User behaviors toward mobile data services: The role of perceived fee and prior experience

Expert Systems with Applications 36 (2009) 8528–8536

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Expert Systems with Applications journal homepage: www.elsevier.com/locate/eswa

User behaviors toward mobile data services: The role of perceived fee and prior experience Byoungsoo Kim *, Minnseok Choi, Ingoo Han Korea Advanced Institute of Science and Technology, Business School, 207-43 Cheongrangri-Dong, Dongdaemun-Gu, 130-722 Seoul, Republic of Korea

a r t i c l e

i n f o

Keywords: Mobile data services Perceived fee Prior experience Technology acceptance model Technology acceptance Technology continuance

a b s t r a c t Rapid advancements in information and communication technologies (ICT) have allowed people some opportunities to access digitalized contents without restrictions in time or place. Mobile data service (MDS) is an important emerging ICT, thus many studies on information systems (IS) and marketing examine key predictors of MDS user behaviors. However, although the usage-based pricing of MDS is considered as a unique feature, most studies on MDS have paid little attention to its influences on MDS user behaviors. This study proposes a theoretical framework integrating perceived fee into Van der Heijden’s model to understand the role of perceived fee in wireless pay-per-use services. To capture the moderating effect of prior experience, this study also investigates the differences in determinants for the adoption decision stages and the continued usage decision stages. The proposed model is empirically tested by using survey data collected from 149 inexperienced users and 393 experienced users. The findings indicate that, compared with traditional IS such as a corporate IS and a website, perceived fee explains a large portion of the variances in adoption intention and continued usage intention toward MDS. This study also reveals that the antecedents leading to MDS user behavior vary in terms of prior experience. Finally, theoretical and practical implications of this study are described. Ó 2008 Elsevier Ltd. All rights reserved.

1. Introduction Because of the increasing relevance of the emergence of the information and communication technologies (ICT), understanding key predictors of their acceptance and continuance is an important issue in the information systems (IS) literature (e.g., Strader, Ramaswami, & Houle, 2006). One of emerging ICT is mobile data service (MDS), and it has experienced rapid growth worldwide. MDS provides wireless access to digitalized contents of the internet via mobile devices. It provides a variety of information and data services, ranging from productivity-oriented services such as traffic information, to pleasure-oriented services such as mobile games, and helps the user easily access these services anywhere and anytime (Ferguson, 2007). Despite the vast of number of services and the useful characteristics of MDS, over half of mobile phone users still use it purely for calling and sending short message service (SMS), according to a recent survey conducted by mobile interaction management developers (Williams, 2008). Thus, understanding the key drivers of MDS user behaviors is an important avenue of research. Among a variety of theoretical perspectives to explain the adoption and usage of IS, the technology acceptance model (TAM) is * Corresponding author. Tel.: +82 2 958 3131; fax: +82 2 958 3685. E-mail address: [email protected] (B. Kim). 0957-4174/$ - see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2008.10.063

popularly used to explain the user’s intention to adopt a target IS (e.g., King & He, 2006; Schepers & Wetzels, 2007). While TAM was originally used to predict the future user’s IS acceptance in a pre-adoption situation, recent works have found that TAM also explains a reasonable portion of the variance in IS continuance (Hong, Thong, & Tam, 2006; Premkumar & Bhattacherjee, 2008). Recently, researchers have pointed out that it is necessary to take into account the nature and specific influences of technological and usage-context factors in order to reflect the unique characteristics of a target IS in the original TAM (Venkatesh & Brown, 2001). In this vein, Van der Heijden (2004) incorporated perceived enjoyment into the original TAM to understand the role of the intrinsic motivation in website acceptance. They found that perceived enjoyment is a stronger predictor of website acceptance than perceived usefulness. Comparing traditional IS with MDS, usage-based pricing is a distinct attribute (Blechar, Constantiou, & Damsgaard, 2006). Several consumer surveys on MDS identify an expensive usage fee as a proxy barrier to prompt its usage (e.g., Lawson, 2006; Loechner, 2006). But, prior works on MDS have not investigated that exact role of perceived fee despite the fact that it is the key characteristic of MDS. Some studies only considered perceived usefulness, perceived enjoyment, and perceived ease of use that are conceived as important constructs in the traditional IS environments (Hong et al., 2006; Nysveen, Pedersen, & Thorbjornsen, 2005; Shin, 2007; Thong, Hong, & Tam, 2006), and other studies

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substitute perceived value for perceived fee to capture the influence of an economic feature of MDS on behavioral intention (Ha, Yoon, & Choi, 2007; Hong & Tam, 2006). However, perceived value is defined as a user’s overall assessment of the utility of a service based on perceived benefits and sacrifices (Zeithaml, 1988), so this construct does not fully explain the role of perceive fee on MDS behaviors. This study, therefore, develops a theoretical framework considering perceived fee to capture its exact effect on MDS user behaviors. Several prior studies on IS focused on the sequence of activities that leads to IS acceptance and its subsequent continuance. Understanding the sequence of activities helps IS providers target investment and marketing for each group. Most of these studies have tested the differences between the initial adoption decision processes and the continued usage decision processes in a mandatory environment such as corporate IS (e.g., Karahanna, Straub, & Chervany, 1999; Taylor & Todd, 1995). According to IS discipline, user’s decision processes are different in a voluntary environment and in a mandatory environment (Venkatesh, Morris, Davis, & Davis, 2003), because the effects of the antecedents affecting behaviors in a voluntary environment, such as a website, may be not the same as those of the antecedents in a mandatory environment such as corporate IS. Nevertheless, there are few works that articulated the differences in a user’s perceptions between voluntary IS acceptance and continuance. (e.g., Ha et al., 2007; Yu, Ha, Choi, & Rho, 2005). Since MDS is often used for personal purposes rather than task-related purposes, it provides a good opportunity to advance our knowledge of the different criteria in voluntary IS acceptance and continuance. In this regard, this study examines the relative importance of the factors affecting behavioral intention toward MDS for both the adoption decision stage and the continued usage decision stage. In summary, this study has two objectives. First, this study attempts to develop a theoretical framework considering perceived fee, and compares the results of the structural equation modeling (SEM) with those of the original TAM and Van der Heijden’s model. This model may help us better understanding the role of the unique characteristic in MDS user behaviors, and provides supplementary information about MDS user behaviors. Second, we examine the moderating role of prior experience in MDS user behaviors. To achieve this goal, this study empirically compares the antecedents of adoption intention with those of continued usage intention. The rest of the paper is organized as follows. The next section develops the theoretical background and proposes the research model, including several hypotheses. In Section 3, the research methodology is described. The analytical methods and the results of SEM are presented in Section 4. Section 5 discusses the findings and their implications for research and practice. Finally, the last section concludes the paper and identifies its limitations.

2. Basic concepts, research model, and hypotheses 2.1. Role of prior experience Researchers in psychology have been interested in the moderating effect of prior experience with a product or service. The cognitive dissonance theory (Cummings & Venkatesan, 1976; Festinger, 1957) suggested that the prior experience with a product or service has influence on the change of users’ perceptions. As a result, the antecedents of initial adoption are not the same as those of continued usage, and the relative importance of the determinants varies over time. Zanna and Rempel (1998) noted that a user’s perceptions may be formed based on three general types of information: past behavior, affective information, and cognitive information.

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They provided some evidence that inexperienced users’ perceptions are formed based on indirect experience such as affective and cognitive information while experienced users’ perceptions are formed based primarily on past behavior. Therefore, perceptions formed by prior experience are more enduring and predict behavior better than those formed by indirect experience. Some studies on IS have articulated that IS acceptance and continuance are affected by different sets of determinants. Taylor and Todd (1995) examined the important differences between the two groups with regard to the relative influence of perceived usefulness, which was the strongest predictor of behavioral intention for the inexperienced group, while experienced users placed less weight on perceived usefulness. Karahanna et al. (1999) confirmed the role of prior experience by a similar study in the context of corporate IS. They found that IS acceptance is solely determined by subjective norms, whereas IS continuance is solely determined by attitude. Yu et al. (2005) compared the factors influencing Tcommerce by inexperienced and experienced users, and showed the perceived enjoyment is the most important factor affecting users’ behavioral intention toward T-commerce for both groups. 2.2. Technology acceptance model (TAM), Van der Heijden’s model, and research model Based on the theory of reasoned action (Fishbein & Ajzen, 1975), TAM posits two perceptual factors, perceived usefulness and perceived ease of use, as key predictors of a user’s attitude toward IS. This attitude, in turn, leads to the user’s IS adoption intention (Davis, 1989). The attitude is a psychological tendency expressed by evaluating a particular entity in terms of the degree of positiveness about IS. However, a great deal of research on TAM shows that this attitude only weakly mediates the user’s perceptions of IS adoption behavior, and TAM retains its robustness even without including attitude (Adams, Nelson, & Todd, 1992). Therefore, as presented in Fig. 1a, recent TAM-based studies omit attitude without any loss of generality. Previous research on TAM has demonstrated the validity of this model across a wide variety of corporate IS (e.g., King & He, 2006; Schepers & Wetzels, 2007). Since TAM was originally developed to explain an employee’s adoption in an organizational setting, these studies have focused on how IS to increase the employee’s task performance. However, researchers on IS noted that TAMs fundamental constructs do not fully explain the user’s behavior in a non-working setting (e.g., Adams et al., 1992; Hong & Tam, 2006; Venkatesh & Brown, 2001). To capture the hedonic feature of a hedonic-oriented IS in a non-working place, Van der Heijden (2004) extended the original TAM by integrating perceived enjoyment as depicted in Fig. 1b. The study examined how the hedonic nature of websites influences on website acceptance. MDS has some similar attributes with websites in respect to providing multipurpose services. Thus, both the utilitarian and hedonic natures of MDS would affect MDS user behaviors in the same manner as Van der Heijden’s model (Wakefield & Whitten, 2006). However, the website usage has generally been free for users while MDS is a pay-peruse scheme. Actually, there are three charges for MDS (Geng & Whinston, 2001): the monthly subscription fee, the packet transmission fee, and the content fee. The packet transmission fee is calculated based on the volume of data transmitted and the content fee is charged based on the amount of contents used. When comparing to Van der Heijden’s model, it is necessary to take into account the perception of fee as the unique feature of MDS. Thus, this study develops a theoretical framework incorporating perceived fee into Van der Heijden’s model to provide a better theoretical understanding of the effect of perceived fee on user’s behaviors toward MDS. Based on the above discussions, the theoretical framework is presented in Fig. 1c.

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of prior experience (e.g., Karahanna et al., 1999; Taylor & Todd, 1995; Yu et al., 2005). Most empirical studies have provided some evidence that the link between perceived usefulness and behavioral intention is stronger among experienced users than inexperienced users (Karahanna et al., 1999). This is because the efficacy and capabilities of IS can be more confidently assessed by users with prior experiences with IS. In the consumer behavior discipline, the user’s perceptions of efficacy and usefulness of a product or a service are more confident and solid as experience accumulates (Homburg, Koschate, & Hoyer, 2006). Thus, perceived usefulness formed by prior experience may predict behavioral intention better than that formed by second-hand experience.

Perceived Usefulness Behavioral Intention Perceived Ease of Use

(a) Technology acceptance model

Perceived Usefulness

H1: Perceived usefulness has a stronger impact on continued usage intention than adoption intention.

Perceived Ease of Use

Behavioral Intention

Perceived Enjoyment

(b) Van der Heijden’s model Perceived Fee

Prior Experience

Perceived Usefulness Behavioral Intention Perceived Ease of Use

2.3.2. Perceived enjoyment Perceived enjoyment is defined as the extent to which the activity of an IS is perceived to be personally enjoyable in its own right, aside from the functional benefit of the technology (Davis et al., 1992). Perceived enjoyment thus represents an intrinsic motivation, and several works in MDS have found a significant role of perceived enjoyment in behavioral intention toward MDS (Ha et al., 2007; Hong & Tam, 2006; Hong et al., 2006; Kim et al., 2007; Nysveen et al., 2005; Shin, 2007; Thong et al., 2006). Gefen, Karahanna, and Straub (2003) noted that understanding the role of perceived enjoyment over time is an important avenue of research to pursue. Inexperienced users base the perception of their enjoyment on relatively superficial information, so it is difficult for them to evaluate and identify fun from using MDS. After users gain pleasurable experience with MDS, their perceptions are more convincing and enduring than those of inexperienced users. According to affective processing theory (Cohen & Charles, 1991), pleasurable experiences leave strong affective traces in episodic memory. When users evaluate the relative usage, the affective traces are readily retrieved. Thus, it is expected that perceived enjoyment has a greater impact on continued usage intention than adoption intention. We therefore hypothesize: H2: Perceived enjoyment has a stronger impact on continued usage intention than adoption intention.

Perceived Enjoyment

(c) Proposed model Fig. 1. Research models.

2.3. Research hypotheses 2.3.1. Perceived usefulness Perceived usefulness is defined as the degree to which an IS is perceived as providing benefits in performing certain activities (Davis, 1989). The motivation-oriented perspective views the user’s perception of usefulness as a measure of extrinsic motivation (Davis, Bagozzi, & Warashaw, 1992). Most studies on MDS have strongly supported perceived usefulness as a crucial determinant of forming user’s behavior toward MDS since it provide some useful services such as video calls, wireless positioning, and mobile TV program watching (Hong & Tam, 2006; Hong et al., 2006; Kim, Chan, & Gupta, 2007; Nysveen et al., 2005; Shin, 2007; Thong et al., 2006). Several studies have examined the relative importance of perceived usefulness in determining the user’s behaviors in terms

2.3.3. Perceived ease of use Perceived ease of use refers to the degree to which a user perceives that an IS is easy to understand and use (Davis, 1989). A technology that is perceived to be easier to use would facilitate its acceptance and use. Since MDS has limited resources compared to other systems due to the small screen size, manipulation difficulty, and low computing power, users need more mental and physical effort. Previous works on MDS have shown that, as theorized in the original TAM, perceived ease of use has both a direct effect and an indirect effect via perceived usefulness on behavioral intention (e.g., Hong & Tam, 2006; Lu, Liu, Yu, & Wand, 2008; Nysveen et al., 2005; Thong et al., 2006). In addition, the Van der Heijden’s model (2004) provides some evidence that it has an indirect impact on behavioral intention through perceived usefulness as well as perceived enjoyment. In the MDS context, it is also expected that perceived ease of use would affect behavioral intention both directly and indirectly via perceived usefulness and perceived enjoyment. H3(a, b): Perceived ease of use is positively related to (adoption intentiona, continued usage intentionb).

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H4(a, b): Perceived ease of use is positively related to perceived usefulness for (inexperienced usersa, experienced usersb). H5(a, b): Perceived ease of use is positively related to perceived enjoyment for (inexperienced usersa, experienced usersb). Davis (1989) found that perceived ease of use plays a critical role in forming the user’s behavioral intention after one hour of IS use, but it has no effect on behavioral intention after 14 weeks of usage. Lower familiarity with MDS may result in increasing levels of mental and physical effort. However, the strength of the relationship between perceived ease of use and behavioral intention becomes weaker as the user’s understanding of how to use MDS increases by virtue of their prior experiences with MDS. Thus, we hypothesize: H6: Perceived ease of use has a weaker impact on continued usage intention than adoption intention.

2.3.4. Perceived fee Perceived fee refers to the amount of economic outlay that must be sacrificed in order to obtain a product or use a service (Lichtenstein, Ridgway, & Netemeyer, 1993). For IS in an organizational setting, users are not concerned with the cost of IS use since the cost is borne by the organization. However, since the fee of MDS use is paid by the users, the monetary price of MDS may exert an important influence on MDS user behavior. Kim et al. (2007) empirically showed that perceived fee is negatively related to adoption intention toward MDS. This study proposes that perceived fee would affect behavioral intention in two ways: (1) by indirectly influencing behavioral intention through perceived usefulness and perceived enjoyment, and (2) by directly influencing behavioral intention. The direct effect suggests that perceived fee could be a potential barrier to decrease the likelihood of the user’s behavioral intention to adopt and use MDS. The indirect effect is explained as stemming from a situation where, other things being equal, the cheaper the MDS is to use, the more useful and enjoyable it can be. In the consumer behavior literature, perceived monetary cost exerts a negative effect on the user’s perceptions and evaluation (Zeithaml, 1988). Thus, it is expected that perceived fee will serve as a critical predictor in forming MDS behaviors. H7(a, b): Perceived fee is negatively related to (adoption intentiona, continued usage intentionb). H8(a, b): Perceived fee is negatively related to perceived usefulness for (inexperienced usersa, experienced usersb). H9(a, b): Perceived fee is negatively related to perceived enjoyment for (inexperienced usersa, experienced usersb). There is no research that examines the change in the relative strength of the link between perceived fee and behavioral intention in terms of prior experience. It may be difficult for inexperienced users to judge the level of a service fee imposed since an MDS usage fee will be charged according to the volume of data transmitted and the amount of contents used. Therefore, users with no prior experience encode a usage-based fee based on uncertain information. On the other hand, users with prior experience have a richer understanding and more concrete knowledge of the MDS fee structure, and therefore the certainty of the usage fee should increase. Recent consumer surveys (Loechner, 2006; Williams, 2008) show that users with prior experience consider the usage-based fee of MDS as excessively expensive, so they decide to discontinue to use it. As users accumulate the negative information of the usage fee, the negative role of a usage-based fee is more enduring than that of the user with no prior experience (Mittal,

Ross, & Baldasare, 1998). Thus, experienced users have more readily accessible memories, resulting in a negatively stronger relationship between perceived fee and behavior intention. H10: Perceived fee has a negatively stronger impact on continued usage intention and adoption intention.

3. Research methodology 3.1. Instrument development All constructs and measures are derived from prior studies to ensure their content validity. The question items are reworded to fit the MDS context. Perceived usefulness is measured by four items adapted from Davis (1989), and perceived enjoyment is adopted from Davis et al. (1992). The four items for perceived ease of use are adapted from Parthasarathy and Bhattacherjee (1998). Perceived fee is measured by three-items adapted from Voss, Parasuramna, and Grewal (1998). Adoption intention and continued usage intention are measured using the three-item scales developed by Davis (1989) and Bhattacherjee (2001), respectively. Two questionnaires were developed, one for inexperienced users and one for experienced users. The first question of our survey is designed to divide the respondents into inexperienced and experienced users. Inexperienced users are defined as people who had never used MDS, and experienced users are defined as people who had used MDS with the exception of experience with SMS. SMS has different characteristics from other MDS in that users do not need to access MDS to use it. Each question is measured on a 7-point, Likert-type scale, ranging from 1 (strongly disagree) to 7 (strongly agree). Before implementing the survey, six doctoral students in the MIS domain and three practitioners engaged in the MDS industry reviewed the instrument. After minor changes were made based on their suggestions, the modified questionnaire was pilot tested on 63 university students. All the questionnaires are proven to have reliability and validity, and they are provided in Appendix A. 3.2. Subjects Empirical data for this study was collected via a paper-based survey. In the data collection, two middle school teachers, two high school teachers, and four professors at two universities were asked to gather data from the students. Small gifts were given to the respondents. We distributed around 700 questionnaires, and finally obtained 569 responses. Among them, 27 responses were discarded because they were only partially completed. The final sample for inexperienced users is 149 and for experienced users 393. Table 1 shows the demographic data on the respondents in the final sample. The means and standard deviations of the main constructs in this study for inexperienced and experienced users are presented Table 1 Sample demographics. Inexperienced users (N = 149)

Experienced users (N = 393)

Frequency

Percentage

Frequency

Percentage

Gender Male Female

89 60

59.7 40.3

208 185

52.9 47.1

Age <20 20–30 >30

33 75 41

22.2 50.3 27.5

201 146 46

51.1 37.2 11.7

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Table 2 Descriptive statistics. Construct

AIN/CUI PUS PEN PEOU PFE

Inexperienced users

Experienced users

Independent t-test

Mean

Std. dev.

Mean

Std. dev.

4.27 4.07 4.43 4.32 5.35

1.626 1.327 1.368 1.443 1.306

3.84 3.70 4.41 5.00 5.52

1.503 1.349 1.403 1.438 1.256

t-value

Significance

4.963 5.581 0.177 9.763 2.300

p < 0.001 p < 0.001 ns p < 0.001 p < 0.05

Notes: AIN: adoption intention; CUI: continued usage intention; PUS: perceived usefulness; PEN: perceived enjoyment; PEOU: perceived ease of use; PFE: perceived fee; ns: non-significant.

in Table 2. This table also provides the results of the two sample independent t-tests, which test the difference between inexperienced and experienced users on these constructs. The two groups differ significantly on four of the five common constructs of the study with the exception of perceived enjoyment. Inexperienced users have significantly more positive behavioral intention toward MDS than experienced users. Compared with inexperienced users, experienced users view using MDS as being less useful as well as more expensive, but they feel that MDS is easier to use because they are familiar with it. 4. Research results 4.1. Measurement model A confirmatory factor analysis (CFA) using LISREL 8.5 (Jöreskog & Sörbom, 1996) is conducted to test the measurement model. In

this study, model fit is assessed in terms of four different indices: root mean square error approximation (RMSEA), comparative fit index (CFI), nonnormed fit index (NNFI), and standardized root mean square residual (SRMR). These fit indices are recommended based on their relative stability and insensitivity to sample size (Hu & Bentler, 1999). According to Gerbing and Anderson (1992), the criteria for an acceptable model are as follows: RMSEA of 0.08 or lower; CFI of 0.90 or higher; NNFI of 0.90 or higher; and SRMR of 0.10 or lower. Although RMSEA for the inexperienced users’ data is a little above the recommended value, most recommended fit indices were within the recommended level, representing good model fit [Inexperienced users RMSEA = 0.11, CFI = 0.92, NNFI = 0.91, and SRMR = 0.09; Experienced users RMSEA = 0.07, CFI = 0.97, NNFI = 0.96, and SRMR = 0.07]. Therefore, we proceed to evaluate the psychometric properties of the measurement for reliability, convergent validity, and discriminant validity of the instrument items.

Table 3 Scale reliabilities. Construct (a) Inexperienced users Adoption intention

Perceived usefulness

Perceived enjoyment

Perceived ease of use

Perceived fee

(b) Experienced users Continued Usage intention

Perceived usefulness

Perceived enjoyment

Perceived ease of use

Perceived fee

Item

Mean

Std. dev.

Factor loading

CR

AVE

AIN1 AIN2 AIN3 PUS1 PUS2 PUS3 PUS4 PEN1 PEN2 PEN3 PEOU1 PEOU2 PEOU3 PEOU4 PFE1 PFE2 PFE3

4.15 4.19 4.46 4.14 3.97 3.98 4.17 4.59 4.32 4.37 4.30 4.28 4.48 4.23 5.71 5.09 5.26

1.646 1.574 1.650 1.310 1.297 1.333 1.369 1.395 1.386 1.317 1.460 1.488 1.487 1.331 1.264 1.289 1.291

0.930 0.982 0.855 0.778 0.852 0.815 0.605 0.871 0.907 0.819 0.878 0.927 0.874 0.665 0.697 0.914 0.829

0.946

0.853

0.850

0.590

0.900

0.751

0.906

0.709

0.857

0.669

CUI1 CUI2 CUI3 PUS1 PUS2 PUS3 PUS4 PEN1 PEN2 PEN3 PEOU1 PEOU2 PEOU3 PEOU4 PFE1 PFE2 PFE3

4.19 3.31 4.04 3.52 3.62 3.77 3.91 4.48 4.41 4.34 5.03 4.99 5.21 4.76 5.70 5.28 5.56

1.417 1.521 1.423 1.427 1.280 1.350 1.306 1.439 1.375 1.392 1.443 1.455 1.370 1.450 1.202 1.241 1.290

0.863 0.711 0.762 0.778 0.888 0.837 0.666 0.828 0.923 0.927 0.886 0.949 0.886 0.710 0.768 0.750 0.849

0.823

0.610

0.873

0.634

0.922

0.799

0.920

0.743

0.833

0.624

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First, to check the reliability, composite reliability (CR) and average variance extracted (AVE) are calculated (Fornell & Larcker, 1981). The reliability is acceptable if CR is 0.70 or higher and AVE is 0.50 or higher. As shown in Table 3, all factors meet both criteria for acceptable reliability. Second, convergent validity can be established if item loadings are 0.60 or higher (Chin, Gopal, & Salisbury, 1997). The lowest loading of this study is 0.61 for inexperienced users and 0.67 for experienced users, satisfying convergent validity. Third, to examine discriminant validity, we compare the shared variances between factors with the AVE of the individual factors (Chin et al., 1997). The diagonal of Table 4 contains the square root of the AVEs. All AVEs are greater than the off-diagonal elements in the corresponding rows and columns, confirming discriminant validity. 4.2. Structural model In order to confirm the hypothesized relations among this study constructs, SEM is performed. As shown in Table 5, the same set of

Table 4 Correlation matrix and discriminant assessment. AIN

PUS

(a) Inexperienced users AIN 0.924 PUS 0.408** PEN 0.276** PEOU 0.231* PFE 0.416**

PEN

0.768 0.143** 0.195* 0.549**

CUI

0.867 0.015 0.416*

PUS

(b) Experienced users CUI 0.781 PUS 0.484** PEN 0.444** PEOU 0.033 PFE 0.364**

PEOU

PEN

0.796 0.534** 0.015 0.420**

0.894 0.157** 0.078

PFE

0.842 0.195*

0.818

PEOU

PFE

0.862 0.220**

0.790

Note: Diagonal elements are the square root of AVE. * p < 0.05. ** p < 0.01.

fit indices is used to test the fit of the structural model, and it has a reasonable fit. The results of the SEM for inexperienced and experienced users are presented in Fig. 2. First, we examine the effects of inexperienced user’s perceptions on MDS adoption intention. Perceived usefulness (b = 0.241, t = 2.415) and perceived enjoyment (b = 0.185, t = 2.299) positively affect adoption intention. Perceived fee (b = 0.207, t = 2.025) negatively influences adoption intention, so H7a is supported. However, contrary to the original TAM, perceived ease of use (b = 0.141, t = 1.809) is found to have an insignificant effect on adoption intention, resulting in rejecting H3a. The effects of perceived ease of use on perceived usefulness (b = 0.092, t = 1.118) and on perceived enjoyment (b = 0.038, t = 0.431) are also found to be insignificant, so H4a and H5a are rejected. However, perceived fee is negatively related with perceived usefulness (b = 0.531, t = 5.250), and perceived enjoyment (b = 0.274, t = 2.904). Therefore, H7a and H8a are accepted. This implies that perceived fee has direct and indirect effects on adoption intention via perceived usefulness and perceived enjoyment. The research model explains 27% of the variance in adoption intention toward MDS. For experienced users, perceived usefulness (b = 0.226, t = 3.722), perceived enjoyment (b = 0.324, t = 6.010), and perceived fee (b = 0.245, t = 3.737) are found to be significant for continued usage intention, explaining 30% of its variance. Therefore, H7b is supported. The effect of perceived ease of use (b = 0.034, t = 0.637) on continued usage intention is not significant, resulting in rejecting H3b. However, it does have a significant indirect effect on continued usage intention through perceived usefulness and perceived enjoyment, and also has a significantly negative direct effect on perceived usefulness (b = 0.123, t = 2.320) and perceived enjoyment (b = 0.198, t = 3.565), respectively. Thus, H4b and H5b are accepted. Perceived fee has negative indirect effects on continued usage intention via perceived usefulness (b = 0.463, t = 7.441) and perceived enjoyment (b = 0.169, t = 2.908), so H8b and H9b are supported. Hypotheses 1, 2, 6, and 10 are tested by statistically comparing the path coefficients from perceived usefulness, perceived enjoyment, perceived ease of use, and perceived fee to adoption intention with the corresponding path coefficients to continued usage

Table 5 Result of SEM. Original TAM Effect AIN/CUI

PUS PEN

Cause PUS PEN PEOU PFE PEOU PFE PEOU PFE

Squared multiple correlation AIN/CUI PUS PEN

Inexp. 0.426

**

0.481

0.141 0.198

Van der Heijden’s model Exp.

Inexp. **

0.036 *

0.014

**

0.353 0.197* 0.161*

Proposed model Exp.

Inexp. **

0.347 0.313** 0.080

0.197*

0.019

0.018

0.157**

*

Exp.

0.241 0.185* 0.141 0.207* 0.092 0.531** 0.038 0.274**

0.226** 0.324** 0.034 0.245** 0.123* 0.463** 0.198** 0.169**

23 4

23 0

21 4 0

22 0 2

27 31 7

30 20 5

142.25 41 0.13 0.93 0.91 0.08

174.54 41 0.09 0.96 0.94 0.08

229.65 72 0.12 0.91 0.89 0.15

332.56 72 0.10 0.95 0.93 0.16

317.32 110 0.11 0.92 0.91 0.09

403.19 110 0.07 0.97 0.96 0.07

Model fit

v2 df RMSEA CFI NNFI SRMR

Note: Inexp: inexperienced user; Exp.: experienced user. * p < 0.05. ** p < 0.01.

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Perceived Fee

Perceived Fee

-0.245**

-0.207* -0.463**

-0.531** Perceived Usefulness

-0.092

R2=0.31

Adoption Intention -0.141

-0.274**

Perceived Usefulness

0.241*

0.123*

2

-0.169**

Continued Usage Intention R2=0.30

Perceived Ease of Use 0.324**

0.185*

0.038

R2=0.20 -0.034

R =0.27

Perceived Ease of Use

0.226**

0.198** Perceived Enjoyment

Perceived Enjoyment

: Significant Path : Non-significant Path

R2=0.05

2

R =0.07

(a) Inexperience users *: p<0.05; **: p<0.01

(b) Experienced users Fig. 2. Analysis results.

intention, respectively. These statistical comparisons are performed using a multi-group analysis presented in Keil et al. (2000). Unexpectedly, the path from perceived usefulness to behavioral intention does not differ for inexperienced and experienced users (t = 1.631, p > 0.05), resulting in rejecting H1. Consistent with our expectations, the path from perceived enjoyment to continued usage intention is stronger than the path from that to continued usage intention (t = 20.730, p < 0.001), resulting in supporting H2. The path from perceived ease of use to behavioral intention is not significant for both groups while the path from perceived fee to continued usage intention is negatively stronger than the path from that to adoption intention (t = 2.561, p < 0.05). Therefore, H6 is rejected whereas H10 is supported. 5. Discussion and implications 5.1. Discussion of findings In this study, the theoretical framework in the MDS context has been formulated and empirically tested. The proposed model, the original TAM, and Van der Heijden’s model are tested on each of the two groups. Six separated SEMs are conducted, and Table 5 reports the results of path coefficients, squared multiple correlation, and model fit. The fit indices indicate that the proposed model explains both inexperienced and experienced groups better than the Van der Heijden’s model. In addition to the model fit, the proposed model surpasses the other models in explaining variance in adoption intention and continued usage intention toward MDS. Taken together, these results strongly suggest that the proposed model is a more reasonable representation of MDS user behaviors than the other models. The results of this study reveal that there are some significant differences in the relative influence of the determinants of behavioral intention toward MDS depending on prior experiences with MDS. While the influence of perceived usefulness does not change with respect to prior experience with MDS, the link between perceived enjoyment and behavioral intention is stronger for users with prior experiences. According to Dhar and Wertenbroch (2000), users view extrinsic aspects of consumption as a means of preserving the status-quo in consumption experiences while

they view intrinsic aspects of consumption as a way to increase the level of consumption. Consistent with their findings, fun experiences with MDS drive its users to increase the level of MDS usage in the post-adoption stages. More importantly, the total effect of perceived fee on behavioral intention is 0.386 for inexperienced users and 0.404 for experienced users. Perceived fee is the strongest predictor of MDS user behaviors for both groups, and its effect increases over time. The lack of a significant link between perceived ease of use and behavioral intention for both groups contradicts some prior TAM studies which have found this relationship to be significant. It is quite possible that because MDS users have sufficient knowledge gained through prior experience with similar services such as websites and SMS as well as similar systems such as a portable multimedia player and an mp3 player, perceived ease of use is not a big issue for them. In addition, young people occupying the main population of MDS actual users have sufficient knowledge sources in learning how to use the new innovative technology, and they are familiar with mobile-based services (Hong & Tam, 2006; Sun & Zhang, 2006; Venkatesh & Brown, 2001). Other reason of the findings is that Korea already has an advanced wireless broadband internet infrastructure such as a high-speed downlink packet access service, resulting in the rapid advancement of human–computer interface in mobile phones. Samsung and LG, that are leading mobile device manufacturers, are developing mobile devices with enhanced human interfaces such as a touch screen, a large LCD panel, and high computing power. 5.2. Theoretical implications The theoretical framework takes into account the perception of the MDS usage fee, representing important theoretical advances in MDS acceptance and continuance. Most IS has generally been free or requires one-time fee for users, thus the perception of fee is not considered in the traditional IS. However, this study clarifies the impact of perceived fee on MDS user behaviors. Specifically, this study reveals that perceived fee significantly influences MDS user intention in two ways: (1) by indirectly affecting behavioral intention through perceived usefulness and perceived enjoyment, and (2) by directly affecting behavioral intention. This study advances

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our understanding of the important role of perceived fee on MDS behaviors. This study also examines whether the antecedents leading to a user’s behavior change at different stages, and provides preliminary evidence suggesting that adoption intention and continued usage intention are determined by different criteria. For inexperienced users, perceived usefulness has a more positive effect on adoption intention than perceived enjoyment. As users have more concrete information on MDS, the role of perceived enjoyment becomes more prominent in generating positive MDS user behavior. This means that an extrinsic motivational factor has a more powerful effect than an intrinsic factor at the pre-adoption stage, but the importance of the intrinsic motivational factor increases in the post-adoption stage. The results of this study confirm the moderating role of prior experience in IS behavior. This study also reveals that continued usage intention is better predicted by the antecedents than adoption intention. This is in line with the notion that beliefs developed through prior experiences are more enduring and more resistant to attack than those developed through indirect experience (Fazion & Zanna, 1978; Mittal, Ross, & Baldasare, 1998). 5.3. Practical implications This study provides several important implications for MDS practitioners. Many IS practitioners have argued that the key barrier of IS acceptance or continuance is perceived ease of use due to the lack of user friendliness of IS. The traditional approach toward increasing usability has focused on perceived ease of use. However, the findings of this study suggest that users are deterred more by perceived fee than by perceived ease of use. In line with our findings, consumer surveys showed that a high price keeps many users from trying MDS if they are not sure about it (Lawson, 2006; Loechner, 2006). MDS practitioners need to understand the significant role of a monetary fee, and must pay close attention to improving the user’s perception of the usage fee of MDS. For example, MDS practitioners can change the fee systems into a flat rate that is similar to the fee structure of the stationary Internet, because a flat rate may encourage users to assume that MDS is not too expensive to use. This strategy would improve the level of perceived usefulness and perceived enjoyment, and, in turn, increase the user’s usage level. Understanding the mechanism leading to MDS user behaviors is critical to foster MDS diffusion (Sohn & Kim, 2008). The findings of this study help MDS managers understand the different criteria between inexperience and experience users, resulting in facilitating more efficiently targeted marketing for MDS in each group. For example, for users with prior experience, the hedonic features of MDS are enhanced by providing them with new fun and enjoyable services. By addressing the key drivers in each group, MDS providers can ensure profitability by retaining their users. 6. Conclusions This study proposes a theoretical framework considering perceived monetary fee of wireless pay-per-use services. The proposed model is conducted as a preliminary test using survey data from 149 inexperienced users and 393 experienced users, and the data is analyzed using LISREL. The results of this study contribute to the development of a more comprehensive understanding of MDS acceptance and continuance compared to the original TAM and the Van der Heijden’s model. This study shows that MDS researchers should give careful consideration to the perceived fee of MDS when investigating user adoption and continued usage decision-making processes. This study also demonstrates the

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moderating role of prior experience on MDS user behaviors. Understanding the different decision criteria of the adoption decision stage and at the continued usage decision stage enables MDS practitioners to employ more targeted investment and marketing efforts for each group. There is, of course, a limitation in this study. As a cross-sectional study of inexperienced and experienced users, this study may not fully capture the dynamics of their MDS adoption and continued usage decision processes. Therefore, the findings should be viewed as preliminary evidence with respect to the varying criteria that dominate the different stages of its decision process. Further research needs to examine how the key factors of the same users evolve temporally. Appendix A. List of model construction and items

Inexperienced users Perceived usefulness PUS1: Using MDS would help me accomplish tasks more quickly. PUS2: Using MDS would enhance my task effectiveness. PUS3: Using MDS would make it easier to do my tasks. PUS4: Overall, using MDS would be useful. Perceived enjoyment PEN1: Using MDS would be pleasurable. PEN2: Using MDS would provide me with enjoyment. PEN3: Overall, using MDS would be interesting. Perceived ease of use PEOU1: Learning how to use MDS would be difficult for me (reversed). PEOU2: It would be difficult for me to become skillful at using MDS (reversed). PEOU3: My interaction with MDS would be unclear (reversed). PEOU4: Overall, using MDS would be easy for me. Perceived fee PFE1: The fee that I have to pay for the use of MDS would be too high. PFE2: The fee that I have to pay for the use of MDS would be reasonable (reversed). PFE3: I would be pleased with the fee that I have to pay for the use of MDS (reversed). Adoption intention AIN1: I intend to use MDS in the future. AIN2: I expect that I would use MDS in the future. AIN3: I plan to use MDS in the future. Experienced users Perceived usefulness PUS1: Using MDS helped me accomplish task more quickly. PUS2: Using MDS enhanced my tasks effectiveness. PUS3: Using MDS made it easier to do my tasks. PUS4: Overall, using MDS was useful. Perceived enjoyment PEN1: Using MDS was pleasurable. PEN2: Using MDS provided me with enjoyment. PEN3: Overall, using MDS was interesting. Perceived ease of use PEOU1: Learning how to use MDS was difficult for me (reversed). PEOU2: It was difficult for me to become skillful at using MDS (reversed). PEOU3: My interaction with MDS was unclear (reversed). PEOU4: Overall, using MDS was easy for me. Perceived fee

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