Climate change drives policymakers to reduce emissions and enhance the integration of variable renewable energy sources (VRES) into the power system. Wind power is considered among the most beneficial VRES as it can generate cost-effectively carbon-free electricity but comes with inherent intermittency. Hydropower is a proposed solution among the research community to handle VRES output volatility and ensure balanced energy output to the electricity grid. This thesis addresses the problem by investigating the integration of intermittent wind power into a hydropower system cost-effectively. The research question "How does the integration of wind power affect the hydro operations and the cost of purchased electricity?" is answered within the Design Science Research framework by optimizing a subset of the Røldal-Suldal hydropower system in Norway's NO2 region. The cost-minimization model utilizes historical data from 2018 on water inflows, hourly electricity prices, hourly wind production, and hourly energy consumption for a smelter within the NO2 zone. To reduce the 8,760-time steps and computational concerns, the kmeans clustering algorithm is implemented to obtain four representative weeks. A multiperiod linear programming model is structured to assess the joint operation of wind and hydropower while ensuring a minimum energy production to satisfy the system's power demands. A benchmark scenario with no wind capacity is formed to serve as the basis for comparisons. Ten scenarios with 100-MW incremental steps of wind capacity are implemented. The minimized cost for the benchmark scenario is €104,981,312.34, with electricity purchases covering more than 75% of the energy demand and hydropower satisfying the remaining 25%. Adding 100 MW of wind capacity reduces costs by more than €2,000,000, restricting the purchased energy’s share by 1.49%, which is the equivalent increased share of wind power during each incremental step. A wind capacity of 1,000 MW leads to a 21.24% cost reduction. Hydropower production remains unaffected by the wind integration based on terminal values of reservoir level or turbined water volume. However, the distribution of hydropower production throughout the year changes after installing wind capacity enabling hydropower to utilize stored water optimally to minimize the costs of purchasing energy. A sensitivity analysis to assess the uncertainties tied with the model coefficients shows that increasing initial reservoir levels and adding 1,000 MW of wind capacity is the most influential factor in the optimization model.

<p>Online reviews are frequently utilized to determine a product's quality before purchase along with the photographs and one-to-five star ratings. The research addressed the two distinct problems observed in the review systems. </p> <p>First, due to thousands of reviews for a product, the different characteristics of customer evaluations, such as consumer sentiments, cannot be understood by manually reading only a few reviews. Second, from these reviews, it is extremely hard to understand the change in these sentiments and other important product aspects over the years (temporal analysis). To address these problems, the study focused on 2 main research parts.</p> <p>Part one of the research was focused on answering how topic modelling and sentiment analysis can work together to give deeper understanding on attribute-based product review. The second part compared different topic modelling approaches to evaluate the performances and advantages of emerging NLP models. For this purpose, a dataset consisting of 469 publicly accessible Amazon evaluations of the Kindle E-reader and 15,000 reviews of iPhone products was utilized to examine sentiment Analysis and Topic modelling. Latent Dirichlet Allocation topic model and BERTopic topic model were used to perform topic modelling and to acquire the diverse topics of concern. Sentiment Analysis was carried out to better understand each topic's positive and negative tones. Topic analysis of Kindle user evaluations revealed the following major themes: (a) leisure consumption, (b) utility as a gift, (c) pricing, (d) parental control, (e) reliability and durability, and (f) charging. While the main themes emerged from the analysis of iPhone reviews depended on the model and year of the device, some themes were found to be consistent across all the iPhone models including (a) Apple vs Android (b) utility as gift and (c) service. The study's approach helped to analyze customer reviews for any product, and the study results provided a deeper understanding of the product's strengths and weaknesses based on a comprehensive analysis of user feedback useful for product makers, retailers, e-commerce platforms, and consumers.</p>

Yes / Purpose – The purpose of this article is to identify how the organisations are able to improve their business value through acquisition of business analytics capabilities and by improving their performance. Design/Methodology/Approach – With the help of literature survey, along with standard resource-based view framework, a conceptual model has been developed. These have been statistically tested by collecting the data using the survey questionnaire from 306 selected respondents from various service sector and product based organisations in India. To analyse the data we have used partial least square based structural equation modelling. Findings – The study highlights that by the help of data acquisition and tool acquisition as two vital components, the acquisition of business analytics capabilities could improve the business value of the organisation by strengthening its organisational performance. The findings of this research also indicated that acquisition of business analytics capabilities has a significant influence on organisation’s business process performance and business decision, which in turn significantly influence organisational performance. And, organisational performance eventually positively influences its business value. The model was found to provide an explanative power of 71%. Research Implication – The proposed research model can provide effective recommendations to the management of the organisations to realise the importance of acquisition of effective business analytics capabilities to eventually improve the business value of the organisation. Originality/Value – No specific studies, as yet, have analysed the effects of acquisition of business analytics capabilities for improving organisational performance mediated through business process performance and business decision. Therefore, this research has explored the distinctive effort to empirically validate this understanding.

Maturity models have become a widely used framework for assessing various capabilities and technologies among businesses. This thesis develops a maturity model for assessing Business Analytics (BA) in Swedish e-commerce firms. Business Analytics has become an increasingly important part of modern businesses, and firms are continuously looking for new ways to perform analysis of the data available to them. The prominent previous maturity models within BA have mainly been developed by IT-consultancy firms with the underlying intent of selling their IT services. Consequently, these models have a primary focus on the technical factors towards Business Analytics maturity, partly neglecting the importance of organisational factors. This thesis develops a Business Analytic Maturity Model (BAMM) which fills an identified research gap of academic maturity models with emphasis on the organisational factors of BA maturity. Using a qualitative research design, the BAMM is adapted to the Swedish e-commerce industry through two sequential evaluation stages. The study finds that organisational factors have a greater impact on BA maturity than previous research suggests. The BAMM and the study's results contribute with knowledge of Business Analytics, as well as providing e-commerce firms with insights into how to leverage their data.

Atualmente vivemos em uma sociedade com a maior quantidade de dados já disponíveis em toda a história, e ao mesmo tempo que ocorre o crescimento desta vasta quantidade de informações dispersas, os ambientes empresariais tornaram-se cada vez mais complexos e competitivos. Nos quais gestores necessitam detectar e, se possível, prever tendências para estruturar planos de ação através de análises simples e/ou, por vezes, extremamente complexas dos dados. Dessa forma, o potencial impacto nas organizações referentes à utilização dessas informações em sua gestão tem chamado a atenção tanto de executivos com de pesquisadores. Esta pesquisa buscou identificar os fatores de sucesso de sistemas de Business Analytics (BA) e avaliar empiricamente suas relações de causalidade, sendo utilizada a metodologia de pesquisa científica de Levantamento tipo Survey e a técnica estatística de Modelagem de Equações Estruturais. Além de contribuir com a expansão do conhecimento relacionado a área de Business Analytics, esta dissertação apresentou uma discussão e proposta de delimitação do conceito de BA frente demais termos relacionados a literatura de sistemas de suporte a decisão (i.e. BI, Big Data e Inteligência Competitiva) e a estruturação de uma ferramenta de mensuração de sucesso de SI de BA baseado no modelo apresentado por Delone e McLean. Após a delimitação do conceito de BA, foi discutido os fatores críticos de sucesso (FCS) presentes na literatura e suas particularidades frente a sistemas transacionais (e.g. Enterprise Resource Planning). Os quais foram estruturados em 3 dimensões e 4 construtos: Tecnologia (Qualidade dos dados), Cultura organizacional (Gestão Baseada em Fatos e Engajamento dos executivos) e Pessoas (Qualidade da Equipe). Nesta análise, a Cultura Organizacional apresentou a maior relevância no sucesso de SI (i.e. Uso da Informação e Impacto Individual) dentre as 3 dimensões. Como alta impacto tanto do engajamento dos executivos, como da Cultura organizacional de gestão baseada em fatos. / We have never lived in a society with such amount of data available where, at the same time of this dispersed information growth, managers and decision makers are facing the most challenging and competitive business environment they have ever seen. Being necessary to detect and, if it is possible, predict trends based on simple and/or complex data analysis in order to structure action plans. In this context, the potential impact of data based management on organizations has increased and have been drawing attention of scholars and executives. This research focused on identify critical success factors of Business Analytics (BA) systems and analyze their causal relationship. It was conducted by survey methodology and the statistical technique selected was structural equation modeling (Partial Least Square). Besides the contribution to the body of knowledge of Business Analytics field, this dissertation presents a theoretical discussion about BA definition, its relationship with order support decision systems terms often present on literature (i.e. Business Intelligence, Big Data and Competitive Intelligence), and a search tool for information system success based on DeLone and McLean model. The proposition of critical success factors of Business Analytics systems were based on a comprehensive literature review and were classified into 3 groups and 4 constructs: Technology (Data Quality), Organizational culture (Fact-based management and Executive engagement) and People (Team knowledge and skill). Organizational Culture showed more relevance on Business Analytics system success (i.e. Information Use and Individual Impact) them Technology and People, with high impact of both constructs (Fact-based management and Executive engagement).

The purpose of this study was to determine the criteria used when choosing small package transportation companies. The results suggested that small package transportation industry marketers should focus their marketing efforts towards on-time delivery.

Atualmente vivemos em uma sociedade com a maior quantidade de dados já disponíveis em toda a história, e ao mesmo tempo que ocorre o crescimento desta vasta quantidade de informações dispersas, os ambientes empresariais tornaram-se cada vez mais complexos e competitivos. Nos quais gestores necessitam detectar e, se possível, prever tendências para estruturar planos de ação através de análises simples e/ou, por vezes, extremamente complexas dos dados. Dessa forma, o potencial impacto nas organizações referentes à utilização dessas informações em sua gestão tem chamado a atenção tanto de executivos com de pesquisadores. Esta pesquisa buscou identificar os fatores de sucesso de sistemas de Business Analytics (BA) e avaliar empiricamente suas relações de causalidade, sendo utilizada a metodologia de pesquisa científica de Levantamento tipo Survey e a técnica estatística de Modelagem de Equações Estruturais. Além de contribuir com a expansão do conhecimento relacionado a área de Business Analytics, esta dissertação apresentou uma discussão e proposta de delimitação do conceito de BA frente demais termos relacionados a literatura de sistemas de suporte a decisão (i.e. BI, Big Data e Inteligência Competitiva) e a estruturação de uma ferramenta de mensuração de sucesso de SI de BA baseado no modelo apresentado por Delone e McLean. Após a delimitação do conceito de BA, foi discutido os fatores críticos de sucesso (FCS) presentes na literatura e suas particularidades frente a sistemas transacionais (e.g. Enterprise Resource Planning). Os quais foram estruturados em 3 dimensões e 4 construtos: Tecnologia (Qualidade dos dados), Cultura organizacional (Gestão Baseada em Fatos e Engajamento dos executivos) e Pessoas (Qualidade da Equipe). Nesta análise, a Cultura Organizacional apresentou a maior relevância no sucesso de SI (i.e. Uso da Informação e Impacto Individual) dentre as 3 dimensões. Como alta impacto tanto do engajamento dos executivos, como da Cultura organizacional de gestão baseada em fatos. / We have never lived in a society with such amount of data available where, at the same time of this dispersed information growth, managers and decision makers are facing the most challenging and competitive business environment they have ever seen. Being necessary to detect and, if it is possible, predict trends based on simple and/or complex data analysis in order to structure action plans. In this context, the potential impact of data based management on organizations has increased and have been drawing attention of scholars and executives. This research focused on identify critical success factors of Business Analytics (BA) systems and analyze their causal relationship. It was conducted by survey methodology and the statistical technique selected was structural equation modeling (Partial Least Square). Besides the contribution to the body of knowledge of Business Analytics field, this dissertation presents a theoretical discussion about BA definition, its relationship with order support decision systems terms often present on literature (i.e. Business Intelligence, Big Data and Competitive Intelligence), and a search tool for information system success based on DeLone and McLean model. The proposition of critical success factors of Business Analytics systems were based on a comprehensive literature review and were classified into 3 groups and 4 constructs: Technology (Data Quality), Organizational culture (Fact-based management and Executive engagement) and People (Team knowledge and skill). Organizational Culture showed more relevance on Business Analytics system success (i.e. Information Use and Individual Impact) them Technology and People, with high impact of both constructs (Fact-based management and Executive engagement).

The way organizations make decisions today is very often purely based on intuition or gut-feeling. It does not matter whether decisions are of high risk for the company’s future or not, managers golden-gut is the only thing that determines whether invest- ments should be made or not. Analytics is the opposite of this intuition-based decision making. If taken seriously, almost all decisions in organizations are made on facts that are analytically derived from massive amount of data from internal and external sources such as customer relationship systems to social networks. Business leaders are becoming more aware of analytically based decisions, and some use it more than others. Analytics is usually practiced in finance, customer relationships or marketing. There is, however, one area where analytics is practiced by a small number of companies, and that is on the organization’s workforce. The workforce is usually seen as one of the most complicated areas to practice analytics. An employee is, of course, more com- plicated than a product. Despite this fact, companies usually forget that conducting analytics on employees is very similar to conducting analytics on customers, which has been practiced for many decades. Some organizations are showing great success with applications of Workforce Analytics (WA). Most of these organizations are located in the US or outside of Sweden. This thesis has conducted research on to what extent Workforce Analytics is practiced in Sweden. Empirical findings show that some com- panies use WA in Sweden. The practice is not of highest sophistication of WA. Also, they show aspiration towards the idea of WA and some are locally conducting various of applications.

This dissertation joins an ongoing discussion in the business management and information technology literature surrounding the measurement of an organization’s business analytic capability, the benefits derived from maturing the capability and the improvements being made toward maturity. The dissertation specifically focuses on the healthcare industry in the United States and more specifically independent physician practices specializing in orthopaedics. After an extensive literature review along with expertise from industry leaders and experienced academic faculty, a survey instrument was developed to measure organizational capabilities, technology capabilities and people capabilities which together measured an organizations overall business analytic capability maturity. The survey instrument was delivered to 89 C-suite executives in the target population. A response rate of 36% was achieved resulting in a total of 32 completed responses. The research study provides evidence that improving an organization’s business analytic capability leads to an improvement in the use of analytics to drive business performance. The research study also explored whether or not the use of analytics would improve business outcomes. The results were inconclusive. This could be due to the lag time between the use of analytics and business performance. In addition, the study did not have access to actual outcome data but rather asked the CEO’s whether or not performance in several areas had improved, remained stable or had declined. This measure may not have been precise enough to provide the predictive value needed. As such, this is an area that should be explored further. Finally, the research shows that over the past two years, physician practices have been focused on and successful in improving their business analytic capabilities. Despite these improvements, opportunities exist for physician practices to further their maturity, particularly in the areas of technology capabilities and people capabilities.

Map compiled by the East Tennessee State University Research Advisory Council showing population centers at different radiuses from the East Tennessee State University Campus. Population statistics were drawn from the population projections of the 1970 census. The legend includes overall population totals in 50 mile intervals. Majid Ejlali is listed as a primary author. In his capacity as Director of University Marketing and Promotion, Dr. Ejlali created a series of series of census maps as part of a university proposal to establish a medical school at ETSU. This is one such map. Physical copy resides in the Government Information, Law and Maps Department of East Tennessee State University’s Sherrod Library. / https://dc.etsu.edu/rare-maps/1004/thumbnail.jpg