A multi-facet analysis of factors affecting the adoption of multimedia messaging service (MMS).(Report).

Mobile applications such as multimedia messaging service (MMS) promises a new way to share rich content of information that enhances its users' personal connectivity experiences as well as productivity. However, the adoption of MMS seems to be unexpectedly slow (Bonte, 2008). As mobile phones become ever smarter (or complex) in functions, understanding the adoption behaviors of complex mobile services such as MMS becomes utterly important to both practitioners and academic. This paper introduces a multi-facet model for MMS adoption by integrating the well-known behavioral models such as TAM and TPB with other factors including intrinsic motivation, personal innovativeness and critical mass. An internet survey of 213 subjects with prior experience in MMS usage found strong support for the proposed model. The results show that the adopter's attitude toward MMS is the most dominating factor in shaping his/her intention to use MMS, followed by subjective norm and perceived behavioral control. Moreover, the results further suggest adopter's intrinsic motivation is the most important motivating factor for attitude toward using MMS. Implications of these findings are discussed for researchers and practitioners.

Keywords: Behavioral Intention, Critical Mass, Intrinsic Motivation, MMS, Mobile Services, Personal Innovativeness

Full Text :COPYRIGHT 2009 IGI Global
INTRODUCTION

In recent years mobile communication has experienced a tremendous growth and quickly become the preferred means for voice communication around the world. According to Pyramid Research, the number of mobile subscriptions outpaced the numbers of fixed lines in service in many countries since 2002 (Ebrahim, 2004). This explosive penetration rate of mobile device motivates practitioners as well as researchers to innovate and provide increasingly diverse services. Data communication is among one of them. With its rapid improvement in bandwidth and handset functions, mobile device is able to surf online, get emails, and send multimedia messages.

Take multimedia messaging service (MMS) for example. It is a global messaging standard that sends multimedia messages between mobile devices. By using this technology, users can exchange rich contents (e.g., color pictures, audio/video, and animations) anytime, anywhere. This leap in messaging capabilities not only enriches mobile phone user's personal connectivity experiences (e.g., sharing photos), but it also enhances his/her productivity (e.g., sending rich-text files) (Lee et al., 2007). The penetration of MMS applications is affecting our everyday life in many ways. The city of New York, for example, makes good uses of multimedia messages and upgraded its 911 emergency call centers to handle picture messaging so that pictures, instead of voices, could be sent using MMS during emergency (Mobiletracker, 2007).

The potential adoption of MMS seems to promise an important source of revenue for network operators as well as content and services providers. Jupiter Research estimates that the volume of MMS messages will reach 4.1 Billion [pounds sterling] in Europe by 2011, accounted for 42 percent of European active mobile phone users (Husson et al., 2007). The use of MMS in marketing also promises potential returns for business sectors. BMW, for example, increased its winter tire sale volume by effectively sending MMS ads to its new car owners (Ahonen and Moore, 2008). Thus, mobile marketing using MMS opens another window of opportunity for all industries.

As promising as it may seem, achieving widespread penetrations for mobile services such as MMS are not without obstacle. In fact, statistics show overall adoption of MMS is still significantly below the level for short messaging service (SMS) (Strother and Ask, 2008). Major hurdles such as lacks of device penetration and interoperability between carriers as well as price issues have slowed down the adoption rate (Husson et al., 2007). While adoption seems slower than expected (Bonte, 2008), mobile industries, on the other hand, have different expectations for MMS future. According to Charles Lafage, Senior Analyst at Juniper Research, and MMS specialist, "picture-messaging has a great potential as it satisfies a widespread customer need: to share the precious moments of our everyday lives" (Cellula.co.za 2004). Achieving this sense of sharing is getting ever so easy as nowadays about half of mobile phones sold had integrated cameras (Strother and Ask, 2008). As the progressive mobile technology provides a perfect adoption atmosphere for MMS (Kou and Yu, 2006), it becomes more and more compelling to look into the factors affecting the adoption of MMS.

While some attention has been paid in the past to research issues related to MMS users adoption behavior (Hsu et al., 2007; Lee et al., 2007), little research has been done on multi-facet perspectives to explore actual adopters' behaviors on MMS. For example, Hsu et al. (2007) applied innovation diffusion theory (IDT) to examine customer intention to use MMS. Additionally, Lee et al. (2007) applied motivational theory and media richness theory to investigate the acceptance of MMS. Their study also revealed that media richness was empirically shown to determine the technology acceptance model (TAM) factors such as perceived usefulness, perceived ease of use and enjoyment. These two studies highlight the technical values of exploring MMS because the Hsu et al (2007) emphasized the nature of innovative technology and Lee et al. (2007) stressed the importance of technology acceptance. Nevertheless, according to a recent cross-country study of handset usages, people send MMS messages to far few persons as compared to other types of message services (i.e. SMS) since most MMS messages are considered more personal (Verkasalo and Hammainen, 2007). Thus, to gain a comprehensive understanding of MMS adoption behavior, one needs to look into this issue from both technical view point and non-technical factors. Moreover, due to its demand in platform specification, the use of MMS also will be affected by network effect. That is, the more people adopt MMS, the higher interpersonal communications value MMS will be perceived. Thus, how well MMS is received socially becomes an important factor in adopting this service. To achieve this goal, we proposed and empirically tested a behavioral model based on technology acceptance model (TAM), theory of planned behavior (TPB) and other related literature in attempt to better understand the adoption of MMS. In addition, this study further investigates the purposes and problems of using MMS. Findings from this study could be of immense managerial use in developing strategies to promote mobile services such as MMS.

THEORETICAL BACKGROUND

For past decades many MIS researchers applied Davis' technology acceptance model (TAM) and Ajzen's theory of planned behavior (TPB) as theoretical foundations to explore user's IT/IS acceptance behavior (e.g. Hsu and Lin, 2008; Lin and Lu, 2000; Pavlou and Fygenson, 2006). Moreover, studies based on these two theories incorporate additional variables to help explain the user's behavior of IT/IS usage for specific context (e.g. Wang et al. 2006). In this study, these two prevalent theories and additional variables such as intrinsic motivation, personal innovativeness and perceived critical mass will be used to develop a richer understanding of MMS acceptance.

Technology Acceptance Model (TAM)

The technology acceptance model (TAM) has received considerable attention of researchers in the Information System (IS) field since its adoption since 1989 (e.g Gefen, and Straub, 1997; Pikkarainenk et al., 2004; Lai and Li, 2005; Hsu and Lu, 2007). Previous studies have empirically confirmed the TAM's validity in explaining the individual's acceptance of various IT/IS (e.g., Lin and Lu, 2000; Moon and Kim, 2001; Hsu and Lu, 2004; Kim et al., 2007; Hernandez et al., 2008). TAM is an adaptation of the theory of reasoned action (TRA) (Fishbein and Ajzen, 1975) from psychology specifically tailored to model user acceptance of IT/IS. TRA postulates that belief (individual's subjective probability of the consequence if a particular behavior is performed) influences attitude (individual's positive and negative feelings if a particular behavior is performed), which in turn shapes a behavioral intention (Fishbein and Ajzen 1975). Davis (1989) adapted the belief-attitude-intention- behavior causal chain to predict user acceptance of IT/IS. He used the cost-benefit paradigm and self-efficacy theory to propose two influential beliefs: perceived usefulness (PU) and perceived ease of use (PE). PU is defined as "the degree to which a person believes that using a particular system would enhance his or her job performance", and PE as "the degree to which a person believes that using a particular system would be free of effort". According to TAM, the system usage is determined by individuals' attitudes toward using the system and PU. Meanwhile, attitude toward using the system is jointly determined by PU and PE.

While TAM has been widely applied in acceptance behavior across a broad range of information technology, much research extends TAM to enhance the understanding of user acceptance behavior for specific contexts (Luo and Strong 2000; Chen et al. 2002; Kaasinen 2005; Hsu and Lin, 2008). For example, Moon and Kim (2001) proposed a variable 'playfulness' for studying WWW acceptance. Extending WWW factors into the TAM model enabled better explanation of WWW usage behavior. In this study, comparing to task-oriented IS, factors contributing to the acceptance of a mobile application are likely to vary sharply according to purpose of usage, innovation and communication effects. Thus, intrinsic motivation, personal innovativeness and perceived critical mass were proposed in terms of following reasons.

1. Purpose of MMS usage Traditionally, the main reason for using mobile devices such as cell phones, laptops, GPS and PDA was to enhance communication effectiveness and work productivity such as banking and commerce (Lopez-Nicola's et al., 2008). However, with the arrival of the third generation (3G) standards and technology for mobile communication, the use of mobile devices is not only just for work, but also for entertainment. For example, MMS users send text messages and multimedia messages incorporating pictures, voice recordings, animated characters, and video clips to others (Cry et al., 2006). Intrinsic motivations such as enjoyment seem to play an influential role in intention to use because users can enjoy multimedia effects via colorful graphics, animation and ring tones.

2. Innovation MMS, like many innovative mobile applications, requires the users to learn and configure their own handsets. As mentioned earlier, one of the hurdles that delays the wide spreads of MMS adoption is the complexity of use for end users (Husson et al., 2007). Therefore, the adoption of MMS will require individual's willingness to put into effort to adopt. In innovation diffusion research, personal innovativeness is identified as an important determinant influencing the acceptance of innovation (Roger, 2003). Individual who possesses higher degree of personal innovativeness will be more willing to accept the challenge and learn to use new technology. As the result, personal innovativeness may play a significant role in adopting MMS.

3. Value-added by communication The successful adoption of network-based applications (such as telephone and facsimile) requires the development of a network externality, which refers to the fact that the value of technology to a user increases with the number of its adopters (Luo and Strong, 2000). Mobile applications such as MMS, mobile games and mobile video telephony also require network effects to succeed. As MMS increases in popularity, for example, it becomes increasingly valuable and attracts more users to adopt. MMS users may develop perceived critical mass via communications with others. Perception of critical mass is rapidly strengthened as more people connect in everyday activities.

Theory of Planned Behavior

The theory of planned behavior (TPB) has also been widely studied in social psychology to explain an individual's behavior (Hsu and Chiu, 2004; Luarn and Lin, 2005). TPB extends from TRA by incorporating an additional construct, namely perceived behavioral control (PBC), to account for situations in which an individual is able to control the performance of behavior personally (Ajzen, 1991). According to TPB, an individual's behavioral intention is jointly determined or predicted by attitude, subjective norms (SN), and PBC. Attitude refers to an individual's positive or negative evaluation of the performance effect of a particular behavior. SN refers to an individual's perception that important others would approve or disapprove of his or her performing a given behavior. PBC refers to the belief that one is able to control personally the performance of a behavior.

In subsequent studies, TPB has been decomposed to incorporate additional variables with specific contexts to improve the understanding of IT usage. For example, Hsu and Chiu (2004) decompose the SN component into interpersonal norm and subjective norm and PBC component into self-efficacy and perceived controllability for studying e-service acceptance. In addition, combining the technology acceptance model (TAM) has also received considerable attention over the years. For example, Riemenschneider et al. (2003) identified the appended model (TAM and TPB combined) performed a better understanding of user's behavioral intention to adoption decisions in small business than either model alone. Similarly, the study by Chau and Hu (2002) showed the similar results in adopting telemedicine technology. In this study, therefore, we integrate TPB constructs such as SN and PBC and extended TAM into a more comprehensive model for investigating user's acceptance of MMS.

CONCEPTUAL MODEL AND HYPOTHESES DEVELOPMENT

Figure 1 illustrates the research model, which was built based on TAM and TPB. It asserts that the intention to use a MMS is a function of its perceived usefulness, ease of use, intrinsic motivation, critical mass and personal innovativeness by an individual and TPB constructs such as subjective norms, perceived behavioral control and attitude. The definition of constructs, network of relationships illustrated in the model, and the rationale for the proposed links are explained in the following section.

TAM Constructs

Perceived usefulness is defined as the degree to which a person believes that using a MMS would enhance his or her job performance and communication effectiveness. Perceived ease of use is defined as the degree to which a person believes that using MMS would be free of effort. Additionally, attitude here is defined as user preferences regarding MMS usage. Intention is the extent to which the user would like to reuse MMS in the future. Previous empirical studies applying TAM have empirically confirmed that the significant relationship between these constructs (Lin and Lu, 2000; Pin and Lin, 2005). Therefore, it is one of our research intentions to verify the following TAM hypothesized relationship in the context of MMS.

H1a: Perceived usefulness will positively affect customer attitude toward using MMS.

H1b: Perceived usefulness will positively affect customer intention to use MMS.

H2a: Perceived ease of use will positively affect customer perceived usefulness.

H2b: Perceived ease of use will positively affect customer attitude toward using MMS.

H3: Attitude will positively affect customer intention to use MMS

Intrinsic Motivation

While extrinsic motivation (i.e., perceived usefulness) emphasizes performing a behavior to achieve specific goals/rewards (Vellerand, 1997), intrinsic motivation (i.e., perceived enjoyment) refers to the pleasure and satisfaction from performing a behavior (Deci and Ryan, 1987). User may engage in MMS usage if it yields fun and enjoyment. Past studies have verified that the use of computer technology was influenced by intrinsic motivation (Davis et al. 1992; Venkatesh et al., 2002; Lee et al., 2005). In addition, perceive ease of use may have influence on intrinsic motivation because MMS that is difficult to use is less likely to be perceived as enjoyment. Teo et al. (1999) present and empirically evaluate a conceptual model of factors contributing to acceptance of Internet. Their findings show that ease of use has significant effects on perceived enjoyment. Accordingly, we hypothesize:

H4a: intrinsic motivation will positively affect customer attitudes toward using MMS.

H4b: intrinsic motivation will positively affect customer intention to use MMS.

H4c: Perceived ease of use will positively affect customer intrinsic motivation.

Personal Innovativeness

Personal innovativeness, which defined as the degree of the willingness of a user trying out any new IT (Agarwal and Prasad, 1998), often plays an influential role in the new IT usage. Personal innovativeness is seen as one of the individual's trait. As noted by Rogers (2003), innovators who own this trait are able to cope with high levels of uncertainty and develop more positive intentions toward acceptance. As a new IT such as MMS announces in market, most people have not much information or experience to help them develop clear perception. Individual's trait of innovativeness may play an important factor that drives them to adoption owing to sheer curiosity and boldness. In addition, past empirical studies also verified that adopter innovativeness significantly affects perceptions and behavior toward the innovation (Agarwal and Karahanna, 2000; Lu et al., 2005). Therefore, we hypothesize:

H5a: Personal innovativeness will positively affect customer attitude toward using MMS.

H5b: Personal innovativeness will positively affect customer intention to use MMS.

Perceived Critical Mass

Perceived critical mass denotes the extent to which the user believes that most of his/her peers are using MMS. A user may develop this perception from communication channels such as interpersonal network, mass media and web sites other than direct observation of usage. For example, a person may get an impression that MMS is widely used from his/her surrounding because many advertisements were announced or his/her family/friends were talking about it. Luo and Strong (2000) pointed out that the users may develop perceived critical mass through interactions with others in the context of groupware. The findings of their study showed that perceived critical mass had a significant effect on intention to use. Therefore, we hypothesize:

H6a: Perceived critical mass will positively affect customer attitude toward using MMS.

H6b: Perceived critical mass will positively affect customer intention to use MMS.

TPB Constructs

Prior research provides evidence for the notion that subjective norms have a significant impact on intentions to adopt IT. Subjective norm is defined as the degree to which the user perceives that others approve of their usage of MMS. Hsu and Lu (2004) showed that subjective norm was a significant determinant of intention to play online games. Its significance in affecting IT adoption was also confirmed by several empirical studies (e.g., Venkatesh and Morris, 2000; Lucas and Spitler, 2000).

Moreover, while the formation of individual intention to perform behavior is influenced by subjective norm in TPB, PBC also plays another key role to determine user's behavioral intention to adopt. Here, PBC refers to user's perception of the ease of difficulty of using MMS. It is to reflect individual's possessed resources (i.e. money, time, skills) or opportunities necessary for using MMS. PBC has also empirically confirmed the significant effect on user's intention to adoption IT (Riemenschneider et al., 2003; Hsu and Kuo, 2002). Therefore, we hypothesize:

H7a: Subjective norm will positively affect customer intention to use a MMS toward using MMS.

H7b: Perceived behavioral control will positively affect customer intention to use MMS.

METHODOLOGY

Sample

Empirical data was collected by conducting an online field survey of MMS users. The questionnaire was designed based on prior related literature and placed on the home page of a web site. A program using Javascript was developed to collect and tabulate the data. In order to effectively target our subject (MMS users) and increase the response rate, we placed messages on several heavily trafficked online message boards on popular mobile-related web sites and mobile-related bulletin board systems (BBS) where active mobile phone users usually visit for two months. These survey sites were chosen because of their wide reach and tolerance of such survey advertisement messages. The message outlined the aim of this study, provided a hyperlink to the survey form, and as an incentive, offered respondents an opportunity to join a draw for prizes. The following sections describe the demographic profile as well as the general perception of our sample.

Demographic Profile

The purpose of our study is to better understand the MMS adoption behavior. Therefore, we only collected and analyzed responses of subjects with some MMS experiences. This yielded 213 usable responses. Of which, 61.5% of the respondents were male, and 38.5% were female. 55.4% respondents ranged from 16 to 25 years of age. 70% had completed college, university degree or graduated degree. Table 1 summarizes the profile of the respondents. The composition of our sample was similar to the result of the survey on the profile of populations of mobile application users as conducted by Find.org (www.find.org.tw), a popular research site in Taiwan (FIND, 2004).

The demographic profile presented that users are relatively young and generally well educated. In general, young users (age 16-25) easily accept new technology although they have not much income to spare. The data showed that most users spent less than NT$300 (The acronym NT$ is New Taiwan dollar) on monthly MMS expenditure. Nevertheless, understanding the needs and preferences of these innovative users is important and desirable because such young users will eventually become the active mobile application users as well as influential consumers in the mobile application field.

Since understanding MMS adoption is the main purpose of this study, our questionnaire was therefore designed to obtain the following information:

1. Purposes: To find out users' purposes of using MMS, users were asked why they use MMS.

2. Problems: To investigate the problems of MMS usage, users were asked what they perceived to be the main hurdles to adopt MMS

Table 2 and 3 present the results of our investigation. Table 2 shows that the top 3 reasons of using MMS are exchanging photo messages, obtaining personalized services and getting audio/video clips. As high as 93% of MMS users uses MMS to exchange digital photos. This seems to be a popular trend considering as many as half of mobile phones sold nowadays had integrated cameras (Strother and Ask, 2008). 70% of respondents indicate that they have used MMS for personalized services (e.g., screen personalization services). 55% of the respondents use MMS to enjoy video clips. 39% uses MMS for emails. Respondents also use MMS for obtaining games (30%), news (16%), GIS services (25%) as well as completing transactions (28%). Only 23.5% respondents claimed to use MMS for work related services. From the results of the data, we can conclude that respondents use MMS mainly for the enrichment of personal messages.

The respondents' perceptions of the problems for using MMS are tabulated in Table 3. The top 3 hurdles for adopting MMS are incompatibility of mobile handsets, high cost of MMS services and not enough subscribers due to network effect. The result is in tune with the general understanding of the main setbacks for slow demands for MMS services (Husson, et al., 2007). Other concerns include poor transmission quality (46%), incompatible services across mobile phone operators (46%), small screen for displaying multimedia messages (44%), and slow transmission rate (41%). Some respondents think that the alternatives such as Internet are widely available and feel no immediate need to use MMS services (38%).

Measurement Development

The questionnaires were developed from literature, and the list of items is displayed in Table 4. Many related studies developed and validated instruments for measuring TAM and TPB constructs. This includes perceived usefulness, perceived ease of use, subjective norms, perceived behavioral control, attitude and intention (Liker and Sindi, 1997; Hsu and Lu, 2004). Hence, items in the instrument were derived form existing literature and modified to suit the context of our study. Furthermore, to develop a scale to measure intrinsic motivation, personal innovativeness and perceived critical mass, we utilized measures of Venkatesh et al. (2002), Agarwal and Prasad (1998) and Luo and Strong (2000), with modifications to suit the setting of MMS. Each item was measured on a five-point Likert scale, ranging from "disagree strongly" (1) to "agree strongly" (5).

Before conducting the main survey, we performed both a pre-test and pilot test to validate the instrument. The pre-test involved ten respondents who were experienced users in MMS. Respondents were asked to comment on listed items that corresponded to the constructs, including scales wording, instrument length, and questionnaire format. Finally, to further reduce possible ambiguity, a pilot test that involved 119 self-selected respondents was performed.

RESULTS

Descriptive Statistics

Table 4 presents the means and standard deviations of the constructs. On average, users responded positively to using MMS (the averages are all greater than 3 out of 5, except for perceived critical mass). Rationally, respondents have high degree of personal innovativeness because they are relatively young and well-educated. Therefore, they usually tend to accept innovation in new market. In addition, owing to the major target users of MMS were respondents' friends and classmates, respondents would feel normative effects from interacting with target users if they were involved in mobile activities. Finally, the means of perceived critical mass was slightly lower than average. This may explain that the MMS is still in its infancy stage of mobile application life cycle and many respondents do not feel critical mass was reached.

Analytic Strategy for Assessing the Model

The proposed model was analyzed primarily using the structural equation modeling (SEM). SEM is a powerful second-generation multivariate technique for analyzing causal models involving an estimation of the two-stage model-building process, in which measurement model is tested before testing the structural model. The measurement model is estimated using confirmatory factor analysis to test whether the constructs possessed sufficient validation and reliability. Internal consistency, convergence reliability, discriminant validity were performed to ensure data validation and reliability. Subsequently, the structural model is to investigate the strength and direction of the relationship among the theoretical constructs (Joresko and Sorbom, 1996). Such analyzed technique has been widely applied by IS researchers in recent years (Hsu and Lu, 2004; Yang et al., 2005). In our study, LISREL 8.7 was used to assess the measurement and the structural model.

The Measurement Model

The test results of the measurement model are showed in Table 5. Data show that item reliabilities range from 0.86 to 0.99, which exceeds the acceptable value of 0.50. The internal consistency of the measurement model was assessed by computing the composite reliability. Consistent with the recommendations of Fornell (1982), all composite reliabilities were above the benchmark of 0.60. The average variance extracted for all constructs exceeded the threshold value of 0.5 recommended by Fornell and Larcker (1981). Since the three values of reliability were above the recommended thresholds, the scales for evaluating these constructs were deemed to exhibit adequate convergence reliability.

Table 6 shows the correlations for the constructs studied with the average variance extracted (AVE) indicated in the diagonals. The results show that the variances extracted by constructs are greater than any squared correlation among constructs (Igbaria and Iivari, 1995). This implies that constructs are empirically distinct. In summary, the measurement model test, including convergent and discriminant validity measures, is satisfactory.

The fitness measures for the measurement models are shown in Table 7. For models with good fit, it is suggested that [chi square]/df should not exceed 5 (Bentler, 1989). Bentler (1989) suggested that model fit indices should be used, and scores of 0.9 or higher on NFI, NNFI, CFI should be considered evidence of good fit. All the fitness measures are acceptable. The only exception was GIF index, which was slightly below 0.8 (Seyal, 2002). Consequently, all the measures taken in this work show that the model provides a decent fit to the data.

Tests of the Structural Model

We examined the structural equation model by testing the hypothesized relationships among various constructs, as shown in Figure 2. The results showed that attitude, subjective norm and perceived behavioral control had significant effects on the intention to use ([beta]=0.43, p<0.001; [beta]=0.11, p<0.05; [beta]=0.07, p<0.05), supporting Hypotheses 3, 7a, 7b. Together, these three paths accounted for approximately 88% of the variance on intention to use. In addition, the results showed that usefulness ([beta]=0.17, p<0.001), intrinsic motivation ([beta]=0.94, p<0.001), personal innovativeness ([beta]=0.06, p<0.01) and critical mass ([beta]=0.31, p<0.001) significantly affected attitude, providing supports for Hypotheses 1a, 4a, 5a and 6a. The model accounts for 73% of the variance in attitude. Unexpectedly, these four perceptions had no direct influence on intention. Hypotheses 1b, 4b, 5b, and 6b are not supported. However, they influenced the intention to use indirectly through attitude, as shown in Table 8.

Perceived ease of use influences both usefulness and intrinsic motivation significantly ([beta]=0.32, p<0.001; [beta]=0.46, p<0.001), supporting Hypotheses 2a and 4c. Contrary to expectations, perceived ease of use has no direct influence on attitude. Therefore, Hypothesis 2b is not supported. Nevertheless, perceived ease of use has indirect effect on intention to use MMS ([beta]=0.19, p<0.001), as shown in Table 8.

DISCUSSION

This paper proposes a behavioral model to generate better insights into understanding Internet users' adoptions on MMS. The results show a number of interesting findings.

First, intention to adopt MMS services is associated with attitudes, subjective norm and perceived behavioral control ([R.sup.2] = 0.88) with attitudinal factors as the most dominating one ([beta]=0.43). This once again confirmed the significant role attitudes play in mediating beliefs and the intention to use (Cheong and Park, 2005; Ajzen and Fishbein, 1980). Therefore, understanding what factors influencing user's attitude becomes imperative to the successful promotion of MMS.

Subjective norm is the second influential factor ([beta]=0.11) to adopter's intention to use MMS in the future. The result indicates that the importance of referential group in affecting individual's IT adoption cannot be neglected (Teo and Pok, 2003). The small coefficient of PBC ([beta]=0.07) may suggest that knowing how to use MMS is merely a basic requirement for future usage of MMS and will not contribute significantly to boost adopter's intention to use MMS.

The findings of study also identified some salient antecedents of attitudes. It includes personal innovativeness, usefulness, intrinsic motivation and critical mass. Together, they explain 73% of the variance in respondents' attitudes toward MMS. Among those antecedents, intrinsic motivation was found to be the most significant influencer on attitudes, with coefficient much higher than others ([beta]= 0.94). This finding not only coincides with most respondents' purposes of using MMS which is to enhance their social lives (see Table 2), but it also provides an important message to MMS practitioners. The most effective way to promote adopters' positive attitude toward MMS will be to enhance their perceptions of the enjoyment MMS will bring to their everyday lives. This will in turn strengthen their intentions to use MMS.

Moreover, according to the results of this study, adopters' perception of how popular MMS is (i.e., critical mass) is also an important factor in influencing their attitudes toward adopting this service ([beta]= 0.31). This finding is in tune with the significance of network externalities (or so called network effects), where "the value of a product to one user depends on how many other users there are" (Shapiro and Varian 1999, p. 13). Communication technologies such as MMS need to achieve critical mass to grow explosively. Therefore, MMS practitioners need to focus on developing strategies to publicize and popularize the functions of MMS to general public.

Although the perception of his/her personal innovativeness is also a significant factor in shaping respondent's positive attitudes toward MMS, it is not as a strong influencer ([beta]= 0.06) as others. This result is interesting but not unexpected. The samples in our study are MMS adopters and possess high scores in personal innovativeness (see Table 4). Therefore, the characteristic of personal innovativeness becomes a necessary but not significant factor in influencing his/her attitude toward MMS usage.

One thing worth noting is that although personal innovativeness, critical mass, intrinsic motivation as well as perceived usefulness are significant factors in affecting mobile phone users' attitude toward using MMS services, none of these four factors has any significant influence on the user's intention to use MMS. Instead, they exert their influence indirectly through attitudes. Thus, to increase mobile phone user's intention to use MMS, the practitioners should focus their resources in developing effective strategies to cultivate their customer's favorable feeling toward MMS.

IMPLICATIONS

Industries such as consumer products are always eager to find out what make adopters adopt a new IT. This study incorporated TAM and TPB and proposed a model to investigate into the adoption of a new IT service (i.e., MMS) in a wireless context. The results may provide following research as well as managerial implications.

Implications for Researchers

This study combined TAM and TPB in attempt to better understand the adoption of MMS. The findings showed that the proposed model exhibits a high predictive capability for explaining MMS adoption intention with attitude as the most influential factor. This once again reconfirms the role of the attitude in predicting the intention and, thus, future studies in technology acceptance should consider attitude as an important predictor of individual's behavioral intention.

Implications for MMS Practitioners

This study also provides some interesting insights that are valuable for practitioners when promoting MMS services. First, the study found intrinsic motivation as the dominating factor to influence potential adopter's attitude toward MMS. Thus, to increase adopter's willingness to use MMS, the practitioners need to create a positive view that emphasizes mostly on the entertaining capabilities which MMS can facilitate in everyday life. Strategies could include making sending/receiving MMS messages as easy as possible, publicizing the rich experiences to use MMS in life, and etc.

Second, this study confirmed the importance of network effects. Like other communications technologies, MMS needs to achieve critical mass to grow explosively. Potential adopter's perception of how popular MMS is (i.e., critical mass) will influence his/her attitude toward MMS, and thus become critical in promoting this services. MMS practitioners, therefore, need to put in more effort to make MMS known.

LIMITATIONS AND FUTURE RESEARCH

Though the results have demonstrated some interesting findings, the factors identified as possible influences on the MMS adoption are not exhaustive. Examples of such factors include compatibility (Rogers, 2003), costs (Constantinides, 2002; Cheong and Park, 2005) and image (Moore and Benbasat, 1991; Teo and Pok, 2003).

The results of this study need to be interpreted with caution due to the following limitations. First, this study was conducted online using self-report scale to measure research variables. Some of the results might have a common method bias. Second, MMS was the IT analyzed in this study; care should be exercised when generalizing these results to other settings. However, results consistent with other related researches and thus enhance confidence in the findings. Thus, the results can still provide insights into the adoption of IT of similar nature.

DOI: 10.4018/jthi.2009062502

ACKNOWLEDGMENT

This study was supported by grants from the National Science Council of the Republic of China under Contract Number NSC 93-2416H-031-005. Both authors have contributed equally to this article.

REFERENCES

Agarwal, R., & Karahanna, E. (2000). Time flies when you're having fun: cognitive absorption and beliefs about information technology usage. MIS Quarterly, 24(4), 665-694. doi:10.2307/3250951

Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition personal innovativeness in the domain of information technology. Information Systems Research, 9(2), 204-215. doi:10.1287/ isre.9.2.204

Ahonen, T., & Moore, A. (n.d.). http://communities-dominate. blogs.com/brands/2008/09/ engagementma1.html, accessed on November 15, 2008.

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. doi:10.1016/07495978(91)90020-T

Ajzen, I., & Fishbein, M. (1980). Understanding Attitude and Predicting Social Behavior. Englewood Cliffs, NJ: Prentice-Hall.

Bagozzi, R. P., & Yi, Y. (1998). On the Evaluation of Structural Equation Models. Journal of the Academy of Marketing Science, 16, 74-94. doi:10.1007/ BF02723327

Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88, 588-606. doi:10.1037/0033-2909.88.3.588

Bentler, R. M. (1989). EQS structural equations program manual (Los Angeles: BMDP Statistical Software).

Bonte, D. (2008, February 19). Is handset-based navigation the long awaited mobile data killer application? ABI Research.

Cellular.co.za. MMS has the potential to generate revenues in excess of $8.3bn by 2004.http://www. cellular.co.za/news_2004/Jan/011204-mms_has_ the_potential_to_generat.htm, Jan. 7, 2004, Accessed on November 15, 2008.

Chau, P. Y. K., & Hu, H. P. (2002). Investigating healthcare professionals' decisions to accept telemedicine technology: an empirical test of competing theories . Information & Management, 39, 297-311. doi:10.1016/S0378-7206(01)00098-2

Chen, L.-D., Gillenson, M. L., & Sherrell, D. L. (2002). Enticing online consumers: an extended technology acceptance perspective. Information & Management, 39, 705-719. doi:10.1016/S03787206(01)00127-6

Cheong, J. H., & Park, M. C. (2005). Mobile internet acceptance in Korea. Internet Research, 15(2), 125-140. doi:10.1108/10662240510590324

Constantinides, E. (2002). The 4S web-marketing mix model. Electronic Commerce Research and Applications, 1(1), 57-76. doi:10.1016/S15674223(02)00006-6

Cyr, D., Head, M., & Ivanov, A. (2006). Design aesthetics leading to m-loyalty in mobile commerce. Information & Management, 43(8), 950-963. doi:10.1016/j.im.2006.08.009

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13, 319-339. doi:10.2307/249008

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22(14), 1111-1132. doi:10.1111/j.1559-1816.1992. tb00945.x

Deci, E. L., & Ryan, R. M. (1987). Accessibility and stability of predictors in the theory of planned behaviour. Journal of Personality and Social Psychology, 63(5), 754-765.

Ebrahim, A.-H. (2004). Telecommunications in the 21st Century. ICT Week 2004 Arab Technology for Development Conference. http://www.pca.org.lb/ docs/Mr.%20Ebrahim%20Haddad%20(2).ppt, Accessed on January 2, 2009.

FIND. (2004). http://www.find.org.tw/0105/howmany/ howmany_disp.asp?id=93.

Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research. Reading, MA: Addison-Wesley.

Fornell, C. R. (1982). A second generation of multivariate analysis methods (Vol. I & II). New York: Praeger Special Studies.

Fornell, C. R., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error. JMR, Journal of Marketing Research, 18, 39-50. doi:10.2307/3151312

Gefen, D., & Straub, D. W. (1997). Gender differences in the perception and use of E-mail: An extension to the technology acceptance model. MIS Quarterly, 21(4), 389-400. doi:10.2307/249720

Hernandez, B., Jimenez, J., & Martin, M. J. (2008). Extending the technology acceptance model to include the IT decision-maker: A study of business management software. Technovation, 28(3), 112-121. doi:10.1016/j.technovation.2007.11.002

Hsu, C. L., & Lin, J. C.-C. (2008). Acceptance of blog usage: The roles of technology acceptance, social influence and knowledge sharing motivation. Information & Management, 45, 65-74. doi:10.1016/j. im.2007.11.001

Hsu, C. L., & Lu, H. P. (2004). Why do people play online games? An extended TAM with social influences and flow experience. Information & Management, 41(7), 853-868. doi:10.1016/j.im.2003.08.014

Hsu, C. L., & Lu, H. P. (2007). Consumer behavior in on-line game communities: a motivational factor perspective. Computers in Human Behavior, 23, 1642-1659. doi:10.1016/j.chb.2005.09.001

Hsu, C. L., Lu, H. P., & Hsu, H. H. (2007). Adoption of the mobile internet: an empirical study of multimedia message service (MMS). OMEGA: International Journal of Management Science, 35, 715-726. doi:10.1016/j.omega.2006.03.005

Hsu, M.-H., & Chiu, C.-M. (2004). Predicting electronic service continuance with decomposed theory of planned behaviour. Behaviour & Information Technology, 23(5), 359-373. doi:10.1080/014492 90410001669969

Hsu, M. H., & Kuo, F. Y. (2003). An Investigation of Volitional Control in Information Ethics. Behaviour & Information Technology, 22(1), 53-62.

Husson, T., Fogg, I., & Mulligan, M. (2007, May 3). Multimedia messaging, Jupiter Research.

Igbaria, M., & Iivari, J. (1995). The effects of self-efficacy on computer usage. Omega, 23(6), 587-605. doi:10.1016/0305-0483(95)00035-6

Joreskog, K. G., & Sorbom, D. (1996). LISREL 8: Users' reference guide. Chicago: Scientific Software International.

Kaasinen, E. (2005). User acceptance of location-aware mobile guides based on seven field studies. Behaviour & Information Technology, 24(1), 37-49. doi:10.1080/01449290512331319049

Kim, B. G., Park, S. C., & Lee, K. J. (2007). A structural equation modeling of the Internet acceptance in Korea. Electronic Commerce Research and Applications, 6(4), 425-432. doi:10.1016/j. elerap.2006.08.005

Kou, Y.-F., & Yu, C.-W. (2006). 3G telecommunication operators' challenges and roles: A perspective of mobile commerce value chain. Technovation, 26, 1347-1356. doi:10.1016/j.technovation. 2005.08.004

Lai, V. S., & Li, H. (2005). Technology acceptance model for internet banking: an invariance analysis. Information & Management, 42(2), 373-386. doi:10.1016/j.im.2004.01.007

Lee, M. K. O., Cheung, C. M. K., & Chen, Z. (2005). Acceptance of internet-based learning medium: the role of extrinsic and intrinsic motivation. Information & Management, 42, 1095-1104. doi:10.1016/j. im.2003.10.007

Lee, M. K. O., Cheung, C. M. K., & Chen, Z. (2007). Understanding user acceptance of multimedia messaging services: An empirical study. Journal of the American Society for Information Science and Technology, 58(13), 2066-2077. doi:10.1002/ asi.20670

Liker, J. K., & Sindi, A. A. (1997). User acceptance of expert systems: a test of the theory of reasoned action. Journal of Engineering and Technology Management, 14, 147-173. doi:10.1016/S09234748(97)00008-8

Lin, J. C.-C., & Lu, H. (2000). Toward an understanding of the behavioural intention to use a web site. International Journal of Information Management, 20, 197-208. doi:10.1016/S0268-4012(00)00005-0

Lopez-Nicola, Molina-Castillo, F. J., & Bouwman, H. (2008). An assessment of advanced mobile services acceptance: Contributions from TAM and diffusion theory models. Information and Management, 45, 359-364.

Lu, J., Yao, J. E., & Yu, C.-S. (2005). Personal innovativeness, social influences and adoption of wireless internet services via mobile technology. The Journal of Strategic Information Systems, 14, 245-268. doi:10.1016/j.jsis.2005.07.003

Luarn, P., & Lin, H.-H. (2005). Toward an understanding of the behavioral to use mobile banking. Computers in Human Behavior, 21, 873-891. doi:10.1016/j.chb.2004.03.003

Lucas, H. C., & Spitler, V. K. (2000). Implementation in a world of workstations and networks. Information & Management, 38(2), 119-128. doi:10.1016/ S0378-7206(00)00059-8

Luo, W., & Strong, D. (2000). Perceived critical mass effect on groupware acceptance. European Journal of Information Systems, 9(2), 91-103. doi:10.1057/ palgrave/ejis/3000358

Mobiletracker. Fee slow MMS adoption. January 19, 2007, http://www.mobiletracker.net/archives/ 2007/01/19/nyc-picture-messaging

Moon, J.-W., & Kim, Y.-G. (2001). Extending the TAM for a World-Wide-Web context. Information & Management, 38(4), 217-230. doi:10.1016/S03787206(00)00061-6

Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192-222. doi:10.1287/ isre.2.3.192

Pavlou, P. A., & Fygenson, M. (2006). Understanding and Predicting Electronic Commerce Adoption: An Extension of the Theory of Planned Behavior. MIS Quarterly, 30(1), 115-143.

Pikkarainenk, T., Pikkarainen, K., Karjaluoto, H., & Pahnila, S. (2004). Consumer acceptance of online banking: an extension of the technology acceptance model. Internet Research, 14(3), 224-235. doi:10.1108/10662240410542652

Pin, L., & Lin, H.-H. (2005). Toward an understanding of the behavioral intention to use mobile banking. Computers in Human Behavior, 21(6), 873-891. doi:10.1016/j.chb.2004.03.003

Riemenschneider, C. K., Harrison, D. A., & Jr, P. P. M. (2003). Understanding it adoption decisions in small business: integrating current theories. Information & Management, 40, 269-285. doi:10.1016/S0378-7206(02)00010-1

Rogers, E. M. (2003). Diffusion of Innovations (5th ed.). New York: Free Press.

Seyal, A. H., Rahman, M. N., & Rahim, M. M. (2002). Determinants of academic use of the Internet: a structural equation model. Behaviour & Information Technology, 21(1), 71-86. doi:10.1080/01449290210123354

Shapiro, C., & Varian, H. (1999). Information Rules: A Strategic Guide to the Network Economy. Harvard Business School Press.

Strother, N., & Ask, J. (2008, September 9). MMS marketing. JupiterResearch.

Teo, T. S. H., Lim, V. K. G., & Lai, R. Y. C. (1999). Intrinsic and extrinsic motivation in Internet usage. OMEGA: International Journal of Management Science, 27(1), 25-37. doi:10.1016/S03050483(98)00028-0

Teo, T. S. H., & Pok, S. H. (2003). Adoption of WAP-enabled mobile phones among Internet users. OMEGA: International Journal of Management Science, 31, 483-498. doi:10.1016/j.omega.2003.08.005

Vellerand, R. J. (1997). Toward a hierarchical model of intrinsic and extrinsic motivation. Advances in Experimental Social Psychology, 29, 271-360. doi:10.1016/S0065-2601(08)60019-2

Venkatesh, V., & Morris, M. G. (2000). Why don't men ever stop to ask for directions? gender, social Influence, and their role in technology acceptance and usage behavior. MIS Quarterly, 24(1), 115-139. doi:10.2307/3250981

Venkatesh, V., Speier, C., & Morris, M. G. (2002). User Acceptance Enablers in Individual Decision Making About Technology: Toward an Integrated Model. Decision Sciences, 33(2), 297-315. doi:10.1111/j.1540-5915.2002.tb01646.x

Verkasalo, H., & Hammainen, H. (2007). A handset-based p latform f o r measuring mobile service usage . Info, 9(1), 80-96. doi:10.1108/14636690710725085

Wang, Y. S., Lin, H. H., & Luarn, P. (2006). Predicting consumer intention to use mobile service. Information Systems Journal, 16, 157-179. doi:10.1111/j.13652575.2006.00213.x

Yang, Z., Cai, S., Zhou, Z., & Zhou, N. (2005). Development and validation of an instrument to measure user perceived service quality of information presenting web portal. Information & Management, 42, 575-589. doi:10.1016/S0378-7206(04)00073-4

Judy Chuan-Chuan Lin, Soochow University, Taiwan

Chin-Lung Hsu, Da-Yeh University, Taiwan

Table 1. Demographic profile

Frequency Percent (%)
Gender

Male 131 61.5
Female 82 38.5

Age

Under 15 1 0.5
16-25 118 55.4
26-35 85 39.9
36-45 9 4.2
Over 45 0 0

Education

High school or less 42 19.8
Some college 28 13.1
Bachelor's degree 105 49.3
Graduate degree 38 17.8

Monthly income
Under NT10000 88 41.3
NT10000-NT30000 49 23
NT30000-NT50000 56 26.3
NT50000-NT80000 15 7.0
Over NT80000 5 2.4

Experience in cell phone use

Under 1 year 11 5.2
1-2 years 10 4.7
2 years--3years 19 8.9
3 years--4 years 26 12.2
Over 4 years 47 69.0

Expenditure for cell phone use per month

Under NT100 6 2.8
NT100-NT300 29 13.6
NT300-NT500 45 21.1
NT500-NT1000 71 33.3
NT1000-NT2000 44 20.7
Over NT2000 18 8.5

Experience in MMS use
Under 3 months 74 34.7

3 months--6 months 42 19.7
6 months--1year 44 20.7
1 year--2 years 35 16.4
Over 2 years 18 8.5

Expenditure for MMS use per month

Under NT100 132 62.0
NT100-NT300 53 24.9
NT300-NT600 19 8.9
NT600-NT900 5 2.3
Over NT900 4 1.9

Target users of MMS

Friends or Classmates 160 75.1
Family 14 6.6
Work-related partner 18 8.5
Internet friends 3 1.4
Others 18 8.4
US$ 1 = NT$ 32.7 in Nov 2008.

Table 2. Purposes of using MMS

Items Numbers of respondents Percent

Exchange photo message * 199 93.4%
Obtain personalized service * 150 70.4%
Exchange audio/video messages * 117 54.9%
Send/Receive emails 83 39.0%
Play games 64 30.0%
mCommerce Transaction 60 28.2%
GIS services 54 25.4%
Work related services 50 23.5%
News 35 16.4%

* denotes top 3 rankings

Table 3. Problems of using MMS

Items Numbers of Percent
respondents

Incompatibility of mobile handset * 162 76.1%

High costs of MMS services and
handsets * 124 58.2%

Not enough subscribers * 107 50.2%

Poor transmission quality 99 46.5%

Incompatibility of service
carriers [] 98 46.0%

Small display of mobile handset [] 93 43.7%

Slow transmission rate [] 88 41.3%

Other alternatives (e.g.,
Internet) are available [] 80 37.6%

* denotes top 3 rankings

Table 4. Descriptive statistics

Means S.D.

Usefulness 3.19 .95
Ease of use 3.99 .89
Intrinsic motivation 3.89 .90
Personal innovativeness 4.10 .82
Perceived critical mass 2.68 1.07
Subjective norms 3.49 .86
Perceived behavioral control 3.65 .95
Attitude 3.72 .92
Behavioral intention 3.62 .87

Table 5. Item reliabilities, composite reliability and AVE

Average
Item Composite variance
Items reliability reliability extracted

Perceived usefulness (PU) 0.97 0.924 0.859
1. Using MMS improves my
performance in my
job/life. 0.88

2. Using MMS enhances my
effectiveness in my
job/life.

Perceived ease of use (PE) 0.95 0.935 0.879
1. Learning to use MMS is
easy for me. 0.92

2. It is easy to use MMS.

Intrinsic motivation (IM) 0.91 0.930 0.869
1. The actual process of
using MMS is pleasant. 0.95

2. I have fun using MMS.

Personal innovativeness (PI) 0.94 0.935 0.879
1. If I heard about a new 0.93
information technology, I
would look for ways to
experiment with it.

2. In general, I have the
willingness to try any new
information technology.

Subjective norm (SN) 0.93 0.907 0.831
1. People who influence my
behavior think that I
should use MMS. 0.90

2. People who are important
to me think that I should
use MMS.

Perceived critical mass (PCM) 0.95 0.938 0.884
1. Most people in my
community use MMS
frequently. 0.93

2. Most people in my class/
office use MMS frequently.

Perceived behavioral control (PB0.99 0.921 0.855
1. I would be able to use
MMS. 0.86

2. Using MMS is entirely
within my control.

Attitude (ATT) 0.97 0.966 0.935
1. I feel good about using
MMS. 0.97

2. I think positively towards
using MMS.

Intention (INT) 0.77 0.805 0.674
1. I plan to use MMS in the
future. 0.87

2. I expect my use of MMS to
continue in the future.

Table 6. Discriminant validity

PU PE IM PI PCM SN

PU 0.859
PE 0.064 0.879
IM 0.267 0.195 0.869
PI 0.050 0.145 0.156 0.879
PCM 0.226 0.091 0.142 0.018 0.884
SN 0.291 0.083 0.263 0.087 0.304 0.831
PBC 0.117 0.360 0.281 0.206 0.136 0.239
ATT 0.312 0.172 0.592 0.150 0.214 0.417
INT 0.272 0.106 0.403 0.100 0.251 0.423

PBC ATT INT
PU
PE
IM
PI
PCM
SN
PBC 0.855
ATT 0.316 0.935
INT 0.339 0.663 0.674

AVEs are shown in diagonal; All correlations are significant
at 0.05 or lower

Table 7. Overall fits of the model

Fit index Recommended Results Suggested by authors
criteria

[chi square]/df < 5 4.943 Bentler (1989)
GFI > 0.8 0.77 Seyal (2002)
NFI > 0.9 0.93 Bentler and Bonett (1980)
NNFI > 0.9 0.94 Bentler and Bonett (1980)
RMSEA < 0.05 0.00 Bagozzi and Yi (1988)

Table 8. Effects on intention to use MMS (n = 213)

Construct Direct Indirect Total
effects effects effects

Perceived ease of use 0 0.19 (***) 0.19 (***)
Perceived usefulness 0 0.07 (**) 0.07 (**)
Intrinsic motivation 0 0.40 (***) 0.40 (***)
Personal innovativeness 0 0.03 (*) 0.03 (*)
Critical mass 0.08 0.13 (***) 0.21 (***)
Subjective norm 0.11 (*) 0 0.11 (*)
Perceived behavioral
control 0.07 (*) 0 0.07 (*)
Attitude 0.43 (***) 0 0.43 (***)

(*) p < 0.05; (**)
p < 0.01; (***) p < 0.00Source Citation
Lin, Judy Chuan-Chuan, and Chin-Lung Hsu. "A multi-facet analysis of factors affecting the adoption of multimedia messaging service (MMS)." International Journal of Technology and Human Interaction 5.4 (2009): 18+. Computer Database. Web. 3 Dec. 2009. .

Gale Document Number:A209105190