Evaluating e-governance: a comparative analysis and way forward
Vinod Bhatia & Shabani Bhatia
Abstract
Purpose This study aims to assess the technical efficiency of selected countries in e-governance and benchmark the countries. This study delves into a possible way of measuring e-government and digitalization infrastructure through available metrics. Design/methodology/approach This study examines and compares e-governance in 19 countries and attempts to address the research gap by using the E-Government Development Index (EGDI) subcomponents as inputs and outputs of data envelopment analysis (DEA). Findings This study reflects how a country uses information technologies to facilitate access and inclusion of its citizens. Despite good infrastructure for e-governance in some countries, the technical efficiency values are low. The authorities in these countries have to provide additional services and facilitate openness, flow of information and transparency to improve e-participation. Countries like India, Indonesia and Türkiye have used lesser inputs but can produce higher outputs. This study further reveals that under CRS and VRS models, Australia, Brazil, China, France, India, Indonesia, Japan, Korea and Great Britain are always efficient. Other countries should emulate the best practices of e-governance in these countries. This study’s findings are a benchmark for other scholars conducting research in e-governance. Originality/value The inputs in terms of Institutional Framework, Content Provision, Technology, Telecom Infrastructure Index and Human Capital Index have been considered to calculate the technical efficiencies of the countries while measuring outputs in terms of E-Participation and Services Provision. The selection of input and output parameters is unique, and such a study is unavailable in the literature.
3 citations
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.32 × 0.4 = 0.13 |
| M · momentum | 0.57 × 0.15 = 0.09 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.