Causal Factors of Competitive Advantages and Influencing the Performance of  E-commerce in Nanjing

Ziyuan Liang; Sirachaya  Karawek; Buraporn  Kumboon; Suchart  Prakthayanon

doi:10.60027/iarj.2025.288012

Authors

Ziyuan Liang Faculty of Business Administration, Bangkokthonburi University, Thailand https://orcid.org/0009-0006-3595-3970
Sirachaya Karawek Faculty of Business Administration, Bangkokthonburi University, Thailand https://orcid.org/0009-0005-4428-9189
Buraporn Kumboon Faculty of Business Administration, Bangkokthonburi University, Thailand https://orcid.org/0009-0009-3425-8134
Suchart Prakthayanon Faculty of Business Administration, Bangkokthonburi University, Thailand https://orcid.org/0009-0003-4859-4119

DOI:

https://doi.org/10.60027/iarj.2025.288012

Keywords:

Competitive Advantage, E-Commerce, Business Performance

Abstract

Background and Aims: Nanjing's rapid emergence as a leading e-commerce hub in China necessitates a deeper understanding of the drivers shaping competitive advantage and business performance. This study aims to (1) identify causal factors of competitive advantage, (2) analyze their impact on e-commerce performance, and (3) develop a structural model to elucidate these relationships.

Methodology: A mixed-methods approach was employed, combining quantitative surveys of 400 employees from 480 Nanjing e-commerce firms and qualitative interviews with 7 senior executives. Structural Equation Modeling (SEM) validated the hypothesized relationships, supported by descriptive statistics and thematic analysis.

Results: Policy support (β = 0.581), technological innovation (β = 0.884), and company capabilities (β = 0.512) significantly drive competitive advantage, with policy support being the most influential. Tax incentives (TLS) and industry regulations (LRS) underpin policy support, while big data analytics (BDA) and artificial intelligence (ALE) enhance technological innovation. Competitive advantage mediates 46% of performance variance (β = 0.462), with cost leadership (mean = 4.02) and differentiation strategies (e.g., AR-powered services boosting conversions by 30%) being critical. Service quality emerged as the strongest performance driver, supported by cost control and innovation. The SEM model demonstrated excellent fit (χ²/df = 2.61, CFI = 0.939, RMSEA = 0.047).

Conclusion: The study presents a validated model where policy support, technological innovation, and internal capabilities synergistically shape competitive advantage, directly and indirectly enhancing e-commerce performance. Practical insights include prioritizing AI adoption, leveraging tax incentives, and blending cost efficiency with differentiation strategies. These findings offer actionable guidance for firms and policymakers to strengthen Nanjing’s digital economy sustainably.

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