Role of Artificial Intelligence in Shaping Consumer Demand in E-Commerce
Open accessAbstract
The advent and incorporation of technology in businesses have reformed operations across industries. Notably, major technical shifts in e-commerce aim to influence customer behavior in favor of some products and brands. Artificial intelligence (AI) comes on board as an essential innovative tool for personalization and customizing products to meet specific demands. This research finds that, despite the contribution of AI systems in e-commerce, its ethical soundness is a contentious issue, especially regarding the concept of explainability. The study adopted the use of word cloud analysis, voyance analysis, and concordance analysis to gain a detailed understanding of the idea of explainability as has been utilized by researchers in the context of AI. Motivated by a corpus analysis, this research lays the groundwork for a uniform front, thus contributing to a scientific breakthrough that seeks to formulate Explainable Artificial Intelligence (XAI) models. XAI is a machine learning field that inspects and tries to understand the models and steps involved in how the black box decisions of AI systems are made; it provides insights into the decision points, variables, and data used to make a recommendation. This study suggested that, to deploy explainable XAI systems, ML models should be improved, making them interpretable and comprehensible.
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Transparency and reasoning from XAI contribute to reducing abandoned products, increasing order value, and boosting revenue and conversion rates.
“This transparency and reasoning contribute to reducing abandoned products, increasing order value, and consequently boosting revenue and conversion rates, as noted [78].”
From Recent Emerging Techniques in Explainable Artificial Intelligence to Enhance the Interpretable and Understanding of AI Models for Human (2025) · cites this paper as [78]
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Abbasi, R., & Esmaili, M. (2024) · Advances in marketing, customer relationship management, and e-services book series · doi:10.4018/979-8-3693-1339-8.ch003 · Cited by 5 · see citing works
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Ara, A., & Thomas, A. (2024) · Advances in business information systems and analytics book series · doi:10.4018/979-8-3693-6537-3.ch015 · Cited by 4 · see citing works
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Association Rule Algorithm based Personalized Recommendation System for E-Commerce Platforms
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Advancements in Food Technology Using Artificial Intelligence- Deep Learning
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The Impact of Artificial Intelligence Effect on e-Commerce: A Framework for Key Research Areas
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How mobile phone application enhance human interaction with e-retailers in the middle east
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A novel hybrid machine learning model for shopping trip estimation: A case study of Tehran, Iran
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- Authors
- Khrais, L. T.
- Affiliations
- Imam Abdulrahman Bin Faisal University (SA)
- Corresponding author
- Khrais, Laith T.
- ORCID
- Khrais, Laith T.
- Venue
- Future Internet
- Publisher
- MDPI AG
- Published
- 2020-12-08
- Volume
- 12
- Issue
- 12
- Pages
- 226
- ISSN
- 1999-5903 (ISSN-L)
- OpenAlex
- W3110686510
- Cited by
- 228
- References
- 24
- Citations by year
- 2026: 24 · 2025: 56 · 2024: 69 · 2023: 41 · 2022: 26 · 2021: 11
- FWCI
- 7.66
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Future Internet (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
- Keywords
- artificial intelligence in e-commerce, consumer demand, explainable AI, explainability, ethical issues of AI, personalization, consumer behavior, black-box decision-making, interpretability, recommender systems
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