Telematics and Informatics 31 (2014) 376–385

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Telematics and Informatics

journal homepage: www.elsevier .com/locate / te le

An Integrated Adoption Model of Mobile Cloud Services:Exploration of Key Determinants and Extension of TechnologyAcceptance Model

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

E-mail addresses: pa1324@kaist.ac.kr (E. Park), veritate@skku.edu (K.J. Kim).

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 a b s t r a c t

Article history:Received 19 September 2013Received in revised form 14 November 2013Accepted 20 November 2013Available online 4 December 2013

Keywords:Mobile cloud computing servicesTechnology acceptance modelPerceived mobilityPerceived connectednessPerceived securityPerceived service and system quality

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 modelinganalysis is employed on data collected from 1099 survey samples, and results reveal thatuser 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 study’s 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 consideredone of the fastest growing areas of information and communication technology (ICT), as well as related industrial andacademic 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 firstidentifies 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, andinternal validity of the proposed model via structural equation modeling (SEM).

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The present study is organized as follows. Section 2 provides a definition and overview of mobile cloud services. Section 3discusses 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 inSection 6.

2. Mobile cloud services

Mobile cloud computing is defined as ‘‘an infrastructure and system where both the data storage and data processinghappen 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 environmentsand 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 cloudservers from a variety of electronic devices with wireless connectivity, while utilization and sharing of the data are processedremotely 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 thesemultimedia 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 capacityand 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, storagedisk, and display screen (Davis, 1993; Paulson, 2003; Mayo and Ranganathan, 2003). However, these attempts requirechanges in hardware structure and cannot be directly applied to mobile technology without significant increases in costsand technological advancements. As a feasible solution to this challenge, cloud computing allows migration of the complexprocessing from a mobile device (resource-limited) to remote cloud servers (resource-rich). Prior studies have demonstratedthat such computational offloading shortens program executions and therefore extends battery life. For example, Rudenkoet al. (1998) reported that performing large matrix calculations in the cloud computing environment rather than on a mobiledevice can save up to 50% of the energy used. In addition, Cuervo et al. (2010) found that cloud applications significantlyreduce 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. Theyalso provide users with various security-related services and software, including personal authentication, virus scanning anddetection, 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 thesematerials (Zou et al., 2010).

Due to these strengths and advantages, mobile cloud computing has emerged as an attractive platform for the upcomingera of Web 3.0. In 2007, Schmidt (2007), CEO of Google, referred to Web 3.0 as a computing application model and defined itas 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 preciselycorrespond to the key components and strengths of mobile cloud computing, suggesting the significant potential of mobilecloud 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, which Davis (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 technologiesand systems and demonstrated that perceived ease of use and perceived usefulness largely determine user attitude towarda 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 andservices, including smartphones (Joo and Sang, 2013; Park and Chen, 2007), mobile banking (Lee and Chung, 2009), mobilegames (Ha et al., 2007), and long-term evolution (LTE) services (Park and Kim, 2013). By extension, TAM is also likely to beapplicable 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 approachand 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 individual’s intention to engage in specific behaviors is primarilydetermined by his/her subjective norm and attitude (Ajzen, 1991; Ajzen and Fishbein, 1977). TRA refers to attitude as theamount of affect for or against some objects or simply, feelings about doing target behaviors (Ajzen, 1991; Ajzen andFishbein, 1977). The causal relationship between attitude and behavioral intention has also been emphasized by TAM andconfirmed by ample studies. Therefore, the present study applies and extends this TRA-based relationship between attitudeand 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 informationsystems and services (Davis, 1989, 1993, 1992). Numerous studies (e.g., Joo and Sang, 2013; Park and Chen, 2007; Leeand Chung, 2009; Ha et al., 2007; Park and Kim, 2013) have replicated this TAM framework and demonstrated that perceivedusefulness 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). Forexample, users may prefer to communicate with others in a virtual system at their physical and locational conveniencerather than actually meeting in person. Similarly, mobile cloud services can provide users with a more positive feeling ofconnectedness in virtual reality.

In the wireless network, online spaces offer various dynamic and convenient functions, including sharing files and postinginformation, and provide users with means of interacting with others (Shin and Kim, 2008). Although social interactions inthese 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 enjoycognitive 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 followinghypotheses.

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 by DeLone 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 serviceand 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 usemobile 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 behavioralintention 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 raiseincreasing 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 acritical 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 andutilize mobile cloud services. Based on this rationale, the current study defines perceived security as the level of user beliefin 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. Perceivedmobility 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 user’s device. Siau and Shen (2003) found that perceived portability is the most representativecharacteristic of wireless communication networks. Huang et al. (2007) reported that perceived mobility of portable devicesplays 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 withan 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 followinghypothesis:

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 uniquecharacteristics of mobile cloud services, and (C) to create valid and reliable survey questions. Participants in the interviewwere 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 andknowledge 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 experimenterthen sorted the notes into the six constructs (i.e., mobility, security, connectedness, service and system quality, satisfaction,

Fig. 1. The proposed research model.

Table 1Survey questionnaire items.

Construct Item Source

Perceivedmobility

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

Perceivedusefulness

PU1 I think mobile cloud computing services are useful for my job Davis (1989)PU2 Using mobile cloud computing services increases my productivityPU3 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 devicesPC3 I feel emotionally comforted because I can do something interesting with mobile cloud

computing services at my convenience

System andservice quality

SSQ1 Mobile devices with cloud computing services provide more services in line with thepurpose 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 servicesSSQ3 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 issecure

Yenisey et al. (2005), Shin andShin (2011)

PS2 I believe nobody can view my information or data stored in mobile cloud computingservices without my agreement

PS3 I believe my information or data in mobile cloud computing services will not bemanipulated 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 think it is desirable to 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 buynew 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 andKim (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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usefulness) and developed a survey questionnaire by adopting measures from previously validated studies for assessingrespondents’ perceptions of each of these constructs.

Table 2Internal reliability and convergent validity.

Construct Item Internal reliability Convergent validity

Cronbach’salpha

Item-totalcorrelation

Factor loading Compositereliability

Average variance extracted

Perceived mobility PM1 0.92 0.87 0.85 0.90 0.75PM2 0.87 0.92PM3 0.79 0.83

Perceived usefulness PU1 0.90 0.84 0.87 0.92 0.79PU2 0.84 0.90PU3 0.87 0.90

Perceived connectedness PU1 0.87 0.80 0.88 0.90 0.75PU2 0.82 0.86PU3 0.85 0.85

System and service quality SSQ1 0.91 0.84 0.80 0.83 0.62SSQ2 0.84 0.80SSQ3 0.89 0.77

Perceived security PS1 0.90 0.87 0.85 0.86 0.67PS2 0.80 0.79PS3 0.85 0.82

Attitude ATT1 0.83 0.85 0.85 0.86 0.68ATT2 0.81 0.83ATT3 0.80 0.79

Satisfaction ST1 0.85 0.79 0.87 0.87 0.76ST2 0.86 0.85ST3 0.82 0.85ST4 0.86 0.92

Intention to use IU1 0.90 0.82 0.84 0.87 0.69IU2 0.82 0.82IU3 0.84 0.84

Table 3Results 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 0.872. PU 0.10 0.893. PC 0.79 0.12 0.874. SSQ 0.05 0.78 0.27 0.795. PS 0.39 0.11 0.22 0.61 0.826. ATT 0.15 0.13 0.45 0.40 0.42 0.827. ST 0.38 0.31 0.23 0.37 0.70 0.36 0.878. 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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Based on the results of the interview, the experimenter created and administered a pretest to examine the reliability andvalidity of the questionnaire. Thirty undergraduate students took part in the pretest. Respondents were instructed to notifythe experimenter if any of the questionnaire items were misleading or unclear. The experimenter then utilized the resultsand respondent feedback to create a final set of questionnaire items for the main survey. Quantitative research experts wereasked to review and modify the descriptions and wordings of the questionnaire items. To evaluate the reliability of the items,we calculated Cronbach’s 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 agencyadministered the survey on the Internet for one month. Table 1 reports 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 1498samples, 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 twomonths of regular mobile cloud service use in their workspace, research, educational environment, or home. SPSS 18.0was 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 allfactor loadings and values of average variance extracted (AVE) should be greater than 0.70 and 0.50, respectively. As reportedin Table 2, the measurement model satisfied these recommendations. For discriminant validity, Fornell and Lacker (1981)

Table 4Descriptive analysis of the constructs.

Construct Mean Standard deviation

Perceived mobility 5.17 0.94Perceived usefulness 5.22 1.02Perceived connectedness 5.18 1.00System and service quality 5.11 1.04Attitude 5.22 0.98Satisfaction 5.31 1.29Intention to use 5.24 1.09

Table 5Fit indices for the measurement model and overall model.

Fit index Measurement model Research model Recommended value Source

v2/d.f. 3.44 3.81 <5.00 Shin and Shin (2011)GFI 0.904 0.911 >0.90 Bagozzi and Yi (1988)AGFI 0.921 0.914 >0.80 Fornell and Lacker (1981)RMSEA 0.049 0.045 <0.06 Joreskog and Sorbom (1996)SRMR 0.055 0.066 <0.08 Bagozzi and Yi (1988)NFI 0.909 0.912 >0.90 Bentler and Bonnet (1980)NNFI 0.929 0.932 >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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also suggested that the correlation shared between two constructs should be less than the square root of the AVE. As shownin Table 3, the measurement model showed strong discriminant validity.

5. Results

5.1. Descriptive analysis

Descriptive statistics of all measured variables are reported in Table 4. The means ranged from 5.11 to 5.31, suggestingthat 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 andproposed 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 in Table 5, the fit indices of both models were satisfactory.

5.3. Hypothesis tests

As summarized in Table 6 and Fig. 2, the results supported all hypotheses in the research model. PU had significantpositive 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 alsohad 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 toPU 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 6Summary of hypothesis tests.

Hypotheses Standardized coefficient SE CR Supported

H1. ATT ? IU 0.128* 0.014 7.362 YesH2. PU ? IU 0.521* 0.009 40.538 YesH3. PU ? ATT 0.252* 0.017 11.312 YesH4. PC ? PU 0.716* 0.016 37.340 YesH5. PC ? ATT 0.187* 0.017 8.527 YesH6. SSQ ? IU 0.488* 0.011 30.669 YesH7. SSQ ? ATT 0.234* 0.020 10.287 YesH8. PS ? SSQ 0.727* 0.013 45.289 YesH9. PS ? ATT 0.568* 0.016 25.433 YesH10. PM ? PU 0.290* 0.016 15.118 YesH11. PM ? SSQ 0.433* 0.014 26.981 YesH12. ST ? IU 0.358* 0.006 31.461 Yes

* p < 0.001.

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

Table 7Squared multiple correlations of the proposed research model.

Constructs Values

Perceived usefulness 59.6System and service quality 71.7Attitude 75.2Intention to use 85.8

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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 notableeffects 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% ofthe 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, serviceproviders, and academic researchers. User-behavior analysis such as that of our research model is essential for greaterunderstanding 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

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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, whichidentifies key psychological factors that largely determine the adoption pattern of mobile cloud services and explicate theircausal 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 perceivedusefulness, attitude, satisfaction, and service and system quality. Perceived mobility and perceived security emerged asmeaningful 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; Parkand Kim, 2013; Wang et al., 2008; Wu et al., 2007), successfully establishes valid links between the key psychological factorsof 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), therebyextending adoption theories on mobile technology.

More specifically, perceived connectedness and perceived security emerged as influential antecedents of attitude towardmobile 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 statisticalevidence showing the importance of these two factors. In addition, perceived mobility and perceived connectedness werefound 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 positiveeffects 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 witha 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 oftheir services. In the long term, the industry should prepare new ubiquitous environments and platforms for the upcomingera 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 oftheir services. While most current cloud service providers offer their services free of charge, their long-term goal is to enterthe 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 study’s verification of the influential roles of perceived usefulness and quality ofservice 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 areseveral issues that should be taken into consideration in future research on related topics. First, individual differences ofthe 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 towardand intention to use a specific technology. Given that the respondents were recruited from South Korea, users from Westernsocieties are likely to have individual and social experiences that may lead to different adoption patterns. Future studies mayconsider 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, afollow-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 understandingof mobile services, and future studies may extend and refine our findings by addressing these limitations.

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