||The Effect of Inhibiting and Enabling Factor On Freemium Mobile Game
||The Effect of Inhibiting and Enabling Factor On Freemium Mobile Game
||Institute of International Management
IS Dual Factor
Keywords: Freemium game, In-app purchase, Flow, Status-Quo Bias, IS Dual Factor, Control, Skill/Challenge, Loss Aversion, Cognitive Inertia, Regret Avoidance, Cognitive Lock-in
There is a big problem behind freemium business model in mobile application when people could download and use application for free that makes developer suffer because of it. This research tries to investigate what factors that urge people to do in-app purchase and do not. This research applied IS dual factor model as a theoretical lens towards behavioral intention that linked flow theory and status quo bias theory an enabler and an inhibitor. The enabler part, we employed experimental design with 3x2 factorial design using two variables, which are control and skill/challenge to see the different effect among groups towards perceived enjoyment as flow experience. The lower part as an inhibitor, we applied questionnaire design to see what factors could influence player’s resistance to buy in-game item. The result interestingly showed that imbalance condition could induce flow more than balance condition. Further, all variables are mostly supported, except regret avoidance.
TABLE OF CONTENTS
TABLE OF CONTENTS III
LIST OF TABLES VII
LIST OF FIGURES IX
CHAPTER ONE INTRODUCTION 1
1.1 Research Background. 1
1.2 Research Gap 8
1.3 Research Objectives and Contributions 9
1.3.1 Research Objectives. 9
1.3.2 Research Contributions 10
1.4 Research Procedure and Scope of Study. 11
1.4.1 Research Structure. 11
CHAPTER TWO LITERATURE REVIEW 13
2.1 Freemium Mobile Game. 13
2.2 IS Dual Factor Theory. 15
2.3. Flow Theory / Cognitive Absorption. 17
2.3.1. Control 21
2.3.2 Challenge/Skill 22
2.3.3 Perceived Enjoyment 22
2.4 Status Quo Bias Theory. 23
2.2.1 Rational Decision Making. 23
2.2.2 Cognitive Misperception. 24
2.2.3. Psychological Commitment. 25
2.5 Cognitive Lock-in. 26
2.6 Hypothesis Development. 27
2.6.1 Control toward Perceived Enjoyment. 27
2.6.2. Challenge/Skill towards Perceived Enjoyment. 27
2.6.3 Loss Aversion towards Cognitive Lock-in. 28
2.6.4 Cognitive Inertia towards Cognitive Lock-in. 29
2.6.5 Regret Avoidance toward Cognitive Lock-in. 29
2.6.6 Perceived Enjoyment towards Intention to In-app Purchase. 30
2.6.7 Cognitive Lock-In towards Intention to In-app Purchase. 30
CHAPTER THREE RESEARCH DESIGN AND METHODOLOGY 31
3.1 Research Background. 31
3.2 Definition of Variables. 32
3.3 Summary of Hypothesis. 32
3.4 Research Design and Methodology. 33
3.5 Manipulation Check for Experimental Independent Variables. 34
3.5.1 Freemium Mobile Game – self-developed. 34
3.5.2 Manipulation Check for Control. 34
3.5.3 Manipulation Check for Challenge/Skill. 35
3.6 Construct Measurement. 36
3.6.1 Control. 36
3.6.2 Challenge/Skill. 36
3.6.3 Loss Aversion. 37
3.6.4 Cognitive Inertia. 38
3.6.5 Regret Avoidance. 39
3.6.6 Perceived Enjoyment. 39
3.6.7 Cognitive Lock-in. 40
3.6.8 Intention to In-app Purchase. 40
3.7 Stimuli Development and Procedure. 40
3.8 Control Variables. 42
3.9 Sampling Plan. 42
3.10 Data Analysis Procedure. 43
3.10.1 Descriptive Statistical Analysis. 43
3.10.2 Factor Analysis and Reliability. 43
3.10.3 Analysis of Variance (MANOVA). 43
3.10.4 Multiple Regression Analysis. 44
CHAPTER FOUR RESEARCH RESULTS 45
4.1 Data Collection. 45
4.2 Characteristics of the Respondents. 45
4.3 Descriptive Statistical Analysis. 47
4.4 Factor Analysis and Reliability Test. 51
4.5. Manipulation Check in Experiment. 53
4.5.1. Manipulation Check for Control. 53
4.5.2 Manipulation Check for Skill/Challenge 54
4.6 Hypothesis Testing. 55
4.7 Control Variables. 66
CHAPTER FIVE CONCLUSION AND SUGGESTIONS 68
5.1 Research Discussion and Conclusions. 68
5.2 Theoretical Contributions and Practical Implications. 71
5.2.1 Theoretical Implications 71
5.2.2 Practical Contributions 73
5.3 Limitation and Future Research. 73
Appendix 1: SPEED RACING’s Interface 80
Appendix 2: SPEED RACING (Low control x High Challenge) 81
Appendix 3: SPEED RACING (Low control x Low Challenge) 82
Appendix 4: SPEED RACING (High control x Low Challenge) 82
Appendix 5: SPEED RACING (High control x High Challenge) 83
Appendix 6: SPEED RACING’s Interface 83
Appendix 7: Indonesia Questionnaire and Experiment Design 84
Appendix 8: English Questionnaire and Experiment Design 90
Agarwal, R., & Karahanna, E. (2000a). Time flies when you're having fun: Cognitive absorption and beliefs about information technology usage. MIS quarterly, 665-694.
Agarwal, R., & Karahanna, E. (2000b). Time Flies When You're Having Fun: Cognitive Absorption and Beliefs About Information Technology Usage. MIS Quarterly, 24(4), 665-694-694.
Agarwal, R., Sambamurthy, V., & Stair, R. M. (1997). Cognitive absorption and the adoption of new information technologies. Paper presented at the Academy of Management Annual Meeting, Boston.
Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211.
Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behaviour.
Bawa, K., & Shoemaker, R. (2004). The effects of free sample promotions on incremental brand sales. Marketing Science, 23(3), 345-363.
Bhattacherjee, A., & Hikmet, N. (2007). Physicians' resistance toward healthcare information technology: a theoretical model and empirical test. European Journal of Information Systems, 16(6), 725-737.
Cenfetelli, R. T. (2004). Inhibitors and enablers as dual factor concepts in technology usage. Journal of the Association for Information Systems, 5(11), 16.
Cheung, C. M., & Lee, M. K. (2005). Consumer satisfaction with internet shopping: a research framework and propositions for future research. Paper presented at the Proceedings of the 7th international conference on Electronic commerce.
Chu, C.-W., & Lu, H.-P. (2007). Factors influencing online music purchase intention in Taiwan: An empirical study based on the value-intention framework. Internet Research, 17(2), 139-155.
Csikszentmihalyi, M. (1975). Beyond boredom and anxiety: The experience of play in work and game. San Francisco, CA: Jossey-Bass.
Csikszentmihalyi, M. (1997). Finding flow: The psychology of engagement with everyday life: Basic Books.
Davidovici-Nora, M. (2014). Paid and free digital business models innovations in the video game industry.
Deci, E., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior: Springer Science & Business Media.
Drachen, A., Thurau, C., Togelius, J., Yannakakis, G. N., & Bauckhage, C. (2013). Game data mining Game analytics (pp. 205-253): Springer.
Engeser, S., & Rheinberg, F. (2008). Flow, performance and moderators of challenge-skill balance. Motivation and Emotion, 32(3), 158-172.
Everard, A., & Galletta, D. F. (2005). How Presentation Flaws Affect Perceived Site Quality, Trust, and Intention to Purchase from an Online Store. Journal of Management Information Systems, 22(3), 56-95.
Fields, T., & Cotton, B. (2011). Social game design: monetization methods and mechanics: CRC Press.
Finneran, C. M., & Zhang, P. (2005). Flow in computer-mediated environments: Promises and challenges. Communications of the Association for Information Systems, 15(1), 82-101.
Ghani, J. A., & Deshpande, S. P. (1994). Task characteristics and the experience of optimal flow in human—computer interaction. The Journal of psychology, 128(4), 381-391.
Grodal, T. (2000). Video games and the pleasures of control. In L. D. Zillmann and P. Vorderermi (Ed.), Media entertainment (pp. 197-213). Mahwah, NJ: Erlbaum.
Hair, J., Black, W., Babin, B., & Anderson, R. (2010). Multivariate Data Analysis Seventh Edition Prentice Hall.
Hamari, J. (2015). Why do people buy virtual goods? Attitude toward virtual good purchases versus game enjoyment. International Journal of Information Management, 35(3), 299-308.
Hamari, J., Hanner, N., & Koivisto, J. (2017). Service quality explains why people use freemium services but not if they go premium: An empirical study in free-to-play games. International Journal of Information Management, 37(1), 1449-1459.
Harter, S. (1978). Pleasure derived from challenge and the effects of receiving grades on children's difficulty level choices. Child Development, 788-799.
Heckhausen, H. (1977). Achievement motivation and its constructs: A cognitive model. Motivation and emotion, 1(4), 283-329.
Herzberg, F. I. (1966). Work and the nature of man.
Hopkins, S. A., Hopkins, W. E., & Hoffman, K. D. (2005). Domestic inter-cultural service encounters: an integrated model. Managing Service Quality: An International Journal, 15(4), 329-343.
Hsiao, K.-L., & Chen, C.-C. (2016). What drives in-app purchase intention for mobile games? An examination of perceived values and loyalty. Electronic Commerce Research and Applications, 16, 18-29.
Hsieh, P.-J., & Lin, W.-S. (2018). Explaining resistance to system usage in the PharmaCloud: A view of the dual-factor model. Information & Management, 55(1), 51-63.
Hsu, C.-L., & Lin, J. C.-C. (2015). What drives purchase intention for paid mobile apps?–An expectation confirmation model with perceived value. Electronic Commerce Research and Applications, 14(1), 46-57.
Jackson, S. A., & Marsh, H. W. (1996). Development and validation of a scale to measure optimal experience: The Flow State Scale. Journal of sport and exercise psychology, 18(1), 17-35.
Jalal, A. N., & bin Ibrahim, O. (2012). Influence of Customer Relationship Management on Online Shopping. International Journal of Computer Science, 9(1), 363-365.
Jin, S.-A. A. (2011). “I feel present. Therefore, I experience flow:” A structural equation modeling approach to flow and presence in video games. Journal of Broadcasting & Electronic Media, 55(1), 114-136.
Kahneman, D., Knetsch, J. L., & Thaler, R. H. (1991). Anomalies: The endowment effect, loss aversion, and status quo bias. The journal of economic perspectives, 5(1), 193-206.
Klarkowski, M., Johnson, D., Wyeth, P., McEwan, M., Phillips, C., & Smith, S. (2016). Operationalising and evaluating sub-optimal and optimal play experiences through challenge-skill manipulation. Paper presented at the Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems.
Klarkowski, M., Johnson, D., Wyeth, P., Smith, S., & Phillips, C. (2015). Operationalising and measuring flow in video games. Paper presented at the Proceedings of the Annual Meeting of the Australian Special Interest Group for Computer Human Interaction.
Klimmt, C., Hartmann, T., & Frey, A. (2007). Effectance and control as determinants of video game enjoyment. Cyberpsychology & behavior, 10(6), 845-848.
Koufaris, M. (2002). Applying the technology acceptance model and flow theory to online consumer behavior. Information systems research, 13(2), 205-223.
Kumar. (2014). Making "Freemium" Work. Harvard Business Review, 2014.
Lee, K. C., Chung, N., & Lee, S. (2011). Exploring the influence of personal schema on trust transfer and switching costs in brick-and-click bookstores. Information & Management, 48(8), 364-370.
Lee, M. K., Shi, N., Cheung, C. M., Lim, K. H., & Sia, C. L. (2011). Consumer's decision to shop online: The moderating role of positive informational social influence. Information & management, 48(6), 185-191.
Li, Y., Liu, H., Yang, F., Lim, E. T., Goh, J. M., & Lee, M. K. (2017). Customer's reaction to cross-channel integration in omnichannel retailing: The mediating roles of retailer uncertainty, identity attractiveness, and switching costs. Decision Support Systems.
Li, Z., & Cheng, Y. (2014). From free to fee: exploring the antecedents of consumer intention to switch to paid online content. Journal of Electronic Commerce Research, 15(4), 281.
Liao, C., Liu, C.-C., Liu, Y.-P., To, P.-L., & Lin, H.-N. (2011). Applying the expectancy disconfirmation and regret theories to online consumer behavior. Cyberpsychology, Behavior, and Social Networking, 14(4), 241-246.
Liao, G.-Y., & Teng, C.-I. (2017). You Can Make It: Expectancy for Growth Increases Online Gamer Loyalty. International Journal of Electronic Commerce, 21(3), 398-423.
Limperos, A. M., Schmierbach, M. G., Kegerise, A. D., & Dardis, F. E. (2011). Gaming across different consoles: exploring the influence of control scheme on game-player enjoyment. Cyberpsychology, Behavior, and Social Networking, 14(6), 345-350.
Lin, T.-C., Hsu, J. S.-C., & Chen, H.-C. (2013). Customer willingness to pay for online music: the role of free mentality. Journal of Electronic Commerce Research, 14(4), 315.
Liu, C. Z., Au, Y. A., & Choi, H. S. (2014). Effects of freemium strategy in the mobile app market: an empirical study of Google play. Journal of Management Information Systems, 31(3), 326-354.
Liu, Y., & Shrum, L. J. (2002). What is Interactivity and is it Always Such a Good Thing? Implications of Definition, Person, and Situation for the Influence of Interactivity on Advertising Effectiveness. Journal of Advertising, 31(4), 53-64.
Lowry, P. B., Gaskin, J., Twyman, N., Hammer, B., & Roberts, T. (2012a). Taking ‘fun and games’ seriously: Proposing the hedonic-motivation system adoption model (HMSAM).
Lowry, P. B., Gaskin, J., Twyman, N., Hammer, B., & Roberts, T. (2012b). Taking ‘fun and games’ seriously: Proposing the hedonic-motivation system adoption model (HMSAM). Journal of the Association for Information Systems, 14(11), 617-671.
Lowry, P. B., Romano, N. C., Jenkins, J. L., & Guthrie, R. W. (2009). The CMC interactivity model: How interactivity enhances communication quality and process satisfaction in lean-media groups. Journal of Management Information Systems, 26(1), 155-196.
Lu, Y., Zhou, T., & Wang, B. (2009). Exploring Chinese users’ acceptance of instant messaging using the theory of planned behavior, the technology acceptance model, and the flow theory. Computers in human behavior, 25(1), 29-39.
Luton, W. (2013). Free-to-play: Making money from games you give away: New Riders.
Nacke, L., & Lindley, C. A. (2008). Flow and immersion in first-person shooters: measuring the player's gameplay experience. Paper presented at the Proceedings of the 2008 Conference on Future Play: Research, Play, Share.
Nakamura, J., & Csikszentmihalyi, M. (2009). Flow theory and research. Handbook of positive psychology, 195-206.
Narula, R. (2002). Innovation systems and ‘inertia’in R&D location: Norwegian firms and the role of systemic lock-in. Research policy, 31(5), 795-816.
Novak, T. P., Hoffman, D. L., & Yung, Y.-F. (2000). Measuring the customer experience in online environments: A structural modeling approach. Marketing science, 19(1), 22-42.
Nozhnin, D. (2012). Predicting churn: Data-mining your game. Gamasutra. URL: http://www. gamasutra. com/view/feature/170472/predicting_churn_datamining_your_. php.
Pauwels, K., & Weiss, A. (2008). Moving from free to fee: How online firms market to change their business model successfully. Journal of Marketing, 72(3), 14-31.
Pilke, E. M. (2004). Flow experiences in information technology use. International journal of human-computer studies, 61(3), 347-357.
Polites, G. L., & Karahanna, E. (2012). Shackled to the status quo: The inhibiting effects of incumbent system habit, switching costs, and inertia on new system acceptance. MIS quarterly, 36(1).
Samuelson, W., & Zeckhauser, R. (1988). Status quo bias in decision making. Journal of risk and uncertainty, 1(1), 7-59.
Seufert, E. B. (2013). Freemium economics: Leveraging analytics and user segmentation to drive revenue: Elsevier.
Shih, H.-P. (2012). Cognitive Lock-In Effects on Consumer Purchase Intentions in the Context of B2C Web Sites. Psychology & Marketing, 29(10), 738-751.
Sifa, R., Hadiji, F., Runge, J., Drachen, A., Kersting, K., & Bauckhage, C. (2015). Predicting purchase decisions in mobile free-to-play games. Proc. of AAAI AIIDE.
Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information systems research, 6(2), 144-176.
Tellegen, A., & Atkinson, G. (1974). Openness to absorbing and self-altering experiences (" absorption"), a trait related to hypnotic susceptibility. Journal of abnormal psychology, 83(3), 268.
Teng, C.-I. (2018). Managing gamer relationships to enhance online gamer loyalty: The perspectives of social capital theory and self-perception theory. Computers in Human Behavior, 79, 59-67.
Trevino, L. K., & Webster, J. (1992). Flow in computer-mediated communication electronic mail and voice mail evaluation and impacts. Communication research, 19(5), 539-573.
Tsiros, M., & Mittal, V. (2000). Regret: A model of its antecedents and consequences in consumer decision making. Journal of Consumer Research, 26(4), 401-417.
Tversky, A., & Kahneman, D. (1991). Loss aversion in riskless choice: A reference-dependent model. The quarterly journal of economics, 106(4), 1039-1061.
Van der Heijden, H. (2004). User acceptance of hedonic information systems. MIS quarterly, 695-704.
Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision sciences, 27(3), 451-481.
Wagner, T. M., Benlian, A., & Hess, T. (2014). Converting freemium customers from free to premium—the role of the perceived premium fit in the case of music as a service. Electronic Markets, 24(4), 259-268.
Wang, C. L., Zhang, Y., Ye, L. R., & Nguyen, D.-D. (2005). Subscription to fee-based online services: What makes consumer pay for online content? Journal of Electronic Commerce Research, 6(4), 304.
Webster, J., & Ho, H. (1997). Audience engagement in multimedia presentations. ACM SIGMIS Database: the DATABASE for Advances in Information Systems, 28(2), 63-77.
Wirth, W., Hartmann, T., Böcking, S., Vorderer, P., Klimmt, C., Schramm, H., et al. (2007). A process model of the formation of spatial presence experiences. Media psychology, 9(3), 493-525.
Yang, X., & Li, G. (2016). Factors influencing the popularity of customer-generated content in a company-hosted online co-creation community: A social capital perspective. Computers in Human Behavior, 64, 760-768.
Zack, M. H. (1993). Interactivity and communication mode choice in ongoing management groups. Information Systems Research, 4(3), 207-239.
Zauberman, G. (2003). The intertemporal dynamics of consumer lock-in. Journal of consumer research, 30(3), 405-419.
Zhou, T. (2013). Understanding the effect of flow on user adoption of mobile games. Personal and ubiquitous computing, 17(4), 741-748.