Multibrand price dispersion

We study a market in which several firms potentially each supply a number of “brands” of fundamentally the same product. In fashion, for example, a single firm might retail similar items under different labels and different prices. Consumers differ in which products they consider for their purchase, and firms compete using (multi-dimensional) mixed pricing strategies for their brands. Using relative elasticity conditions, we discuss when firms choose to offer uniform pricing across their brands, and when they use segmented pricing so that one “discount” brand is always priced below another. We solve duopoly models in which equilibria can be derived for all parameters. We discuss the impact of introducing a new brand, of imposing a requirement to set uniform prices across a firm’s brands, and of mergers between single-brand firms

Product and marketing actions in a competitive scenario

We analyze product and marketing actions and their consequences on firm competitive outcomes. These actions are investigates in relative terms compared to a firm’s direct competitors. Our results shed new light on how a firm’s choices regarding product portfolio and marketing postures affect its performance, while accounting for competitive conditions in the external environment. The theory is tested using data from the US apparel industry

Multibrand price dispersion

We study a market in which several firms potentially each supply a number of "brands" of fundamentally the same product. In fashion, for example, a single firm might retail similar items under different labels and different prices. Consumers differ in which products they consider for their purchase, and firms compete using (multi-dimensional) mixed pricing strategies for their brands. Using relative elasticity conditions, we discuss when firms choose to offer uniform pricing across their brands, and when they use segmented pricing so that one "discount" brand is always priced below another. We solve duopoly models in which equilibria can be derived for all parameters. We discuss the impact of introducing a new brand, of imposing a requirement to set uniform prices across a firm's brands, and of mergers between single-brand firms

Optimization and revenue management in complex networks

This thesis consists of three papers in optimization and revenue management over complex networks: Robust Linear Control in Transmission Systems, Online Learning and Optimization Under a New Linear-Threshold Model with Negative Influence, and Revenue Management with Complementarity Products. This thesis contributes to analytical methods for optimization problems in complex networks, namely, power network, social network and product network. In Chapter 2, we describe a robust multiperiod transmission planning model including renewables and batteries, where battery output is used to partly offset renewable output deviations from forecast. A central element is a nonconvex battery operation model plus a robust model of forecast errors and a linear control scheme. Even though the problem is nonconvex we provide an efficient and theoretically valid algorithm that effectively solves cases on large transmission systems. In Chapter 3, we propose a new class of Linear Threshold Model-based information-diffusion model that incorporates the formation and spread of negative attitude. We call such models negativity-aware. We show that in these models, the expected positive influence is a monotone sub-modular function of the seed set. Thus we can use a greedy algorithm to construct a solution with constant approximation guarantee when the objective is to select a seed set of fixed size to maximize positive influence. Our models are flexible enough to account for both the features of local users and the features of the information being propagated in the diffusion. We analyze an online-learning setting for a multi-round influence-maximization problem, where an agent is actively learning the diffusion parameters over time while trying to maximize total cumulative positive influence. We develop a class of online learning algorithms and provide the theoretical upper bound on the regret. In Chapter 4, we propose a tractable information-diffusion-based framework to capture complementary relationships among products. Using this framework, we investigate how various revenue-management decisions can be optimized. In particular, we prove that several fundamental problems involving complementary products, such as promotional pricing, product recommendation, and category planning, can be formulated as sub-modular maximization problems, and can be solved by tractable greedy algorithms with guarantees on the quality of the solutions. We validate our model using a dataset that contains product reviews and metadata from Amazon from May 1996 to July 2014. We also analyze an online-learning setting for revenue-maximization with complementary products. In this setting, we assume that the retailer has access only to sales observations. That is, she can only observe whether a product is purchased from her. This assumption leads to diffusion models with novel node-level feedback, in contrast to classical models that have edge-level feedback. We conduct confidence region analysis on the maximum likelihood estimator for our models, develop online-learning algorithms, and analyze their performance in both theoretical and practical perspectives

Essays on Complementary Products and Strategies

Complementary products contribute significantly to the growth and sustenance of primary products and platforms in many high technology product markets. Although different literatures have investigated issues related to complementary products, our understanding on this topic is limited. This dissertation aims to address some of the questions in the growing literature on complementary products. Following the literature review, the third chapter develops the conceptual underpinnings of product complementarity and examines commonly specified definitions to clarify the dimensions of product complementarity. The fourth chapter addresses the boundary question from the perspective of a primary product firm. The theoretical model identifies the antecedents of the internalization decision emphasizing the influence of type of product complementarity and key environmental contingences, viz., technological and market demand uncertainty. The fourth and fifth chapters of the thesis examine the role of type of complementarity in predicting the governance choices of 31 public businesses over a time frame of 26 years in the PC industry, a setting where complementary products have significantly influenced the competitive and technological landscape. The study findings reveal that type of complementarity along with environmental contingences influence a firm's choice of internalization, alliances or complementor make. Market demand uncertainty influences the choice of strategy towards complementary product for moderately increasing levels of uncertainty while technological uncertainty predicts the governance choices for both low and moderately increasing levels of uncertainty. In addition, in accordance with emerging literature in the Transaction Cost Economics logic (Leiblein & Miller, 2002; Jacobides, 2005) the findings highlight the role of firm capabilities. The dissertation attempts to contribute to the strategy literature by explicating the importance of nature of complementary products, so far not addressed in traditional TCE work.Ph.D., Business Administration -- Drexel University, 201