Mohamed M. Mostafa

Shades of green: A psychographic segmentation of the green consumer in Kuwait using self-organizing maps

This study uses self-organizing maps (SOM) to examine the effect of various psychographic and cognitive factors on green consumption in Kuwait. SOM is a machine learning method that can be used to explore patterns in large and complex datasets for linear and non-linear patterns. The results show that major variables affecting green consumption are related to altruistic values, environmental concern, environmental knowledge, skepticism towards environmental claims, attitudes toward green consumption, and intention to buy green products. The study also shows that SOM models are capable of improving clustering quality while extracting valuable information from multidimensional data.

Clustering the ecological footprint of nations using Kohonen's self-organizing maps

Per capita ecological footprint (EF) is one of the most widely recognized measures of environmental sustainability. It seeks to quantify the Earths biological capacity required to support human activity. Self-organizing maps (SOM) is a machine learning method that can be used to explore patterns in large and complex datasets for linear and non-linear patterns. This study uses SOM to model and cluster the EF of 140 nations. The results show that major variables affecting a nations EF are related to the nations world system position (WSP), GDP, urbanization level, export as a percent of the GDP, services intensity, and literacy rate. The study also shows that SOM models are capable of improving clustering quality while extracting valuable information from multidimensional environmental data.

Benchmarking the US specialty retailers and food consumer stores using data envelopment analysis

Purpose – The purpose of this paper is to measure the relative efficiency of the US specialty retailers and food consumer stores using cross‐sectional data for the year 2007.Design/methodology/approach – This study uses a non‐parametric data envelopment analysis approach to measure the relative efficiency of 45 retailers in the USA.Findings – The results indicate that the performance of several retailers is sub‐optimal, suggesting the potential for significant improvements over both profitability and marketability dimensions. Separate benchmarks were derived for possible reductions in resources used, and significant savings are possible on this account.Originality/value – From a policy perspective, this paper highlights the economic importance of encouraging increased efficiency throughout the retailing sector in the USA.

Recognition of Western style musical genres using machine learning techniques

This study uses machine learning techniques (ML) to classify and cluster different Western music genres. Three artificial neural network models (multi-layer perceptron neural network [MLP], probabilistic neural network [PNN]) and self-organizing maps neural network (SOM) along with support vector machines (SVM) are compared to two standard statistical methods (linear discriminant analysis [LDA] and cluster analysis [CA]). The variable sets considered are average frequencies, variance frequencies, maximum frequencies, amplitude or loudness of the sound and the median of the location of the 15 highest peaks in the periodogram. The results show that machine learning models outperform traditional statistical techniques in classifying and clustering different music genres due to their robustness and flexibility of modeling algorithms. The study also shows how it is possible to identify various dimensions of music genres by uncovering complex patterns in the multidimensional data.

A neuro-computational intelligence analysis of the ecological footprint of nations

The per capita ecological footprint (EF) is one of the most-widely recognized measures of environmental sustainability. It seeks to quantify the Earths biological capacity required to support human activity. This study uses three neuro-computational methodologies: multi-layer perceptron neural network (MLP), probabilistic neural network (PNN) and generalized regression neural network (GRNN) to predict and classify the EF of 140 nations. Accuracy indices are used to assess the prediction and classification accuracy of the three methodologies. The study shows that neuro-computational models outperform traditional statistical techniques such as regression analysis and discriminant analysis in predicting and classifying per capita EF due to their robustness and flexibility of modeling algorithms.

An Investigation of Egyptian Consumers’ Attitudes Toward Ethical Issues in Advertising

This article investigates Egyptian consumers’ attitudes toward ethical issues in advertising held by a sample of 306 participants. The subjects completed a 20-item instrument originally designed to measure respondents’ attitudes toward controversial issues in advertising. The study validates the scale in an Arab non-Western context. The results revealed that Egyptian consumers have negative attitudes toward ethical issues in advertising. There are significant differences between males’ and females’ perceptions of ethics in advertising. Finally, the study detects a significant difference between Muslims and non-Muslims in Egypt regarding their attitudes toward ethical issues in advertising. These results lend strong support to the adaptation hypothesis and suggest that ads produced in one country cannot be standardized or directly translated for use in another, particularly if they are different culturally.

Profiling blood donors in Egypt: A neural network analysis

This study uses intelligent modeling techniques to examine the effect of various demographic, cognitive and psychographic factors on blood donation in Egypt. Two artificial neural network models (multi-layer perceptron neural network [MLP] and probabilistic neural network [PNN]) are compared to a standard statistical method (linear discriminant analysis [LDA]). The variable sets considered are sex, age, educational level, altruistic values, perceived risks of blood donation, blood donation knowledge, attitudes toward blood donation, and intention to donate blood. The paper shows how it is possible to identify various dimensions of blood donation behavior by uncovering patterns in the dataset, and also shows the classification abilities of two neural network techniques.

Do Domestic Firms Benefit from Multinational Enterprises? A Meta-Analysis of the Empirical Research

Meta-analysis is a statistical technique that allows one to combine the results from multiple studies to glean inferences on the overall importance of a certain phenomenon. This study employs a substantive meta-analysis approach to quantitatively summarize the results of empirical studies of the direct impact of multinational companies on productivity spillovers in host countries. When all the available estimates are combined and averaged, there seems to be a small significant positive effect of multinational companies on productivity spillovers in host countries (average effect size = .06, aggregate N = 124,143). The findings of this study significantly refine the body of knowledge concerning the impact of multinational companies on productivity spillovers in host countries, and thereby offer an improved conceptual framework for researchers and policymakers.

Through the Eye of a Child

Meta-analysis is a statistical technique that allows one to combine the results from multiple studies to glean inferences on the overall importance of a certain phenomenon. This study employs a substantive meta-analysis approach to quantitatively summarize the results of empirical studies of the direct impact of age on childrens understanding of advertising intent. When all the available estimates are combined and averaged, there seems to be a genuine and positive effect of age on childrens understanding of advertising (average effect size = 0.367, aggregate N = 9307). The findings of this study significantly refine the body of knowledge concerning the impact of age on childrens understanding of advertising intent, and thereby offer an improved conceptual framework for marketers and policy makers.