@article {880,
	title = {The Evolution of SEO in the Era of Generative AI: A Business Intelligence Perspective},
	journal = {Electronics},
	volume = {15},
	year = {2026},
	pages = {3541},
	abstract = {<p>The rapid paradigm shift from traditional keyword-matching algorithms to AI-driven answer engines has fundamentally disrupted Search Engine Optimization (SEO). As Large Language Models (LLMs) power modern Search Generative Experiences (SGEs), organizations must transition from legacy web analytics to sophisticated Business Intelligence (BI) frameworks to capture visibility. Despite the immense strategic implications of this shift, academic literature remains fragmented across computer science, information systems, and digital marketing management. To bridge this gap, this paper adopts an integrative literature review methodology, synthesizing 70 high-value studies selected from an initial corpus of 11,382 papers filtered to 2962 on-topic studies. Rather than utilizing restrictive systematic protocols (e.g., PRISMA) that isolate empirical data within narrow boundaries, the integrative approach enables a holistic synthesis of emerging, multi-disciplinary concepts necessary to decode a rapidly evolving phenomenon. Through this methodological lens, this study introduces the Signal{\textendash}Structure{\textendash}Surface{\textendash}Score (4S) lifecycle framework, illustrating how AI-BI systems capture conversational search intents (Signal), architect machine-readable, entity-based data (Structure), optimize content for LLM retrieval and Generative Engine Optimization (Surface), and define novel attribution metrics for zero-click environments (Score). Furthermore, the paper maps the critical technical and strategic landscape, systematically evaluating prevailing trends (e.g., zero-click searches, AI-generated content velocity), core organizational challenges (e.g., search data attribution loss, algorithmic opacity), and emerging strategic opportunities (e.g., real-time intent mapping, competitor LLM audit trails). Ultimately, this paper bridges the gap between AI search mechanics and strategic BI measurement, providing a robust future research agenda designed to guide scholars and practitioners in navigating data-driven visibility in the age of generative search.</p>
},
	keywords = {business intelligence (BI), digital marketing strategy, Explainable AI, generative AI, generative engine optimization (GEO), Information systems, integrative literature review, large language models (LLMs), search engine optimization (SEO), search generative experience (SGE)},
	issn = {2079-9292},
	doi = {10.3390/electronics15163541},
	url = {https://www.mdpi.com/2079-9292/15/16/3541},
	author = {Roumeliotis, Konstantinos I. and Margaris, Dionisis and Spiliotopoulos, Dimitris and Vassilakis, Costas}
}
@article {879,
	title = {Large Language Models as Explainable AI Ensemble Aggregators for Business Review Sentiment Analysis: A Comparative Study with Classical Ensembles},
	journal = {Applied Sciences},
	volume = {16},
	year = {2026},
	pages = {6479},
	abstract = {<p>Online business reviews encode rich customer sentiment that is critical for commercial decision making, yet accurately predicting star ratings from free text remains a challenging five-class classification problem. Classical ensemble methods{\textemdash}Soft Voting, Weighted Voting, and Stacking{\textemdash}aggregate complementary base-model outputs to improve predictive performance, but they produce opaque decisions that are unintelligible to business stakeholders. This paper proposes using a large language model (LLM), specifically unsloth/LLaMA-3.3-70B-Instruct, as an Explainable AI (XAI) ensemble aggregator: the LLM receives the predictions and confidence scores of four heterogeneous base models (Logistic Regression, Support Vector Machine, Na{\"\i}ve Bayes, and BERT-base-uncased) and reasons over them to produce both a final star-rating prediction and a natural-language explanation. We evaluate the full pipeline on 10,000-sample balanced and natural-distribution test sets derived from the Yelp Academic Dataset, with additional cross-lingual validation on Spanish Amazon Reviews. The LLM aggregator (LLAMA_AGG) achieves the highest macro-F1 on both pipelines (0.6800 on balanced; 0.6720 on natural) and the best ordinal calibration (QWK = 0.9111 on balanced; 0.9337 on natural), outperforming all classical aggregators and base models. A detailed Explainable AI analysis reveals that the LLM revises 28.07\% of its standalone predictions after observing the ensemble outputs, improving the accuracy by +22.2 percentage points on the revised cases. The aggregator corrects severe polar bias in the standalone LLM ({\textpm}0.35 recall improvement on mid-range star classes) and produces longer explanations when evidence is conflicted{\textemdash}a quantitative signal of deliberative reasoning. A formal human evaluation with two judges confirms high explanation faithfulness (4.47/5) and readability (4.82/5). Model scale ablation shows an 8B parameter variant achieves 90.8\% agreement with the 70B model, enabling practical deployment. These findings demonstrate that Explainable AI can be achieved through LLM-based ensemble aggregation, establishing a principled approach for business-review sentiment analysis.</p>
},
	keywords = {BERT, business reviews, chain-of-thought, ensemble learning, Explainable AI, large language models, Llama, meta-reasoning, Natural language processing, Sentiment Analysis, Soft Voting, Stacking, star rating prediction, Yelp},
	issn = {2076-3417},
	doi = {https://doi.org/10.3390/app16136479},
	url = {https://www.mdpi.com/2076-3417/16/13/6479},
	author = {Roumeliotis, Konstantinos I. and Margaris, Dionisis and Spiliotopoulos, Dimitris and Vassilakis, Costas}
}
