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

Author

Listed:
  • Day Yang Liu

    (Graduate Institute of Finance, National Taiwan University of Science and Technology, Taipei, Taiwan)

  • Wen Chun Tsai

    (Graduate Institute of Finance, National Taiwan University of Science and Technology, Taipei, Taiwan)

  • Pei Leen Liu

    (Graduate School of Resources Management and Decision Science, Management College of National Defense University, Taipei, Taiwan)

  • Chung Yi Fang

    (Department of Financial Management, Management College of National Defense University, Taipei, Taiwan)

Abstract

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. Key Words:System Dynamics, Innovation Diffusion, Sports Lottery

Suggested Citation

  • Day Yang Liu & Wen Chun Tsai & Pei Leen Liu & Chung Yi Fang, 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), Center for the Strategic Studies in Business and Finance, vol. 10(4), pages 43-58, June.
    • Handle: RePEc:rbs:ijbrss:v:10:y:2021:i:4:p:43-58
    DOI: 10.20525/ijrbs.v10i4.1198
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    References listed on IDEAS

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    1. Stefan Szymanski, 2010. "The Economic Impact of the World Cup," Palgrave Macmillan Books, in: Football Economics and Policy, chapter 11, pages 226-235, Palgrave Macmillan.
    2. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    3. Kaplanski, Guy & Levy, Haim, 2010. "Exploitable Predictable Irrationality: The FIFA World Cup Effect on the U.S. Stock Market," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 45(2), pages 535-553, April.
    4. Yelkur, Rama & Tomkovick, Chuck & Traczyk, Patty, 2004. "Super Bowl Advertising Effectiveness: Hollywood Finds the Games Golden," Journal of Advertising Research, Cambridge University Press, vol. 44(1), pages 143-159, March.
    5. Sterman, John., 1994. "Learning in and about complex systems," Working papers 3660-94., Massachusetts Institute of Technology (MIT), Sloan School of Management.
    6. Clement Karani Mbore & Jane Sang & Joyce Komen, 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), Center for the Strategic Studies in Business and Finance, vol. 8(6), pages 229-239, October.
    7. Mark Paich & John D. Sterman, 1993. "Boom, Bust, and Failures to Learn in Experimental Markets," Management Science, INFORMS, vol. 39(12), pages 1439-1458, December.
    8. Bruce Robinson & Chet Lakhani, 1975. "Dynamic Price Models for New-Product Planning," Management Science, INFORMS, vol. 21(10), pages 1113-1122, June.
    9. Nerilee Hing & Lorraine Cherney & Alex Blaszczynski & Sally M. Gainsbury & Dan I. Lubman, 2014. "Do advertising and promotions for online gambling increase gambling consumption? An exploratory study," International Gambling Studies, Taylor & Francis Journals, vol. 14(3), pages 394-409, December.
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