Determinants of sales revenue in innovation diffusion effects of Taiwan sports lottery during the FIFA World Cup 2018




System Dynamics, Innovation Diffusion, Sports Lottery


This article analyzes the factors affecting the sales revenue of sports lottery from the perspective of innovative diffusion theory by system dynamics analysis. With the quantification and simulation of system dynamics, the sales revenue of sports lottery is affected is found. With the daily sales amount during the FIFA World Cup 2018 as samples, six variables (reach frequency, adoption rate, betting among per person per day, advertisement expenditure, advertisement successful rate, and potential bettor increase rate) are used to find out the key factors. According to the simulation result of this study, it indicates that all the variables exert a positive influence on the sales revenue. The magnitude of influence on sales, from large to small, they are betting among per person per day, reach frequency and adoption rate in word-to-mouth, potential bettors increase rate, advertisement expenditure and advertisement successful rate in the advertisement effects. During the FIFA World Cup 2018, advertising effects initiated the diffusion of sports lottery. Compared to the advertising effects, word-to-mouth effects were bigger. In the same situation and with the same resources, Taiwan Sports Lottery, the operator could change the betting among per person per day and change the word-to-mouth advertising with priority. When major matches take place in the future, Taiwan Sports Lottery is suggested to judge if it maintains an optimistic attitude for future growth, it shall begin to promote advertising effects. When more people learn more about the sports lottery, with the diffusion of word-to-mouth advertising, the effects will be most significant.   


Bass, F. M. 1969. A new product growth for model consumer durables. Management science, 15, 215-227.

Blackman Jr, A. W. 1974. The market dynamics of technological substitutions. Technological Forecasting and Social Change, 6, 41-63.

Capital, H. G. 2013. There’s nothing virtual about the opportunity in real-money gambling. H2 Gambling Capital & Odobo Gibraltar.

Compliance, G. 2013. Gambling advertising and sport: A comparison of regulated markets. Sydney: Australian Wagering Council. Retrieved from ….

Cornelissen, S. 2010. Football’s tsars: proprietorship, corporatism and politics in the 2010 FIFA World Cup. Soccer & Society, 11, 131-143.

Denzin, N. 1970. Strategies of multiple triangulation. The research act in sociology: A theoretical introduction to sociological method, 297, 313.

Denzin, N. K. 2017. The research act: A theoretical introduction to sociological methods, Transaction publishers.

Forrester, J. W. 2007. System dynamics—the next fifty years. System Dynamics Review: The Journal of the System Dynamics Society, 23, 359-370.

Herskowitz, S. 2016. Gambling, saving, and lumpy expenditures: Sports betting in Uganda. University of California, Berkeley.

Hing, N., Cherney, L., Blaszczynski, A., Gainsbury, S. M. & Lubman, D. I. 2014. Do advertising and promotions for online gambling increase gambling consumption? An exploratory study. International Gambling Studies, 14, 394-409.

Hing, N., Russell, A. M., Thomas, A. & Jenkinson, R. 2019. Wagering advertisements and inducements: Exposure and perceived influence on betting behaviour. Journal of gambling studies, 35, 793-811.

Jenkinson, R., De Lacy-Vawdon, C. & Carroll, M. L. 2019. Weighing up the odds: Sports betting and young men, Australian Gambling Research Centre, Australian Institute of Family Studies.

Kaplanski, G. & Levy, H. 2010. Exploitable predictable irrationality: The FIFA World Cup effect on the US stock market. Journal of Financial and Quantitative Analysis, 535-553.

La Fleur, B. 2019. La Fleur’s World Lottery Almanac. Boyds: TLF Publications, Inc.

Lee, C.-K. & Taylor, T. 2005. Critical reflections on the economic impact assessment of a mega-event: the case of 2002 FIFA World Cup. Tourism management, 26, 595-603.

Lewis, C. D. 1982. Industrial and business forecasting methods: A practical guide to exponential smoothing and curve fitting, Butterworth-Heinemann.

Mahajan, V. 2010. Innovation diffusion. Wiley International Encyclopedia of Marketing.

Mahajan, V., Muller, E. & Wind, Y. 2000. New-product diffusion models, Springer Science & Business Media.

Mahajan, V. & Peterson, R. A. 1979. First-purchase diffusion models of new-product acceptance. Technological Forecasting and Social Change, 15, 127-146.

Mbore, C. K., Sang, J. & Komen, J. 2019. Management control system, organizational processes and institutional performance of technical training institutions in Kenya. International Journal of Research in Business and Social Science (2147-4478), 8, 229-239.

Milling, P. M. 1996. Modeling innovation processes for decision support and management simulation. System Dynamics Review: The Journal of the System Dynamics Society, 12, 211-234.

Mwadime, A. 2017. Implications of Sports Betting In Kenya: Impact of Robust Growth of the Sports Betting Industry. United States International University-Africa.

Paich, M. & Sterman, J. D. 1993. Boom, bust, and failures to learn in experimental markets. Management science, 39, 1439-1458.

Prahalad, C. K. & Ramaswamy, V. 2000. Co-opting customer competence. Harvard business review, 78, 79-90.

Robinson, B. & Lakhani, C. 1975. Dynamic price models for new-product planning. Management science, 21, 1113-1122.

Rogers, E. M. 1961. Bibliography on the Diffusion of Innovations.

Rogers, E. M. 1963. What are innovators like? Theory into Practice, 2, 252-256.

Rogers, E. M. 2010. Diffusion of innovations, Simon and Schuster.

Rogers, E. M. & Williams, D. 1983. Diffusion of. Innovations (Glencoe, IL: The Free Press, 1962).

Senge, P. M. & Forrester, J. W. 1980. Tests for building confidence in system dynamics models. System dynamics, TIMS studies in management sciences, 14, 209-228.

Sharif, M. N. & Ramanathan, K. 1984. Temporal models of innovation diffusion. IEEE Transactions on Engineering Management, 76-86.

Sterman, J. 2000. Instructor's Manual to Accompany Business Dyanmics: Systems Thinking and Modeling for a Complex World, McGraw-Hill.

Sterman, J. D. 1994. Learning in and about complex systems. System dynamics review, 10, 291-330.

Szymanski, S. 2002. The economic impact of the World Cup. World Economics, 3, 169-177.

Uzochukwu, C. 2021. An Assessment of Pattern, Motivation and Effects of Online Sports Betting Among Youths in South-East Nigeria. International Journal of Innovative Science and Research Technology, 6.

Weibe, J. 2008. Internet gambling: Strategies to recruit and retain customers. Guelph: Ontario Problem Gambling Research Centre.

Yelkur, R., Tomkovick, C. & Traczyk, P. 2004. Super Bowl advertising effectiveness: Hollywood finds the games golden. Journal of Advertising Research, 44, 143-159.

Yeon, S.-J., Park, S.-H. & Kim, S.-W. 2006. A dynamic diffusion model for managing customer's expectation and satisfaction. Technological Forecasting and social change, 73, 648-665.




How to Cite

Liu, D. Y., Tsai, W. C., Liu, P. L., & Fang, C. Y. (2021). Determinants of sales revenue in innovation diffusion effects of Taiwan sports lottery during the FIFA World Cup 2018. International Journal of Research in Business and Social Science (2147- 4478), 10(4), 43–58.



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