The first essay studies the channel relationship between the reseller and the manufacturer based on a social network theory framework. We propose a conceptual model that approaches this topic from a relational embeddedness perspective. Our analysis shows how the reseller can strategically develop relational ties with a manufacturer that transform the latters common marketing mix into unique resources that enhance the resellers own profit. Results from a large scale survey of beer resellers in a local Chinese market suggest that in a channel setting, social norms (e.g. communication effectiveness and conflict resolution) and social relations influence the resellers access to the manufacturers valuable resources. Furthermore, we find that over embeddedness affects the resellers profit in a non-linear manner. That is, a resellers effort to develop a relationship with a particular manufacturer may generate information that lacks freshness, objectivity or usefulness, thereby diminishing the resellers profitability. Theory of social contagion states that individuals adoption of new product depends on the adoption of his immediate neighbors in a social network in addition to the influence from other sources. This research models the dynamic diffusion process of new drug in a social network of physicians. We simulated the information transmission process in a social network, where each network entity repetitively influences the probability of connected entitys new product adoption. The simulation approach integrates two seemingly contradictive concepts of cohesion and structural equivalence into a single modeling framework. Besides, it incorporates a coefficient that describes an individual entitys efficiency of information transmission. On the one extreme it assumes that information transmits to only one of the network neighbors and on the other extreme it assumes that information transmits to all of the network neighbors. We revisited Medical Innovation data and empirically find an optimum point for each of the four cities in this data set, using a discrete time hazard model. The four cities demonstrate different patterns of information transmission. Managerially, we suggest different ways of pinpointing initial adopters in different types of social networks. / Marketing

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Global marketing managers are keenly interested in being able to predict the sales of their new products. Understanding how a product is adopted over time allows the managers to optimally allocate their resources. With the world becoming ever more global, there are strong and complex interactions between the countries in the world. My work explores how to describe the relationship between those countries and determines the best way to leverage that information to improve the sales predictions. In Chapter II, I describe how diffusion speed has changed over time. The most recent major study on this topic, by Christophe Van den Bulte, investigated new product di ffusions in the United States. Van den Bulte notes that a similar study is needed in the international context, especially in developing countries. Additionally, his model contains the implicit assumption that the diffusion speed parameter is constant throughout the life of a product. I model the time component as a nonparametric function, allowing the speed parameter the flexibility to change over time. I find that early in the product's life, the speed parameter is higher than expected. Additionally, as the Internet has grown in popularity, the speed parameter has increased. In Chapter III, I examine whether the interactions can be described through a reference hierarchy in addition to the cross-country word-of-mouth eff ects already in the literature. I also expand the word-of-mouth e ffect by relating the magnitude of the e ffect to the distance between the two countries. The current literature only applies that e ffect equally to the n closest countries (forming a neighbor set). This also leads to an analysis of how to best measure the distance between two countries. I compare four possible distance measures: distance between the population centroids, trade ow, tourism ow, and cultural similarity. Including the reference hierarchy improves the predictions by 30 percent over the current best model. Finally, in Chapter IV, I look more closely at the Bass Diffusion Model. It is prominently used in the marketing literature and is the base of my analysis in Chapter III. All of the current formulations include the implicit assumption that all the regression parameters are equal for each country. One dollar increase in GDP should have more of an eff ect in a poor country than in a rich country. A Dirichlet process prior enables me to cluster the countries by their regression coefficients. Incorporating the distance measures can improve the predictions by 35 percent in some cases.

Applying Agent-Based Modeling and Simulation (ABMS) methodology, this paper analyzes the impact of alternative production-sales policies on the diffusion of a new product and the generated NPV of profit. The key features of the ABMS model, that captures the marketplace as a complex adaptive system, are: (i) supply chain capacity is constrained; (ii) consumers' new product adoption decisions are influenced by marketing activities as well as positive and negative word of mouth (WOM) between consumers; (iii) interactions among consumers taking place in the context of their social network are captured at the individual level; and (iv) the new product adoption process is adaptive. Conducting over 1 million simulation experiments, we determined the "best" productionsales policies under various parameter combinations based on the NPV of profit generated over the diffusion process. The key findings are as follows: (1) on average, the build-up policy with delayed marketing is the preferred policy in the case of only positive WOM as well as the case of positive and negative WOM. This policy provides the highest expected NPV of profit on average and it also performs very smoothly with respect to changes in build-up periods. (2) It is critical to consider the significant impact of negative word-of-mouth on the outcomes of alternative production-sales policies. Neglecting the effect of negative word-of-mouth can lead to poor policy recommendations, incorrect conclusions concerning the impact of operational parameters on the policy choice, and suboptimal choice of build-up periods. (authors' abstract)

Submitted by JOSIANE SANTOS DE OLIVEIRA (josianeso) on 2018-04-11T16:07:01Z No. of bitstreams: 1 Pedro Nascimento de Lima_.pdf: 14461751 bytes, checksum: 473cf6676a373e7639a581816a08b7a5 (MD5) / Made available in DSpace on 2018-04-11T16:07:01Z (GMT). No. of bitstreams: 1 Pedro Nascimento de Lima_.pdf: 14461751 bytes, checksum: 473cf6676a373e7639a581816a08b7a5 (MD5) Previous issue date: 2018-01-26 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / A avaliação de decisões estratégicas em condições de profunda incerteza é um desafio significativo para as organizações. Em condições onde informação disponível permite que stakeholders cheguem a um consenso sobre o futuro que será mais provável, ferramentas de planejamento baseadas em predição podem suportar o processo decisório de modo confiável. No entanto, em situações de instabilidade, onde o futuro é altamente incerto, a avaliação de decisões estratégicas utilizando predições pode levar a decisões equivocadas. Tais condições de incerteza frequentemente ocorrem em mercados nascentes, onde há alta incerteza relacionada ao processo de difusão de um novo produto. Na Indústria da Manufatura Aditiva, enquanto alguns especialistas estimam que a indústria pode chegar a faturar 21 bilhões de dólares em 2020, outros estimam que este mercado pode valer até 550 bilhões até 2025. Esta pesquisa emprega a simulação computacional de dinâmica de sistemas utilizando o método Robust Decision Making (RDM) para avaliar decisões estratégicas de fabricantes de sistemas de impressão 3D profissional. Para tanto, este trabalho amplia modelos de dinâmica competitiva e difusão de novos produtos para permitir a simulação no contexto da manufatura aditiva. Em seguida, são desenvolvidos algoritmos necessários para a análise RDM. Para avaliar decisões estratégicas em um amplo conjunto de futuros plausíveis, 10.800 simulações são realizadas. Em seguida, a robustez das estratégias avaliadas é testada, e as vulnerabilidades da estratégia mais robusta localizada são examinadas utilizando técnicas estatísticas. Finalmente, o trabalho identifica estratégias alternativas à estratégia mais robusta. Os resultados da simulação sugerem que fabricantes de sistemas de impressão 3D profissional deveriam perseguir uma estratégia de dominação do mercado agressiva, com um modelo de Pesquisa e Desenvolvimento e proteção intelectual fechado. Finalmente, o trabalho discute implicações gerenciais e teóricas relacionadas à avaliação de decisões estratégicas em condições de incerteza profunda. / Strategic Decision Making under deep uncertainty is a relevant challenge to organizations. When the available information allows sound decision making based on predictions, traditional decision making tools based on maximum expected value can lead to the right decision. Under conditions of deep uncertainty, however, decision making based on predict-then-act approaches might mislead and build overconfidence. In the 3D printing industry, uncertainty is highly relevant. While some experts forecast that this industry will worth 21 billion dollars by 2020, other estimates point that this market can have an economic impact of 550 billion by 2025. This dissertation leverages system dynamics simulation, using the Robust Decision Making (RDM) approach as the analytical framework to evaluate 3D printing Systems Manufacturers’ strategic decisions. I extend an existing competitive dynamics model allowing it to take into account expiring patents dynamics, an important aspect of the 3D printing industry. Then, I test 54 different strategies under 200 different scenarios, highlighting the most robust strategies. Afterwards, I examine the vulnerabilities of a candidate strategy using machine learning algorithms. The experiments showed that aggressive strategies dominate their conservative counterparts, using robustness as a criteria. Also, the results do not lend support to open source Research and Development strategies. Finally, I discuss managerial implications to the 3D printing industry, and theoretical contributions to the Strategic Decision-Making literature.

In this thesis, we investigate the potential of improving demand management activities in the global supply chains. In the increasingly global world, commerce is becoming more complex with an incredible amount of internal and external information available for businesses to select, analyze, understand and react. We identify opportunities for companies to convert data and business information into actionable intelligence. We first study the logistics industry with real data. In the Less-than-Truckload (LTL) market, we analyze an extensive historical shipment database to identify important factors to estimate LTL market rates. Quantifying critical expert knowledge, we develop a price estimation model to help shippers reduce their logistics cost and carriers to better manage their demand. In our second study, we analyze a global supply chain in the high tech industry. Using the demand dependency structure of certain products, we identify collaboration opportunities in the ordering practices that results in increased forecast accuracy. In our third study, we focus on using historical product adoption patterns for developing good pre-launch forecasts for new product introductions. Through a normalization approach and algebraic estimation procedures that use intuitive parameters, our models provide opportunities to significantly improve pre-launch forecast accuracy. Finally, in our fourth study, we develop novel approaches for modeling and mitigating the impact of demand seasonality in new product diffusion context. Focusing mainly on practical applications, our research shows that companies can find innovative ways for turning raw data into valuable insights leading to better demand management activities.