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Fatores críticos de sucesso para ferramentas de Business Analytics. / Critical success factors of business analytics tools.Cezar Sayão 15 September 2017 (has links)
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).
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Business Intelligence, Analytics and Human Capital: Current State of Workforce Analytics in SwedenGustafsson, Daniel January 2012 (has links)
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.
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East Tennessee State University Area Location MapEast Tennessee State University Research Advisory Council, Ejlali, Majid 01 January 1969 (has links)
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
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FACTORS INFLUENCING WILLINGNESS TO ADOPT ADVANCED ANALYTICS IN SMALL BUSINESSESGrant, Navneet Chattha 15 September 2020 (has links)
No description available.
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Development of a Performance Management System Artifact Based on Business Intelligence for SMEs: A Design Science Research Methodology ApproachSeifollahi, Mohammadreza 03 October 2022 (has links)
Although interest in Performance Measurement and Management Systems (PMMS) and their application in various organizations has grown substantially, they are mainly developed for larger companies, and practitioners find it challenging to implement these systems in Small and Medium-sized Enterprises (SMEs). SME characteristics such as limited budget, lack of experience and knowledge, and pace of change usually make it difficult for managers to adopt and use traditional PMMS. One of the solutions to the lack of applicability of PMMS in the SME context is provided by integrating traditional PMMS with Business Intelligence (BI) and Business Analytics (BA) tools and technologies. The main goal of this thesis is to design and evaluate a Performance Management System based on Business Intelligence (PMS-BI) artifact that contains frameworks, architectures, tools and guidelines to help SMEs evaluate their current Performance Management System (PMS), select the most suitable PMS-BI system components, and design their own PMS based on BI. This artifact also guides them throughout the year by providing tools to assess their PMS-BI and maintain it. For this thesis, I applied a Design Science Research (DSR) methodology to collect the relevant information needed in the design step, design and develop the artifact, and evaluate it. I used the case study approach in the design's information collection and artifact evaluation phases. The final artifact consists of an executive document that guides the SME managers to design their own PMS-BI and helps them have a perception of its efficiency. In the data collection phase, I systematically reviewed the relevant literature to understand best practices, then interviewed eleven participants from seven SMEs to find out the gaps and requirements regarding PMS in SMEs. These participants are directly responsible for the PMS in their organization. Then by combining this information, I extracted the PMS-BI features. In the creative process of system design, I used these features to develop the artifact. Then, I presented the results to the same participants in the form of an executive summary. Finally, I used a “usability questionnaire” to understand participants' opinions about the artifact and document their feedback. My research fills the gap in the application of DSR in the PMS artifact development. This thesis helps researchers understand the application of DSR in designing and developing PMS artifacts for SMEs. In addition, the final artifact can be used by SMEs to develop and apply their own PMS-BI.
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The Impact of Artificial Intelligence in the Customer Journey: A Case Study of Bosch USA and Defy South AfricaMunyengeterwa, Tariro S 01 May 2021 (has links)
Artificial intelligence (AI) continues to gain traction and is increasingly reshaping the media and marketing communications field. While significant research has been conducted on the impact of AI in other fields, there is little empirical evidence on how AI is affecting the customer journey. The present study explored both organizations’ current use of AI tools and how customer perceptions about AI affect AI usage and adoption through the lens of diffusion of innovation theory. The research was conducted using mixed-method qualitative research. In-depth interviews and a case study content analysis were conducted to collect and analyze the data. The results suggest that consumer perceptions about AI impact levels of adoption when AI is recognized, but there appears to be cognitive dissonance regarding what constitutes AI and complicit acceptance of some of its benefits. Companies in different geographical locations have different levels of AI adoption along the diffusion of innovation stages.
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Supply Chain Analytics implications for designing Supply Chain Networks : Linking Descriptive Analytics to operational Supply Chain Analytics applications to derive strategic Supply Chain Network DecisionsBohle, Alexander, Johnson, Liam January 2019 (has links)
Today’s dynamic and increasingly competitive market had expanded complexities for global businesses pressuring companies to start leveraging on Big Data solutions in order to sustain the global competitions by becoming more data-driven in managing their supply chains.The main purpose of this study is twofold, 1) to explore the implications of applying analytics designing supply chain networks, 2) to investigate the link between operational and strategic management levels when making strategic decisions using Analytics.Qualitative methods have been applied for this study to gain a greater understanding of the Supply Chain Analytics phenomenon. An inductive approach in form of interviews, was performed in order to gain new empirical data. Fifteen semi-structured interviews were conducted with professional individuals who hold managerial roles such as project managers, consultants, and end-users within the fields of Supply Chain Management and Big Data Analytics. The received empirical information was later analyzed using the thematic analysis method.The main findings in this thesis relatively contradicts with previous studies and existing literature in terms of connotations, definitions and applications of the three main types of Analytics. Furthermore, the findings present new approaches and perspectives that advanced analytics apply on both strategic and operational management levels that are shaping supply chain network designs.
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The Effect of Corporate Positions on Social and Political Justice on Consumer Behavior and Financial PerformanceSrinivasan, Esha 01 January 2019 (has links)
A proliferation of social movements and a large group of young, politically active individuals have pushed corporate firms to pay more attention to increase resource allocation to corporate social responsibility. As of now, corporate social responsibility refers to a wide definition of general efforts made by firms to support society through social impacts. A review of current literature on corporate social responsibility reveals a gap in the way the quality is calculated as well as does not take into account the influence of social movements in the past couple of years. This paper assesses the specific social and political justice positions that firms have taken and whether these positions have had an effect on financial performance given the more actively conscious consumer base. Tobin’s Q is used to quantify these effects and show that positive corporate social responsibility increases financial performance, supporting the hypothesis, while negative corporate social responsibility does not significantly affect financial performance. Future research recommends a closer look at the industry differences in the subject as well as a clearer definition of the variance between the different issues that constitute corporate social responsibility.
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A NOVEL FRAMEWORK FOR SOCIAL INTERNET OF THINGS: LEVERAGING THE FRIENDSHIPS AND THE SERVICES EXCHANGED BETWEEN SMART DEVICESAbed, Javad 01 January 2019 (has links)
As humans, we tackle many problems in complex societies and manage the complexities of networked social systems. Cognition and sociability are two vital human capabilities that improve social life and complex social interactions. Adding these features to smart devices makes them capable of managing complex and networked Internet of Things (IoT) settings.
Cognitive and social devices can improve their relationships and connections with other devices and people to better serve human needs. Nowadays, researchers are investigating two future generations of IoT: social IoT (SIoT) and cognitive IoT (CIoT). This study develops a new framework for IoT, called CSIoT, by using complexity science concepts and by integrating social and cognitive IoT concepts. This framework uses a new mechanism to leverage the friendships between devices to address service management, privacy, and security. The framework addresses network navigability, resilience, and heterogeneity between devices in IoT settings. This study uses a new simulation tool for evaluating the new CSIoT framework and evaluates the privacy-preserving ability of CSIoT using the new simulation tool. To address different CSIoT security and privacy issues, this study also proposes a blockchain-based CSIoT. The evaluation results show that CSIoT can effectively preserve the privacy and the blockchain-based CSIoT performs effectively in addressing different privacy and security issues.
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A Framework for Monitoring and Adapting Business Processes Using Aspect-Oriented URNPourshahid, Alireza 28 April 2014 (has links)
Context:
Organizations strive to improve their business processes, and adaptive business processes have recently attracted much attention in that context. However, much research in that area has a narrow focus and does not consider a comprehensive view of the organization and its goals. In addition, Business Intelligence-based monitoring methods are useful for business process improvement but they often present information in a format that is not entirely suited for decision making.
Objectives:
The main objectives of this thesis are to provide:
• A framework to model goals, processes, performance, situations, and improvement patterns using one modeling notation, in an iterative and incremental manner;
• A method for the modeling and analysis of cause-effect relationships between indicators used to measure goal satisfaction; and
• A technique allowing the detection of undesirable, sub-optimal conditions and the application of improvement patterns to the context
Method:
We develop an iterative framework based on the User Requirements Notation (URN) for modeling, monitoring and improving business organizations and their business processes. In addition, we introduce a formula-based evaluation algorithm allowing better analysis of the relationships between the business performance model elements (namely indicators). Furthermore, we use a profiled version of the Aspect-oriented URN (AoURN) with extensions (Business Process Pattern profile), for detecting undesirable conditions and for business process adaptation. We validate the novelty and feasibility of our approach by performing a systematic literature review, by assessing it against Zellner’ mandatory elements of a method, by developing tool support, by performing a pilot experiment and by using real-life examples from different sectors (healthcare and retail).
Results:
The two examples show that through the framework’s iterative approach, organizations at different levels of maturity in their business improvement journey can benefit from the framework. Furthermore, our systematic literature review shows that although there are existing works that enable our vision, most of them have a narrow focus and do not cover the three organization views that are of interest in this research. AoURN allows analysts to find repeated patterns in a context and bundle goal, performance and process models as a self-contained unit. AoURN hence enables the modeling of complex circumstances together with analysis techniques for what-if analysis and process adaptation, all using a unified and integrated modeling language. Finally, the pilot experiment suggests that, with some level of documentation and training, users who are already familiar with URN can use the profiled AoURN provided in this thesis as well as the discussed improvement patterns.
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