An Integrated Adoption Model of Mobile Cloud Services

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An Integrated Adoption Model of Mobile Cloud Services:

Exploration of Key Determinants and Extension of Technology

Acceptance Model

Eunil Park a, Ki Joon Kim b,

a Graduate School of Innovation and Technology Management, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Koreab Interaction Science Research Center, Sungkyunkwan University, Seoul, Republic of Korea

a r t i c l e i n f o

Article history:

Received 19 September 2013

Received in revised form 14 November 2013

Accepted 20 November 2013

Available online 4 December 2013

Keywords:

Mobile cloud computing services

Technology acceptance model

Perceived mobility

Perceived connectedness

Perceived security

Perceived service and system quality

a b s t r a c t

This study identifies and investigates a number of cognitive factors that contribute to shap-

ing user perceptions of and attitude toward mobile cloud computing services by integrat-

ing these factors with the technology acceptance model. A structural equation modeling

analysis is employed on data collected from 1099 survey samples, and results reveal that

user acceptance of mobile cloud services is largely affected by perceived mobility, connect-

edness, security, quality of service and system, and satisfaction. Both theoretical and prac-

tical implications of the studys findings are discussed.

2013 Elsevier Ltd. All rights reserved.

1. Introduction

Mobile devices, such as tablet computers and smartphones, have become essential tools for communication ( Dinh et al.,

2011). In particular, users increasingly benefit from mobile cloud computing that provides instant access to wireless net-

works and stored data on remote servers. With its efficiency and convenience, mobile cloud computing is now considered

one of the fastest growing areas of information and communication technology (ICT), as well as related industrial and

academic fields (Satyanarayanan, 1996). While earlier mobile devices and services faced a number of challenges (e.g.,

difficult user interfaces, security threats, limited resources) in maintaining and providing adequate services (Ali, 2009;

Satyanarayanan, 1996), mobile cloud computing has gained significant public interest as a suitable and realistic next-

generation computing service that offers a potential solution to these challenges.

In spite of the rapidly growing popularity of cloud computing in the mobile environment, only a few studies have exam-ined how user perceptions are shaped in mobile cloud computing, and these studies provide little information on how psy-

chological factors involved in the mobile context determine user acceptance of the service. Therefore, this study first

identifies user perceptions of mobility, security, connectedness, service and system quality, and satisfaction as key compo-

nents of mobile cloud services and then examines how these factors affect user perceptions and acceptance of the services.

More importantly, this study integrates these psychological factors with the technology acceptance model (TAM) and devel-

ops a new research model to predict the adoption of mobile cloud services by confirming the convergent, discriminant, and

internal validity of the proposed model via structural equation modeling (SEM).

0736-5853/$ - see front matter 2013 Elsevier Ltd. All rights reserved.http://dx.doi.org/10.1016/j.tele.2013.11.008

Corresponding author. Address: Interaction Science Research Center, Sungkyunkwan University, 326 International Hall, 53 Myeongnyun-dong 3-ga,

Seoul 110-745, Republic of Korea. Tel.: +82 2 740 1867; fax: +82 2 740 1856.

E-mail addresses: [email protected](E. Park), [email protected](K.J. Kim).

Telematics and Informatics 31 (2014) 376385

Contents lists available at ScienceDirect

Telematics and Informatics

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http://dx.doi.org/10.1016/j.tele.2013.11.008mailto:[email protected]:[email protected]://dx.doi.org/10.1016/j.tele.2013.11.008http://www.sciencedirect.com/science/journal/07365853http://www.elsevier.com/locate/telehttp://www.elsevier.com/locate/telehttp://www.sciencedirect.com/science/journal/07365853http://dx.doi.org/10.1016/j.tele.2013.11.008mailto:[email protected]:[email protected]://dx.doi.org/10.1016/j.tele.2013.11.008http://-/?-http://-/?-http://-/?-http://crossmark.crossref.org/dialog/?doi=10.1016/j.tele.2013.11.008&domain=pdf


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The present study is organized as follows. Section 2 provides a definition and overview of mobile cloud services. Section 3

discusses key characteristics of mobile cloud services and examines their psychological effects on user acceptance by devel-

oping an integrated research model. Sections 4 and 5 report the data collection procedure and results of the statistical anal-

ysis. This study then concludes with a discussion of the theoretical and practical implications of the study findings in

Section 6.

2. Mobile cloud services

Mobile cloud computing is defined as an infrastructure and system where both the data storage and data processing

happen outside of the mobile device (Dinh et al., 2011). Since its emergence, cloud computing has gained significant indus-

trial and consumer attention as a promising mobile paradigm in which data processing and storage occur in a network

cloud via a wireless connection. Cloud computing technologies can reduce the maintenance and development costs of mo-

bile services and applications, promote research on efficient methods and promising solutions for ubiquitous environments

and green IT systems, and provide users with various mobile services at low cost ( Aepona, 2010).

Greater storage capacity is one of the key advantages of mobile cloud services. Users can access their stored data via cloud

servers from a variety of electronic devices with wireless connectivity, while utilization and sharing of the data are processed

remotely within the servers (Vartiainen and Mattila, 2010). Well-known examples of such cloud services are iCloud and Goo-

gle Drive, which provide data storage and sharing service for images, movie clips, games, and documents. Given that these

multimedia files tend to be large and mobile devices typically have smaller storage area compared to conventional comput-

ers, storage capacity has always been an important technical limitation of mobile-platform devices and services. However,

mobile cloud services now offer a practical solution to this issue by allowing users to save large files in the cloud server viathe wireless networks (e.g., 3G, LTE, Wi-Fi) of their mobile devices. Mobile clouds significantly increase data storage capacity

and therefore allow more convenient data management and synchronization in a ubiquitous online workspace.

Long-lasting battery life is an essential component of mobile technology due to the associated portability and mobility.

The electronics industry has long invested in energy-efficient technology by working to develop low-power CPU, storage

disk, and display screen (Davis, 1993; Paulson, 2003; Mayo and Ranganathan, 2003). However, these attempts require

changes in hardware structure and cannot be directly applied to mobile technology without significant increases in costs

and technological advancements. As a feasible solution to this challenge, cloud computing allows migration of the complex

processing from a mobile device (resource-limited) to remote cloud servers (resource-rich). Prior studies have demonstrated

that such computational offloading shortens program executions and therefore extends battery life. For example, Rudenko

et al. (1998) reported that performing large matrix calculations in the cloud computing environment rather than on a mobile

device can save up to 50% of the energy used. In addition, Cuervo et al. (2010) found that cloud applications significantly

reduce energy consumption in computer games.

Saving files on cloud servers is an effective way to enhance reliability and reduce potential threats to data loss. The major-ity of cloud service providers are equipped with their own means of security and backup systems that protect user data. They

also provide users with various security-related services and software, including personal authentication, virus scanning and

detection, and protection of private information (Oberheide et al., 2008). Furthermore, cloud computing services can be ap-

plied to protect copyrighted online contents (e.g., books, movies, MP3s) and prevent unauthorized distribution of these

materials (Zou et al., 2010).

Due to these strengths and advantages, mobile cloud computing has emerged as an attractive platform for the upcoming

era of Web 3.0. In 2007,Schmidt (2007), CEO of Google, referred to Web 3.0 as a computing application model and defined it

as applications that are pieced together so that they (1) are relatively small, (2) are very fast and customizable, (3) can oper-

ate on any device (PC or mobile), and (4) store data in the cloud. These characteristics of the predicted Web 3.0 precisely

correspond to the key components and strengths of mobile cloud computing, suggesting the significant potential of mobile

cloud computing as the future mainstream technology.

3. User acceptance model of mobile cloud services

3.1. Technology acceptance model (TAM)

TAM consists of two main beliefs known as perceived ease of use and perceived usefulness, whichDavis (1989, 1993)

defined as the degree to which a person believes that using a specific system would be free of mental and physical efforts

and the degree to which a person believes that using a specific system would enhance his/her job performance, respec-

tively. Numerous studies have successfully utilized and replicated TAM to predict user acceptance of novel technologies

and systems and demonstrated that perceived ease of use and perceived usefulness largely determine user attitude toward

a specific technology, while attitude and perceived usefulness significantly affect behavioral intention to use the technology.

The TAM framework has been particularly useful in exploring user acceptance of recent novel mobile technologies and

services, including smartphones (Joo and Sang, 2013; Park and Chen, 2007), mobile banking (Lee and Chung, 2009), mobile

games (Ha et al., 2007), and long-term evolution (LTE) services (Park and Kim, 2013). By extension, TAM is also likely to be

applicable to examining the adoption of mobile cloud services and is likely to show causal relationships among the

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constructs that are similar to the findings of earlier studies. Therefore, the current study employs the TAM-based approach

and examines the role of the following psychological factors in determining the user acceptance of mobile cloud services.

3.2. Attitude (ATT)

The theory of reasoned action (TRA) argues that an individuals intention to engage in specific behaviors is primarily

determined by his/her subjective norm and attitude (Ajzen, 1991; Ajzen and Fishbein, 1977). TRA refers to attitude as the

amount of affect for or against some objects or simply, feelings about doing target behaviors (Ajzen, 1991; Ajzen and

Fishbein, 1977). The causal relationship between attitude and behavioral intention has also been emphasized by TAM and

confirmed by ample studies. Therefore, the present study applies and extends this TRA-based relationship between attitude

and intention to the context of mobile cloud services.

H1. Attitude toward mobile cloud services will have positive effects on intention to use the services.

3.3. Perceived usefulness (PU)

TAM posits that perceived usefulness is a strong predictor of attitude toward and intention to use specific information

systems and services (Davis, 1989, 1993, 1992). Numerous studies (e.g., Joo and Sang, 2013; Park and Chen, 2007; Lee

and Chung, 2009; Ha et al., 2007; Park and Kim, 2013) have replicated this TAM framework and demonstrated that perceived

usefulness have positive effects on user attitude and behavioral intention. In a similar vein, this study defines perceived use-

fulness as the degree to which users believe that using mobile cloud services improves their job performance and predictsthat it will have similar positive effects on attitude toward and intention to use mobile cloud services. Therefore, the follow-

ing hypotheses are proposed.

H2. Perceived usefulness will have positive effects on intention to use mobile cloud services.

H3. Perceived usefulness will have positive effects on attitude toward mobile cloud services.

3.4. Perceived connectedness (PC)

In collaborative environments, users tend to share and communicate with others via a particular system (Shin, 2010). For

example, users may prefer to communicate with others in a virtual system at their physical and locational convenience

rather than actually meeting in person. Similarly, mobile cloud services can provide users with a more positive feeling of

connectedness in virtual reality.In the wireless network, online spaces offer various dynamic and convenient functions, including sharing files and posting

information, and provide users with means of interacting with others (Shin and Kim, 2008). Although social interactions in

these spaces do not require users simultaneous presence at the same space and time, they still experience the sense of con-

nectedness with their friends and colleagues. Users feelings of perceived connectedness are the degrees to which they be-

lieve that they are cognitively connected with the network, its people, and its resources ( Shin, 2010). Users may enjoy

cognitive connectedness through mobile cloud services and experience a strong sense of co-presence while using the ser-

vices (Boyd and Ellison, 2007; Shin, 2010). The current study adopts this notion of connectedness and proposes the following

hypotheses.

H4. Perceived connectedness will have positive effects on perceived usefulness of mobile cloud services.

H5. Perceived connectedness will have positive effects on attitude toward mobile cloud services.

3.5. Service and system quality (SSQ)

Coined byDeLone and McLean (1992), the term system and service quality refers to the perceived level of general per-

formance of a particular system and its service. Ample research has revealed positive relationships between quality of service

and system and user perceptions of that service and system. For example,DeLone and McLean (1992)demonstrated that users

behavioral intentions to use a particular information service and system are largely determined by the service and system qual-

ity. In addition,Park and del Pobil (2013)found that service and system quality is a significant determinant of intention to use

mobile services. Since mobile cloud services consist of both a service (e.g., cloud program) and a system (e.g., mobile devices),

the current study examines them as one construct. This construct is likely to have notable effects on attitude and behavioral

intention to use mobile cloud services. Therefore, the current study set forth the following hypotheses.

H6. Service and system quality will have positive effects on intention to use mobile cloud services.

H7. Service and system quality will have positive effects on attitude toward mobile cloud services.

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3.6. Perceived security (PS)

Although mobility, immediacy, and availability are key strengths of mobile cloud services, these characteristics also raise

increasing concerns related to privacy and security of data stored and accessed via mobile clouds. Earlier studies defined per-

ceived security as the degree to which users believe in the security of a particular service and demonstrated that it plays a

critical role in determining user attitude toward and perceived usefulness of online services (Shin, 2010; Yenisey et al., 2005).

By extension, perceived security is also likely to have similar psychological effects on the ways in which users accept and

utilize mobile cloud services. Based on this rationale, the current study defines perceived security as the level of user belief

in the security of the mobile cloud services and examines the following related hypotheses.

H8. Perceived security will have positive effects on service and system quality of mobile cloud services.

H9. Perceived security will have positive effects on attitude toward mobile cloud services.

3.7. Perceived mobility (PM)

This study examines perceived mobility as a determinant of perceived usefulness and service and system quality of mo-

bile cloud services because mobility (portability) is a core factor of any wireless or ubiquitous network service. Perceived

mobility refers to the degree to which users are aware of the mobility value of mobile services and systems ( Huang et al.,

2007). In the context of this study, perceived mobility is defined as the perceived capability to wirelessly access and use par-ticular mobile services via a users device.Siau and Shen (2003)found that perceived portability is the most representative

characteristic of wireless communication networks.Huang et al. (2007)reported that perceived mobility of portable devices

plays a significant role in enhancing perceived usefulness of mobile education services. In accordance with these findings,

the current study hypothesizes the following:

H10. Perceived mobility will have positive effects on perceived usefulness of mobile cloud services.

H11. Perceived mobility will have positive effects on service and system quality of mobile cloud services.

3.8. Satisfaction (ST)

Numerous prior studies have demonstrated that user satisfaction with a particular service or system is positively asso-ciated with behavioral intention to use the service. For example,Battacherjee (2001)found that initial user satisfaction with

an information system was positively related to actual use of the system. Similarly,Park and Kim (2013) discovered that sat-

isfaction with mobile services positively affected user intention to use the services. Therefore, this study posits the following

hypothesis:

H12. Satisfaction will have positive effects on intention to use to use mobile cloud services.

3.9. Research model

The following research model (Fig. 1) was examined in order to validate the proposed hypotheses.

4. Method

4.1. Survey development

In-depth interviews were conducted to select potentially important psychological factors that are closely related to mo-

bile cloud services. The purpose of the interviews was (A) to reconfirm factors from prior studies, (B) to examine unique

characteristics of mobile cloud services, and (C) to create valid and reliable survey questions. Participants in the interview

were undergraduate students who were recruited from a large private university in South Korea. The interviewees were se-

lected using the method of purposeful sampling developed by Shin and Shin (2011), in which a good deal of information and

knowledge is believed to be gained from a small number of interview respondents. The experimenter interviewed 16 under-

graduate students (8 males and 8 females) with different majors and class standings. Their ages ranged from 19 to 30 years

(mean = 23.3, SD = 2.21), and they all had experience using mobile cloud services. Previous studies have shown that under-

graduate students are a generally representative group of mobile service users ( Shin and Shin, 2011; Hargittaii, 2007). Stu-

dents were instructed to write their feelings and perceptions of mobile cloud services on post-it notes. The experimenter

then sorted the notes into the six constructs (i.e., mobility, security, connectedness, service and system quality, satisfaction,

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usefulness) and developed a survey questionnaire by adopting measures from previously validated studies for assessing

respondents perceptions of each of these constructs.

Fig. 1. The proposed research model.

Table 1

Survey questionnaire items.

Construct Item Source

Perceived

mobility

PM1 Mobility of mobile cloud computing services makes it possible to acquire real-time data Huang et al. (2007), Park and

Kim (2013)PM2 It is convenient to use mobile cloud computing services anytime and anywhere

PM3 Mobility is an outstanding advantage of mobile devices offering mobile cloud

computing services

Perceived

usefulness

PU1 I think mobile cloud computing services are useful for my job Davis (1989)

PU2 Using mobile cloud computing services increases my productivity

PU3 Using mobile cloud computing services improves my work performance and

effectiveness

Perceivedconnectedness PC1 I feel like I am connected to external reality because I can search for desired information Shin (2010)PC2 I feel good because I can access the services anytime via mobile devices

PC3 I feel emotionally comforted because I can do something interesting with mobile cloud

computing services at my convenience

System and

service quality

SSQ1 Mobile devices with cloud computing services provide more services in line with the

purpose of the system

DeLone and McLean (2003),

Lee and Chung (2009)

SSQ2 I have not had any limitations or problems with using mobile cloud computing services

SSQ3 Mobile devices with cloud computing services fully meet my needs

Perceived security PS1 I am confident that the private information in mobile cloud computing services is

secure

Yenisey et al. (2005), Shin and

Shin (2011)

PS2 I believe nobody can view my information or data stored in mobile cloud computing

services without my agreement

PS3 I believe my information or data in mobile cloud computing services will not be

manipulated by inappropriate parties

Attitude ATT1 I have positive feelings toward mobile cloud computing services in general Davis (1989, 1993)

ATT2 It is a good idea to use mobile cloud computing servicesATT3 I thinkit is desirableto use mobile cloud computing services as opposed to other mobile

services

Satisfaction ST1 Overall, I am satisfied with mobile cloud computing services DeLone and McLean (2003),

Lee and Chung (2009)ST2 The mobile cloud computing services I am currently using meet my expectations

ST3 I recommend mobile cloud computing services to others who intend to use and buy

new mobile phones

ST4 Mobile cloud computing services are a beneficial tool for performing my job

Intention to use IU1 I am very likely to continue to use mobile cloud computing services Davis (1989, 1993), Park and

Kim (2013)IU2 I intend to use mobile cloud computing services as much as possible

IU3 I will continue to use mobile cloud computing services if I have access to the service



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Based on the results of the interview, the experimenter created and administered a pretest to examine the reliability and

validity of the questionnaire. Thirty undergraduate students took part in the pretest. Respondents were instructed to notify

the experimenter if any of the questionnaire items were misleading or unclear. The experimenter then utilized the results

and respondent feedback to create a final set of questionnaire items for the main survey. Quantitative research experts were

asked to review and modify the descriptions and wordings of the questionnaire items. To evaluate the reliability of the items,

we calculated Cronbachs alphas and confirmed that they were all greater than the recommended value of 0.70 (Hair et al.,

2006; ATT = 0.93, PU = 0.91, PC = 0.90, SSQ = 0.93, PS = 0.92, PM = 0.94, IU = 0.91). After these tests, a professional survey agency

administered the survey on the Internet for one month. Table 1reports the complete questionnaire used in this main survey.

Participants were instructed to respond to each questionnaire item using a 7-point Likert scale. The agency collected 1498

samples, with a 41% response rate. After data filtering, 1099 valid samples remained as the final sample data in the study.

Males made up 57.2% of the respondents and females made up 42.8%. Respondents reported that they had at least two

months of regular mobile cloud service use in their workspace, research, educational environment, or home. SPSS 18.0

was used to analyze the data and obtain descriptive statistics of the constructs.

4.2. Measurements

For statistically acceptable internal reliability and convergent validity, Fornell and Lacker (1981)recommended that all

factor loadings and values of average variance extracted (AVE) should be greater than 0.70 and 0.50, respectively. As reported

inTable 2, the measurement model satisfied these recommendations. For discriminant validity, Fornell and Lacker (1981)

Table 2

Internal reliability and convergent validity.

Construct Item Internal reliability Convergent validity

Cronbachs

alpha

Item-total

correlation

Factor loading Composite

reliability

Average variance extracted

Perceived mobility PM1 0.92 0.87 0.85 0.90 0.75

PM2 0.87 0.92

PM3 0.79 0.83

Perceived usefulness PU1 0.90 0.84 0.87 0.92 0.79

PU2 0.84 0.90

PU3 0.87 0.90

Perceived connectedness PU1 0.87 0.80 0.88 0.90 0.75

PU2 0.82 0.86

PU3 0.85 0.85

System and service quality SSQ1 0.91 0.84 0.80 0.83 0.62

SSQ2 0.84 0.80

SSQ3 0.89 0.77

Perceived security PS1 0.90 0.87 0.85 0.86 0.67

PS2 0.80 0.79

PS3 0.85 0.82

Attitude ATT1 0.83 0.85 0.85 0.86 0.68

ATT2 0.81 0.83

ATT3 0.80 0.79

Satisfaction ST1 0.85 0.79 0.87 0.87 0.76

ST2 0.86 0.85

ST3 0.82 0.85

ST4 0.86 0.92

Intention to use IU1 0.90 0.82 0.84 0.87 0.69

IU2 0.82 0.82

IU3 0.84 0.84

Table 3

Results of discriminant tests; square roots of the average variance extracted are presented as diagonal elements.

1 2 3 4 5 6 7 8

1. PM 0.87

2. PU 0.10 0.89

3. PC 0.79 0.12 0.87

4. SSQ 0.05 0.78 0.27 0.795. PS 0.39 0.11 0.22 0.61 0.82

6. ATT 0.15 0.13 0.45 0.40 0.42 0.82

7. ST 0.38 0.31 0.23 0.37 0.70 0.36 0.87

8. IU 0.04 0.69 0.25 0.65 0.17 0.44 0.43 0.83

Abbreviations: PM = Perceived mobility, PU = Perceived usefulness, PC = Perceived connectedness, SSQ = System and service quality, PS = Perceived security,

ATT = Attitude, ST = Satisfaction, IU = Intention to use.

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also suggested that the correlation shared between two constructs should be less than the square root of the AVE. As shown

inTable 3, the measurement model showed strong discriminant validity.

5. Results

5.1. Descriptive analysis

Descriptive statistics of all measured variables are reported inTable 4. The means ranged from 5.11 to 5.31, suggesting

that participants generally had positive impressions of mobile cloud services.

5.2. Model fit

Confirmatory factor analysis (CFA) was conducted to examine the following model-fit indices of the measurement and

proposed research models:

A. Chi-square/degree of freedom (v2/d.f.).

B. Goodness-of-Fit Index (GFI).

C. Adjusted Goodness-of-Fit Index (AGFI).

D. Root Mean Square Error of Approximation (RMSEA).

E. Standardized Root Mean square Residual (SRMR).

F. Normed Fit Index (NFI).

G. Non-Normed Fit Index (NNFI).

H. Comparative Fit Index (CFI).

I. Incremental Fit Index (IFI).

As described inTable 5, the fit indices of both models were satisfactory.

5.3. Hypothesis tests

As summarized in Table 6 andFig. 2, the results supported all hypotheses in the research model. PU had significant

positive effects on IU and ATT (H2,b= 0.521, CR = 40.538,p< 0.001; H3,b= 0.252, CR = 11.312,p< 0.001). Similarly, SSQ also

had notable effects on IU and ATT (H6,b = 0.488, CR = 30.669,p < 0.001;H7, b = 0.234, CR = 10.287,p < 0.001). Compared to

PU and SSQ (H2 and H6), ATT and ST had moderately weaker effects on IU (H1, b= 0.128, CR = 7.362, p< 0.001; H12,

b= 0.358, CR = 31.461,p

< 0.001). PC had significant effects on PU (H4,b= 0.716, CR = 37.340,p

< 0.001), which was also pos-

itively influenced by PM (H10, b= 0.290, CR = 15.118, p< 0.001). Similarly, PS had positive effects on SSQ (H8, b= 0.727,

Table 4

Descriptive analysis of the constructs.

Construct Mean Standard deviation

Perceived mobility 5.17 0.94

Perceived usefulness 5.22 1.02

Perceived connectedness 5.18 1.00

System and service quality 5.11 1.04

Attitude 5.22 0.98

Satisfaction 5.31 1.29Intention to use 5.24 1.09

Table 5

Fit indices for the measurement model and overall model.

Fit index Measurement model Research model Recommended value Source

v2/d.f. 3.44 3.81 0.90 Bagozzi and Yi (1988)

AGFI 0.921 0.914 >0.80 Fornell and Lacker (1981)

RMSEA 0.049 0.045 0.90 Bentler and Bonnet (1980)CFI 0.941 0.939 >0.90 Fornell and Lacker (1981)

IFI 0.924 0.908 >0.90 Widaman and Thompson (2003)

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CR = 45.289,p< 0.001), which was also affected by PM (H11,b = 0.433, CR = 26.981,p< 0.001). Lastly, PS and PC had notable

effects on ATT (H9, b = 0.568, CR = 25.433,p < 001;H5, b = 0.187, CR = 8.527,p < 0.001).

With regard to the variances of the constructs (Table 7), PU, ATT, ST, and SSQ explained 85.8% of the variance in IU. Com-

pared to other factors, PU had the strongest effects on IU, while PS showed the strongest effects on ATT. Moreover, 59.6% of

the variance in PU was explained by PM and PC, while 71.7% of the variance in SSQ was contributed by PM and PS. PU, PC, PS,

and SSQ explained 75.2% of the variance in ATT.

6. Discussion

The findings of the current study have several theoretical and practical implications for device manufacturers, service

providers, and academic researchers. User-behavior analysis such as that of our research model is essential for greater

understanding and success of mobile cloud services, which have become a pronounced segment in the mobile environment.

The current study provides an extended framework based on the structural equation modeling method that elucidates a

Table 6

Summary of hypothesis tests.

Hypotheses Standardized coefficient SE CR Supported

H1. ATT? IU 0.128* 0.014 7.362 Yes

H2. PU? IU 0.521* 0.009 40.538 Yes

H3. PU? ATT 0.252* 0.017 11.312 Yes

H4. PC? PU 0.716* 0.016 37.340 Yes

H5. PC? ATT 0.187* 0.017 8.527 Yes

H6. SSQ? IU 0.488*

0.011 30.669 YesH7. SSQ? ATT 0.234* 0.020 10.287 Yes

H8. PS? SSQ 0.727* 0.013 45.289 Yes

H9. PS? ATT 0.568* 0.016 25.433 Yes

H10. PM? PU 0.290* 0.016 15.118 Yes

H11. PM? SSQ 0.433* 0.014 26.981 Yes

H12. ST? IU 0.358* 0.006 31.461 Yes

* p< 0.001.

Fig. 2. Results of hypothesis tests; p< 0.001.

Table 7

Squared multiple correlations of the proposed research model.

Constructs Values

Perceived usefulness 59.6

System and service quality 71.7

Attitude 75.2

Intention to use 85.8

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user-centered decision process. The excellent fit indices between the model and the collected sample data, as well as con-

firmations of the hypothesized causal paths, indicate the validity of the proposed integrated user acceptance model, which

identifies key psychological factors that largely determine the adoption pattern of mobile cloud services and explicate their

causal relationship.

As summarized in Fig. 2, the integrated model shows that the combinatory effects of perceived usefulness, perceived con-

nectedness, perceived security, and service and system quality explained 75.2% of the variance in user attitude toward mo-

bile cloud services, while 85.8% of the variance in user intention was found to be explainable by the combination of perceived

usefulness, attitude, satisfaction, and service and system quality. Perceived mobility and perceived security emerged as

meaningful predictors of service and system quality by explaining 71.7% of its variance. All these findings statistically dem-

onstrate that our proposed model, as in prior research on the adoption of novel mobile technology (Huang et al., 2007; Park

and Kim, 2013; Wang et al., 2008; Wu et al., 2007), successfully establishes valid links between the key psychological factors

of the services (i.e., perceived mobility, perceived connectedness, perceived security, service and system quality, and satis-

faction) and the constructs from the original TAM framework (i.e., perceived usefulness, attitude, intention to use), thereby

extending adoption theories on mobile technology.

More specifically, perceived connectedness and perceived security emerged as influential antecedents of attitude toward

mobile cloud computing services. Given that increasing emphasis is being placed on the immediate, convenient connected-

ness to stored data and guaranteed security from intrusion of private information, our findings offer compelling statistical

evidence showing the importance of these two factors. In addition, perceived mobility and perceived connectedness were

found to be strong motivational factors of service and system quality and perceived usefulness, which then significantly af-

fected user attitude and intention to use mobile cloud services. In accordance with prior studies that revealed the positive

effects of service and system quality and perceived usefulness on attitudes toward mobile technology ( Park and del Pobil,

2013; Park and Kim, 2013; Shin et al., 2011), our findings add to the existing literature in that these factors serve as influ-

ential determinants for mobile cloud services as well. The implication is that enhanced mobility of and user satisfaction with

a mobile cloud service are critical to the failure or success of the service, encouraging the industry to invest more in devel-

oping stable, reliable infrastructures and platforms that guarantee enhanced mobility and satisfaction with the quality of

their services. In the long term, the industry should prepare new ubiquitous environments and platforms for the upcoming

era of Web 3.0 while addressing the following questions:

A. How concerned are users about system and service quality in decisions about using mobile cloud computing services?

B. How can service providers improve service quality?

From a practical perspective, the industry can utilize our integrated model to develop strategic plans for the success of

their services. While most current cloud service providers offer their services free of charge, their long-term goal is to enter

the mainstream mobile market and maximize profits. To do so, service providers should pay close attention to how user atti-

tudes and behaviors are shaped. The current studys verification of the influential roles of perceived usefulness and quality of

service and system in determining user intention indicates that the industry should put its efforts into improving users over-

all psychological perceptions of these factors. More importantly, providers of mobile cloud services ought to set up an effi-

cient and reliable connection via stable wireless networks.

Although the findings of the current study provide meaningful insights on adoption of mobile cloud services, there are

several issues that should be taken into consideration in future research on related topics. First, individual differences of

the survey respondents were not examined in this study. In their unified theory of acceptance and use of technology

(UTAUT), Venkatesh et al. (2003) demonstrated that individual differences (e.g., gender, age, race) and social influences

(e.g., performance and effort expectancy, voluntariness, subjective norms) have significant effects on user attitude toward

and intention to use a specific technology. Given that the respondents were recruited from South Korea, users from Western

societies are likely to have individual and social experiences that may lead to different adoption patterns. Future studies may

consider investigating the potential moderating effects of these factors and employ diverse samples for greater generalizabil-

ity of the proposed model.

In addition, results of the data analysis revealed that the proposed model included several highly correlated variables (i.e.,

perceived mobility perceived connectedness, service and system quality perceived usefulness, satisfaction attitude),

which suggests that there might have been inaccurate measures and missing pathways of causality in the model. Thus, a

follow-up analysis on the indirect and direct relationships among these factors is recommended. While there still exist ques-

tions to be further investigated on this and related topics, the current study contributes to a more systematic understanding

of mobile services, and future studies may extend and refine our findings by addressing these limitations.

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