With the rapid development of artificial intelligence technologies, recommendation algorithms have become a crucial tool for e-commerce platforms to enhance user experience and improve conversion rates. Based on value chain theory, this paper systematically examines the influence mechanisms of AI recommendation algorithms on consumer purchasing behavior in e-commerce, focusing on key stages such as user acquisition, information matching, decision support, and post-purchase feedback.By reviewing relevant literature and constructing a theoretical model, the study explores the functional pathways of recommendation algorithms across different stages of the value chain and their combined effects on consumer cognition, emotions, and behavioral decisions. The findings indicate that recommendation algorithms significantly promote purchase intention and actual purchasing behavior by improving information matching efficiency, enhancing user experience, and reducing decision-making costs. Meanwhile, trust mechanisms and perceived value play mediating roles in this process. This study provides both theoretical support and practical insights for e-commerce platforms to optimize recommendation strategies and improve user conversion rates.

Zhang Leyi. AI Recommendation Algorithms and E-commerce Purchasing Behavior: A Value Chain Perspective. Academic Journal of Business & Management (2026), Vol. 8, Issue 5: 84-91. https://doi.org/10.25236/AJBM.2026.080512.

[1] JIN J, LIU Q, YANG Y,et al . How social crowding impacts mobile shopping: a perspective from information processing[J]. Information & Management, 2025, 62(7).

[2] ZHOU M, HUANG J, WU K,et al. Characterizing Chinese consumers’ intention to use live e-commerce shopping[J]. Technology in Society, 2021, 67.

[3] SULLIVAN Y W, KIM D J. Assessing the effects of consumers’ product evaluations and trust on repurchase intention in e-commerce environments[J]. International Journal of Information Management, 2018, 39: 199-219.

[4] WANG L, CHEN L, LI C. Research on strategies for improving green product consumption sentiment from the perspective of big data[J]. Journal of Retailing and Consumer Services, 2024, 79.

[5] ZHANG Y, ZHANG T, YAN X. Understanding impulse buying in short video live E-commerce: the perspective of consumer vulnerability and product type[J]. Journal of Retailing and Consumer Services, 2024, 79.

[6] XU X Y, JIA Q D, TAYYAB S M U. Exploring the stimulating role of augmented reality features in E-commerce: a three-staged hybrid approach[J]. Journal of Retailing and Consumer Services, 2024, 77.

[7] PAYAL R, SHARMA N, DWIVEDI Y K. Unlocking the impact of brand engagement in the metaverse on real-world purchase intentions: analyzing pre-adoption behavior in a futuristic technology platform[J]. Electronic Commerce Research and Applications, 2024, 65.

[8] ZHANG Y, LI K, QIAN C,et al. How real-time interaction and sentiment influence online sales? Understanding the role of live streaming danmaku[J]. Journal of Retailing and Consumer Services, 2024, 78.

[9] LI L, CHEN X, ZHU P. How do e-commerce anchors’ characteristics influence consumers’ impulse buying? An emotional contagion perspective[J]. Journal of Retailing and Consumer Services, 2024, 76.

[10] NUGROHO A, WANG W T. Consumer switching behavior to an augmented reality (AR) beauty product application: push-pull mooring theory framework[J]. Computers in Human Behavior, 2023, 142.

[11] WANG Q, ZHANG W, LI J,et al. Effect of online review sentiment on product sales: the moderating role of review credibility perception[J]. Computers in Human Behavior, 2022, 133.

[12] PILLAI S G, KIM W G, HALDORAI K,et al. Online food delivery services and consumers’ purchase intention: integration of theory of planned behavior, theory of perceived risk, and the elaboration likelihood model[J]. International Journal of Hospitality Management, 2022, 105.