Empirical Analysis of Indirect Network Effects in the Market for Personal Digital Assistants

Nair, Harikesh S.; Dubé, Jean-Pierre

doi:10.2139/ssrn.408280

Cited by 99 publications

(143 citation statements)

References 30 publications

(36 reference statements)

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“…One approach is to just include the number of games like [Nair et al, 2004, Dube et al, 2010, but in our case we would not be able to capture the bundle's included software appropriately. Another would be to model the software utility like Gowrisankaran et al [2010], who account for a consumer's portfolio of owned products explicitly but link consumer preference structures across hardware and software markets only by using the number of available products.…”

Section: Modelmentioning

confidence: 99%

The Dynamic Effects of Bundling as a Product Strategy

Derdenger

Kumar²

2013

Marketing Science

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Several key questions in bundling have not been empirically examined: Is mixed bundling more effective than pure bundling or pure components? Does correlation in consumer valuations make bundling more or less effective? Does bundling serve as a complement or substitute to network effects? To address these questions, we develop a consumer-choice model from micro-foundations to capture the essentials of our setting, the handheld video game market. We provide a framework to understand the dynamic, long-term impacts of bundling on demand. The primary explanation for the profitability of bundling relies on homogenization of consumer valuations for the bundle, allowing the firm to extract more surplus. We find bundling can be effective through a novel and previously unexamined mechanism of dynamic consumer segmentation, which operates independent of the homogenization effect, and can in fact be stronger when the homogenization effect is weaker. We also find that bundles are treated as separate products (distinct from component products) by consumers. Sales of both hardware and software components decrease in the absence of bundling, and consumers who had previously purchased bundles might delay purchases, resulting in lower revenues. We also find that mixed bundling dominates pure bundling and pure components in terms of both hardware and software revenues. Investigating the link between bundling and indirect network effects, we find that they act as substitute strategies, with a lower relative effectiveness for bundling when network effects are stronger.

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Section: Modelmentioning

confidence: 99%

The Dynamic Effects of Bundling as a Product Strategy

Derdenger

Kumar²

2013

Marketing Science

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“…For example, in two-sided offline media such as TV or magazines, consumers typically only buy and consume media content while professional editors produce and sell content. A wide body of empirical research exists in two-sided markets (see for example, Bucklin and Sismeiro 2003, Nair et al 2004, Ellison and Ellison 200, Tucker and Zhang 2010. However, mobile Internet settings are distinct in that people can take on the dual role of content creators as well as content consumers.…”

Section: Literature Reviewmentioning

confidence: 99%

An Empirical Analysis of User Content Generation and Usage Behavior on the Mobile Internet

2011

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We quantify how user mobile Internet usage relates to unique characteristics of the mobile Internet. In particular, we focus on examining how the mobile-phone-based content generation behavior of users relates to content usage behavior. The key objective is to analyze whether there is a positive or negative interdependence between the two activities. We use a unique panel data set that consists of individual-level mobile Internet usage data that encompass individual multimedia content generation and usage behavior. We combine this knowledge with data on user calling patterns, such as duration, frequency, and locations from where calls are placed, to construct their social network and to compute their geographical mobility. We build an individuallevel simultaneous equation panel data model that controls for the different sources of endogeneity of the social network. We find that there is a negative and statistically significant temporal interdependence between content generation and usage. This finding implies that an increase in content usage in the previous period has a negative impact on content generation in the current period and vice versa. The marginal effect of this interdependence is stronger on content usage (up to 8.7%) than on content generation (up to 4.3%). The extent of geographical mobility of users has a positive effect on their mobile Internet activities. Users more frequently engage in content usage compared to content generation when they are traveling. In addition, the variance of user mobility has a stronger impact on their mobile Internet activities than does the mean. We also find that the social network has a strong positive effect on user behavior in the mobile Internet. These analyses unpack the mechanisms that stimulate user behavior on the mobile Internet. Implications for shaping user mobile Internet usage behavior are discussed. Sang-Pil HanPostdoctoral Researcher, Stern School of Business, New York University AbstractWe quantify how users' mobile Internet use is related to some unique characteristics of the mobile Internet space like usage-based data pricing, the extent of their geographical mobility and their social network behavior. We use a unique panel dataset consisting of individual-level mobile Internet usage data encompassing their multimedia content uploading and downloading behavior along with detailed data on their voice and text calls, and demographics. We build and estimate an individual-level simultaneous equations panel data model using three-stage least-squares (3SLS) after controlling for the endogeneity of the social network. We find that there exists negative temporal interdependence between the content generation and usage behavior for a given user. While the extent of geographical mobility of users is positively associated with their mobile Internet activities, users more frequently engage in content downloading compared to content uploading when they travel. We find statistically significant social network effects that are robust across model specific...

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“…In addition to this aspect of competition, platformbased businesses generally produce network effects, indicating that, as was noted earlier, a consumer's willingness to buy is likely to increase with the network size of the compatible technology standard (Farrell and Saloner 1986). Many empirical studies have supported positive network externalities by using data from various industries such as the software, VCR, video games and/or PDA industries (Brynjolfsson and Kemerer 1996;Ohashi 2003;Nair, Chintagunta and Dubé 2004).…”

Section: Literature Reviewmentioning

confidence: 99%