Big Data and Artificial Intelligence (AI) In E-Business: Opportunity and Challenges in Business Growth

This research article delves into the multifaceted implications of Big Data and Artificial Intelligence (AI) on e-businesses, emphasizing both their potential advantages and the associated challenges. With a rapidly evolving market characterized by intricate digital integrations, personalization and predictive analytics emerge as paramount drivers for e-business growth. Statistical evidence reveals that 80% of consumers favor businesses offering personalized experiences, and AI-driven predictive analytics have contributed to significant revenue increases in companies. However, this monumental shift isn't without its challenges. Concerns about data breaches, AI biases, and the infrastructural investments needed to harness these technologies effectively have risen. Through an exhaustive review of current literature and pertinent case studies, this article aims to provide a comprehensive understanding of how e-businesses can optimally leverage Big Data and AI, while concurrently navigating the complexities presented by ethical, security, and infrastructural challenges. This dual exploration addresses the research objectives: understanding the leverage points for e-business growth using personalization and predictive analytics, and delineating the obstacles faced in Big Data and AI integration within e-commerce. Drawing from secondary data, this research offers a holistic perspective to stakeholders on the evolving landscape of e-business in the context of Big Data and AI.

E-business, Big Data, Artificial Intelligence (AI), Data

1. Brown, R., & Green, T. (2021). Navigating predictive analytics in the e-business landscape. Journal of Digital Business Insights, 16(3), 210-225. [Google Scholar] [Crossref]

2. Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS quarterly, 1165-1188. [Google Scholar] [Crossref]

3. Choi, T. Y., Kim, Y., & Kim, J. K. (2020). Big Data and AI strategies for e-commerce: Bridging gaps between knowledge and application. Journal of Business Analytics, 3(2), 126-139. [Google Scholar] [Crossref]

4. Clark, A. & Lee, D. (2021). Harnessing Big Data and AI: The Future of E-businesses. International Journal of E-commerce Studies, 18(2), 34-48. [Google Scholar] [Crossref]

5. Dataconomy. (2020). How much data does the average online shopper generate? http://www.dataconomy.com/2020/02/how-much-data-does-the-average-online-shopper-generate/ [Google Scholar] [Crossref]

6. Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108-116. [Google Scholar] [Crossref]

7. eMarketer. (2019). Global e-commerce forecast 2019. https://www.emarketer.com/content/global-ecommerce-2019 [Google Scholar] [Crossref]

8. eMarketer. (2019). Global e-commerce trends. eMarketer Research Journal, 24(7), 102-110. Retrieved from http://www.emarketer.com/journal/global-e-commerce-trends. [Google Scholar] [Crossref]

9. Epsilon (2018). The Power of Personalization: How Tailored Interactions Boost Business. Epsilon Data Management, LLC. [Google Scholar] [Crossref]

10. Epsilon. (2018). Epsilon’s new digital media delivery platform offers marketers optimized performance and consumer privacy protection. Retrieved from [Google Scholar] [Crossref]

11. Epsilon. (2018). The power of me: The impact of personalization on marketing performance. Epsilon. [Google Scholar] [Crossref]

12. Gartner. (2019). AI-driven predictive analytics and its effects on business revenue. Gartner Research Publications. [Google Scholar] [Crossref]

13. Gartner. (2019). The rise of AI in e-business. Gartner Research, 45(3), 45-54. Retrieved from http://www.gartner.com/research/ai-rise-ebusiness. [Google Scholar] [Crossref]

14. Haleem, A., Javaid, M., & Vaishya, R. (2020). Effects of COVID-19 pandemic in the field of orthopaedics. Journal of Clinical Orthopaedics and Trauma, 11(3), 498-499. https://us.epsilon.com/pressroom/new-digital-media-delivery-platform-offers-marketers-optimized-performance-and-consumer-privacy-protection [Google Scholar] [Crossref]

15. James, R., & Peters, S. (2021). AI biases in e-business: Implications and solutions. Journal of Ethical Computing, 4(1), 10-22. [Google Scholar] [Crossref]

16. Jupiter Research. (2018). AI investments in e-commerce to touch new highs by 2022. https://www.jupiterresearch.com/ [Google Scholar] [Crossref]

17. Lu, Y., Sadiq, S., & Padmanabhan, V. (2014). Incorporating temporal and spatial variations to evaluate the vulnerability of sensor networks against node capture attacks. Sensors, 14(4), 7245-7268. [Google Scholar] [Crossref]

18. Mittelstadt, B., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 2053951716679679. [Google Scholar] [Crossref]

19. RiskBased Security. (2019). Cyber risk analytics half year report. RiskBased Security, Inc. [Google Scholar] [Crossref]

20. RiskBased Security. (2019). Data breach QuickView report 2019 Q3 trends. Retrieved from https://www.riskbasedsecurity.com/2019/11/13/report-data-breach-quickview-report-2019-q3-trends/ [Google Scholar] [Crossref]

21. Russell, S. J., & Norvig, P. (2010). Artificial intelligence: A modern approach. Malaysia; Pearson Education Limited. [Google Scholar] [Crossref]

22. Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of Big Data challenges and analytical methods. Journal of Business Research, 70, 263-286. [Google Scholar] [Crossref]

23. Smith, J., & Jones, M. (2020). Consumer behavior in the age of personalization. Consumer Psychology Journal, 14(5), 445-460. [Google Scholar] [Crossref]

24. Statista. (2022). E-commerce trends and growth predictions. Statista Research Department. [Google Scholar] [Crossref]

25. Statista. (2022). E-commerce worldwide - Statistics & Facts. Retrieved from https://www.statista.com/statistics/379046/worldwide-retail-e-commerce-sales/ [Google Scholar] [Crossref]

26. Thompson, L., & Wright, R. (2020). Infrastructure needs for big data and AI integration. Journal of Business Infrastructure, 9(4), 300-312. [Google Scholar] [Crossref]

27. White, C., & Black, D. (2020). Cybersecurity in the age of big data and AI. Journal of Digital Threats and Solutions, 12(1), 50-65. [Google Scholar] [Crossref]

28. Xia, L., Monroe, K., & Cox, J. (2017). The price is unfair! A conceptual framework of price fairness perceptions. Journal of Marketing, 68(4), 1-15. [Google Scholar] [Crossref]

29. Xia, Q., Ma, J., Lu, J. J., & He, Y. (2017). Applications of Big Data analytics in supply chain management: A literature review. Computers & Industrial Engineering, 101, 529-543. [Google Scholar] [Crossref]

30. Xia, Q., Ma, Q., Lu, Y., & He, J. (2017). Predictive big data analytics in supply chain management: A study on advantages, challenges, and business process. Pacific Asia Journal of the Association for Information Systems, 9(2), 1-26. [Google Scholar] [Crossref]

• IFRS S1 and S2: A Global and Indian Perspective with Implications for Auditors
• A Study on Consumer Attitudes and Buying Behavior towards organic Food Products
• India’s Transfer Pricing Framework: Evolution, Challenges, and Future Directions
• Application of Statistical Methods in Business Administration: A Quantitative Study on Organizational Performance
• Decoding Fintech Adoption: The Role of Trust, Risks and Perceived Benefits