Abdallah Al Shawabkeh, Ph.D

Deputy Dean, College of Business

Abu Dhabi Campus

+971 2 6133525

business_ad@aau.ac.ae

Education

PhD: University of Greenwic-UK, Knowledge Management and Data Mining in Banks, 2010

PGCert in Higher Education, University of Greenwich, London, UK, 2011

MSc: Arab Academy for Banking and Finance-Jordan, Information Systems, 2004

BSc: University of Jordan, Mathematics, 1999

Research Interests

Business Information Systems, Knowledge Management and Data Minning, and Social Media usage in Business

Selected Publications

  • Al Shawabkeh, A., Razmak, J., Qasim, A., Kharbat, F.(2018) “Enhancing internal communication in organisations using enterprise social networking ”, International Journal of Economics and Business Research, 15(1), pp. 72-86
  • A., Razmak, J., Al Shawabkeh., A., Qasim, A., Kharbat, F.(2018) “Examining the factors affecting the adoption of e-health innovative technology ”, International Journal of Economics and Business Research, 16(2), pp. 196-209
  • Sun, J., Garibaldi, M., and Al-Shawabkeh, A., (2016). “A Multi-cycled Sequential Memetic Computing Approach for Constrained Optimisation”, Journal of Information Sciences, Vol. 340, pp. 175-190
  • Al-Shawabkeh, A., and Tambyrajah, A. (2009). “The Impact of Knowledge Management on Credit Risk Management in Jordanian Banks”, Published in the proceedings the 10th European Conference on Knowledge Management, Vicenza, Italy, 3-4 September 2009 (peer reviewed paper).
  • Al-Shawabkeh, A., and Tambyrajah, A. (2009). “Knowledge Management in Jordanian Banks”, the International Journal of Knowledge, Culture and Change Management, 9(9), pp.105-124.
  • Rennols, K., and Al-Shawabkeh, A. (2008). “Formal structures for data mining, knowledge discovery and communication in a knowledge management environment”, Published in the proceedings Intellectual Data Analysis Journal, 12(2), pp.147-163.

Teaching Courses

Analytic Data Sciences (MBA)

Business System Analysis and Design (Undergraduate)

Principles of MIS (Undergraduate)

 

Memberships

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

 

 

Article

Enhancing internal communication in organisations using enterprise social networking

Published in: International Journal of Economics and Business Research

Apr 04, 2018

/ Abdallah Al Shawabkeh / Faten Kharbat / Jamil Razmak / Amer Qasim

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.


Article

Examining the factors affecting the adoption of e-health innovative technology

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


Article

A multi-cycled sequential memetic computing approach for constrained optimisation

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.