Development and evaluation of neural network freeway incident detection models using field data

This paper discusses a multi-layer feedforward (MLF) neural network incident detection model that was developed and evaluated using field data. In contrast to published neural network incident detection models which relied on simulated or limited field data for model development and testing, the model described in this paper was trained and tested on a real-world data set of 100 incidents. The model uses speed, flow and occupancy data measured at dual stations, averaged across all lanes and only from time interval t. The off-line performance of the model is reported under both incident and non-incident conditions. The incident detection performance of the model is reported based on a validation-test data set of 40 incidents that were independent of the 60 incidents used for training. The false alarm rates of the model are evaluated based on non-incident data that were collected from a freeway section which was video-taped for a period of 33 days. A comparative evaluation between the neural network model and the incident detection model in operation on Melbourne's freeways is also presented. The results of the comparative performance evaluation clearly demonstrate the substantial improvement in incident detection performance obtained by the neural network model. The paper also presents additional results that demonstrate how improvements in model performance can be achieved using variable decision thresholds. Finally, the model's fault-tolerance under conditions of corrupt or missing data is investigated and the impact of loop detector failure/malfunction on the performance of the trained model is evaluated and discussed. The results presented in this paper provide a comprehensive evaluation of the developed model and confirm that neural network models can provide fast and reliable incident detection on freeways. (C) 1997 Elsevier Science Ltd. All rights reserved.

Expectation Propagation with Factorizing Distributions: A Gaussian Approximation and Performance Results for Simple Models

We discuss the expectation propagation (EP) algorithm for approximate Bayesian inference using a factorizing posterior approximation. For neural network models, we use a central limit theorem argument to make EP tractable when the number of parameters is large. For two types of models, we show that EP can achieve optimal generalization performance when data are drawn from a simple distribution.

The effects of information request ambiguity and construct incongruence on query development

This paper examines the effects of information request ambiguity and construct incongruence on end user's ability to develop SQL queries with an interactive relational database query language. In this experiment, ambiguity in information requests adversely affected accuracy and efficiency. Incongruities among the information request, the query syntax, and the data representation adversely affected accuracy, efficiency, and confidence. The results for ambiguity suggest that organizations might elicit better query development if end users were sensitized to the nature of ambiguities that could arise in their business contexts. End users could translate natural language queries into pseudo-SQL that could be examined for precision before the queries were developed. The results for incongruence suggest that better query development might ensue if semantic distances could be reduced by giving users data representations and database views that maximize construct congruence for the kinds of queries in typical domains. (C) 2001 Elsevier Science B.V. All rights reserved.

Monitoring hotel performance

There is a pressing need to address productivity analysis in the hospitality industry if hotels are to exist as sustainable business entities in rapidly maturing markets. Unfortunately, productivity ratios commonly used by managers are narrowly defined. This study illustrates data envelopment analysis of cross-sectional data that benchmark hotels on observed best performances. Data envelopment analysis enables management to integrate unlike multiple inputs and outputs to make simultaneous comparisons. Findings from the cross-sectional data suggest that some of the hotels have the potential to reduce number of beds and number of part-time staff while increasing revenue.

Design gráfico : análise da relação com a performance da micro e pequena empresa

O design possui um papel central nos negócios, participando de forma crucial no intercambio cultural e econômico da sociedade. Uma das competências do profissional de design gráfico é o desenvolvimento da Identidade Visual Corporativa (IVC). Ela tem como função definir visualmente o perfil de uma empresa, configurando-se, assim, como um composto mercadológico de fundamental importância para diferenciação das organizações, possuindo um importante papel para o crescimento das micro e pequenas empresas (MPEs). O objetivo desta pesquisa é verificar de que forma os gestores de MPEs percebem a atuação do design gráfico sobre a performance do seu negócio, avaliada a partir da sua experiência com a IVC desenvolvida por um profissional de design e aplicada pela empresa em suas atividades. A pesquisa é aplicada, em relação a sua finalidade, e exploratória, quanto ao seu objetivo. Sua condução se deu a partir de pesquisa bibliográfica, entrevista semiestruturada com os gestores das MPEs investigadas e pesquisa documental em materiais fornecidos pelos sujeitos da pesquisa, realizando-se, desta forma, uma triangulação metodológica a fim de contribuir para o exame do fenômeno. Como estratégia de investigação foi utilizado o estudo de casos múltiplos, realizado com 7 (sete) MPEs que haviam incorporado sua IVC há pelo menos 2 (dois) anos, dos setores de comércio e serviço, localizadas nos municípios de Vitória, Vila Velha, Serra ou Cariacica. Os dados coletados foram analisados por meio da Análise de Conteúdo, com base nas etapas descritas por Bardin (1977) e Laville (1999), distribuindo as unidades de análise (frases e parágrafos) nas categorias desenvolvidas: Motivação, Integração da IVC, Gestão da IVC, Relevância da IVC, IVC e Performance e Expressão Visual. A análise utilizou a abordagem quantitativa, realizada por meio da técnica de percentagem, e qualitativa, com maior ênfase na última, onde foram analisadas as categorias e seus elementos, assim como as relações entre elas, buscando extrair os significados construídos. Desta forma, foi possível verificar que a maior parte dos gestores das MPEs investigadas identificam que o design gráfico, por meio da IVC, contribui de forma positiva para a performance do seu negócio, proporcionando diferentes tipos de benefícios, dentre os mais citados foram: Identificação/Reconhecimento, Fortalecimento (solidez, estabilidade, profissionalismo), Novos Clientes, Imagem, Receptividade, Agregação de Valor para a Marca e Diferenciação.

Design gráfico : análise da relação com a performance da micro e pequena empresa

O design possui um papel central nos negócios, participando de forma crucial no intercambio cultural e econômico da sociedade. Uma das competências do profissional de design gráfico é o desenvolvimento da Identidade Visual Corporativa (IVC). Ela tem como função definir visualmente o perfil de uma empresa, configurando-se, assim, como um composto mercadológico de fundamental importância para diferenciação das organizações, possuindo um importante papel para o crescimento das micro e pequenas empresas (MPEs). O objetivo desta pesquisa é verificar de que forma os gestores de MPEs percebem a atuação do design gráfico sobre a performance do seu negócio, avaliada a partir da sua experiência com a IVC desenvolvida por um profissional de design e aplicada pela empresa em suas atividades. A pesquisa é aplicada, em relação a sua finalidade, e exploratória, quanto ao seu objetivo. Sua condução se deu a partir de pesquisa bibliográfica, entrevista semiestruturada com os gestores das MPEs investigadas e pesquisa documental em materiais fornecidos pelos sujeitos da pesquisa, realizando-se, desta forma, uma triangulação metodológica a fim de contribuir para o exame do fenômeno. Como estratégia de investigação foi utilizado o estudo de casos múltiplos, realizado com 7 (sete) MPEs que haviam incorporado sua IVC há pelo menos 2 (dois) anos, dos setores de comércio e serviço, localizadas nos municípios de Vitória, Vila Velha, Serra ou Cariacica. Os dados coletados foram analisados por meio da Análise de Conteúdo, com base nas etapas descritas por Bardin (1977) e Laville (1999), distribuindo as unidades de análise (frases e parágrafos) nas categorias desenvolvidas: Motivação, Integração da IVC, Gestão da IVC, Relevância da IVC, IVC e Performance e Expressão Visual. A análise utilizou a abordagem quantitativa, realizada por meio da técnica de percentagem, e qualitativa, com maior ênfase na última, onde foram analisadas as categorias e seus elementos, assim como as relações entre elas, buscando extrair os significados construídos. Desta forma, foi possível verificar que a maior parte dos gestores das MPEs investigadas identificam que o design gráfico, por meio da IVC, contribui de forma positiva para a performance do seu negócio, proporcionando diferentes tipos de benefícios, dentre os mais citados foram: Identificação/Reconhecimento, Fortalecimento (solidez, estabilidade, profissionalismo), Novos Clientes, Imagem, Receptividade, Agregação de Valor para a Marca e Diferenciação.

Comparative analysis between statistical and artificial intelligence models in business failure prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.

Comparative Analysis between Statistical and Artificial Intelligence Models in Business Failure Prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.

STRATEGIES FOR SUPERIOR PERFORMANCE IN RECESSIONS: PRO OR COUNTER-CYCLICAL?

Recessions are recurring events in which most firms suffer severe impacts while others are less affected or may even prosper. Strategic management has made little progress in understanding such performance differences. In a scenario of decreased demand, intensified competition, and higher uncertainty, most firms try to survive by pro-cyclically cutting costs and investments. But firms could take advantage of undervalued resources in the market to counter-cyclically invest in new business opportunities to overtake competitors. We survey Brazilian firms in various industries about the 2008-2009 recession and analyze data using PLS-SEM. We find that while most firms pro-cyclically reduce costs and investments in recessions, a counter-cyclical strategy of investing in opportunities created by changes in the market enables superior performance. Most successful are firms with a propensity to recognize opportunities, an entrepreneurial orientation to invest, and the flexibility to efficiently implement investments.

CORPORATE SOCIAL RESPONSIBILITY AND FINANCIAL PERFORMANCE: THE SPANISH CASE

ABSTRACT The enormous interest aroused by corporate social responsibility both in the academic and the business worlds forms the background for this study. Its objective is to analyze the relationship between corporate social responsibility and financial performance in view of the debate in the literature on the subject. The study focuses on a sample of Spanish companies taken from the IBEX 35 stock market index, using panel data methodology, which offers advantages in comparison to methodologies used in other studies. We analyzed the period from 2003 to 2010. Our findings suggest that there is no obvious relationship between corporate social responsibility and financial results, at least in the case of Spain.

Performance management in health centres: evidence from a field study

There has been a growing interest in research on performance measurement and management practices, which seems to reflect researchers’ response to calls for the need to increase the relevance of management accounting research. However, despite the development of the new public management literature, studies involving public sector organizations are relatively small compared to those involving business organizations and extremely limited when it comes to public primary health care organizations. Yet, the economic significance of public health care organizations in the economy of developed countries and the criticisms these organizations regularly face from the public suggests there is a need for research. This is particularly true in the case of research that may lead to improvement in performance measurement and management practices and ultimately to improvements in the way health care organizations use their limited resources in the provision of services to the communities. This study reports on a field study involving three public primary health care organisations. The evidence obtained from interviews and archival data suggests a performance management practices in these institutions lacked consistency and coherence, potentially leading to decreased performance. Hierarchical controls seemed to be very weak and accountability limited, leading to a lack of direction, low motivation and, in some circumstances to insufficient managerial abilities and skills. Also, the performance management systems revealed a number of weaknesses, which suggests that there are various opportunities for improvement in performance in the studied organisations.

Learning business strategies for competitive electronic marketplaces

This paper presents a Multi-Agent Market simulator designed for developing new agent market strategies based on a complete understanding of buyer and seller behaviors, preference models and pricing algorithms, considering user risk preferences and game theory for scenario analysis. This tool studies negotiations based on different market mechanisms and, time and behavior dependent strategies. The results of the negotiations between agents are analyzed by data mining algorithms in order to extract rules that give agents feedback to improve their strategies. The system also includes agents that are capable of improving their performance with their own experience, by adapting to the market conditions, and capable of considering other agent reactions.