Assessing Digital Readiness of Small Medium Enterprises: Intelligent Dashboard Decision Support System

The implication of the Covid-19 global pandemic is driving the transition of SMEs’ business towards digitalization. However, despite the use of the digital platform, many SMEs are unable to survive. Therefore, this study included a focus on Decision Support System (DSS)-based dashboard model as a new feature in assessing SMEs’ digitalization readiness. The twenty-four criteria appraisals are regarded in this sense as two views of business and Information Technology (IT) dimensions which include the Fuzzy-Analytical Hierarchy Process Method (F-AHP) for the weighting measurement and Objective Matrix (OMAX) for the performance mapping analysis, and both are embedded in the Business Intelligent (BI) dashboard development. In Riau Province, Indonesia, a total of 118 SMEs were interested in this study and fact thus revealed the general performance of SMEs as rated at an “Average” level of index value 4.95 with comprehensive parameters for index contribution viz., 3.79, 3.84, 7.75, 4.68, 4.32, and 5.43 for Business Activity (BA), Transaction (TC), Marketing (MC), Management (MG), Micro Environment (MI) and Macro Environment (MA) respectively. Furthermore, the dashboard prepares a tracking and analysis system with the graphical diagram extracted from each criteria hierarchy’s root cause to sub-criteria. The DSS dashboard’s information and knowledge have been developed into a promotional framework for stakeholders relevant to a digital business’s success and sustainability performance initiatives.

@article{Okfalisa2022,
  title     = {Assessing Digital Readiness of Small Medium Enterprises: Intelligent Dashboard Decision Support System},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {4},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Okfalisa and Mahyarni and Wresni Anggraini and Saktioto and B. Pranggono},
  doi       = {10.14569/IJACSA.2022.0130412},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130412}
}