The impact of artificial intelligence (AI) application on marketing performance in small and medium-sized enterprises in Hanoi
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DOIhttp://dx.doi.org/10.21511/ppm.24(3).2026.25
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Article InfoVolume 24 2026, Issue #3, pp. 392–405
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Creative Commons Attribution 4.0 International License
Type of the article: Research Article
Abstract
The rapid development of artificial intelligence (AI) and digital transformation has significantly reshaped marketing activities, particularly for small and medium-sized enterprises (SMEs) operating under resource constraints. In emerging economies such as Vietnam, empirical evidence regarding the effectiveness of AI application in marketing remains limited and fragmented. Therefore, this study aims to examine the impact of AI application on marketing performance of SMEs in Hanoi, Vietnam. The study applies the resource-based view (RBV) and dynamic capabilities theory (DCT) as the theoretical foundation to analyze the relationships between AI-enabled marketing capabilities and marketing performance. The study used quantitative research through a survey of 237 SME managers and employees in Hanoi. The collected data were analyzed using SPSS and structural equation modeling (SEM).
The findings indicate that AI application in customer data analytics positively affects market performance (β = 0.219, p < 0.01), customer performance (β = 0.186, p < 0.05), financial performance (β = 0.174, p = 0.050), and communication performance (β = 0.620, p < 0.001). AI-enabled marketing personalization is positively associated with market, financial, and communication performance. However, customer performance showed a statistically significant negative coefficient. In addition, AI-based marketing automation and interactive customer communication demonstrate varying effects across different dimensions of marketing performance. The study contributes to the literature on AI-driven marketing by providing empirical evidence from an emerging economy context and offering managerial implications for SMEs seeking to improve marketing effectiveness through AI adoption.
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JEL Classification (Paper profile tab)M31, O33, L26
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References18
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Tables7
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Figures2
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- Figure 1. Proposed research model
- Figure 2. SEM model analysis
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- Table 1. Demographic statistics
- Table 2. Cronbach's alpha results
- Table 3. Summary of CFA analysis results
- Table 4. Results of CR, AVE, MSV and SQRTAVE assessment
- Table 5. Structural path estimates and hypothesis-testing results
- Table A1. Measurement scales
- Table A2. EFA analysis results
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- Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99-120.
- Barney, J.B., Ketchen, D.J., & Wright, M. (2011). The future of resource-based theory: Revitalization or decline? Journal of Management, 37(5), 1299-1315.
- Clark, B. H. (1999). Marketing performance measures: History and interrelationships. Journal of Marketing Management, 15(8), 711-732.
- Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108-116.
- Davenport, T.H., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48, 24-42.
- Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Pearson.
- Huang, M. H., & Rust, R. T. (2021). Artificial intelligence in service. Journal of Service Research, 24(1), 3-18.
- Kaplan, A. M., & Haenlein, M. (2019). Siri, Siri, in my hand: Who's the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15-25.
- Kumar, V., Dixit, A., Javalgi, R. G., & Dass, M. (2016). Research framework, strategies, and applications of intelligent agent technologies in marketing. Journal of the Academy of Marketing Science, 44, 24-45.
- Morgan, N. A., Vorhies, D. W., & Mason, C. H. (2009). Market orientation, marketing capabilities, and firm performance. Strategic Management Journal, 30(8), 909-920.
- Rust, R. T. (2020). The future of marketing. International Journal of Research in Marketing, 37(1), 15-26.
- Rust, R. T., Lemon, K. N., & Zeithaml, V. A. (2004). Return on marketing: Using customer equity to focus marketing strategy. Journal of Marketing, 68(1), 109-127.
- Rust, R.T., Ambler, T., Carpenter, G.S., Kumar, V., & Srivastava, R.K. (2004). Measuring marketing productivity: Current knowledge and future directions. Journal of Marketing, 68(4), 76-89.
- Teece, D. J. (2007). Explicating dynamic capabilities: The nature and microfoundations of (sustainable) enterprise performance. Strategic Management Journal, 28(13), 1319-1350.
- Teece, D. J., Pisano, G., & Shuen, A. (2008). Dynamic capabilities and strategic management. In Technological Know-How, Organizational Capabilities, and Strategic Management Business Strategy and Enterprise Development in Competitive Environments (pp. 27-51). World Scientific Publishing Co. Pte. Ltd.
- Verhoef, P. C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Dong, J. Q., Fabian, N., & Haenlein, M. (2021). Digital transformation: A multidisciplinary reflection and research agenda. Journal of Business Research, 122, 889-901.
- Verhoef, P. C., Lemon, K. N., Parasuraman, A., Roggeveen, A., Tsiros, M., & Schlesinger, L. A. (2009). Customer experience creation. Journal of Retailing, 85(1), 31-41.
- Wedel, M., & Kannan, P. K. (2016). Marketing analytics for data-rich environments. Journal of Marketing, 80(6), 97-121.


