[pt] Os sistemas de mensuração de desempenho buscam acompanhar o alcance dos objetivos estratégicos a partir de um conjunto de indicadores que suportem os processos de tomada de decisão. Várias iniciativas, entretanto, têm se mostrado ineficazes devido à subjetividade no desdobramento desses objetivos em indicadores. Métodos de business analytics vêm sendo utilizados para auxiliar esse desdobramento via análise de dados, com maior geração de valor para as organizações. O presente trabalho apresenta a aplicação das técnicas de Random Forest e Bayesian Belief Network para, respectivamente, selecionar indicadores e mapear suas relações em um estudo prático numa empresa do setor de transporte ferroviário de cargas, com foco no suporte ao indicador de disponibilidade de locomotivas. Para o processo de seleção de variáveis, observou-se que o algoritmo Variable Selection Using Random Forest obteve o melhor desempenho em acurácia e tempo de processamento. Na elaboração do mapa estratégico, a combinação do algoritmo Tabu Search com o critério estatístico Bayesian Information Criteria levou à escolha de um arranjo parcimonioso em suas relações, aderente à expectativa inicial associada ao critério estatístico utilizado. Foi observado um significativo vínculo entre a disponibilidade de locomotivas e indicadores operacionais da empresa em estudo, revelando o potencial de influência do modelo operacional nos resultados da disponibilidade. Verifica-se a oportunidade de emprego de técnicas de séries temporais para a previsão de desempenho a partir dos relacionamentos entre indicadores, bem como para aperfeiçoar a fase de seleção de variáveis, com a identificação de possíveis defasagens existentes nesses relacionamentos. / [en] Performance measurement systems seek to monitor the achievement of strategic objectives through a set of indicators that support decision-making processes. Several initiatives, however, have been shown to be ineffective due to the subjectivity in the unfolding of these objectives into indicators. Business analytics methods have been used to assist this deployment via data analysis, with greater value generation for organizations. The present work presents the application of Random Forest and Bayesian Belief Network techniques to, respectively, select indicators and map their relationships in a practical study in a company in the rail freight sector, with a focus on supporting the locomotive availability indicator. For the variable selection process, it was observed that the Variable Selection Using Random Forest algorithm obtained the best performance in accuracy and computation time. In the preparation of the strategic map, the combination of the Tabu Search algorithm with the Bayesian Information Criteria statistical criterion led to the choice of a parsimonious arrangement in its relations, adhering to the initial expectation associated with the statistical criterion used. A significant link was observed between the locomotive availability and operational indicators of the company under study, revealing the potential influence of the operational model on the availability results. There is an opportunity to use time series techniques to predict performance based on the relationships between indicators, as well as to improve the variable selection phase, with the identification of possible lags in these relationships

O processo de tomada de decisão, em diferentes ambientes gerenciais, enfrenta um momento de mudança no contexto organizacional. Nesse sentido, Business Analytics pode ser visto como uma área que permite alavancar o valor dos dados, contendo ferramentas importantes para o processo de tomada de decisão. No entanto, a presença de dados em diferentes formatos representa um desafio. Nesse contexto de variabilidade, os dados de texto têm atraído a atenção das organizações, já que milhares de pessoas se expressam diariamente neste formato, em muitas aplicações e ferramentas disponíveis. Embora diversas técnicas tenham sido desenvolvidas pela comunidade de ciência da computação, há amplo espaço para melhorar a utilização organizacional de tais dados de texto, especialmente quando se volta para o suporte à tomada de decisões. No entanto, apesar da importância e disponibilidade de dados em formato textual para apoiar decisões, seu uso não é comum devido à dificuldade de análise e interpretação que o volume e o formato de dados em texto apresentam. Assim, o objetivo desta tese é desenvolver e avaliar um framework voltado ao uso de dados de texto em processos decisórios, apoiando-se em diversas técnicas de processamento de linguagem natural (PNL). Os resultados apresentam a validade do framework, usando como instância de demonstração de sua aplicabilidade o setor de turismo através da plataforma TripAdvisor, bem como a validação interna de performance e a aceitação por parte dos gestores da área consultados. / The decision-making process, in different management environments, faces a moment of change in the organizational context. In this sense, Business Analytics can be seen as an area that leverages the value of data, containing important tools for the decision-making process. However, the presence of data in different formats poses a challenge. In this context of variability, text data has attracted the attention of organizations, as thousands of people express themselves daily in this format in many applications and tools available. Although several techniques have been developed by the computer science community, there is ample scope to improve the organizational use of such text data, especially when it comes to decision-making support. However, despite the importance and availability of textual data to support decisions, its use is not common because of the analysis and interpretation challenge that the volume and the unstructured format of text data presents. Thus, the aim of this dissertation is to develop and evaluate a framework to contribute with the expansion and development of text analytics in decision-making processes, based on several natural language processing (NLP) techniques. The results presents the validity of the framework, using as a demonstration of its applicability the tourism sector through the TripAdvisor platform, as well as the internal validation of performance and the acceptance by managers.

This paper examines data from Spotify’s API for 2017-2018 to determine the effects of song attributes on the success of tracks on Spotify’s Top 200 Chart across five different countries: the U.S., Norway, Taiwan, Ecuador, and Costa Rica. Two dependent variables are used to measure the success of a song – a track’s peak position on the charts and the number of days it survives on a country’s Top 200 Chart. Using ten separate regressions, one for each dependent variable in all five countries, it is concluded that the presence of a featured guest on a track increases a song’s peak position and the number of days it survives on the charts in almost every country. Further, songs that are perceived as “happier” are more successful for both metrics in Norway and Taiwan while those that are louder and more aggressive have a shorter lifespan on the charts in three out of five of the countries studied. The paper concludes that further research should be conducted with a larger, more diverse dataset to see if these findings hold and if they are present in other countries as well.

To promote higher air quality and reduce greenhouse gas emissions, the Californian government is investing heavily in developing public charging infrastructure to meet its electric vehicle adoption goal of five million zero-emission vehicles on the road by 2030. This thesis investigates the effect of public charging infrastructure availability on electric vehicle adoption at the zip code level in California. The analysis considers other factors that may influence electric vehicle adoption such as education level, income, commute time, gas prices, and public transportation rate. The findings suggest that public charging infrastructure availability does significantly positively correlate with electric vehicle registrations. Linear regressions were run using data from the U.S Department of Energy Alternative Fuels Data Center, IHS Markit vehicle registration data, and the US Census Bureau. The findings support continued investment in public charging infrastructure as a means of promoting electric vehicle adoption.

Terrorist attacks can cause massive casualties and severe property damage, resulting in terrorism crises surging across the world; accordingly, counter-terrorism analytics that take advantage of big data have been attracting increasing attention. The knowledge and clues essential for analyzing terrorist activities are often spread across heterogeneous data sources, which calls for an effective data integration solution. In this study, employing the goal definition template in the Goal-Question-Metric approach, we design and implement an automated goal-driven data integration framework for counter-terrorism analytics. The proposed design elicits and ontologizes an input user goal of counter-terrorism analytics; recognizes goal-relevant datasets; and addresses semantic heterogeneity in the recognized datasets. Our proposed design, following the design science methodology, presents a theoretical framing for on-demand data integration designs that can accommodate diverse and dynamic user goals of counter-terrorism analytics and output integrated data tailored to these goals.

The Swedish health care sector must improve productivity in order to deal with anincreased demand from an aging population with limited resources. In the traditiondriven health care sector, transitioning towards becoming more data-driven has beenidentified as a potential solution. This explorative qualitative case study explores howindividual employees perceive this development at two Swedish hospitals. The resultscomplement theory by presenting propositions that explains drivers and barriers ofthe transition, but also the outcomes of it as perceived by the employees. The studyprimarily concludes that (1) a lack of trust in data and a tradition to base decisions ongut feelings in conjunction with low IT competence make hospital culture a majorobstacle for the transition, and that (2) it is important to understand the employees’perceived outcomes of becoming data-driven as it affects their support of thetransition. The results provide a platform for future research to build on and arevaluable for practitioners as they seek to utilize the drivers and mitigate the barriers.

This dissertation primarily proposes data-driven methods to handle uncertainty in problems related to Enterprise-wide Optimization (EWO). Datadriven methods are characterized by the direct use of data (historical and/or forecast) in the construction of models for the uncertain parameters that naturally arise from real-world applications. Such uncertainty models are then incorporated into the optimization model describing the operations of an enterprise. Before addressing uncertainty in EWO problems, Chapter 2 deals with the integration of deterministic planning and scheduling operations of a network of batch plants. The main contributions of this chapter include the modeling of sequence-dependent changeovers across time periods for a unitspecific general precedence scheduling formulation, the hybrid decomposition scheme using Bilevel and Temporal Lagrangean Decomposition approaches, and the solution of subproblems in parallel. Chapters 3 to 6 propose different data analytics techniques to account for stochasticity in EWO problems. Chapter 3 deals with scenario generation via statistical property matching in the context of stochastic programming. A distribution matching problem is proposed that addresses the under-specification shortcoming of the originally proposed moment matching method. Chapter 4 deals with data-driven individual and joint chance constraints with right-hand side uncertainty. The distributions are estimated with kernel smoothing and are considered to be in a confidence set, which is also considered to contain the true, unknown distributions. The chapter proposes the calculation of the size of the confidence set based on the standard errors estimated from the smoothing process. Chapter 5 proposes the use of quantile regression to model production variability in the context of Sales & Operations Planning. The approach relies on available historical data of actual vs. planned production rates from which the deviation from plan is defined and considered a random variable. Chapter 6 addresses the combined optimal procurement contract selection and pricing problems. Different price-response models, linear and nonlinear, are considered in the latter problem. Results show that setting selling prices in the presence of uncertainty leads to the use of different purchasing contracts.

O processo de tomada de decisão, em diferentes ambientes gerenciais, enfrenta um momento de mudança no contexto organizacional. Nesse sentido, Business Analytics pode ser visto como uma área que permite alavancar o valor dos dados, contendo ferramentas importantes para o processo de tomada de decisão. No entanto, a presença de dados em diferentes formatos representa um desafio. Nesse contexto de variabilidade, os dados de texto têm atraído a atenção das organizações, já que milhares de pessoas se expressam diariamente neste formato, em muitas aplicações e ferramentas disponíveis. Embora diversas técnicas tenham sido desenvolvidas pela comunidade de ciência da computação, há amplo espaço para melhorar a utilização organizacional de tais dados de texto, especialmente quando se volta para o suporte à tomada de decisões. No entanto, apesar da importância e disponibilidade de dados em formato textual para apoiar decisões, seu uso não é comum devido à dificuldade de análise e interpretação que o volume e o formato de dados em texto apresentam. Assim, o objetivo desta tese é desenvolver e avaliar um framework voltado ao uso de dados de texto em processos decisórios, apoiando-se em diversas técnicas de processamento de linguagem natural (PNL). Os resultados apresentam a validade do framework, usando como instância de demonstração de sua aplicabilidade o setor de turismo através da plataforma TripAdvisor, bem como a validação interna de performance e a aceitação por parte dos gestores da área consultados. / The decision-making process, in different management environments, faces a moment of change in the organizational context. In this sense, Business Analytics can be seen as an area that leverages the value of data, containing important tools for the decision-making process. However, the presence of data in different formats poses a challenge. In this context of variability, text data has attracted the attention of organizations, as thousands of people express themselves daily in this format in many applications and tools available. Although several techniques have been developed by the computer science community, there is ample scope to improve the organizational use of such text data, especially when it comes to decision-making support. However, despite the importance and availability of textual data to support decisions, its use is not common because of the analysis and interpretation challenge that the volume and the unstructured format of text data presents. Thus, the aim of this dissertation is to develop and evaluate a framework to contribute with the expansion and development of text analytics in decision-making processes, based on several natural language processing (NLP) techniques. The results presents the validity of the framework, using as a demonstration of its applicability the tourism sector through the TripAdvisor platform, as well as the internal validation of performance and the acceptance by managers.

This paper aims to measure the economic impact of different NBA player achievements on the athletic apparel companies, Nike, Adidas, and Under Armour. It looks at players affiliated with the brands who participated in three different events in the NBA from from 2007 to 2017: the All-Star game, the All-NBA awards, and starting lineups in the Finals. Monthly stock returns for each company were calculated for the months the events took place: January, February, May, and June. The return of each company was then regressed on total number of players each company had in the events. Four total regressions were run for the months of January, February, May, and June. Understanding the economic implications of endorsed players participating in these events can help athletic apparel companies draft more cost efficient endorsement contracts.

In the eyes of Hollywood producers, film remakes are popular endeavors to undertake. Part of the logic behind remakes is that they will likely perform successfully in the domestic box office because of the tried-and-tested formula that was the remake’s source material as well as the pre-existing fanbase. In addition, over the past two decades the international box office, particularly countries like China and Russia, are overtaking the United States in generating box office revenue. Hence, with the increasing popularity of producing remakes, as well as the growing significance of foreign markets for the entertainment industry, I test whether the international share of the box office is higher for remakes compared to other types of films. I control for several standard variables including type of film, genre, production budget, critical review score, recency of film, and number of installments in the film franchise. In conclusion, I find that although film remakes do not achieve significantly higher foreign box office shares, factors like sequel films, the horror genre, production budget and critical review scores play a significantly positive role in determining foreign box office share, thus indicating the preferences of foreign film audiences.