The Empirical Analysis of Free-sample Promotion Effect in Mobile Games

Transcription

1 Association for Information Systems AIS Electronic Library (AISeL) PACIS 2017 Proceedings Pacific Asia Conference on Information Systems (PACIS) Summer The Empirical Analysis of Free-sample Promotion Effect in Mobile Games Moonkyoung Jang Seoul National University, Follow this and additional works at: Recommended Citation Jang, Moonkyoung, "The Empirical Analysis of Free-sample Promotion Effect in Mobile Games" (2017). PACIS 2017 Proceedings This material is brought to you by the Pacific Asia Conference on Information Systems (PACIS) at AIS Electronic Library (AISeL). It has been accepted for inclusion in PACIS 2017 Proceedings by an authorized administrator of AIS Electronic Library (AISeL). For more information, please contact

2 The Empirical Analysis of Free-sample Promotion Effect in Mobile Games Indicate Submission Type: Doctoral Consortium Moonkyoung Jang Department of Management Information Systems College of Business Administration Seoul National University 1 Gwanak-Ro, Gwanak-Gu Seoul, KOREA Phone: (82) moonk14@snu.ac.kr Supervisor: Byungjoon Yoo February 2017 Abstract The objective of this research is to understand the factors related to in-app purchase in freemium mobile games. The primary research question is whether free-sample items have a positive or negative effect on virtual item selling. The empirical dataset used for this research is a very large user-level gameplay log dataset collected from a Korean mobile game company. To test the hypotheses, negative binomial regression is carried out since item purchase as our dependent variable is over-dispersed count variable. Generalized method of moments model is also employed to understand the recursive relationship with purchasing and playing behaviors. Preliminary results show that the impact of freesample promotion is positive on the item purchase in the same period, but its impact becomes negative in the subsequent period. This study also examines the impact of habitual and goal-oriented behaviors on the in-app purchase and finds that users are more likely to buy virtual items when they play more frequently and when they reach a higher stage. Finally, expected contribution and the major challenge of this research are discussed. Keywords: In-app purchases, free item promotion, mobile games, casual games, freemium model, negative binomial regression, generalized method of moments Introduction The growth of the mobile game market has been remarkable. Many mobile game companies continue to ride the growing wave of the freemium pricing strategy for their monetizing methods, and they Twenty First Pacific Asia Conference on Information Systems, Langkawi

3 depend on sales of virtual items as their main source of revenue (Hanner et al. 2015; Kimppa et al. 2016). Freemium (Free & Premium) means that users play a game for free and can then obtain additional functionality by paying extra money. Since this model offers users free entry into a game, the game providers can easily obtain many new users. However, the freemium model for mobile games does not guarantee to gain a profit because only a few users spend money on in-app purchases, normally far below 5% of users for most social casual games (Runge et al. 2014). In this regard, it is important to figure out the factors that affect users in-app purchase in mobile games. Therefore, this study specifically investigates the following research question: what factors affect in-app purchasing in mobile games? More specifically, this study focuses the effect of free-sample items on virtual item selling. To find the answer, the aim of this study is to investigate purchasing behaviors for virtual items in a mobile game with the freemium model by analyzing a huge user-level log dataset from three viewpoints: goal-oriented, habitual and cherry-picking behaviors. Literature Review and Research Hypothesis According to goal-setting theory, people tend to be highly motivated by elaborated goals that are specific, difficult but achievable (Fishbein and Ajzen 1977; Khansa et al. 2015). In the game case, users normally try to accomplish explicit and attractive goals such as going to higher stages, gaining more score or getting new items (Choi and Kim 2004). Game developers make multi-tiered goal structures and offer many types of virtual items to entice users to keep achieving their goals by effectively reducing users boredom (Cotton 2011; Zarnekow 2015). Accordingly, the goal-oriented user can be described as a person who wants to achieve a higher stage within a short time regardless of paid-item use. This study considers the level of stages presents a user s goal-oriented propensity, so the first hypothesis is defined as follows. H1-1: The goal-oriented propensity of users has a positive impact on in-app purchases in mobile games. Also, habitual use has a positive impact on purchasing behaviors in e-commerce website as well as online games (Guo and Barnes 2011; Venkatesh and Agarwal 2006). In this regards, we assume that the users, who frequently login the game, have a higher probability of buying virtual items because they are exposed more often to item lists and have more chance to buy virtual items. Therefore, this study investigates the relationship between habitual game usage and in-app purchases. The frequency of past behavior is strongly associated with habit and is used as a proxy when measuring the strength of habits (Hartmann et al. 2012; Neal et al. 2006). Thus, to figure out the relationship between habitual game usage and in-app purchases, this study measures the number of game play as the strength of habitual game usage. Therefore, the second hypothesis is postulated as follows. H2-1: The stronger strength of gameplay habits has a positive impact on in-app purchases in mobile games. Furthermore, this study also examines the effect of free items on in-app purchases. Game developers normally offer free items as one of the promotions. For example, they give to newcomers welcome-free items for users easy adaptation or special items to loyal users as a reward. The effect of free samples on product sales is a blended which is mixed from acceleration, cannibalization, and expansion (Bawa and Shoemaker 2004). In the freemium games, users can play the game for free with some functional restriction. Some users intentionally do not pay extra money but enjoy the game by only consuming the free items because they consider themselves as economical users. This study regards these kinds of users as cherry pickers who receive free virtual items only and do not buy virtual items. The term of cherry pickers is used for describing people who only pick the best part of products and ignore the others (McAlister et al. 2009). In this sense, game developers need to figure out the true effect of free items whether offering free items cannibalize or boost item selling. Accordingly, the third hypothesis is formulated as follows. H3-1: The effect of free samples has a negative impact on in-app purchases in mobile games. Furthermore, this study also looks the effect of free item in the subsequent period since the effect of the free item may be different depending the time. For example, users who receive free items and they don t need to buy extra items in the same period. However, in the subsequence period, they may realize playing with extra items is more fun since they already know the effect of extra items to alleviate game restriction such as time limit. Also, this study tries to figure out the recursive relationship with playing and purchasing behaviors. Users may play more after purchasing items and vice versa. Buying behavior could represent users willingness to continuous play the game. They can consider the amount of money for purchased items as an investment for playing games, so they tend Twenty First Pacific Asia Conference on Information Systems, Langkawi

4 to play the game continuously. With previous theoretical background, our hypotheses are postulated as follows. H1-2: The goal-oriented propensity of users in the previous period has a positive impact on in-app purchases in the current period. H2-2: The stronger strength of gameplay habits in the previous period has a positive impact on inapp purchases in the current period. H3-2: The effect of free samples in the previous period has a negative impact on in-app purchases in the current period. Data and Method The dataset used for this paper is a huge user-level gameplay log dataset collected from one Korean mobile game company. The strength of analyzing the user-level log data is that it provides behavior information more objectively and accurately than self-reported behavior data from surveys. The chosen game is one of the famous mobile mahjong games in Korea and Japan. Players can go to the next stage after finding all card pairs with the same patterns in a certain time. Although there is no rule of thumb regarding the suitable length of a time window, about nine weeks is selected based on opinions of field experts. According to them, the lifecycle of mobile games quite short related to other IT hedonic systems. Users normally start to buy virtual items within three weeks and often leave within five weeks, especially in mobile casual games. Thus, the number of utilized dates records a total of 66 days from April 9 to June 13, The data contains the gameplay and in-app purchasing behavior of each user. Positioning user identifier variable as the panel variable, this study reformulates the dataset into a daily panel dataset. The key variables are as follows. - purchased_frequency: The frequency of item purchases as of today - tenure: The number of days that s/he joined the game as of today - stage: The maximum number of stages as of today - play_ frequency: The frequency of gameplay during today - free_frequency: The frequency of received free items during today This study estimates the effect of the level of the stage, the frequency of gameplay, and the number of receiver-free items on users purchases to test the hypothesis. To do this, this study uses purchased_frequency as our dependent variable. This study formally tests using techniques of multiple panel data analysis including the ordinary least square (OLS) or pooling panel data model, the fixed effect model, and the random effect model. Doing so allows us to conduct robustness checks to choose the model best fitted to our data. To test for random effects, this study conducts Breusch- Pagan Lagrange multiplier (LM) test. The LM test helps decide between a simple OLS model and a random effect model. The null hypothesis of the LM test states no significant difference across units. The result of LM test demonstrates that the random effect model is more appropriate for our dataset. This study also considers whether the fixed effect model or the random effect model is more suitable for our dataset. The fixed effect model allows individual-specific effects to be correlated with the independent variables and treats the individual error components as a set of parameters which need to be evaluated using OLS. The random effect model, on the other hand, is used when the individual error components are not correlated with the regressors, but result in correlation across composite error terms. Giving this strong assumption, the fixed effect model is commonly preferred to the random effect model. For checking whether the random effect model or the fixed effect model is proper for our dataset, the Hausman test is conducted, and its result reveals the regressors are not correlated with the error terms. Therefore, our estimation incorporates fixed effects for users to consider unobserved characteristics of individual users. For the independent variable, this study adds the tenure variable to investigate which it may influence item purchases. Equation 1, presented below, captures our econometric model. In this equation, users are indexed by i, and time is indexed by t. β i is coefficients estimates for the frequency of item purchases today. μ i accounts for the individual crosssectional effect, that is, user characteristics, and the error term ε i,t control for the idiosyncratic effects. Since the dependent variable utilized in this paper is overdispersed count variable, to confirm the validity of the analyses, negative binomial regression is carried out. Equation1: purchased_frequency i,t = β 1stage i,t + β 2play_frequency i,t + β 3free_frequency i,t + β 4tenure i,t + μ i + ε i,t Twenty First Pacific Asia Conference on Information Systems, Langkawi

5 This study also looks into the recursive relationship with purchasing items and playing the game as postulated in the hypothesis. Equation 2, presented below, captures our econometric models with the recursive relationship. Since the lagged dependent variable in dynamic models is likely to be correlated with the user-specific effect, estimating dynamic models is more challenging than estimating static models. Because of this challenge, many researchers argue that OLS estimators, whether achieved through fixed or random effects, give inconsistent estimates with dynamic panel data models (Arellano and Bond 1991; Blundell and Bond 1998). To overcome this challenge, Arellano and Bond (1991) suggest using the generalized method of moments (GMM) model in first differences. The cross-sectional effects are removed by first-order differencing. The model is also estimable by using instruments variables. To test the consistency of the GMM estimators, this study conducts the Arellano and Bond s serial autocorrelation test and Sargan test. The result of Arellano and Bond s serial autocorrelation test confirms the errors in the first-difference regression do not show first- or second-order serial correlation. The result of Sargan test also demonstrates the validity of the GMM estimators. Therefore, this study conducts GMM analysis to look into the recursive relationship with playing and purchasing behavior. Equation2: purchased_frequency i,t = β 1Stage i,t-1 + β 2play_frequency i,t-1 + β 3free_frequency i,t-1 + purchased_frequency i,t-1 + β 4tenure i,t + φ i + ω i,t Results and Discussion The result of our first model is demonstrated in Table 1. The result shows that the user with a higher stage is likely to more frequently buy items (H1-1 supported). The user frequently playing the game is more likely to purchase more items (H2-1 supported). The number of received-free items and the tenure also shows a positive relationship to item purchases (H3-1 not supported). In addition, the noticeable point is that offering free items do not cannibalize item selling. The effect of free item is the biggest among the effects of the suggested variables. The result represents that the cherry pickers think they enjoy the game for free, but, in fact, they purchase items eventually. Therefore, this study can find that offering free items works well to promote selling items. << Insert Table 1 here>> The results of our second model are demonstrated in Table 2. The result shows that the stage in t-1 is not related to item purchases in t (H1-2 not supported). The user frequently playing the game in t-1 is more likely to purchase more items in t (H2-2 supported). The number of received-free items in t-1 shows a negative relationship to item purchases in t (H3-2 supported). Therefore, the results of both cases indicate that the free item promotion increases item selling in short time, but the cherry picking behaviors show in the subsequent period. The game item can be a kind of experience goods since users cannot know exact functionality or benefit unless they use the items. Accordingly, game developers provide free items when newcomers start playing the game as a welcome gift or when new items are released. After experiencing the item, users know its benefit and consider buying it. Especially for the mobile casual game, since many mobile casual games are released with similar functionality and design, the retention rate of mobile casual games is relatively low (Runge et al. 2014). Therefore, the game developers try to entice users into spending money before users leave. The noticeable point of our results is the effect of free-item promotion shows in the current period only, not in the subsequent period. Thus, the game developers need to consider the duration of the free-item effect and provide the free item in suitable time depending on users situation. Expected Contribution << Insert Table 2 here>> This study empirically investigates the factors on virtual item purchases in a mobile casual game. It especially focuses on the goal-oriented/habitual behaviors and the effect of free-item promotion. All suggested variables in the current period have a positive effect on the frequency of in-app purchases. The frequency of playing in the previous period has a positive effect on the frequency of in-app purchases in the current period. However, the impact of free item promotion in the previous period is negative on the frequency of in-app purchases in the current period. This study is expected to offer one of the first empirical evidence that examines the factors on virtual item purchases in the mobile casual game based on a large-scale user log dataset. It is also expected that this study can give significant implications to game developers who use freemium pricing strategy as their revenue model. For example, both goal-oriented and habitual users are important to sell items. Offering free Twenty First Pacific Asia Conference on Information Systems, Langkawi

6 items does not cannibalize item selling in the same period, but they need to beware of the negative effect of free item in the subsequence period. Potential Pitfalls Perhaps the most significant challenge facing this study includes the lack of prior IS literature in this research area. Although prior research in other reference disciplines may inform us, a significant amount of work still needs to be done to find the factors to affect item purchase, a solid research model, hypotheses, and measurement. To remedy this risk, I will ensure that I thoroughly review all the relevant literature and that the research process is systematic and rigorous. Another major challenge is due to data deficiency. Since this study relies on data on actual usage and purchase in mobile game, the significant challenge for the completion of this study involves obtaining such data in real mobile games. This study does not use the demographic information of users such as gender, age, and nationality due to privacy issue so far. If additional analysis including these factors could be conducted, the result will be more luxuriant. Finally, the generalizability of the findings may become problematic because this paper uses the data from one casual mobile game. This study only analyzes the effect of free item in a suggested game, so it is hard to figure out the differences of free-item effect among other games since the effect can be various depending on game genre. Despite the complexities in the data collection, I am trying to obtain data from other games including demographic information and consider these factors to strengthen the current results for additional research. Current Status and Timeline I have completed research problem formulation, literature review, conceptualization of key constructs, data collection, and preliminary data analysis. My goal for this research is to produce publishable quality papers that I will submit to leading IS journals before graduation. The following timeline indicates the current status and the proposed schedule for completion of this research. Appendix << Insert Table 3 here>> Table 1. Main Results from Fixed Effect Analysis Dep. variable purchased_frequency i,t Independent stage i,t *** (0.0001) variables play_frequency i,t *** (0.0003) free_frequency i,t *** (0.0083) tenure i,t *** (0.0619) Observation 136,903 Number of users 3,820 Standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1 Table 1. Main Results from Fixed Effect Analysis Dep. variable Independent variables Table 2. Main Results from GMM Analysis purchased_frequency i,t stage i,t (0.0000) play_frequency i,t *** (0.0000) free_frequency i,t *** (0.0000) tenure i,t (0.0000) purchased_frequency i,t *** (0.0037) Observation 136,903 Number of users 3,820 Standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1 Twenty First Pacific Asia Conference on Information Systems, Langkawi

7 Table 2. Main Results from GMM Analysis Table 3. Research Project Timeline Phase of Research Date Status Research Problem Formulation 03/ /2016 Done Literature Review 06/ /2016 Done Conceptual Development 08/ /2016 Done Data Collection 10/2016 Done Preliminary Data Analysis 11/ /2017 Done Proposal Defense 02/2017 Done Large Scale Data Collection 02/ /2017 Done Data Analysis 04/ /2017 In Progress Writing Thesis and Revision 07/ /2017 References Final Thesis Defense 12/2017 Table 3. Research Project Timeline Arellano, Manuel, and Stephen Bond. "Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations." The Review of Economic Studies (58:2), 1991, pp Bawa, Kapil, and Robert Shoemaker. "The effects of free sample promotions on incremental brand sales." Marketing Science (23:3), 2004, pp Blundell, Richard, and Stephen Bond. "Initial conditions and moment restrictions in dynamic panel data models." Journal of Econometrics (87:1), 1998, pp Choi, D., and Kim, J. Why people continue to play online games: In search of critical design factors to increase customer loyalty to online contents, CyberPsychology & Behavior (7:1), 2004, pp Cotton, B. Social Game Design: Monetization Methods and Mechanics, Taylor & Francis (eds.), Fishbein, M., and Ajzen, I. Belief, attitude, intention, and behavior: An introduction to theory and research, Addison-Wiley Publishing Company (eds.), Guo, Y., and Barnes, S. Purchase behavior in virtual worlds: An empirical investigation in Second Life, Information & Management (48:7), 2011, pp Hanner, N., Heppner, K., and Zarnekow, R. Counting Customers in Mobile Business The Case of Free to Play, Proceedings of the 19th Pacific Asia Conference on Information Systems, Hartmann, T., Jung, Y., and Vorderer, P. What Determines Video Game Use?, Journal of Media Psychology, Khansa, L., Ma, X., Liginlal, D., and Kim, S. S. Understanding Members Active Participation in Online Question-and-Answer Communities: A Theory and Empirical Analysis, Journal of Management Information Systems (32:2), 2015, pp Kimppa, K. K., Heimo, O. I., and Harviainen, J. T. First dose is always freemium. ACM SIGCAS Computers and Society (45:3), 2016, pp McAlister, L., George, E. I., & Chien, Y. H. (2009). A basket-mix model to identify cherry-picked brands. Journal of Retailing, 85(4), Neal, D. T., Wood, W., and Quinn, J. M. Habits A repeat performance, Current Directions in Psychological Science (15:4), 2006, pp Runge, J., Gao, P., Garcin, F., and Faltings, B. Churn prediction for high-value players in casual social games, Proceedings of the Computational Intelligence and Games, Venkatesh, V., and Agarwal, R. Turning visitors into customers: a usability-centric perspective on purchase behavior in electronic channels, Management Science (52:3), 2006, pp Zarnekow, N. H. R. Purchasing Behavior in Free to Play Games: Concepts and Empirical Validation, Proceedings of the 48th Hawaii International Conference on System Sciences, Twenty First Pacific Asia Conference on Information Systems, Langkawi