Virtual Is Real: Demystifying Consumer Behaviour in the Metaverse—A Review and Future Research Agenda

The increasing use of digital technologies has significantly reshaped marketing and consumer behavior (CB) as online communities and cutting-edge innovations such as artificial intelligence (AI) disrupt and advance consumer attitudes on specific products and services. As such, online communities that are supported by AI technologies creating new knowledge from consumer interactions through platforms like social media as consumers share experiences on specific products or services. Since AI is designed to “learn” and improve with data generated from digital technologies linked to consumer interactions, AI relies on consumer knowledge-sharing (KS) activities to replicate new knowledge for product and service improvement. However, given the knowledge gap in this area, this article applies the fsQCA technique to data generated from 291 participants to develop CB metaframework predicted on the concepts of AI, CB, and KS. Our results suggest that AI advances consumer attitudes and behaviors when knowledge is acquired while online communities promote curiosity and engage consumers to learn by sharing experiences about specific products or services. Furthermore, understanding the causality between AI, CB, and KS concepts offers critical decision-making insights to marketing experts across the industry.

Artificial intelligence (AI) is fundamentally transforming the marketing landscape, enabling significant progress in customer engagement, personalization, and operational efficiency. The retail sector has been at the forefront of the AI revolution, adopting AI technologies extensively to transform consumer interactions, supply chain management, and business performance. Given its early adoption of AI, the retail industry serves as an essential case context for investigating the broader implications of AI for consumer behavior. Drawing on 404 articles published between 2000 and 2023, this study presents a comprehensive bibliometric and content analysis of AI applications in retail marketing. The analysis used VOSviewer (1.6.20.0 version) and Bibliometrix (version 4.3.1) to identify important contributors, top institutions, and key publication sources. Co-occurrence keyword and co-citation analyses were used to map intellectual networks and highlight emerging themes. Additionally, a focused content analysis of 50 recent articles was selected based on their relevance, timeliness, and citation influence. It revealed six primary research streams: (1) consumer behavior, (2) AI in retail marketing, (3) business performance, (4) sustainability, (5) supply chain management, and (6) trust. These streams were categorized through thematic relevance and theoretical significance, emphasizing AI’s impact on the retail sector. The contributions of this study are twofold. Theoretically, it integrates existing research on AI in retail marketing and outlines future research in areas such as AI’s role in the domain of consumer behavior. From an empirical standpoint, the study highlights how AI can be applied to enhance customer experiences and improve business operations.

In an era characterised by unprecedented technological advancements and shifting societal norms, the burgeoning trust in Artificial Intelligence (AI) has notably surpassed the reliance traditionally placed in human judgement. This profound shift in trust dynamics is pivotal, particularly within the realm of marketing, where AI's impact is increasingly evident. The present contribution draws from three significant studies conducted during the period 2022 - 2024, offering an in-depth examination of the evolving interplay between AI, trust, and marketing practices. The first of these studies surveyed 1,389 scholars across the US, UK, Germany, and Switzerland, providing a broad perspective on societal attitudes towards AI. The findings reveal an increasing dependence on AI technologies, propelled by disillusionments in human interactions and a growing appreciation for the perceived objectivity and accuracy of technological analyses. Notably, the European Commission’s guidelines on Trustworthy AI, which prioritise data protection and ethical governance, have likely reinforced public trust, positioning AI as a reliable and transformative entity. Concurrently, the second study focuses on the burgeoning role of virtual influencers in social media marketing. Engaging 357 participants, this investigation centres on the trust, credibility, expertise, and their effects on purchase intentions attributed to these digital personas. The results indicate a significant trend: consumers are not only drawn to but also place greater trust in virtual influencers compared to their human counterparts. This shift suggests a fundamental transformation in the landscape of trust and credibility, where AI's influence extends beyond operational efficiency to shaping consumer relationships and preferences. Further exploration is warranted into the underlying motivations for this shift, as discussed in a third study involving 451 scholars from UK business schools. This study highlights a critical preference for AI over humans, driven by a quest for unbiased and accurate information, and a growing disenchantment with human influencers across various sectors, including politics and marketing. This evolution poses unique challenges and opportunities for marketers. The imperative now is to leverage this newfound trust in AI while navigating the complexities of authenticity and ethical standards in digital engagements. As AI continues to reshape consumer expectations and behaviours, marketers must adapt by developing strategies that not only harness AI’s capabilities but also respect and uphold the ethical dimensions of consumer engagement. In synthesising these insights, this contribution not only maps the current landscape but also anticipates future trajectories in the integration of AI within marketing frameworks beyond the existing data collection and analysis tools. It underscores the need for ongoing research into the effects of AI on societal norms and consumer behaviours, aiming to foster a balanced approach to technology adoption in marketing practices.

This systematic literature analysis examines the many effects of artificial intelligence (AI) on consumer behavior. It consolidates data from a carefully chosen to set of English-language papers acquired using a Web of Science search. The selected studies provide useful insights into the complex interaction between AI, consumer attitudes, preferences, decision-making, and the larger consequences for companies. These research cover many areas of AI applications in marketing and consumer domains. The research covers various topics, such as the positive impact of AI on consumer attitudes, potential drawbacks of AI recommendations, the influence of AI-driven recommendation agents on privacy risk, personalized engagement marketing, consumer evaluations of GAN-generated fashion products, AI in ethnic clothing consumption, the adoption of AI in the leisure economy, AI in digital marketing, automation of services using AI in Industry 4.0, AI-powered applications in the service profit chain, the role of AI-powered learning apps in education, AI in B2B settings, the security of AIoT using the HoneyNet approach, the impact of digital AI technologies in India, and the role of AI in the Internet of Things (IoT). This study presents a thorough analysis of the current state of AI and consumer behavior research, providing insights that are relevant for both academic and industrial sectors.

Backgrounds. Artificial Intelligence (AI) has emerged as a transformative force in modern business, influencing competitive dynamics and reshaping consumer behavior. As AI applications expand across industries, understanding their strategic impact on market competition and consumer engagement becomes increasingly vital for business sustainability and innovation. Methods. This study employs a mixed-method approach combining bibliometric analysis and systematic literature review to examine the interrelationship between AI, market competition, and consumer behaviour. Bibliometric analysis was conducted using the Scopus database for the period 2015–2025, with VOSviewer utilized to map keyword co-occurrences and thematic clusters. Subsequently, a qualitative literature review was performed on thematically relevant and highly cited articles to extract insights on AI’s practical implementations, competitive implications, consumer analytics, and ethical concerns. Results. The findings reveal a marked increase in scholarly attention to AI-driven business strategies, particularly between 2023 and 2024. AI is shown to influence market competition by enhancing operational efficiency, fostering innovation, supporting data-driven decision-making, and improving strategic adaptability. In terms of consumer behaviour, AI enables pattern recognition, real-time responsiveness, personalized marketing, and demand forecasting, contributing to customer satisfaction and loyalty. Additionally, AI-powered business operations—such as dynamic pricing and product recommendation systems—further optimize performance. However, ethical challenges, including data privacy, algorithmic bias, and regulatory gaps, underscore the need for responsible AI adoption. Conclusions. AI serves as both a technological enabler and strategic asset in contemporary business ecosystems. Its influence extends beyond automation, offering firms competitive advantage through improved agility and consumer-centered strategies. To fully leverage AI’s potential, businesses must balance innovation with ethical considerations, ensuring transparent governance and human oversight in AI integration.

This chapter reviews the emerging literature on consumer interactions with artificial intelligence (AI) in marketing. Over the past decade, the rapid proliferation of AI technology has dramatically altered how businesses deliver products and services to consumers, giving rise to a groundswell of research. Consumer research has revealed important differences in attitudes and behaviors resulting from AI interactions as compared to human to human interactions. First, the chapter reviews domains where AI interactions are preferred as compared to domains where consumers are more averse to AI interactions. Next, the chapter identifies key process mechanisms that have been identified linking AI with key consumer outcomes. The chapter concludes with the enumeration of predictions about future directions for AI research in consumer behavior and marketing.

The rise of artificial intelligence (AI) has significantly transformed the landscape of consumer engagement, enabling brands to deliver highly personalized experiences. This review presents a conceptual framework for AI-driven personalization, emphasizing its implications for consumer behavior and brand loyalty. The framework explores how AI technologies, including machine learning algorithms and data analytics, can be utilized to tailor marketing strategies and interactions to individual consumer preferences and behaviors. AI-driven personalization leverages vast amounts of consumer data to create customized experiences, which can enhance engagement and satisfaction. By analyzing data such as browsing history, purchase patterns, and social media interactions, AI systems can predict consumer preferences and deliver relevant content, product recommendations, and targeted promotions. This process of personalization not only improves the relevance of marketing efforts but also fosters a deeper connection between consumers and brands. The framework examines key components of AI-driven personalization, including data collection, analysis, and application. It discusses how advanced algorithms process consumer data to identify patterns and trends, enabling brands to anticipate consumer needs and tailor their offerings accordingly. Additionally, the framework highlights the role of real-time data processing in providing immediate and contextually relevant interactions, which can further enhance consumer satisfaction. Implications for consumer behavior are explored, focusing on how personalized experiences influence purchasing decisions, brand perception, and overall consumer loyalty. Personalized marketing efforts are shown to increase customer satisfaction and retention by providing more relevant and engaging interactions. Moreover, the framework addresses potential challenges, such as data privacy concerns and the need for ethical AI practices, which are crucial for maintaining consumer trust. This paper proposes a conceptual framework for understanding the impact of AI-driven personalization on consumer behavior and brand loyalty. The framework examines the mechanisms through which personalized marketing, enabled by advanced machine learning algorithms, influences consumer preferences, purchase intentions, and long-term loyalty. By integrating insights from behavioral psychology and digital marketing, the paper highlights the potential benefits and challenges of AI personalization strategies. It also addresses the ethical considerations involved in data usage and provides recommendations for marketers aiming to enhance customer engagement and loyalty through personalized experiences. In conclusion, the conceptual framework for AI-driven personalization underscores its transformative impact on consumer behavior and brand loyalty. By leveraging AI technologies to deliver customized experiences, brands can strengthen consumer relationships and enhance loyalty, driving long-term success. The framework provides valuable insights into how AI can be effectively utilized to meet evolving consumer expectations and maintain competitive advantage.

The bibliometric analysis presented in this article delves into the use of Artificial Intelligence (AI) in Rheumatology, aiming to fill a gap in the existing relevant scientific literature. In the article a holistic comprehensive overview of key trends and research clusters in the field are provided, exploiting a number of widely recognised bibliometric techniques, such as citation analysis, co-authorship analysis, co-occurrence analysis, and bibliographic coupling analysis. Notably, the citation analysis reveals a diverse array of highly cited papers, underscoring the multidimensional nature of research in rheumatology. The co-authorship analysis illuminates complex collaborative networks among countries, with prominent clusters such as the European, USA and the Asian-Pacific clusters, highlighting the dynamic and interconnected nature of international collaborations. The co-occurrence analysis identifies four thematic clusters, emphasising the interconnectedness of rheumatic diseases, prediction methods, artificial intelligence algorithms considerations and patient characteristics. Addressing limitations, including the potential bias introduced by specific keywords and database restrictions, in conclusion, the article provides valuable insights for researchers, paving the way for further refinements in understanding the evolving use of AI in rheumatology.

This paper aims to investigate the impact of Artificial Intelligence (AI) on consumer behaviors within the retailing sector in Egypt. The research depended on the quantitative research method. The primary data was collected through the online questionnaire. Convenience sampling was used. The sample size in this research is 400. A total of 384 responses were collected and valid. The data was analyzed using the Statistical Package for the Social Science (IBM SPSS v22) for Windows computer software. The Results highlighted that there is a significant relationship between Artificial Intelligence and consumer behavior. In addition, The model has a high ability to predict and explain the consumer purchase behavior through Artificial Intelligence, and this was proved by the validity of the first hypothesis (H1) through the value of (R-Sq = 0.95.8) in the model. The study recommends online retailers to employ Artificial Intelligence in each step in the consumer journey, from need recognition, information search, evaluation, and purchase decision making to post-purchase behavior to predict consumer's purchase behavior in the online platform.

The article discusses topical issues of the use of Artificial Intelligence (AI) in marketing and its impact on the efficiency of modern marketing strategies. In the conditions of high competition in the market of products and services, special attention is paid to the introduction of innovative approaches that contribute to improving the quality of product promotion. In particular, AI is becoming an increasingly perspective tool for automating processes, saving time, and performing routine operations. There is an emphasis on various aspects of the use of AI, such as behavioral targeting, dynamic content, e-marketing, customer service with chat-bots, and increasing sales through personalized interactions. The study provides an overview of recent scientific papers that examine the impact of AI on marketing communications and consumer interaction. One of the key benefits of AI is its ability to personalize marketing campaigns. By analyzing large amounts of data and consumer behavioral patterns, AI can accurately predict each user’s interests and needs. In addition, AI allows you to automate many processes, which reduces the burden on marketing teams and helps reduce the risk of human error and provide a faster response to changes in consumer behavior. However, the use of AI in marketing also has a number of challenges and drawbacks. One of the biggest risks is the issue of data privacy. For personalization and targeting, it is necessary to collect a large amount of information about consumers, which can raise concerns about violating their privacy. Another major challenge is the technical limitations of AI. While AI algorithms are capable of analyzing vast amounts of data, they cannot always properly adapt to a changing market environment or predict consumer behavior in non-standard situations. Furthermore, AI algorithms can reproduce biases that exist in the data they are trained on. This can lead to discrimination against certain groups of consumers or incorrect targeting. The article analyzes the top ten AI tools that will drive the success of personalized marketing strategies in 2024. These tools include Undetectable.ai, Grammarly, ChatGPT-4, Copy.ai, SurferSEO, Trellis, Crayon, Manychat, ChatFuel, and LivePerson.

The rapid advancement of artificial intelligence (AI) has revolutionized the e-commerce industry by enabling personalized shopping experiences that cater to individual consumer preferences and behaviors. This study employs a systematic literature review methodology, analyzing peer-reviewed articles from the Scopus database published between 2020-2024, to comprehensively examine the impact of AI-driven personalization on consumer behavior in e-commerce. The review methodology followed the PRISMA protocol, ensuring a rigorous and transparent selection process of relevant literature. The findings reveal three key impacts of AI-driven personalization. First, it enhances customer engagement through personalized recommendations, leading to a 15-30% increase in conversion rates. Second, it improves operational efficiency through AI-powered chatbots and automated customer service, reducing response times by up to 80%. Finally, it strengthens brand loyalty through personalized marketing campaigns, resulting in a 20-40% increase in customer retention rates. However, the study also highlights the importance of addressing ethical considerations, particularly data privacy concerns and the need for transparent data practices. The implications suggest that e-commerce businesses should prioritize AI integration while maintaining responsible data management to build consumer trust. The limitations of this research include the focus on a specific time frame and the reliance on a single database. Future research directions could explore the impact of emerging technologies, such as AR/VR, on personalized shopping experiences and investigate the long-term effects of AI-driven personalization on consumer behavior patterns. This systematic literature review contributes to a deeper understanding of how AI is transforming e-commerce and shaping consumer behavior. The findings underscore the significant potential of AI in enhancing customer engagement, operational efficiency, and brand loyalty, while emphasizing the critical importance of addressing ethical considerations in the rapidly evolving e-commerce landscape.

The rise of artificial intelligence (AI) and, more recently, generative AI (GAI) has transformed digital marketing, particularly within social media. However, academic research on this intersection remains dispersed, requiring a structured synthesis to identify prevailing trends and gaps. Given the increasing integration of AI in digital marketing, understanding its implications for consumer behaviour is crucial for both researchers and practitioners. This study conducts a systematic literature review (SLR) following the SPAR‐4‐SLR protocol to analyse existing research on AI, GAI, social media, and consumer behaviour. In addition, the 5W1H framework is used to organise information and answer questions that arise. Specifically, it examines how AI is portrayed in social media and consumer behaviour literature, whether as an enabler, risk, or neutral factor, the perspective taken by the studies, and the application given to it. Findings show that AI is primarily framed as a driver of personalisation, engagement, and analytics, yet notable concerns about ethical risks like algorithmic bias and privacy persist. Research perspectives vary, spanning consumer, business, and integrative views that reflect the complex AI influence on user experience and organisational strategy. Empirical studies mainly treat AI as a core subject, focusing on applications such as chatbots, recommendation systems, and virtual influencers (VIs). A smaller number employ AI methodologically for social media data analysis through machine learning (ML) and natural language processing (NLP). Despite growth, significant gaps remain in understanding AI's long‐term effects, cross‐cultural nuances, and theoretical integration. Ethical issues highlight the need for responsible AI frameworks balancing innovation and fairness. This review synthesises current knowledge and outlines future research directions, aiming to guide academic inquiry and responsible implementation of AI in digital consumer contexts.

The advancement of artificial intelligence (AI) technology and its applications has drastically transformed consumer behavior (CB). As consumers interact with these applications on multiple platforms and touchpoints, it becomes crucial to understand how these interactions affect consumer behavior and its components, including personality, attitude, engagement, decision‐making, and trust. The research on the relationship between artificial intelligence and consumer behavior (hereafter referred to as AI CB) revolves around these topics and has grown exponentially in recent years. A rigorous review is required to provide directions for future studies by comprehending the extensive literature, understanding research gaps, and identifying the future directions for scholarly work. This article aims to address this research gap by analyzing 107 AI CB articles using the bibliometric and framework‐based methodology to provide insights into publication trends, dominant theories, methods, antecedents, decisions, and outcomes in the AI CB literature. Most importantly, the review identifies clusters of research fronts and provides a thematic framework for current research. These clusters or themes relate to AI interaction with consumer behavior dimensions, including consumer acceptance and trust, consumer interaction and engagement, attitude and personality, decision‐making, and adoption. This thematic framework and TCM‐ADO analysis offer future research directions to advance theory development and have implications for industry and society.

Today, changes in society and business are associated with digital transformation, which affects society as a whole and consumer behaviour. This requires addressing the problem of using the opportunities in digitalisation of marketing communications based on artificial intelligence. Despite a significant number of publications on marketing communications and consumer behaviour, there are gaps in the consideration of strategies for applying marketing communications based on artificial intelligence and immersive technologies that significantly affect consumer behaviour in a digital society. Based on the analysis of scientific research and the practice of applying artificial intelligence, the article defines a strategy for using artificial intelligence capabilities in marketing communications, which includes the areas of artificial intelligence use and the opportunities they provide through their implementation. These include creating content for marketing communications, personalising content, improving analytics and forecasting the use of marketing communications, increasing sales through optimising marketing communications, direct interaction of artificial intelligence with consumers, social listening, and engaging consumers in cooperation through improving the efficiency of communications. To implement the strategy, an algorithm for adapting enterprises to the use of artificial intelligence in marketing communications has been defined, which provides new opportunities for influencing consumer behaviour. It includes stages related to assessing the company's readiness for the introduction of artificial intelligence, possible areas of implementation, training of marketing personnel, adjusting work processes, tracking the nuances of consumer perception and monitoring innovations in the field of artificial intelligence. Based on the different attitudes of consumers towards artificial intelligence, the features of its application are identified, including for emotional and personalised communication, analytical and routine tasks. This allows enterprises/organisations to effectively use the capabilities of artificial intelligence and immersive technologies. Further research in this area requires empirical studies of consumer behaviour in various areas of activity when applying artificial intelligence and the development of methodological approaches to determining effective digital marketing communications strategies.

This study examines the evolution and emerging trends in consumer behavior by integrating Artificial Intelligence (AI), digital marketing, and consumer buying behavior through bibliometric analysis. Using data from the Web of Science (2014–2023), 645 articles were analyzed to identify publication trends, key themes, and leading contributors. Bibliometric indicators, including citation counts and keyword analysis, were visualized using VOSviewer. The analysis revealed six keyword clusters, highlighting key areas such as AI-driven data analytics, customer experience, and conversational AI. The findings provide valuable insights into how AI and digital marketing influence consumer behavior, offering directions for future research. The conclusion synthesizes the key findings and outlines potential research avenues at the intersection of AI, digital marketing, and consumer behavior. This study contributes to the field by providing a comprehensive bibliometric analysis, identifying major trends, influential authors, contributing countries, and dominant themes while suggesting areas for further exploration.