The rapid growth of digital platforms has fundamentally reshaped network and viral marketing, profoundly transforming how information spreads across social networks and influences consumer behavior. This comprehensive review synthesizes theoretical, computational, and ethical perspectives into an integrated narrative, providing novel insights into the mechanisms driving information diffusion within contemporary interactive marketing. By integrating foundational concepts from social network theory, advanced graph models, and behavioral dynamics, the paper demonstrates how the interplay between network structures, influencer behaviors, and AI-driven algorithms significantly redefines traditional marketing paradigms. A distinctive theoretical contribution of this study lies in its innovative combination of Big Data analytics with AI-based predictive modeling, explicitly revealing how real-time algorithmic personalization not only enhances marketing effectiveness but also creates new ethical tensions surrounding misinformation, algorithmic bias, and consumer vulnerability. Addressing recent calls for greater theoretical originality and narrative coherence in interactive marketing research, this review explicitly highlights how these insights resolve critical theoretical puzzles and clarify contemporary ethical dilemmas. Additionally, the paper identifies emerging trends—including Web3 marketing, decentralized platforms, and neuroscience-driven targeting—offering clear future research directions. Through its integrative, narrative-driven framework, this study significantly advances interactive marketing theory, providing essential guidance for scholars and practitioners navigating the evolving complexities of digital influence.

In the big data era, understanding and influencing consumer behavior in digital marketing increasingly relies on large-scale data and AI-driven analytics. This narrative, concept-driven review examines how big data technologies and machine learning reshape consumer behavior analysis across key decision-making areas. After outlining the theoretical foundations of consumer behavior in digital settings and the main data and AI capabilities available to marketers, this paper discusses five application domains: personalized marketing and recommender systems, dynamic pricing, customer relationship management, data-driven product development and fraud detection. For each domain, it highlights how algorithmic models affect targeting, prediction, consumer experience and perceived fairness. This review then turns to synthetic data as a privacy-oriented way to support model development, experimentation and scenario analysis, and to dark data as a largely underused source of behavioral insight in the form of logs, service interactions and other unstructured records. A discussion section integrates these strands, outlines implications for digital marketing practice and identifies research needs related to validation, governance and consumer trust. Finally, this paper sketches future directions, including deeper integration of AI in real-time decision systems, increased use of edge computing, stronger consumer participation in data use, clearer ethical frameworks and exploratory work on quantum methods.

Enterprise decision making in marketing, finance, and audit remains fragmented, leading to inefficient budget allocation and incomplete risk assessment. This study proposes an integrated, Big Data-driven decision-support framework that unifies Large Language Models (LLMs), attention-based marketing mix modeling, and multi-agent, game-theoretic optimization to coordinate cross-functional decisions. The architecture combines five modules: LLM-enhanced customer segmentation and customer lifetime value prediction, attention-weighted marketing mix modeling, multi-agent LLM systems for hierarchical budget optimization, attention-informed Markov multi-touch attribution, and LLM-augmented audit quality assessment. Empirical validation on a large-scale e-commerce dataset with 2.8 million customers and USD 156 million in marketing expenditure shows that marketing return on investment increases from 4.2 to 6.78 (61.4% relative improvement), financial forecasting error (MAPE) decreases from 12.8% to 4.7% (63.3% reduction), fraud detection accuracy improves by 29.8%, the Audit Quality Index reaches 0.951, and customer lifetime value prediction accuracy improves from 76.4% to 91.3%. By operationalizing the convergence of LLMs, attention mechanisms, and game-theoretic reasoning within a unified and empirically validated framework, the study delivers both theoretical advances and practically deployable tools for integrated business intelligence in digital economies.

Tourism is increasingly shaped by data, and the convergence of Big Data and Machine Learning is quietly rewriting how destinations, firms, and policymakers make decisions, compete, and create value. This survey synthesizes peer-reviewed research on core applications such as demand forecasting, dynamic pricing, context-aware recommendation, and sentiment analysis across reviews and social media, while also tracing their economic implications for productivity, revenue management, and new business formation. To complement the literature, we include a structured cross-regional survey of tourism organizations that reports widespread awareness and adoption alongside uneven technological readiness. Results indicate generally positive perceived economic effects, yet they also suggest that performance gains are not guaranteed by organization size or years of technology use, which may hint that strategic alignment and data governance matter more than scale alone. We distill the principal technical and managerial challenges that limit impact in practice, including privacy and security risks under multi-jurisdictional regulation, fragmented and heterogeneous data infrastructures, skill shortages, model opacity, and the lack of standardized, open benchmarks tailored to tourism. Building on these findings, we outline a research and practice agenda that emphasizes privacy-preserving learning, interpretable and domain-specific models for real-time operations, interoperable architectures spanning IoT and streaming data, and shared evaluation datasets to enable cumulative progress. The review’s contribution is twofold: it integrates the methods and use cases of Big Data and Machine Learning in tourism with a clear view of economic outcomes, and it offers actionable priorities for responsible scaling. For academics, it maps fertile areas for rigorous, comparable studies; for industry and public agencies, it provides guidance on where investments, capability building, and governance can most plausibly translate data into resilient and inclusive tourism development.