Leveraging Emerging Technologies in Pricing Strategies and Consumer Behavior: Case Studies from China's Innovative Markets

Authors

  • Lifeng Wang Graduate School, University of the East, Manila, Philippines Author
  • Christi Blandina Aldave University of the East, Manila, Philippines Author

DOI:

https://doi.org/10.62677/IJETAA.2406121

Keywords:

Emerging technologies, Pricing strategies, Consumer behavior, Case studies, Chinese market

Abstract

This study explores the impact of emerging technologies on pricing strategies and consumer behavior in China’s innovative markets. Through case studies of companies such as Alibaba, JD.com, Ctrip, Fliggy, Meituan, Hema, Douyin, and Kuaishou, we reveal how dynamic pricing, artificial intelligence, machine learning, and big data analytics are reshaping corporate pricing strategies and consumer behavior. The research finds that companies adopting dynamic pricing increased sales by an average of 15-20%; AI-driven personalized pricing improved con version rates by 25%; and big data analytics helped companies increase inventory turnover by 30%. In terms of consumer behavior, emerging technologies enabled companies to more accurately predict consumer demand, increasing user retention rates by an average of 18%. Social e-commerce platforms extended average user stay time by 40% and improved purchase conversion rates by 22% through technological innovation. This study provides valuable insights and practical guidance for companies formulating pricing strategies and understanding consumer behavior in technology-driven market environments.

Downloads

Download data is not yet available.

References

J. Wang, Y. Yang, and H. Wang, ”The role of big data analytics in China’s digital economy,” Journal of Management Information Systems, vol. 37, no. 2, pp. 424-452, 2020.

Y. Chen and C. Zhang, ”The impact of emerging technologies on pricing strategies: Evidence from China’s e-commerce market,” Journal of Marketing, vol. 84, no. 4, pp. 50-68, 2020.

L. Liu, M. Lu, and J. Shang, ”Dynamic pricing in the presence of social learning: An empirical study of China’s ride-hailing market,”Management Science, vol. 66, no. 5, pp. 2075-2096, 2020.

H. Zhang and J. Zhu, ”Artificial intelligence in consumer behavior prediction: The case of Alibaba,” Journal of Consumer Research, vol.47, no. 3, pp. 377-397, 2020.

R. Yin and Y. Wang, ”Big data analytics in Chinese retail industry: A case study of JD.com,” Journal of Retailing and Consumer Services, vol.53, pp. 101887, 2020.

S. Guo, T. Li, and H. Xu, ”The role of AI in dynamic pricing: A study of ride-hailing platforms in China,” Information Systems Research, vol.31, no. 4, pp. 1037-1060, 2020.

X. Li, K. Jiang, and L. Murphy, ”The impact of AI-driven personalization on consumer behavior: A study of Chinese social commerce platforms,” Marketing Science, vol. 39, no. 4, pp. 644-665, 2020.

Y. Wang and H. Xu, ”Artificial intelligence in pricing strategies: A study of China’s online-to-offline market,” Journal of Marketing Research, vol.58, no. 3, pp. 502-520, 2021.

Y. Li and K. Xie, ”Dynamic pricing and inventory management with dual-channel sales: A data-driven approach,” Production and Operations Management, vol. 29, no. 9, pp. 2069-2090, 2020.

L. Wu and E. Brynjolfsson, ”The future of prediction: How AI is transforming decision making in China’s retail sector,” Management Science, vol. 67, no. 5, pp. 2668-2686, 2021.

H. Liu and W. Chen, ”Dynamic pricing in omnichannel retailing: The case of JD.com,” Journal of Retailing, vol. 97, no. 2, pp. 276-293, 2021.

X. Zhang, J. Li, and Y. Liu, ”The impact of AI-driven personalization on consumer behavior: Evidence from Taobao,” Marketing Science, vol.40, no. 1, pp. 62-84, 2021.

J. Li and F. Zhu, ”Information disclosure and consumer behavior: A study of China’s e-commerce platforms,” Management Science, vol. 67,no. 3, pp. 1527-1546, 2021.

Y. Wang and H. Xu, ”Artificial intelligence in pricing strategies: A study of China’s online-to-offline market,” Journal of Marketing Research, vol.58, no. 3, pp. 502-520, 2021.

R. Gao and B. Gu, ”The impact of AI on consumer trust: A study of China’s sharing economy platforms,” MIS Quarterly, vol. 45, no. 2, pp.885-918, 2021.

H. Zhang, Y. Li, and W. Chen, ”The role of machine learning in personalizing pricing: Evidence from Ctrip,” Journal of Marketing, vol.85, no. 1, pp. 1-20, 2021.

S. Li and Y. Liu, ”Artificial intelligence in revenue management: The case of China’s airline industry,” Tourism Management, vol. 84, pp.104-116, 2021.

X. Wu and Y. Chen, ”The impact of AI-driven recommendation systems on consumer behavior: A study of Alibaba’s Taobao,” Information Systems Research, vol. 32, no. 1, pp. 11-26, 2021.

L. Chen and H. Wang, ”The challenges of implementing AI in pricing strategies: A case study of Chinese retailers,” Journal of Business Research, vol. 131, pp. 499-510, 2021.

L. Xu, J. He, and H. Chen, ”Big data analytics in omnichannel retailing: The case of Hema Fresh,” Decision Support Systems, vol. 141, pp.113447, 2021.

Y. Li and W. Wang, ”The impact of dynamic pricing on consumer behavior: Evidence from DiDi Chuxing,” Transportation Research Part A: Policy and Practice, vol. 145, pp. 71-91, 2021.

H. Chen and X. Li, ”Artificial intelligence in social commerce: A study of live streaming platforms in China,” Journal of Marketing, vol. 85, no.6, pp. 17-33, 2021.

R. Liu and S. Zhao, ”The impact of AI on consumer decision-making: Evidence from Tmall,” Journal of Consumer Research, vol. 48, no. 3,pp. 488-508, 2021.

J. Wang and Y. Sun, ”Privacy concerns in AI-driven pricing: A study of Chinese consumers,” Information & Management, vol. 58, no. 5, pp.103411, 2021.

X. Li, Y. Chen, and J. Zhu, ”Balancing data-driven decision making and managerial intuition: Evidence from China’s e-commerce giants,” Strategic Management Journal, vol. 42, no. 2, pp. 386-412, 2021.

Z. Yang and W. Xu, ”The role of AI in personalized content recommendation: Evidence from Douyin,” Information Systems Research, vol. 32,no. 3, pp. 738-755, 2021.

J. Li and Y. Wang, ”Social network analysis in e-commerce: A study of Kuaishou’s recommendation system,” MIS Quarterly, vol. 45, no. 4,pp. 1967-1994, 2021.

R. Gao et al., ”The impact of AR/VR technologies on consumer decision-making: A study of Taobao’s virtual fitting room,” Journal of Marketing Research, vol. 58, no. 6, pp. 1132-1151, 2021.

L. Wu and X. Chen, ”Ethical considerations in AI-driven personalization: A study of Chinese e-commerce platforms,” Journal of Business Ethics, vol. 170, no. 4, pp. 637-654, 2021.

Y. Li et al., ”The role of emerging technologies in pricing strategies: A comprehensive review of China’s innovative markets,” Journal of the Academy of Marketing Science, vol. 50, no. 1, pp. 5-35, 2022.

H. Zhang and J. Wang, ”Artificial intelligence in dynamic pricing: A meta-analysis of empirical studies in China,” Marketing Science, vol.41, no. 1, pp. 1-22, 2022.

Z. Jiang et al., ”Policy implications of AI-driven pricing and consumer behavior: Lessons from China’s experience,” Journal of Public Policy & Marketing, vol. 41, no. 1, pp. 45-63, 2022.

R. Liu and H. Xu, ”Future research directions in AI-driven pricing and consumer behavior: Insights from China’s innovative markets,” Journal of the Association for Consumer Research, vol. 7, no. 1, pp. 160-178,2022.

Downloads

Published

2024-07-26

Issue

Section

Research Articles

Categories

How to Cite

[1]
L. Wang and C. B. Aldave, “Leveraging Emerging Technologies in Pricing Strategies and Consumer Behavior: Case Studies from China’s Innovative Markets”, ijetaa, vol. 1, no. 6, pp. 6–12, Jul. 2024, doi: 10.62677/IJETAA.2406121.

Similar Articles

1-10 of 21

You may also start an advanced similarity search for this article.