Artificial Intelligence: An Analytical Study: Its Impact on Marketing Rate Through Electronic Applications in Saudi Arabia
Keywords:
Artificial Intelligence (AI), E-commerce, Personalized Recommendations, Chatbots, Predictive Analytics, Saudi Arabia, Digital Marketing.Abstract
This study examines the role of artificial intelligence (AI) in shaping e-commerce trends and its impact on consumer behavior in Saudi Arabia. With the increasing integration of AI technologies, such as machine learning algorithms, chatbots, and virtual assistants, e-commerce platforms are experiencing a transformation in customer engagement, sales performance, and personalized shopping experiences. The research utilizes a mixed-methods approach, combining quantitative and qualitative data from a sample of 349 participants across various regions in Saudi Arabia. The findings reveal that AI-driven features significantly influence online shopping behaviors, with users reporting enhanced satisfaction, increased shopping frequency, and improved decision-making processes through personalized recommendations and dynamic pricing. Key insights from the study include gender-based preferences for specific e-commerce platforms and distinct challenges related to AI adoption, such as privacy concerns, mistrust in AI recommendations, and system complexity. The study also highlights the need for e-commerce platforms to address these challenges by enhancing transparency, refining AI-driven tools, and ensuring a balance between personalization and consumer privacy. Despite these challenges, the data suggest optimism regarding AI's potential to further transform the e-commerce landscape in Saudi Arabia, offering valuable opportunities for businesses to improve customer experiences and operational efficiency. This research provides a comprehensive analysis of AI's impact on digital shopping in Saudi Arabia, contributing to the growing body of knowledge on AI’s influence in the global e-commerce market..
Downloads
References
Sharma, A. and M.O. Shafiq, A comprehensive artificial intelligence based user intention assessment model from online reviews and social media. Applied Artificial Intelligence, 2022. 36(1): p. 2014193.
Dumitriu, D. and M.A.-M. Popescu, Artificial intelligence solutions for digital marketing. Procedia Manufacturing, 2020. 46: p. 630-636.
Forrest, E. and B. Hoanca, Artificial intelligence: Marketing's game changer. Trends and innovations in marketing information systems, 2015: p. 45-64.
Rizvi, A.T., et al., Artificial intelligence (AI) and its applications in Indian manufacturing: a review. Current Advances in Mechanical Engineering: Select Proceedings of ICRAMERD 2020, 2021: p. 825-835.
Liu, Y. and W. Chen, Optimization of brand marketing strategy of intelligent technology under the background of artificial intelligence. Mobile Information Systems, 2021. 2021(1): p. 9507917.
Gupta, T. and S. Bansal, The AI Advantage: Assessing Personalization Effects on E-commerce Shopping Behaviors.
Holmström, E. and A. Larsson, From Data to Loyalty: A quantative study of consumer's response to AI-driven personalized marketing. 2024.
Sanakuiev, M., et al., Information Marketing: A Comprehensive Review. Futurity Economics&Law, 2023. 3(1): p. 28-50.
Qi, B., Y. Shen, and T. Xu, An artificial-intelligence-enabled sustainable supply chain model for B2C E-commerce business in the international trade. Technological forecasting and social change, 2023. 191: p. 122491.
Lopez, S., Optimizing Marketing ROI with Predictive Analytics: Harnessing Big Data and AI for Data-Driven Decision Making. Journal of Artificial Intelligence Research, 2023. 3(2): p. 9-36.
Haleem, A., et al., Artificial intelligence (AI) applications for marketing: A literature-based study. International Journal of Intelligent Networks, 2022. 3: p. 119-132.
Techlabs, M. Can chatbots help reduce customer service costs by 30? . 2017; Available from: https://chatbotsmagazine.com/how-with-thehelp-of-chatbots-customer-service-costs-could-be-reduced-up-to30-b9266a369945.
Meuter, M.L., et al., Choosing among alternative service delivery modes: An investigation of customer trial of self-service technologies. Journal of marketing, 2005. 69(2): p. 61-83.
Takale, D.G., Revolutionizing Customer Interaction: NLP-Powered Virtual Shopping Assistants and Sentiment Analysis in E-commerce.
Misra, R.R., S. Kapoor, and M. Sanjeev, The Impact of Personalisation Algorithms on Consumer Engagement and Purchase Behaviour in AI-Enhanced Virtual Shopping Assistants. 2024.
Kyrylenko, Y., Chatbots and virtual assistants vs. human agents in IT customer support. 2024.
Jankovic, S.D. and D.M. Curovic, Strategic integration of artificial intelligence for sustainable businesses: implications for data management and human user engagement in the digital era. Sustainability, 2023. 15(21): p. 15208.
Gerlick, J.A. and S.M. Liozu, Ethical and legal considerations of artificial intelligence and algorithmic decision-making in personalized pricing. Journal of Revenue and Pricing Management, 2020. 19: p. 85-98.
Shrestha, Y.R., V. Krishna, and G. von Krogh, Augmenting organizational decision-making with deep learning algorithms: Principles, promises, and challenges. Journal of Business Research, 2021. 123: p. 588-603.
Owen, J., AI-Driven Personalization: Balancing Customer Benefit and Ethical Concerns. 2023.
Kim, W., Shopping with AI: Consumers' perceived autonomy in the age of AI, in Human-Centered Artificial Intelligence. 2022, Elsevier. p. 157-171.
Bjørlo, L.V., Freedom from interference: Decisional privacy as a dimension of consumer privacy online. AMS Review, 2024: p. 1-25.
Markula, A., The Use of Artificial Intelligence in Dynamic Pricing Strategies. 2023.
Phillips, R.L., Pricing and revenue optimization. 2021: Stanford university press.
Dash, R., et al., Application of artificial intelligence in automation of supply chain management. Journal of Strategic Innovation and Sustainability, 2019. 14(3).
ACQUA ERA, C., Dynamic Pricing and Consumer Behaviour: Analysis from a Consumers' Perspective. 2018.
East, A. and J. Smith, The Future of E-commerce: AI’s Role in Customer Experience Optimization. 2024, EasyChair.
Zimmermann, R., et al., Enhancing brick-and-mortar store shopping experience with an augmented reality shopping assistant application using personalized recommendations and explainable artificial intelligence. Journal of Research in Interactive Marketing, 2023. 17(2): p. 273-298.
Shin, D., User perceptions of algorithmic decisions in the personalized AI system: Perceptual evaluation of fairness, accountability, transparency, and explainability. Journal of Broadcasting & Electronic Media, 2020. 64(4): p. 541-565.
Rane, N.L., A. Achari, and S.P. Choudhary, Enhancing customer loyalty through quality of service: Effective strategies to improve customer satisfaction, experience, relationship, and engagement. International Research Journal of Modernization in Engineering Technology and Science, 2023. 5(5): p. 427-452.
Gal, M.S., Algorithmic challenges to autonomous choice. Mich. Tech. L. Rev., 2018. 25: p. 59.
Burlacu, C., The Impact Of Ai-Powered Content Generation On Customer Experience. 2023, University of Twente.
Riegger, A.-S., et al., Technology-enabled personalization in retail stores: Understanding drivers and barriers. Journal of Business Research, 2021. 123: p. 140-155.
Krishnan, C., et al., Impact of artificial intelligence-based chatbots on customer engagement and business growth, in Deep learning for social media data analytics. 2022, Springer. p. 195-210.
Bharadiya, J.P., Machine learning and AI in business intelligence: Trends and opportunities. International Journal of Computer (IJC), 2023. 48(1): p. 123-134.
Yau, K.-L.A., N.M. Saad, and Y.-W. Chong, Artificial intelligence marketing (AIM) for enhancing customer relationships. Applied Sciences, 2021. 11(18): p. 8562.
Bag, S., et al., Journey of customers in this digital era: Understanding the role of artificial intelligence technologies in user engagement and conversion. Benchmarking: An International Journal, 2022. 29(7): p. 2074-2098.
Jayawardena, N.S., et al., Artificial intelligence (AI)-based market intelligence and customer insights, in Artificial intelligence for marketing management. 2022, Routledge. p. 120-141.
Vemulapalli, G., AI-Driven Predictive Models Strategies to Reduce Customer Churn. International Numeric Journal of Machine Learning and Robots, 2024. 8(8): p. 1-13.
4Balakrishnan, J. and Y.K. Dwivedi, Conversational commerce: entering the next stage of AI-powered digital assistants. Annals of Operations Research, 2024. 333(2): p. 653-687.
Chopdar, P.K. and J. Balakrishnan, Consumers response towards mobile commerce applications: SOR approach. International Journal of Information Management, 2020. 53: p. 102106.
Daqar, M.A.A. and A.K. Smoudy, The role of artificial intelligence on enhancing customer experience. International Review of Management and Marketing, 2019. 9(4): p. 22.
Sarker, I.H., et al., Mobile data science and intelligent apps: concepts, AI-based modeling and research directions. Mobile Networks and Applications, 2021. 26(1): p. 285-303.
Arachchi, H.D.M. and G.D. Samarasinghe, Impact of embedded AI mobile smart speech recognition on consumer attitudes towards AI and purchase intention across Generations X and Y. European Journal of Management Studies, 2024. 29(1): p. 3-29.
Raji, M.A., et al., E-commerce and consumer behavior: A review of AI-powered personalization and market trends. GSC Advanced Research and Reviews, 2024. 18(3): p. 066-077.
Krishnan, C. and J. Mariappan, The AI Revolution in E-Commerce: Personalization and Predictive Analytics, in Role of Explainable Artificial Intelligence in E-Commerce. 2024, Springer. p. 53-64.
Chowdhury, S., et al., Influence of AI driven Digital Marketing on Consumer Purchase Intention: An Empirical Study. Journal of Informatics Education and Research, 2024. 4(2).
EMB, T., Impact of Innovative App Features on User Engagement 2024.
Le, W., et al., Can App Reviews Help Developers to Improve Mobile User Interface Design? Journal of Internet Technology, 2023. 24(4): p. 955-964.
Li, X., et al., User Interaction Interface Design and Innovation Based on Artificial Intelligence Technology. Journal of Theory and Practice of Engineering Science, 2024. 4(03): p. 1-8.
Roffarello, A.M. and L. De Russis, Understanding and streamlining app switching experiences in mobile interaction. International Journal of Human-Computer Studies, 2022. 158: p. 102735.
Smith, R., M. Cubino, and E. McKeon, The AI Revolution in Customer Service and Support: A Practical Guide to Impactful Deployment of AI to Best Serve Your Customers. 2024: Pearson Education.
Sharma, R., et al., Digital literacy and knowledge societies: A grounded theory investigation of sustainable development. Telecommunications Policy, 2016. 40(7): p. 628-643.
Lee, J., et al., Emerging technology and business model innovation: the case of artificial intelligence. Journal of Open Innovation: Technology, Market, and Complexity, 2019. 5(3): p. 44.
Kopalle, P.K., et al., Examining artificial intelligence (AI) technologies in marketing via a global lens: Current trends and future research opportunities. International Journal of Research in Marketing, 2022. 39(2): p. 522-540.
Chin, Y.-C. and J. Zhao, Governing cross-border data flows: International trade agreements and their limits. Laws, 2022. 11(4): p. 63.
Taylor, R.D., “Data localization”: The internet in the balance. Telecommunications Policy, 2020. 44(8): p. 102003.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
