The impact of superstar and non-superstar software on hardware sales: the moderating role of hardware lifecycle
In the context of two-sided markets, we propose hardware lifecycle as a key moderator of the impact of superstar and non-superstar software on hardware adoption. A hardware’s earlier adopters are less price sensitive and have a higher preference for exciting and challenging software. In contrast, later adopters are more price sensitive and prefer simplicity in software. Superstar software tend to be more expensive and more complex compared to non-superstars. Therefore, earlier (later) adopters prefer superstars (non-superstars), which leads to higher impact of superstars (non-superstars) on hardware adoption in the early (later) stages of the hardware lifecycle. Using monthly data over a 12-year timeframe (1995–2007) from the home video game industry, we find that both superstar and non-superstar software impact hardware demand, but they matter at different points in the hardware lifecycle. Superstars are most influential when hardware is new, and this influence declines as hardware ages. In contrast, non-superstar software has a positive impact on hardware demand later in the hardware lifecycle, and this impact increases with hardware age. Findings reveal that eventually the amount of available non-superstar software impacts hardware adoption more than the amount of available superstar software. We provide several managerial implications based on these findings.
KeywordsIndirect network effect Superstars Two-sided markets Lifecycle theory Relationship marketing
This research benefited from generous support provided by the Carolan Research Institute and a Bradley University Foster College of Business Administration Faculty Development Grant. The authors would like to thank participants at the 2017 American Marketing Association Winter Educators’ Conference and the 2015 American Marketing Association Winter Educators’ Conference. Additionally, the authors thank BJ Allen for helpful comments on an earlier draft and four anonymous referees, an associate editor, and the editor for insightful comments throughout the review process. Lastly, the authors thank Katlyn Brinkley and Aaron Gleiberman for their copyediting prowess. The usual disclaimer applies.
- Allenby, G., Fennell, G., Huber, J., Eagle, T., Gilbride, T., Horsky, D., et al. (2005). Adjusting choice models to better predict market behavior. Marketing Letters, 16(3–4), 197–208.Google Scholar
- Avnet, T., & Higgins, E. T. (2006). How regulatory fit affects value in consumer choices and opinions. Journal of Marketing Research, 43(1), 1–10.Google Scholar
- Basuroy, S., Chatterjee, S., & Ravid, S. A. (2003). How critical are critical reviews? The box office effects of film critics, star power, and budgets. Journal of Marketing, 67(4), 103–117.Google Scholar
- Bernbeck, S. (2015). What drives a review score? https://www.gamesindustry.biz/articles/2015-02-09-what-drives-a-review-score. Accessed 1 June 2018.
- Berry, S. T. (1994). Estimating discrete-choice models of product differentiation. The Rand Journal of Economics, 25(2), 242–262.Google Scholar
- Binken, J. L., & Stremersch, S. (2009). The effect of superstar software on hardware sales in system markets. Journal of Marketing, 73(2), 88–104.Google Scholar
- Chatterjee, R. A., & Eliashberg, J. (1990). The innovation diffusion process in a heterogeneous population: A micromodeling approach. Management Science, 36(9), 1057–1079.Google Scholar
- Clements, M. T. (2004). Direct and indirect network effects: Are they equivalent? International Journal of Industrial Organization, 22(5), 633–645.Google Scholar
- Clements, M. T., & Ohashi, H. (2005). Indirect network effects and the product cycle: Video games in the U.S., 1994-2002. The Journal of Industrial Economics, 53(4), 515–542.Google Scholar
- Coughlan, P. J. (2001). Note on home video game technology and industry structure. Harvard Business School Case.Google Scholar
- Cox, J. (2014). What makes a blockbuster video game? An empirical analysis of US sales data. Managerial and Decision Economics, 35(3), 189–198.Google Scholar
- Day, G. S. (1981). The product life cycle: Analysis and application issues. Journal of Marketing, 45, 60–67.Google Scholar
- Dranove, D., & Gandal, N. (2003). The dvd-vs.-divx standard war: Empirical evidence of network effects and preannouncement effects. Journal of Economics & Management Strategy, 12(3), 363–386.Google Scholar
- Dubé, J. H., Hitsch, G. J., & Chintagunta, P. K. (2010). Tipping and concentration in markets with indirect network effects. Marketing Science, 29(2), 216–249.Google Scholar
- Fennell, G. (1978). Consumers' perceptions of the product. use situation. The Journal of Marketing, 38–47.Google Scholar
- Gandal, N., Kende, M., & Rob, R. (2000). The dynamics of technological adoption in hardware/software systems: The case of compact disc players. The Rand Journal of Economics, 43–61.Google Scholar
- Gaston, M. (2014). Titanfall launch sees Xbox One sales almost double in the UK. http://www.gamespot.com/articles/titanfall-launch-sees-xbox-one-sales-almost-double-in-the-uk/1100-6418348/. Accessed 1 June 2018.
- Gilbert, B. (2017). The PlayStation 4 is selling about twice as fast as the Xbox One,. http://www.businessinsider.com/playstation-4-ps4-xbox-one-sales-2017-6. Accessed 1 June 2018.
- Golder, P. N., & Tellis, G. (2004). Growing, growing, gone: Cascades, diffusion, and turning points in the product life cycle. Marketing Science, 23(2), 207–218.Google Scholar
- Gooding, C., & Stephenson, E. F. (2017). Superstars, uncertainty of outcome, and PGA tour television ratings. Journal of Sports Economics, 18(8), 867–875.Google Scholar
- Gretz, R. T. (2010). Hardware quality vs. network size in the home video game industry. Journal of Economic Behavior and Organization, 76(2), 168–183.Google Scholar
- Gretz, R. T., & Basuroy, S. (2013). Why quality may not always win: The impact of product generation life cycles on quality and network effects in high-tech markets. Journal of Retailing, 89(3), 281–300.Google Scholar
- Hagiu, A., & Wright, J. (2015). Multi-sided platforms. International Journal of Industrial Organization, 43, 162–174.Google Scholar
- Hausman, J. A., & Leonard, G. K. (1997). Superstars in the national basketball association: Economic value and policy. Journal of Labor Economics, 15(4), 586–624.Google Scholar
- Hofmann, J., Clement, M., Völckner, F., & Hennig-Thurau, T. (2017). Empirical generalizations on the impact of stars on the economic success of movies. International Journal of Research in Marketing, 34(2), 442–461.Google Scholar
- Jahanmir, S. F., & Lages, L. F. (2015). The lag-user method: Using laggards as a source of innovative ideas. Journal of Engineering and Technology Management, 37, 65–77.Google Scholar
- Jahanmir, S. F., & Lages, L. F. (2016). The late-adopter scale: A measure of late adopters of technological innovations. Journal of Business Research, 69(5), 1701–1706.Google Scholar
- Katz, M. L., & Shapiro, C. (1994). Systems competition and network effects. Journal of Economic Perspectives, 8(2), 93–115.Google Scholar
- Kharpal, A. (2017). Sony profits rise 346% thanks to strong performance from PlayStation gaming unit. Retrieved February 18, 2018, from https://www.cnbc.com/2017/10/31/sony-earnings-q2-2017.html.
- Kim, J. H., Prince, J., & Qiu, C. (2014). Indirect network effects and the quality dimension: A look at the gaming industry. International Journal of Industrial Organization, 37, 99–108.Google Scholar
- Kretschmer, T., & Claussen, J. (2016). Generational transitions in platform markets – The role of backward compatibility. Strategy Science, 1(2), 90–104.Google Scholar
- Lee, R. S. (2013). Vertical integration and exclusivity in platform and two-sided markets. The American Economic Review, 103(7), 2960–3000.Google Scholar
- Lee, Y., & O’Connor, G. C. (2003). New product launch strategy for network effects products. Journal of the Academy of Marketing Science, 31(3), 241–255.Google Scholar
- Liu, Y. (2006). Word of mouth for movies: Its dynamics and impact on box office revenue. Journal of Marketing, 70(3), 74–89.Google Scholar
- Malshe, A. V., Colicev, A., & Mittal, V. (2017). How main steet drives wall street: Customer satisfaction, short sellers, and shareholder value. https://ssrn.com/abstract=3021327. Accessed 1 June 2018.
- Marchand, A., & Hennig-Thurau, T. (2013). Value creation in the video game industry: Industry economics, consumer benefits, and research opportunities. Journal of Interactive Marketing, 27(3), 141–157.Google Scholar
- McFadden, D. (1973). Conditional logit analysis of qualitative choice behavior.Google Scholar
- Moorhead, P. (2013). Xbox One: buy now, later, or never? https://www.forbes.com/sites/patrickmoorhead/2013/12/03/xbox-one-probably-better-off-waiting/. Accessed 1 June 2018.
- Nair, H., Chintagunta, P. K., & Dubé, J. P. (2004). Empirical analysis of indirect network effects in the market for personal digital assistants. Quantitative Marketing and Economics, 2(1), 23–58.Google Scholar
- Nevo, A. (2000). A practitioner’s guide to estimation of random-coefficients logit models of demand. Journal of Economics and Management Strategy, 9(4), 513–548.Google Scholar
- Newey, W. K., & West, K. D. (1994). Automatic lag selection in covariance matrix estimation. The Review of Economic Studies, 61(4), 631–653.Google Scholar
- Pearce, C. (2002). Emergent authorship: The next interactive revolution. Computers & Graphics, 26(1), 21–29.Google Scholar
- Phan, M. H., Keebler, J. R., & Chaparro, B. S. (2016). The development and validation of the game user experience satisfaction scale (GUESS). Human Factors, 58(8), 1217–1247.Google Scholar
- Reddy, S. K., Swaminathan, V., & Motley, C. M. (1998). Exploring the determinants of Broadway show success. Journal of Marketing Research, 370–383.Google Scholar
- Rietveld, J., & Eggers, J. P. (2018). Demand heterogeneity in platform markets: Implications for complementors. Organization Science, 29(2), 304–322.Google Scholar
- Rogers, E. M. (2003). Diffusion of innovations. Simon and Schuster.Google Scholar
- Rosen, S. (1981). The economics of superstars. The American Economic Review, 71(5), 845–858.Google Scholar
- Schreier, J. (2015). Metacritic matters: how review scores hurt video games. https://kotaku.com/metacritic-matters-how-review-scores-hurt-video-games-472462218. Accessed 1 June 2018.
- Song, R., Kim, H., Lee, G. M., & Jang, S. (2017). Does deceptive marketing pay? The evolution of consumer sentiment surrounding a pseudo-product-harm crisis. Journal of Business Ethics, 1–19. https://doi.org/10.1007/s10551-017-3720-2.
- Squire, K., & Jenkins, H. (2002). The art of contested spaces. In Game On (pp. 97–102). New York.Google Scholar
- Stanko, M. A., Bonner, J. M., & Calantone, R. J. (2007). Building commitment in buyer–seller relationships: A tie strength perspective. Industrial Marketing Management, 36(8), 1094–1103.Google Scholar
- Steiner, M., Wiegand, N., Eggert, A., & Backhaus, K. (2016). Platform adoption in system markets: The roles of preference heterogeneity and consumer expectations. International Journal of Research in Marketing, 33(2), 276–296.Google Scholar
- Stock, J. H., & Watson, M. H. (2007). Introduction to econometrics. Boston: Pearson-Addison Wesley.Google Scholar
- Stock, J. H., & Yogo, M. (2002). Testing for weak instruments in linear IV regression. Cambridge. Retrieved February 1, 2018 from http://www.nber.org/papers/t0284.
- Stock, J. H., Wright, J. H., & Yogo, M. (2002). A survey of weak instruments and weak identification in generalized method of moments. Journal of Business and Economic Statistics, 20(4), 518–529.Google Scholar
- Stremersch, S., Tellis, G. J., Franses, P. H., & Binken, J. L. (2007). Indirect network effects in new product growth. Journal of Marketing, 71(3), 52–74.Google Scholar
- Totilo, S. (2012, July 9). The difference between a good video game and a bad one. Kotaku. https://kotaku.com/5924387/the-difference-between-a-good-video-game-and-a-bad-one. Accessed 29 April 2018.
- Usher, W. (2015). Destiny’s poor Metacritic score could cost Bungie millions. Retrieved February 1, 2018, fromhttps://www.cinemablend.com/games/Destiny-Poor-Metacritic-Score-Could-Cost-Bungie-Millions-67350.html.
- Wooldridge, J. (2010). Econometric analysis of cross section and panel data. MIT press.Google Scholar
- Zeelenberg, M. (1999). Anticipated regret, expected feedback and behavioral decision making. Journal of Behavioral Decision Making, 12(2), 93–106.Google Scholar
- Zhu, F., & Zhang, X. (2010). Impact of online consumer reviews on sales: The moderating role of product and consumer characteristics. Journal of Marketing, 74(2), 133–148.Google Scholar