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HomeAcademic staffDr Sarah Sabbaghan
Dr Sarah Sabbaghan

Dr Sarah Sabbaghan

sabbaghs@lsbu.ac.uk

Innovation, Leadership, Strategy and Management

https://orcid.org/0000-0001-5497-5012

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I joined LSBU from University of Auckland, New Zealand in 2019. I am currently a permanent Lecturer in Business Research Methods at London South Bank University. I have been awarded a PhD in Information Systems from the University of Auckland, New Zealand in 2018.

For me, research is about collaboration, when different perspectives, skills and knowledge come together and complement each other. As a methodologist, my main research interest is developing new ways to obtain evidence for, measure and analyse phenomena. My last work was on creating a variant q-sorting methodology for building Diagnostic Trees. Diagnostic theories are fundamental to Information System (IS) practice and are represented as trees. While there are approaches to validating diagnostic trees, these validate the overall performance of the tree rather than identifying ways incorrect diagnoses can occur. It is important to fully validate diagnostic trees, because even if the tree gives the correct decision “most of the time,” it is possible for incorrect decisions traveling down little used branches of the tree to result in catastrophic decisions. My work describes the process of using a variant

of q-sorting to validate diagnostic trees. In this methodology, diagnostic trees that independent experts develop are transformed into a quantitative form, and that quantitative form is tested to determine the inter-rater reliability of the individual branches in the tree. The trees are then successively transformed to incrementally test if they branch in the same way. The results help researchers not only identify quality items for use in a diagnostic tree, but also facilitate diagnoses of problems with those items and facilitate the reconciliation of discrepant trees by experts. The methodology validates not only the whole tree, but also its subparts.

My work has been published in several high-quality journals such as IEEE Transactions on Engineering Management and in top conferences such as The European Conference on Information Systems. I have also been a reviewer for several top Journals such as Quality and Quantity and conferences such as ICIS, ECIS and PACIS. In addition, I was awarded Certificate of Merit at the PHIS-NZ Information Systems Doctoral Thesis Award July 2019 for being second place in New Zealand.

Postgraduate Research Supervision
Current
Mr Andreas ScheelSocial Media as Innovation Laboratory - Competitive Advantages of the Open, Networked EnterprisePhD
PhD in Information Systems

University of Auckland

2014
2018
Guest lecturer for New Perspectives on Organisations and Information Systems
University of Auckland

2016
2017
Education
Teaching Assistant for Data Visualisation
University of Auckland

There were three major roles, first was to plan, teach and create a customized workshop, second to provide feedback to the students, and finally mark assignments and exams.

2015
2016
Education
Knowledge Management Consultant
The Horizon Education & Training SDN BHD

I performed 4 major roles in this job function. The first was to create an environment for knowledge sharing and facilitate the flow of knowledge within the organization. The second was to create courses on knowledge management for other organizations. The third is, I assembled and managed teams for various projects. Finally, I assisted in completing an eight-month project that compared historical customer satisfaction data to current customer service metrics. I was responsible for developing a system for monitoring customer service issues that was integrated into the sales contact system.

2012
2014
Commercial/industry

A Variant Q-Sorting Methodology for Building Diagnostic Trees
Sabbaghan, S., Chua, C. and Gardner, L. (2021). A Variant Q-Sorting Methodology for Building Diagnostic Trees. IEEE Transactions on Engineering Management. https://doi.org/10.1109/TEM.2021.3078582.

Statistical measurement of trees’ similarity
Sabbaghan, S., Cecil Eng Huang Chua and Lesley Ann Gardner (2020). Statistical measurement of trees’ similarity. Quality and Quantity: international journal of methodology. https://doi.org/10.1007/s11135-019-00957-8

A test of a computer-adaptive survey using online reviews
Sabbaghan, S, Chua, CEH and Gardner, LA (2018). A test of a computer-adaptive survey using online reviews.

A threshold for a q-sorting methodology for computer-adaptive surveys
Sabbaghan, S, Gardner, L and Chua, CEH (2017). A threshold for a q-sorting methodology for computer-adaptive surveys. pp. 2896-2906

Computer-Adaptive Surveys (CAS) as a means of answering questions of why
Sabbaghan, S, Gardner, L and Chua, CEH (2017). Computer-Adaptive Surveys (CAS) as a means of answering questions of why.

A Q-sorting methodology for Computer-Adaptive Surveys - Style "Research"
Sabbaghan, S, Gardner, L and Chua, CEH (2016). A Q-sorting methodology for Computer-Adaptive Surveys - Style "Research".