This study examines the association between e-governance service quality and perceived business ease among small and medium-sized enterprises (SMEs) in Kathmandu, Nepal. Using a sequential explanatory mixed-methods design, the study first analysed survey data from 350 SME owners and managers and then conducted 30 semi-structured interviews to explain the quantitative findings. The quantitative results showed that system quality, information quality, administrative support quality and trust in digital government services were all positively associated with perceived business ease. Administrative support quality showed the strongest standardised association, suggesting that SMEs evaluate e-governance not only through platform access but also through the availability of procedural guidance, problem resolution and confirmation support. The qualitative findings reinforced this interpretation by showing that online services reduced routine burden but often remained incomplete when information was unclear, support was weak or digital records required manual verification. The study extends e-governance service-quality research by showing that SMEs assess digital government services through procedural completion rather than access alone. The study concludes that SME-facing e-governance should be assessed through a procedural completion logic rather than an access logic.

E-governance service quality, perceived business ease, small and medium-sized enterprises, administrative burden, procedural completion, Nepal

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Appendix A

Table A1. Detailed Online Government Service Use Among Small and Medium-sized Enterprises.

Table A2. Item-level Descriptive Statistics and Reliability Diagnostics.

Table A3. Exploratory Factor Analysis Loading Diagnostics.

Note: MR1 = perceived business ease; MR2 = administrative support quality; MR3 = system quality; MR4 = information quality; MR5 = trust in digital government services.

Table A4. Confirmatory Factor Analysis Results.

Notes: CFA was estimated using the WLSMV estimator for five-point Likert-scale items. The fit indices indicate very good model fit, but they are interpreted cautiously alongside factor loadings, composite reliability and average variance extracted. AVE: Average variance extracted; CFI: Comparative fit index; CR: Composite reliability; RMSEA: Root mean square error of approximation; SRMR: Standardised root mean square residual; TLI: Tucker–Lewis index.

Notes: CFA was estimated using the WLSMV estimator for five-point Likert-scale items. AVE: Average variance extracted; CFI: Comparative fit index; CR: Composite reliability; RMSEA: Root mean square error of approximation; SRMR: Standardised root mean square residual; TLI: Tucker–Lewis index.

Table A5. Full Regression Model and Diagnostic Checks.

Table A6. Common Method and Robustness Checks.

Table A7. Qualitative Case Validation, Themes and Codes.

Table A8. Detailed Mixed-methods Joint Display with Representative Quotations.

Figure A1. Parallel Analysis for Exploratory Factor Analysis.

Figure A2. Regression Diagnostic Plots. (a) Residuals Versus Fitted Values. (b) Distribution of Standardised Residuals. (c) Cook’s Distance by Case.