A design of evaluation method for SaaS in cloud computing

Chekfoung Tan, Kecheng Liu, Lily Sun


Purpose: This paper aims to design an evaluation method that enables an organization to assess its current IT landscape and provide readiness assessment prior to Software as a Service (SaaS) adoption.

Design/methodology/approach: The research employs a mixed of quantitative and qualitative approaches for conducting an IT application assessment. Quantitative data such as end user’s feedback on the IT applications contribute to the technical impact on efficiency and productivity. Qualitative data such as business domain, business services and IT application cost drivers are used to determine the business value of the IT applications in an organization.

Findings: The assessment of IT applications leads to decisions on suitability of each IT application that can be migrated to cloud environment.

Research limitations/implications: The evaluation of how a particular IT application impacts on a business service is done based on the logical interpretation. Data mining method is suggested in order to derive the patterns of the IT application capabilities.

Practical implications: This method has been applied in a local council in UK. This helps the council to decide the future status of the IT applications for cost saving purpose.

Originality/value: The proposed method has gauge various factors including the organizational, business, IT, SaaS assessment, risk assessment prior to SaaS adoption. SaaS providers and consumers hence can have a better understanding on the SaaS adoption from the top level.


Cloud Computing, Software as a Service, Application Portfolio Management, Application Rationalization, SaaS Risk Assessment

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DOI: https://doi.org/10.3926/jiem.661

Licencia de Creative Commons 

This work is licensed under a Creative Commons Attribution 4.0 International License

Journal of Industrial Engineering and Management, 2008-2024

Online ISSN: 2013-0953; Print ISSN: 2013-8423; Online DL: B-28744-2008

Publisher: OmniaScience