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Measuring the Effects of Regulation Policy on Online Game: A Vector Autoregressive (VAR) approach

Published: 03 August 2015 Publication History

Abstract

The purpose of this study is to study the effects of regulatory policy on online gambling, one of increasingly popular types in the online game industry. Previous information systems (IS) studies on online game primarily aim user behavior. Nowadays, instead of heuristic approaches on individual behavior, there is a growing need to examine the effects of regulatory policy on dynamic changes of games or game providers. Standing above the approaches of prior studies, we empirically test the regulatory policy effect with two theoretical viewpoints: social influence and previous experience. We use a vector autoregression (VAR) analysis to predict game usage and to model various forms of the co-movement of online games. We provide also evidence of strong Granger-causal interdependencies within games and game providers. This study offers one of the first empirical evidences studying the effects of regulatory policies on online game industry. In research methodology point, this study also introduces an explanation of VAR methodology in IS research. Thus, it delivers advanced knowledge on gaming behaviors as well as helps develop suitable regulatory policies to satisfy policymakers and to protect users of online game.

References

[1]
Auer, M., and M. D. Griffiths. 2013. Voluntary limit setting and player choice in most intense online gamblers: An empirical study of gambling behaviour. Journal of Gambling Studies, 29, 4, 647--660.
[2]
Awokuse, T. O., and D. A. Bessler. 2003. Vector autoregressions, policy analysis, and directed acyclic graphs: An application to the US economy. Journal of Applied Economics, 6, 1, 1--24.
[3]
Bagliano, F. C., and C. A. Favero. 1998. Measuring monetary policy with VAR models: An evaluation. European Economic Review, 42, 6, 1069--1112.
[4]
Bernanke, B. S., J. Boivin, and P. Eliasz. 2005. Measuring the effects of monetary policy: A factor-augmented vector autoregressive (FAVAR) approach. Quarterly Journal of Economics, 120, 1, 387--422.
[5]
Business Korea Report. 2013. Online game addiction bill: Undergoing rapid legislation amid blowing-up opposition, Retrieved from http://www.businesskorea.co.kr/article/2717/online-gaming-addiction-bill-undergoing-rapid-legislation-amid-blowing-opposition
[6]
Castañeda, J. A., F. Muñoz-Leiva, and T. Luque. 2007. Web acceptance model (WAM): Moderating effects of user experience. Information & Management, 44, 4, 384--396.
[7]
Chandra, S. R., and H. Al-Deek. 2009. Predictions of freeway traffic speeds and volumes using vector autoregressive models. Journal of Intelligent Transportation Systems, 13, 2, 53--72.
[8]
Cooley, T. F., and M. Dwyer. 1998. Business cycle analysis without much theory: A look at structural VARs. Journal of Econometrics, 83, 1, 57--88.
[9]
Cotte, J., and Kathryn A. Latour. 2009. Blackjack in the kitchen: Understanding online versus casino gambling. Journal of Consumer Research, 35, 5, 742--758.
[10]
Eadington, W. R. 2004. The future of online gambling in the United States and elsewhere. Journal of Public Policy and Marketing, 23, 2, 214--219.
[11]
Enders, W. 2008. Applied Econometric Time Series. John Wiley & Sons.
[12]
Etnews. 2015. Need to change the regulation policy on online gambling (in Korean). Retrieved from http://www.etnews.com/20150213000164
[13]
Friesendorf, C. 2005. Squeezing the balloon? Crime, law and social change, 44, 1, 35--78.
[14]
Gainsbury, S., J. Parke, and N. Suhonen. 2013. Consumer attitudes towards Internet gambling: Perceptions of responsible gambling policies, consumer protection, and regulation of online gambling sites. Computers in Human Behavior, 29, 1, 235--245.
[15]
Gainsbury, S., R. Wood, A. Russell, N. Hing, and A. Blaszczynski. 2012. A digital revolution: Comparison of demographic profiles, attitudes and gambling behavior of Internet and non-Internet gamblers. Computers in Human Behavior, 28, 4, 1388--1398.
[16]
Gainsbury, S. M., N. Suhonen, and J. Saastamoinen. 2014. Chasing losses in online poker and casino games: Characteristics and game play of Internet gamblers at risk of disordered gambling. Psychiatry Research, 217, 3, 220--225.
[17]
Granger, C. W. 1969. Investigating causal relations by econometric models and cross-spectral methods. Econometrica: Journal of the Econometric Society, 424--438.
[18]
Grohman, C. 2006. Reconsidering regulation: A historical view of the legality of Internet poker and discussion of the Internet gambling ban of 2006. Journal of Legal Technology Risk Management, 1, 34, 34--74.
[19]
Hamilton, J. D. 1994. Time Series Analysis. Princeton University Press Princeton.
[20]
Heckman, J. J. 2000. Causal parameters and policy analysis in Economics: a twentieth century retrospective. Quarterly Journal of Economics, 115, 1, 45--97.
[21]
Hsu, C.-L., and H.-P. Lu. 2004. Why do people play on-line games? An extended TAM with social influences and flow experience. Information & Management, 41, 7, 853--868.
[22]
Huhh, J.-S. 2008. Culture and business of PC bangs in Korea. Games and Culture, 3, 1, 26--37.
[23]
KOCCA. 2013. White paper on Korean games: Guide to Korean games industry and culture. Korea Creative Content Agency.
[24]
Ma, X., S.-H. Kim, and S.-S. Kim. 2014. Online gambling behavior: The impacts of cumulative outcomes, recent outcomes, and prior use. Information Systems Research, 25, 3, 511--527.
[25]
Monaghan, S. 2009. Responsible gambling strategies for Internet gambling: The theoretical and empirical base of using pop-up messages to encourage self-awareness. Computers in Human Behavior, 25, 1, 202--207.
[26]
Siemens, J. C., and S. W. Kopp. 2011. The influence of online gambling environments on self-control. Journal of Public Policy & Marketing, 30, 2, 279--293.
[27]
Sims, C. A. 1986. Are forecasting models usable for policy analysis? Federal Reserve Bank of Minneapolis Quarterly Review, 10, 1, 2--16.
[28]
Stewart, D. O., and L. Gray. 2011. Online gambling five years after UIGEA. Washington, DC: American Gaming Association.
[29]
Stock, J. H., and M. W. Watson. 2001. Vector autoregressions. Journal of Economic perspectives, 101--115.
[30]
Taylor, S., and P. Todd. 1995. Assessing IT usage: The role of prior experience. MIS Quarterly, 19, 4, 561--570.
[31]
Venkatesh, V., M. G. Morris, G. B. Davis, and F. D. Davis. 2003. User acceptance of information technology: Toward a unified view. MIS Quarterly, 27, 3, 425--478.
[32]
Watson, S., P. Liddell Jr, R. S. Moore, and W. D. Eshee Jr. 2004. The legalization of Internet gambling: A Consumer Protection Perspective. Journal of Public Policy & Marketing, 23, 2, 209--213.

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ICEC '15: Proceedings of the 17th International Conference on Electronic Commerce 2015
August 2015
268 pages
ISBN:9781450334617
DOI:10.1145/2781562
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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  • KRF: Korea Research Foundation

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 August 2015

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Author Tags

  1. online game
  2. policy effect
  3. regulation policy
  4. time series analysis
  5. vector autoregression

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ICEC '15

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ICEC '15 Paper Acceptance Rate 39 of 55 submissions, 71%;
Overall Acceptance Rate 150 of 244 submissions, 61%

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