Over the recent years, there has been a significant increase in the number of women belonging to the Internet population. With this increase, researchers have begun to investigate the role of gender in information technology (IT) adoption and its related innovations, including e-commerce. Despite the large body of research on the topic, we know of no work that specifically reviews the literature regarding gender differences in these domains. To address this gap, we systematically reviewed the recent literature (in the period 2000–2017) regarding gender differences in IT adoption and six related behaviors and responses: online shopping adoption, online trust, attitude and satisfaction with e-commerce, website design preferences, and information processing strategies. Findings from the current review indicated that, in general, men are more likely to try a new IT and shop online than women

Study question: What are the overall estimate of the different indicators of infertility in the Middle East and North African (MENA) region? Summary answer: The overall clinical primary and total infertility rate in the MENA region are (3.8%, 95% Cl = 1.7-8.4) and (17.2%, 95% Cl = 10.6-26.7) respectively.

Purpose

The paper aims to re-interpret the role of corporate social responsibility (CSR) in limiting the extreme practices in earnings management (EM) by using evidence from large UK companies.

Design/methodology/approach

The study has used content analysis and disclosure index to measure the level of CSR. The authors measured EM based on discretionary accruals by using cross-sectional version of the modified Jones model.

Findings

The findings of this study reveal that companies with a higher commitment to CSR activities are less likely to manage earnings through accruals.

Originality/value

This study shed more light on the potential impact of CSR on earnings management in the context of the UK. Prior research on the impact of CSR on earnings management has used exclusively CSR scores, provided by CSR score indices. The manual measurement used in this study for CSR (disclosure index/content analysis) is considered to provide a more detailed and precise measure.

The development of Semantic Web technology has fueled the creation of a large amount of Linked Open Data. DBpedia is a good example of an open data repository extracted from the crowd sourced knowledge base, Wikipedia. Because of the way Wikipedia and DBpedia were created, the information available there is more vulnerable to grammatical errors, inconsistency, structures, and, data type problems. The introduction of the Arabic chapter of DBpedia created additional problems due to the nature of the language, when it comes to quality assessment issues. In this paper1, we focus on identifying challenges in quality assessment of Arabic DBpedia, as well as analysis of existing tools and methodologies used for Linked Data quality assessments.

Abstract

This study examines the impact of the teachability of vocabulary learning strategies (VLSs) on Libyan EFL teachers and students at university level. It investigates the extent to which instructing language teachers in how to integrate strategic intervention into regular classes impacts upon teachers’ and students’ use, promotion of the use, and perceptions of usefulness of vocabulary learning strategies. The empirical research in this area has been very limited, and the vast majority of studies have centred around examining the influence of strategy instruction on language learners’ achievements. Bearing this in mind, the present study provides an exciting opportunity to advance knowledge of the impact of direct teaching of VLSs on EFL teachers and students alike. The findings make an important contribution to literature in the field of vocabulary learning and teaching. The study recruited 109 participants, including 13 EFL teachers from two different university levels (Year 1 and 2) and two English language faculties at the Al-jabal Al-gharbi University in the cities of Tiji and Badr. The aims were 1) to identify the current situation in terms of VLSs known, promoted or used, 2) to design a strategy training programme for teachers and students, and 3) to trial and evaluate the effectiveness of the training programme on students’ and teachers’ use, promotion, and adoption of learning strategies. To achieve the research aims, three studies were carried out and two phases of training arranged. Teachers were targeted in the first phase, which was administered by the researcher and ran for a 2-week period, with students targeted in the second phase, which was carried out by trainee teachers, with the researcher’s guidance, for 10 weeks. Data for this research were collected using a multi-method approach in the form of VLS questionnaires, semi-structured interviews, observations, and an evaluation form. The quantitative and qualitative data obtained from the instruments were triangulated to allow for a more comprehensive understanding, as well as to render reliable conclusions. Microsoft Office Excel programmes, SPSS (Statistical Package for the Social Science), and a qualitative content analysis were used to analyse the data gathered. The results of the study reveal that the training had a positive impact on both the teachers’ and students’ attitudes, perceptions and adoption of VLSs. What is more, teachers and students showed an increased awareness of using/promoting the use of learning strategies even when the training had finished, which in turn indicates that the impact of VLS training has been durable.

Hadoop has been developed as a platform solution for processing a large scale of data in parallel for different applications in Cloud com-puting. A Hadoop system can be characterized based on three main factors: cluster, workload, and user. Each of these factors can be described in heterogeneous environment, which reflects the heterogeneity degree of the Hadoop system. This paper investigates the effect of heterogeneity in each of these factors on the performance of Hadoop for different schedulers. Three schedulers which consider differ-ent levels of Hadoop heterogeneity are used for the analysis: FIFO, Fair sharing, and COSHH (Classification and Optimization based Scheduler for Heterogeneous Hadoop). Performance issues are introduced for Hadoop schedulers and comparative performance analysis between different cases of jobs submission. These jobs are processed in heterogeneous data environments and, under fixed or reconfigu-rable slot between map and reduce tasks for Hadoop MapReduce java programming clustering model. The results showed that when assigning tunable knob between map and reduce tasks under certain scheduler like FIFO algorithm, the performance enhanced about 81.42% especially in cases of heterogeneity environment where the workload is decreased significantly and the utilization of computa-tional resources in increased obviously.

Hadoop is a Java-based programming framework that supports the storing and processing of large data sets in a distributed computing environment and it is very much appropriate for high volume of data. It uses HDFS for data storing and uses MapReduce for processing that data. MapReduce is a popular programming model to support data-intensive applications using shared-nothing clusters. The main objective of MapReduce programming model is to parallelize the job execution across multiple nodes for execution. Nowadays, all focus of the researchers and companies toward to Hadoop. Due to this, many scheduling algorithms have been proposed in the past decades. There are three important scheduling issues in MapReduce such as locality, synchronization and fairness. The most common objective of scheduling algorithms is to minimize the completion time of a parallel application and also achieve to these issues. Performance issues are introduced for Hadoop schedulers, and comparative performance analysis between different cases of jobs submission. These jobs are processed in different homogenous data environment and, under fixed or reconfigurable slot between map and reduce tasks for Hadoop MapReduce java programming clustering model. The results showed that when assigning tunable knob between map and reduce tasks under certain scheduler like FIFO algorithm, the performance enhanced 16.66% in inverted index, 55.55% in word count and 11.76% in classification process.