Ai And Consumer Behavior Trends Technologies And Future Directions From A Scopus Based Systematic Review
Digital Business Study Program, Faculty of Economics and Business, Padjadjaran University, Sumedang, Indonesia
ABSTRACT
Section titled “ABSTRACT”This systematic literature review, conducted following the PRISMA 2020 guidelines, synthesizes 117 Scopus-indexed articles (2009–April 4, 2025) to address six research questions on artificial intelligence’s (AI) role in consumer behavior. The study examines research trends (RQ1), prevalent AI technologies (RQ2), key variables (RQ3), methodological approaches (RQ4), future research directions (RQ5), and AI’s transformative impact on consumer behavior (RQ6). Findings report exponential publication surges after 2020, driven by machine learning and natural language processing applications. Consumer attitudes (trust, privacy) and behavioral intentions emerge as primary mediators of AI adoption, while ethical concerns underline the need for open algorithmic architectures. Quantitative survey methods dominate, though longitudinal and cross-cultural investigations remain infrequent. Generative AI is a disruptive technology enabling hyper-personalization but also authenticity threats. Practical implications call for ethical imperatives of AI governance and hybrid human-machine processes to weigh innovation against consumer trust. In noting limitations of geographic coverage and linguistic bias, the review proposes future research agendas like cultural mediation of AI acceptance and longitudinal behavioral impact. By examining these dimensions systematically, the research enhances academic understanding of AI’s multifaceted influence and supports actionable insight to ethically grounded marketing practice in evolving technology contexts.
KEYWORDS
Section titled “KEYWORDS”Artificial intelligence; consumer behavior; marketing; systematic literature review; PRISMA
SUBJECTS
Section titled “SUBJECTS”Business, Management and Accounting; Consumer Psychology; Artificial Intelligence
1. Introduction
Section titled “1. Introduction”The rapid evolution of Artificial Intelligence (AI) has significantly transformed several domains, especially when it comes to its application in analyzing and influencing consumer behavior (Gansser & Reich, 2021; Kumar et al., 2024). Promoted as autonomous systems with the capacity to learn, adapt, and make decisions with minimal human involvement, artificial intelligence differs from traditional statistical models by its capacity for adaptive machine learning, real-time data processing, and multimodal data fusion (Hermann & Puntoni, 2024; P. Zhang et al., 2025). For example, neural networks improve artificial intelligence’s ability to interpret intricate consumer desires, providing firms with mechanisms to predict behavior, personalize marketing, and ethically resolve moral issues (Guo et al., 2024; Naz & Kashif, 2025).
AI in consumer research reacts to technological advances and emerging demands in research. In 1995, strong computational power and algorithmic modeling were enabled through comprehensible approaches such as decision trees to examine structured consumer data (Vaid et al., 2023). These early methods bridged traditional statistical approaches with AI’s predictive potential, making them appealing to marketing researchers. Between 2009 and 2018, breakthroughs in deep learning, coupled with the rise of big data and cloud computing, facilitated the application of neural networks to unstructured data like social media content and images (Kumar et al., 2024). This study explains that AI in consumer behavior research has grown rapidly, as reflected in publication trends from year to year. Although initial exploration began in 2009 with two publications, research activity remained low until 2020. Since then, the field has experienced exponential growth, with a significant jump from 14 publications in 2021 to 45 publications in 2024, marking the peak of academic interest (see Figure 2). Although the data for 2025 shows 12 publications through April, the overall trend shows a consistent increase, driven by advances in AI technology and increasingly widespread applications in marketing.
This growth is driven by the need to process vast digital consumer data, respond to preferences swiftly, and model complex multi-channel decision-making (Hoffmann et al., 2024; Klaus & Zaichkowsky, 2022). AI delivers substantial benefits, including behavior prediction, campaign personalization, and enhanced customer service, boosting outcomes like retail order values and customer retention (Guo et al., 2024; Q. Wang et al., 2024). Yet, ethical concerns—such as potential manipulation and the need for transparency to maintain trust—persist as critical issues (Li et al., 2024; Naz & Kashif, 2025). Despite this progress, the literature reveals key gaps. While individual studies proliferate, systematic reviews synthesizing these findings remain scarce, limiting a holistic understanding and practical business applications (Chen & Prentice, 2024; W.-J. Lee et al., 2023). Research often conflates basic automation with advanced AI, obscures ethical implications, and underexplores areas like cognitive biases, habit formation, and cultural differences (Josimovski et al., 2023; Mills et al., 2023; Puntoni & Wertenbroch, 2024). These gaps hinder firms’ ability to leverage AI effectively.
This research aims to address several research questions (RQ):
RQ1: What is the research trend of AI in consumer behavior research?
RQ2: What AI types are used related to consumer behavior?
RQ3: What are the variables used in previous research?
RQ4: What methods were used in previous research?
RQ5: What are the future research opportunities in AI and consumer behavior?
RQ6: How does AI transform consumer behavior?
By synthesizing existing findings, this study aims to clarify AI’s role in consumer behavior, provide actionable insights for optimizing business strategies, and identify critical future research directions, particularly in ethical and transformative dimensions.
2. Theoretical underpinnings
Section titled “2. Theoretical underpinnings”2.1. Artificial intelligence
Section titled “2.1. Artificial intelligence”Artificial Intelligence (AI) is an autonomous system with the capacity to learn, adapt, and make decisions with minimal human involvement. AI differs from traditional statistical models by its capacity for adaptive machine learning, real-time data processing, and multimodal data fusion (Hermann & Puntoni, 2024; P. Zhang et al., 2025). AI based from a computer science discipline that deals with the development of systems capable of performing tasks that typically require human intellectual capacity (Haenlein & Kaplan, 2019). Contemporary AI entails an array of technologies, including machine learning algorithms, natural language processing, visual computing, and predictive modeling, that enable systems to learn from data and improve their performance autonomously without the requirement of manual programming (Davenport et al., 2020). The advancements in technology have significantly enhanced the functionality of artificial intelligence in numerous sectors, including business operations, strategic management, and customer relations.
To ensure clarity and replicability, this study defines key AI-related terms: AI capability refers to the ability of AI systems to execute tasks like predictive analytics and recommendation generation using machine learning and natural language processing (Davenport et al., 2020). Intelligence level denotes the sophistication of AI systems, from basic rule-based algorithms to advanced neural networks mimicking human-like reasoning (Hermann & Puntoni, 2024). Technological maturity describes the developmental stage of AI, from early-stage chatbots to scalable, integrated systems like generative AI, assessed by reliability and adoption (Haenlein & Kaplan, 2019).
2.2. Consumer ‑behavior
Section titled “2.2. Consumer ‑behavior”Consumer behavior is a comprehensive examination of how individuals, groups, or organizations select, buy, use, and eventually discard products, services, or experiences to satisfy their needs and desires (X. Ma & Wang, 2024). Classical theories have emphasized the role of rationality in the decision-making process; however, contemporary theoretical notions acknowledge the preponderant role played by psychological, social, cultural, and personal factors in shaping consumer choice (Lemon & Verhoef, 2016). The dawn of digital transformation has changed consumer behavior, and models such as omnichannel retailing and social commerce have emerged. Consequently, modern consumers create extensive behavioral datasets, providing excellent insights into their choices and mental processes when consuming (Verhoef et al., 2021).
2.3. AI and consumer behavior
Section titled “2.3. AI and consumer behavior”The intersection of consumer behavior and artificial intelligence (AI) is a seismic change in the understanding and management of purchasing behaviors. AI-powered applications allow organizations to research consumer behavior to an extent of thoroughness and granularity unachievable by conventional analysis techniques (Davenport et al., 2020). The influence of artificial intelligence on consumer behavior is mediated by several mechanisms, including sophisticated recommendation systems that assist users in making purchasing decisions, personalized content that informs unique user experiences, and behavior nudges that are enabled by the strategic organization of information (Puntoni et al., 2021).
Empirical studies have demonstrated how far-reaching the influence of AI-driven interactions is on customers’ choices, brand awareness, and brand loyalty development. For example, personalized recommendations were shown to push search expenses down, lift purchase likelihood, as well as provide enhanced overall satisfaction (W. Wang & Benbasat, 2016). Yet, such results are contingent upon factors like algorithm transparency, perceived user autonomy, and privacy issues, which significantly contribute to the building of consumer trust, a critical driver of AI success in this industry (Longoni et al., 2019).
2.4. Application of AI in marketing
Section titled “2.4. Application of AI in marketing”The use of artificial intelligence in marketing operations has significantly transformed the way companies interact and persuade consumers. By leveraging AI competencies, marketers can implement extremely data-driven approaches for enhancing the consumer lifecycle, such as acquisition, engagement, and retention, across various digital and physical touchpoints (Paschen et al., 2019). Examples are personalized marketing, where AI is leveraged to learn individual consumer information to tailor content and provide promotions; predictive marketing, utilized to forecast future consumer behavior and needs; programmatic advertising, applied to streamline campaign efficiency; and AI-driven customer service via chatbots and virtual agents (Overgoor et al., 2019).
Although promising, the marketing application of artificial intelligence creates strong moral concerns, specifically regarding data privacy, algorithmic transparency, and minimizing bias. Since consumers are becoming more aware of how their data is utilized and how algorithmic systems influence their actions, the demand for ethical responsibility in using AI has increased (Martin et al., 2017). This establishment of consumer confidence, therefore, rests upon a fine line of ethics—a balancing act in bringing technology developments in tune with ethical measures toward responsible and sustainable use of artificial intelligence in advertising as it deeper integrates in marketing spaces for consumer engagement (Puntoni et al., 2021).
3. Methods
Section titled “3. Methods”There are two main methods to conduct review research. A researcher can use bibliometric analysis if the objective is to determine the research mainstream and have more than 100 literature items to be reviewed (Arviansyah et al., 2024). Another option is a systematic literature review (SLR) if the objective is to do content analysis. The SLR method collects insights from previous studies (Thorpe et al., 2005). Therefore, this study combines SLR with bibliometric analysis to comprehensively map the intellectual structure of the field. The bibliometric analysis conducted are co-occurrence analysis using the VOSViewer application and co-authorship analysis using Bibliometrix application (Aria & Cuccurullo, 2017). This article follows the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) (Page et al., 2021). There are three main steps to conducting an SLR: determining the search strategy, establishing journal selection with inclusion/exclusion criteria, and analyzing based on the research question (S. Sharma & Sharma, 2024).
3.1. Search strategy
Section titled “3.1. Search strategy”First, authors should choose an electronic database. Choosing a reliable electronic database is one of the steps in the quality assessment to ensure the quality of the literature obtained (Xiao & Watson, 2019). The authors used Scopus as an electronic database to find relevant literature related to the topic. The Scopus electronic database ensures the quality of the obtained literature (Baas et al., 2020; Harzing & Alakangas, 2016).
In an SLR, a researcher should focus on a specific topic or keywords, otherwise, they will experience information overload (Pittaway et al., 2004). The keywords used in this article were ‘AI’ OR ‘Artificial Intelligence’ AND ‘Consumer Behavior’ OR ‘Consumer Behaviour’. The search strategy was conducted on April 4, 2025.
3.2. Journal selection and inclusion/exclusion criteria
Section titled “3.2. Journal selection and inclusion/exclusion criteria”The most crucial step in conducting SLR is the review process and transparency in the method (Hiebl, 2023). Transparency in conducting SLR should be detailed to minimize bias (Tranfield et al., 2003). Researchers have widely used PRISMA to conduct SLR (Dhingra et al., 2024; Labib, 2024; Mauludina et al., 2024; S. Sharma & Sharma, 2024). Figure 1 shows the PRISMA diagram conducted in this research.
The search was conducted using the Scopus database, applying a combination of keywords: ‘AI’ OR ‘Artificial Intelligence’ AND ‘Consumer Behavior’ OR ‘Consumer Behaviour’, which initially yielded 1126 documents. Inclusion Criteria: documents categorized as articles; published in the final stage; subject areas limited to ‘Business, Management and Accounting and Economics’ and ‘Econometrics and Finance’; written in English; with accessible full-text; and passed abstract relevance screening. Exclusion Criteria: non-article document type (e.g. book chapter, conference paper, book, review); not in the final stage; outside of the defined subject areas; non-English articles; duplicates and inaccessible documents; and those irrelevant based on abstract screening.
After applying the above criteria:
- A total of 474 article-type documents were identified.
- Filtering for the publication stage reduced the dataset to 428.
- Subject area filtering further narrowed the results to 186.
- English language filtering resulted in 180 articles.
- Abstract screening produced 137 relevant articles.
- After removing inaccessible full texts, 117 documents were included in the final analysis.
In this study, we used the Scopus database as the database source. The Scopus database was chosen for this study because it is considered the most extensive interdisciplinary content and citation database, containing publications from scientific publishers worldwide, and is used globally for many large-scale analyses (Rong & Bahauddin, 2023). In addition, Scopus was chosen because it has extensive coverage of scientific literature across a large number of disciplines (Pahari et al., 2024). The Scopus database by Elsevier has more than 50 million records from more than 5000 publications (Lada et al., 2023). Scopus has some major benefits over Web of Science in that it contains a larger and more encompassing collection of journal coverage, with particular journals from a wide range of fields, countries, and languages. This enables a more in-depth and representative examination of the literature and an increase in accuracy in the determination of relevant sources not included in other databases, facilitating the ability to carry out more extensive and globally linked research (Mongeon & Paul-Hus, 2016).
Additionally, we utilize solely published articles as they have undergone a strict peer review and exhaustive revision process, thus offering up-to-date research that is always reflective of the dynamics and
Figure 1. PRISMA diagram.
Figure 2. Publication Trends throughout the Years.
advancements of the times. Conclusive papers provide valid empirical research, state-of-the-art methodologies, and pertinent research frameworks associated with digital innovation, social media, and mobile marketing. In this way, the literature reviewed will be current and capable of accommodating emerging findings continually, thereby enhancing the validity and exhaustiveness of analysis in systematic literature reviews (Lamberton & Stephen, 2016). Quality assessment was conducted using the Scopus database, and the authors also screened abstracts to ensure only relevant and high-quality articles were included.
4. Results and discussion
Section titled “4. Results and discussion”We found 117 articles relevant to the study’s scope. In conducting an in-depth analysis, a spreadsheet was created to review the literature (Foltýnek et al., 2020). The spreadsheet included data on study design, key variables measured (e.g. trust in AI, privacy concerns), outcomes assessed, and effect measures such as frequencies, correlations, and mean differences. These details facilitated a systematic comparison across studies. The authors checked and ensured all the content in the spreadsheet of reviewed articles. We also conducted a descriptive analysis to determine the research trend, the journal source, the most cited article, and the top authors who contributed to the topic.
4.1. Descriptive analysis
Section titled “4.1. Descriptive analysis”Figure 2 illustrates the publication trend of articles on AI and consumer behavior from 2009 to 2025, based on data retrieved from Scopus as of April 4, 2025. The exploration of this topic began modestly in 2009 with two publications, followed by a period of low activity, with two articles in 2017, six in 2018, and a dip to one in 2019. The year 2020 marked the start of a growth phase, with three publications, which rose sharply to 14 in 2021 and continued to increase to 15 in 2022 and 17 in 2023. The most significant surge occurred in 2024, with 45 publications, reflecting a peak in academic interest. Despite a drop to 12 publications in 2025, likely due to the data being collected early in the year on April 4, 2025, the overall trend demonstrates a consistent increase in publication activity, particularly from 2021 to 2024. Given this steady upward trajectory and the significant rise in 2024, it is highly plausible that by the end of 2025, the number of publications could increase further, potentially surpassing previous years. This projection is supported by the rapid development of AI technologies and their growing influence on consumer behavior, which are likely to continue driving academic interest and research output in this field.
Figure 3 shows the distribution of most journals that published articles on AI and consumer behavior in various journals based on the final dataset. Journal of Business Research and Journal of Retailing and Consumer Services lead with 10 articles each, followed by Psychology and Marketing with 6 articles, and Technological Forecasting and Social Change with 5 articles. Cogent Business and Management and Journal of Research in Interactive Marketing published 4 articles each, while Electronic Commerce Research, Journal of Service Management, and Journal of Theoretical and Applied Electronic Commerce Research contributed 3 articles each. Transportation Research Part E: Logistics and Transportation Review,
Figure 3. Publication Distribution of AI and Consumer Behavior Research by Journal.
Technovation, Technology in Society, Marketing Letters, and Journal of the Association for Consumer Research each published 2 articles. This concentration on a few key journals confirms their important role in the field, especially journals focused on business, marketing, and consumer studies, which are highly relevant to the crossover between AI and consumer behavior.
These 117 articles were spread across 73 different journals, reflecting a very diverse publication landscape. Of these, 59 journals published only one article, indicating a high degree of dispersion in the choice of publication outlet. This fragmentation likely stems from the interdisciplinary nature of AI and consumer behavior research, which crosses various domains such as business, marketing, technology, psychology, economics, to specialized areas such as hospitality and tourism. The absence of dominant journals implies that research within this field is in its infancy, with no significant dominant outlets. This variety might also indicate the range of research focus and methods, along with the wide applicability of artificial intelligence to the research on consumer behavior, which has prompted researchers to publish in specialist journals like the Journal of Hospitality and Tourism Technology, alongside generalist journals like the Journal of Business Ethics. This dispersion does not necessarily reflect a fragmented research foundation negatively, but does reflect extensive and diverse exploration of a new topic. This indicates that there is no flagship journal for AI and consumer behavior research, but instead reflects strength in the form of innovation and cross-disciplinary collaboration. But this spread also serves to underscore the demand for more research to locate future trends and guide the field towards more specific publication hubs as research continues to evolve. As the field evolves with the rapid advancement of AI, this fragmented landscape may begin to consolidate, potentially giving rise to leading journals specialized in this crossover.
Table 1 provides the top ten most highly cited artificial intelligence and consumer behavior research articles, citation-adjusted by year to eliminate the advantages conveyed by publication timing. Normalization enables a more equitable comparison between publication periods by controlling for older papers’ natural bias to have more citations over time. The data shows that ‘Brave new world: service robots in the frontline’ (Wirtz et al., 2018) has higher impact with 173.63 citations per year, far beyond the other articles in the data set. The substantial citation disparity is an interesting research phenomenon in our field of study.
There are numerous explanations for why the article is so high on citations. Primarily, it enjoys unrestricted access provided by a Creative Commons license, which makes it more accessible to researchers regardless of budget constraints. Secondly, its cross-disciplinary theme of embracing service management, robotics, artificial intelligence, and business draws citations from across academic disciplines. Third, the paper boasts high-profile authorship, namely Jochen Wirtz, an award-winning researcher and Vice
Table 1. Top 10 most cited articles.
| Document title | Author | Year | Citation per year |
|---|---|---|---|
| Brave new world: service robots in the frontline | Jochen Wirtz, Paul G. Patterson, Werner H. Kunz, Thorsten Gruber, Vinh Nhat Lu, Stefanie Paluch, Antje Martins | 2018 | 173.63 |
| Artificial intelligence in marketing: A systematic literature review | Srikrishna Chintalapati, Shivendra Kumar Pandey | 2022 | 44.25 |
| A new acceptance model for artificial intelligence with extensions to UTAUT 2: An empirical study in three segments of application | Oliver Alexander Gansser, Christina Stefanie Reich | 2021 | 38.00 |
| Consumer trust and perceived risk for voice-controlled artificial intelligence: The case of Siri | Rajibul Hasan, Riad Shams, Mizan Rahman | 2021 | 36.40 |
| AI-powered marketing: What, where, and how? | V. Kumar, Abdul R. Ashraf, Waqar Nadeem | 2024 | 31.00 |
| Digital servitization value co-creation framework for AI services: a research agenda for digital transformation in financial service ecosystems | Elizabeth H. Manser Payne, Andrew J. Dahl, James Peltier | 2021 | 28.20 |
| A look back and a leap forward: a review and synthesis of big data and artificial intelligence literature in hospitality and tourism | Hui Lv, Si Shi, and Dogan Gursoy | 2022 | 27.50 |
| Exploring the influence of artificial intelligence technology on consumer repurchase intention: The mediation and moderation approach | Sajjad Nazir, Sahar Khadim, Muhammad Ali Asadullah, Nausheen Syed | 2023 | 26.33 |
| The digital transformation of business. Towards the datafication of the relationship with customers | Cristina Fernández-Rovira, Jesús Álvarez Valdés, Gemma Molleví, Ruben Nicolas-Sans | 2021 | 25.60 |
| Consumers’ reasons and perceived value co-creation of using artificial intelligence-enabled travel service agents | Lidija Lalicic & Christian Weismayer | 2021 | 25.40 |
Dean of MBA Programs and Marketing Professor at the National University of Singapore, and author of classic publications like ‘Services Marketing: People, Technology, Strategy’ (9th edition, 2022), which is bound to raise the work’s profile through well-established academic networks. Fourth, as a conceptual paper, it provides frameworks that follow-up empirical research tends to cite. Lastly, its publication in the Journal of Service Management, with a high impact factor, guarantees high penetration into top academic circles.
The second tier of citations per year (26.33–44.25) is more balanced, with emerging contributions gaining significant momentum despite their shorter circulation times. Surprisingly, ‘Artificial intelligence in marketing: A systematic literature review’ (Chintalapati & Pandey, 2022) receives 44.25 citations per year despite being published four years after Wirtz et al.’s article, suggesting rapid-developing field advances. Similarly, Kumar et al.’s 2024 paper on ‘AI-powered marketing’ already has 31.00 citations per year, indicating growing interest in AI applications to marketing in particular.
The third tier of citations, ranging from 25.40 to 28.20 annually, mirrors a consistent contribution to specialized subjects such as digital transformation, AI-driven travel services, and financial service ecosystems. These papers primarily offer application-specific knowledge instead of developing extensive theoretical frameworks, which accounts for their more narrow citation profiles.
The normalized citation pattern, when viewed annually, holds significant information on the development of research, from foundational theoretical models to specialized applications across various domains. The dramatic citation lead of the Wirtz et al. work, even after adjusting for time, underscores its pioneering status in opening up future research streams and offers necessary context for appreciating the development of the field. Rather than a statistical anomaly to be discarded, this pattern of citations is in line with expected dynamics for new fields of research in which seminal articles establish the fundamental ideas that are then differentiated into specialized research streams. The citation distribution is thus an illuminating indicator of the field’s intellectual structure and development.
Figure 4 displays the distribution of top contributing authors in our dataset, illustrating the number of publications per researcher. Among the 117 articles retrieved from our systematic literature review, Stefano Puntoni emerges as the most productive scholar with 3 publications, distinguishing him as the only researcher exceeding the two-publication threshold in this domain. Following Puntoni, a cohort of 13 researchers—Simoni F. Rohden, Sebastian Molinillo, Rafael Anaya-Sánchez, Phil Klaus, Ohbyung Kwon, Keng-Boon Ooi, Georgios Tsourvakas, Garry Wei-Han Tan, Francisco Rejón-Guardia, Erik Hermann, Eleonora Pantano, Dimitra Skandali, and Anastasios Magoutas—have each contributed 2 publications to the field.
The rest of the authors in our data set (not shown in Figure 4) each have contributed one publication, which points towards a wide but relatively dispersed research community. Such a pattern of distribution indicates that research on AI in consumer behavior contexts is a developing scholarly field without a
Figure 4. Top author.
very highly consolidated core of leading authors. The lack of authors with considerably more publications could reflect the relative youth of the discipline, with opportunities remaining for researchers to become established authorities through long-term academic investigation. The fragmentation might also reflect the interdisciplinary nature of the discipline, with researchers coming from a diverse range of academic backgrounds and, therefore, possessing different research foci and methodological commitments.
4.2. Bibliometric analysis
Section titled “4.2. Bibliometric analysis”4.2.1. Co-occurrence network
Section titled “4.2.1. Co-occurrence network”Figure 5 displays the co-occurrence network of all keywords with five colors identified. Network Visualization helps researchers to identify trends in a research area (McAllister et al., 2022). Each color shows a thematic cluster that can be used to explain the themes and topics covered under that cluster (Donthu et al., 2021). Recent bibliometric reviews show clusters by these software-generated colours are needed to facilitate discussion (Martins et al., 2022; Yu et al., 2020). Therefore, the five thematically consistent clusters that describe the intellectual structure of artificial intelligence research in consumer behavior. The theme of the clusters are determined based on the keywords identified for each cluster.
The largest cluster, colored blue, is defined by terms such as machine learning, electronic commerce, sales, and forecasting, indicating a strong area of research concerned with predictive analytics related to online retailing performance. Nearby, the green cluster deals with consumption behavior, technology acceptance, and perceived value, revealing a significant use of the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) to elucidate consumer interaction with AI-powered services. A third, red-colored cluster emphasizes social media, electronic marketing, and service robots, pointing to emerging interdisciplinary scholarship at the nexus of robotics, media richness, and anthropomorphic design in hospitality contexts. Conversely, the yellow cluster—founded on big data, branding, and purchase intention—emphasizes the strategic use of large-scale analytics to brand equity and confirmation-expectation models. Finally, the purple cluster, including trust, online consumer behavior, and services marketing, emphasizes sustained research attempts at conceptualizing and quantifying the effect of consumer trust in electronic commerce transactions, with frequent applications of extensions of the Theory of Planned Behavior (TPB).
Figure 6 is a kernel density heatmap of the same collection of keywords, offering another view of research intensity and topic emergence. The most heated sections of the heatmap are those for machine learning and consumption behavior, confirming these as the most widely explored themes and thus representative of mature streams of research. Mid-density sections appear close to social media, forecasting, and purchasing intention, mirroring solid but still emerging scholarship. In contrast, comparatively
Figure 5. Co-occurrence network visualization.
Figure 6. Co-occurrence heatmap visualization.
low-density ‘cold’ sections in the vicinity of ChatGPT, anthropomorphism, and service robots signify nascent streams with lower publication density but ample room for future growth. This density distribution not only underpins the network clusters but also forms a temporal proxy, whereby colder regions on the map might be interpreted as fertile ground for pioneering research that challenges or expands leading theoretical paradigms.
Together, the network and heatmap visualizations create a bifocal content analysis that not only maps out current lines of investigation but also discloses emerging trajectories. The co-occurrence network outlines the structural topology of theoretical bases—i.e. TAM/UTAUT, Diffusion of Innovations, and TPB while the density heatmap quantifies locus-specific research momentum. By integrating these bibliometric perspectives, the study offers a stringent rigorous foundation for the identification of consolidated topics and prioritization of future subjects, and hence addresses the reviewer’s call for an exhaustive content analysis connecting descriptive and theoretical discussion.
4.2.2. Co-author network
Section titled “4.2.2. Co-author network”Figure 7 shows the author collaboration network reveals a fragmented landscape of scholarly interaction within the research domain. The visualization shows several small, distinct clusters of co-authors, with most groups comprising two to three individuals who collaborate closely within their circle. Notably, there is no evidence of a central or highly connected author acting as a bridge between these clusters, indicating that research efforts are largely isolated rather than collaborative across groups. For instance, authors such as Anaya-Sánchez, Molinillo, and Guardia form a tightly knit trio, suggesting a consistent pattern of co-authorship, while other pairs such as Bhatti and Alshemeili or Bilgihan and Bai reflect dyadic collaboration without broader network integration. The absence of cross-group linkages suggests that, while active research is taking place, it tends to be siloed, possibly due to disciplinary boundaries, institutional affiliations, or the emerging nature of the field. This structure highlights opportunities for future interdisciplinary collaboration and knowledge exchange to strengthen the research community.
4.2.3. Co-citation analysis
Section titled “4.2.3. Co-citation analysis”Figure 8 shows the five clusters connected by the co-citation analysis therefore reflect the theoretical and topical structuring of research into AI-consumer behavior. Cluster 1 consolidates core frameworks central to understanding the adoption of AI, with the Theory of Planned Behavior (Ajzen, 1991) and the Technology Acceptance Model (Davis, 1989) forming the basis for inquiry into perceived usefulness and
Figure 7. Co-author collaboration visualization.
Figure 8. Co-citation visualization.
behavioral intentions. They are complemented by systematic reviews (Chintalapati & Pandey, 2022; Mustak et al., 2021) that map the path of AI in marketing, emphasizing methodological rigor and new applications. Cluster 2 shifts its attention to consumer-brand relationships and experiential AI interactions, with foundations in Aaker’s (1997) brand personality theory and Fournier’s (1998) relationship models. Key research like Hasan et al. (2021) into voice AI trust and Hoffman & Novak’s (2018) IoT experiential model recognize the psychological relationship between anthropomorphism and consumer engagement.
Cluster 3 ranges from theory to service applications, such as Huang & Rust’s (2018) theorizing of AI in service settings and Wirtz et al.’s (2018) examination of frontline service robots. The inclusion of Gray & Wegner’s (2012) uncanny valley hypothesis and Luo et al.’s (2019) chatbot disclosure study underscores tensions between AI’s instrumental efficacy and consumer unease. Cluster 4 discusses ethical and anthropomorphic issues, with Longoni et al. (2019) describing opposition to AI in delicate domains and Epley et al. (2007) speculating on anthropomorphism’s psychological motives. Srinivasan and Sarial-Abi’s (2021) examination of algorithmic failure emphasizes the vulnerability of consumer trust to inscrutable systems. Lastly, Cluster 5 examines the transformative impact of artificial intelligence on business strategy, as exemplified by Davenport et al.’s (2020) AI-driven marketing model and the fintech innovations described by Belanche et al. (2019), with Du and Xie (2021) warning against the moral concerns of automation.
4.3. Research context
Section titled “4.3. Research context”The use of artificial intelligence (AI) in consumer behavior research is a significant development in modern business and marketing scholarship. This necessitates a keen and thorough exploration in academic literature (Kalogiannidis et al., 2024; Muthuswamy, 2024). The fast and strong growth of AI technologies has initiated a new era, shifting how consumers interact with brands. It is changing the way consumers make decisions and changing what consumers expect from service quality and experience (Deryl et al., 2023; Dong, 2025; Emon & Khan, 2025; Giroux et al., 2022; Gyeong Kim & Chang Lee, 2025; Josimovski et al., 2023; Kalogiannidis et al., 2024; Lopes et al., 2025; Lyndyuk et al., 2024; Potwora et al., 2024; Rohden & Zeferino, 2023). The worldwide challenges posed by the COVID-19 pandemic have fast-tracked the usage of AI-driven solutions in many industries, establishing a novel form of interaction between brands and consumers (Adulyasak et al., 2024; Oancea, 2021; Silva & Bonetti, 2021).
It has been demonstrated through research that AI adoption is expanding exponentially to personalize services, automate business-to-customer interactions, and improve the prediction of customer behaviors. It illustrates a clear trajectory of technology being used more in the realm of consumers (Beyari et al., 2024; Chintalapati & Pandey, 2022; Dong, 2025; Gyeong Kim & Chang Lee, 2025; Hoffmann et al., 2024; Kumar et al., 2024; Laksmidewi et al., 2024; Lyndyuk et al., 2024; Naz & Kashif, 2025; Omoge et al., 2022; Potwora et al., 2024; Sohaib et al., 2025). At the same time, this growth in technology is accompanied by some very important problems, such as weak data privacy, lower consumer trust, and challenging ethical dilemmas—problems that must be examined thoroughly (Hasim & Mohd Nazri, 2025; Pahari et al., 2024; Puntoni & Wertenbroch, 2024).
Placing AI in consumer platforms has shifted the way that companies compete to the advantage of those who can leverage AI within their business and marketing systems (Zhu et al., 2023). Nonetheless, this paradigm shift introduces pressing ethical and regulatory considerations concerning the responsible deployment of AI, the safeguarding of consumer trust, and the maintenance of robust data privacy standards (Fernández-Rovira et al., 2021; Mills et al., 2023; Mou & Meng, 2024). To elucidate the breadth and depth of this research domain, Table 2 delineates the research contexts emergent from the systematic literature review (SLR). This table sorts the 117 studies into 14 general fields of research and shows the percentage of attention each receives from researchers.
The most significant focus is laid on Consumer Behavior & Decision-Making (24 studies), understanding how AI influences consumer buying behavior, how customers form a penchant for a product, and how choice is made. Then there is Technology Adoption & Acceptance (18 studies), examining what enables people and what discourages people from accepting AI in consumer solutions. The AI & Human Interaction field (16 studies) investigates how automated systems and humans work together, sometimes in conflict, which is one of the central features of how AI is used to provide services. AI in Service Industries (14 studies) includes applications in customer service robots, CRM systems, and improving frontline services.
AI in Marketing & Advertising and AI in Retail & E-commerce each have 13 studies. This shows how AI helps with targeted ads, product recommendations, and improving e-commerce, like the understanding of return behavior. Machine Learning & Data Analytics has 12 studies. It looks at the computer
Table 2. Research context.
| Research context | Qty |
|---|---|
| Consumer Behavior & Decision-Making | 24 |
| Technology Adoption & Acceptance | 18 |
| AI & Human Interaction | 16 |
| AI in Service Industries | 14 |
| AI in Marketing & Advertising | 13 |
| AI in Retail & E-commerce | 13 |
| Machine Learning & Data Analytics | 12 |
| Ethical & Social Implications | 11 |
| Legal & Regulatory Issues | 10 |
| Behavioral Economics & Psychology | 9 |
| Emerging Tech & Innovation | 8 |
| Supply Chain & Operations | 4 |
| Health & Wellness | 3 |
| Sustainability & Green Marketing | 3 |
infrastructure that allows AI to predict trends and understand consumer behavior. The Ethical & Social Implications category (11 studies) challenges the normative questions at stake in AI, including bias, fairness, and social responsibility. Legal & Regulatory Issues (10 studies) addresses ongoing and emerging policy paradigms for controlling the use of AI, and Behavioral Economics & Psychology (9 studies) offers information on cognitive and affective decision foundations made through AI.
The other prominent new areas are Emerging Tech & Innovation (8 studies), which explore new AI technologies; Supply Chain & Operations (4 studies), which look at more streamlined and automated logistics; and Health & Wellness and Sustainability & Green Marketing (3 studies each), reflecting how AI is becoming more relevant to health behavior research and green marketing strategies.
4.4. Type of AI technology & benefit
Section titled “4.4. Type of AI technology & benefit”In recognition of the considerable heterogeneity characterizing artificial intelligence (AI) applications, this review systematically delineates distinct AI subtypes to avoid treating the construct as analytically monolithic. By disaggregating AI technologies—such as machine learning algorithms, natural language processing (NLP), recommendation systems, generative AI, and autonomous systems—we enable a more nuanced interpretation of their respective roles in shaping consumer behavior. This typological differentiation allows for greater contextual precision in understanding behavioral outcomes such as trust formation, purchase intention, perceived autonomy, and ethical sensitivity.
Contemporary SLR findings (Table 3) delineate eleven principal AI categories that collectively enhance consumer-centric value through personalization, efficiency, and trust. Types of AI consist of machine learning, machine vision, natural learning processes, expert systems, robotics (Collins et al., 2021). With improvements to our research, we added recommendation systems, predictive analytics, generative AI, AR/VR with AI, image processing, blockchain in AI, fuzzy logic, and explainable AI. At the core of these technological advancements lie machine learning (ML) and deep learning (DL) architectures, which facilitate the detection of complex patterns in consumer purchase histories and demographic profiles. These capabilities enable high-resolution segmentation, real-time dynamic pricing strategies, and anomaly detection mechanisms critical for fraud mitigation (Lazić et al., 2024; Sanober et al., 2021).
Natural language processing (NLP) technologies, which power intelligent chatbots and sentiment analysis systems, provide continuous conversational support while extracting emotional and contextual cues from consumer-generated content, such as reviews and social media, thereby informing iterative product enhancements (Olujimi & Ade-Ibijola, 2023). Complementing these, AI-powered recommendation systems leverage collaborative, content-based, and hybrid filtering algorithms to streamline consumer decision-making. By narrowing choice sets and mitigating decision fatigue, these systems not only enhance user experience but also foster consumer loyalty and increase repurchase intent (S. Sharma et al., 2022).
Autonomous systems and robotics automate routine service tasks, such as self-driving kiosks and warehouse logistics, thereby lowering labor costs and error rates, while simultaneously creating immersive brand interactions that differentiate customer experiences (Ghazal et al., 2021). Predictive analytics frameworks leverage regression and time-series forecasting to anticipate demand fluctuations and customer lifetime value, optimizing inventory management and enabling proactive, lead-scored marketing campaigns (Henrys, 2021). Generative AI models (e.g. GANs, large-scale transformers) accelerate creative asset production, from ad mockups to personalized content, facilitating rapid ideation and co-creation experiences that deepen consumer engagement (Brüns & Meißner, 2024).
Extended-reality applications integrate AI with AR/VR platforms to offer virtual try-ons and context-aware storytelling, reducing purchase hesitation and reinforcing brand narratives (Babics & Jermolajeva, 2024). Computer vision systems apply convolutional neural networks to visual search (‘see-and-buy’ interfaces) and behavioral analytics (gaze and emotion recognition), informing both digital UX design and physical store layouts (Beyari et al., 2024). Blockchain-AI hybrids introduce immutable provenance tracking and secure data-sharing protocols, bolstering consumer confidence in product authenticity and privacy. Finally, fuzzy logic frameworks handle imprecise preference inputs with interpretable rule-based reasoning, and explainable AI (XAI) methodologies provide transparent decision rationales that satisfy regulatory mandates (e.g. GDPR) and enhance user trust.
Table 3. Type of AI technology.
| Type of AI technology | Qty | Usage |
|---|---|---|
| Machine Learning & Deep Learning | 50 | Machine learning and deep learning techniques are fundamental building blocks for predictive analytics, predicting consumer behavior, sentiment analysis, and market segmentation. These advanced computational models are designed to examine high levels of data to recognize behavioral patterns, refine adaptive pricing strategies, and enable extremely targeted marketing initiatives. Their application spreads across critical segments like recommendation systems, fraud detection, and demand forecasting, particularly in the domains of e-commerce, financial services, and the hospitality sector. |
| Natural Language Processing (NLP) | 47 | Natural language processing (NLP) is at the core of many AI-powered applications like chatbots, voice command assistants, and sentiment analysis tools. These systems, exemplified by Siri, Alexa, and Google Assistant, facilitate easy human-machine interactions using natural language. Furthermore, NLP techniques are instrumental in the analysis of qualitative customer feedback, reviews, and social media responses to pick up on subtle consumer sentiments and preferences, thus informing product development and customer outreach strategies. |
| Recommendation Systems | 22 | Recommendation systems apply collaborative filtering, content-based filtering, and hybrid methods to generate personalized product or service suggestions. Through the alleviation of information overload and facilitation of more efficient decision-making processes, recommendation systems enhance user engagement and overall satisfaction. Prominent examples are Netflix’s content suggestions and Amazon’s personalized shopping recommendations. |
| Autonomous Systems/Robot | 17 | Autonomous systems comprise a variety of intelligent agents, including service robots, humanoid interfaces, and autonomous vehicles. They find application in operational environments to perform tasks such as guest service, check-in management, and autonomous navigation. The incorporation of such systems increases operational efficiency, lowers labor costs, and improves customer experience through customized interactions. |
| Predictive Analysis | 17 | Predictive analytics uses machine learning algorithms to forecast consumer behavior, market trends, and business performance. Such models improve strategic decision-making in pricing, inventory, and campaign effectiveness by analyzing historical and real-time data. Practical applications involve dynamic pricing algorithms, churn prediction, and customer lifetime value modeling. |
| Generative AI | 15 | Generative AI technologies produce novel content, ranging from videos and images to text, given input prompts. This capability enables use cases like personalized product design, artificial ad generation, and virtual influencer creation. Some well-known examples are Midjourney for luxury fashion design, ChatGPT for interactive text generation, and generative adversarial networks (GAN s) for stylized visual content. |
| AR/VR with AI | 10 | AI-powered augmented and virtual reality (AR/VR) solutions offer immersive consumer experiences through virtual try-ons, branded virtual environments, and interactive tours. These applications overlay digital elements onto real-world environments, thereby enriching consumer engagement and deepening personalization. |
| Computer Vision/ Image Processing | 8 | Computer vision platforms facilitate image and face recognition, visual tracking, and real-time analysis in applications ranging from virtual product demos, AR platforms, and autonomous robots. Such features augment user experience by facilitating product visualization, emotion detection, and real-time video analysis. |
| Blockchain in AI | 6 | The integration of blockchain technology and artificial intelligence enhances transparency, ensures data traceability, and enhances transactional security. Blockchain technology is being utilized more to validate sustainability claims, safeguard consumer data, and facilitate decentralized digital systems. The convergence of blockchain and AI fosters a greater degree of trust and accountability in marketing and supply chain practices. |
| Fuzzy Logic/ Heuristics | 4 | Fuzzy logic and heuristic algorithms are particularly effective in handling ambiguity and uncertainty in decision-making contexts. These approaches enable the modeling of complex consumer behavior and provide interpretable decision rules for managerial applications. Use cases include optimization of supply chain networks, advancement of recommendation algorithms, and refinement of customer segmentation strategies. |
| Explainable AI (XAI) | 3 | Explainable AI (XAI) enhances transparency by offering interpretable justifications for AI-generated decisions. Utilizing tools such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations), XAI facilitates regulatory compliance and fosters consumer trust. It is especially critical in high-stakes domains like healthcare and financial services, where accountability and clarity in automated decisions are paramount. |
Although AI is beneficial for companies and marketers, there are various issues related to its usage. Data privacy and security risks arise from an extensive collection of personal and transactional data, exposing consumers to unauthorized profiling, breaches, and identity theft (Josimovski et al., 2023; Potwora et al., 2024). Algorithmic bias can perpetuate discriminatory outcomes when training datasets reflect historical prejudices, and the opacity of ‘black-box’ models undermines trust and complicates compliance with emerging data-protection frameworks such as GDPR and CCPA (Nizette et al., 2025). Abrupt automation also increases ethical concerns about job displacement and the reduction of human judgment in decision-making fields of significance (Bernovskis et al., 2024).
Practical and organisational hurdles also hinder the straightforward integration of AI technologies. Fragmented legacy systems, low internal expertise, and expensive computational infrastructure costs can hinder both deployment and scalability (Josimovski et al., 2023; Nizette et al., 2025). Additionally, adversarial threats—model inversion attacks and data poisoning—require ongoing security reviews and stringent governance controls. Including human-in-the-loop governance, mandating standardized explainability protocols, and promoting cross-disciplinary stakeholder engagement are essential risk-management strategies to ensure that AI’s transformative capability is realized ethically and sustainably.
4.5. Variables used in previous research
Section titled “4.5. Variables used in previous research”Table 4 shows the use of variables used in AI in consumer behavior research. The literature review systematically identified eight specific categories of variables that are the most common subjects of research at the intersection of AI and consumer behavior. Not only do these variables reveal the many different dimensions of AI’s influence on consumer behavior, but they also provide a theoretical framework for understanding the complex relationship between AI and the art of getting consumers to make purchase decisions. This section examines each variable category (the review identified no fewer than 49 different specific variables), connects it to well-known consumer behavior theories, and presents an emerging synthesis of the relevant empirical findings that shed light on our still-imperfect understanding of what happens when AI and consumers interact with each other.
Table 4. Variable in AI and consumer behavior research.
| Variable | Definition | Qty |
|---|---|---|
| Consumer Perceptions | Subjective evaluations of AI by consumers. | 29 |
| Examples: | ||
| - Trust (reliability of AI recommendations). | ||
| - Privacy Concerns (fear of data misuse). | ||
| - Perceived Risk (uncertainty about AI outcomes). | ||
| - Brand Authenticity (alignment of AI with brand values). | ||
| Behavioral Intentions | Planned actions driven by AI interactions. | 28 |
| Examples: | ||
| - Purchase Intention (likelihood to buy via AI). | ||
| - Reuse Intention (continued use of AI tools). | ||
| - Recommendation Intention (WOM promotion). | ||
| - Resistance (avoidance of AI due to distrust). | ||
| Technology Interaction | User engagement with AI systems. | 21 |
| Examples: | ||
| - User Experience (UX) (ease of navigation). | ||
| - Modality (voice/text interfaces). | ||
| - Engagement Metrics (time spent, interaction frequency). | ||
| Ethical & Privacy Concerns | - Interface Design (visual/aesthetic appeal). Moral or societal issues arising from AI adoption. | 19 |
| Examples: | ||
| - Data Security (protection of user data). | ||
| - Algorithmic Bias (unfair outcomes due to flawed training data). | ||
| - Transparency Gaps (lack of explainability). | ||
| - Job Displacement (AI replacing human roles). | ||
| Marketing Performance | Business outcomes influenced by AI. | 19 |
| Examples: | ||
| - ROI (return on AI-driven campaigns). | ||
| - Campaign Efficiency (cost per conversion). | ||
| - Customer Retention (repeat purchases). | ||
| - Demand Forecasting Accuracy (predicting sales trends). | ||
| AI Characteristics | Attributes or design features of AI systems that influence user interaction. | 15 |
| Examples: | ||
| - Anthropomorphism (human-like traits, e.g. voice, appearance). | ||
| - Self-efficacy (user confidence in AI). | ||
| - Transparency (clarity of AI decision-making). | ||
| - Adaptability (AI’s ability to adjust to user needs). | ||
| Psychological Factors | Cognitive or emotional drivers of behavior. | 15 |
| Examples: | ||
| - Hedonic Motivation (enjoyment of AI interactions). | ||
| - Social Influence (peer pressure to use AI). | ||
| - Fear of Missing Out (FOMO) (fear of AI trends). | ||
| - Personal Relevance (AI in line with user values). | ||
| Demographic & Contextual | User background or environmental attributes. | 12 |
| Examples: | ||
| - Age/Gender (demographic segmentation). | ||
| - Cultural Background (regional preferences). | ||
| - Geographic Location (urban vs rural access). | ||
| - Use of devices (smartphone vs desktop). |
4.5.1. Consumer perceptions
Section titled “4.5.1. Consumer perceptions”Perceived consumer attitudes constitute a highly studied variable, encompassing subjective consumer judgments regarding artificial intelligence. This salience is consistent with the extensions of the Technology Acceptance Model (TAM) (Kasilingam, 2020) and the Unified Theory of Acceptance and Use of Technology (UTAUT) (Dwivedi et al., 2022), which contend that perceived usefulness and ease of use exert deep influences on technology adoption. Trust in AI-recommended advice is an essential cognitive factor, as found in the revelation that perceived trust has a direct influence on consumers’ intentions to act on advice recommended by AI (Gansser & Reich, 2021; Heirati et al., 2024; Muthuswamy, 2024; Yim et al., 2024). The trust aspect functions within the general framework of modern relationship marketing frameworks, in which dependability is one of the fundamental prerequisites for building relationships between consumers and artificial intelligence technology (Muthuswamy, 2024).
Privacy concern rises as a significant perceptual variable, with a theoretical underpinning available in existing Privacy Calculus approaches (Bol et al., 2018), where it is argued that consumers balance perceived value with presumed privacy threats (Mou & Meng, 2024). Empirical evidence states that concerns become heightened wherever artificial intelligence processes vulnerable behavioral data within contexts of personalization. Perceived risk, as operationalized within the framework of contemporary protection motivation models (Hasan et al., 2021), is uncertainty about the result of artificial intelligence. Uncertainty surrounding the process of algorithmic decision-making produces perceptions of risk, influencing technology acceptance (Mou & Meng, 2024; Pramod et al., 2025).
Brand authenticity perceptions, in the course of consumer interactions with AI systems, constitute a newer dimensional expansion that has come under investigation under theories of contemporary brand relationship frameworks (Brüns & Meißner, 2024; Muthuswamy, 2024; Vo et al., 2025). Customer-brand relationships are significantly influenced by perceived fit between AI functionality and brand values, particularly in cases where anthropomorphized AI agents serve as brand ambassadors (Wu et al., 2023).
4.5.2. Behavioral intentions
Section titled “4.5.2. Behavioral intentions”Behavioral intentions constitute the second most common type of variable, which are indicative of the anticipated behavior as a consequence of interaction with artificial intelligence. This focus finds echoes in recent uses of the Theory of Planned Behavior (Dang et al., 2025), according to which intentions immediately precede behavior. Intention to purchase via AI interfaces is a key concern, fostered by the conversational agent’s impact on transaction intention of customers (Heirati et al., 2024). It conceptually revolves around expectation-confirmation theory models within the technological setting that explain determinants for continued use post-initial adoption, as seen from research quantifying AI tool reuse intention (Thuy An Ngo et al., 2024).
Recommendation intention reflects the diffusion process in updated Innovation Diffusion models whereby consumers disseminate new technologies through word-of-mouth promotion (Gerlich, 2023). Positive AI experiences are connected to willingness to recommend, particularly if consumers perceive value congruence between AI features and the interest of their social network (Alabed et al., 2022; Gerlich, 2023). Conversely, the intention to resist aligns with the Technology Resistance Theory, where perceived threats to job security, privacy, or personal autonomy significantly predict avoidance behaviors against artificial intelligence systems (Lobera et al., 2020; Manser Payne et al., 2018).
4.5.3. Technology interaction
Section titled “4.5.3. Technology interaction”Technology interaction factors include patterns of user engagement with AI systems, with theoretical grounding in Human-Computer Interaction (HCI) models and revised task-technology fit models. User experience (UX) measures emphasize navigability, with research indicating that interfaces with high intuitive appeal substantially lower cognitive load when engaging with AI (Beyari et al., 2024). This result supports modern cognitive load theory applications in the digital environment, with the implication that AI systems with low extraneous processing improve consumer satisfaction. Modality preferences (voice/ text) capture recent advances in Media Richness Theory with contextual determinants of interface selection. Consumers utilize voice interfaces for hedonic consumption contexts and text interfaces for deliberative utilitarian activities (Flavián et al., 2023).
Engagement metrics evaluate the intensity and duration of interaction as indicators of behavioral manifestations of consumer commitment that translate to contemporary digital engagement models (Gyeong Kim & Chang Lee, 2025). The dimensions of interface design consist of visual and aesthetic properties, according to Processing Fluency Theory, wherein design adherence to cognitive processing expectations enhances persuasive effectiveness (Beyari et al., 2024; Bilgihan et al., 2024).
4.5.4. Ethical and privacy concerns
Section titled “4.5.4. Ethical and privacy concerns”Ethical and privacy concerns address moral and societal challenges as a consequence of AI adoption, theoretically grounded in contemporary stakeholder frameworks and Information Boundary Theory (Campbell et al., 2022; Puntoni & Wertenbroch, 2024; Wirtz et al., 2018). Data security concerns illustrate consumers’ boundaries for information with heightened sensitivity regarding data protection for AI contexts involving financial or health information (Manser Payne et al., 2018; Tereszkiewicz & Cichowicz, 2024). Concerns regarding algorithmic bias also find resonance within new paradigms of Digital Justice, particularly where consumer recognition of the unfair outcomes resulting from insufficiently trained data is especially acute within marginalized demographic groups (Lyndyuk et al., 2024).
Lack of transparency is connected to existing elaboration likelihood models in the sense that the perceived ease of understanding of information determines the cognitive processing pathways of consumers and the resulting persuasive impacts. Job replacement issues involve more general views of technological advancement, where the associated anxieties regarding labor security hurt attitudes towards artificial intelligence deployment, especially in service settings where human contact has been more prevalent (Boustani, 2022; Dogan et al., 2024).
4.5.5. Marketing performance
Section titled “4.5.5. Marketing performance”Marketing performance metrics capture business effects spurred by AI implementations, aligning with Dynamic Capability theory and contemporary Market Orientation theory. Financial justification for AI investments using return on investment (ROI) calculations quantifies financial gains from AI implementations, e.g. recommender systems, in retail settings (Lyndyuk et al., 2024; Potwora et al., 2024). Campaign performance metrics examine cost-per-conversion performance improvements, theoretically explained by digital marketing performance models (Potwora et al., 2024; Shumanov et al., 2022), whereby AI reduces information asymmetry and coordination costs. Customer retention rates are aligned with modern Customer Relationship Management paradigms, as AI-based personalization radically improves repeat purchase rates, particularly when algorithmic recommendations accurately foresee latent demands (Nazir et al., 2023). Demand forecasting accuracy is a supply chain optimization metric, where machine learning algorithms outperform traditional statistical approaches in predicting sales patterns, particularly for products with complex seasonality patterns or numerous demand drivers (Jackson & Ivanov, 2023).
4.5.6. AI characteristics
Section titled “4.5.6. AI characteristics”Artificial intelligence features consist of design or attribute aspects defining user interactions, informed by Human-AI Interaction theories as well as the Computers Are Social Actors paradigm. A primary feature is anthropomorphism, in that the design of human-like qualities strongly facilitates consumer trust and engagement, specifically in service contexts traditionally characterized by individual interaction (Gyeong Kim & Chang Lee, 2025). Such a finding would be consistent with contemporary social presence theories in demonstrating perceived social richness to act as a mediator of technological agent relationship building (Hsieh & Lee, 2021).
Self-efficacy scales examine user confidence in AI interaction skills, corresponding to existing Social Cognitive Theory applications in technology (W.-J. Lee et al., 2023). Perceived capability to manage AI systems is a positive predictor of adoption intentions across demographic segments (Nizette et al., 2025). Transparency measures gauge the transparency of AI decision-making, in theory grounded in algorithmic transparency approaches, with explainable AI vastly enhancing consumer trust in high-stakes decision-making scenarios (Nizette et al., 2025). Adaptability measures gauge AI receptivity to the needs of users, reflecting contemporary technology appropriation processes (Gyeong Kim & Chang Lee, 2025).
4.5.7. Psychological factors
Section titled “4.5.7. Psychological factors”Psychological factors include cognitive and emotional drivers of consumer behavior in AI environments, drawing on multiple psychological frameworks. Hedonic motivation variables relate to Technology Hedonic-Motivation frameworks, with enjoyment significantly predicting engagement length with the AI interface (Laksmidewi et al., 2024). Social influence variables represent revised network influence models, since peer pressure influences AI adoption, especially for high-profile uses such as voice assistants (Gansser & Reich, 2021; Hsieh & Lee, 2021; Molinillo et al., 2023).
The theory of fear of missing out (FOMO) is an emerging psychological construct, theoretically linked with existing models of digital social psychology. The construct indicates that concern for technological trends strongly predicts hastened adoption timelines, frequently at the cost of complete evaluation (Ng et al., 2023). Personal relevance dimensions examine the congruence of AI with consumer self-concept, referencing Self-Determination Theory online applications, where perceptions of contribution to autonomy, competence, and relatedness needs determine a strongly significant impact on sustained engagement with AI systems (He et al., 2024).
4.5.8. Demographic and contextual factors
Section titled “4.5.8. Demographic and contextual factors”Demographic and contextual influences consist of user background and environmental factors that moderate AI interaction patterns. Analyses of age and gender segmentation reveal significant diversity in technology adoption timelines, theoretically aligned with contemporary digital divide theory and updated gender technology adoption models (Chakraborty et al., 2025; Waliszewski & Warchlewska, 2020). Technology proficiency mediates age-related adoption barriers, while gender variation arises primarily in usage patterns rather than adoption levels, with differential interest in AI applications and interface modes. Cultural background variables capture contemporary cross-cultural technology adoption frameworks, with pronounced differences in AI trust and privacy issues between individualistic and collectivistic societies (Bernovskis et al., 2024; Rasheed et al., 2023; Silva & Bonetti, 2021; Waliszewski & Warchlewska, 2020). Geographic location variables test urban-rural digital divides, identifying infrastructure and exposure differences that moderate AI adoption, supported theoretically by recent digital inclusion models (J. Lee et al., 2021; Zenobia et al., 2009).
4.5.9. Theoretical integration and implications
Section titled “4.5.9. Theoretical integration and implications”The variable categories identified together comprise a cohesive theoretical framework for explaining the influence of AI on consumer behavior. The analysis here exposes the multidimensionality of AI-consumer interactions, with variables functioning at the individual (perceptions, psychology), technological (AI features, interfaces), and contextual (ethics, demographics) levels. The analysis illustrates how traditional theories of consumer behavior need to be extended to incorporate AI’s distinctive features, especially in terms of anthropomorphism, algorithmic transparency, and human-machine trust relationships. For practitioners, these variables provide quantifiable frameworks for AI use improvement, most prominently highlighting the need to balance privacy against the benefits of personalization, to promote transparency to build trust, and to shape interfaces to align with demographic trends. The prevalence of perceptual and intentional variables highlights the significance of consumer cognitive processes in the determination of AI adoption outcomes, indicating that technological advancement is not enough without a corresponding focus on psychological factors. Relative frequency between variable categories also reveals research maturation trends, with established constructs (intentions, perceptions) being investigated more than emerging fields (psychological factors, ethical concerns). This distribution also identifies areas of future research, particularly longitudinal examination of variable changes throughout the AI adoption life cycle and cross-cultural examination of the moderation of variable relationships by socio-cultural contexts.
4.6. Method used in previous research
Section titled “4.6. Method used in previous research”The artificial intelligence and consumer behavior research has utilized several methodological frameworks, each of which has made distinct contributions to this cross-disciplinary research area. This section
Table 5. Methods used in AI and consumer behavior research.
| Methods | Qty | Example article |
|---|---|---|
| Quantitative Surveys | 40 | (Boustani, 2022; Gansser & Reich, 2021; Gerlich, 2023; Hasim & Mohd Nazri, 2025; Kang et al., 2024; Laksmidewi et al., 2024; WJ. Lee et al., 2023; Lopes et al., 2025; Manser Payne et al., 2018; Muthuswamy, 2024; Oancea, 2021; Omoge et al., 2022; Piehlmaier, 2022; Pramod et al., 2025; Skandali et al., 2024; Šola et al., 2024; Tereszkiewicz & Cichowicz, 2024; Zhu et al., 2023) |
| Experimental Studies | 19 | (Ameen et al., 2022; Brüns & Meißner, 2024; Giroux et al., 2022; T. Kim et al., 2023; Kirshner, 2024; Kovács, 2024; Letheren et al., 2021; Sohn et al., 2020; Y. Zhang & Wang, 2023) |
| Conceptual/Theoretical Frameworks | 17 | (Bilgihan et al., 2024; Cooke & Zubcsek, 2017; Crews, 2019; Huang & Rust, 2022; Lazić et al., 2024; Mills et al., 2023; Q. Wang et al., 2024; Wirtz et al., 2018; Zenobia et al., 2009) |
| Machine Learning Models | 12 | (Fiore et al., 2017; Guo et al., 2024; Hajek & Sahut, 2022; Jackson & Ivanov, 2023; J. Lee et al., 2021; Temple University et al., 2018; Trivedi et al., 2024) |
| Mixed-Method | 11 | (Adamopoulos et al., 2018; Chakraborty et al., 2025; M. J. Kim et al., 2024; Shumanov et al., 2022; Sohaib et al., 2025; Taghikhah et al., 2021) |
| Systematic Literature Review (SLR) | 9 | (Chintalapati & Pandey, 2022; Deryl et al., 2023; Emon & Khan, 2025; Potwora et al., 2024) |
| Qualitative Interviews | 6 | (Angmo & Mahajan, 2024; Campbell et al., 2022; Hermann, 2022; Pantano et al., 2024) |
| Bibliometric Analysis | 5 | (Akbari et al., 2022; Dong, 2025; Pahari et al., 2024) |
| fsQCA/NCA | 4 | (He et al., 2024; Kang et al., 2024) |
| Content Analysis | 3 | (Babics & Jermolajeva, 2024; Campbell et al., 2022) |
| Thematic Analysis | 3 | (Dogan et al., 2024; Potwora et al., 2024) |
| Case Studies | 2 | (Lyndyuk et al., 2024) |
| Field Experiments | 1 | (Simchi-Levi & Wu, 2018) |
provides a critical review of prominent methodologies used in previous studies based on their theoretical robustness, applicability, and limitations in the context of AI-facilitated consumer interactions. A brief explanation of these methods, along with their weaknesses and strengths, is summarized in Table 5.
4.6.1. Quantitative surveys
Section titled “4.6.1. Quantitative surveys”Quantitative questionnaires were the prevailing methodology, being depicted in 40 investigations (e.g. Boustras et al., 2022; Hasim & Mohd Nawi, 2022; Kang et al., 2023). This entails presenting individuals with structured questionnaires to large samples that yield quantifiable information for statistical processing in order to determine patterns and correlations between AI applications and consumer behavior.
Strengths: The method has a significant capacity for extrapolating findings to different demographic populations, as evidenced by its reproducibility and standardized data collection protocols. In addition, its concordance with statistical methods allows hypotheses to be tested rigorously (Yang et al., 2020).
Weaknesses: Limitations include susceptibility to response biases, an inherent limitation in best representing the fine-grained complexities of consumer attitudes, and the difficulty in creating questions that can fully reflect the multifaceted nature of AI-consumer interactions (Peterson & Merunka, 2014).
4.6.2. Experimental studies
Section titled “4.6.2. Experimental studies”Empirical research, as manifested in 19 studies (e.g. Ameen et al., 2022; Kim et al., 2023; Kovac, 2024), involves the manipulation of artificial intelligence-based factors (e.g. personalized recommendation algorithms) in controlled environments to analyze their effect on consumer decisions.
Strengths: This method’s capacity to demonstrate causality, supported by high internal validity and isolation of particular AI effects, is its strongest aspect (Cui et al., 2021).
Weaknesses: Problems are posed by restricted external validity as a result of artificial conditions, possible participant reactivity, and resource-demanding requirements that would sacrifice sample heterogeneity (Jimenez-Buedo & Guala, 2016).
4.6.3. Conceptual/theoretical frameworks
Section titled “4.6.3. Conceptual/theoretical frameworks”Conceptual or theoretical frameworks were used in seventeen studies (e.g. Bilghar et al., 2021; Wirtz et al., 2018; Lee et al., 2024), with many of them borrowing classic paradigms, e.g. the Technology Acceptance Model, to interpret the dynamics of AI-consumer interactions.
Strengths: These models offer a general outline for comprehension, integrating existing knowledge, and guiding future empirical investigation (Gupta et al., 2024).
Weaknesses: Their tendency toward speculation, periodic lack of empirical support, and limited practical application without verification lower their utility (J. Ma et al., 2025).
4.6.4. Machine learning models
Section titled “4.6.4. Machine learning models”Machine learning programs were a focus area in 12 studies (e.g. Jackson & Ivanov, 2023; Guo et al., 2024), applying algorithms to predict consumer behavior or tailor experience based on the insights created through AI.
Strengths: Foremost in predictive accuracy, capacity to manage enormous amounts of data, and sensitivity to shifting consumer trends (R. Sharma et al., 2021).
Weaknesses: The drawbacks include the imprecise ‘black box’ nature of decision-making processes, ethical issues in terms of data privacy, and the required technical expertise that may prevent widespread use (Akter et al., 2022).
4.6.5. Mixed methods
Section titled “4.6.5. Mixed methods”Mixed methods, integrating quantitative and qualitative approaches, were utilized in 11 studies (e.g. Adamopoulos et al., 2018; Soliman et al., 2023).
Strengths: This approach enables data triangulation and thus enhances validity while covering both breadth and depth simultaneously; it also allows flexibility in covering multifaceted research questions (Pentina et al., 2022).
Weaknesses: Weaknesses include the complexity of integrating heterogeneous data types, potential inconsistencies in results, and increased resource requirements (Truong et al., 2020).
4.6.6. Systematic literature reviews (SLRs)
Section titled “4.6.6. Systematic literature reviews (SLRs)”SLRs, conducted in 9 studies (e.g. Chintalapati & Panday, 2022; Porwal et al., 2024), synthesize systematically available literature to delineate trends and gaps in research.
Strengths: Their broad scope, avoidance of bias through formal protocols, and usefulness in determining research agendas are most important (Bhukya & Paul, 2023).
Weaknesses: Subjectivity in the selection of studies, challenges in resolving heterogeneous literature, and reliance on the quality of previous research are limitations (Paul & Barari, 2022).
4.6.7. Qualitative interviews
Section titled “4.6.7. Qualitative interviews”Qualitative interviews, included in 6 studies (e.g. Angmo & Mahajan, 2024; Pantano et al., 2024), provide an opportunity for in-depth exploration of consumer attitudes towards AI technologies.
Strengths: Strengths: Depthful, detailed information, responsiveness to emerging themes, and skill at accessing subjective experience mark this method (Hasija & Esper, 2022).
Weaknesses: Limited sample sizes restrict generalizability, interviewer bias and the lengthy analysis process being other obstacles (Lim, 2024).
4.6.8. Bibliometric analysis
Section titled “4.6.8. Bibliometric analysis”Bibliometric analysis, employed by 5 studies (e.g. Akbar et al., 2022; Pahari et al., 2024), quantitatively measures publication patterns and citation networks.
Strengths: Objectivity, seminal work identification capability, and research trajectory visualization are its key strengths (Haque et al., 2024).
Weaknesses: Focus on quantitative aspects at the cost of content substance, coupled with reliance on completeness of the database, limits its depth (Samala et al., 2024).
4.6.9. Content analysis
Section titled “4.6.9. Content analysis”Content analysis, employed in 4 studies (e.g. Babic & Jermolajev, 2022), systematically classifies textual or visual data to reveal AI-consumer behavior themes.
Strengths: Its structured approach, scalability to unstructured data, and reproducibility are advantageous (Vaid et al., 2023).
Weaknesses: Limitations include the potential for coder subjectivity, reduction of complicated narratives to simplified versions, and requirement of strong coding schemes (Volkmar et al., 2022).
4.6.10. Thematic analysis
Section titled “4.6.10. Thematic analysis”Thematic analysis, employed by 3 studies (e.g. Dogan et al., 2024), identifies recurring qualitative themes to describe consumer responses to AI.
Strengths: Flexibility, depth of consumer insight, and iterative refinement contribute to its worth (Christou, 2024).
Weaknesses: Subjectivity in deriving themes, lack of standardization, and issues of reliability take away from its rigor (Naeem et al., 2023).
4.6.11. Case studies
Section titled “4.6.11. Case studies”Case studies, described in 2 studies (e.g. Lyndvuk et al., 2024), provide detailed examinations of single AI applications in consumer contexts.
Strengths: They offer depth of context, analysis of original circumstances, and real-world implications (Lyndyuk et al., 2024).
Weaknesses: Restricted applicability, tendency to be biased towards successful experiences, and excessive resource requirements restrict their scope (Alawadh & Barnawi, 2025).
4.6.12. Field experiments
Section titled “4.6.12. Field experiments”Field experiments, as applied in 1 study (e.g. Simch-Levi & Wu, 2018), involve manipulating AI-related factors in real settings to observe their immediate impact on consumer behavior.
Strengths: High external validity due to naturalistic settings; captures real consumer responses, enhancing applied relevance (Malodia et al., 2023).
Weaknesses: Difficulty in managing extraneous variables; time-intensive; ethical concerns regarding participant awareness and consent (Mollen, 2024).
Every methodological tradition makes a unique contribution to our knowledge about the influence of artificial intelligence on consumer behavior. Quantitative surveys and experimental designs form the backbone of the evidence base, providing generalizable and causal findings, but at the cost of ecological validity and contextual depth. Conceptual pieces and systematic reviews provide theoretical and structural clarity, but at the expense of empirical testing. Machine learning studies yield robust predictive models but struggle with interpretability and data availability. Mixed-method designs supply whole-picture perspectives but necessitate significant resources and methodological expertise to synthesize findings optimally.
By acknowledging these strengths and limitations, future research can adopt more balanced designs leveraging the causal leverage of experiments, the representativeness of surveys, the theoretical grounding of conceptual work, and the depth of qualitative inquiry—to generate both robust and actionable knowledge on AI in consumer behavior.
5. Conclusion
Section titled “5. Conclusion”This systematic literature review synthesizes 117 peer-reviewed articles to address six research questions (RQs) on AI’s role in consumer behavior. The findings illuminate critical trends, technological applications, methodological approaches, and theoretical gaps while offering actionable insights for academia and industry. Below, we present a structured synthesis of RQs.
5.1. Research trends (RQ1)
Section titled “5.1. Research trends (RQ1)”The field has witnessed exponential growth since 2018, with annual publications surging from 2 to 45 by 2024, reflecting AI’s escalating centrality in consumer research. Early studies (2009–2018) focused on predictive analytics and chatbots, while post-2020 works emphasize ethical dilemmas, generative AI, and anthropomorphism. A paradigm shift occurred post-2022, driven by ChatGPT’s emergence, which catalyzed research on AI-driven creativity and consumer co-creation. This trajectory underscores academia’s responsiveness to technological advancements but reveals a lag in addressing longitudinal and cross-cultural dynamics. The concentration of studies in Western contexts highlights a critical gap in understanding Global South perspectives, necessitating geographically diversified inquiries to capture heterogeneous adoption patterns.
5.2. Dominant AI technologies (RQ2)
Section titled “5.2. Dominant AI technologies (RQ2)”Machine learning (ML) and natural language processing (NLP) dominate the landscape, underpinning the majority of studies through predictive modeling and conversational agents. Post-2022, generative AI (e.g. GANs, transformers) emerged as a disruptive force, enabling hyper-personalized content creation but raising ethical concerns about authenticity and intellectual property. Autonomous systems (17 studies) and AR/VR (10 studies) demonstrate AI’s capacity to enhance experiential marketing, yet their reliance on data-intensive architectures exacerbates privacy risks. Notably, blockchain (6 studies) and explainable AI (XAI, 3 studies) remain underexplored despite their potential to mitigate transparency deficits. Bifurcation between ‘traditional’ AI (ML/NLP) and generative AI necessitates alternate theoretical frameworks to account for their disparate impacts on consumer trust and creativity.
5.3. Key variables (RQ3)
Section titled “5.3. Key variables (RQ3)”Consumer perceptions particularly trust (29 studies), privacy concerns (27 studies), and perceived risk (19 studies) emerge as pivotal mediators of AI adoption. These constructs map to extensions of the Technology Acceptance Model (TAM) but are complicated by generative AI’s opaqueness, which exacerbates uncertainty in decision-making. Behavioral intentions, including purchase intention (22 studies) and resistance (14 studies), are increasingly influenced by emotional factors such as FOMO (fear of missing out) and hedonic motivation, echoing the dual role of AI as a utilitarian tool and as a social actor. Ethical issues, including algorithmic bias (11 studies) and job displacement (9 studies), are theoretically fragmented, with insufficient empirical investigation into potential mitigation strategies.
5.4. Methodological approaches (RQ4)
Section titled “5.4. Methodological approaches (RQ4)”Quantitative surveys (40 studies) and experiments (19 studies) dominate, offering generalizable results but at the expense of ecological validity. While machine learning models (12 studies) excel on predictive accuracy, their ‘black-box’ quality limits interpretability, with few actionable implications for practitioners. Mixed-method designs (11 studies) and SLRs (9 studies) bridge this divide but are still underutilized in examining the societal impact of generative AI. Text analysis techniques could be more used to unpack hidden themes in consumer-AI interactions. The absence of longitudinal (2 studies) and cross-cultural designs (4 studies) undermines the discipline’s ability to witness unfolding consumer-AI relationships in different contexts.
5.5. Future research opportunities (RQ5)
Section titled “5.5. Future research opportunities (RQ5)”Four imperatives emerge: First, cross-cultural research must examine the impact of collectivist versus individualistic norms on trust in AI, particularly in service robotics and generative content. Second, longitudinal approaches are necessary to map AI’s cumulative effect on cognitive bias and brand loyalty. Third, ethical frameworks must move away from conceptual conjectures (e.g. XAI) towards empirically validated models that proportion innovation and responsibility. Fourth, generative AI demands dedicated exploration of its dual role as a creativity enhancer and societal disruptor, e.g. its impacts on digital literacy divides. These agenda priorities highlight the imperative of paradigm-specific analysis in the post-ChatGPT era and temporally phased research agendas.
5.6. AI’s transformative impact (RQ6)
Section titled “5.6. AI’s transformative impact (RQ6)”AI reforms consumer behavior across three axes: decision efficacy, experiential personalization, and relational dynamics. As ML-driven recommendation engines reduce search costs, generative AI introduces unmatched agency in product co-creation, blurring the lines between creator and consumer. However, this revolution is tempered by ethical dangers—algorithmic manipulation, data exploitation, and human autonomy erosions—that demand urgent scholarly attention. The advent of virtual influencers and chatbots that are emotionally intelligent complicates brand-consumer relationships further, and it demands novel theory models that capture the role of AI as a quasi-social agent. AI must be introduced not as a tool but as an active participant in consumer settings, and this opposes anthropocentric marketing theory traditions.
5.7. Managerial implications
Section titled “5.7. Managerial implications”The synthesis of 117 studies yields six strategic imperatives for businesses working in AI-driven consumer ecosystems. First, companies need to develop AI governance ethical frameworks that surpass regulatory compliance. This calls for instituting bias-auditing protocols for training data sets, particularly in multicultural settings, as well as incorporating explainable AI (XAI) interfaces for interpreting algorithmic decisions. For instance, the integration of SHAP values in recommendation systems can enhance transparency while also fostering ethical norms. Second, generative AI’s dual function as creative amplifier and societal disruptor requires strategic application of guardrails. Companies can use tools such as ChatGPT for hyper-personalized content, but institute watermarking protocols to address misinformation and have clear boundaries around AI-generated and human-curated content. Third, the paradigm shift to generative AI calls for cross-cultural adaptation strategies. Businesses in collectivist cultures need to emphasize community-based recommendations over individualistic personalization, whereas individualistic markets can capitalize on AI agents that enhance self-expression. Fourth, human-AI symbiosis models integration is a necessity in high-touch sectors like luxury retail and hospitality.
Hybrid workflows, where AI handles data-intensive tasks (e.g. demand forecasting) and humans handle emotionally rich interactions (e.g. conflict resolution), can mitigate job displacement concerns. Fifth, firms must invest in privacy-by-design architectures to pre-empt regulatory threats and consumer distrust. Techniques such as federated learning for decentralized data processing and blockchain-based transparency logs can balance personalization with privacy. Finally, the advent of emotionally intelligent AI agents makes anthropomorphism calibration unavoidable. While human-like characteristics in chat agents facilitate interactivity, excessive anthropomorphization risks triggering uncanny valley reactions. Pilot testing optimum degrees of human-likeness across demographics can continue to refine this balance. For DMOs and retailers, these tactics transform the SLR’s findings into actionable paths, converting scholarly knowledge into competitive advantages.
5.8. Limitations
Section titled “5.8. Limitations”This systematic review acknowledges several limitations. First, the exclusive focus on English-published articles and Scopus-indexed ones might have omitted valuable insights from research conducted in other languages or regionally targeted studies, especially in environments of the underrepresented Global South. Second, the rapid development of AI technologies—especially advances in generative AI post-2022 (e.g. ChatGPT)—places temporal constraints, in that more recent advances may already be revolutionizing consumer engagement beyond the scope covered by this review. Third, geographical asymmetry of the literature undermines generalizability to complex cultural and regulatory contexts. Fourth, methodological homogeneity, founded on quantitative surveys and experiments, risks oversimplifying intricate consumer-AI realities, particularly in affectively rich or longitudinal contexts. Fifth, while ethical concerns like algorithmic bias are familiar, empirical validation of mitigation strategies leaves a critical gap between theoretical recommendations and real-world implementation.
These limitations underscore the necessity for future research to adopt interdisciplinary, inclusive, and adaptive approaches to fully capture AI’s evolving role in global consumer behavior.
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| List of reviewed articles. A Appendix Table A1. | |
|---|---|
| Title | Method | Sample | AI technology | Behavioral outcomes |
|---|---|---|---|---|
| Shocks on the Accuracy of AI Forecasting in the Beauty A Beautiful Shock: Exploring the Impact of Pandemic Care Industry | Causal mediation analysis using backtesting for AI accuracy. order data (2019–2021); | Beauty product manufacturer data (3 years). | XGBoost, Random Forest, Prophet, AutoArima. | Demand volatility, sales volume, forecasting error (MAPE). |
| A Bibliometric Analysis of Digital Advertising in Social Media: The State of the Art and Future Research Agenda | OSviewer); citation/keyword co-occurrence. Bibliometric analysis (R, V | Literature on digital advertising in social media. | AI, data analytics, AR, social media tools. | Keyword trends, citation analysis, institutional collaboration. |
| A Comparison and Interpretation of Machine Learning Algorithm for the Prediction of Online Purchase Conversion | Google Analytics data; 8ML Empirical systematic approach; models compared (CARET package). | Google Merchandise Store clickstream data. | NN, Random Forest, GBM, XGB. Logistic Regression, SVM, ANN, K Classification Tree, | SHAP/XGB Explainer for interpretability. AUC, sensitivity, specificity, accuracy; |
| A look back and a leap forward: a review and synthesis of big data and artificial intelligence literature in hospitality and tourism | → Systematic review (516 records 270 articles); bibliometric/ thematic analysis. | Hospitality/tourism literature (Web of Science, ScienceDirect). | ML, deep learning, neural networks, fuzzy logic. | individual/organizational/industry Structured vs. unstructured data; themes. |
| New Acceptance Model for Artificial Intelligence with UTAUT2: An Empirical Study in Three Segments of Application Extensions to A | Quantitative survey (PLS-SEM); 21,841 respondents. | German consumers (mobility, household, health sectors). | Smart home devices, virtual assistants, health AI. | Convenience, health, safety, social influence, habit. |
| Systematic Literature Review on Sustainability Integration and Marketing Intelligence in the Era of Artificial Intelligence A | SLR (2018–2023); bibliometric analysis. | Peer-reviewed publications (Google IEEE Xplore). Scholar, | ML, NLP, AI chatbots, blockchain. | Sustainable marketing integration, ethical challenges, campaign effectiveness. |
| AI as Customer | Conceptual literature review. | Theoretical analysis of AI-as customer role. | decision-making in consumption. Autonomous AI | AI autonomy in consumption tasks; augmentation vs. replacement. |
| disclosure, performance expectations and intention to AI assistant is my new best friend! Role of emotional reuse | OS v.24); 644 SEM (AM valid responses. Survey-based | AI assistant users in Pakistan. | NLP-based AI assistants (Siri, Google Assistant, Alexa). | Emotional disclosure, user engagement, reuse intention. |
| AI increases unethical consumer behavior due to reduced anticipatory guilt | experiments (t-tests, logistic Four incentive-compatible regression). | Participants in lottery/shopping/ return scenarios. | Amazon Alexa, XT-1000 AI agent. | misuse); anticipatory guilt mediation. Unethical behavior (cheating, coupon |
| AI Like ChatGPT, Users Like Us: How ChatGPT Drivers and AI Voice Bots: A Services Marketing Research Agenda Efficacy Affect Consumer Behaviour AI | Literature review (service marketing Online survey (PLS analysis); 251 ChatGPT users. theories). | Theoretical framework for voice ChatGPT users. bots. | Alexa, Google Assistant, Siri. GPT-based generative AI. | Decision-making, branding, customer Perceived humanness, self-efficacy, serendipity, satisfaction. experience. |
| AI-empowered scale development: Testing the potential of ChatGPT | Cross-country survey validation. | US, Australia, UK, Consumers in Germany. | ChatGPT (v3.5). | Convenience, privacy, decision-making dependency. |
| AI-Powered Eye Tracking for Bias Detection in Online AI-powered marketing: What, where, and how? Course Reviews: A Udemy Case Study | Dynamic/static eye-tracking (Tobii Literature review+survey of 40 marketing professionals. ANOVA/t-tests. X2-30); | Marketing professionals across Udemy course review pages. industries. | AI-powered Predict system (Stanford collaboration). Marketing automation, chatbots, predictive | Marketing efficiency, customer experience, Attention metrics (focus, cognitive demand), engagement, clarity. ethical implications. |
| AI, Behavioural Science, and Consumer Welfare | Literature review (AI/behavioral science intersection). | Studies on AI and behavioral biases. | Bias detection, personalized interventions. analytics. | Behavioral biases, consumer welfare, social impact. |
Impulse Buying in E-Retailing Context
shoppers.
recommendations.
motivations, impulse buying.
| Title | Method | Sample | AI technology | Behavioral outcomes |
|---|---|---|---|---|
| customer perception, experience and engagement in AI’s invisible touch: how effortless browsing shapes online retail | Online survey (PLS-SEM); 1,438 Portuguese consumers. | E-commerce consumers. | Personalized recommendations, chatbots, voice assistants. | Awe experience, perceived control, purchase intention. |
| privacy concerns on consumer resistance to intelligent Alexa, it is creeping over me – Exploring the impact of voice assistants | ESS ANOVA, PROC macro); online panels. Five experiments ( | IVA users (e.g. Alexa, Siri). | Intelligent voice assistants (IVAs). | Privacy concerns, perceived creepiness, resistance. |
| Analysis of Online Consumer Behavior – Design of | CRISP-DM framework (clustering, | 185,706 purchases from 4,111 | k-means, CART/C5.0, association | Transactional metrics, cluster assignments, |
| CRISP-DM Process Mode | decision trees, Apriori). | companies. | rule mining. | product associations. |
| Animating Arousal and Engagement: Empirical Insights into | Online survey (PLS-SEM); 339 | Tourists at AI-enhanced robotic | Interactive AI robots (artistic | Interactivity, novelty, eWOM intentions, |
| AI-enhanced Robotic Performances and Consumer Reactions | respondents. | performances. | features). | emotional responses. |
| Anthropomorphized Artificial Intelligence, Attachment, and | Qualitative exploratory approach. | Consumers interacting with | Siri, Alexa, emotional-capable | AI anthropomorphism, emotional |
| Consumer Behavior | anthropomorphized AI. | chatbots. | attachment, brand loyalty. | |
| Artificial intelligence and consumer behavior: From | Literature review+conceptual | Predictive vs. generative AI | Predictive ML, generative AI | Algorithm reactions, preference for AI |
| Artificial Intelligence and Declined Guilt: Retailing Morality predictive to generative AI | Three experimental studies (scenario analysis. | Consumers interacting with AI/ applications. | Self-service checkout AI. (ChatGPT, DALL-E). | Moral intention (error reporting), guilt types, decision automation. |
| Comparison Between Human and AI | manipulation). | human cashiers. | perception. | |
| Artificial Intelligence and Empirical Consumer Research: A | Topic modeling of 119 journal | Top-tier consumer research journals. | Regression, decision trees, NLP, | Supervised/unsupervised learning |
| Topic Modeling Analysis | abstracts. | neural networks. | applications in consumer research. | |
| Artificial Intelligence and Predictive Marketing: An Ethical | Semi-structured interviews (thematic | Marketing professionals. | ML, NLP, deep learning in | Customer prioritization, market |
| Framework from Managers’ Perspective | analysis); 14 professionals. | marketing. | concentration, manipulation. | |
| Who has the control when interacting with a chatbot? Artificial intelligence and the new forms of interaction: | ESS macro); Two experiments (PROC mock-up webpages. | Participants in mobile/car rental scenarios. | non-anthropomorphic Anthropomorphic vs. | Reactance, choice difficulty, satisfaction. |
| Artificial Intelligence Impact on Banks’ Clients and | Quantitative survey (SPSS); 250 | Lebanese bank stakeholders. | AI-based CRM, chatbots, chatbots. | Customer satisfaction, time efficiency, job |
| Employees in an Asian Developing Country | clients, 50 employees. | automated transactions. | impact. | |
| Artificial Intelligence in Marketing: A Systematic Literature | SLR of 57 publications; qualitative/ | Marketing literature (2010–2022). | Recommendation systems, | AI effectiveness in digital/content/ |
| Review | quantitative analysis. | chatbots, marketing automation. | experiential marketing. | |
| Artificial Intelligence in the Fashion Industry: Consumer GAN) Responses to Generative Adversarial Network ( | ANOVA/regression); 163 participants. Experiment ( | Generation Y fashion consumers. | GAN-generated fashion images. | Functional/social/emotional values, purchase intent, WTP. |
| Artificial intelligence, firms and consumer behavior: A | Systematic review of ML-based AI | Empirical/theoretical papers on AI | ML-based prediction vs. | Labor markets, firm innovation, consumer |
| survey | economics. | impacts. | traditional automation. | biases. |
| Artificial intelligent housekeeper based on consumer | ML algorithms for purchase | Taobao platform consumer data. | AIH (machine learning, big | Preferences, purchase history, brand |
| purchase decision: a case study of online E-commerce | prediction. | data). | loyalty. | |
| Artificial markets: A review and assessment of a new | OT analysis of artificial Review+ SW | Agent-based market simulations. | Agent-based social simulation | Innovation diffusion, consumer |
| venue for innovation research | markets. | (heterogeneous agents). | heterogeneity, market dynamics. | |
| Attitudes towards Artificial Intelligence in the Area of | ING survey; Quantitative analysis ( | 14,824 respondents across 15 | Robo-advisors, self-service | Acceptance of AI financial services, |
| Personal Financial Planning: A Case Study of Selected Countries | Mann-Whitney U tests). | countries. | technologies. | socio-demographic correlations. |
| Being Human in the Age of AI | Literature review+conceptual | AI in marketing automation. | Recommendation systems, | Consumer welfare, privacy, |
| analysis. | pricing algorithms. | human-technology interaction. | ||
| Brave New World: Service Robots in the Frontline | Conceptual framework (sRAM | Service robotics literature. | Voice/facial recognition, | Operational efficiency, customer |
| model). | autonomous decision-making. | experience, ethical concerns. | ||
| Bridging Artificial Intelligence-Based Services and Online | SEM analysis; 470 Chinese online | E-commerce consumers in China. | Chatbots, personalized | System/info quality, hedonic/utilitarian |
Table A1. Continued.
.
| Title | Method | Sample | AI technology | Behavioral outcomes |
|---|---|---|---|---|
| Consumer Behaviour Analysis for AI Services in the Tourism Industry | Online survey (PLS-SEM); 301 international tourists. | Tourists in Greece using AI apps. | AI-powered mobile apps recommendations). (bookings, | Happiness, immersion, trust, willingness to pay. |
| Measuring the Impact of Value-Based Adoption Model Consumer Behaviour on AI Applications for Services: on Luxurious AI Resorts’ Applications | PLS-SEM (SmartPLS); 311 luxury resort guests. | Guests at Greek luxury resorts. | recommendation engines). AI resort apps (chatbots, | Perceived value, WTA/WTP, habit change. |
| Uncertainty and Reduced Perceived Control in Decisions Consumer Reactions to Technology in Retail: Choice Assisted by Recommendation Agents | ESS); Prolific samples. ANOVA, Three experiments ( PROC | Online grocery shoppers (wine selection). | AI recommendation agents (e.g. wine suggestions). | Perceived control, uncertainty, purchase intent. |
| Consumer trust and perceived risk for voice-controlled artificial intelligence: The case of Siri | PLS-SEM (Amazon MTurk); 675 iPhone users. | Siri users in the U.S. | Voice-controlled IVA (Siri). | Trust, interaction ease, privacy risk, brand loyalty. |
| using artificial intelligence-enabled travel service agents Consumers’ reasons and perceived value co-creation of | PLS-SEM+fsQCA; 99 business students. | Users of travel chatbots. | AI travel chatbots (flight/hotel bookings). | Value co-creation, adoption/rejection reasons. |
| Determinants of consumers’ emotions and willingness to use artificial intelligence in Indonesia | PLS-SEM (SmartPLS); 208 chatbot users. | Indonesian consumers with chatbot experience. | NLP-based chatbots. | Anthropomorphism, hedonic motivation, willingness to use. |
| Development of Social Platforms and New Opportunities in Digital humans in fashion: Will consumers interact? Digital Marketing | Content analysis+trend modeling Quantitative survey (descriptive/ (polynomial regression). | Global sample of fashion consumers. Global/Latvian social media penetration data. | Digital human avatars (AR/VR/ AI/ML, AR/VR, voice search, blockchain. | Interaction likelihood (speech/text/ Internet/social media adoption, personalization trends. |
| services: a research agenda for digital transformation in Digital servitization value co-creation framework for AI | Conceptual literature review. inferential stats). | Financial services (banking). | Chatbots, fraud detection, investment AI. MR). | engagement, firm performance. Digital servitization, consumer gestures), device influence. |
| Disruptive technology and AI in the banking industry of an financial service ecosystems emerging market | SEM analysis; 400 Nigerian bank customers. | Customers of 10 Nigerian banks. | AI-based CRM systems. | Technology usage, service quality, buying behavior. |
| artificial intelligence for social media content creation Do you create your content yourself? Using generative diminishes perceived brand authenticity | ANOVA); Three experiments (t-tests, Qualtrics surveys. | Consumers evaluating brand posts. | Generative AI (ChatGPT, Midjourney). | Brand authenticity, credibility, EWOM intentions. |
| Effects of voice assistant recommendations on consumer behavior | Two experiments (PLS-SEM, logistic regression). | Participants receiving voice/text recommendations. | Voice assistants (Amazon Alexa). | Perceived credibility, usefulness, purchase intent. |
| Embracing the Power of AI in Retail Platform Operations: Considering the Showrooming Effect and Consumer Returns | Theoretical modeling+numerical simulations. | Duopoly market (online/physical retailers). | Virtual fitting rooms, AI chatbots. | Pricing, service effort, return policies. |
| machine learning techniques and data collected from Evaluating the sales potential of new products using mobile applications | NN, ML pipeline (Random Forest, C PCA); 223K records. | Fashion mobile app users. | Hybrid unsupervised/supervised learning. | Product/user feature importance, like/ dislike prediction. |
| Examining the Impact of Keyword Ambiguity on Search Advertising Performance: A Topic Model Approach | LDA topic modeling+hierarchical Bayesian model. | 12,800 Google search keywords. | Latent Dirichlet Allocation (LDA). | CTR, ad position, semantic ambiguity effects. |
| Exploring AI-enabled green marketing and green intention: An integrated PLS-SEM and NCA approach | UAE Mixed-method (PLS-SEM+ NCA); survey. | ENA. 837 online shoppers in M | AI-enabled green marketing strategies. | Trust, satisfaction, green purchase intention. |
| Exploring consumer intentions to continue: Integrating task technology fit and social technology fit in generative AI-based shopping platforms | analysis+PLS-SEM); 472 users. Mixed-method (thematic | Users of generative AI shopping platforms. | Generative AI for personalized recommendations. | usefulness, continuance intention. Task/social technology fit, perceived |
| Exploring Consumer-Robot Interaction in the Hospitality Sector: Unpacking the Reasons for Adoption (or Resistance) to Artificial Intelligence | Semi-structured interviews; 22 respondents. | Restaurant users/non-users in Pakistan. | Food-ordering service robots. | Perceived ease of use, enjoyment, privacy concerns. |
(Continued)
| Continued. Table A1. | ||||
|---|---|---|---|---|
| Title | Method | Sample | AI technology | Behavioral outcomes |
| Exploring the influence of artificial intelligence technology on consumer repurchase intention: The mediation and moderation approach | PLS-SEM (SmartPLS); 308 Omani hotel customers. | Online hotel bookers in Oman. | AI-integrated social media/WEB 2.0. | Social media engagement, CRO, repurchase intention. |
| Findings from the Polish InsurTech market as a roadmap for regulators | CAWI survey; 2,136 respondents. | Polish insurance consumers. | ML/AI for analytics, claims management. | Awareness, usage, cybersecurity/privacy concerns. |
| Fog Computing-Based Smart Consumer Recommender Systems | Conceptual framework. | IoT/edge computing applications. | Fog computing+ IoT devices. | Latency, bandwidth, consumer decision-making. |
| GPT and CLT: The impact of ChatGPT’s level of abstraction on consumer recommendations | ANOVA, regression); GPT-4/Bard tests. Eight studies ( | ChatGPT/Bard-generated recommendations. | GPT-3.5, GPT-4, Google Bard. | Construal level (abstract/concrete), |
| Hey Alexa: examining the effect of perceived socialness in | PLS-SEM (Amazon MTurk); 391 users | Smart speaker users (Alexa, Google | AI-enabled smart speakers. | Media richness, parasocial interaction, recommendation preferences. |
| usage intentions of AI assistant-enabled smart speaker How Deepfakes and Artificial Intelligence Could Reshape | Qualitative synthesis+practitioner + 151 non-users. | Industry reports (U.S., Europe, Home). | GANs for synthetic Deepfakes, | Stakeholder promises/perils (brand continuance intention. |
| the Advertising Industry: The Coming Reality of AI Fakes and Their Potential Impact on Consumer Behavior | consultations. | Asia-Pacific). | ads. | managers, creatives). |
| psychological contracts determine purchase hesitation? How do sales promotions, communication agents, and Evidence from live stream influencers’ fan groups | ESS); ANOVA, PROC Credamo samples. Two experiments ( | Chinese Douyin users in fan groups. | AI chatbots vs. human agents in LSI groups. | Purchase hesitation, transactional/ relational contracts. |
| How does AI drive branding? Towards an integrated theoretical framework for AI-driven branding | Thematic analysis of 414 documents. | Literature across psychology, marketing, IS. | Sentiment analysis, AI-driven recommendations. | Brand trust, loyalty, subjective norms. |
| How to Improve Voice Assistant Evaluations: Understanding the Role of Attachment with a Socio-Technical Systems Perspective | M+fsQCA). Longitudinal survey (PLS-SE | VA users in the U.S. | Speech recognition, NLP algorithms. | Emotional/functional attachment, WOM recommendations. |
| I am attracted to my Cool Smart Assistant! Analyzing Attachment-Aversion in AI-Human Relationships | PLS-SEM; 308 IVA owners. | Users of Alexa/Google Home. | Intelligent voice assistants (IVAs). | Sensory/affective experiences, IVA coolness, continued use. |
| Behaviour Intention: Moderating Role of Customer Trust Impact of AI and E-Business Related Factors on Consumer | Two questionnaires (CFA, regression); Saudi Arabia. | 247 customers + 174 employees. | AI-driven decision-making (AIDD). | Empathy, facilitating conditions, behavioral intentions. |
| Impact of Perceived Value on Intention to Use Voice Assistants: The Moderating Effects of Personal Innovativeness and Experience | Two surveys (PLS-SEM); Spanish VA users. | Experienced VA users in Spain. | NLP-based voice assistants. | Perceived value (quality/price/emotional/ social), continuance intention. |
| Impacts and Potential of Autonomous Vehicles in Tourism | Mixed-method (survey+Kruskal Wallis tests); 671 Hungarians. | Potential space tourists in South Korea. | SAE Level 4–5 AVs (navigation, automation). | Motivations, constraints, behavioral intention. |
| Implementation of Artificial Intelligence for Brand Equity | OSviewer); Bibliometric analysis (V 519 Scopus articles. | AI-branding literature (1988–2024). | ML, NLP, AR, chatbots, generative AI. | Brand equity clusters (AI, sales, consumer behavior). |
| Influence of AI Tools on Consumer Behavior Management in Digital Marketing | Meta Ads experiment (Kohonen maps); four groups. | Competent/incompetent ad audiences. | Meta Advantage+ AI (ML algorithms). | Reflexive choice, interaction intensity, purchase intention. |
| Is it really unreal? A two-theory approach on the impact of deepfakes technology on the protection motivation of consumers | PLS-SEM (SmartPLS); 317 consumers post-deepfake exposure. | Consumers exposed to viral deepfakes. | Deepfake AI (synthetic celebrity videos). | Threat/coping appraisal (PMT), attitude, protective intent. |
| augmented reality, chatbots, and social media on the body image and self-esteem of Generation Z female It’s all part of the customer journey: The impact of consumers | M+experiments); Malaysian samples. Multi-study (PLS-SE | Gen Z women (beauty product users). | AI chatbots (friend/assistant modes) + AR. | Body image, self-esteem, purchase breadth/depth. |
| Let us talk about something: The evolution of e-WOM from the past to the future | Bibliometrics (Web of Science); 468 articles. | e-WOM literature (2003–2021). | Robotic e-WOM (r-WOM), AI personalization. | Consumer/firm outcomes, platform affordances. |
.
| Title | Method | Sample | AI technology | Behavioral outcomes |
|---|---|---|---|---|
| modelling by a descriptive induction approach based Marketing Intelligent Systems for consumer behaviour on Genetic Fuzzy Systems | KDD process (fuzzy rule induction NSGA-II). via | Consumer behavior causal model data. | Genetic Fuzzy Systems (fuzzy GA). logic+ | Fashion consciousness, conservatism, hedonism. |
| Mining behavioural and sentiment-dependent linguistic patterns from restaurant reviews for fake review detection | embedding+neural network. NN-based sentiment C | YelpZip dataset (608K reviews). | Sentiment-aware deep learning NN, MLP). (C | Linguistic/behavioral features, fake review classification. |
| differential effects of technological and non-technological factors on digital natives’ perceptions and behavior Mobile banking and AI-enabled mobile banking: The | regression); 218 undergraduates. Online survey (multivariate | Digital natives (millennials). | AI chatbots in banking apps. | Relative advantage, trust, AI comfort. |
| of artificial intelligence and customers’ digital experience Neuromarketing: Understanding the effect of emotion and memory on consumer behavior by mediating the role | ENA PLS-SEM (SurveyMonkey); 837 M online shoppers. | Electrical equipment shoppers. | ML, NLP, recommendation systems. | Emotional appeal, memory encoding, purchase decisions. |
| Neurotourism Insights: Eye Tracking and Galvanic Analysis of Tourism Destination Brand Logos and AI Visuals | GSR experiment; 40 students. ET + | Tourism brand logo viewers. | AI-generated visuals (BP dimensions). | GSR amplitude. Fixation duration, |
| Orbital and sub-orbital space tourism: motivation, constraint and artificial intelligence | ANN; 664 South PLS-SEM+fsQCA + Koreans. | Potential space tourists. | AI for mission design, astronaut assistance. | Motivations, constraints, AI awareness/ trust. |
| Overconfidence and the adoption of robo-advice: why overconfident investors drive the expansion of | NFCS Investor Sample analysis (probit models). | 2,000 U.S. investors. | Robo-advisors (algorithmic wealth management). | Overconfidence, adoption, financial satisfaction. |
| Powering retailers’ digitization through analytics and automated financial advice | Case studies (Rue La La, Groupon, | Retail transaction data. | ML for demand forecasting, | Revenue, bookings, profit, units sold. |
| Predicting consumer healthy choices regarding type 1 wheat flour automation | ML modeling (Random Forest, SVM); B2W); field experiments. 467 respondents. | Italian flour consumers. | Random Forest classifier. dynamic pricing. | Flour type preference, health knowledge. |
| Proposing a metaverse engagement model for brand | Conceptual framework | Metaverse marketing examples | AI for avatar creation, | Engagement/immersiveness dimensions, |
| Proposing the ‘Digital Agenticity Theory’ to analyze user development | SEM); (literature+case studies). Mixed-method (literature+ | IGA chatbot interactions. (Nike, Gucci). High/low D | Conversational AI (NLP, procedural content. | Digital agenticity (topicality, proactivity, brand equity. |
| Recommendation agents: an analysis of consumers’ risk engagement in conversational AI chatbot | Mixed-method (interviews+PLS chatbot users. | Online shoppers (Study 1: 17; Study | Collaborative/content-based predictive modeling). | Privacy risk, trust mediation, RA influence. intra-activity). |
| Revisiting the social commerce paradigm: the social perceptions toward artificial intelligence | Scoping review+bibliometrics (765 SEM); Brazilian shoppers. | SC literature (2003–2023). 2: 308). | filtering (e.g. Amazon). AI, blockchain, IoT in SC | Commerce/behavior/social/technology |
| commerce (SC) framework and a research agenda | articles). | platforms. | dimensions. | |
| intelligence’s transformative journey in tourism and Revolutionizing getaways: Automation and artificial | Thematic literature review. | Tourism/hospitality AI applications. | recognition, chatbots. Service robots, facial | experience, job displacement. Operational efficiency, customer |
| Robots should be seen and not heard…sometimes: hospitality | ANOVA); 3x3 factorial experiment ( | U.S. adults evaluating cooking | Humanoid/mechanical/android | Liking, compatibility, purchase intention. |
| Anthropomorphism and AI service robot interactions | 953 MTurkers. | robots. | robots. | |
| Shifts in consumer behavior towards organic products: | AN); 1,003 Mixed-method (RF, HDBSC Australians. | Organic wine consumers. | Random Forest, SVM, decision trees. | Price sensitivity, emotions, normative cues. |
| Society 5.0: Shaping the future of e-commerce Theory-driven data analytics | Literature review+case studies | Decentralized e-commerce examples. | AOs/ AI, blockchain, IoT in D | Consumer awareness, control, ethical |
| Symmetrical and asymmetrical approaches to brand loyalty | PLS-SEM+fsQCA; U.S. IVA users. (Web3 platforms). | Users of Siri/Alexa/Google Assistant. | NLP-based IVAs. AOs. H | Psychological needs (autonomy, transactions. |
| Taking the fiction out of science fiction: (Self-aware) robots – The case of intelligent voice assistants | Conceptual/theoretical synthesis. | Future self-aware AI scenarios. | AGI, NLP, self-awareness | Robot self-expression, human-robot competence), brand loyalty. |
| and what they mean for society, retailers and marketers The Connected Consumer: Connected Devices and the | Conceptual framework (2052 | Futuristic AI-driven marketing. | ML, causal modeling, affective algorithms. | Real-time consumer behavior prediction. interaction. |
Evolution of Customer Intelligence
scenario).
computing.
(Continued)
| Continued. |
|---|
| Table A1. |
| Continued. Table A1. | ||||
|---|---|---|---|---|
| Title | Method | Sample | AI technology | Behavioral outcomes |
| The convenience of shopping via voice AI: Introducing AIDM | Literature review+conceptual study. | Voice AI in retail decision-making. | Amazon Alexa. | Consumer involvement, voice assistant reliance. |
| The digital transformation of business. Towards the datafication of the relationship with customers | SLR (47 articles); snowball technique. | Big Data/AI literature (2010–2020). | BDA, recommendation systems. | Personalization, loyalty, ethical concerns. |
| GAI) and Large Language Models (LLMs) are transforming the The form of AI-driven luxury: how generative AI ( | designs+consumer interviews). Qualitative (Midjourney | 46 Italian luxury consumers. | GAI (Midjourney) for bag design. | GAI Brand essence, emotional bond, disclosure effects. |
| The Future of Electronic Commerce in the IoT Environment creative process | Theoretical analysis+case studies. | IoT-integrated e-commerce. | NNs, edge/fog computing. ML, C | Operational efficiency, personalization, security. |
| Unethical Behavior: A Social Judgment Perspective The Impact of AI Identity Disclosure on Consumer | Experiment (disclosed/undisclosed AI); Chinese sample. | Participants in ethical decision tasks. | Text-based AI chatbots. | Unethical behavior, perceived social judgment. |
| The impact of artificial intelligence on marketing | Case studies (Netflix, Amazon) + | Large corporations+ Ukrainian retailer. | ML, NLP, predictive analytics. | Loyalty, ROI, advertising costs. |
| communications: New business opportunities and challenges The Impact of User Personality Traits on Word of Mouth: | Text-mining+survival analysis; 198K Rozetka strategy. | Social media users. | NLP, deep learning for | Personality-driven WOM, purchase |
| The implications of behavioural economics for pricing in a Text-Mining Social Media Platforms | Survey (300+ respondents) + Twitter transactions. | Airline pricing strategies. | personality inference. ML for dynamic offer | Choice architecture, revenue management. likelihood. |
| The influence of anthropomorphic appearance of artificial world of offer optimisation | Four experiments; 1,172 Chinese conceptual modeling. | Hedonic/utilitarian AI product users. | Anthropomorphic AI (cleaning optimization. | Perceived entertainment/usefulness, |
| intelligence products on consumer behavior and brand | respondents. | robots, speakers). | purchase intent. | |
| evaluation under different product types | ||||
| Communication Channels: Opportunities and Challenges The Integration of Artificial Intelligence in Business | Survey (SPSS); 384 Greek experts. | Business/technology professionals. | Chatbots, email filtering, speech recognition. | Communication efficiency, AI adoption barriers. |
| The Power of Virtual Influencers: Impact on Consumer | Survey (Likert scale); 357 | Followers of human/virtual | AI-generated virtual influencers. | Trust, purchase intent, credibility. |
| Behaviour and Attitudes in the Age of AI | participants. | influencers. | ||
| The Role of Cuteness on Consumer Attachment to Artificial Intelligence Agents | Survey (SEM); VA users. | Users of Alexa/Siri/Google Assistant. | Cute-design AI agents. | Emotional attachment, benevolence trust, usage frequency. |
| The Role of Mobile Application Design, Branding, AI-Driven | PLS-SEM (SmartPLS); 431 Gen Z | Kuala Lumpur mobile app users. | AI personalization | App design, branding, purchase intention. |
| Purchase Intention Among Generation Z: The Mediating Personalization, and Social Commerce Integration on | students. | (recommendations, pricing). | ||
| Role of Mobile App Marketing | ||||
| The Role of Recommendation Sources and Attribute | ANOVA, mediation Five experiments ( | Online recommendation recipients. | AI vs. human recommenders. | Warmth/competence perceptions, |
| The Turing test of online reviews: Can we tell the Framing in Online Product Recommendations | Three studies (chi-square, logit models). | Participants classifying Yelp reviews. | GPT-4 Turbo-generated reviews. | Review authenticity detection, trust in willingness to pay. |
| difference between human-written and GPT-4-written | regression). | platforms. | ||
| online reviews? | ||||
| The use of artificial intelligence in marketing strategies: | SLR+thematic analysis. | AI in marketing literature. | ML, chatbots, recommendation | Campaign efficiency, engagement, |
| Automation, personalization, and forecasting | systems. | demand prediction. | ||
| Transformative privacy calculus: Conceptualizing the personalization-privacy paradox on social media | Two experiments (PLS-SEM); French/ SNS users. UK | Social media users. | AI-driven personalization algorithms. | Willingness to disclose, trust beliefs, InfCC. |
| Understanding the Consumer Dynamics of AI in North | ANOVA/correlation); AI users. Survey ( | North Macedonian e-business | ML-based recommendation | AI awareness, trust, ethical concerns. |
| Macedonian E-Business | consumers. | systems. | ||
| Unlocking my heart: Fostering hotel brand love with | ANN; hotel guest survey. M+ SE | Guests interacting with service | Humanoid service robots. | Perceived authenticity, brand love. |
| Using AI predicted personality to enhance advertising service robots | Mixed-method (surveys+interviews). | Australian banking customers. robots. | Personality-prediction AI. | CTR, conversion rate, Big Five traits. |
| effectiveness |
| Continued. | |
|---|---|
| Table A1. |
| Title | Method | Sample | AI technology | Behavioral outcomes |
|---|---|---|---|---|
| Virtual influencer marketing: a study of millennials and gen Z consumer behaviour | groups+interviews); 29 Indians. Qualitative (focus | MZ Instagram users following VIs. | AI-generated virtual influencers. | Credibility, authenticity, engagement. |
| What Machine Learning Can Learn from Foresight: A Human-Centered Approach | Conceptual framework (corporate foresight). | ML forecasting applications. | ML for market/consumer prediction. | Forecast acceptance, strategic alignment. |
| What prompts consumers to purchase online? A machine learning approach | ML (ISD classifier); e-commerce dataset. | Online shopping sessions. | Stacked ML (Random Forest, Decision Trees). | Purchase intent prediction (administrative/ informational features). |
| When the recipe is more important than the ingredients: Unveiling the complexity of consumer use of voice assistants | fsQCA; 296 VA users (single/ multimodal). | Users of Alexa/Google Nest Hub. | Single vs. multimodal VAs. | Continued use intention, humanlike/tech attributes. |
| Why should I trust you? Influence of explanation design on consumer behavior in AI-based services | Two experiments (PLS-SEM); healthcare/insurance. | Prolific participants. | XAI (detailed/on-demand explanations). | Understanding, assurance, trust, acceptance. |