Abu Dhabi Campus
Dr. Abdallah AlShawabkeh is an Associate Professor of Business Information Systems with over 15 years of teaching and research experience in higher education. He holds a PhD in Information Systems from the University of Greenwich, UK, where he also earned a Postgraduate Certificate in Higher Education. His research interests include data mining, knowledge management, decision support systems, and artificial intelligence. Dr. AlShawabkeh has contributed to numerous peer-reviewed publications, particularly focusing on technology's role in enhancing business processes and education. He has supervised PhD students, secured research grants, and held key academic leadership roles, including serving as Deputy Dean and Head of the MIS Department at Al Ain University. His work is recognized globally, with publications in leading academic journals and conferences.
AlShawabkeh, A., Kharbat, F., & Razmak, J. (2023). Knowledge Management Role in Enhancing Customer Relationship Management in Hotels Industry in the UK. In Proceedings of the 9th International Conference on Social Networks Analysis, Management and Security (SNAMS 2022)
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all.
This person’s work contributes towards the following SDG(s):
FHEA | Fellowship | Higher Education/UK |
Since 2011 |
MIET | Member | Member of the Institute of Engineering and Technology | Since 2014 |
MCMI | Member | Member of the Chartered Management Institute |
Since 2014 |
Published in: International Journal of Electronic Customer Relationship Management
Feb 04, 2020
Facebook is the leading social media platform, with more than 1.8 billion users. The growth of Facebook has been a phenomenon, and the demographics of its users cut across all social strata. Businesses have taken note of Facebook's online presence and are increasingly advertising on Facebook. To understand the role of Facebook in creating the link between businesses and clients, we drew on a theoretical framework that identifies the importance of technology mediation between subjects and objects. The cultural historical activity theory (CHAT) explicates the role of technology as a mediator between different groups of users. This theory identifies five key components, or 'mediating factors', that enhance the users' ability to beneficially employ technology. The use of Facebook to reach customers was explored within the theoretical lens of CHAT. This research examined the advertising trend on Facebook over the last ten years by focusing on the reasons why it has gained popularity among firms. The study proposes that marketing professionals consider using the CHAT framework in order to understand the mediating role of social media platforms in facilitating effective marketing campaigns.
Published in: International Journal of Economics and Business Research
Apr 04, 2018
Effective internal communication is crucial for organisations' success as it affects the ability of strategic managers to engage employees and achieve objectives. At the end of year 2013, over 90% of Fortune 500 companies had partially or fully implemented an enterprise social network within their organisation (Fee, 2013). As the knowledge shared over enterprise social networking has been proven to have a significant positive impact on work performance. It should be in every organisation's best interest to utilise this tool to its maximum potential. The research aims to examine the impact of internal communication and enterprise social networking. This was tested through the formation of eight sub-hypothesis and analysis of data from the survey. The study showed that there were positive correlations between each of the key success factors of using enterprise social networking and internal communication. This implies that enterprise social networking is a tool which can be utilised to improve internal communication between employees.
Published in: International Journal of Economics and Business Research
Apr 03, 2018
In today's world, many modern health facilities have started using e-health with the aim of improving health services by managing its costs, patient waiting time, and other services. Nevertheless, there are numerous studies exploring the barriers to e-health adoption. Concentrating on innovation in the healthcare industry, the present study explores the external factors that predict patients' behavioural intention to use a personal health record (PHR) as an important part of the electronic patient-physician relationship. Empirical research is used to identify a conceptual framework illustrating the relation between patients' behavioural intention and the proposed factors: governmental incentives, physician support and hospital management support. The framework is tested by using data collected from Canada as a case study through a well-designed survey. The results of multiple regression analysis indicate that the proposed factors were significantly predicted as the perceived ease of use and perceived usefulness of PHR innovative technology. The perceived usefulness factor was significantly predicted in the behavioural intention to use PHR. Some procedures and actions should be considered by government and healthcare policy makers to manage the adoption and support the usage of PHR application
Published in: Information Sciences
May 01, 2016
In this paper, we propose a multi-cycled sequential memetic computing structure for constrained optimisation. The structure is composed of multiple evolutionary cycles. At each cycle, an evolutionary algorithm is considered as an operator, and connects with a local optimiser. This structure enables the learning of useful knowledge from previous cycles and the transfer of the knowledge to facilitate search in latter cycles. Specifically, we propose to apply an estimation of distribution algorithm (EDA) to explore the search space until convergence at each cycle. A local optimiser, called DONLP2, is then applied to improve the best solution found by the EDA. New cycle starts after the local improvement if the computation budget has not been exceeded. In the developed EDA, an adaptive fully-factorized multivariate probability model is proposed. A learning mechanism, implemented as the guided mutation operator, is adopted to learn useful knowledge from previous cycles. The developed algorithm was experimentally studied on the benchmark problems in the CEC 2006 and 2010 competition. Experimental studies have shown that the developed probability model exhibits excellent exploration capability and the learning mechanism can significantly improve the search efficiency under certain conditions.
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