Understanding the Adoption of Mobile Data Services: Differences among Mobile Portal and Mobile Internet Users

Dimitrios C. Karaiskosa, Panos Kourouthanassisb, Panagiota Lantzounic, George M. Giaglisd

Department of Management Science and Technology Athens University of Economics and Business

Athens, Greece e-mail: {dimkara, pkourb, giaglisd}@aueb.gr,

platz@dmst.aueb.grc

Christos K. Georgiadis

Department of Applied Informatics University of Macedonia

Thessaloniki, Greece e-mail: geor@uom. gr

Abstract—This study attempts to investigate the potential differences among individual adoption patterns between m-portal and m-internet services usage. A theoretical model that reflects a set of unique predominant factors of IS usage was developed. The model was empirically tested using data collected on mobile data services adoption. The results indicate that there are differences among the adoption behavior of mobile portal users and mobile internet users. Specifically, mobile internet use is likely to be driven by utilitarian expectations and overall usefulness of accessing the web through this alternative medium. On the contrary, mobile portals use is likely to be dependent on the users’ current state of innovativeness and not being affected by the actual utility of the mobile data service. The paper concludes with theoretical and practical implications of our findings.

Keywords: mobile internet, mobile portals, acceptance, theory of planned behavior

I. INTRODUCTION Mobile Data Services (MDS) have been recognized

by researchers in the area (refs) as all non-voice services afforded through mobile networks, except for interpersonal SMS exchanges, that the end users can employ whilst mobile. While in their origination MDS were exclusively provided by the MNOs (through their portals), the last years we observe the emergence of mobile internet services. The proliferation of suitable handsets (providing better user experience) and the deployment of high-speed mobile networks (from 3G networks to 3.5G) motivate mobile phone users to an increased utilization of internet through their mobile phone. As penetration of mobile internet is increasing in the European countries and US (penetration rate is above 10%), it is expected that more than one third of the European mobile subscribers will be using mobile internet services by the end of 2013 [1].

Nevertheless, adoption studies in the area treat MDS as a homogeneous set of services with unvarying value for the mobile user and with uniform provision plan regarding the service provider. This study claims

that MDS need to be separated, and studied as such, in two distinctive categories:

• m-portal services, which include services accessed by the mobile users through the MNOs portals (e.g. the iMode portal)

• m-internet services, which include services accessed by the mobile users through internet

The common basis for both categories is that the users utilize their mobile phone to access the services through mobile network. The important difference is whether the service provision is mediated by the MNO (m-portal services) or it is not (m-internet services) and the mobile phone user directly accesses the services via internet.

Based on this MDS distinction, the objective of the current study is to investigate the potential differences among individual adoption patterns between m-portal and m-internet services usage. In order to do so, this paper presents the construction of a model that arrays a set of factors relevant to understanding the underlying rationale for the intention to use (or not use) MDS (section 2). The model is tested against a set of empirical data collected through the means of a large-scale survey (N=224) conducted in Greece in 2009 (Section 3). Results provide rigorous explanations of MDS usage intention, along with useful information regarding the differences that the two MDS categories impose (section 4).

II. THEORETICAL SETTING Our theoretical setting has been based on theories

that predict human behavior, the Theory of Planned Behavior (TPB) [2, 3], and the Triandis theory of human behavior [4]. Both theories posit that human behavior is directly predicted by behavioral intention which in turn is influenced by three kinds of beliefs: a) behavioral beliefs about the consequences (positive or not) of the behavior, b) normative beliefs about the normative expectations of others and c) control beliefs

2009 Eighth International Conference on Mobile Business

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DOI 10.1109/ICMB.2009.10

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about the presence of factors that may facilitate or impede performance of the behavior.

A. Behavioral Beliefs

Behavioral beliefs in our study consider the individual’s perceived consequences and affect evoked by using MDS.

Perceived consequences illustrate “the certainty that a behavior is having some consequences with value to the performer” [5]. IS research has heavily relied on perceived consequences to predict actual use under multiple notations. One of them is performance expectancy [6] which is used in our study along with the items of time and place flexibility that better define MDS’s utilitarian value. Based on the theories aforementioned we hypothesize that:

H1: Perceived Consequences will have a positive effect on the intention to use MDS moderated by gender and MDS type.

On the other hand, affect is the direct emotional

response to the thought of a behavior and is referred to as “the feelings of joy, elation, or pleasure, or depression, disgust, displeasure, or hate associated by an individual with a particular act” [5]. Affect represents the individual's emotional response to the thoughts of performing a given behavior, in our case using MDS. In the case of MDS, the combination of mobility and entertainment appear intuitively interesting, especially in situations when relative wired services cannot be accessed. Furthermore, MDS are reported as interesting and stimulating, a fact that intrinsically motivates people to engage with them [7]. Hence, people tend to evaluate MDS in an affective manner when MDS involve fun and enjoyment characteristics. Consequently, we formulate the hypothesis that:

H2: Affect will have a positive effect on the intention to use MDS moderated by gender and MDS type.

B. Normative Beliefs

Normative beliefs in our study try to capture the congruency between social norms (social influence) and individual beliefs (image and personal innovativeness) and how the human part of an individual’s environment influences one in using MDS.

Social influence is defined “as the extent to which users believe that “important others” would approve or disapprove of their performing a given behavior” [3]. Social influence tries to capture how people (friends, family, and colleagues) who are considered important by an individual, have an effect on him towards using MDS. Hong et Tam [8] empirically

proved the strong effect of social pressure on adopting MDS, as MDS is considered a form of public consumption with social projections for the individual. Again following the theories of TPB and Triandis the accommodated hypothesis is:

H3: Social influence will have a positive effect on the intention to use MDS moderated by gender and MDS type.

Image, the second normative belief studied, is

defined as “the degree to which use of an innovation is perceived to enhance one’s image or status in one’s social system” [9]. MDS can serve as a means for achieving social differentiation and uniqueness and offer the user a favorable social outcome that will elevate his or her standing within the (social) group. Consequently, we formulate the hypothesis that:

H4: Image will positively affect adoption intention moderated by gender and MDS type.

Furthermore, personal innovativeness is defined “as the willingness of an individual to try out any new information technology” [10]. Rogers claims in his study [11] that people can be characterized as innovators and non-innovators. Innovators tend to place less reliance on the subjective evaluation of other members of their social system about the expected consequences of adopting an innovation, and thus they are considered as early adopters. As MDS is an innovative set of services, personal innovativeness is expected to serve as direct determinant for adoption decision [1]. Consequently, we hypothesize that:

H5: Personal innovativeness will positively affect adoption intention moderated by gender and MDS type. C. Control Beliefs

Control beliefs in our study group together “the objective factors, out there in the environment, that several judges or observers can agree make an act easy or hard to do” [5].

Under the Triandis perspective, behavioural intention and IT usage would not be expected to occur as less time and money are available and as technical compatibility decreases [12]. Adopting the concept of facilitating conditions in the MDS domain, we align to the technology and cost factors. In particular, lack of confusing billing schemes and low flat rate costs will diminish the financial barriers that act as an important determinant of MDS adoption. Thus the perceived monetary value of MDS will be a potential predictor of their usage adoption. According to Triandis we hypothesize that:

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H6: Perceived monetary value will positively affect

adoption intention moderated by gender and MDS type.

Moreover, technology barriers, encompassing lack of coverage, low speed connections, and device deficiencies in usability erode users’ adoption tendencies [13]. Thus we hypothesize that:

H7: Technology barriers will negatively affect adoption intention moderated by gender and MDS type. D. Intention and Moderating Variables

Intention in our study considers the individual’s intention to use MDS in the near future. We employed the construct proposed in the study of Venkatesh et. al [6]. As aforementioned, intention has seven precedents, namely perceived consequences, affect, social influence, image, personal innovativeness, perceived monetary value and technology barriers. Furthermore, we hypothesize that gender and MDS category will play a moderate role on the influential strength of the independent variables to intention to adopt MDS. Our theoretical setting is depicted in Figure 1.

III. RESEARCH METHODOLOGY AND RESULTS The instrument used to measure the behavioral

intention of MDS usage was based on previous IS adoption studies and was modified in terms of phrasing to suit the context of MDS (Table 2 concentrates the items used for each factor). Prior to conducting the survey and in order to cater for the content validity of the instrument, a pre-test procedure was performed with 14 selected participants, who were also interviewed through phone. The participants were selected in order to comprise a representative sample of the given quotas.

A national phone-based survey following stratified sampling was executed to select the survey participants. Stratification took into account the distribution of population and gender among Greek regions. The survey lasted for three weeks during January 2009. A total of 224 valid responses were obtained resulting to a response rate of 35%.

We used SPSS 15.0 to perform a confirmatory factor analysis and to test the reliability of the model’s constructs. Initially, single indices were developed for the constructs under examination, by averaging responses obtained from the corresponding scales (see Table II). Reliability was measured via Cronbach's alpha. Results depicted in Table 2 show that all constructs were reliable, as the reliability estimate for each construct ranged from 0.742 to 0.951, exceeding the 0.70 acceptable threshold values.

Figure 1 . Research Model

Table 1 summarizes the demographics of the

sample.

Table 1. Sample Demographics Demographics MDS User

Group (%) N=224

GenderMale 56.7 Female 43.3 Age 15-24 44.6 25-34 28.1 35-44 19.6 45+ 7.6 Education Level Elementary School 1.8 Middle/ High School 45.1 College/ Bachelor 45.6 Master (or higher) 7.6

Three stepwise, forward, multiple regression

analyses were executed to test the model for the different MDS user groups (only mobile portal users, only mobile internet users, either mobile portal or mobile internet users). Likewise, two stepwise, forward, regression analyses were executed to test the model for behavioral differences among males and females.

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Table 2. Construct Reliability

Constructs Measurements Adopted from

Cronbach's alpha

Perceived Consequences (PC)

Using MDS enables me to accomplish tasks more quickly

[6] .901

Using MDS improves my performance in accomplishing tasks Using MDS increases my productivity in accomplishing tasks Using MDS enhances my effectiveness on accomplishing tasks Using MDS provides me flexibility to accomplish tasks anywhere Using MDS makes it easier to accomplish tasks anytime

Affect (AF)

Using MDS is enjoyable

[5] .856 Using MDS is exciting Using MDS is interesting Using MDS is pleasant

Social Influence (SI)

People who are important to me would use MDS [8] .885 People who influence my behavior would use MDS

People whose opinions I value would use MDS

Personal Innovativeness (PI)

If I heard about a new technology, I would look for ways to experiment with it [10] .842 Among my peers, I am usually the first to explore new technologies. I like to experiment with new technologies.

Image (IMG) People consider that using MDS improves the user's social status

[14] .861 People consider MDS users to have more prestige People who use the system are considered to have a high social profile

Perceived Monetary Value

(PMV)

MDS costs are reasonable [15] .742

MDS offer good value for money

Technology Barriers (TB)

MDS access speeds are low created for the study

.743 Mobile phone screen sizes are small for MDS usage It takes a lot of time to download MDS data My phone screen makes it hard to use MDS

Intention (INT) I intend to use Mobile Data Services in the near future

[6] .951 It is likely that I will use Mobile Data Services in the near future I expect to use Mobile Data Services in the near future

Table 3 summarizes the results of the regression

analyses. Most of the hypothesized relationships are validated. The strong effects of personal innovativeness and perceived monetary value on behavioral intention to use MDS for the entire sample and sub-samples (mobile portal users, mobile internet users, males, and females) are noticeable. On the contrary, image was not perceived as a determining factor of MDS usage in any of the regression models. The differences in the regression models’ R2 among the diverse gender and MDS type, as well as the differences in the models’ prediction factors, support our assumption of moderating effect. The model explains 34.6% of the variance in adoption intention for the entire sample.

Table 3. Regression Analysis Results

Behavioral Intention (All, N=224) R2 = 0.346

Variables Beta Hypothesis PC 0.093 Not Supported AF 0.096 Not Supported

IMG 0.072 Not Supported SI 0.158 Supported PI 0.431 Supported TB - 0.156 Supported PMV 0.293 Supported Behavioral Intention (Mobile Portal Users, N=178) R2 = 0.342

Variables Beta Significance PC 0.028 Not Supported AF 0.055 Not Supported IMG 0.078 Not Supported SI 0.159 Supported PI 0.401 Supported TB - 0.215 Supported PMV 0.254 Supported Behavioral Intention (Mobile Internet Users, N=116) R2 = 0.493

Variables Beta Significance PC 0.359 Supported AF 0.205 Supported IMG 0.112 Not Supported SI 0.079 Not Supported

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PI 0.264 Supported TB - 0.068 Not Supported PMV 0.221 Supported Behavioral Intention (Males, N=127) R2 = 0.353

Variables Beta Significance PC 0.283 Supported AF 0.058 Not Supported IMG 0.039 Not Supported SI 0.047 Not Supported PI 0.369 Supported TB - 0.020 Not Supported PMV 0.188 Supported Behavioral Intention (Females, N=97) R2 = 0.457

Variables Beta Significance PC - 0.112 Not Supported AF 0.009 Not Supported IMG 0.061 Not Supported SI 0.219 Supported PI 0.405 Supported TB - 0.318 Supported PMV 0.319 Supported

IV. DISCUSSION The current study investigated the adoption of

MDS. We developed and empirically tested an adoption model that incorporates seven determinant dimensions that have proved to be strong predictors of desktop-based and mobile computing systems throughout the IS literature. We used gender and MDS type as moderators to explore whether they influence the balance among the predominant factors of intention of MDS use. Our intention was to investigate whether there are statistically significant differences among the behavioral usage intention of mobile portal users compared to mobile internet ones.

One striking finding is that perceived consequences and affect do not seem to have a strong statistical power over the prediction of MDS usage. This finding contradicts the results of previous adoption studies (e.g. [8]), which showcased a strong correlation among perceived usefulness/ perceived affect and intention of use. At the same time, the degree of personal innovativeness (the degree to which a particular person is eager to experiment with new technologies) is the most important factor that affects MDS usage along with service monetary value. This generates some interesting managerial implications for the commercialization and marketing of MDS, especially if we further explore differences in adoption patterns between m-portal users and m-internet users.

M-portal users seem to be influenced neither by the perceived usefulness of the services nor the extent to which they enjoy using them. Thus, the only types of users attracted by this particular business model are those that are innovative in character, relatively

sensitive to cost and to technology barriers. These results first indicate that the access medium (mobile device), and its consequent effect on usability and ease of use, still strongly affects the behavioral intention of MDS usage. This factor might have a diminishing effect over the future if we take into account the eventual widespread launch of more user friendly mobile devices such as the iPhone and the further expansion of 3G networks. Moreover, m-portal services use seems to be primarily driven by how innovative a particular individual is in terms of technology usage. This finding might imply that as in the innovativeness of the m-portal services fades off, their user base might be eventually diminished in the future. Consequently, this raises a major challenge for m-portal providers, who need to raise the perceived value of the portals’ services portfolio. The aforementioned finding is further supported by the fact that social influence was also found to be a significant predictor of mobile portal usage indicating that mobile portals might be considered as a trend that is mostly used through word-of-mouth among their user base possibly as a factor of personal projection. Finally, perceived monetary value represents a significant factor influencing the usage intention of MDS suggesting that mobile portal providers should continue to give additional emphasis on the formulation of appropriate pricing strategies tailored to the unique requirements of their customer base.

On the other hand, m-internet users are mostly driven in their intention to use such services either by their enjoyment and/or their usefulness. Moreover, monetary value and personal innovativeness are once again important predictors of behavioral usage intention. These findings should be interpreted under the perspective of current mobile internet usage which may be summarized into mainly e-mail and basic web browsing. E-mail, browsing and remote access to corporate data are primarily work-related activities therefore perceived usefulness is expected to play a major role in the service’s usage. At the same time, browsing, even in a basic form because of the inherent limitations of contemporary mobile phone web browsers, may stimulate emotions related to enjoyment. It is also noteworthy that technology barriers seem not to affect behavioral intention of m-internet use, a finding that contradicts with mobile portal usage behavior. We interpret this contradiction under the prism of context-of-use in each MDS type. M-portals use is more effort intensive compared to simple web or e-mail browsing because it involves many searches and clicks among the mobile portals’ decks. At the same time, because navigation in the different services of the m-portal is charged, any usability problems imposed by the mobile device are

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likely to generate frustration and, hence, minimize intended use.

Differences on MDS use among gender have also been explored. On the one hand, males are primarily driven to use MDS by utilitarian objectives and cost efficiency. Moreover, personal innovativeness remains a strong predictor of MDS use. On the other hand, females are more likely to use MDS if others in their personal or work life are using or have a positive opinion on MDS. Furthermore, perceived ease of use (in the form of technology barriers) positively influences MDS usage. As in the case of males, females also relate their behavioral intention to use MDS based on cost efficiency and degree of personal innovativeness. These results suggest that mobile network operators might need to approach males and females following a different marketing strategy that emphasizes on service usefulness (for males) and differentiation through MDS use (for females). Especially for females, the strong direct impact of social influence on intention might indicate that mobile service providers could brand MDS to niche target groups (e.g. young, high income women) and receive the benefits from group affiliation.

V. CONCLUSIONS This research presented a theoretical model that

measures behavioral intention of MDS usage. The model comprised of seven predictors out of which four were evaluated as statistically significant. Building on previous adoption studies that considered MDS uniformly, the main contribution of this research refers to the exploration of differences among the adoption behavior between m-portal users and m-internet users. The results showcase that there are indeed differences among the adoption behaviors between the two user groups, which are not evinced in the generic assessment of the model (incorporating both user groups). We consider that this knowledge provides a first insight to MDS providers about the factors that drive MDS use for their user base and therefore, provide an indication that the two user groups should be addressed differently especially considering activities related to commercialization and marketing.

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[4] H. C. Triandis, Interpersonal Behavior. Monterey, 1977.

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[10] R. Agarwal and J. Prasad, "A Conceptual and Operational Definition of Personal Innovativeness in the Domain of Information Technology," Information Systems Research, vol. 9, pp. 204-215, 1998.

[11] E. M. Rogers, Diffusion of Innovations,4th edition. New York: Free Press, 1995.

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[13] S. Sarker and J. D. Wells, "Understanding mobile handheld device use and adoption," Communications of the ACM, vol. 46, pp. 35-40, 2003.

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[15] M. Pura, "Linking perceived value and loyalty in location-based mobile services," Service Innovation Management, vol. 15, pp. 509-538, 2005.

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