A Strategic Motivation for Commodity Bundling

Meza, David de; Carabjo, J.; Seidmann, Daniel J.

doi:10.2307/2098499

Cited by 191 publications

(126 citation statements)

References 5 publications

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“…In our model, incompatibility (exclusive alliances) is used as a semicollusive device rather than a foreclosing device, and so it is more closely related to the facilitating-device story where bundling relaxes competition by allowing competing firms to better differentiate themselves (see Carbajo, De Meza, and Seidman, 1990;Seidman, 1991;Chen, 1997;and Denicolo, 2000). 16 This line of research usually examines the case where only one firm bundles and the other sells only one component, while we consider cases where all firms are active in equilibrium, selling their components or systems.…”

Section: Resultsmentioning

confidence: 99%

Mix-and-Match Compatibility in Asymmetric System Markets

Hahn¹,

Kim²

2012

JITE

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This paper shows that the private incentive for mix-and-match compatibility in system markets diverges from the social planner's incentive if competing suppliers are asymmetric in production cost or product quality. There can be too much or too little compatibility when the market is served by fully integrated system suppliers. Also, the market outcome involves socially too much incompatibility in the form of exclusive technological alliances when the market is composed of independent component suppliers. These results contrast with the standard one obtained in the symmetric setup and shed new light on public policy towards compatibility, technological alliances, and bundling practices in system markets.

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Section: Resultsmentioning

confidence: 99%

Mix-and-Match Compatibility in Asymmetric System Markets

Hahn¹,

Kim²

2012

JITE

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“…Although not directly related to our study, see also Ansari et al (1996) for the determination of the optimal number of items to be included in a service bundle, Ben-Akiva and Gershenfeld (1998) for customer choice behavior for bundles with correlated demand, Carbajo et al (1990) for incentives for bundling under imperfect competition, Hanson and Martin (1990) for the calculation of optimal bundle prices in a deterministic setting, using mixed integer linear programming, Ernst and Kouvelis (1999) for the effect of selling product bundles (as opposed to price bundles in our case) on inventory decisions, and Stremersch and Tellis (2002) for a clear discussion of bundling terms which are used in marketing, economics and law literature in a somewhat unclear way. Finally, we note the growing literature on bundling of information goods (see, for example, Bakos and Brynjolfsson (1999)).…”

Section: Introductionmentioning

confidence: 99%

Bundle pricing of inventories with stochastic demand

Bulut

Gürler

Şen

2009

European Journal of Operational Research

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We consider a retailer selling a fixed inventory of two perishable products over a finite horizon. The products are sold individually or as part of a bundle. The customers arrive following a Poisson Process and each customer makes a purchasing decision based on her reservation prices of individual products: she either buys one of the individual products, buys the bundle or leaves without a purchase. Assuming that the customer reservation prices follow a bivariate distribution, we determine the optimal product and the bundle prices that maximize the expected revenue. The performances of three bundling strategies (mixed bundling, pure bundling and unbundling) under different reservation price distributions, demand arrival rates and starting inventory levels are compared. The impact of the shape of the reservation prices is also investigated by considering both the bivariate normal and bivariate gamma densities, the latter of which has not been considered before in the literature. Our numerical results based on the bivariate normal reservation prices indicate that the performances of the policies heavily depend on the parameters of the demand process and the initial inventory levels. Bundling is observed to be most effective with negatively correlated reservation prices and when the starting inventory levels are high. When the starting inventory levels of two products are equal, most of the benefits of bundling can be achieved through pure bundling in case of excess supply. However, the mixed bundling strategy clearly dominates the other two when the starting inventory levels are not equal. Our numerical results show that bundling becomes more effective and the expected revenues increase as the products become less substitutable and more complementary. We also observe that the correct modeling of the reservation price distribution is important, and the use of sub-optimal prices resulting from assuming bivariate normal density when in fact the appropriate distribution is bivariate gamma may result in significant losses in the expected revenue especially if the reservation prices are negatively correlated. Finally, the model is extended to allow for price changes during the selling horizon using a dynamic programming formulation. It is shown that offering price bundles mid-season may be a more effective mechanism than changing individual product prices.

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“…29 This bundling example is taken from Section III.E of Nalebuff (2004). See section II of Whinston (1990), Carbajo, De Meza, and Seidman (1990) and Chen (1997a) for earlier analyses. p A 12 ≈ 0.61 ; p B 2 ≈ 0.24.…”

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confidence: 99%