Kebangkitan E-commerce Bertenaga AI: Mengubah Lanskap Bisnis di Tahun 2024
DOI:
https://doi.org/10.61132/prosemnasimkb.v1i1.20Keywords:
artificial intelligence, business strategy, digital transformation, disruptive innovation, e-commerceAbstract
The rapid adoption of artificial intelligence (AI) in e-commerce is revolutionizing the business landscape. This study explores the rise of AI-powered e-commerce and its impact on business models, strategies, and market dynamics in 2024. Through a multiple case study approach, focusing on leading e-commerce companies such as Amazon, Alibaba, and Shopee, the research reveals that AI is fundamentally transforming the way businesses operate in the digital economy. AI enables enhanced personalization, operational efficiency, and improved customer experiences, driving the emergence of new business models and competitive advantages. However, the adoption of AI also creates significant challenges, including implications for the workforce, ethical concerns surrounding data privacy and algorithmic bias, and potential impacts on market dynamics and competition. The study highlights the need for a strategic and ethical approach to AI adoption, collaboration among stakeholders, and adaptive regulatory frameworks. It concludes with recommendations for businesses, policymakers, and future research to navigate the transformative impact of AI in e-commerce. The findings contribute to the literature on digital transformation and disruptive innovation, offering valuable insights for managers, practitioners, and researchers.
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Alekseeva, L., Azar, J., Gine, M., Samila, S., & Taska, B. (2019). The demand for AI skills in the labor market. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3470610
Amina, B., & Hadjira, L. C. (2023). Artificial intelligence and its applications in e-commerce. International Forum on Artificial Intelligence Strategy and Its Controls. https://doi.org/10.37018/jfjmu.724
Areiqat, A. Y., Hamdan, A., Alheet, A. F., & Alareeni, B. (2021). Impact of artificial intelligence on e-commerce development (pp. 571–578). https://doi.org/10.1007/978-3-030-69221-6_43
Boratto, L., Fenu, G., & Marras, M. (2019). The effect of algorithmic bias on recommender systems for massive open online courses (pp. 457–472). https://doi.org/10.1007/978-3-030-15712-8_30
Borges, A. F. S., Laurindo, F. J. B., Spínola, M. M., Gonçalves, R. F., & Mattos, C. A. (2021). The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions. International Journal of Information Management, 57, 102225. https://doi.org/10.1016/j.ijinfomgt.2020.102225
Braun, V., & Clarke, V. (2021). Thematic analysis: A practical guide. SAGE Publications Ltd.
Bughin, J., Hazan, E., Ramaswamy, S., Chui, M., Allas, T., Dahlstrom, P., Henke, N., & Trench, M. (2017). Artificial intelligence – The next frontier in IT security? McKinsey Global Institute. https://doi.org/10.1016/S1353-4858(17)30039-9
Chen, J., Zhang, C., & Guo, R. (2021). The D-Day, V-Day, and bleak days of a disruptive technology: A new model for ex-ante analysis of the timing of AI adoption. Technological Forecasting and Social Change, 162.
Chen, Y., Shen, Y., & Zheng, K. (2021). Digital innovation in the sharing economy: A bibliometric analysis and integrative conceptual framework. Journal of Cleaner Production, 299.
Christensen, C. M. (1997). The innovator’s dilemma: When new technologies cause great firms to fail. Harvard Business School Press.
Christensen, C. M., Raynor, M. E., & McDonald, R. (2015). What is disruptive innovation. Harvard Business Review, 93(12), 44–53.
Chui, M., Manyika, J., Miremadi, M., Henke, N., Chung, R., Nel, P., & Malhotra, S. (2021). Notes from the AI frontier: Applications and value of deep learning. McKinsey Global Institute. https://www.mckinsey.com/featured-insights/artificial-intelligence/notes-from-the-ai-frontier-applications-and-value-of-deep-learning
Creswell, J. W., & Poth, C. N. (2018). Qualitative inquiry and research design: Choosing among five approaches (4th ed.). SAGE Publications.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319. https://doi.org/10.2307/249008
Denzin, N. K. (2017). The research act. Routledge. https://doi.org/10.4324/9781315134543
Eisenhardt, K. M., & Graebner, M. E. (2007). Theory building from cases: Opportunities and challenges. Academy of Management Journal, 50(1), 25–32.
Fang, J., Liu, S., & Sun, Y. (2021). The impact of AI customer service on customer satisfaction and loyalty in e-commerce. Journal of Retailing and Consumer Services, 63.
Ferreira, B., & Reis, J. (2023). Artificial intelligence in supply chain management: A systematic literature review and guidelines for future research. In Industrial engineering and operations management (pp. 339–354). Springer. https://doi.org/10.1007/978-3-031-47058-5_27
Gal, M. S. (2019). Law and technology illegal pricing algorithms. Communications of the ACM, 62(1), 18–20. https://doi.org/10.1145/3292515
Garg, S., Gupta, S., & Sahu, M. (2021). Artificial intelligence in e-commerce: A systematic literature review and future research agenda. International Journal of Electronic Commerce, 25(2), 175–196.
Grand View Research. (2022). Artificial intelligence in e-commerce market size, share & trends analysis report by technology, by application, by deployment, by region, and segment forecasts, 2022 to 2030. https://www.grandviewresearch.com/industry-analysis/artificial-intelligence-e-commerce-market-report
Hambrick, D. C., & Mason, P. A. (1984). Upper echelons: The organization as a reflection of its top managers. The Academy of Management Review, 9(2), 193. https://doi.org/10.2307/258434
Kaplan, A., & 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.
Kharlamov, A. (2022). The impact of artificial intelligence on labor markets: A systematic literature review. In The impact of artificial intelligence on governance, economics and finance (pp. 103–131). Springer Cham.
Kotler, P., & Armstrong, G. (2021). Principles of marketing (18th ed.). Pearson.
Kraus, M., Feuerriegel, S., & Oztekin, A. (2022). Deep learning in business analytics and operations research: Models, applications and managerial implications. European Journal of Operational Research, 300, 401–412.
Krippendorff, K. (2018). Content analysis: An introduction to its methodology (4th ed.). SAGE Publications.
Kumar, A., Garg, A., & Rahman, Z. (2019). Customer engagement and retention in e-commerce: A theoretical perspective. International Journal of Electronic Marketing and Retailing, 10(3), 209–225.
Kumar, P., Dwivedi, Y. K., Rana, N. P., & Sahu, G. P. (2021). Artificial intelligence in e-commerce: A bibliometric study and future research agenda. Electronic Markets.
Lee, K., & Seo, Y. (2021). Application of artificial intelligence in e-commerce inventory management. Journal of the Korean Institute of Industrial Engineers, 47(2), 144–155.
Li, H., Jiang, H., & Liu, Y. (2021). Artificial intelligence in supply chain management: A systematic literature review. International Journal of Production Research, 59(12), 3535–3553.
Lobschat, L., Mueller, B., Eggers, F., Brandimarte, L., Diefenbach, S., Kroschke, M., & Wirtz, J. (2021). Corporate digital responsibility. Journal of Business Research, 122, 875–888. https://doi.org/10.1016/j.jbusres.2019.10.006
Ma, L., & Sun, B. (2020). Machine learning and AI in marketing – Connecting computing power to human insights. International Journal of Research in Marketing. https://api.semanticscholar.org/CorpusID:225277368
Mahroof, K., Weerakkody, V., Onkal, D., & Hussain, Z. (2020). Artificial intelligence in the public sector: A systematic literature review. Government Information Quarterly, 38(3).
Mashalah, H. Al, Hassini, E., Gunasekaran, A., & Bhatt (Mishra), D. (2022). The impact of digital transformation on supply chains through e-commerce: Literature review and a conceptual framework. Transportation Research Part E: Logistics and Transportation Review, 165, 102837. https://doi.org/10.1016/j.tre.2022.102837
McKinney, S. M., Karthikesalingam, A., Tse, D., Kelly, C. J., Liu, Y., Corrado, G. S., & Shetty, S. (2020). Reply to: Transparency and reproducibility in artificial intelligence. Nature, 586(7829), E17–E18. https://doi.org/10.1038/s41586-020-2767-x
Nowell, L. S., Norris, J. M., White, D. E., & Moules, N. J. (2017). Thematic analysis: Striving to meet the trustworthiness criteria. International Journal of Qualitative Methods, 16(1), 1–13.
Patton, M. Q. (2015). Qualitative research & evaluation methods: Integrating theory and practice (4th ed.). SAGE Publications.
Peña-Vinces, J. C., Solakis, K., & Guillen, J. (2021). The impact of artificial intelligence on the labor market: Challenges and opportunities. Human Resource Management Review.
Puddy, I. S. Z., & Ren, D. (2021). Artificial intelligence impact on jobs and skills: A systematic literature review. Journal of Business and Economic Analysis, 4(1), 1–20.
Rai, A. (2020). Explainable AI: From black box to glass box. Journal of the Academy of Marketing Science, 48(1), 137–141. https://doi.org/10.1007/s11747-019-00710-5
Rai, A., Constantinides, P., & Sarker, S. (2019). Editor’s comments: Next-generation digital platforms: Toward human-AI hybrids. MIS Quarterly, 43(1), iii–ix.
Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347–1358. https://doi.org/10.1056/NEJMra1814259
Ramaswamy, R., & Singh, S. (2022). Ethical and legal implications of artificial intelligence in e-commerce. Journal of Business Ethics, 1–21.
Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.
Saldaña, J. (2021). The coding manual for qualitative researchers (4th ed.). SAGE Publications Ltd.
Samani, S., Zhan, Z., Park, C. H., & Tang, O. (2022). Adoption of artificial intelligence in logistics and supply chain management: Insights from expert interviews. International Journal of Logistics Research and Applications, 1–26.
Seyedghorban, Z., Tahernejad, H., Meriton, R., & Graham, G. (2020). Supply chain digitalization: Past, present, and future. Production Planning & Control, 31(2–3), 96–114.
Singh, R., Gupta, A., & Srivastava, V. (2020). Artificial intelligence in supply chain management: A systematic literature review and future research agenda. International Journal of Logistics Research and Applications, 1–24.
Solomon, M. R. (2020). Consumer behavior: Buying, having, and being (13th ed.). Pearson Education.
Soni, A., & Dubey, S. (2024). The impact of AI-powered chatbots on customer satisfaction in e-commerce marketing (TAM approach). Journal of Public Relations and Advertising, 3(1), 12–18.
Stahl, B. C., & Wright, D. (2021). Ethics and artificial intelligence: An agenda for future research. AI & Society, 1–13.
Statista. (2023). Retail e-commerce sales worldwide from 2014 to 2027 (in billion U.S. dollars). https://www.statista.com/statistics/379046/worldwide-retail-e-commerce-sales/
Sun, Y., Shao, X., Li, X., Guo, Y., & Nie, K. (2019). How live streaming influences purchase intentions in social commerce: An IT affordance perspective. Electronic Commerce Research and Applications, 37, 100886. https://doi.org/10.1016/j.elerap.2019.100886
Tao, Q., Ren, C., Zhang, M., Du, W., & Li, X. (2019). AI-based intelligent customer service for e-commerce. 2019 IEEE 23rd International Conference on Computer Supported Cooperative Work in Design (CSCWD), 534–539.
Thomas, V., Schubert, K., & Mößlang, G. (2021). Occupational roles in the age of artificial intelligence - An analysis of job advertisements using natural language processing. 2021 6th Swiss Conference on Data Science (SDS), 7–12.
Verma, S., Sharma, R., Deb, S., & Maitra, D. (2021). Artificial intelligence in marketing: Systematic review and future research direction. International Journal of Information Management Data Insights, 1(1), 100002. https://doi.org/10.1016/j.jjimei.2020.100002
Wedel, M., Bigné, E., & Zhang, J. (2020). Virtual and augmented reality: Advancing research in consumer marketing. International Journal of Research in Marketing, 37(3), 443–465.
Yang, Q., Pang, C., Liu, L., Yen, D. C., & Tarn, J. M. (2019). Exploring consumer perceived risk and trust for online payments: An empirical study in China’s younger generation. Technological Forecasting and Social Change, 144, 63–73.
Yin, R. K. (2018). Case study research and applications: Design and methods (6th ed.). SAGE Publications.
Youn, S., & Jin, S. V. (2021). “In A.I. we trust?” The effects of parasocial interaction and technopian versus luddite ideological views on chatbot-based customer relationship management in the emerging “feeling economy.” Computers in Human Behavior, 119, 106721. https://doi.org/10.1016/j.chb.2021.106721
Zaki, M. (2019). Digital transformation: Harnessing digital technologies for the next generation of services. Journal of Services Marketing, 33(4), 429–435. https://doi.org/10.1108/JSM-01-2019-0034
Zheng, Y., Li, G., & Zheng, Z. (2018). A survey on fraud detection in e-commerce using machine learning methods. 2018 IEEE International Conference on Big Data (Big Data), 4535–4539.
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