Modeling the impact of product preannouncements in the context of indirect network externalities

Nagard-Assayag, Emmanuelle Le; Manceau, Delphine

doi:10.1016/s0167-8116(00)00031-8

Cited by 52 publications

(34 citation statements)

References 25 publications

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“…In management and marketing, the model helps to identify markets for content providers and end consumers, and to determine the optimum size of the two user networks (Le Nagard- Assayag & Manceau, 2001;Nair, Chintagunta, & Dubé, 2004). It also attempts to determine which markets to subsidize in order to avoid a "chicken and egg" situation (Parker & Van Alstyne, 2005).…”

Section: Value Propositions and Two-sided Market Theorymentioning

confidence: 99%

Two-sided Internet platforms: A business model lifecycle perspective

Muzellec

Ronteau

Lambkin

2015

Industrial Marketing Management

211

186

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Research Highlights: Two-sided Internet platforms need to formulate two different value propositions -one for the end-user side (B2C) and one for the business side (B2B). In two-sided Internet platforms, end-users are part of the value proposition for business customers.  The business model and the marketing orientation (B2B or B2C) of a two-sided Internet Platform tend to evolve over time towards the B2B side. In two-sided Internet platforms, the monetization of the business model is "B2B oriented". 4 ABSTRACT Multi

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Section: Value Propositions and Two-sided Market Theorymentioning

confidence: 99%

Two-sided Internet platforms: A business model lifecycle perspective

Muzellec

Ronteau

Lambkin

2015

Industrial Marketing Management

211

186

View full text Add to dashboard Cite

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“…Dranove and Gandal 2003;Gandal, Kende and Rob 2000;LeNagard-Assayag and Manceau 2001;Ohashi 2003;Park 2004;Rysman 2004). Second, it is unlikely that the variables of focal interest to us, such as superstar introductions, software quantity, and software quality, depend upon contemporaneous hardware unit sales.…”

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confidence: 99%

The Effect of Superstar Software on Hardware Sales in System Markets

Binken

Stremersch

2009

Journal of Marketing

134

157

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AND KEYWORDS AbstractSystems are composed of complementary products (e.g., video game systems are composed of the video game console and video games). Prior literature on indirect network effects argues that, in system markets, sales of the primary product (often referred to as "hardware") largely depend on the availability of complementary products (often referred to as "software").Mathematical and empirical analyses have almost exclusively operationalized software availability as software quantity. However, while not substantiated with empirical evidence, case illustrations show that certain "superstar" software titles of very high quality (e.g., Super Mario 64) may have had disproportionately large effects on hardware unit sales (e.g., Nintendo N64 console sales). In the context of the U.S. home video game console market, we show that the introduction of a superstar increases video game console sales on average by 14%, over a

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“…The rationale behind this is that DVD players/recorders and digital cameras are likely to substitute their analogue predecessors in the long run. For the CD players, the market potential estimated by Le Nagard‐Assayag and Manceau () was used. The method by Franses () is a unique way to avoid exogenous estimations of m when using a Gompertz curve approach, but as noted by Goodwin and Meeran (, p. 4), fitting the Gompertz curve to small numbers of data points can lead to “infeasible high estimates of m .” In such cases, even the suggested limitation of m to 15 times the cumulative level of sales by the end of year 7 need not necessarily allow an appropriate estimation of m and reliable forecasts.…”

Section: Methodological Considerationsmentioning

confidence: 99%

“…In order to further investigate the estimation of m , the Gompertz curve approach was applied to the CD player data published in Le Nagard‐Assayag and Manceau (), and also used by Decker and Gnibba‐Yukawa () . Based on interviews of marketing managers in the CE industry, Le Nagard‐Assayag and Manceau (, p. 210) concluded that the “ultimate market potential corresponds to the number of households in France M = 20,000,000.” As Table shows, the Gompertz curve approach is sensitive to the different levels of m . Forecasting accuracy is lowest when

\overset{m}{true}

is generated by using Franses's () approach and the concerning value is more than 15 times the number of households in France at that time.…”

Section: Endogenous Estimation Of the Market Potential As Suggested Bmentioning

confidence: 99%

Comments on the Model Testing by Goodwin and Meeran (2012): Comparison of the Utility‐based and the Gompertz Curve Approach for High‐technology Product Sales Forecasting

Gnibba-Yukawa¹,

Decker²

2012

J of Product Innov Manag

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Starting from a comprehensive examination of recent empirical studies focusing on consumer behavior in high‐technology markets and the resulting identification of factors probably affecting individual buying decisions as well as aggregate product sales, Decker and Gnibba‐Yukawa developed and empirically verified a utility‐based sales forecasting approach in their earlier work. Based on data for 14 consumer electronic products and using the Gompertz curve as a benchmark, Goodwin and Meeran carried out a “more extensive testing” of this proposal. However, at least from a practical point of view, the plausibility of their testing framework regarding the market potential m is not unquestionable. This paper, therefore, first discusses some theoretical aspects of both approaches by addressing issues challenged by Goodwin and Meeran, especially regarding the use of short time series and the consideration of replacement purchases. Then, the quasi‐endogenous estimation method for m favored by Goodwin and Meeran for the Gompertz curve is examined in terms of sensitivity to better understand its influence on sales forecasts, and the adequacy of the suggested range for m in the case of the approach by Decker and Gnibba‐Yukawa is investigated. In addition, the results presented in Goodwin and Meeran are considered from a more distant perspective, and possible causes of the variations in forecasting accuracy are discussed, which finally reveals that the forecasting performance of the utility‐based approach is not that “disappointing” as claimed. It provides more accurate (or at least equivalent) forecasts than the Gompertz curve approach in 64% of the cases considered. Furthermore, if product 14 (portable MP3 players) is excluded from the analysis because of the nonconsideration of probably existing product improvement effects, then the utility‐based approach, on average, outperforms the benchmark in all forecasting years. Altogether, this suggests that the approach by Decker and Gnibba‐Yukawa could achieve more accurate forecasts when applying a more reasonable range for m, rather than varying it between 2 and 15 times the cumulative sales by the end of year 7 as proposed by Goodwin and Meeran. It turns out that the Gompertz curve approach can perform on a par with the utility‐based approach in high‐technology product sales forecasting based on short time series if the market potential m is estimated exogenously. A combination of the outcomes of both approaches can even lead to more accurate forecasts as when being used individually insofar as composite forecasting seems to be a practicable approach to the problem of shorter time series compelled by the accelerated diffusion speed in high‐technology markets, rather than relying on one presumably “best” model.

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Modeling the impact of product preannouncements in the context of indirect network externalities

Cited by 52 publications

References 25 publications

Two-sided Internet platforms: A business model lifecycle perspective

Two-sided Internet platforms: A business model lifecycle perspective

The Effect of Superstar Software on Hardware Sales in System Markets

Comments on the Model Testing by Goodwin and Meeran (2012): Comparison of the Utility‐based and the Gompertz Curve Approach for High‐technology Product Sales Forecasting

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