This study surveys the theoretical literature dealing with the behavior of technology suppliers in the presence of network externalities with a focus on economies of compatibility setting and promotional pricing. Positive network externalities arise when a good is more valuable to a user because more users adopt the same good or compatible ones. There are two issues with network externalities: demand side and supply side. This paper focuses on the supply side, and it relates the way that technologies are chosen and promoted. On the supply side, product compatibility choice, technology sponsorship, penetration pricing, and product pre-announcement are the competing strategies of firms operating in a market with network externalities. Among these strategies, compatibility choice decisions and promotional pricing are presented in the two different subsections, which follows. / Master of Arts

本文以三階段賽局，在消費者偏好異質、只有下游廠商 (即線上音樂服務提供廠商) 擁有消費者偏好資訊、上游 (即傳統唱片音樂製作廠商)只 能透過猜測之下，分析資訊財上游內容製造、實體提供廠商、及下游平台銷售廠商之間的互動關係。 在上游廠商猜測需求的值對其利潤的影響方面：其猜測值若小於實體唱片的願付價格，表示廠商對消費者願付 價格的猜測較低，此時提高猜測的值能使利潤增加；反之，當猜測需求的值大於實體唱片的願付價格，此時提高猜測的值會使利潤下降。 並且發現，上游廠商若能提升實體唱片的價值，即能提高其利潤。 在下游提供的平台水準方面，不論上游廠商是否提供資訊財、下游廠商是否選擇商品組合或個別出售，下游廠商的最適平台投入水準皆相同。 在上游廠商決定不提供實體資訊財，只藉由授權下游廠商以獲利下，下游廠商在商品組合的內容夠多時，商品組合能為其帶來較個別銷售高的 利潤。因在此情況下，只有商品組合的最適定價考慮了消費者偏好，使廠商利潤得以較個別銷售下提升。 在上游廠商提供實體資訊財下，下游廠商在上游對實體資訊財的定價高於消費者對此資訊財實體收藏價值的評價，且商品組合內容夠多時， 採商品組合能為其帶來較個別銷售高的利潤。 最後，本論文認為線上音樂的興起除了受消費者收聽習慣改變及網路普及的影響外，更重要的是由原本傳統唱片業者組成的市場環境有利於 線上音樂的發展，並認為因應線上音樂的崛起，傳統實體唱片廠商並非走入夕陽，仍可透過其身為資訊財內容提供者、握有曲目版權的優勢 ，和下游廠商透過授權契約議定有利於自身的契約，或是更進一步，推動產業進行上、下游的整併。

<p>The ability to digitize information goods such as music and movies and the growing accessibility of the Internet has led to online piracy and the emergence of a new class of retailers that specialize in digital downloads. Both online piracy and digital retailers have changed the dynamics of the information goods distribution channel. In my dissertation I focus on issues related to this change.</p><p>In the first chapter, "Digital music set free: the flip side of DRM," I study the effect of Digital Rights Management (DRM) mechanisms on the competition between traditional and digital retailers and on online piracy. DRM refers to technologies designed to control how end users may access, copy, or convert digital media. In the context of music downloads, DRM makes piracy of digital music more difficult, and until recently, most legal outlets for downloadable music only sold songs with DRM protection. Recently download retailers have convinced record companies to allow them to sell DRM-free music. The introduction of DRM-free music raises several important questions: Will music piracy increase as the opponents of DRM-free music predict? Will the music industry profits go up or down? How will CD retailers be affected? Will all labels start selling the unprotected (DRM-free) content? </p><p>I address these and related questions by developing a model of a music distribution channel that allows a record label to sell through both traditional CD retailers and iTunes-like download services at different wholesale prices. Among the interesting results, the analysis indicates that the level of piracy may decline when DRM protection is removed and that the traditional retailers much prefer to compete with distributors of pirated digital music rather than with legal music download services.</p><p>The competition between online and traditional retailers has led to interesting pricing policies on which I focus in the second chapter, "Digital movies at one simple price: the effect on competition." Online retailers tend to prefer uniform pricing (e.g. iTunes Store) where all "products" carry a single price, while traditional retailers do not have a policy of uniform prices. It is important to understand why one retailer should choose a single, uniform price and what impact it has on the competing retailer who chooses multiple prices. I focus specifically on the impact that single price policy adopted by digital retailer has on the traditional retailer. I also analyze the choice of uniform vs. differentiated pricing by modeling the competition between online and traditional retailers for vertically differentiated information goods. Importantly, I demonstrate how the asymmetric equilibrium we observe in the market today can change systematically with the nature of competition between the retailers.</p> / Dissertation

This thesis makes a contribution to the algorithmic pricing literature by proposing and analyzing techniques for automatically pricing digital and information goods in order to maximize profit in different settings. We also consider the effect on social welfare when agents use these pricing algorithms. The digital goods considered in this thesis are electronic commodities that have zero marginal cost and unlimited supply e.g., iTunes apps. On the other hand, an information good is an entity that bridges the knowledge gap about a product between the consumer and the seller when the consumer cannot assess the utility of owning that product accurately e.g., Carfax provides vehicle history and can be used by a potential buyer of a vehicle to get information about the vehicle. With the emergence of e-commerce, the customers are increasingly price sensitive and search for the best opportunies anywhere. It is almost impossible to manually adjust the prices with rapidly changing demand and competition. Moreover, online shopping platforms also enable sellers to change prices easily and quickly as opposed to updating price labels in brick and mortar stores so they can also experiment with different prices to maximize their revenue. Therefore, e-marketplaces have created a need for designing sophisticated practical algorithms for pricing. This need has evoked interest in algorithmic pricing in the computer science, economics, and operations research communities. In this thesis, we seek solutions to the following two algorithmic pricing problems: (1) In the first problem, a seller launches a new digital good (this good has unlimited supply and zero marginal cost) but is unaware of its demand in a posted-price setting (i.e., the seller quotes a price to a buyer, and the buyer makes a decision depending on her willingness to pay); we look at the question --- how should the seller set the prices in order to maximize her infinite horizon discounted revenue? This is a classic problem of learning while earning. We propose a few algorithms for this problem and demonstrate their effectiveness using rigorous empirical tests on both synthetic datasets and real-world datasets from auctions at eBay and Yahoo!, and ratings on jokes from Jester, an online joke recommender system. We also show that under certain conditions the myopic Bayesian strategy is also Bayes-optimal. Moreover, this strategy has finite regret (independent of time) which means that it also learns very fast. (2) The second problem is based on search markets: a consumer is searching for a product sequentially (i.e., she examines possible options one by one and on observing them decides whether to buy or not). However, merely observing a good, although partially informative, does not typically provide the potential purchaser with the complete information set necessary to execute her buying decision. This lack of perfect information about the good creates a market for intermediaries (we refer to them as experts) who can conduct research on behalf of the buyer and sell her this information about the good. The consumer can pay these intermediaries to learn more about the good which can help her in making a better decision about whether to buy the good or not. In this case, we study various pricing schemes for these information intermediaries in a search-based environment (e.g., selling a package of $k$ reports instead of selling a single report or offering a subscription based service). We show how subsidies can be an effective tool for a market designer to increase the social welfare. We also model quality level in the experts and study competition dynamics by computing equilibrium strategies for the searcher and two experts with different qualities. Surprisingly, sometimes an improvement in the quality of the higher-quality expert (holding everything constant) can be pareto-improving: not only that expert's profit increase, so does the other expert's profit and the searcher's utility. / Ph. D.

As técnicas computacionais de aprendizagem de máquina para classificação ou categorização de dados estão sendo cada vez mais utilizadas no contexto de extração de informações ou padrões em bases de dados volumosas em variadas áreas de aplicação. Em paralelo, a aplicação destes métodos computacionais para identificação de padrões, bem como a classificação de dados relacionados ao consumo dos bens de informação é considerada uma tarefa complexa, visto que tais padrões de decisão do consumo estão relacionados com as preferências dos indivíduos e dependem de uma composição de características individuais, variáveis culturais, econômicas e sociais segregadas e agrupadas, além de ser um tópico pouco explorado no mercado brasileiro. Neste contexto, este trabalho realizou o estudo experimental a partir da aplicação do processo de Descoberta do conhecimento (KDD), o que inclui as etapas de seleção e Mineração de Dados, para um problema de classificação binária, indivíduos brasileiros que consomem e não consomem um bem de informação, filmes em salas de cinema, a partir dos dados obtidos na Pesquisa de Orçamento Familiar (POF) 2008-2009, pelo Instituto Brasileiro de Geografia e Estatística (IBGE). O estudo experimental resultou em uma análise comparativa da aplicação de duas técnicas de aprendizagem de máquina para classificação de dados, baseadas em aprendizado supervisionado, sendo estas Naïve Bayes (NB) e Support Vector Machine (SVM). Inicialmente, a revisão sistemática realizada com o objetivo de identificar estudos relacionados a aplicação de técnicas computacionais de aprendizado de máquina para classificação e identificação de padrões de consumo indica que a utilização destas técnicas neste contexto não é um tópico de pesquisa maduro e desenvolvido, visto que não foi abordado em nenhum dos trabalhos estudados. Os resultados obtidos a partir da análise comparativa realizada entre os algoritmos sugerem que a escolha dos algoritmos de aprendizagem de máquina para Classificação de Dados está diretamente relacionada a fatores como: (i) importância das classes para o problema a ser estudado; (ii) balanceamento entre as classes; (iii) universo de atributos a serem considerados em relação a quantidade e grau de importância destes para o classificador. Adicionalmente, os atributos selecionados pelo algoritmo de seleção de variáveis Information Gain sugerem que a decisão de consumo de cultura, mais especificamente do bem de informação, filmes em cinema, está fortemente relacionada a aspectos dos indivíduos relacionados a renda, nível de educação, bem como suas preferências por bens culturais / Machine learning techniques for data classification or categorization are increasingly being used for extracting information or patterns from volumous databases in various application areas. Simultaneously, the application of these computational methods to identify patterns, as well as data classification related to the consumption of information goods is considered a complex task, since such decision consumption paterns are related to the preferences of individuals and depend on a composition of individual characteristics, cultural, economic and social variables segregated and grouped, as well as being not a topic explored in the Brazilian market. In this context, this study performed an experimental study of application of the Knowledge Discovery (KDD) process, which includes data selection and data mining steps, for a binary classification problem, Brazilian individuals who consume and do not consume a information good, film at theaters in Brazil, from the microdata obtained from the Brazilian Household Budget Survey (POF), 2008-2009, performed by the Brazilian Institute of Geography and Statistics (IBGE). The experimental study resulted in a comparative analysis of the application of two machine-learning techniques for data classification, based on supervised learning, such as Naïve Bayes (NB) and Support Vector Machine (SVM). Initially, a systematic review with the objective of identifying studies related to the application of computational techniques of machine learning to classification and identification of consumption patterns indicates that the use of these techniques in this context is not a mature and developed research topic, since was not studied in any of the papers analyzed. The results obtained from the comparative analysis performed between the algorithms suggest that the choice of the machine learning algorithms for data classification is directly related to factors such as: (i) importance of the classes for the problem to be studied; (ii) balancing between classes; (iii) universe of attributes to be considered in relation to the quantity and degree of importance of these to the classifiers. In addition, the attributes selected by the Information Gain variable selection algorithm suggest that the decision to consume culture, more specifically information good, film at theaters, is directly related to aspects of individuals regarding income, educational level, as well as preferences for cultural goods

As técnicas computacionais de aprendizagem de máquina para classificação ou categorização de dados estão sendo cada vez mais utilizadas no contexto de extração de informações ou padrões em bases de dados volumosas em variadas áreas de aplicação. Em paralelo, a aplicação destes métodos computacionais para identificação de padrões, bem como a classificação de dados relacionados ao consumo dos bens de informação é considerada uma tarefa complexa, visto que tais padrões de decisão do consumo estão relacionados com as preferências dos indivíduos e dependem de uma composição de características individuais, variáveis culturais, econômicas e sociais segregadas e agrupadas, além de ser um tópico pouco explorado no mercado brasileiro. Neste contexto, este trabalho realizou o estudo experimental a partir da aplicação do processo de Descoberta do conhecimento (KDD), o que inclui as etapas de seleção e Mineração de Dados, para um problema de classificação binária, indivíduos brasileiros que consomem e não consomem um bem de informação, filmes em salas de cinema, a partir dos dados obtidos na Pesquisa de Orçamento Familiar (POF) 2008-2009, pelo Instituto Brasileiro de Geografia e Estatística (IBGE). O estudo experimental resultou em uma análise comparativa da aplicação de duas técnicas de aprendizagem de máquina para classificação de dados, baseadas em aprendizado supervisionado, sendo estas Naïve Bayes (NB) e Support Vector Machine (SVM). Inicialmente, a revisão sistemática realizada com o objetivo de identificar estudos relacionados a aplicação de técnicas computacionais de aprendizado de máquina para classificação e identificação de padrões de consumo indica que a utilização destas técnicas neste contexto não é um tópico de pesquisa maduro e desenvolvido, visto que não foi abordado em nenhum dos trabalhos estudados. Os resultados obtidos a partir da análise comparativa realizada entre os algoritmos sugerem que a escolha dos algoritmos de aprendizagem de máquina para Classificação de Dados está diretamente relacionada a fatores como: (i) importância das classes para o problema a ser estudado; (ii) balanceamento entre as classes; (iii) universo de atributos a serem considerados em relação a quantidade e grau de importância destes para o classificador. Adicionalmente, os atributos selecionados pelo algoritmo de seleção de variáveis Information Gain sugerem que a decisão de consumo de cultura, mais especificamente do bem de informação, filmes em cinema, está fortemente relacionada a aspectos dos indivíduos relacionados a renda, nível de educação, bem como suas preferências por bens culturais / Machine learning techniques for data classification or categorization are increasingly being used for extracting information or patterns from volumous databases in various application areas. Simultaneously, the application of these computational methods to identify patterns, as well as data classification related to the consumption of information goods is considered a complex task, since such decision consumption paterns are related to the preferences of individuals and depend on a composition of individual characteristics, cultural, economic and social variables segregated and grouped, as well as being not a topic explored in the Brazilian market. In this context, this study performed an experimental study of application of the Knowledge Discovery (KDD) process, which includes data selection and data mining steps, for a binary classification problem, Brazilian individuals who consume and do not consume a information good, film at theaters in Brazil, from the microdata obtained from the Brazilian Household Budget Survey (POF), 2008-2009, performed by the Brazilian Institute of Geography and Statistics (IBGE). The experimental study resulted in a comparative analysis of the application of two machine-learning techniques for data classification, based on supervised learning, such as Naïve Bayes (NB) and Support Vector Machine (SVM). Initially, a systematic review with the objective of identifying studies related to the application of computational techniques of machine learning to classification and identification of consumption patterns indicates that the use of these techniques in this context is not a mature and developed research topic, since was not studied in any of the papers analyzed. The results obtained from the comparative analysis performed between the algorithms suggest that the choice of the machine learning algorithms for data classification is directly related to factors such as: (i) importance of the classes for the problem to be studied; (ii) balancing between classes; (iii) universe of attributes to be considered in relation to the quantity and degree of importance of these to the classifiers. In addition, the attributes selected by the Information Gain variable selection algorithm suggest that the decision to consume culture, more specifically information good, film at theaters, is directly related to aspects of individuals regarding income, educational level, as well as preferences for cultural goods

Information goods pricing is an essential and emerging topic in the era of information economy. Myriad researchers have devoted considerable attention to developing and testing methods of information goods pricing. Nevertheless, in addition; there are still certain shortcomings as the challenges to be overcome. This study encompasses several unexplored concepts that have attracted research attention in other disciplines lately, such as collaborative prototyping, prospect theory, ERG theory, and maintenance from design, economic, psychological, and software engineering respectively. This study proposes a novel conceptual framework for information goods pricing and investigates the impact of three advantages: (1) provides collaborative process that could generate several prototypes via trial and error in pricing process, (2) deliberates the belief of consumer and producer by maximizing utility and profit, and (3) offers an appropriate service bundle by interacting with consumer and discovering the actual needs. Due to the unique cost structure and product characteristics of information goods, conventional pricing strategies are unfeasible, and a differential pricing strategy is crucial. Nevertheless, few models exist for pricing information goods in the e-service industry. This study proposes a novel collaborative pricing model in which customers are active participants in determining product prices and adopt prices and services that meet their changing needs. This study also shows that the collaborative pricing model generates an optimal bundle price at equilibrium with optimal profit and utility. Theoretical proofs and practical implications justify this pricing model, which is essential for future information goods pricing in information economy. Moreover, we apply iCare e-service delivery as an exemplar and scenario for our system. The objective of iCare is to provide quality e-services to the elderly people anywhere and anytime. The new pricing method will go beyond the current iCare e-service delivery process which furnishes personalized and collaborative bundles. iPrice system for pricing information goods fills the gap among previous literatures which only considers consumers or providers. Different from existing works, iPrice system is novel in integrating distinctively important concepts yielding more benefits to consumers and profits to more providers. Thus, iPrice also guides and provides a roadmap for information goods pricing for future research. / Information goods pricing is an essential and emerging topic in the era of information economy. Myriad researchers have devoted considerable attention to developing and testing methods of information goods pricing. Nevertheless, in addition; there are still certain shortcomings as the challenges to be overcome. This study encompasses several unexplored concepts that have attracted research attention in other disciplines lately, such as collaborative prototyping, prospect theory, ERG theory, and maintenance from design, economic, psychological, and software engineering respectively. This study proposes a novel conceptual framework for information goods pricing and investigates the impact of three advantages: (1) provides collaborative process that could generate several prototypes via trial and error in pricing process, (2) deliberates the belief of consumer and producer by maximizing utility and profit, and (3) offers an appropriate service bundle by interacting with consumer and discovering the actual needs. Due to the unique cost structure and product characteristics of information goods, conventional pricing strategies are unfeasible, and a differential pricing strategy is crucial. Nevertheless, few models exist for pricing information goods in the e-service industry. This study proposes a novel collaborative pricing model in which customers are active participants in determining product prices and adopt prices and services that meet their changing needs. This study also shows that the collaborative pricing model generates an optimal bundle price at equilibrium with optimal profit and utility. Theoretical proofs and practical implications justify this pricing model, which is essential for future information goods pricing in information economy. Moreover, we apply iCare e-service delivery as an exemplar and scenario for our system. The objective of iCare is to provide quality e-services to the elderly people anywhere and anytime. The new pricing method will go beyond the current iCare e-service delivery process which furnishes personalized and collaborative bundles. iPrice system for pricing information goods fills the gap among previous literatures which only considers consumers or providers. Different from existing works, iPrice system is novel in integrating distinctively important concepts yielding more benefits to consumers and profits to more providers. Thus, iPrice also guides and provides a roadmap for information goods pricing for future research.

The subject of this diploma thesis "The proposal of a marketing plan for a company specialized in information products" is to suggest a system based on the theoretical background that generates the marketing plan from the input data. The important requirement for this system is to respect the specialization of the company, which operates in the field of information products. The thesis contains the detailed proposal of a prototype system, in which the focus is on the user-friendliness and the complexity considering the scale of the issue analyzed. The proposal also contains the possibilities and the limitations which are the results from the mentioned solution. The proposed prototype is implemented successfully. The work also includes the demonstration of all implemented functions and the description of the improvements that lead to its use also in other areas of business than the market of information products.

The thesis considers theoretical bases for understanding the product, company, competition and strategies. The thesis compares material and immaterial products and examines the properties of information goods. The thesis considers regularities of strategic management and strategic concepts. The thesis examines analysis used in strategic management, analyses requirements for the system data and describes implemented analysis and their usage.