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Situational Effects on the Usage Intention of Mobile Games Ting-Peng Liang and Yi-Hsuan Yeh Department of Information Management, National Sun Yat-sen University, Kaohsiung, Taiwan [email protected], [email protected]

Abstract. As value-added services on mobile devices are developing rapidly, text messaging, multi-media messaging, music, video, games, GPS navigation, RFID, and mobile TV are all accessible from a single device. Mobile games that combine mobile communication with computer games are an emerging industry. The purpose of this research is to explore what situation factors may affect the intention to play mobile game. We propose a research model to fit the nature of mobile games and conducted an online survey to examine the effect of situational factors. The model integrates constructs in TAM and TRA. The findings are as follows. First, Subjective norm affects a user’s intention in using mobile games when a user has no other task. Second, perceived playfulness affects a user’s intention to use mobile games when the user has another task. Keywords: Mobile games, Situation influences, Technology Acceptance Model, Theory of Reasoned Action.

1 Introduction As mobile devices become more and more popular, value-added mobile services develop rapidly. Text and multi-media messaging, music, video, games, GPS navigation, RFID, mobile TV, and many innovative applications are available on mobile devices. An application that has gained much attention in entertainment is mobile games that allow the user to play games on their mobile devices. A recent survey by Informa Telecoms & Media (2007) shows that the global revenue of mobile games in 2006 was more than $2.5 billion, and that will exceed $7.5 billion in 2011, tripled in five years. Many experts forecast that cell phones will play a pivotal role in meeting personal demands in a variety of environments in the near future. As a major characteristic of mobile games its availability in different places, situation is considered a critical factor in affecting consumer’s intention to play. Previous literature has indicated that consumer decisions are frequently affected by situational factors, such as particular occasions, time restrictions, or task characteristics [9]. A major advantage of mobile technology is its ability to provide users with instant, new and useful information at appropriate time and in a variety of places. If mobile technology is not able to satisfy user needs instantly, m-commerce will lose its important economic value. Therefore, when we investigate issues related to mobile commerce, situational factors must be considered. C. Weinhardt, S. Luckner, and J. Stößer (Eds.): WEB 2008, LNBIP 22, pp. 51–59, 2009. © Springer-Verlag Berlin Heidelberg 2009

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T.-P. Liang and Y.-H. Yeh

With the above rationale, this study targets at the following two questions: 1. 2.

What factors affect a user’s intention to use mobile games; and Do situational factors have moderating effects on a user’s intention to use mobile games?

The remainder of the paper is organized as follows. Section 2 reviews related literature. Section 3 describes our research framework that is based on an integration of TAM and TRA model and augmented with situational factors. Section 4 outlines research method and instruments. Section 5 describes research findings. Finally, Section 6 concludes the paper and discusses potential implications.

2 Background and Literature Review 2.1 Mobile Commerce and Mobile Games The convergence of the Internet and mobile technology has resulted in the development of electronic commerce via mobile devices—m-commerce. M-commerce can be defined as any direct or indirect transaction performed using a mobile device such as a cell phone or personal digital assistant (PDA). As a new channel of electronic commerce, m-commerce has received much attention in both academic and trade journals (e.g., [6], [8], [10], [12], and [14]). M-commerce has been viewed as the next big wave of technology evolution, and revenues created from transactions conducted through mobile devices are estimated to reach more than $554 billion in 2008 [20]. Among various value-added applications, mobile entertainment is considered to have a great potential. The introduction of 3G broadband capabilities provides a fast platform for transmitting videos, which is expected to have a considerable effect on mobile entertainment consumption. At present, mobile games, broadly defined as games played on mobile devices, are one of the primary entertainment services. Mobile games gain popularity in some countries recently and are expected to grow rapidly in coming years. The adoption of mobile services is a research direction that gains popularity recently. For example, Ngai et al. [17] used a case study to examine RFID applications in m-commerce. The results show that RFID can benefit the operators of a container depot. Mallat [15] presented a qualitative study on consumer adoption of mobile payment technology and found that the relative advantage of mobile payments is different from that specified in adoption theories. These prior studies indicate that the adoption of mobile services must have some additional factors as compared to the adoption of other technology. Since a unique feature of mobile commerce is its sensitivity to time and location, a potential direction for investigation is the factor related to the situation in which a mobile service is used. 2.2 Situational Effects Situation is viewed as a critical mediating factor in consumer behavior research. Consumers making decisions often encounter many situational factors, such as occasion, time limitation, or tasks [18]. For instance, Hansen [7] proposed three situational characteristics in the decision making process: consumption situation, purchase situation, and communication situation. Belk [1] classified situational variables into

Situational Effects on the Usage Intention of Mobile Games

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five categories: physical surroundings, social surroundings, temporal perspective, task definition, and antecedent states. Some prior research has studied the effect of situation variables. For examples, Topi et al. [21] investigated the relationship between task complexity and time availability in database query. The results show that time availability does not have any effect on task performance whilst task complexity has a strong influence on performance. Schmitt and Shultz [19] studied the influence of situational variables (such as the purchasing situation and the purchasing target) on consumer preferences toward the image product of men’s fragrances. In m-commerce research, some scholars suggest that the design of m-commerce customer interfaces should take into account the particular mobile setting. Researchers used three characteristics to describe the mobile setting: spatiality, temporality, and contextuality. Spatiality refers to users being able to carry their mobile device anywhere they go. Temporality refers to users being able to access the Internet instantly. Contextuality is concerns with the dynamic environments in which mobile devices are used [11]. Mallat [15] pointed out that the adoption of mobile payments is dynamic, depending on certain situational factors such as a lack of other payment methods or urgency.

3 Research Model and Hypothesis In this section, we present a research model that is based on the Theory of Reasoned Action (TRA) and Technology Acceptance Model (TAM), and augmented with situational factors. 3.1 TRA and TAM TRA and TAM are two models that have been used to interpret and predict the intention of technology use in the information systems area. TRA was derived from social psychology and proposed by Ajzen and Fishbein [5]. It has three major constructs: behavioral intention, attitude, and subjective norm. Attitude is affected by beliefs. Attitude, combined with subjective norm, determines behavioral intention. This theory has been applied to study many information technology applications and is certainly appropriate for investigating the intention to use mobile games. However, a shortcoming of TRA is that it does not have a clearly definition of what precedents may affect attitude. Another popular theory in predicting technology adoption is the technology acceptance model (TAM) [3]. Its two main tenets are perceived usefulness and perceived ease of use (EOU). Perceived usefulness refers to the degree to which a person believes that using a particular system would enhance his/her job performance. Perceived EOU refers to the degree to which a person believes that using a particular system would be free from effort. Perceived usefulness and perceived EOU are also influenced by external factors. TAM proposed that perceived usefulness and perceived EOU will affect the usage attitude, and further affect user behavior. Perceived EOU will enhance the perceived usefulness of technology and further influence the attitude toward using IT. TAM is usually used to measure user cognition of IT applications and behavioral attitude [3] and [4].

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A shortcoming of TAM is its assumption that user attitude is the sole factor that determines user intention and tends to ignore the influences of reference groups and other contextual factors. Therefore, it seems that the integration of these two models can provide a better predictive power. Integration of TAM with other models is not new in information systems research. For example, López-Nicolás et al. [13] combined TAM and innovations diffusion theory (IDT) models to explore the usage behavior of mobile services. Yi et al. [23] integrated TAM, IDT, and the theory of planned behavior (TPB), and measured the adoption of PDAs in medical treatment. However, most of these works did not include the situational factors in their combined models. 3.2 Perceived Playfulness for Games Another issue related to using TAM for mobile games is that the perceived usefulness may be different for mobile games than for other business applications. We replace it with perceived playfulness, which is well-supported by existing literature. For instance, Moon and Kim [16] used an extended TAM in different task contexts (entertainment-purpose vs. work-purpose). They found that perceived playfulness is an important factor for entertainment-oriented tasks and perceived usefulness is an important factor for work-oriented tasks. Van der Heijden [22] argued the hedonic nature of an information system is an important boundary condition to the validity of TAM. The result showed that perceived enjoyment and perceived EOU are stronger determinants of intention to use than perceived usefulness. Thus, perceived usefulness loses its dominant predictive value in hedonic domains. Mobile gaming is obviously a pleasure-oriented use of information technology. Therefore, TAM is revised by replacing usefulness with playfulness. We posit the following hypothesis: Hypothesis 1: Attitude toward playing mobile games is affected by the perceived playfulness and perceived ease of use of the game. 3.3 Subjective Norm Subjective norm (SN) refers to the social pressure exerted on an individual to perform or not perform a particular behavior [5]. Consequently, the social pressure causes the relevant behavior to become the individual’s normative beliefs with which he/she would comply. Motivation to comply refers to his/her wanting or being willing to comply with these beliefs. That is, a user may exhibit different motivations for complying with the opinions of relevant people on the adoption of mobile technology. In TRA, subjective norm is a major factor that can influence attitude. Hence, we posit the following hypothesis: Hypothesis 2: Intention to play mobile games is affected by perceived playfulness, perceived ease of use, the attitude toward playing the game, and the subjective norm of the user with respect to playing the game. 3.4 Situational Factors As many decisions may be affected by certain situational factors, we augment our research model with a major situational factor related to mobile games: it is a


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psychological factor of whether the user has another task on hand. This situational factor makes two different usage situations for examining their effects. The following hypotheses can be posited: Hypothesis 3: The combined TRA and TAM model is moderated by situational factors. Hypothesis 3-1: Different situations will affect the relationship between perceived playfulness and intention to use a mobile game. Hypothesis 3-2: Different situations will affect the relationship between perceived ease of use and intention to use a mobile game. Hypothesis 3-3: Different situations will affect the relationship between attitude toward a mobile game and intention to use. Hypothesis 4: Different situations will affect the relationship between the SN and intention to use a mobile game.

．

Situational Effect Task Playfulness

Attitude

Easy of use

Intention to use

Subjective Norm Fig. 1. Theoretical Framework

Based on the above description, we can put together a research model based on the integration of TRA and TAM, with the augmentation of situational factors. Figure 1 shows the schematic illustration of the model.

4 Instrument Development and Research Methodology 4.1 Instrument Development A cross-sectional survey was conducted to evaluate the proposed research model. Validated items were used to measure perceived playfulness [2], perceived EOU [3],

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attitude [3], SN [5], and intention to use [3]. We chose task as our situational variable (have task vs. no task) for study. Likert scales (ranging from 1 to 5) with anchors ranging from “strongly disagree” to “strongly agree” were used for all questions. After pretesting the measures, these items were modified to fit the mobile game context. 4.2 Measure and Data Collection Voluntary mobile game users were recruited to participate in the online survey. Each person was randomly assigned three situations when completing the questionnaire. There were three versions of the questionnaire for different situations. Each version had 78 respondents. There were 234 usable responses and the overall response rate was 95.12%. Respondents ranged from 20 to 30 years of age. The ratio of male to female was 53.8% to 46.2%, and student to non-student was 46.2% to 53.8%. Of all the respondents, 89.8% have at least a college degree.

5 Analysis of Results 5.1 Measurement Model Partial Least Squares (PLS) were used to analyze the measurement model. We proceeded to evaluate the psychometric properties of the measurement model in terms of reliability, convergent validity, and discriminant validity. Reliability and convergent validity of the factors were estimated by composite reliability and average variance extracted (AVE). The composite reliability value must be above 0.70, and the AVE value must be above 0.05. Discriminant validity verifies whether the squared correlation between a pair of latent variables is less than the average variable extracted for each variable. All constructs in all models satisfied the criteria, thus requiring no changes to the constructs (shown in Table 1). Table 1. Reliability, Convergent Validity, and Discriminant Validity AVE

Composite Reliability

R Square

Cronbachs Alpha

Attitude

0.760517

0.904966

0.381940

0.842805

EOU

0.772038

0.910202

0.851067

Intension

0.854324

0.946201

0.648250

0.914610

Playfulness

0.581126

0.917012

0.142316

0.896536

SN

0.817764

0.930850

0.889096

5.2 Structural Model Figure 2 presents a graphical depiction of the PLS results. This is the original model without any situational influence. Most of paths are significant with the model accounting for 14.2% of the variance in playfulness, 38.2% of the variance in attitude, and 64.8% of the variance in intention. The results reported that perceived EOU has significant influence on perceived playfulness and intention to use mobile games, but


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Playfulness R2 = 0.142

0.661*** (7.689)

0.020 (0.200)

Attitude

0.377** (2.672)

R2 = 0.382 0.040 (0.484)

0.718*** (8.029)

Intention R2 = 0.648

0.173* (1.996)

0.023 (0.329)

EOU Path coefficients with t -values in parentheses. * Significant at .05 level; * *significant at .01 level

SN

Fig. 2. Path analysis of mobile games

not on users’ attitude toward using mobile games; Perceived playfulness has a strong significant effect on users’ attitude toward using mobile games, but not on the intention of mobile games; SN has no significant effects on the intention to use mobile games. Therefore, hypotheses 1 and 2 are partially supported. That is, TRA and TAM can help predict the intention to play mobile games. 5.3 Situational Factor Given the base model being reasonably supported, we further analyze the moderating effect of situational factor by analyzing the model in two different situations. We found that different situations have moderating effects in our model. SN has a positive significant effect on intention while a user has no task, but does not have any effect on intention while a user has a task. It implies that a user’s perception of peer attitude has a different effect on intention in different situations. Perceived playfulness has a positive significant effect on intention when a user has another task; on the contrary, it has no effect on intention when a user has no other task. It seems that when a user is busy, he/she may be distracted by a mobile game because, for example, there is a need to lift his/her mood or to relax for a while (Shown in Figure 3 and 4). Playfulness R2 = 0.187

0.786*** (11.630 )

0.097 (0.762)

Attitude

0.432** (2.889)

R2 = 0.543 -0.139 (1.730)

0.574*** (5.182)

0.109 (1.245)

Intention R2 = 0.578 0.150* (2.226 )

EOU Path coefficients with t -values in parentheses. *Significant at .05 level; * *significant at .01 level

Fig. 3. No Task

SN

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Playfulness R2 = 0.095

0.698*** (9.166)

0.139* (2.055 )

Attitude

0.308** (2.945)

R2 = 0.471 -0.042 (0.613)

0.836*** (13.852 )

-0.061 (1.746)

Intention R2 = 0.814

-0.047 (0.846)

EOU Path coefficients with t -values in parentheses. *Significant at .05 level; * *significant at .01 level

SN

Fig. 4. Having a Task

6 Conclusion This paper aims to shed light on consumer adoption of mobile games using an integrated TRA and TAM model. In the theoretical model, we examined the moderating effects of alternative situations on the relationship between playfulness and intention, attitude and intention, EOU and intention, and SN and intention. The findings are as follows. First, SN affects a user’s intention in using mobile games only when a user has no other task. A user’s perception of peer attitude has a different effect on intention in different situations. Secondly, perceived playfulness affects a user’s intention to use mobile games when he/she has a task. When a user is in that situation, the playfulness of mobile games can stimulate a user’s intention to use mobile games. The contributions of this study are as follows. This paper has proposed a hybrid intension model to explore the moderating effects of certain situations on a user’s intention to use mobile games. This is the first study that considers situational influences in the hedonic information systems context. The findings provide critical information for marketers and advertisers. Situational factors should be taken into consideration when new mobile services are marketed. One potential limitation of this research surrounds the size of the sample collected. Also, the convenient sampling used to solicit respondents for the survey may not be perfectly random. Another measurement limitation is that only one contextual effect was investigated in this study. Other situational factors that may affect users’ intentions may be explored in the future.

References 1. Belk, R.W.: An Exploratory Assessment of Situational Effects in Buyer Behavior. J. Marketing Res. 11, 156–163 (1974) 2. Csikzentmihalyi, M.: Flow, the Psychology of Optimal Experience. Harper & Row, New York (1990) 3. Davis, F.D.: Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quart. 13(3), 319–340 (1989)


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4. Davis, F.D., Bagozzi, R.P., Warshaw, P.R.: User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Manage. Sci. 35(8), 982–1003 (1989) 5. Fishbein, M., Ajzen, I.: Belief, Attitude, Intentions and Behavior: An Introduction to Theory and Research. Addison-Wesley, Reading (1975) 6. Gebauer, J., Shaw, M.J.: Success Factors and Impacts of Mobile Business Applications: Results from A Mobile E-Procurement Study. Int. J. Electron. Comm. 8(3), 19–42 (2004) 7. Hansen, F.: Consumer Choice Behavior: A Cognitive Theory. The Free Press, NY (1972) 8. Herzberg, A.: Payments and Banking with Mobile Personal Devices. Commun. ACM 46(5), 53–58 (2003) 9. Jaeger, S.R., Rose, J.M.: Stated Choice Experimentation, Contextual Influences and Food Choice: A Case Study. Food Qual. Prefer. 19, 539–564 (2008) 10. Lee, Y.E., Benbasat, I.: Interface Design for Mobile Commerce. Commun. ACM 46(12), 49–52 (2003) 11. Lee, Y.E., Benbasat, I.: A Framework for the Study of Customer Interface Design for Mobile Commerce. Int. J. Electron. Comm. 8(3), 79–102 (2004) 12. Liang, T.P., Wei, C.P.: Introduction to the Special Issue: A Framework for Mobile Commerce Applications. Int. J. Electron. Comm. 8(3), 7–14 (2004) 13. Lopez-Nicolas, C., Molina-Castillo, F.J., Bouwman, H.: An Assessment of Advanced Mobile Services Acceptance: Contributions from TAM and Diffusion Theory Models. I&M (in press, 2008) 14. Maamar, Z.: Virtual extension: Commerce, E-Commerce, and M-Commerce: What Comes Next? Commun. ACM 46(12), 251–257 (2003) 15. Mallat, N.: Exploring Consumer Adoption of Mobile Payments-A Qualitative Study. J. Strategic Inf. Syst. 16, 413–432 (2007) 16. Moon, J.W., Kim, Y.G.: Extending the TAM for a World-Wide-Web Context. I&M 38, 217–230 (2001) 17. Ngai, E.W.T., Cheng, T.C.E., Au, S., Lai, K.H.: Mobile Commerce Integrated with RFID Technology in a Container Depot. Decis. Support Syst. 43, 62–76 (2007) 18. Park, C.W., Iyer, E.S., Smith, D.C.: The Effects of Situational Factors on In-Store Grocery Shopping Behavior: The Role of Store Environment and Time Available for Shopping. J. Consum. Res. 15(4), 422–433 (1989) 19. Schmitt, B.H., Shultz, C.J.: Situational Effects on Brand Preferences for Image Products. Psychol. Market. 12(5), 433–446 (1995) 20. Smith, A.D.: Exploring M-Commerce In Terms Of Viability, Growth and Challenges. Int. J. Mobile Commun. 4(6), 682–703 (2006) 21. Topi, H., Valacich, J.S., Hoffer, J.A.: The Effects of Task Complexity and Time Availability Limitations on Human Performance in Database Query Tasks. Int. J. Hum.-Comput. St. 62, 349–379 (2005) 22. Van der Heijden, H.: User Acceptance of Hedonic Information Systems. MIS Quart. 28(4), 695–704 (2004) 23. Yi, M.Y., Jackson, J.D., Park, J.S., Probst, J.C.: Understanding Information Technology Acceptance by Individual Professionals-Toward An Integrative View. I&M 43, 350–363 (2006)