Computers in Human Behavior 29 (2013) 2512–2518

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Computers in Human Behavior

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Exploring Koreans’ smartphone usage: An integrated modelof the technology acceptance model and uses and gratificationstheory

0747-5632/$ - see front matter � 2013 Published by Elsevier Ltd.http://dx.doi.org/10.1016/j.chb.2013.06.002

⇑ Corresponding author. Tel.: +1 (512) 815 0338.E-mail addresses: hyukjoo@kdu.ac.kr (J. Joo), ysang@utexas.edu (Y. Sang).

1 Tel.: +82 43 879 3654.

Jihyuk Joo a,1, Yoonmo Sang b,⇑a Department of Journalism and Communication, Far East University, Daehakgil 76-32, Gamgok Eumseong, Chungbuk, South Koreab Department of Radio-Television-Film, University of Texas at Austin, 2504 Whitis Ave., Stop A0800, United States

a r t i c l e i n f o a b s t r a c t

Article history:Available online 30 June 2013

Keywords:SmartphoneTechnology acceptance modelUses and gratificationsInstrumental useRitualized use

This study investigates factors that influence adoption and use of smartphones among Koreans and seeksto integrate two theoretical approaches: the technology acceptance model (TAM) and the uses and grat-ifications (U&G) approach. To that end, the study used data from a self-reported survey of 491 Koreanadults who use Apple’s iPhone. A structural equation model employed in the current study demonstratesthat Koreans’ smartphone use is affected more by motivations based on instrumental and goal-orienteduse than by ritualized and less-goal oriented use. The findings suggest that to spread information systemwith innovative and active features, developers should pay attention to users’ intrinsic motivations aswell as to their extrinsic perceptions.

� 2013 Published by Elsevier Ltd.

1. Introduction As Apple’s iPhone began to shake up the mobile phone industry

The Organization for Economic Cooperation and Development(OECD) recently reported that South Korea (hereafter ‘‘Korea’’) be-came the first country to reach 100% wireless penetration (Os-borne, 2012). Arguably, this powerful telecommunicationsinfrastructure has made it possible for the nation to support morethan 30 million smartphone users even though the introduction ofsmartphones to Korea came later than in other countries. As of Au-gust 2012, the total number of smartphone users in Korea sur-passed 30 million, representing 60% of the population, accordingto a report released by SK Telecom and other major telecommuni-cations companies (Kwon, 2012).

Four months after Apple introduced its iPhone to Korea inNovember 2009, the number of iPhone users reached 500,000.When the iPhone was first introduced to Korea, the country ranked85th in terms of iPhone users worldwide. However, within only1 year, the country had become one of the first nations (7th) toboast more than 500,000 iPhone users (Shin, 2010). Korean mediareported that a smartphone craze was sweeping the nation, servingas a springboard for Korea’s second digital revolution (SBS, 2010).Korea Telecom (KT), the largest telecommunications company inKorea, predicted that the iPhone would lead to an overall reformof Korea’s mobile ecosystem (Cho, Chung, & Huh, 2010).

in Korea, Korea’s domestic industry turned its attention towardsmartphone development. Yet, because the smartphone is a rela-tively new technology, it has received scant attention in academicresearch, in terms of understanding users’ mindset about the adop-tion of this new technology.

This study builds upon and extends the technology acceptancemodel (TAM) by integrating the uses and gratifications (U&G) ap-proach and seeking to develop and validate a theoretical model.Specifically, this study looks at the effects of users’ motivationson user acceptance of smartphones in Korea based on a researchmodel for explaining and predicting the adoption of the Informa-tion System (IS). While the theory of TAM has been tested in work-place environments where adoption and use often involve extrinsicfactors (Luo, Remus, & Chea, 2006), this study looks at individualusers’ intrinsic motivations. Specifically, this study looks at useradoption in a consumer context, by combining TAM with anothertheory from mass communication known as the uses and gratifica-tions approach (U&G approach) (Luo et al., 2006; Ruggiero, 2000).The U&G approach offers insights into consumer viewpoints tobroaden TAM’s perspective, which focuses on IS uses in the work-place settings (Luo et al., 2006).

2. Theoretical background and hypotheses

2.1. Technology acceptance model (TAM)

A variety of models, drawn from various disciplines such as psy-chology, information systems, and sociology, have been employed

J. Joo, Y. Sang / Computers in Human Behavior 29 (2013) 2512–2518 2513

to explain people’s intention to adopt new technology, with TAMbeing among the most frequently cited (Davis, Bagozzi, & War-shaw, 1989; Rose & Fogarty, 2006). Since Davis (1986) introducedthe TAM model, it has been widely used to identify the determi-nants of technology acceptance in many contexts, especially forpredicting people’s acceptance of information technology (Daviset al., 1989).

TAM is built on the theory of reasoned action (TRA) (Fishbein &Ajzen, 1975), which suggests that an individual’s behavior is initi-ated by his or her behavioral intention to carry out a specifiedbehavior. In turn, one’s behavioral intention is determined by one’sattitude and subjective norms regarding the behavior in question(Fishbein & Ajzen, 1975). According to the TRA, the intention toact directly determines behavior, because people, in general, be-have as they intend to.

TAM adapts the causal links of TRA to explain an individual’s ISacceptance behaviors (Dishaw & Strong, 1999), positing that users’beliefs determine their attitudes toward use of a system. In otherwords, attitudes determine behavioral intentions to use that, inturn, lead to actual system use.

TAM is based on two salient behavioral beliefs that affectbehavioral intentions: perceived ease of use (PEOU) and perceivedusefulness (PU). Davis (1989) defines PEOU as ‘‘the degree to whicha person believes that using a particular system would be free ofeffort’’ (p. 320). That is, a perception that a particular system orapplication is easy to use (Davis, 1989). PU is defined as, ‘‘the de-gree to which a person believes that using a particular systemwould enhance his or her job performance’’ (Davis, 1989, p. 320).

Numerous studies using TAM have shown that both PEOU andPU are determining factors for acceptance and use (Davis, 1989;Malhotra & Galletta, 1999; Moon & Kim, 2001). Previous studieshave also demonstrated that PEOU influences, either directly orindirectly, one’s behavioral intention through PU (Davis et al.,1989; Jackson, Chow, & Leitch, 1997).

Since its inception, a considerable amount of attention has beengiven to TAM (Farn, Fan, & Chen, 2006), showing that TAM is a ro-bust and powerful model for evaluating technologies and makingcomparisons between user groups of a particular technology. How-ever, TAM has two limitations. First, since the original model wasintended to be general and parsimonious, it pays little attentionto identification of antecedent variables that could influence PEOUand PU (Dishaw & Strong, 1999; Park, 2010). Moreover, eventhough the model is useful in identifying factors that influencepeople’s technology acceptance and use, as Park (2010) noted,the model cannot fully explain why people accept and use a partic-ular technology. In an attempt to overcome these limitations ofTAM, this study introduces the U&G approach, which has beenwidely employed in communication studies, particularly media ef-fects (Palmgreen, Wenner, & Rosengren, 1985; Park, 2010; Rubin,1994).

2.2. Uses and gratifications approach

U&G explores questions about why and how people seek to usemedia to fulfill their needs and motives (Rubin, 1984). The ap-proach assumes that people’s use of media is purposive and thatusers actively seek to satisfy their various needs (Katz, Blumler,& Gurevitch, 1974). The U&G approach also posits that users’ moti-vations are triggered by their individual needs and characteristics(Park, 2010; Rosengren, 1974); motivations exert an important rolein facilitating individuals’ behavioral intention and actual mediausage (Park, 2010; Park, Lee, & Cheong, 2007).

The approach can be plausibly applied to a variety of technolo-gies (Park, 2010). The U&G approach has been applied to a widerange of new media and communication technologies, such asthe video cassette recorder (VCR) (Cohen, Levy, & Golden, 1988;

Rubin & Bantz, 1987), cable TV (Bantz, 1982), the World WideWeb (Ferguson & Perse, 2000), online services (Lin, 1999), theInternet in general (Flanagin & Metzger, 2001), the pager (Leung& Wei, 1998), the mobile phone (Aoki & Downes, 2003; Leung &Wei, 2000), the mobile Internet (Gillenson & Stafford, 2004), andcomputer-based VoIP phone (Park, 2010).

The popularity of the U&G approach is based on its capacity todescribe audience behavior as well as assorted content in terms ofaudience appeal (Livaditi, Vassilopoulou, Lougos, & Chorianopou-los, 2003). The foundation of U&G is the belief that people’s choicesabout consuming media are motivated by their desire to gratify awide range of needs. The approach further assumes that (i) audi-ences are active, (ii) audiences’ previous experience with mediahelps them make "motivated choices" and that (iii) audiences usemedia as one of several ways to satisfy every day needs (Livaditiet al., 2003).

Rubin (1983,1984) and Windahl (1981) divided media usageinto two types: ritualized and instrumental. According to Rubin(1984), ritualized media use is more habitual and used more fordiversionary reasons (e.g., companionship, time consumption,relaxation) with a greater affinity for the medium itself. Instru-mental use, on the other hand, refers to a more goal-oriented useof media content for the purpose of gratifying "informational needsor motives" (Rubin, 1984, p. 69). Like instrumental use, ritualizeduse is concerned with utility but in a manner that is less activeor goal-oriented (Livaditi et al., 2003). By contrast, instrumentaluse seeks certain content and perceives that content from a utili-tarian perspective (Livaditi et al., 2003).

Ritualized uses serve needs that pertain to ‘‘companionship,entertainment, personal identify, and escape’’ (Livaditi et al.,2003, p. 2) and that satisfy certain abstract needs, for example,the need for ‘‘curiosity, adventure, advice seeking and communityfeelings’’ (p. 2). In contrast, instrumental use serves informationneeds that meet the users’ goal-oriented needs, such as gaining afinancial edge or a useful piece of information for business oreveryday living (Livaditi et al., 2003). Studies have distinguishedritualized needs in which the audience goes into a passive modefrom instrumental needs that require a more active mode (Bernoff,2000; Livaditi et al., 2003). Furthermore, instrumental or cognitiveneeds motivate users to seek informational content and to becomeinvolved in cognitive processes (Rubin, 1983, 1984; Rubin & Perse,1987; Rubin & Rubin, 1982). Based on these theoretical discus-sions, this study seeks to understand motivations for use of smart-phones in the Korean context.

2.3. Hypotheses

This study combines the U&G approach with the TAM model fortwo reasons. First, the combination overcomes the limitations ofTAM while encompassing users’ motivations yet retaining the par-simonious feature of TAM. Second, the integration allows us to pre-dict the intention of smartphone use in Korea.

Smartphones, as a technology, are ideal for this study in thatusers are able to use the same technology to satisfy both theirinstrumental or cognitive needs as well as their ritualized or habit-ual needs. Moreover, because users have the ability to install appli-cations that meet their own individual tastes or purposes,smartphones are regarded user-centered and user-completedmedia. In other words, in their instrumental use of smartphones,users are able to select and use repeatedly a preferred applicationgenre repertoire or they can select several different genres of di-verse content (Lee, Kim, Seoh, & Lee, 2010; Lee, Ryu, & Kim,2010). Given these facts, it is clear that smartphones are used, tosome degree, in habitual or ritualized modes, in which content ishabitually chosen from one or more specific genres without seriousconsideration.

Table 1Demographic profile.

Demographic N Percentage

Gender Male 272 55.4Female 219 44.6

Age 20–29 74 15.130–39 227 46.240–59 190 38.7

Education Less than a high school diploma and highschool graduate

165 33.6

Current college student and bachelor’sdegree

287 58.5

Current graduate student and master’sdegree or higher

39 7.9

Fig. 1. Hypothesized research model.

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Park’s (2010) study also combined the theoretical approach ofTAM and U&G to examine factors influencing adoption and useof computer-based voice over Internet protocol phone serviceand discovered that motivation for communication and instrumen-tal use significantly affect PEOU, PU, and actual VoIP use. Thosefindings, he concluded, suggest that integration of TAM and U&Gare fruitful for understanding user acceptance of other new com-munication technologies.

Based on Park’s (2010) work, this research sets forth the follow-ing hypotheses:

H1. Motivation for ritualized (or habitual) use of smartphones willhave an effect on PEOU.

H2. Motivation for ritualized use of smartphones will have aneffect on PU.

H3. Motivation for instrumental (or cognitive) use of smartphoneswill have an effect on PEOU.

H4. Motivation for instrumental use of smartphones will have aneffect on PU.

A considerable amount of research over the past two decadessupports the idea that PEOU has a significant effect on PU. And pre-vious studies have found that PU had a direct effect on behavioralintention and PEOU had direct and indirect effects through PU onbehavioral intention (Davis, 1989; Malhotra & Galletta, 1999;Moon & Kim, 2001).

Thus, based on a variety of TAM studies, this study puts forwardthe following additional hypotheses.

H5. PEOU will have a positive effect on PU.

H6. PEOU will have a positive effect on intention to use (ITU).

H7. PU will have a positive effect on ITU.The proposed research model of this study is illustrated in

Fig. 1.

3. Method

3.1. Sample

Participants chosen for this study, which is based on a conve-nience sampling method, were 491 Korean adults all iPhone users,whose demographic profiles are presented in Table 1.

As Table 1 shows, 55.4% of the respondents were male with44.6% female. As for age composition, 46.2% of the respondentswere between 30 and 39 years old; 38.7% were 40–49; 15.1% were

20–29. In terms of education, 58.5% of the respondents were eithercurrent college students or having a bachelor’s degree. 33.6% of therespondents were either having less than a high school diploma orhigh school graduates. Of the total, 7.9% of respondents were eithercurrent graduate students or having a master’s or higher. The par-ticipants lived in Seoul and the surrounding metropolitan area.

3.2. Survey administration

To examine the hypotheses, this study employed a self-reportedsurvey of Koreans who use Apple’s iPhone. Questionnaires wereadministered by trained interviewers. The survey was adminis-tered for two weeks from May 24 to June 6, 2010. At that time,about 6 months had passed since Apple introduced iPhones intoKorea so that iPhone users were regarded as early adopters, willingand able to buy a high priced iPhone. Substantially, purchasers ofiPhone triggered the adoption throughout Korea of smartphonesdeveloped domestically. Although the method of convenient sam-pling limits generalization, we expect that early adopters of thesmartphone may have academic and practical implicationsthrough tests of motivations to use and users’ perceptions suchas usefulness and ease-of-use regarding new technology.

3.3. Measurement

The current research examines the relationship between ritual-ized and instrumental motivations to use the smartphone, PEOU,and PU with intention to use the smartphone. The research instru-ments consist of a 5-part questionnaire that was modified from avariety of sources to gather information regarding demographics,motivations to use the smartphone, PEOU, PU and intention touse the smartphone. Five-point Likert scales, anchored withstrongly disagree to strongly agree, were used to measure theaforementioned constructs.

The items of motivations to use smartphone were selected torepresent the two types of motivations to use media from previousstudies (e.g., Kim & Rubin, 1997; Livaditi et al., 2003; Rubin, 1984,1994) and modified to suit a smartphone context. The motivationsto use the smartphone were measured through eight items, includ-ing ‘‘I use the iPhone because it entertains me,’’ ‘‘I use the iPhonebecause it informs me of things that happen in everyday life,’’ ‘‘Iuse the iPhone in order to help my job or learning,’’ and ‘‘I usethe iPhone because it helps me kill time.’’

Questions regarding PEOU, PU and intention to use consisted ofthree items each, adapted from Davis (1989).

The construct PEOU was measured as follows:

� Learning to operate the iPhone is easy for me (PEOU1).� It would be easy for me to become skillful at using my iPhone

(PEOU2).

Table 2Factor analysis for motivations.

Items Factorloadings

1 2

Motivation for ritualized use (MR)I use the iPhone in order to escape for everyday life (MR1) .917I use the iPhone because it passes the time away, particularly

when I’m bored (MR2).889

I use the iPhone because it relaxes me (MR3) .842I use the iPhone because it entertains me (MR4) .788

Motivation for instrumental use (MI)I use the iPhone because it informs me for things that happen in

everyday life (MI1).889

I use the iPhone in order to a variety of news (MI2) .855I use the iPhone in order to get information about products and

services (MI3).828

I use the iPhone in order to help my job or learning (MI4) .795Eigenvalue 5.37 1.22Common variance% 67.2 15.2

Table 3Results of Cronbach’s alpha test.

Construct Number of items Cronbach’s alphas

Motivation for ritualized use 4 0.94Motivation for instrumental use 4 0.92Perceived usefulness 3 0.90Perceived ease of use 3 0.84Intention to use 3 0.94

J. Joo, Y. Sang / Computers in Human Behavior 29 (2013) 2512–2518 2515

� My interaction with the iPhone is clear and understandable(PEOU3).

The PU was measured as follows:

� Using iPhone enables me to accomplish tasks more quickly(PU1).� Using iPhone enhances my effectiveness on the job (PU2).� Using iPhone makes it easier to do my job (PU3).

The intention to use (ITU) iPhone was surveyed as follows:

� I intend to use the iPhone in the near future (ITU1).� I intend to keep using my iPhone (ITU2).� I predict that I will use iPhone in the short term (ITU3).

3.4. Data analysis

This study employed a structural equation modeling (SEM) ap-proach to develop a model that represents the relationships amongthe five underlying constructs: perceived ease-of-use, perceivedusefulness, intention to use iPhone, motivation for ritualized use,and motivation for instrumental use.

Data were collected through use of survey questionnaires thatasked about participants’ own characteristics and multiple itemsfor each construct. In this study, SEM was used because it has beenproven to allow for the ‘‘simultaneous analysis to be performed forassessing the relationships among variables and errors for eachvariable to be independently estimated, something that traditionalregression technique cannot do’’ (Teo, 2009, p. 110).

As noted in Teo’s (2009) study, there are usually a few steps re-quired to conduct SEM. The following steps were followed in thisstudy: (a) ‘‘data were screened for missing data and outliers,’’ (b)‘‘convergent and discriminant validities of the data were estab-lished,’’ (c) ‘‘issues pertinent to structural equation modeling wereaddressed’’ (p. 110). The sample size of this study was 491, thusexceeding the recommended sample size of 100–150 needed to ob-tain reliable results in SEM (e.g., Hair, Black, Babin, Anderson, & Ta-tham, 2006).

4. Results

4.1. Motivation to use smartphone

Respondents indicated their agreement with each of the eightstatements of motivations for using smartphone. Patterns of moti-vation of usage were determined by intercorrelating the items andconducting a principal factors analysis with varimax rotation. Serv-ing as the criteria were eigenvalues of at least 1.0 and a minimumof three primary loadings of .50 or greater. The factor solution,summarized in Table 2, identifies two factors and explains 82.4%of total variance.

Factor 1 had an eigenvalue of 5.37 and explained 67.2% of com-mon variance. On this factor were loaded escape, passing time,relaxation and entertainment. Thus, we named this factor Motiva-tion for Ritualized Use (MR). Factor 2, Motivation for InstrumentalUse (MI), had an eigenvalue of 1.22 and accounted for 15.2% of thecommon variance. That factor consisted of daily life and commer-cial information, news, and help with job and learning.

4.2. Internal consistency reliability test

Internal consistency reliability represents the degree to whichitems within a dimension measure the same constructs. The testis based on the Chronbach’s alpha coefficients (Chronbach & Snow,

1977). Cronbach’s alpha reliability coefficient normally ranges be-tween 0 and 1. The closer Cronbach’s alpha coefficient is to 1.0, thegreater the internal consistency of the items on the scale.

Table 3 summarizes the results of internal consistency reliabil-ity tests with regard to constructs used in current research (Cron-bach’s alpha values). The Cronbach’s alpha values range from 0.84to 0.94.

4.3. Confirmatory factor analysis

A confirmatory factor analysis was conducted to assess validityof the constructs. To judge the model fit, the study used the com-parative fit index (CFI), goodness of fit index (GFI), normed fit index(NFI), and root mean square error of approximation (RMSEA). CFI isthe recommended index of overall fit (Gerbring & Anderson, 1993).GFI is usually used to measure the fitness of a model in comparisonwith another model (Hair, Anderson, Tatham, & Black, 2003); NFI isfrequently used to measure the degree of improvement of the fit-ness of a model compared to a base model (Hair et al., 2003);and RMSEA gives information about the discrepancy per degreeof freedom for a model (Steiger, 1990).

As suggested in the literature (Bollen & Long, 1993; Kline,1998), model fit was assessed by several indices. An acceptable ra-tio for v2/df value should be below 3.0 —for the values of CFI. GFIand NFI should be greater than 0.90; and RMSEA is recommendedto be under 0.05 though acceptable up to 0.08 (Schumacker & Lo-max, 1996).

Table 4 shows the results of confirmatory factor analysis. Thev2/df ratio in this study was under 3 (2.838 = 309.327/109). GFI,CFI, and NFI were over 0.90 and RMSEA was 0.061. All model fitswere acceptable and, according to the literature, the validity ofthe measurements in the current study met the criteria.

Table 4Result of confirmatory factor analysis.

Constructs Items Standardizedregression weights

SE CR p

Perceived usefulness PU1 0.844PU2 0.898 0.042 24.578 ***

PU3 0.858 0.043 23.211 ***

Perceived ease ofuse

PEOU1 0.799PEOU2 0.780 0.056 17.358 ***

PEOU3 0.825 0.055 18.183 ***

Intention to use ITU1 0.882ITU2 0.947 0.033 32.624 ***

ITU3 0.913 0.036 30.347 ***

Motivation forinstrumental use

MI1 0.911MI2 0.919 0.032 32.447 ***

MI3 0.818 0.035 25.144 ***

MI4 0.763 0.038 21.992 ***

Motivation forritualized use

MR1 0.868MR2 0.942 0.036 31.052 ***

MR3 0.905 0.037 28.629 ***

MR4 0.842 0.039 24.854 ***

v2 = 309.327, df = 109, p < .001, GFI = .932, CFI = .972, NFI = .958, RMSEA = .061.Note: v2 = Chi Square, df = degree of freedom, GFI = goodness-of-fit index,CFI = comparative fit index, NFI = normed fit index, RMSEA = root mean square errorof approximation. SE = standard error, CR = critical ratio. Table 4 shows the result ofconfirmatory factor analysis. The v2/df ratio in this research is under 3(2.838 = 309.327/109). GFI, CFI, and NFI are over 0.90 and RMSEA is 0.061. All modelfits are acceptable. Namely, the validity of the measurements in current studymeets the criteria set out by previousliterature.*** p < .001.

Table 5Fit indices for the research model.

Model fit indices Values Recommended guidelines

v2 (df) 306.182(108) p < .001 Non-significantv2/df 2.835 <3GFI .933 P0.9CFI .973 P0.9NFI .958 P0.9RMSEA .061 <0.05(recommend)

<0.08(acceptable)

Table 6Hypotheses testing results.

Hypotheses Path Standardized pathcoefficient (b)

t-Value Results

H1 MR ? PEOU .271 3.983*** Supported(two-tailedtest)

H2 MR ? PU .119 2.080* Supported(two-tailed test)

H3 MI ? PEOU .337 4.939*** Supported(two-tailedtest)

H4 MI ? PU .437 7.213*** Supported(two-tailedtest)

H5 PEOU ? PU .297 5.965*** Supported(one-tailedtest)

H6 PEOU ? ITU .375 7.156*** Supported(one-tailedtest)

H7 PU ? ITU .423 8.294*** Supported(one-tailedtest)

* p < .05.*** p < .001.

Fig. 2. Standardized path coefficients.

2516 J. Joo, Y. Sang / Computers in Human Behavior 29 (2013) 2512–2518

4.4. Test of the measurement model

To determine the relationship of the constructs in the proposedmodel, the structural equation model was tested using AMOS 18with the default maximum likelihood estimation method. Table 5indicates the level of acceptable fit and the fit indices for the pro-posed research model in the current study. With the exception ofthe v2, the fit indices considered in this study satisfy the recom-mended level of acceptable fit. A chi-square has been found to betoo sensitive to sample size (Hair et al., 2006). Thus, the ratio ofv2 to its degree of freedom (v2/df) is used under the condition thatan acceptable fit for the proposed model should be below three(Teo, 2009).

4.5. Test of the structural model

The results of the hypotheses test and path coefficients of theproposed research model are shown in Table 6 and Fig. 2 below.All seven hypotheses are supported by the data. MR and MI directlypredicted PEOU, resulting in an R2 of 0.317. Therefore, MR and MIexplained 31.7% of the variance in PEOU. PU was predicted by MR,MI, and PEOU positively and the three constructs together ex-plained 54.0% of the variance in PU. PEOU directly and indirectly

and PU directly predicted ITU positively. ITU variance was ex-plained by its determinants in the amount of 50.6%.

5. Discussion and conclusion

There is a presumption that Koreans are very likely to readilyadopt new technologies (Kim, 2006). This perception is particu-larly strong regarding the adoption of information communica-tion technologies (ICTs). For instance, Koreans change their cellphones more frequently than people in other countries as indi-cated by the fact that Koreans, on average, change their cellphones every 12 months, whereas Americans do so every21 months, followed by Canadians at every 30 months (Kim,2006). It is commonly held that the Korean people’s readinessto adopt new technologies is based on two factors: (1) their abil-ity to diffuse information communication technologies quicklyand (2) Korea’s widespread and sophisticated infrastructure tosupport new technology.

This study, which questioned why the use of smartphones grewrapidly among Koreans sought to better understand the reasons byrelying on an integrated model of U&G and TAM. Both U&G andTAM are well-known theories for understanding the adoption ofICTs. Specifically, this study aimed to suggest a model that couldexplain and predict the adoption of ICTs in the context of smart-phone usage. While TAM is effective at explaining extrinsic factors

J. Joo, Y. Sang / Computers in Human Behavior 29 (2013) 2512–2518 2517

of IS adoption, U&G approach addresses intrinsic factors; thus, weemployed the U&G approach to help us understand intrinsic fac-tors of individual users, a weak point in TAM.

The smartphone is one of the most innovative of mobile devices.The user’s adoption of this innovation comes with much consider-ation that may consist of need to use, perception to use, and so on.This research thus employs and integrates U&G and TAM to explainand predict relationships between motivations to use and percep-tions about using the smartphone.

We proposed and tested hypotheses predicting intent to use thesmartphone and found that the motivation for ritualized use (MR)and the motivation for instrumental use (MI) had positive effectson both perceived ease of use (PEOU) and perceived usefulness(PU). Especially MI affected PEOU more than did MR. MI had agreater influence on PU than it did on PEOU, whereas MR influ-enced PEOU more than it did PU. These results lead us to the con-clusion that within the Korean context smartphone use is affectedmore by cognitive and goal-oriented motivations than by habitualand less goal-oriented use motivation. Therefore, early Koreanadopters appear to have purchased smartphones based mainly ontheir perceptions of usefulness, especially active features that al-low them to optimize and customize their smartphones by select-ing from a range of applications that met their needs for use. Bycontrast, the previous communication devices widely used in Kor-ea, traditional cell phones, are developed with software that comeswith limited built-in applications that cannot be modified to meetindividual needs.

The findings of this study suggest that users’ motivations con-stitute an important feature in the development of future informa-tion devices and services. In particular, designers should considerthe fact MI on adoption is greater within some populations thanMR. By combining TAM with U&G approach to analyze users’ moti-vations to optimize active features, this study redirected attentionto the users with the recommendation that developers pay closeattention to consumers’ needs especially to the motivation forinstrumental use.

The findings of this study validate a theoretical framework thatintegrates TAM with U&G as applied to a practical model for under-standing the effects of users’ motivations to adopt smartphones.This study extended TAM by combining it with U&G that resultedin statistical significance. This study found that different usagemotivations predict PEOU and PU. That is, MR influences PEOUmore than PU, and MI influences PU more than PEOU. The relation-ship that less-active motivation (MR) has with PEOU is deeper; therelationship more-active motivation (MI) has with PU is deeper,relatively. From a practical aspect, this study suggests that bothintrinsic motivations and extrinsic perceptions are important con-siderations when developing new electronic devices. Particularly,in Korea, smartphone use is affected more by motivations basedon instrumental and goal-oriented use than by ritualized andless-goal oriented use. The findings of this study suggest that tospread information system with innovative and active features,intrinsic motivations should be significant, as well as extrinsicperceptions.

Although the present study provides significant implications forsmartphone use in Korea, some limitations are inherent in thisstudy. The survey was conducted conveniently in the metropolitanarea surrounding Seoul so that there may be a built-in bias with re-gard to a highly urbanized population that can be addressed in fu-ture studies that target participants who live in diverse regions andhold different jobs with different levels of education. Moreover, be-cause the U&G approach used in this study is intentionally parsi-monious, we excluded diverse use motives identified in otherU&G studies. Therefore, future studies should employ diverse mo-tives to represent a broader range of users’ needs.

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