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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Frank M. Bass developed the Bass Diffusion Model to predict how innovative consumer durable products diffuse through consumer markets. This thesis will use data from 1999-2011 to examine the applicability of the Bass Diffusion Model to the introduction of alternative fuel vehicles (AFVs) in the automobile market. The findings in this thesis indicate the Bass Diffusion Model fit the diffusion pattern exhibited by AFVs well, but failed to accurately forecast diffusion patterns outside a given range of data. This thesis investigates potential reasons for the inaccurate 'Out of Sample Forecast', and gives recommendations for directions of future research on AFV diffusion.

In recent years, social networking sites have become a prevailing communication technology in the evolution of the digital era for today’s Internet users (Ipsos Insight 2007). As more and more marketers attempt to harness the power of electronic word-of-mouth (eWOM) in social networking sites (Williamson 2006), rigorous investigation of determinants that lead to consumers’ engagement in eWOM via the social networks is becoming critical. A central question to answer is what factors influence eWOM behavior in social networking sites and what are the underlying processes of eWOM communications in this new social medium. This study focuses on five social relationship variables: social capital, tie strength, homophily, trust, and consumer susceptibility to interpersonal influence that are all related to eWOM behavior in social networking sites. An online survey with a sample drawn from a large southwestern university was conducted to examine predictors of eWOM in social networking sites. Results from a series of multiple regression analyses indicate that certain social relationship variables are significant predictors that relate to social networking site users’ eWOM behavior. Out of the five relationship variables, social capital, homophily, trust, and interpersonal influence were found to significantly relate to users’ engagement in eWOM communications, whereas no effect was found with regard to tie strength. My dissertation research provides a theoretical understanding of consumers’ use of social networking sites as a vehicle for eWOM and contributes to the literature on computer-mediated communication with specific emphasis on online social media. Managerially, findings from this research could provide marketers with valuable information to establish their long-term relationships with consumers and use beneficial eWOM to promote selected brands. In conclusion, examining social relationships in social networking sites could contribute to our understanding of the determinants of consumer engagement in eWOM, which in turn influences the extent and pattern of eWOM and enables companies to deliberate their product diffusion strategies. / text

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.

Understanding how innovations are accepted in a dynamic and complex market environment is a crucial factor for competitive advantage. To understand the relevant factors for this diffusion and to predict success, empirically grounded agent-based models have become increasingly popular in recent years. Despite the popularity of these innovation diffusion models, no common framework that integrates their diversity exists. This article presents a flexible, modular and extensible common description and implementation framework that allows to depict the large variety of model components found in existing models. The framework aims to provide a theoretically grounded description and implementation framework for empirically grounded agent-based models of innovation diffusion. It identifies 30 component requirements to conceptualize an integrated formal framework description. Based on this formal description, a java-based implementation allowing for flexible configuration of existing and future models of innovation diffusion is developed. As a variable decision support tool in decision-making processes on the adoption of innovations the framework is valuable for the investigation of a range of research questions on innovation diffusion, business model evaluation and infrastructure transformation.

Les protéines à radical S-adénosyl-L-méthionine (SAM) utilisent un centre [Fe4S4] réduit pour initier le clivage réductive homolytique de la SAM et la formation d'une espèce hautement réactive - le radical 5'-déoxyadénosyl ou 5'-dA•. Dans la quasi-totalité de cas ce radical alkyl va arracher un atome d'hydrogène sur le substrat et déclencher ainsi sa conversion en produit. On trouve ces enzymes au niveau d'étapes clé de la synthèse de certaines vitamines, antibiotiques, précurseurs de l'ADN ou encore cofacteurs protéiques où elles sont souvent impliquées dans le clivage ou la formation des liaisons C-C, C-N, C-S ou encore C-P. Les travaux réalisés au cours de cette thèse ont été focalisés sur l'étude structurale et fonctionnelle de la protéine HydE ; une enzyme à radical SAM, qui intervient dans la biosynthèse du site actif organométallique de l'hydrogénase à [FeFe]. L'objectif principal était d'identifier le substrat de HydE et d'étudier les détails du fonctionnement d'une protéine à radical SAM. Nous avons réussi à identifier un groupe de molécules, dérivées de la cystéine, contentant un cycle thiazolidine avec un ou deux groupements carboxylates, qui ont une très bonne affinité pour le site actif de HydE. Certains de ces ligands se sont montrés d’être des substrats non physiologiques de l’enzyme. Grâce à ces substrats nous avons pu mettre en évidence un nouveau mécanisme d’attaque radicalaire dans les protéines à radical SAM. En effet, dans HydE nous avons observé une attaque directe du radical 5'-dA• sur l’atome soufre du thioéther appartenant au cycle thiazolidine. Cette réaction constitue un exemple pas comme les autres d’une insertion d’un atome de soufre (ou de sélénium) catalysée par une enzyme à radical SAM. Il s'agit également d'une première observation d'une réaction radicalaire dans les cristaux protéiques d'une enzyme à radical SAM et également un premier suivi en temps réel par la RMN du 13C et 1H de l'accumulation d'un des produits de la réaction catalysée par ces enzymes. Les résultats de calculs théoriques basés sur nos structures cristallographiques de haute résolution suggèrent que dans le cas de cette superfamille de protéines le radical 5'-dA• serait plutôt un état de transition et donc pas une espèce intermédiaire isolable. / Radical S-adenosyl-L-methionine (SAM) proteins use a reduced [Fe4S4] cluster to initiate homolytic reductive cleavage of SAM, which leads to the formation of highly reactive 5'-deoxyadenosyl radical species or 5'-dA•. In almost all cases this alkyl radical will abstract a hydrogen atom from the substrate and thus trigger its conversion into product. These enzymes are found in key steps of the synthesis of certain vitamins, antibiotics, DNA precursors or protein cofactors. They are often involved in the cleavage or formation of C-C, C-N, C-S or C-P bonds. The present thesis work has been focused on the structural and functional study of HydE protein; a radical SAM enzyme, involved in the biosynthesis of the organometallic active site of [FeFe]-hydrogenase. The main goal was to identify the substrate of HydE and to study details of how radical SAM proteins control the highly oxidizing 5'-dA• species. We managed to identify a group of molecules, derived from cysteine, containing a thiazolidine ring with one or two carboxylate groups, which have a very good affinity for the active site of HydE. We have demonstrated some of these ligands are non-physiological substrates of the enzyme. With these substrates we could highlight a new radical attack mechanism in radical SAM proteins. Indeed, in HydE we observed a direct attack on the 5'-dA • radical on the sulfur atom of the thioether belonging to the thiazolidine ring. This is an unprecedented reaction that contrasts with sulfur (or selenium) atom insertion reactions catalysed by some radical SAM enzymes. This is also the first observation of a radical reaction in the protein crystal of a radical SAM enzyme and also the first real-time monitoring by 1H- & 13C-NMR spectroscopy of the accumulation of products of the reaction catalysed by these enzymes. Theoretical calculations based on our high-resolution crystal structures suggest that in the case of this protein superfamily the 5'-dA• radical, which triggers the reaction in radical SAM enzymes, is a transition state and therefore not an isolable intermediate species.