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Journal of the Academy of Marketing Science

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13-02-2019 | Original Empirical Research

The impact of superstar and non-superstar software on hardware sales: the moderating role of hardware lifecycle

Authors: Richard T. Gretz, Ashwin Malshe, Carlos Bauer, Suman Basuroy

Published in: Journal of the Academy of Marketing Science | Issue 3/2019

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Abstract

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.

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We acknowledge that certain types of software may act as platforms too, e.g., operating systems.

We follow this convention throughout the paper and refer to the platform as “hardware” and the content as “software.”

We thank an anonymous reviewer for this suggestion.

These prices are significantly different at p < 0.01 using a standard T-test. Average price calculated for each game adjusting for inflation using the Consumer Price Index for Urban Consumers (2015 = 100).

Emphasis ours.

We note that games are physically distinct from consoles in that no console is preloaded with gaming software.

While we do not differentiate between exclusive and non-exclusive games in the main econometric analysis in the paper, we provide similar analysis using only exclusive games and non-exclusive games in Web Appendix B.

Though results are robust to classifying games as superstars according to sales criteria.

For face validity, we identify games in our dataset that sold 1 million copies or more. We find the average quality of these games (81.77) is significantly higher than the average quality of games that do not achieve this threshold (58.44). These averages are significantly different with >99% confidence using standard t-test.

Our concern is that quality ratings by users may be influenced by sales performance, given that users enter their ratings after the game is released. As a robustness check, we also used a quality measure based on the average of the three ratings and found similar results to those presented below.

Metacritic touts this policy on their website (http://www.metacritic.com/faq) as a way to protect critics from outside influences that may pressure them to change their scores.

This is generated in a manner similar to Fig. 1 in Binken and Stremersch (2009).

We perform a series of robustness checks using different quality ratings to classify games as superstars. All estimations from robustness checks are available from the authors upon request.

The outside option is the fraction of people in the market per period who do not buy any console. The market in any period is defined as the number of U.S. homes with TV sets who have not purchased a video game console. The number of U.S. homes with a TV set was obtained from The Nielsen Company (http://en-us.nielsen.com/). We estimate the number of households that do not own a video game console from our data—in each generation we calculate total generational sales up to the period in question and subtract from the number of U.S. homes with a TV set. Importantly, we depreciate generational sales by 90% per year to account for the fraction of consumers who purchase multiple consoles in each generation. In other words, we assume consumers can buy only one console a month, but some fraction returns to the market to make another purchase later. This approach has been used in Clements and Ohashi (2005), Gretz (2010), and Kretschmer and Claussen (2016). We should mention that our results are robust to various depreciation rates for total generational sales.

Market share of the focal console is calculated as the sales of the focal console relative to the total sales of all consoles in the same console generation as the focal console. Sales of previous versions of the console are not included in the market share calculation of the focal console.

Using the logit demand model derived from the structural approach to model hardware demand has the advantage of controlling for the effect of competing product characteristics in a tractable way without entering them directly into the estimation equation (McFadden 1973). Otherwise, as Berry (1994) notes “a system of N goods gives N² elasticities to estimate” in order to account for competitive effects, which quickly becomes impractical to estimate as degrees of freedom decrease quadratically with the addition of competing products. However, as a robustness check we estimate the model for hardware demand using the natural log of console sales for console k in period t as the dependent variable and obtain qualitatively similar results to those presented below. These results are available from the authors upon request.

We thank an anonymous reviewer for suggesting this operationalization of backward compatible superstars. While we obtain similar results to those presented below using the number of backward compatible superstars (rather than the share), this variable was highly correlated with the number of backward compatible games which lead to collinearity concerns in our estimations. Using the share of backward compatible superstars alleviates this issue.

We use the levels of the number of available superstar and non-superstar games as the dependent variables in Equations 2 and 3 instead of the natural log as suggested by Gretz (2010) to be consistent with how these variables enter the hardware demand specification in Equation 1. Similarly, we use the level of installed base rather than the natural log. However, our results are qualitatively similar if we use a log-log or linear-log specification for software supply.

We treat the number of backward compatible superstar and non-superstar games as exogenous, since they are more likely influenced by the market share of new buyers for the previous generation console they were originally designed for rather than the backward compatible, next-generation console.

VIF tables are available from the authors upon request.

We cluster on console and use the optimal cluster robust weighing matrix to obtain the single equation GMM estimations. A similar cluster-robust weighing matrix is not feasible in the joint GMM estimation because there are more moments in the system than clusters (i.e., the cluster robust weighing matrix is not invertible). Instead we use a heteroskedastic and autocorrelation consistent weighing matrix with the lag order optimally selected using the Newey and West (1994) algorithm. Standard errors robust to clustering at the console level are presented in every estimation.

Restricting the coefficients on the interaction terms in Column 4 to zero yields χ²(2) = 10.54, p < 0.01, implying that the model with interactions has a higher explanatory power.

Testing restrictions in Columns 3 and 4 that the effect of superstars and non-superstar games are equal yields χ²(1) = 4.06, p < 0.05 for Column 3 and χ²(1) = 5.92, p < 0.05 for Column 4. Thus, in both cases we reject the null hypothesis that superstar and non-superstar games have equivalent effects on console market share.

Restricting the coefficients on the interaction terms in Estimation 4 to be the same yields χ²(1) = 4.22, p < 0.05, implying that the two interaction terms do not have the same magnitude.

This is found by solving 0.685–0.009 × \( \mathrm{Console}\ {\mathrm{Age}}_t^k \) = −0.048 + 0.001 × \( \mathrm{Console}\ {\mathrm{Age}}_t^k \).

A joint test of the hypothesis that the interaction terms are zero in Column 8 in Table 5 and Column 12 in Table 6 yields χ²(2) = 184.13, p < 0.01.

A test of the hypothesis that the coefficients on \( {\mathrm{IB}}_t^k \) are equal in Column 8 in Table 5 and Column 12 in Table 6 yields χ²(1) = 156.53, p < 0.01.

These elasticities are calculated from the linear models of Equations 2 and 3. We do not calculate elasticities for the non-linear model presented in Equation 1 given the risk of misinterpretation (Ai and Norton 2003) and note this as a limitation of our current level of analysis.

We note that these elasticities could be quite different if we use different variable values (Ai and Norton 2003).

We also test to see if elasticities are the same over time within the same software type (separately for both superstars and non-superstar games). We reject the null hypothesis of similar coefficients in every case with p < 0.01. We suppress the test statistics given the number of individual tests conducted. The results are available from authors upon request.

For the hardware demand estimations, the Durbin-Wu-Hausman test comparing OLS and column (1) in Table 4 yields χ²(3) = 14.15, p < 0.01 while comparing OLS and column (2) yields χ²(5) = 15.65, p < 0.01. Similarly, for the non-superstar supply estimations, the Durbin-Wu-Hausman test comparing OLS and column (5) results in χ²(1) = 147.38, p < 0.01 while comparing OLS and (6) yields χ²(2) =86.62, p < 0.01. For the superstar supply estimations, the Durbin-Wu-Hausman test comparing OLS and column (9) yields χ²(1) = 138.29, p < 0.01 while comparing OLS and column (10) yields χ²(2) = 119.33, p < 0.01.

We thank an anonymous reviewer for this suggestion.

We thank an anonymous reviewer for pointing out this issue.

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Title: The impact of superstar and non-superstar software on hardware sales: the moderating role of hardware lifecycle
Authors: Richard T. Gretz
Ashwin Malshe
Carlos Bauer
Suman Basuroy
Publication date: 13-02-2019
Publisher: Springer US
Published in: Journal of the Academy of Marketing Science / Issue 3/2019
Print ISSN: 0092-0703
Electronic ISSN: 1552-7824
DOI: https://doi.org/10.1007/s11747-019-00631-3

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