Beykoz Lojistik Meslek Yüksekokulu

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Consumer Boycotts as a Consequence of Consumerism
(IGI Global, 2014) Yener, D.
Consumerism is not a new concept for marketing, but it has grown in importance in the recent years. Researchers have studied consumerism from within different dimensions. However, its relationship with consumer boycotts has not been dealt with accurately. A consumer boycott is a type of consumer behaviour in which consumers collectively prefer not to use their purchasing power towards a product, brand, or all products of a country. Motivations for participating in boycotts differ in accordance with various factors such as consumers' beliefs, needs, or attitudes. Being boycotted by consumers may cause economic damage and decreased amount of reputation incurred in return. Organizing a boycott and calling for people's participation is much easier today than it used to be in the past. The Internet, especially social media, is an effective tool to inform people about boycotts and free of charge. However, that does not mean all the information circulating in the Internet is always of a reliable nature. In this chapter, the case of Danone in Turkey is thoroughly analyzed. Danone has been the target of Turkey's biggest Internet smear campaign which resulted in 26% shrinkage in its whole category sales. The aim of this chapter is to examine the case of Danone in Turkey as an example of the relationship between consumerism and consumer boycotts. The research for the case of Danone, which has a special importance in Turkey, uses secondary sources such as the daily newspapers, news pages in Internet, and Danone's web page. © 2014, IGI Global. All rights reserved.
Feature Selection for the Prediction of Tropospheric Ozone Concentration Using a Wrapper Method
(Taylor & Francis Ltd, 2011) Sakar, C. Okan; Demir, Goksel; Kursun, Olcay; Ozdemir, Huseyin; Altay, Gokmen; Yalcin, Senay
High concentrations of ozone (O-3) in the lower troposphere increase global warming, and thus affect climatic conditions and human health. Especially in metropolitan cities like Istanbul, ozone level approximates to security levels that may threaten human health. Therefore, there are many research efforts on building accurate ozone prediction models to develop public warning strategies. The goal of this study is to construct a tropospheric (ground) ozone prediction model and analyze the effectiveness of air pollutant and meteorological variables in ozone prediction using artificial neural networks (ANNs). The air pollutant and meteorological variables used in ANN modeling are taken from monitoring stations located in Istanbul. The effectiveness of each input feature is determined by using backward elimination method which utilizes the constructed ANN model as an evaluation function. The obtained results point out that outdoor temperature (OT) and solar irradiation (Si) are the most important input features of meteorological variables, and total hydrocarbons (THC), nitrogen dioxide (NO2) and nitric oxide (NO) are those of air pollutant variables. The subset of parameters found by backward elimination feature selection method that provides the maximum prediction accuracy is obtained with six input features which are OT, SI, NO2, THC, NO, and sulfur dioxide (SO2) for both validation and test sets.
Identifying Effective Variables Using Mutual Information and Building Predictive Models of Sulfur Dioxide Concentration with Support Vector Machines
(Foundation Environmental Protection & Research-FEPR, 2010) Sakar, C. Okan; Kursun, Olcay; Ozdemir, Huseyin; Demir, Goksel; Yalcin, Senay
Sulfur dioxide (SO2) is an issue of increasing public concern due to its recognized adverse effects on human health. Therefore, accurate SO2 prediction models are very important tools in developing public warning strategies. The goal of this study is to identify the relevance of meteorological and air pollutant variables using a classical and widely used measure of dependence, Shannon's Mutual Information (MI), and to build an accurate SO, prediction model using the relevant variables as inputs. Specifically, features ranked by MI measure are tested on how much joint predictive power they have of the target using a popular machine learning tool, support vector machines (SVM), and in comparison to multilayer perceptron (MLP), which is the most commonly used machine learning tool in previous studies for the prediction and analysis of air pollutants. It was found that the SVM model gave a higher correlation coefficient (r) and less root mean squared error (RMSE) than MLP for both test and validation sets. The predictive model used 6 input variables for both data sets as the relevant features for maximum SO, concentration prediction at time t+1, which are the average SO,, maximum SO2, outdoor temperature (OT), average nitrogen dioxide (NO2), average ozone (O-3), and average wind speed at time t. The results of this study indicate that MI can be used efficiently in determining the importance of input variables in the prediction of SO2 concentration and SVM is a popular machine learning tool well suited for use in air pollution modeling.
Using TOPSIS Method with Laplace Criterion to Select Optimum Airline
(Iura Edition Spol Sro, 2010) Eker, Ipek; Turan, Gokhan; Ergin, Ayfer; Alkan, Guler
In this study, for evaluating subjective features that provides preference of airline companies to others the method TOPSIS has been used. Whilst calculating the weights of the criteria Laplace Criterion had been used. The importance of the study is that this is a unique application in air cargo industry.

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