded app. User reviews are an important window for both marketing and
communication between brands and consumers and between consumers and consumers
(i.e., consumers networked via branded apps). Branded apps can be a new tool for
relationship building between brands and consumers.
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In addition to practical implications, this study makes a theoretical contribution to the
field of mobile communication research by suggesting a conceptual model for the
acceptance of mobile apps. Within the information systems perspective, Davis’s (1989)
original purpose in developing TAM was to explain the motivations of technology
adoption in organizations. However, because this study focused on using technology in the
context of everyday life (i.e., media technology such as mobile apps), it was supplemented
by the uses and gratification approach, which assumes that media (or media technology)
use is based on individuals’ needs in everyday life. According to Katz and Blumler (1974),
uses and gratification theory makes a foundational contribution to integrated research in
media, sociology, and social psychology. Thus, it is reasonable that the current model
includes both utilitarian and hedonic factors perceived informative, entertaining, and
social usefulness affecting intentions to use mobile apps. Finding the effect of user
reviews on intentions to use mobile apps, this study also confirmed the importance of
social influences, assumed in TRA (Fishbein and Ajzen 1975). Another variable missed in
the traditional TAM was perceived behavioral control. TPB assumes that this variable
reflects the internal and external constraints on behavior (Ajzen 1991). In this study, cost-
effectiveness was considered as one possible controlling factor. Although it had no
significant effect, cost-effectiveness or cost still may be an important variable extended
from TAM, especially in the context of paid mobile services. In sum, this study offers
insight into various theoretical applications to mobile communication research.
Limitations and future research
This study attempted to solve the problem of why people are willing to accept new mobile
technology (i.e., mobile apps) despite small but necessary effort required, such as
searching an app, downloading it, and learning how to use it. The model revealed that
smartphone users were likely to prefer playful, informative, and easy-to-handle apps. The
model also showed that users were affected by others’ favorable evaluations toward apps.
However, cost-effectiveness was not a significant consideration, probably because of the
inherent low price of apps. These findings may help mobile app practitioners not only
understand consumer needs and behavior but also decide marketing direction.
Nonetheless, it is still doubtful whether the current model would exactly reflect various
cognitive, emotional, and behavioral motives of smartphone users who are willing to
accept mobile apps. TAM, assuming acceptance of a particular technology, was used as a
framework model in this study, which defined acceptance of general mobile apps as
acceptance of technology. However, different mobile apps have different functions that
depend on different technologies. It may be questionable to generalize the current findings
and apply them to all cases of mobile apps. If this study focused on specific apps (e.g.,
game apps and branded apps), the application of TAM might be more theoretical and
scientific.
Future research may examine antecedents affecting acceptance of a particular app or a
particular kind of apps so that it identifies what successful factors are or not. In addition,
future research needs to define mobile app usage more elaborately, whether it is
acceptance of technology, use of media, or purchase of retail product.
Notes
1. Email: dyoon@ou.edu
2. Email: bird24@skku.edu
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Notes on contributors
Sang Chon Kim is a doctoral candidate in the Gaylord College of Journalism and Mass
Communication, University of Oklahoma. His research interests include relationship marketing,
web- and mobile-based information processing, and advertising effectiveness.
Doyle Yoon (Ph.D., University of Missouri) is an Associate Professor of advertising in the Gaylord
College of Journalism and Mass Communication, University of Oklahoma. His research interests
include brand advertising, web-based information processing, and brand consumer relationship
marketing.
Eun Kyoung Han (Ph.D., SungKyunKwan University) is a Professor of journalism and mass
communication in the College of Social Sciences, SungKyunKwan University, Seoul, South Korea.
Her research interests include advertising, brand communication, and reputation communication.
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... Price concern (PB) Characterised as the degree to which consumers show concerns due to price threats to use m-payment. Kim et al., 2016 Innovation resistance (IR) It is the consumer"s resistance to the m-payment innovation because it threatens to disrupt their current status quo or clashes with their belief system owing to the dark sides of m-payment. In simple terms, it is the refusal to accept the innovation of m-payment due to multitudinous concerns i.e., usage concern, price concern etc. Ram and Sheth, 1989 Selfcompassion (SC) From a mindfulness perspective, it is the degree to which an individual feels sympathetic pity and concern towards m-payment due to the dark sides. ...
... The hypothesised conceptual model adopted the construct IR from IRT, whereas the constructs UC, RC, VC, IC, TC, CC, PC, NAI, and SC are introduced to extend the IRT and also to meet the research objectives. Ram and Sheth, 1989;Borraz-Mora et al., 2017;Kim et al., 2016;Neff, 2003;Gu et al., 2009) ...
... The price barrier for mobile apps is low, and their distribution is likely to be very positively skewed. Price has a lower likelihood of constraining behavioural intentions or actual actions (Kim et al., 2016). The study opined that the price has an ambiguous effect on consumers" willingness to use m-payment i.e., some consumers may pay to update their phones to use m-payments, as well as the processing fees and other charges, and the rest may not. ...
Article
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Purpose The new ways to complete financial transactions have been developed by setting up mobile payment (m-payment) platforms and such platforms to access banking in the financial mainstream can transact as never before. But, does m-payment have veiled consequences? To seek an answer, the research was undertaken to explore the dark sides of m-payment for consumers by extending the theory of innovation resistance (IR) and by measuring non-adoption intention (NAI). Design/methodology/approach Three hundred individuals using popular online m-payment apps such as Paytm, PhonePe, Amazon Pay and Google Pay were surveyed for the primary data. IBM AMOS based structural equation modelling (SEM) was used to analyse the data. Findings Each m-payment transaction leaves a digital record, making some vulnerable consumers concerned about privacy threats. Lack of global standards prevents consumers from participating in the m-payment system properly until common interfaces are established based on up-to-date standards. Self-compassion (SC) characteristics such as anxiety, efficacy, fatigue, wait-and-see tendencies and the excessive choice of technology effect contribute to the non-adoption of m-payment. Originality/value This study proposes a threat model and empirically explores the dark sides of m-payment. In addition, it also unveils the moderator's role of SC in building the structural relationship between IR and NAI.
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