Renewable energy has attracted researcher attention in recent years, and the number of studies conducted on the topic has increased. The importance of renewable energy has increased because certain energy resources are exhaustible and they damage the environment in various ways. Fossil fuel-based energy is the main culprit for environmental damage and lately renewable energy is the main focus as a safe alternative to fossil fuels. However, replacement of fossil fuels by renewables may have a negative impact on human development, even if it has a positive impact on the environment. With this rationale, this study investigates the relationship between renewable energy and human development in 28 OECD (Organization for Economic Cooperation and Development) countries from 1990 to 2017 by using the Westerlund and Edgerton panel cointegration test with structural breaks and the Dumitrescu and Hurlin causality test. The results of the panel data analysis revealed that renewable energy affected human development positively. In addition, the causality test determined the presence of a bidirectional causality relationship between renewable energy and human development. This study is unique in the sense that it is the only study in the literature examining the relationship between human development index and renewable energy for the countries in question. While similar analyses were conducted in the past for different regions or for just one type of renewable energy, no such study has been conducted in this scale with this method. Another differentiating feature of the study is that it demonstrates the bidirectional nature of the study not just the unidirectional causality. Policymakers are advised to invest in renewable energy projects and also create frameworks which provide incentives to the private sector for renewable energy production.

This article investigates the factors affecting primary and secondary education teachers’ behavioral intention to adopt learning management systems (LMSs). Information technology (IT) innovations have the power to change the way we work, educate, learn, and basically the way we live. The effect of IT innovations on education makes it critical to understand the current usage situation of LMSs and the factors affecting their adoption by teachers. The unified theory of acceptance and use of technology (UTAUT) was extended with factors from education and game-based learning literature. In order to see the effect of individual- and organizational-level characteristics, multi-group structural equation modeling (SEM) analysis was conducted and discrepancies in relationships were reported. Evaluation of users and non-users and teachers of different fields were also compared to each other. The findings of this study not only contribute to theory through the development and testing of a thorough model relating technology features and individual characteristics to behavioral intention to use, but also offer strong implications for practitioners who would like to increase LMS usage and create a more effective learning environment.

The aim of this research is to understand the factors behind smartphone purchase decisions of consumers. Nowadays companies make use of various strategies in order to attract new customers, retain existing customers and differentiate their products from those of their competitors. Perhaps, the most important and effective strategy to influence consumer behavior in the product selection is emphasizing the “brand name” of the products. Our aim in this paper is to investigate how smartphone brands can influence consumers’ buying decisions. Brand equity is a set of brand assets and liabilities linked to a brand name and symbol, which add to or subtract from the value provided by a product or service. It enhances the customer’s ability to interpret and process information, improves confidence in the purchase decision and affects the quality of the user experience. Using this construct widely discussed in the literature, we use and build our hypothesis based on Aaker model about the brand equity, including perceived quality, brand awareness, brand association and brand loyalty. The study involved a questionnaire administered to 171 smartphone consumers between December 2015 and March 2016. The consumers were chosen by convenience sampling among the students from a prestigious university in the Istanbul district of Turkey. Our findings indicate that a majority of the smartphone buyers’ decisions are mainly influenced by brand loyalty and brand awareness. Perceived quality and brand association do not seem to influence purchase decisions for the sample of this study.

No abstract

This paper gives a review and synthesis of methods of evaluating dimensionality reduction techniques. Particular attention is paid to rank-order neighborhood evaluation metrics. A framework is created for exploring dimensionality reduction quality through visualization. An associated toolkit is implemented in R. The toolkit includes scatter plots, heat maps, loess smoothing, and performance lift diagrams. The overall rationale is to help researchers compare dimensionality reduction techniques and use visual insights to help select and improve techniques. Examples are given for dimensionality reduction of manifolds and for the dimensionality reduction applied to a consumer survey dataset.Keywords Dimensionality reduction · mapping · solution quality · model selection 1 IntroductionThe problem of dimensionality reduction is core to statistics, machine learning, and visualization. High dimensional data can contain a large amount of noise and importantly for visualization, the human brain can only comprehend three dimensions. Thus, there is a need to reduce data into an interpretable format by converting high dimensional data into two or three dimensions, which can subsequently be visualized using a two or three dimensional scatterplot. To meet the need for dimensionality reduction methods, a plethora of algorithms and associated fitting methods have been developed. A researcher wishing to perform dimensionality reduction for visualization will be presented with a choice of hundreds of algorithms. Which algorithm should be used? This paper describes a visualization framework called QVisVis and associated software tools implemented in R to help choose dimensionality reduction methods, tune these methods, and visually evaluate the quality of dimensionality reduction solutions. The major contributions of these paper are to review and synthesize previous work on evaluating and "visualizing" performance metrics, create an overall visualization framework for "visualizing" visualization quality, and implement the framework in an R toolkit.