Abdullah M. Baabdullah

Big data with cognitive computing: A review for the future

Abstract Analysis of data by humans can be a time-consuming activity and thus use of sophisticated cognitive systems can be utilized to crunch this enormous amount of data. Cognitive computing can be utilized to reduce the shortcomings of the concerns faced during big data analytics. The aim of the study is to provide readers a complete understanding of past, present and future directions in the domain big data and cognitive computing. A systematic literature review has been adopted for this study by using the Scopus, DBLP and Web of Science databases. The work done in the field of big data and cognitive computing is currently at the nascent stage and this is evident from the publication record. The characteristics of cognitive computing, namely observation, interpretation, evaluation and decision were mapped to the five V’s of big data namely volume, variety, veracity, velocity and value. Perspectives which touch all these parameters are yet to be widely explored in existing literature.

Ranking online consumer reviews

The product reviews are posted online in the hundreds and even in the thousands for some popular products. Handling such a large volume of continuously generated online content is a challenging task for buyers, sellers, and even researchers. The purpose of this study is to rank the overwhelming number of reviews using their predicted helpfulness score. The helpfulness score is predicted using features extracted from review text data, product description data and customer question-answer data of a product using random-forest classifier and gradient boosting regressor. The system is made to classify the reviews into low or high quality by random-forest classifier. The helpfulness score of the high-quality reviews is only predicted using gradient boosting regressor. The helpfulness score of the low-quality reviews is not calculated because they are never going to be in the top k reviews. They are just added at the end of the review list to the review-listing website. The proposed system provides fair review placement on review listing pages and making all high-quality reviews visible to customers on the top. The experimental results on data from two popular Indian e-commerce websites validate our claim, as 3-4 new high-quality reviews are placed in the top ten reviews along with 5-6 old reviews based on review helpfulness. Our findings indicate that inclusion of features from product description data and customer question-answer data improves the prediction accuracy of the helpfulness score.

Consumer Adoption of Mobile Government in the Kingdom of Saudi Arabia: The Role of Usefulness, Ease of Use, Perceived Risk and Innovativeness

Utilising Mobile Government (M-Gov) services would raise socio-economic benefits. Thus, it is essential to examine the factors that may increase the adoption of M-Gov services within the context of Saudi Arabia. This research aims to examine potential users’ intentions towards different variables that may be significant for supporting higher behavioural intention to use the M-Gov services in Saudi Arabia. This study embraces the following variables: perceived risk, innovativeness, perceived usefulness, perceived ease of use and behavioural intention. Data was collected by means of a self-administered questionnaire on a convenience sample that consisted of 600 subjects with a response rate of 69.67 %. The findings were gathered and the statistical analysis suggested that the related variables are perceived as significant by participants and they have a strong behavioural intention to use the M-Gov services. Furthermore, the findings show that perceived ease of use significantly influences perceived usefulness.

Consumer use of mobile banking (M-Banking) in Saudi Arabia: Towards an integrated model

Abstract Mobile banking is one of the most promising technologies that has emerged in recent years and could prove to have considerable value to both banks and customers. Thus, this study recognises the need to test the main factors that could predict the use of mobile banking as well as how using such a system could contribute to both customer satisfaction and customer loyalty. The conceptual model of this study combines two models (i.e. UTAUT2 and the D&M IS Success Model). A questionnaire survey was conducted to collect the required data from convenience sampling of Saudi bank customers. The main factors – performance expectancy, price value, facilitating conditions, hedonic motivation, habit, system quality and service quality – were found to have a significant impact on actual use behaviour. This study was cross-sectional, therefore future studies should implement longitudinal studies in order to re-collect the findings. Further, this study adopted convenience sampling of Saudi M-Banking users. This may adversely impact the issue of generalisability to the whole population. The gap in the M-Banking literature in Saudi Arabia would be bridged by proposing a comprehensive conceptual model that scrupulously clarifies the use of M-Banking from the perspective of Saudi users. Furthermore, this study would consider the adoption of numeric data in order to inferentially analyse them using SEM. This in turn would assist in generalising the findings to the whole Saudi population.

Citizens' Awareness, Acceptance and Use of Mobile Government Services in India: An Exploratory Research

The development of mobile technology is not only changing the way business is run, as evidenced by the fast growth of mobile commerce, but also facilitating the transformation in a way that governments deliver their services. Mobile government is one area that has received very little research, but it can have a tremendous impact on the way citizens engage with their government. This research will analyse the data gathered from 25 respondents through online survey on the various aspects of mobile government and in India and their views on some of the key questions related to the use of mobile government in the country.

Evaluating the Current Situation of Mobile Services (M-Services) in the Kingdom of Saudi Arabia

The main aim of this study is to provide further understanding about the adoption of mobile services (mobile Internet and mobile government) over the context of Saudi Arabia. Through a careful evaluation of the current situation of mobile services in Saudi Arabia, researchers have noticed that the lower adoption of these services is the main barrier that could prevent citizens and service providers the full utilisation of these applications. It was also noticed that the related issues of mobile services have been rarely examined in Saudi Arabia as well as there is a necessity to select a theoretical foundation suitable for the perspective of Saudi customers. Therefore, the unified theory of acceptance and use of technology (UTAUT2) was adopted to propose the conceptual model of the current study. This is expanded by considering trust and awareness alongside UTAUT2 factors. Further, it was proposed that a survey questionnaire could be more appropriate to test the conceptual model and verify the research hypotheses. The main limitations and future research directions are discussed further in the last section of this study.

Examining the Factors Affecting Behavioural Intention to Adopt Mobile Health in Jordan

Health organizations worldwide express a considerable attention to utilize Mobile technology applications to provide a better health services to their people. One of the most emerging apps in this regard is Mobile health (M-health). Large amount of efforts, money, and time have been invested to provide such innovative technology. Yet, the adoption rate of these systems is still low. Additionally, such system has never been examined over the Jordanian context. Thus, this study aims to test the most important factors that could shape the intention of Jordanian people to use Mhealth. Four factors: perceived usefulness, social influence, awareness, and innovativeness were proposed as key predictor of behavioural intention. Data was collected using convenience sample size of 365 and was analyzed using structural equation modelling. The main statistical findings supported the role of perceived usefulness, social influence, and innovativeness. More discussion will also be provided regarding the current study practical and theoretical implications.

Identifying reputation collectors in community question answering (CQA) sites: Exploring the dark side of social media

Abstract This research aims to identify users who are posting as well as encouraging others to post low quality and duplicate contents on community question answering sites. The good guys called Caretakers and the bad guys called Reputation Collectors are characterised by their behaviour, answering pattern and reputation points. The proposed system is developed and analysed over publicly available Stack Exchange data dump. A graph-based methodology is employed to derive the characteristic of Reputation Collectors and Caretakers. Results reveal that Reputation Collectors are primary sources of low-quality answers as well as answers to duplicate questions posted on the site. The Caretakers answer limited questions of challenging nature and fetches maximum reputation against those questions whereas Reputation Collectors answers have so many low quality and duplicate questions to gain the reputation point. We have developed algorithms to identify the Caretakers and Reputation Collectors of the site. Our analysis finds that 1.05% of Reputation Collectors post 18.88% of low-quality answers. This study extends previous research by identifying the Reputation Collectors and how they collect their reputation points.