Mazen El-Masri

Extending the UTAUT model to understand the customers’ acceptance and use of internet banking in Lebanon

Purpose n n n n nA number of studies have shown that internet banking (IB) implementation is not only determined by banks or government support, but also by perceptions and experience of IB users. IB studies have showed encouraging results from academics in developed countries. Yet little is known about the user adoption of IB in Lebanon. The purpose of this paper is to investigate the factors that may hinder or facilitate the acceptance and usage of IB in Lebanon. n n n n nDesign/methodology/approach n n n n nA conceptual framework was developed through extending the unified theory of acceptance and use of technology (UTAUT) by incorporating two additional factors namely; perceived credibility (PC) and task-technology fit (TTF). A quantitative approach based on cross-sectional survey was used to collect data from 408 IB consumers. Data were analysed using structural equation modelling based on AMOS 20.0. n n n n nFindings n n n n nThe results of the structural path revealed that performance expectancy (PE), social influence, PC and TTF to be significant predictors in influencing customers’ behavioural intention (BI) to use IB and explained 61 per cent of its variance, with PE was found the strongest antecedent of BI. Contrary to the UTAUT, the effect of effort expectancy on BI was insignificant. In addition, both BI and facilitating conditions were found to affect the actual usage behaviour and explained 64 per cent of its variance n n n n nPractical implications n n n n nThis study would be helpful for bank managers and policy makers to explain the currently relatively low penetration rate of IB in formulating strategies to encourage the adoption and acceptance of IB by Lebanese customers, where IB is still considered an innovation. n n n n nOriginality/value n n n n nThis study is the first research that extend the UTAUT by incorporating two additional factors namely; PC and TTF to study the IB in the Lebanese context. This study contributes to the research on computer technology usage by looking at IB adoption and incorporation into the lives of customers via the BI to use and actual usage of IB in Lebanon.

Successes and challenges of Arabic sentiment analysis research: a literature review

The analysis of sentiment in text has mainly been focused on the English language. The complexity of the Arabic language and its linguistic features that oppose those found in English resulted in the inability to adapt extant research to Arabic contexts limiting advancement in Arabic sentiment analysis. The need for Arabic sentiment analysis research is accentuated by the driving changes in different Arab regions like heavy political movements in some areas and fast growth in others. These changes help shape not just policies and implications of this region but affect the entire world on a global scale. Therefore, it is essential to utilise effective methods of sentiment analysis to analyse Arabic tweets to understand regional and global implications in microblogging mediums such as Twitter. In this paper, we conduct a comprehensive review of Arabic sentiment analysis, present the pros and cons of the different approaches used and highlight the challenges of it. Finally, we outline the relevant gaps in the literature and suggest recommendations for future Arabic sentiment analysis research.

A web-based tool for Arabic sentiment analysis.

Abstract Sentiment analysis can help analyse trending topics such as political crises and predict it before it occurs. Yet, analysing sentiments in Arabic texts has not been explored much in the extant literature. In this paper, we present a new tool that applies sentiment analysis to Arabic text tweets using a combination of parameters. Those parameters are (1) the time of the tweets, (2) preprocessing methods like stemming and retweets, (3) n-grams features, (4) lexicon-based methods, and (5) machine-learning methods. Users can select a topic and set their desired parameters. The model detects the polarity (negative, positive, both, and neutral) of the topic from the recent related tweets and display the results. The tool is trained with 8000 randomly selected and evenly-labelled Arabic tweets. Our experiments show that the Naive Bayes machine-learning approach is the most accurate in predicting topic polarity. The tool is useful for intermediate and expert users and can help guide them in choosing the best combinations of parameters for sentiment analysis.

The effects of using social network sites on academic performance: the case of Qatar

Social network sites (SNSs) have been common applications attracting a large number of users in Qatar. Current literature remains inconclusive about the relationship between SNS usage and users’ academic performance. While one stream confirms that SNS usage may lead to addiction and seriously affect individuals’ academic performance, other studies refer to SNS as learning enablers. The purpose of this paper is twofold: first, it investigates the SNS usage profiles among the young generation in the Gulf Cooperation Council (GCC) represented by Qatar; second, it examines the relationship between the identified SNS usage profiles and their respective users’ academic performance.,The study follows a quantitative survey-based method that was adapted from Chen’s internet Addiction Scale to fit the context of social networks. Data were collected from students of two universities in Qatar, one private and another public. Respondents’ grade point average was also collected and compared across the different usage profiles to understand how SNS usage behavior affects academic performance.,Results reveal that there is no linear relationship between SNS usage and academic performance. Therefore, this study further investigates SNS usage profiles and identifies three groups: passive (low usage), engaged (normal usage) and addicted (high usage). It was found that engaged users demonstrate significantly higher academic performance than their passive and addicted peers. Moreover, there is no significant difference in the academic performance between passive and addicted users.,This study is cross-sectional and based on self-reported data collected from university students in Qatar. Further research venues could employ a more general sample covering a longer period, differentiating between messaging tools (e.g. WhatsApp) and other pure SNS (e.g. Twitter), and to cover other aspects than just academic performance.,This study complements research efforts on the influence of technology on individuals and on the society in the GCC area. It concludes that engaged SNS users achieve better academic performance than the addicted or passive users. Contradicting the strong linear relationship between SNS and performance, as claimed by previous studies, is the main originality of this paper.