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USER ACCEPTANCE OF THE MOBILE INTERNET

Won Jun Leea

, Tae Ung Kimb, and Jay-Young Chung

c

IT Management Research Center

Graduate School of Business

Sungkyunkwan University, Seoul, Korea

leewj@skku.ac.kra

tukim@skku.ac.krb

jyjung@yurim.skku.ac.krc

ABSTRACT

This paper uses the original Technology Acceptance Model (TAM) and its extension models to explainthe factors affecting the use of the mobile Internet services in Korea. Based on data collected from a

questionnaire survey, we show that social influence and self-efficacy variables significantly affectperceived usefulness and perceived ease of use, respectively. The data also reveal that both perceived

usefulness and ease of use explain a significant percentage of the variations in the attitude towardusing the mobile Internet, which in turn influences the actual usage frequency.

Key words: Technology acceptance; mobile Internet; Regression analysis

1. INTRODUCTION

According to a recent survey conducted by Korean National Computerization Agency (NCA), thereare at the end of year 2001 about 24.4 million Internet users in Korea who are over 6 years old and use

the Internet at least once a month on average [14]. This translates into a usage rate of 56.6% defined asthe ratio of the number of Internet users to the number of total potential users, assuming that one canuse the Internet when over 6 years old. Since the end of year 2001, the number of Internet users has

increased by 5.3 million and the usage rate by 11.9% although the rates of increase in these numbersare reportedly decreasing. Among these Internet users, 62.4% are using at least once a day and 95.1%

at least once a week. These statistics indicate that the Internet is fast becoming an important part ofdaily life in Korea.

The same survey by NCA [14] also reports that as of the end of 2001, more than 23 million people in

Korea subscribe to mobile Internet services provided by three major telecommunication companies

SKT, KTF, and LGT. Even though this number represents over 80% of total cellular phone users inKorea (29 million as of the end of 2001), actual users of mobile Internet occupy less than 20% of the

23 million subscribers they only possess cellular phones which can be also used as the mobileInternet device. Furthermore, two thirds of the actual users are using the mobile Internet for less than

10 minutes on average per day. Mobile Internet services provided by SKT, KTF, and LGT generated ameager 3.3% of their total sales during the first half of year 2001. Compared with 15% of Japans NTT

Docomo, the figure 3.3% simply implies that Korean mobile Internet market is still at its early stage.

As discussed above, the mobile Internet is apparently not widely used in Korea despite its potentialvalue. Therefore, understanding what delays penetration of the mobile Internet on a larger scale has

become of particular interest in terms of both theory and practice. In this regard, we attempt to gain an

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understanding of the factors influencing the usage and acceptance of the mobile Internet services in

Korea. We use the Technology Acceptance Model (TAM) which has been widely used and empirically

validated in explaining why individuals use or do not use a particular information technology.

The remainder of this paper is organized as follows. Section 2 reviews previous studies related to

TAM and its application to WWW and mobile Internet acceptance/usage. Based on the literature

review, a research model is proposed along with research hypotheses. Section 3 describes the researchprocedures employed to test the proposed hypotheses. Section 4 presents multiple regression resultsand discussions of these results, which is then followed by concluding remarks.

2. THEORETICAL BACKGROUND AND RESEARCH MODEL

2.1 A Review of the TAM literature

User technology acceptance and use has received extensive attention from researchers andpractitioners for over a decade. This issue has been examined for a variety of information technologies

and user populations. Among the research models that have been proposed to understand whypotential users accept or do not accept a particular information technology, the Technology Acceptance

Model (TAM) seems to be the most widely used and effective.

The theoretical basis of TAM can be found in Fishbein and Ajzens [8] theory of reasoned action(TRA). In TRA, attitudes (ATT), which are influenced by beliefs, form behavioral intentions (BI),

which then trigger actual behaviors (USE). TAM uses this causal sequence of belief-attitude-intention-

behavior to understand the determinants of IT acceptance/use. The original TAM proposed by Davis[6] views the users perception on usefulness (PU) and ease of use (PEU) of a particular IT as the key

factors influencing the behavioral intention and actual use.

Many empirical tests of TAM suggest that PU has consistently been a strong determinant of usageintention. PU is defined as the extent to which a person believes that using a particular IT will enhance

his or her performance. PU is also influenced by PEU, defined as the extent to which a person believes

that using the IT will be free of effort. Many of subsequent studies have modified the original TAM inorder to improve explanatory power of TAM. Davis, Bagozzi, and Warshaw [7] found that attitude

does not play a significant role, and thus several of the subsequent studies did not include the attitude

variable [9, 18, 20].

Given the significance of PU and PEU in TAM, considerable research efforts have been exerted to find

external variables or antecedents of PU and PEU. In order to understand what determines PEU,Venkatesh and Davis [19] and Venkatesh [18] provide and empirically test the antecedents of PEU.

Especially, Venkatesh [18] bases her theoretical development on anchoring and adjustment ofbehavioral decision theory [15, 17]. In the TAM context, anchoring implies that, in the absence of

knowledge about a specific system, individuals are expected to anchor their system-specific PEU ofa new system to their general beliefs regarding computers and computer use. But, with increasing

experience with the system, individuals are then expected to adjust their system-specific PEU to

reflect their interaction with the system. Although meaningful, a review of PEU adjustment is not

provided here since it is beyond the scope of the present study.

Venkatesh [18] lists control, intrinsic motivation, and emotion as the anchor variables. Control refers

to an individuals perception of the availability of knowledge, resources, and opportunities required toperform the specific behavior. Control includes both internal and external dimensions, and thisbidimensionality has been empirically supported [3, 16]. The internal dimension is measured by self-

efficacy or SE individuals beliefs about their ability and motivation to perform specific tasks [2] and

the external dimension relates to individual perception of technology and resource facilitatingconditions or supports (SUP) including the availability of support staff [18], the ease of access to the

system of interest [4], and the availability of user training [10].

Intrinsic motivation, conceptualized as computer playfulness (PLAY), refers to the degree of

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cognitive spontaneity in microcomputer interactions [21]. Those who are more playful with

computers tend to indulge in using a new system just for the sake of using it, and therefore, they in

general underestimate the difficulty associated with using a new system [18]. The final anchor variable,computer anxiety, is defined as an individuals apprehension about using computers. It represents anegative affection toward computer use in general and thus it would have a negative impact on PEU.

With respect to the antecedents of PU, Venkatesh and Davis [20] proposed an extended TAM or TAM2,in which social influence processes and cognitive instrumental processes are incorporated into theoriginal TAM. TAM2 has job relevance, output quality, and result demonstrability for the cognitive

dimension. Although the relationships between the cognitive dimension variables and PU are

conceptually rather straightforward, the social influence processes dimension needs some elaboration.

The social influence construct includes subjective norm and image, among others. Subjective norm(SN) refers to a persons perception that most people who are important to him think he should or

should not perform the behavior in question. Subjective norm tends to make one to incorporate thereferents belief into ones own belief structure. Therefore, if ones referents believe a particular

innovation is useful, he or she may come to believe that it is actually useful. By defining image (IMG)

as the degree to which use of an innovation is perceived to enhance ones status in ones social system,TAM2 posits that subjective norm will improve image because ones status will enhance when one

accepts what ones peers and important people believe that he or she should do. Consequently,

potential improvement in image may make one believe that an innovation in question is useful.

2.2 Acceptance of WWW and Mobile Internet

Chang, Cheung, and Lai [5] and Cheung and Chang [4] studied factors affecting the acceptance of

WWW in workplace settings. Their analyses revealed that facilitating conditions and social factors arethe most significant determinants of WWW usage. Perceived complexity, which represents the

opposite of PEU, and near-term consequence, which is defined as the degree to which using WWW

can increase the performance of users current job, also influence their acceptance and usage behaviors.Lederer, Maupin, Sena, and Zhuang [12] report the results that support TAM in general. Specifically,ease of finding the information in need and ease of understanding explain PEU, and information

quality provided by a web site determines its PEU that increases the probability of returning to the site.Morris and Turner [13] explored the role of experience in changing relationships among TAM

variables in the WWW usage context.Little research can be found in the area of mobile Internet acceptance. Kim and Lee [11] attempted toexplain the mobile Internet acceptance in Korea using the TAM. In their use of TAM, they specifically

considered user characteristics (age, education, use frequency and experience of cellular phones,

income, etc) and social pressure representing subjective norms. Their findings show that theacceptance and usage behavior are affected by some of the user characteristics such as age, education,

and use frequency of cellular phones as well as social pressure. Due to limitations in their statisticalanalysis, Kim and Lee [11] suggest that their results should be interpreted with caution.

2.3 Proposed research model and research hypotheses

Although there are growing interests in and understanding of how to make use of mobile Internet in

businesses and individuals daily life, its actual use is not wide spread enough and thus full potentialbenefits from using it remain to be realized. In this regard, understanding why diffusion and

acceptance of the mobile Internet is delayed is one important research issue to be explored. Given thecurrent status of mobile Internet usage in Korea, the purpose of using mobile Internet by the currentand potential users appears to be mostly personal, as opposed to work-related. Therefore, their use of

mobile Internet is assumed to be voluntary in this study.

Based on the review of TAM literature, we propose a research model in an attempt to explaindifferences in usage of mobile Internet by the current users. We use subjective norms (SN), image

(IMG), service quality (SQ), and personal innovativeness (PI) as the antecedents of PU, and self-efficacy (SE), facilitating conditions or supports (SUP), and playfulness (PLAY) as the antecedents of

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PEU. All of these antecedents, except for SQ and PI, are adopted from Venkatesh and Davis [20] and

Venkatesh [19].

SQ, representing the perceived quality of mobile Internet services, is used as a substitute for perceivedoutput quality because output in the context of personal use of the mobile Internet appears to be hard

to define. PI, which can be defined as willingness of an individual to try out any new IT [1] or the

mobile Internet in our study, captures the risk-taking propensity of an individual. In our study, wesuggest that those with a higher level of PI may believe an innovative IT to be more useful than thosewith a lower PI level. Taking all together, we present our research model in Figure 1.

Since our research model makes use of the TAM and several of the antecedents of PU and PEU fromthe relevant literature, without discussion, we simply present the following hypotheses in the context

of mobile Internet usage.

Figure 1: Proposed research model

Each of the numbers on arrows in Figure 1 corresponds to the hypotheses presented below.

Hypothesis 1. There will be a positive relationship between Attitude and Actual Use.

Hypothesis 2. There will be a positive relationship between Perceived Usefulness and Attitude.

Hypothesis 3. There will be a positive relationship between Perceived Ease of Use and Attitude.

Hypothesis 4. There will be a positive relationship between Perceived Ease of Use and PerceivedUsefulness.

Hypothesis 5. There will be a positive relationship between Subjective Norm and Perceived

Usefulness.

Hypothesis 6. There will be a positive relationship between Image and Perceived Usefulness.

Hypothesis 7. There will be a positive relationship between Service Quality and Perceived

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SUP

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Usefulness.

Hypothesis 8. There will be a positive relationship between Personal Innovativeness and Perceived

Usefulness.

Hypothesis 9. There will be a positive relationship between Self-Efficacy and Perceived Ease ofUse.

Hypothesis 10. There will be a positive relationship between Support and Perceived Ease of Use.

Hypothesis 11. There will be a positive relationship between Playfulness and Perceived Ease of Use.

3. RESEARCH METHOD

3.1 Study context and sample

Data were gathered by means of a questionnaire in the April of 2002 at a large University located in

Seoul, Korea. Out of 300 questionnaires that were distributed, 208 were returned and used forstatistical analysis. Table 1 summarizes sample demographics.

Table 1: Sample demographics

Measure frequency valid NGender 207

Male 122Female 85

Age 206

Under 25 136

25 34 64Over 35 6

Usage Status 208

Current Users 167Quitters 41

Primary purpose of use 196

Communication 46Information 86

Entertainment 60

Commerce 4

3.2 Operationalization of Constructs

Although all the research constructs were operationalized using multi-items scales from the relevantprevious studies, many of these scales were modified to reflect the study context, i.e. the mobile

Internet. To ensure that the measurement scales were adapted appropriately, we conducted a pretestwith one business professional and one MIS professor. Minor changes in wording and structure of the

questionnaire were performed. In the questionnaire, respondents are asked to score on a 7-point Likert-type scale with the endpoints being strongly disagree and strongly agree. The dependent variable,

USE, was measured by the frequency of using mobile Internet services.Factor analysis was performed to confirm the construct validity of all the scales for the endogenous(ATT, PU, PEU) and exogenous (SN, IMG, SQ, PI, SE, SUP, PLAY) variables. Construct validity was

strongly supported by principal component analysis with oblimin rotation, in which all cross-loadingswere lower than 0.30 for both endogenous and exogenous variables after excluding one item for SUP.

However, for the exogenous variables, SN and IMG were loaded as one factor while PLAY was

separated into two factors. Therefore, we combined SN and IMG into one factor, social influence (SI).Furthermore, a careful examination of the seven PLAY scales revealed that the first PLAY factor

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represents focus (not recognizing elapse of time, etc) and the second represents fun. Given this, in the

subsequent analyses, we used FOCUS and FUN instead of the playfulness construct. Based on the new

grouping of the constructs, we performed reliability test, which showed high reliability for allconstructs with Cronbach Alpha coefficients exceeding 0.7.

In order to reflect the factor grouping, we restate Hypotheses 5, 6, and 11 as follows.

Hypothesis 12 There will be a positive relationship between Social Influence and PerceivedUsefulness.

Hypothesis 13 There will be a positive relationship between Focus and Perceived Ease of Use.

Hypothesis 14. There will be a positive relationship between Fun and Perceived Ease of Use.

Hypothesis 12 replaces Hypotheses 5 and 6, and Hypothesis 11 is separated into Hypotheses 13 and 14.

4. RESULTS AND DISCUSSION

All the hypotheses were tested using linear regression analysis for four submodels: USE = ATT, ATT =

PU + PEU, PU = PEU + SI + SQ + PI, PEU = SE + SUP + FOCUS + FUN. We used the stepwisemethod with the criteria of 0.05 for both including in or excluding variables from the final regression

model. Regression results are presented in Table 2.

Table 2: Regression analysis resultsRegression R Changes p-value Hypothesis

Models in R Supported

[1] USE = ATT 0.0671 0.6065 0.0015 H1

[2] ATT = PU + PEU 0.4396

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enhancing his/her status in the relevant social group through adoption of the mobile Internet. Therefore,

one can increase the acceptance by developing social information through the provision of its source

credibility or by developing campaigns that raise the prestige associated with using the mobile Internet.

PI and PEU are also shown to affect PU in a significant way. It has consistently been empirically

validated that PEU influences PU. In this paper, PI was hypothesized and empirically supported to

have direct impact on PU. This means that people who are open to new technology tend to perceive itto be more useful. Another way to look at this is that PI influences PU indirectly through PEU becauseindividuals with a higher level of PI would feel less difficult to use the new technology. Further, those

who perceive it as easier to use will consider it to be more useful.

In explaining PEU, as shown in Table 2, SE turns out to be the most significant with R-square of

42.7% along with the p-value less than 0.0001. Therefore, when an individual is less self-confidence inthe use of the mobile Internet, he or she would feel it more difficult to use. Although less powerful,

FOCUS is another factor explaining the variance in PEU. The more absorbed one is in using themobile Internet, the easier he or she perceives it to use. Therefore, designing the interface of mobile

Internet devices in such a way that users can get more absorbed during the use is an important factor in

diffusing the mobile Internet on a larger scale.

Rejection of the hypothesis 7 indicates that quality of the mobile Internet services does not play a

significant role in forming users perception on their usefulness. Furthermore, people do not feel themobile Internet to be easier to use even when facilitating conditions or supports provided by theservice providers or other users are available (hypothesis 10). Likewise, rejection of hypothesis 14

implies that the mobile Internet is not perceived to be easier to use even by those who perceive it to bemore enjoyable.

Overall, the original TAM is well supported in our study as in other previous related studies. In

addition, PU is found to be a stronger predictor of attitude than PEU, which is consistent with previous

studies. Among the antecedents of PU and PEU tested in the present study, (1) social influence whichcombines subjective norm and image and (2) self-efficacy are empirically shown to determine PU andPEU, respectively, in the most significant way.

5. CONCLUSIONSIn this study, we empirically investigated the usage and acceptance behaviors of mobile Internet

services by university students in Korea. Regression analysis was performed for the survey data, and

the overall results well support the original and subsequently developed extensions of TAM. Testing ofthe proposed hypotheses regarding antecedents of TAM main variables perceived usefulness and

perceived ease of use. Among the antecedents of perceived usefulness and perceived ease of use,social influence and self-efficacy were shown to be most significant. It was also found that a positive

attitude leads to the actual use of the mobile Internet services.

One major limitation of the present study is in the representativenss of the sample. Since university

students were used as the sample, generalizability of the results drawn from their responses is limited

although the current users of the mobile Internet in Korea are mostly younger generations. Despite thisproblem, the present study makes good contribution to theory and practice because there is societal

consensus regarding the practical value of the mobile Internet and because there are few studiesconducted to address the issue of mobile Internet acceptance.

As an extension of this study, it will be interesting to compare the characteristics of users according to

their usage purposes such as communication, contents, and commerce. Understanding differences

between the current users and the quitters will shed light on what hampers diffusion of the mobileInternet at a faster rate. As mentioned earlier, only 20% of the subscribers actually use the mobile

Internet. Understanding why people stopped using it seems to be equally important.

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REFERENCES

1. Agarwal, R., and Prasad, J. The Antecedents and Consequents of User Perceptions in Information

Technology Adoption. Decision Support Systems, 22, 2, (1998), 15-29.

2. Bandura, A. Social Foundations of Thought and Action. Englewood Cliffs, NJ: Prentice Hall, 1986.

3. Beale, D.A., and Mansted, A.R.S. Predicting Mothers Intentions to Limit Frequency if InfantsSugar Intake: Testing the theory of Planned behavior. J. Appl. Soc. Psych, 21. 5, (1991), 409-431.

4. Chang, M.K. and Cheung W. Determinants of the Intention to Use Internet/WWW at work: aConfirmatory study. Information & Management, 39, (2001), 1-14.

5. Cheung, W., Chang, M.K., and Lai, V.S. Prediction of Internet and World Wide Web Usage at work:

a Test of an Extended Triandis Model. Decision Support Systems, 30, (2000), 83-100.

6. Davis, F.D. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of ComputerTechnology. MIS Quarterly, 13, 3, (1989), 319-340.

7. Davis, F.D., Bagozzi, R.P., and Warshaw, P.P. User Acceptance of Computer Technology: AComparison of Two Theoretical Models. Management & Science, 30, 2, (1992), 361-391.

8. Fishbein, M., and Ajzen, I. Belief, Attitude, Intention, and Behavior: An Introduction to Theory and

Research. Addison-Wesley, 1975.

9. Igbaria, M., Zinatelli, N., Cragg, P., and Cavaye, A.L.M. Personal Computing Acceptance Factors in

Small Firms: A Structure Equation Model. MIS Quarterly, 21, 3, (1997), 279-305.

10. Karahanna, E., Straub, D.W., and Chervany, N.L. Information Technology Adoption Across Times:A Cross-Sectional Comparison of Pre-Adoption and Post-Adoption Beliefs. MIS Quarterly, 23, 2,

(1999), 183-213.

11. Kim, I.K., and Lee, J.W. A Study on Factors Affecting Acceptance of Mobile Internet Services.Information System Review (in Korean), 3, 1, (2001), 83-101.

12. Lederer, A.L., Maupin, D.J., Sena, M.P. and Zhuang, Y. The Technology Acceptance Model and

the World Wide Web. Decision Support Systems, 29, (2000), 269-282.

13. Morris, M.G., and Turner, J.M. Assessing Users Subjective of Experience with the World WideWeb: an Exploratory Examination of Temporal Changes in Technology Acceptance. Human-

Computer Studies, 54, (2001), 877-901.

14. National Computerization Agency. 2002 Internet White Book. Seoul, Korea, 2002.

15. Slovic, P., and Lichtenstein, S. Comparison of Bayesian and Regression Approaches to the Study

of Information Processing in Judgement. Behavior and Human Performance, 6, (1971), 611-744.

16. Sparks, P. Attitudes towards Food: Applying, Assessing and Extending the Theory of Planned

Behavior, Rutter, D.R. and Quine, L. (ed.) The Social Psychology of Health and Safety: European

Perspectives, Aldershot: Avebury Press, England, (1994), 25-46.

17. Tversky, A., and Kahneman, D. Judgement under Uncertainty: Heuristics and Biases. Science, 185,

(1974), 1124-1131.

18. Venkatesh, V. Determinants of Perceived Ease of Use: Integrating Control, Intrinsic Motivation,and Emotion into the Technology Acceptance Model. Information Systems Research, 11, 4, (2000),

342-365.

19. Venkatesh, V., and Davis, F.D. A Model of the Antecedents of Perceived Ease of Use:Development and Test. Decision Sciences, 27, 3, (1996), 451-481.

20. Venkatesh, V., and Davis, F.D. A Theoretical Extension of the Technology Acceptance Model: Four

Longitudinal Field Studies. Management Science, 46, 2, (2000), 186-204.

8/2/2019 10.1.1.20.573 (1)

9/9

G `

21. Webster, J., and Martocchio, J.J. Microcomputer Playfulness: Development of a Measure with

Workplace Implications. MIS Quarterly, 16, 2, (1992), 201-226.