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

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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Publication Details

Journal
Interdisciplinary Academic and Research Journal
Published
2025-07-26
DOI
https://doi.org/10.60027/iarj.2025.288012
Citations
1
Primary Topic
E-commerce and Technology Innovations
Type
article
Field-Weighted Citation Impact
2.45
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article

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

Sirachaya Karawek, Buraporn Kumboon, Suchart Prakthayanon, Ziyuan Liang
1 citations
Interdisciplinary Academic and Research Journal
E-commerce and Technology Innovations
2.45
article

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

Sirachaya Karawek, Buraporn Kumboon, Suchart Prakthayanon, Ziyuan Liang
article en
1 citations

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.

Interdisciplinary Academic and Research JournalVol. 5(4)
Bangkokthonburi University (TH)
Openalex Percentile: Top 10%
E-commerce and Technology Innovations
2.45
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