Keywords

Generative Artificial Intelligence, Gen AI, ChatGPT, Consumer Behavior, Consumer Experience, Behavioral Response.

Introduction

Gen AI has ushered in a new era of marketing, significantly enhancing content marketing, lead generation, customer relationship management, customer experience, and more. The implementation of Gen AI in marketing impacts marketers in various aspects of their businesses. Generative AI (Gen AI) represents the latest advancement in AI, specifically designed to create new content, such as text, images, and videos (Huang & Rust, 2023). Many organizations have already adopted Gen AI to drive essential innovative marketing initiatives. For instance, Coca-Cola utilized Gen AI to help develop new beverages, including Coca-Cola Sugar Y 3000. Similarly, Unilever, Nestle, and Mondelez have embraced Gen AI for their advertising efforts (Paola Cillo & Gai Rubera, 2024).

he marketing industry has experienced major changes due to the rise of language models, such as ChatGPT, and the expansion of artificial intelligence. By incorporating Gen AI into traditional marketing practices, businesses can boost customer engagement, enhance customer service, simplify content creation, improve personalization, and attract more leads, ultimately impacting consumer behavior and decisions. Marketers can also gain valuable insights into consumer behavior and preferences, which helps improve the overall customer experience. ChatGPT has been celebrated as a game-changer because it enables marketers to craft personalized content that effectively connects with their target audience (Gupta et al.,2024).

Generative AI tools, such as OpenAI's ChatGPT, Google's Gemini, and Microsoft's Co-Pilot, have become influential technologies shaping consumer engagement and potentially affecting consumer behavior (Dwivedi et al., 2023). Since OpenAI's initial launch of ChatGPT, researchers, industries, and consumers have been fascinated by its potential for diverse, high-quality applications. ChatGPT has also garnered significant public interest, as consumers worldwide are eager to test its capabilities and experience innovation. Its monthly active users are estimated to have surpassed 100 million within two months of its release, making it the fastest-growing consumer application in history (Hu, K. 2023).

Gen AI tools are becoming increasingly popular among consumers as sources of information and recommendations for brands, products, and services. These tools allow consumers to express their creativity, boost their social media presence, and enrich user-generated content with eye-catching images. By providing personalized suggestions, virtual retail assistants can change how consumers interact with products and make buying decisions, influencing consumer behavior through information processing and decision support. By enabling consumers to make smarter choices, Gen AI can reshape their purchasing habits by giving access to reviews and ratings from various sources (Emmanuel Mogaji& Varsha Jain, 2024). It changes consumer behavior by altering how people approach decision-making. Gen AI can revolutionize consumer marketing by enabling hyper-personalization, increased efficiency, and better customer engagement. The research on the impact of Gen AI on consumers and companies is still in its early stages, despite its disruptive potential in marketing (Huang & Rust, 2023; Li et al., 2024; Reisenbichler et al., 2022).

While Gen AI has many practical uses, it also carries risks. One concern is its ability to produce false or misleading content (Dwivedi et al., 2021). Another worry is that depending too much on Gen AI for creating original material may lessen consumers' interest in creativity (Paola Cillo & Gai Rubera, 2024). Additionally, Gen AI comes with several drawbacks. Its use can cause privacy breaches, cybersecurity problems, increased dependence, decreased human skills, and social isolation. Using Gen AI in customer service may lead to confusion, annoyance, and resentment among users. The use of data by businesses presents further challenges, such as unemployment and social inequality. Furthermore, Gen AI's lack of emotion and empathy is concerning and could result in user dissatisfaction. Also, Gen AI is prone to spreading false information, often called AI hallucination (Belanche et al., 2024).

Several studies have explored the connection between Gen AI and consumer behavior, emphasizing how different Gen AI features impact consumer experience, satisfaction, and actions. Gen AI's efficiency, excellence, meaningful recommendations, and conversational abilities influence the likelihood of its use during the initial acceptance phase. Consumers' expectations related to status, respect, ethics, and spirituality in their ChatGPT encounters shape their intention to engage with ChatGPT, encouraging collaboration between ChatGPT and humans (Abadie et al., 2024). Humanlike characteristics (such as perceived humanness, naturalness, and responsiveness), output quality (including perceived ethics, trust, intelligence, and sincerity), and perceived serendipity all affect the intention to adopt Gen AI (Lee & Kim, 2024). A consumer's attitude toward Gen AI advertising depends on their awareness of human authorship, the degree of co-creation, and perceived authenticity (Yin et al., 2024). The adoption of Gen AI influences consumer responses—such as attitude, intention, and loyalty—toward the brand (Brüns & Meißner, 2024). Perceived risks, including privacy, accuracy, and overreliance, also impact ChatGPT's behavioral intentions (Shi et al., 2024). Management responses—such as redress, timeliness, facilitation, apologies, attentiveness, and credibility—also affect customer satisfaction. However, this relationship is mediated by perceived justice dimensions: distributive, procedural, and interactional (Koc et al., 2023). Trust plays a vital role in disclosing information to Gen AI (Morosan, 2025). Brand identity, image, and integrity significantly influence consumer purchase intentions (Hossain, 2024). AI authorship shapes perceptions of word-of-mouth and customer loyalty (Kirk & Givi, 2025). Whether a review summary is human or AI-generated impacts customers' booking intentions (Jia et al., 2025). Factors like age, gender, and product knowledge affect consumers' willingness to use Gen AI systems for product discovery and information gathering (Gude, 2023).

Despite recent advancements in Gen AI technology and its potential to bring transformative changes across various sectors of society and business, particularly regarding consumer behavior, few comprehensive reviews on the subject exist. Kumar & Ratten, (2024) conducted one of the earliest systematic literature reviews relevant to the current investigation; however, their review solely focused on the use of AI in family businesses. Alsharhan et al. (2024) also performed a systematic literature review, but their emphasis was limited to chatbots. This review examined the theories, variables, and methodologies utilized in studies related to chatbot adoption. Zheng et al. (2023) carried out another review titled "Data Science in Finance: Challenges and Opportunities," which centered on the finance sector and the application of Data Science (Gen AI) in algorithmic trading and fraud detection. Madanchian, (2024) conducted a systematic literature review titled "Generative AI for Consumer Behavior Prediction: Techniques and Applications," concentrating on using AI (Gen AI) to forecast customer behavior. In this context, Gen AI serves as a tool to predict consumer behavior. Sarstedt et al. (2024) conducted another review titled "Using large language models to generate silicon samples in consumer and marketing research: Challenges, opportunities, and guidelines," which focused on silicon samples and AI-generated data in consumer and marketing research. Nevertheless, this review aims to understand the influence of Gen AI on customer experience and behavioral intention. In this analysis, Gen AI and its characteristics are treated as independent variables, while various aspects of customer behavior serve as dependent variables. This analysis is notably more comprehensive, examining 139 articles compared to previous reviews.

The previous investigations in Tables 1 & 2 do not provide a comprehensive literature review. First, no review addresses the essential elements of a review paper, according to Paul and Rosado-Serrano's (2019) framework of theory, context, constructions, and methodologies. Second, none of the reviews have examined the impact of Gen AI on consumer experiences and behavioral responses. Therefore, the current SLR aims to fill this gap. This study is the first to integrate bibliometric analysis and SLR using the TCCM framework to address the following questions.

Table 1 Inclusion and Exclusion Criteria
No	Criteria	Inclusion Criteria	Exclusion Criteria
1	Duration of study	2023-2025	Before 2022 - Beyond Feb 2025
2	Search Within	(Article Title, Abstract, Keywords) AND (Article Title, Abstract, Keywords) AND (Article Title, Abstract, Keywords)	All others
3	SearchCriteria (Keyword Search)	"Generative AI" OR "Generative Artificial Intelligence" OR "ChatGPT" OR " Chat-GPT" OR “GAI” AND (“Consumer" OR "Customer”) AND (behavior OR perception OR attitude OR relation OR loyalty OR engagement OR experience)	All others
4	Language	English	Chinese
5	Document Type	Article, Review	Book chapter, Conference paper, Book, Editorial, Conference review, Letter, Note
6	Source Type	Journals	Book, Conference Proceeding, Book series
7	Subject Area	Business, Management and Accounting, Social Sciences, Psychology, Arts and Humanities, Neuroscience	All other subjects

Table 2 List of Literature Review Articles
No	Title	Number of studies reviewed, Years	Literature Review and Techniques Used	Review Focus	Research Questions
1	Artificial intelligence and family businesses: a systematic literature review Kumar, D., & Ratten, V. (2024)	15; 2003-2024; Not mentioned	Systematic Literature Review (SLR), Authors created method	Application of AI (Generative AI, Chatbots) in family business.	1. What are the key trends, opportunities, challenges, and factors influencing AI adoption in family-owned enterprises? 2. How can integrating AI (Generative AI, Chatbots) into family businesses enhance competitiveness, resilience, and sustainability?
2	Chatbot Adoption: A Multi-Perspective Systematic Review and Future Research Agenda Alsharhan et al. (2024)	219; 1966-2023; A	Systematic Literature Review (SLR), PRIZMA Framework	To analyze the technology adoption theories, antecedents, moderators, domains, methodologies and participants	1. What are the main theories and models used to study chatbot adoption? What is their distribution across different domains? 2. What is the geographical distribution of chatbot adoption studies? 3. What are the primary dependent variables, independent variables, mediators, and moderators in chatbot adoption studies? 4. What are the main domains where chatbot adoption studies have been conducted? 5. What are the methodologies used in conducting Chatbot adoption studies? 6. Who are the main participants in Chatbot adoption studies?
3	Data Science in Finance: Challenges and Opportunities Zheng, X., Gildea, E., Chai, S., Zhang, T., & Wang, S. (2024)	31; 2017-2023; Not mentioned (Scopus) Listed)	Narrative Review Method (Traditional Method)	Challenges and opportunities in applying Data Science (GAI) to finance	1. How to use GAI in algorithmic trading?  2. How to apply GAI to fraud detection?
4	Generative AI for Consumer Behavior Prediction: Techniques and Applications Madanchian, M. (2024)	31; 2018-2024; Not mentioned	Systematic Literature Review (SLR); PRISMA framework	The application of Gen AI techniques in predicting consumer behavior.	1. What are the primary themes and trends in the current application of AI (GAI) for predicting consumer behavior, and how are these applications transforming consumer insights across industries? 2. How do different AI models compare in effectiveness and suitability for consumer behavior prediction, and what factors influence their optimal application in various contexts? 3. What significant challenges and limitations are associated with integrating AI in consumer behavior prediction, including ethical, technical, and practical considerations, and how can these challenges be mitigated?
5	How to Bell the Cat? A Theoretical Review of Generative Artificial Intelligence and Its Impact on Digital Disruption in All Walks of Life Mondal, S., Das, S., & Vrana, V. G. (2023)	49; 2020-2023	Theoretical Review Method (Traditional Method)	Impact of GAI on consumers and content creation, AI's potential for businesses, and its future applications.	1. What applications does GAI have across various sectors to promote the development of future customer solutions? 2. In what ways can GAI assist businesses in creating new value propositions and enhancing customer experiences?
6	Using large language models to generate silicon samples in consumer and marketing research: challenges, opportunities, and guidelines Sarstedt et al. (2024)	72; 1979-2023	Narrative Review (Traditional Review Method; Not based on any framework)	Use of silicon samples and data generated by AI in consumer and marketing research	1. Where should silicon samples be situated in a research project? 2. What are the current guidelines that researchers should adhere to in silicon sampling
7	This study Exploring the Influence of Generative AI on Consumer Experiences and Behavioral Responses: A Systematic Literature Review and Future Research Agenda	139, 2023-2025	SLR based on the TCCM framework and bibliometric analysis	To study the influence of Gen AI on consumer experiences and behavioral responses	RQ1: Who are the leading authors, countries, sources, and articles contributing to knowledge about the impact of Gen AI on consumer experiences and behavioral responses? RQ2: What are the most relevant theories, contexts, constructs, and methodologies in the literature that explain the effects of Gen AI on consumer experiences and behavioral responses? RQ3: How can future researchers deepen their understanding of the influence of Gen AI on consumer experiences and behavioral responses?

RQ1: Who are the leading authors, countries, sources, and articles contributing to knowledge about the impact of Gen AI on consumer experiences and behavioral responses?

RQ2: What are the most relevant theories, contexts, constructs, and methodologies in the literature that explain the effects of Gen AI on consumer experiences and behavioral responses?

RQ3: How can future researchers deepen their understanding of the influence of Gen AI on consumer experiences and behavioral responses?

RQ4: What are the key implications for scholars and managers drawn from the existing literature on Gen AI's influence on consumer experiences and behavioral responses?

The structure of this systematic literature review is organized as follows: Section 1 introduces the impact of Gen AI on consumer experiences and behavioral responses. Section 2 outlines the methodology for the literature review and synthesis, explaining the SPAR-4-SLR protocol and TCCM framework utilized in this review. Subsequently, Section 3 provides an overview of the literature review, offering a descriptive analysis of the reviewed articles through bibliometric analysis. Section 4 describes the theories, contexts, constructs, and methodologies employed in all 139 articles reviewed in this paper. Section 5 discusses future research directions within the TCCM framework. Section 6 explores the theoretical and managerial implications of the findings, while Section 7 highlights the study's limitations and proposes avenues for future research. Finally, Section 8 concludes the study.

Literature Review Methodology

SPAR-4-SLR Method

This review employs a rigorous systematic literature review technique, known as the scientific procedures and rationales for systematic literature review (SPAR-4-SLR), proposed by Paul et al. (2021), to investigate existing knowledge and research gaps regarding the impact of Gen AI on consumer experiences and behavioral responses. This strategy is considered to be more reliable and effective in producing innovative insights (Paul et al., 2023). It includes three main processes (Figure 1): assembling, organizing, and assessing, along with six specific stages: identification, acquisition, organization, purification, evaluation, and reporting (Paul et al., 2021).

Assembling: Identification

The present SLR employs a robust technique, the SPAR-4-SLR protocol proposed by Paul et al. (2021). This substage of the SPAR-4-SLR structure involves identifying the SLR's research domain, research questions, source type, and source quality. The goal is to examine the impact of Gen AI on consumer experiences and behavioral responses.

The following research questions that guided this systematic literature review (SLR) were identified during the initial phase:

RQ1: Who are the leading authors, countries, sources, and articles contributing to knowledge about the impact of Gen AI on consumer experiences and behavioral responses?

RQ2: What are the most relevant theories, contexts, constructs, and methodologies in the literature that explain the effects of Gen AI on consumer experiences and behavioral responses?

RQ3: How can future researchers deepen their understanding of the influence of Gen AI on consumer experiences and behavioral responses?

RQ4: What are the key implications for scholars and managers drawn from the existing literature on Gen AI's influence on consumer experiences and behavioral responses?

At this stage, the dataset includes peer-reviewed and published works. These papers were collected from the Scopus database, which was chosen for its comprehensiveness compared to other databases like Web of Science, EBSCOhost, and Google Scholar (Bhukya et al., 2022). The search period spans from 2023 to 2025, aligning with the introduction of Generative Artificial Intelligence (Gen AI) in November 2022. This review topic was selected due to the significant increase in publication volume over the past three years and the growing interest among academics and practitioners.

Acquisition

The articles were sourced from the Scopus database. The review did not specify a start date to ensure comprehensive coverage of the topic. It included all articles indexed by the end of the selection period in February 2025. The following keywords were used: ("Generative AI" OR "Generative Artificial Intelligence" OR "ChatGPT" OR "Chat-GPT" OR "GAI") AND ("Consumer" OR "Customer") AND (behavior OR perception OR attitude OR relation OR loyalty OR engagement OR experience). Ultimately, 139 articles and reviews were selected for this systematic literature review (SLR) based on the inclusion and exclusion criteria outlined in Table 1. The first-year growth rate was 383% (from 18 in 2023 to 87 in 2024), which explains why the topic was chosen for review. Additionally, the fact that no review on this topic had been conducted to date (Table 2) further justified the current systematic literature review (SLR).

Arranging

Organization involves creating a workbook where researchers can code the articles identified during the assembly stage. To build an Excel database, the following codes were used: article name, author, year, journal name, citations, research type, method, research design, data collection method, data analysis type, domain, theory, abstract, context (including country and industry), and future research directions. This data was then used to synthesize findings and generate useful figures and tables. The researcher organized this systematic literature review (SLR) using the TCCM (Theory, Context, Construct, Method) framework provided by Paul and Rosado-Serrano (2019).

Purification

At this stage, the inclusion and exclusion criteria established by Paul and Dhiman (2021) were applied. Articles examining the link between Gen AI and consumer behavior were included in this review. Only articles written in English and published in journals listed in the Scopus database were considered. Articles published from November 2022 to February 2025 were included, while those published after February 2025 were excluded. Only articles and reviews were accepted, whereas other content types such as book chapters, conference papers, editorials, and letters were excluded. Papers from five specific subject areas—Business, Management and Accounting, Social Sciences, Psychology, Arts and Humanities, and Neuroscience—were included. Conversely, papers from fields like economics, engineering, data science, and computer science were excluded. The following table summarizes the study's inclusion and exclusion criteria.

Assessing: Evaluation

In this stage, researchers outlined procedures for data analysis. In this systematic literature review (SLR), the articles were evaluated using the TCCM framework, which helped the researcher synthesize the literature on the impact of Gen AI on customer experiences and behavioral responses. The analysis employed two methods: content analysis and thematic analysis. The researcher collected bibliometric data through content analysis. Bibliometric analysis is a well-established and comprehensive approach for gathering and understanding large volumes of scientific data (Donthu et al., 2021). Article details were extracted using the Bibliometric R program. The most influential authors, countries, journals, papers, and sources were identified. The publications were carefully examined to provide valuable insights and recommendations for future research. Thematic analysis involved reviewing the retrieved codes and grouping them into common themes. To synthesize findings, the researcher identified connections among these themes and organized them within the TCCM framework. Sixteen key theories were identified, along with various independent, dependent, mediating, and moderating variables. The study also explored and analyzed different contexts (countries and sectors) and research methods used in the literature. The TCCM framework was applied to identify research gaps and to propose recommendations for future studies.

Reporting

The final part of the SPAR-4-SLR framework involves presenting the results. The researcher presented the findings using standard reporting formats, including tables and figures. The results were thoroughly discussed, with the TCCM framework serving as a guiding framework for the discussion. The review concluded with an assessment of the study's implications and limitations.

Overview: Bibliometric Analysis and Results

Descriptive Analysis

Table 3 presents the descriptive statistics of the study. This research analyzed all papers and reviews published between 2023 and 2025. The dataset comprised 139 publications, consisting of 133 articles and six literature reviews from 97 different journals.

Table 3 Descriptive Statistics: Main Information about Data
Timespan	2023:2025
Sources (Journals, Books, etc.)	97
Documents	139
Annual Growth Rate %	37.44
Document Average Age	0.885
Average citations per doc	7.547
References	0
DOCUMENT CONTENTS
Keywords Plus (ID)	347
Author's Keywords (DE)	539
AUTHORS
Authors	425
Authors of single-authored docs	21
AUTHORS COLLABORATION
Single-authored docs	21
Co-Authors per Doc	3.24
International co-authorships %	32.37
DOCUMENT TYPES
Article	133
review	6

Publication Trends

Figure 2 shows the yearly publication trend of papers on the impact of Gen AI on customer behavior. The graph was created using the Bibliometric R program. Only 18 articles were published in 2023, since Gen AI technology was introduced in November 2022. However, because of its relevance and usefulness, 87 papers were published in 2024, marking an impressive growth rate of 383.33%. This rapid increase reflects strong interest from scholars and practitioners alike. By February 2025, 34 articles had been published in less than two months. Trend analysis predicts at least 204 publications in 2025, indicating a growth rate of over 134.48% from 2024 to 25. Looking at the growth over the last two years (2023-2025), it will total more than 1033%, which is remarkable. This calls for an examination of the publications from the past two years to understand the technology's uses, benefits, drawbacks, and its effect on customer behavior. Additionally, the review will offer recommendations for future research.

Performance Analysis

Performance analysis across various journals can help future researchers gain a broad understanding of the knowledge base and research progress regarding the influence of Generative AI on consumer behavior (Donthu, Kumar, Pandey, et al., 2021). Table 4 presents the top 20 journals with the most publications on this topic, including their citation counts and selected sample articles. The number of publications shows the research volume, while the number of citations indicates its impact. The Journal of Retailing and Consumer Services is the leading journal in this area, with 12 publications and 132 citations. Applied Marketing Analytics ranks second in total publications, with eight. Other notable journals with significant publication counts include the International Journal of Contemporary Hospitality Management, the International Journal of Hospitality Management, Psychology and Marketing, Sustainability (Switzerland), the International Journal of Information Management Data Insights, and the Journal of Business Research. In terms of citations, the top journals are: Journal of Hospitality Marketing and Management (164 citations), Journal of Retailing and Consumer Services (132 citations), Journal of Business Research (50 citations), International Journal of Information Management Data Insights (48 citations), Technology in Society (42 citations), Journal of Research in Interactive Marketing (41 citations), International Journal of Contemporary Hospitality Management (36 citations), Psychology and Marketing (36 citations), Sustainability Switzerland (33 citations), and Service Industries Journal (32 citations).

Table 4 Top 20 Journals with the Number of Publications and Respective Citations
Rank	Sources	Total Publications	Total Citations	Papers Published
1	JOURNAL OF RETAILING AND CONSUMER SERVICES	12	132	Markovitch et al. (2024a), Park & Ahn (2024b)
				Pathak et al. (2025b), G. Lee & Kim (2024),
				Vhatkar et al. (2024), Yi et al. (2025)
				Brüns&Meißner (2024), Kirshner (2024)
				J. Kim et (2023b)
2	APPLIED MARKETING ANALYTICS	8	5	Sterne & Davenport (2024), Crisp et al. (2024),
				Cutler (2024), Thomas (2024), Thukral et al. (2023)
3	INTERNATIONAL JOURNAL OF CONTEMPORARY HOSPITALITY MANAGEMENT	4	36	Ramos-Henriquez & Morini-Marrero (2024),
				Mogaji et al. (2024), Han et al. (2024)
4	INTERNATIONAL JOURNAL OF HOSPITALITY MANAGEMENT	4	3	Morosan (2025a), Sun et al. (2025), Bilgihan et al. (2024)
				(Jasper) Jia et al. (2025)
5	PSYCHOLOGY AND MARKETING	4	36	Ozuem et al. (2024), Sarstedt et al. (2024), G. Lee & Kim (2024)
6	SUSTAINABILITY (SWITZERLAND)	4	33	Madanchian (2024a), Silva et al. (2024a), Jeong & Lee (2024)
7	INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT DATA INSIGHTS	3	48	M. Gupta et al. (2024), R. Gupta et al. (2024b), Joshi, S., et al. (2025)
8	JOURNAL OF BUSINESS RESEARCH	3	50	I. Kim et al. (2024a), Kirk & Givi (2025a), Hermann &Puntoni (2024)
9	INTERNATIONAL JOURNAL OF ADVERTISING	2	4	Baek, T. H. Kim et al. (2024), Sands et al.  (2025)
10	JOURNAL OF CONSUMER BEHAVIOUR	2	9	Behrens et al. (2024), Cillo & Rubera (2024)
11	JOURNAL OF HOSPITALITY MARKETING AND MANAGEMENT	2	164	Gursoy, Dogan et al. (2023), Saleh, Mahmoud Ibraheam (2025)
12	JOURNAL OF RESEARCH IN INTERACTIVE MARKETING	2	41	Graham, Christian, and Stough, Rusty(2025),
13	JOURNAL OF THE ACADEMY OF MARKETING SCIENCE	2	3	Kunz, Werner H. and Wirtz, Jochen (2024)
14	SERVICE INDUSTRIES JOURNAL	2	32	Belanche, Daniel, et al. (2024)
15	TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE	2	11	Hoffmann, Stefan et al. (2024), Abadie, Amelie et al. 2024)
16	TECHNOLOGY IN SOCIETY	2	42	Koc, Erdogan et al. (2023)
17	ACTA PSYCHOLOGICA	1	5	Hussain, Talib et al. (2024)
18	ASIA PACIFIC JOURNAL OF INFORMATION SYSTEMS	1	0	Lee, Kyoung Jun et al. (2023)
19	ASIA PACIFIC JOURNAL OF MARKETING AND LOGISTICS	1	0	Alizadeh, Hamid and Nazarpour Kashani, Hamed (2024)
20	AUSTRALASIAN MARKETING JOURNAL	1	0	Ferraro, Carla et al. (2024)

Table 5 shows the ten most influential publications based on total citations and their key findings. For example, the article by Gursoy and Song (2023), which has the highest citations (164), demonstrates that information produced by Gen AI and augmented AI is viewed as higher quality than that generated by human or augmented human experts. Another article, with 62 citations, suggests that Gen AI has a significant impact on various management tasks, including financial management, marketing and sales management, operations management, supply chain management, and human resource management (Kar et al., 2023). According to another study, Gen AI enables businesses to develop customized products and services with a high level of individual scalability and performance, a concept known as hyper-personalization, which is a revolutionary approach in the tourism industry (Oviedo-Trespalacios et al., 2023). Additionally, another article shows that Gen AI or ChatGPT can improve customer experience, service quality, and productivity. However, there are risks associated with its use, including ethical, fairness, and privacy concerns (Kim et al., 2023).

Table 5 Top 10 Most Cited Papers and Important Findings
Sr No	Article	Total Citations	Findings
1	ChatGPT and the hospitality and tourism industry: an overview of current trends and future research directions Gursoy, D., Li, Y., & Song, H. (2023).	164	Content generated by Gen AI and augmented AI is perceived as higher quality than that produced by human and augmented human experts. Revealing the source of content production reduces, but does not reverse, the perceived quality gap. This bias in evaluation is due to human favoritism and not AI aversion.
2	Unravelling the Impact of Generative Artificial Intelligence (GAI) in Industrial Applications: A Review of Scientific and Grey Literature Kar et al. (2023)	62	Gen AI is a linguistic model that generates anthropomorphic responses to natural language queries using machine learning and deep learning algorithms. It produces written materials, images, videos, audio, music, and computer code, collectively called a conversational agent. It can significantly impact various management functions, including financial management, marketing and sales management, operations management, supply chain management, and human resource management. It can also significantly impact several industries, including e-commerce, education, startups, and Healthcare.
3	The risks of using ChatGPT to obtain common safety-related information and advice Oviedo-Trespalacios et al. (2023)	60	It can be an effective marketing tool. Gen AI technology dramatically enhances consumer segmentation and personalization of products and services, enabling tourism cities and organizations to create tailored content in real time. It can assist companies in designing personalized products and services with a high level of individual attention. This Scalability and performance is called hyper-personalization, and it has never been seen before in the tourism industry.
4	Decisions with ChatGPT: Reexamining choice overload in ChatGPT recommendations J. Kim et al. (2023a)	55	Some consumers have algorithm aversion, while some have algorithm appreciation. Gen AI can help companies ensure cost-effective service excellence. Gen AI / ChatGPT can help improve customer experience, service quality, and productivity. However, risks (ethical, fairness, privacy) are associated with its use.
5	Adoption and impacts of generative artificial intelligence: Theoretical underpinnings and research agenda R. Gupta et al. (2024b)	46	ChatGPT's management response was extremely satisfactory in all six dimensions: redress, timeliness, facilitation, apology, attentiveness, and credibility. It did well on threejustice dimensions: distributive, procedural, and interactional.
6	Corporate digital responsibility (CDR) in the age of AI: implications for interactive marketing Kunz & Wirtz (2024)	41	Despite the risks, many companies do not prioritize good CDR practices. Financial benefits from collecting and using consumer data, improved customer experience through AI-driven customization and personalization, cost reduction through service automation, and the tradeoffs between organizational goals and CDR practices can prevent companies from prioritizing good CDR practices.
7	Houston, we have a problem! The use of ChatGPT in responding to customer complaints Koc et al. (2023a)	41	AI-generated marketing imagery can surpass human-made images in quality, realism, and aesthetics. Gen AI can help advertisers produce marketing content faster, cheaper and at superhuman effectiveness level with important implications for firms, consumers and policy makers.
8	ChatGPT and generative artificial intelligence: an exploratory study of key benefits and challenges in operations and supply chain management	29	ChatGPT and generative artificial intelligence (Gen AI) are transforming firms and supply chains. The major challenges are related to data, technological and organizational issues. Major benefits are cost savings and enhanced customer experience. Other benefits include automation and sustainability.
9	Is it the end of the technology acceptance model in the era of generative artificial intelligence? Mogaji et al. (2024)	28	Despite TAM's ability to explain the factors influencing adoption of technology in hospitality and tourism sector, it has limitations due to its individual centric perspective, limited scope, statice nature, cultural applicability, and reliance on self-reported measures.
10	Virtual influencer marketing: Evaluating the influence of virtual influencers’ form realism and behavioral realism on consumer ambivalence and marketing performance	27	The VI exhibiting mid (vs. low) form realism triggered heightened perceptions of both eeriness and coolness, with these effects being magnified under the condition of high (vs. low) behavioral realism. Those with high (vs. low) forms were associated with decreased perceptions of eeriness and increased coolness, particularly when combined with high (vs. low) behavioral realism. There is a negative impact of eeriness and the positive influence of coolness on VI performance. VIs’ interface designs are significant factor in VI marketing performance. Also, consumers’ ambivalent emotions play as vital mediators in this relationship.

Table 6 presents the leading authors and countries. The top three authors, each with three articles, were Dwivedi YK, Kumar A, and Wirtz J. Kar AK was the author with the highest number of citations, totaling 72. The following three significant authors with over 20 citations were Wirtz J (46), Dwivedi YK (28), and Kumar A (24). The United States leads in publications related to this topic, with 24, followed by China (12), Korea (10), the United Kingdom (10), and India (9). Countries with the fewest publications in this field include France (3 publications), the UAE (4 publications), Spain (4 publications), and Malaysia (4 publications). Korea recorded the highest number of citations, at 211. Other notable countries with more than 100 citations included the United States (145) and India (108). The country with the fewest citations was Malaysia (10).

Table 6 Leading Authors and Countries by Number of Publications and Corresponding Citations
Authors	Publications	Citations	Countries	Publications	Citations
DWIVEDI YK	3	28	USA	24	145
KUMAR A	3	24	CHINA	12	29
WIRTZ J	3	46	KOREA	10	211
BAEK TH	2	16	UK	10	43
CHAKRABORTY D	2	0	INDIA	9	108
DAVENPORT TH	2	0	GERMANY	7	52
DEMSAR V	2	3	MALAYSIA	4	10
FERRARO C	2	3	SPAIN	4	25
KAR AK	2	72	UAE	4	63
KIM H-Y	2	17	FRANCE	3	29

The TCCM Framework

Theoretical Perspectives (T)

The theories discussed in this section are limited to those examined in the reviewed papers. The discussion surrounding these theories draws on the theoretical foundations used in papers that assess the impact of Gen AI on customer experiences and behavioral responses.

This topic has been explored using various theoretical frameworks from multiple disciplines. Most papers (approximately 73%) did not use any theoretical framework, while only a few (around 27%) employed one. Additionally, it was found that most theories were rarely referenced; only five theories appeared in more than two articles, while most (about ten) were cited in just one or two articles. The most popular theories included the Technology Acceptance Model and the Extended Technology Acceptance Model. Other frequently referenced theories consisted of the Theory of Planned Behavior, the Unified Theory of Acceptance and Use of Technology, the Diffusion of Innovations, Uses and Gratifications, the Stimulus-Organism-Response Theory, and the Mind Perception Theory (MPT). The remaining theories were mentioned in only one study. Since the topic is still in its early stages and continues to evolve, scholars are actively researching the relevance and applicability of theories from various disciplines to understand how and why Gen AI influences consumer experiences and responses, and how marketers can leverage Gen AI to impact these elements in the future. The sixteen most important theories used (as outlined in Table 7), which form the foundation for the papers examined in this research, are discussed and evaluated here.

Table 7 Theories Used in the Articles
No	Theory	No of Articles	Author (s)
1	Technology Acceptance Model (TAM / Extended TAM)	7	Mogaji et al. (2024), Ngo (2024), B. Yue & Li (2023), Alsharhan et al. (2024), Christensen et al. (2024), A. S. Gupta & Mukherjee (2024), Argon et al. (2024)
2	Theory of Planned Behavior (TPB)	5	Shi et al. (2024), Madanchian (2024), R. Gupta et al. (2024), Alsharhan et al. (2024), Christensen et al. (2024).
3	Unified Theory of Acceptance and Use of Technology (UTAUT & UTAUT 2)	5	Abadie et al. (2024), Indrawati et al. (2024), Foroughi et al. (2025), R. Gupta et al. (2024), A. S. Gupta & Mukherjee (2024)
4	Diffusion of Innovation Theory (DOI)	3	Fosso Wamba et al. (2024), A. S. Gupta & Mukherjee (2024), Alsharhan et al. (2024)
5	Usage and Gratifications Theory (U&G)	3	Hussain et al. (2024), A. S. Gupta & Mukherjee (2024), Alsharhan et al. (2024)
6	Stimulus-Organism- Response Theory (SOR)	2	Gu et al. (2024), Alsharhan et al. (2024),
7	Mind Perception Theory (MPT)	2	Tan et al. (2025), G. Lee & Kim (2024)
8	Behavioral Reasoning Theory (BRT)	2	Joshi et al. (2025), Alsharhan et al. (2024b)
9	The theory of Avatar marketing and ambivalence Theory	1	I. Kim et al. (2024)
10	Theory of Choice Overload	1	J. Kim et al. (2023)
11	Frustration-AggressionTheory	1	Ozuem et al. (2024)
12	Construal Level Theory (CLT)	1	Kirshner (2024)
13	Elaboration Likelihood Model (ELM)	1	(Jasper) Jia et al. (2025)
14	Authenticity Theory	1	Kirk & Givi (2025)
15	Self-VerificationTheory	1	Park & Ahn (2024)
16	Service recovery dimension and justice theory	1	Koc et al. (2023)

Davis developed the Technology Acceptance Model in 1989 to explain the adoption of new technologies. It states that the two most critical factors influencing the adoption of new technology are 'perceived usefulness' and 'perceived ease of use.' According to Alsharhan et al. (2024), the Theory of Adoption Model is the most popular theory for explaining consumer adoption of ChatGPT. M. Christensen et al. (2024) found that prior AI experience increases familiarity with AI, which influences perceived ease of use and attitudes toward General AI (Gen AI). It was also revealed that, despite the AI hallucination problem, customers continue to use Gen AI information to make tourism decisions, rather than relying on sources such as government websites, TripAdvisor, and social media. The theory's disadvantages include an individual-centric approach, limited reach, static nature, cultural applicability, and reliance on self-reported data. However, adding contextual factors, such as industry and culture, can enhance the theory's effectiveness (Mogaji et al., 2024). The Theory of Planned Behavior, introduced by Aizen (1985), helps us understand human behavior. It predicts behavioral intention based on attitude, subjective norm, and perceived behavioral control. Their attitude toward it can predict consumers' intention to use ChatGPT, the societal standard set by the appropriate subjective norm, and their perception of ease of use (Gupta et al., 2024).

According to the Unified Theory of Acceptance and Use of Technology model, four key factors determine the likelihood of technology adoption: Performance Expectancy, Effort Expectancy, Social Impact, and Facilitation Conditions (Venkatesh et al., 2023). This model, combined with the consumer value typology, was used to develop a theoretical framework that explains how the efficiency, excellence, meaningfulness of recommendations, and conversational ability of ChatGPT influence the behavioral intention to use it during the pre-acceptance stage (Abadie et al., 2024). Venkatesh, Thonga, and Xu (2012) extended the Unified Theory of Acceptance and Use of Technology, known as Unified Theory of Acceptance and Use of Technology-2, by incorporating three additional constructs, namely “hedonic motivation, price value, and habit,” to enhance the explanatory power of the theory regarding consumer behavior towards technology use. Foroughi et al. (2025) further expanded the Unified Theory of Acceptance and Use of Technology-2 by examining the direct and moderating effects of trust and technology anxiety. Their study found that trust and technology anxiety negatively moderate the relationship between the intention to use ChatGPT and its predictors. The diffusion of innovation theories explains why and how innovations spread (Gupta & Mukherjee, 2025). Several factors influence the diffusion of any given innovation, including technological characteristics (compatibility, complexity, relative advantage, trialability, and observability), organizational characteristics (organizational readiness, organizational culture, and top management support), and environmental characteristics (competitive pressure, standards, government support, and regulations) (Wamba et al., 2024). Usage and Gratification Theory illustrates how individuals use media to satisfy specific needs or gratifications. Using the U&G framework, it was found that material centered on ChatGPT (innovative content) attracted more customer engagement than other types of content produced by the same providers the same providers.

The Stimulus-Organism-Response theory suggests that external stimuli can influence people’s cognitive and emotional states, which in turn affect their behavioral responses. Mind Perception Theory (MPT), developed by Kurt Gray and Daniel M. Wegner in 2007, refers to the ability to infer mental states in oneself or others, helping to predict and explain behavior. People attribute minds to even non-human entities, like computers. They perceive minds along two dimensions: Experience and Agency. Experience relates to the ability to feel and sense emotions, while Agency is the capacity to act intentionally and plan. Consumers tend to see human-designed fashion products as superior to those created by AI, believing that humans have better design expertise (Lee & Kim, 2024). Studies show that human-designed products are generally judged more favorably than AI-designed ones, and human managerial responses to online service recovery are viewed as more effective (Tan et al., 2025). Using Mednick's Theory of Creativity (developed in 1962), one study found that Gen AI can contribute to the creative process of luxury products. Westaby's (2005) behavioral reasoning theory extends the theory of planned behavior and reasoned action to explore why people support or oppose certain actions.

To enhance the customer experience, Crisp et al. (2024) proposed a conceptual model for integrating Gen AI into digital marketing and examined the motivations and barriers to its adoption.

Miao et al. (2022) proposed avatar marketing, defining avatars as “digital entities with an anthropomorphic appearance, controlled by a human or software, that can interact.” The theory introduced ‘form realism’ and ‘behavioral realism’ to define and develop avatars. Form realism refers to the degree to which an avatar resembles a human, while behavioral realism describes how well an avatar mimics human behavior in real-life situations. According to Kim et al. (2024), ambivalence explains mixed emotions—both positive and negative—toward the same person, object, or stimulus. The realism of a virtual influencer's form influences consumers' perceptions of eeriness and coolness. The form realism of virtual influences contributes to their perceived coolness. Additionally, the interactions between form and behavioral realism impact consumers' perceptions of eeriness and coolness. The mixed effects of eeriness and coolness affect marketing performance, including follow and purchase intentions. The Theory of Choice Overload describes a situation where consumers face multiple similar options, leading to complexity and making difficult decisions. J. Kim et al. (2023) found that consumers respond positively to a relatively large number of options—more than 60—generated by ChatGPT.

The Frustration–Aggression theory suggests that aggression results from goal frustration. It is seen as the behavioral response that occurs when someone’s frustration with events and situations increases (Azemi et al., 2020). Ozuem, W. et al. (2024) found that consumers’ emotions, especially frustration and aggression, affect customer loyalty and technology use. The construal level theory (Trope & Liberman, 2010) explains “construal,” or personal interpretations of the world. People develop construals that range from low-level, concrete views to high-level, abstract ideas. It was found that ChatGPT shows abstraction bias, producing responses that match a high-level construal. This basis causes ChatGPT to focus more on high-level construal features (such as desirability) rather than low-level ones (such as feasibility) (Kirshner, 2024). ELM describes how customers process information via central and peripheral routes during decision-making. The central route involves careful thinking and reliance on the message content, while the peripheral route uses superficial cues and requires less mental effort (Jia et al., 2025). Human-written hotel review summaries (compared to AI-generated ones) lead to greater trust, which increases booking intentions. Also, information processing effort could serve as a mediator. When reviews are negative, consumers put more mental effort into reading AI-generated review summaries than those created by humans.

Applying Authenticity Theory, Kirk & Givi (2025) found that when consumers believe emotional marketing communications are written by an AI (as opposed to a human), positive word of mouth and customer loyalty decrease. The AI-authorship effect is mediated by perceived authenticity and moral disgust. According to Park & Ahn (2024), self-verification theory posits that individuals have a fundamental drive to reinforce their self-view, whether positive or negative. Koc et al. (2023) utilized six service recovery dimensions (timeliness, facilitation, redress, apology, credibility, and attentiveness) and three justice dimensions (distributional, procedural, and interactional justice). Their study was grounded in the Service Recovery Dimension and Justice Theory, which is based on principles of justice. Distributional justice emphasizes customer compensation to ensure fairness after a complaint, linking it to the “redress” dimension. Procedural justice focuses on the perceived fairness of policies, procedures, and criteria used by decision-makers when resolving issues, aligning with the “timeliness and facilitation” dimensions of the service recovery model. Interactional justice refers to the customer's perception of fairness in interpersonal treatment when responding to a service failure, aligning with the “apology, credibility, and attentiveness” dimensions of the service recovery model. The study found that ChatGPT can evaluate the severity of service failure based on customer complaints and generate high-quality responses that meet management's needs for efficiency and effectiveness.

Context (C)

The literature on the impact of Gen AI on consumer behavior has explored various countries and industries to improve understanding of the research context. Table 8 shows the classification of articles by country, while Table 9 displays the categorization of articles by industry. Some studies have examined multiple industries, including those by Kar et al. (2023), Markovitch et al. (2024a), Cillo & Rubera (2024), and Pathak et al. (2025). However, only one study has been conducted in a multi-country context, which includes Ahmad et al. (2023).

Table 8 Countries Studied in Research
Countries Investigated	No of Studies	Sample Studies
USA	15	J. Kim et al. (2023a), Brüns&Meißner (2024), Gude (2023) Kovács (2024), Hussain et al. (2024b), Fosso Wamba et al. (2024b) Kirk & Givi (2025a), Jeong & Lee (2024), Haddud (2024) Baek et al. (2024), Morosan (2025a), Park & Ahn (2024a) Ozuem et al. (2024), Ramos-Henriquez & Morini-Marrero (2024)
UK	8	Kirk & Givi (2025a), Fosso Wamba et al. (2024b), Ozuem et al. (2024) Kovács (2024), Hussain et al. (2024b), Hoffmann et al. (2024) Abadie et al. (2024b), Han et al. (2024)
China	5	Shi et al. (2024),Gu et al. (2024b), Ng et al. (2024), Ngo (2024b)
Australia	3	Kovács (2024), Hussain et al. (2024c), Hoffmann et al. (2024)
Korea	3	Han et al. (2024), W.-J. Lee et al. (2023)
India	2	Vhatkar et al. (2024), A. Kumar et al. (2024)
Italy	2	Ozuem et al. (2024), Pasca & Arcese (2024)
Thailand	2	Ahmad et al. (2023),Limna &Kraiwanit (2023a)
Turkey	2	Koc et al. (2023a), Argan et al. (2024)
Brazil	1	Silva et al. (2024)
Botswana	1	Indrawati et al. (2024)
Canada	1	Kovács (2024)
France	1	Ozuem et al. (2024)
Germany	1	Hoffmann et al. (2024)
Iran	1	Alizadeh &Nazarpour Kashani (2024)
Ireland	1	Kovács (2024)
Jordan	1	Al Matalka et al. (2024)
North America and Asia Pacific	1	Christensen et al. (2024b)
Portugal	1	Ramos-Henriquez & Morini-Marrero (2024)
UAE	1	Goher, G. N. (2024)
Multi Country (Egypt, Iraq, Lebanon, Palestine, Philippines, Saudi Arabia, Sudan, Kuwait, India, Jordan, Thailand)	1	Ahmad et al. (2023)

Countries

The current section explores the country of origin of the articles reviewed in the literature. Table 8 shows that the United States produced up to 15 studies. These studies focus on Gen AI-generated content for social media (Brüns & Meißner, 2024), factors affecting ChatGPT adoption for product research and information retrieval (Gude, 2023), a comparison of human versus Gen AI-written online reviews (Kovács, 2024), and the Gen AI authorship effect (Kirk & Givi, 2025). The United Kingdom ranks as the second-largest contributor with eight studies. Research from the UK examined the Gen AI authorship effect (Kirk & Givi, 2025), the main benefits and challenges of Gen AI in operations and supply chain management (Fosso Wamba et al., 2024), how Gen AI service failure recovery influences customer loyalty (Ozuem et al., 2024), and a comparison of human versus ChatGPT-authored online reviews (Kovács, 2024). China contributed five studies focusing on the impact of Gen AI on the hospitality and tourism sectors (Shi et al., 2024), the acceptance of Gen AI-generated advertising (Gu et al., 2024), and how it enhances marketing capabilities to better understand customers' attitudes, desires, and needs (Ngo, 2024).

Australia contributed three papers on scale construction using ChatGPT (Hoffmann et al., 2024) and on comparing human and Gen AI-generated online reviews (Kovács, 2024). Korea also contributed three studies. India contributed two studies: one on developing an instrument to examine attitudes, advantages, and threats associated with using Gen AI as a tool in the education sector (Ahmad et al., 2023) and another on the usage of ChatGPT in the retail industry (Kumar et al., 2024). Italy contributed two studies on the prospects and limitations of ChatGPT in Italy, as well as the impact of Gen AI on service failure recovery and customer loyalty (Ozuem et al., 2024). Thailand and Turkey each contributed two studies. The remaining countries, including Brazil, Botswana, Canada, France, Germany, Iran, Ireland, Jordan, Portugal, and the UAE, each contributed one study. Ahmad et al. (2023) conducted a study that covered multiple countries.

The implication for academics is that more research is needed in nations that have not been examined previously or where the number of studies conducted is minimal. The majority of the studies focused on only 18 countries. The analysis indicates a need for further research in many unexplored countries, such as those in Africa (Kenya, Ethiopia, Uganda, Zimbabwe, Ghana, etc.), Asia (Bangladesh, Sri Lanka, Myanmar, Vietnam, Cambodia, etc.), Europe (Albania, North Macedonia, Serbia, etc.), Latin America (Mexico and Peru), and the Middle East (Oman and Bahrain) to enhance generalizability and understand the cultural differences regarding the impact of Gen AI on consumer behavior.

Industries

Numerous studies have evaluated the impact of Gen AI on consumer behavior across various industries (Table 9), with up to 22 studies specifically conducted in the hospitality and tourism sector. Several research efforts (Dogru et al., 2023; Dwivedi et al., 2024) have explored how hospitality companies might utilize Gen AI for customer relationship management, human resource management, marketing, strategic management, and other functions. ChatGPT shows significant progress in analyzing hotel reviews, providing deeper insights into consumer experiences and attitudes (Jeong et al., 2024). Tan et al. (2025) found that customers were unable to distinguish between Management Responses and ChatGPT-generated responses. However, when consumers were informed about the source of the Management Response, ChatGPT's Management Response had a lower cognitive and affective impact on consumers. Saleh et al. (2025) noted that while Gen AI can enhance customer experience and operational efficiency, its success depends on selecting appropriate prompting strategies, addressing ethical considerations, developing the workforce, and assessing community impacts.

Table 9 Industries Examined in the Research
Industries Examined	No of Articles	Sample Studies
Hospitality and Tourism	22	Sun et al. (2025), Jeong & Lee (2024), Tan et al. (2025c), Gursoy et al. (2023), Gursoy et al. (2023), Sigala et al. (2024), Shi et al. (2024a), Han et al. (2024), Florido-Benítez (2024), M. Gupta et al. (2024), Koc et al. (2023a),Morosan (2025c), Bilgihan et al. (2024), Kovács (2024), Christensen et al. (2024b), Ng et al. (2024), Mogaji et al. (2024), Hossain (2024a), (Jasper) Jia et al. (2025), Luo et al. (2025), Saleh (2025)
Digital Marketing	14	Ramos-Henriquez & Morini-Marrero (2024), Brüns&Meißner (2024), Ngo (2024b), Hussain et al. (2024b), Pranatawijaya et al. (2024), Alghamdi & Abdelwahed (2024), Hartmann et al. (2024a), Baek (2023), Cutler (2024), Hoffman & Novak (2024), de Oliveira et al. (2024),
Education	8	Hermann &Puntoni (2024), Silva et al. (2024a), Ahmad et al. (2023), Al Matalka et al. (2024), Rienties et al. (2024), Bouchard, J. (2024),
Advertising	8	Thomas (2024), Yin et al. (2024), Baek et al. (2024), Gu et al. (2024), Wen & Laporte (2025)
E Commerce	6	Sadiq et al. (2024), Y. Yue et al. (2024),
Retail	5	A. S. Gupta & Mukherjee (2024b), Vhatkar et al. (2024), Yi et al. (2025),
Fashion & Luxury	4	G. Lee & Kim (2024a), Park & Ahn (2024a), Gude (2023), G. Lee & Kim (2024c)
Healthcare	4	Hassan et al. (2024), Leslie-Miller et al. (2024), Argan et al. (2024)
Marketing Research	3	Gude (2023), Mogaji& Jain (2024), Schmitt (2024),
Financial Services Industry / Fin Tech	2	Bartáková et al. (2025), Andronie et al. (2024), Singh (2025)
Multi Industry	10	Kar et al. (2023), Markovitch et al. (2024a), Cillo & Rubera (2024), Pathak et al. (2025), Madanchian (2024a), Yi et al. (2025b), Pantano et al. (2024), Shankar (2024), Liu & Chen (2024), Haupt et al. (2024)

The second most researched industry is digital marketing, featuring 14 studies. Hussain et al. (2024), using the Uses and Gratification theoretical framework, found that creative content, such as ChatGPT-related videos, generates greater engagement than other forms of content on the same YouTube channel. The impact of Gen AI on the education industry has been thoroughly researched, with eight studies conducted in this area. One study examined the utility of Gen AI for distance learning students. The use of ChatGPT marks a new era in educational platforms (Rienties et al., 2024). The negative influence of Gen AI in education leads students to become consumers rather than providers of information (Bouchard, 2024).

Significant research has been conducted in the advertising sector. Gu et al. (2024) investigated how the qualities of AI-generated advertisements influence customers' willingness to embrace them. Yin et al. (2024) found that consumers' awareness of AI authorship, their levels of involvement in the co-creative process, and their perceived authenticity all significantly affect their attitudes toward commercials. However, limited research has been undertaken in the fashion and luxury industries. G. Lee and Kim (2024) employed the Mind Perception Theory to discover that human-designed objects are often rated higher than AI-designed products, as consumers believe that people possess superior design skills. Several additional studies have been conducted in the healthcare sector. Hassan et al. (2024) highlighted the ethical issues facing the public healthcare industry, such as a lack of transparency and accountability, and they recommended the development of an ethical framework for the healthcare sector.

There have been few studies in the marketing research field. Gude (2023) discovered that gender, product expertise, and a preference for online or in-store shopping can all influence people's inclination to use ChatGPT. Only two studies focused on the financial services and FinTech industry. According to Qin Yang and Young-Chan Lee (2024), personalized investment suggestions, sympathetic communication, and continual improvement of Gen AI's proposals all have a significant impact on consumer perceptions of authenticity. They emphasized the importance of incorporating Gen AI financial advice into the regular operations of financial institutions. Some research has also been conducted across multiple industries. According to Kar et al. (2023), Gen AI is being utilized to generate quick reports, provide adaptive responses, and offer insights that enable enterprises across various industries to make better and faster decisions, thereby enhancing overall flexibility, customer experience, and ultimately, profitability.

Constructs (C)

This subsection examines the constructs and variables used in the various articles reviewed in this study. The variables are categorized into four types: independent, mediating, moderating, and dependent. Table 10 presents all 29 papers that explored the effects of different independent, mediating, and moderating variables on customer experiences and behavioral responses. The description in Table 10 will help advance and develop the theory (Chen et al., 2021; Paul & Rosado-Serrano, 2019).

Table 10 Variables Examined in Research
Independent Variables	Mediating Variables		Moderating Variables		Dependent Variables	Title
Efficiency, Excellence, Esteem, Ethics, Spirituality	Intention to use ChatGPT				ChatGPT-Human co-creation	Abadie et al. (2024)
Humanlike characteristics (Perceived humanness, Perceived Naturalness, Perceived Responsiveness), Output performance (Perceived Ethics, Trust, Intelligence, Sincerity);  Perceived Serendipity	AI self-efficacy, Service Satisfaction				Intention to use	W.-J. Lee et al. (2023)
Design Entity (AI vs Human)	Mind Perception (Perceived experience,Perceived agency), Perceived DesignExpertise			Product Type, Perceived AI Threats,Levels of human elements	Consumers' Responses (Attitude,Purchase Intention)	G. Lee & Kim (2024a)
Anthropomorphism, Trust, Interactivity,Enjoyment, Privacy, Communication style, Risk, Social Presence, Design/ User Interface, Innovativeness					Usage Intention, Satisfaction, Continuance/Reuse Intention, Attitude, Purchase Intention, Engagement, Trust, Brand Likeability, Performance, Patronage Intention.	Alsharhan et al. (2024)
Service Recovery Entity (human vs Gen AI)	Perceived Authenticity, Uncanniness				Cognitive, Affective, Conative Outcomes among Potential Customers	Tan et al. (2025c)
Consumers' Awareness of AI authorship, Level of Co-Creation Process, Perceived Authenticity					Attitude towards Gen AI Advertising	Yin et al. (2024)
Service Giver (Chatbot vs Human)	Perceived Empathy (Based on SERVQUAL)				Satisfaction, Patronage Intentions, Recommendation Acceptance, and Recommending	Markovitch et al. (2024a)
Functional Value, Social Value, Emotional Value, Epistemic Value;	Attitude Towards LAAZZIECHAT (AI Chatbot)			Online ShoppingSelf-Efficacy	Continuance Intention to Adopt LAZZIECHAT (AI Chatbot)	Y. Yue et al. (2024)
Risks, Motivations, Innovation Factors					Approach Behavior forChatGPT	Han et al. (2024)
Customer experience with  PA (Perceived Accuracy), PC (Perceived convenience), PR (Perceived Relevance), PP (Perceived Personalization)					SDM- Satisfaction in Digital Marketing)	Alghamdi & Abdelwahed (2024)
Gen AI Adoption	Brand Authenticity				Followers'/ Consumers' Reaction (Attitude, Intention, Loyalty)	Brüns&Meißner (2024)
AI Disclosure	Perceived Advertising Credibility			AI Perception	Attitude and Donation Intention	Baek et al. (2024)
Perceived Risk (Privacy Risk, Accuracy Risk, Overreliance Risk)	Attitude, Subjective Norm, Perceived Behavioral Control				ChatGPT behavioral intention	Shi et al. (2024a)
Characteristics of AI-generated Ads- 1. Verisimilitude (Degree of authenticity), 2. Vitality 3. Imagination. 4. synthesis;	Consumer Eeriness, Consumer Intelligence				Consumers' Willingness to Accept AI generated Ads	Gu et al. (2024b)
Age, Gender, Product Knowledge, Product research	Trust				ChatGPT usage (Consumers' inclination to use Gen AI platforms for discovering Products and getting information)	Gude (2023)
Warmth,Competence	Technology Acceptance, Satisfaction, Customer-Brand Identification			TRI- Technology Readiness Index	Customer Brand Attachment	Ng et al. (2024)
Management Response Dimensions(Redress, Timeliness, Facilitation, Apology, Attentiveness, Credibility)	Perceived Justice Dimensions (Distributive, Procedural, Interactional)				Customer Satisfaction	Koc et al. (2023a)
System Quality, Information Quality, Service Quality, Self-Efficacy, Personal Innovativeness				Anthropomorphic vs Non-Anthropomorphic Chatbot	Cognitive Absorption,	Sarraf et al. (2024a)
Emotional (Excitement), Cognitive (Advocacy, Concern)				Competitive Presser	Perceived Value of HME,Affinity for HME, Perceived Risk of Gen AI, Willingness to Implement Gen AI in Services	Azer & Alexander (2024)
Attitude, perceived AI Service Quality, Perceived Humanness, Perceived Competence, Perceived Innovativeness.	Chatbot Efficiency. Awe Sensation				Chatbot Use Intention	Pathak et al. (2025)
TRUST	Perceived Threat, Perceived Coping Efficacy, Fear, Protective Action, Seeking Help, Avoidance				Disclosure of Information to Gen AI	Morosan (2025a)
Brand Identity, Brand Image, Brand Integrity, Customer Interaction, Sentiment	Customer Satisfaction				Purchase Intention	Hossain (2024a)
AI Authorship	Moral Disgust.	Factual vs Emotional Content, Edit vs write Marketing Communications, Reused Emotional Content			Positive WOM, Customer Loyalty	Kirk & Givi (2025a)
Human-AI collaboration, Outcome expectations	attribution of responsibility	Algorithmic transparency			Evaluations and usage intention	B. Yue & Li (2023b)
Traditional Brand Personality (Excitement, Sophistication, Sincerity, Professionalism, Attractiveness, Materialism) vs AI Generated Brand Personality (Elegant, Luxurious, Feminine, Innovativeness, Artistic)	Self-Brand Connection				Purchase Intention	Park & Ahn (2024a)
AI Technology Prior Use	Prior Gen AI Usage, AI Familiarity, Behavioral Intention, Perceived Ease of Use, Perceived Usefulness, Attitude, Behavioral Control)			Increased Perceived AI Hallucination Potential	Intention to use AI for Tourism Planning	Christensen et al. (2024b)
Source of ReviewSummary (Human vs AI-generated)	TRUST, Information Processing Effort				Booking Intention	(Jasper) Jia et al. (2025)
Form Realism	Eeriness,Coolness			Behavioral Realism	Purchase Intention, Follow Intention	I. Kim et al. (2024a)
Attachment Towards Virtual World	Need for Uniqueness;				Self-Identity with Brand Using Gen AI	Farah et al. (2024)

Independent Variables

Table 10 lists various independent variables that influence consumer experiences and behaviors. These factors fall into five categories: Gen AI quality and performance; Gen AI's anthropomorphism or human-like traits; interaction, which includes Gen AI's co-creation with consumers and the brand; perceived hazards; and ethical issues related to Gen AI (Table 11). The perception of system quality, information quality, and service quality shapes the customer's cognitive absorption experience (Sarraf, 2024). ChatGPT's efficiency, excellence, and conversational abilities influence users' behavioral intentions to use it (Abadie et al., 2024). ChatGPT's human-like features, particularly its perceived naturalness, have a significant impact on consumer willingness to use it. However, perceived humanness and responsiveness do not significantly affect consumers' intentions to use it. The impression of warmth positively correlates with the use of Gen AI and customer satisfaction (Ng et al., 2024). Human-generated review summaries boost consumer trust and booking intentions (Jia et al., 2025). Tourists' perceived hazards, including privacy risk, accuracy risk, and overreliance risk, notably affect their attitudes and intentions to use ChatGPT (Shi et al., 2024). Ethics also plays an important role in the decision to use ChatGPT (Del Río-Lanza et al., 2009; Abadie et al., 2024).

Table 11 Independent Variables Investigated in the Study
No	Independent Variables	Titles
1	Quality and Performance /Gen AI Attributes System Quality Information Quality Service Quality Efficiency Excellence Competence Conversational ability of ChatGPT	Abadie et al. (2024) Sarraf et al. (2024)
2	Anthropomorphism/ Humanlike characteristics Perceived humanness Perceived Naturalness Perceived Responsiveness Perceived Intelligence Sincerely Warmth Trust	W.-J. Lee et al. (2023) Alsharhan et al. (2024) Ng et al. (2024),
3	Interactivity and Co-creation: Enjoyment Design/ User Interface Customer Interaction Human-AI collaboration Level of Co-creation Process, Customer experience with PA (Perceived Accuracy), PC (Perceived Convenience), PR (Perceived Relevance), PP (Perceived Personalization) Perceived authenticity Design Entity (AI vs Human) Source of Review Summary (Human vs AI Generated) Service Giver (Chatbot vs Human) AI Disclosure / AI Authorship Service Recovery Entity (Human vs Gen AI) Gen AI adoption for content creation AI Technology Prior Use	Hossain (2024) B. Yue & Li (2023) Alghamdi & Abdelwahed (2024) Yin et al. (2024) G. Lee & Kim (2024) (Jasper) Jia et al. (2025) Tan et al. (2025c) Markovitch et al. (2024) Brüns&Meißner (2024) Christensen et al. (2024) Baek et al. (2024) Kirk & Givi (2025) Alsharhan et al. (2024)
4	Perceived Risk Privacy Risk, Accuracy Risk, Overreliance Risk)	Shi et al. (2024)
5	Ethics, Spirituality, Privacy	Abadie et al. (2024) W.-J. Lee et al. (2023) Alsharhan et al. (2024)

Mediating Variables

Table 10 lists the independent variables. Only 23 publications (16% of the 139 total articles) examined the role of mediating variables. These variables fall into five main categories: consumer characteristics, interactional dynamics, consumer experience, consumer perception, and behavioral dynamics (Table 12). Consumer characteristics such as 'AI self-efficacy' were found to mediate the relationship between Gen AI anthropomorphism and the intention to use it (Lee et al., 2023), while 'Consumer Eeriness and intelligence' mediated the relationship between 'Characteristics of AI-generated Ads' and 'Consumers' willingness to Accept AI-generated Ads.' According to Gu et al. (2024), the relationship between 'Attachment towards Virtual World' and 'Self-Identity with Brand Using Gen AI' is mediated by the need for uniqueness (Farah et al., 2024). Interactional dynamics, such as the 'perceived justice dimension' (distributive, procedural, and interactional), affected the relationship between 'Management Response Dimensions' (redress, timeliness, facilitation, apology, attentiveness, and credibility) and 'customer satisfaction' (Koc et al., 2023). The consumer satisfaction experience was found to mediate the interaction between 'Brand Identity, Brand Image, Brand Integrity, Customer Interaction, Sentiment' and 'Purchase Intention' (Hossain, 2024). Consumers' experiences with 'moral disgust' mediated the link between 'AI Authorship' and 'Positive WOM & Customer Loyalty' (Kirk & Givi, 2025). Consumer impressions of 'experience, agency, and design expertise' were found to mediate the relationship between 'design Entity (AI versus Human)' and 'Consumers' Responses: Attitude, Purchase intention' (Lee & Kim, 2024). Consumer behavior, especially 'Attitude towards AI Chatbot,' was found to mediate the interaction between 'Functional Value, Social Value, Emotional Value, Epistemic Value of Gen AI' and 'Continuance Intention to Adopt AI Chatbot' (Yue et al., 2024).

Table 12 Mediating Variables Investigated in the Study
No	Mediating Variables	Title
1	Consumer Characteristics AI self-efficacy, Consumer Eeriness, Consumer Intelligence Need for Uniqueness Seeking Help, Avoidance	W.-J. Lee et al. (2023), Pathak et al. (2025), Morosan (2025a), Farah et al. (2024)
2	Interactional Dynamics Technology Acceptance, Customer-Brand Identification, Self-Brand Connection Perceived Justice Dimensions (Distributive, Procedural, Interactional)	Ng et al. (2024), Park & Ahn (2024a), Koc et al. (2023a)
3	Consumer Experience Customer Satisfaction Service Satisfaction TRUST Moral disgust Awe Sensation	Hossain (2024a), Ng et al. (2024), W.-J. Lee et al. (2023), (Jasper) Jia et al. (2025), Kirk & Givi (2025a), Pathak et al. (2025)
4	Consumer Perception Mind Perception (Perceived experience, Perceived agency), Perceived Design Expertise Perceived Empathy (Based on SERVQUAL) Perceived Authenticity, Uncanniness Perceived Advertising Credibility Perceived Behavioral Control Perceived Threat, Perceived Coping Efficacy, Fear Brand Authenticity	G. Lee & Kim (2024), Markovitch et al. (2024a), Tan et al. (2025), Baek et al. (2024), Koc et al. (2023a), Gude (2023), Brüns&Meißner (2024)
5	Behavioral Factors Intention to use ChatGPT Attitude Towards LAAZZIECHAT (AI Chatbot) Attitude	Abadie et al. (2024) , Y. Yue et al. (2024), Shi et al. (2024a), Shi et al. (2024a)

Moderating Variables

Table 10 displays the moderating variables found in the reviewed papers. Only ten articles (7% of those examined) explored the role of these variables. They are generally classified into four categories: consumer characteristics, product features, content features, and consumer perception (Table 13). Consumer characteristics, such as the Technological Readiness Index (TRI), positively affect the relationships between warmth and competence, as well as the acceptability of Gen AI services (Ng et al., 2024). Product features, like product type (functional versus self-expressive), have a somewhat positive influence on receptivity to AI-designed products. The content type influences the AI authorship effect (consumers prefer human-written over AI-authored communication). Consumers’ negative reactions to AI authorship are stronger when the content is emotional but lessen when it is factual (Kirk & Givi, 2025). Consumer perceptions, such as concerns about AI-generated hallucinations, moderate perceived ease of use, enhance behavioral control, and shape consumers' behavioral intentions toward using AI in travel planning (Christensen et al., 2024). The negative effect of AI disclosure decreases when consumers see AI as more human-like than a machine or robot, highlighting the moderating role of AI human-likeness perception (Baek et al., 2024).

Table 13 Moderating Variables Analyzed in the Study
No	Moderating Variables	Title
1	Consumer Characteristics Online Shopping Self-Efficacy TRI- Technology Readiness Index	Y. Yue et al. (2024) Ng et al. (2024)
2	Product Characteristics Product Type Levels of human elements	G. Lee & Kim (2024)
3	Content characteristics Factual vs Emotional Content  Edit vs write Marketing Communications Reused Emotional Content	Kirk & Givi (2025)
4	Consumer Perception AI Perception Increased Perceived AI Hallucination Potential Perceived AI Threats Anthropomorphic vs Non-Anthropomorphic Chatbot Competitive Presser Algorithmic transparency Behavioral Realism	Baek et al. (2024) Christensen et al. (2024b) G. Lee & Kim (2024a) Sarraf et al. (2024) Hossain (2024) B. Yue & Li (2023) I. Kim et al. (2024)

Dependent Variables

Table 10 lists all the dependent variables, along with the variables that influence them and the authors of the corresponding papers. Following a thorough review of the literature, it was determined that the dependent variables explored can be categorized into two groups: customer experiences and behavioral responses (Table 14). Customer experiences are further divided into two categories: cognitive-emotional experiences and interactional-emotional experiences. Behavioral responses are further divided into two categories: consumer attitude and behavioral intention, and adoption, loyalty, and advocacy.

Table 14 Dependent Variables Analyzed in the Research
No	Dependent Variables	Sample articles
1	Customer Experiences Cognitive & Emotional Experiences Cognitive absorption Perceived value of HME Perceived risk of Gen AI Self-Identity with Brand Using Gen AI Affinity for HME  Brand Likeability Customer Brand Attachment SDM- Satisfaction in Digital Marketing) Customer Satisfaction Interactional / Relational Experiences ChatGPT Human co-creation,  Engagement, Trust	Abadie et al. (2024) Alsharhan et al. (2024) Markovitch et al. (2024a) Alghamdi & Abdelwahed (2024) Koc et al. (2023) Sarraf et al. (2024) Hossain (2024b) Farah et al. (2024) Ng et al. (2024) Azer and Alexander (2024)
2	Behavioral Responses  (Attitude& Intention; adoption, loyalty, and advocacy) Consumer Attitude and Behavioral Intention Consumers' Responses (Attitude, Purchase intention) Attitude toward Gen AI adoption and usage (Cognitive, Affective, Conative Outcomes) Cognitive, Affective, Conative Outcomes among Potential Customers Attitude towards Gen AI advertising Consumers' Willingness to Accept AI-Generated Ads Consumer Perception and Trust Intention to use Purchase intention, ChatGPT usage intention (Consumers' inclination to use Gen AI platforms for discovering Products and getting information) ChatGPT behavioral intention Intention to use AI for Tourism Planning Booking Intention Willingness to Implement Gen AI in Services Adoption, loyalty, and advocacy Continuance/Reuse Intention Continuance intention to adopt AI Chatbots Patronage Intention. Recommendation acceptance Recommendations Disclosure of Information to Gen AI Positive WOM, Customer Loyalty Engagement & Co-creation	W.-J. Lee et al. (2023) G. Lee & Kim (2024) Alsharhan et al. (2024 Tan et al. (2025c) Yin et al. (2024) Markovitch et al. (2024a) Y. Yue et al. (2024) Han et al. (2024), Brüns&Meißner (2024), Baek et al. (2024) Shi et al. (2024a) Gu et al. (2024b) Gude (2023), Pathak et al. (2025), Morosan (2025a), Hossain (2024), Christensen et al. (2024b), (Jasper) Jia et al. (2025), I. Kim et al. (2024a) Kirk & Givi (2025a) B. Yue & Li (2023b) Park & Ahn (2024a) Christensen et al. (2024b)

Customer experiences, the dependent variable in this study, are influenced by various independent, mediating, and moderating variables. System quality, information quality, service quality, self-efficacy, and personal innovativeness all impact customer cognitive absorption, mediated by chatbot anthropomorphism (Sarraf et al., 2024). Different forms of HME offer users a range of cognitive and personality outcomes, such as perceived value, affinity, and risk, and affect service providers' willingness to implement HME services and their perceived value of HME. Competitive pressure functions as a moderator (Azer & Alexander, 2024). Management response dimensions—including redress, timeliness, facilitation, apology, attentiveness, and credibility—influence customer satisfaction, with the connection mediated by Perceived Justice Dimensions: distributive, procedural, and interactional (Koc et al., 2023). Efficiency, excellence, esteem, ethics, and spirituality impact ChatGPT-human co-creation, with the intention to use ChatGPT serving as a mediator (Abadie et al., 2024).

Various independent, mediating, and moderating variables influenced the dependent variable: Behavioral Responses. Consumers' responses, including their attitudes and purchase intentions, are shaped by the Design Entity (AI vs. Human). This relationship was mediated by Mind Perception (Perceived experience, Perceived agency) and moderated by product type, perceived AI threat, and the presence of human elements (Lee & Kim, 2024). The Cognitive, Affective, and Conative Outcomes for potential customers were influenced by the service recovery entity (human vs. Gen AI), mediated through perceived authenticity and uncanniness. Furthermore, this paper reviews several other dependent variables such as Intention to Use (W.-J. Lee et al., 2023), Uses and Purchase Intention (Alsharhan et al., 2024), and followers' or consumers' reactions—including attitude, intention, and loyalty (Brüns&Meißner, 2024)—as well as the willingness to accept AI-generated ads (Gu et al., 2024), Positive WOM& Customer Loyalty Kirk & Givi (2025), and Disclosure of Information to Gen AI (Morosan (2025).

Conceptual Framework: Effect of Gen AI on Consumer Experiences and Behavioral Responses

Based on a literature review, a conceptual framework (Figure 3) has been developed that introduces a new dimension to existing knowledge about the influence of Gen AI on consumer experiences and behavioral responses. This framework was developed following a comprehensive review and analysis of 139 articles. All independent variables, along with mediating, moderating, and dependent variables, have been identified and classified to form a model explaining how Gen AI affects consumer experiences and behaviors. The independent variables—such as Gen AI Quality and Performance, Gen AI attributes, Anthropomorphism, Humanlike characteristics, Interactivity, Co-creation with Consumers, and Perceived Risks (including Accuracy, Privacy, Overreliance Risk) and Ethics (covering Values, Transparency, Morality)—impact two dependent variables: customer experiences (both cognitive & emotional and interactional & emotional experiences) and behavioral responses (‘Consumer attitude & behavioral intention’ and ‘adoption, loyalty, and advocacy’). These effects are mediated by variables such as consumer characteristics, Interactional Dynamics, Consumer Experiences, Consumer Perceptions, and Behavioral Factors. Moderating variables may influence the strength of relationships among independent variables and mediating variables, as well as between mediating variables and each dependent variable—customer experiences and behavioral responses. Additionally, consumer experiences will influence behavioral responses. This model offers an original and innovative contribution to the understanding of how Gen AI impacts consumer experiences and their responses, and it will be valuable for future research.

Methods (M)

Data Collection Methods

This section categorizes articles according to the research methodologies employed. Table 15 presents the various data collection methods used in the reviewed papers during this investigation. Of the 139 publications included in this study, 70 (nearly 50%) relied on primary data, while 69 (about 50%) utilized secondary data, which consisted of theoretical, conceptual, and review articles. Among the 70 papers that employed a primary data collection method, 51 (37% of the total articles investigated in this study) used quantitative research methods. Quantitative research offered a structured approach to the investigation. The majority of papers (27) adopted the survey method, followed by the experimental study method (22). Only 12 publications (around 9%) employed qualitative research methods. Nevertheless, this strategy allowed researchers to explore the underlying ideas and concepts within the literature, resulting in a more comprehensive understanding of the subject. Only one study employed content analysis, while two case studies and nine studies utilized thematic analysis and in-depth interviews. There is a need for future studies to incorporate qualitative research methods to explore emerging areas of research related to the subject. Just seven studies (5% of all articles) used a mixed-methods approach. Farah et al. (2024) and Saleh (2025) utilized a mixed-method approach to data collection, integrating in-depth interviews with surveys. A greater number of mixed-methods research papers are needed in the domain of the current review, as this approach yields more holistic and comprehensive results.

Table 15 Types of Data Collection Methods Used in Research
Data Collection Method	No of Studies	%	Sample Studies
Mixed Method	7	5.03 %
Quantitative (Survey) and Qualitative (In Depth Interview)	4		Rienties et al. (2024), Pathak et al. (2025), Farah et al. (2024), Saleh (2025)
Survey (open ended: qualitative) + Closed ended: quantitative)	2		Sun et al. (2025), Chakraborty et al. (2025)
Experiment + Focus Group	1		I. Kim et al. (2024a)
Qualitative Study	12	8.63 %
Content Analysis	1		Oviedo-Trespalacios et al. (2023)
Case study	2		Skórnóg& Kmiecik (2023), Oviedo-Trespalacios et al. (2023)
Thematic analysis and in-depth Interview	9		Limna &Kraiwanit (2023b), A. S. Gupta & Mukherjee (2024b), Cillo & Rubera (2024), Tan et al. (2025c), Ozuem et al. (2024),  Sun et al. (2025), D. Kumar &Ratten (2024), Madanchian (2024a)
Quantitative Method	51	36.70 %
Experiment	22		Hartmann et al. (2024b), Kirk & Givi (2025a), M. Gupta et al. (2024), Yin et al. (2024), (Jasper) Jia et al. (2025), Baek et al. (2024), Kovács (2024), B. Yue & Li (2023b), Gursoy et al. (2023), Brüns&Meißner (2024), Markovitch et al. (2024a), Haupt et al. (2024), J. Kim et al. (2023a), Silva et al. (2024a), Sarraf et al. (2024b), Kirshner (2024), Luo et al. (2025), Wen & Laporte (2025),
Survey	27		Vhatkar et al. (2024), Shi et al. (2024a), Al Matalka et al. (2024), Ahmad et al. (2023), Alghamdi & Abdelwahed (2024), Gude (2023), W.-J. Lee et al. (2023), Abadie et al. (2024b), Park & Ahn (2024a), Gu et al. (2024b), Gude (2023), Koc et al. (2023a), Morosan (2025a), Fosso Wamba et al. (2024b), Haddud (2024), Ng et al. (2024), Christensen et al. (2024b), Yang & Lee (2024), Silva et al. (2024b), Sarraf et al. (2024a), Kirshner (2024c), Abadie et al. (2024), Indrawati et al. (2024), A. Kumar et al. (2025), Foroughi et al. (2025), Wang & Zhang (2024)
Data Extraction from You Tube Videos	1		Hussain et al. (2024b)
Text Mining	1		Ngo (2024)
Others - Theoretical/ conceptual / Review	69	49.64 %	Belanche et al. (2024), Gursoy et al. (2023), Sarstedt et al. (2024), A. Kumar et al. (2024),R. Gupta et al. (2024b), Bouchard, J. (2024), Kunz & Wirtz (2024), Mogaji et al. (2024), Shankar (2024), Hoffman & Novak (2024), Singh (2025), Al-Kfairy (2025)
Total	139	100%

Data Analysis Techniques

Table 16 displays the data analysis techniques used across the various studies reviewed in this analysis. Up to 73 papers (90% of all articles using data analysis techniques) employed quantitative methods, while only seven articles (about 10%) used qualitative techniques. Some papers (Yi et al. (2025b), Tan et al. (2025c), Limna & Kraiwanit (2023), Ozuem et al. (2024), Sun et al. (2025), Farah et al. (2024), Skórnóg & Kmiecik (2023)) implemented qualitative methods such as content analysis, thematic analysis, and case analysis. Among the 73 papers that used quantitative data analysis techniques, SEM was the most common approach, appearing in 21 articles (around 29%), followed by ANOVA, ANCOVA, and MANOVA, used in 14 articles (about 18%). Six articles (roughly 8%) utilized regression and mediation analysis each. Eight articles (approximately 10%) employed CFA (confirmatory factor analysis), and five studies used sentiment analysis, including ABSA (aspect-based sentiment analysis). Data analysis techniques such as Chi-square, fs-QCA (Fuzzy Set Qualitative Comparative Analysis methodology), topic modeling, t-test, MGA (Multi-Group Analysis), co-word network analysis, and cluster analysis were applied in fewer than five studies. There is a clear need for a mixed-methods approach to data analysis, which is currently absent from the existing literature. For example, combining content analysis with sentiment analysis could offer a more comprehensive understanding, especially when a holistic view of the issue is necessary.

Table 16 Data Analysis Techniques Used in Research
Data Analysis Technique	No of Articles	%	Sample Studies
Qualitative Analysis (Content analysis, Thematic analysis, Case Analysis)	7	9.6%	Yi et al. (2025b), Tan et al. (2025c), Limna &Kraiwanit (2023b),Ozuem et al. (2024), Sun et al. (2025), Farah et al. (2024), Skórnóg& Kmiecik (2023)
Quantitative Analysis	73	90.4%
SEM	21	23.3%	Abadie et al. (2024b), Morosan (2025a), Yang & Lee (2024), Gu et al. (2024b), Gude (2023), Christensen et al. (2024b), Gursoy et al. (2023), Pathak et al. (2025), Shi et al. (2024a), Hussain et al. (2024b), Ahmad et al. (2023), Alghamdi & Abdelwahed (2024), Al Matalka et al. (2024), W.-J. Lee et al. (2023), Ng et al. (2024), Vhatkar et al. (2024), Sarraf et al. (2024), Indrawati et al. (2024), A. Kumar et al. (2025), Foroughi et al. (2025)
ANOVA, ANCOVA, MANOVA	14	17.8%	J. Kim et al. (2023a), Kirk & Givi (2025a), Kirshner (2024a), Zhang & Gosline (2023), B. Yue & Li (2023b), Gude (2023), Brüns&Meißner (2024), Baek et al. (2024), Hussain et al. (2024b),Yin et al. (2024), G. Lee & Kim (2024a), I. Kim et al. (2024a), Brüns&Meißner (2024)
Regression analysis	6	8.2%	Markovitch et al. (2024a), Han et al. (2024), Hossain (2024b), Gude (2023), I. Kim et al. (2024a), Hartmann et al. (2024b)
CFA	8	9.6%	Abadie et al. (2024b), Han et al. (2024), Morosan (2025a), Yang & Lee (2024), Al Matalka et al. (2024), W.-J. Lee et al. (2023), Ashour & Rashdan (2024)
Mediation Analysis	6	8.2%	Baek et al. (2024), Kirk & Givi (2025a), G. Lee & Kim (2024a), I. Kim et al. (2024a), Brüns&Meißner (2024), (Jasper) Jia et al. (2025)
Sentiment Analysis, ABSA	5	5.5%	Hossain (2024a), Ngo (2024), Jeong & Lee (2024), Pranatawijaya et al. (2024), Graham & Stough (2025)
Chi-Square	3	4.1%	Gude (2023), Haddud (2024), Gude (2023)
fs-QCA (Fuzzy set Qualitative comparative analysis technique)	3	4.1%	Sarraf et al. (2024a), Han et al. (2024), Gursoy et al. (2023)
Topic Modelling	2	2.70%	Ramos-Henriquez & Morini-Marrero (2024), Ngo (2024)
t test	2	2.70%	(Jasper) Jia et al. (2025), Gude (2023)
MGA (Multi Group Analysis)	2	2.70%	Hussain et al. (2024b), Sarraf et al. (2024a)
Co-word network analysis, Cluster analysis	1	1.40%	Fosso Wamba et al. (2024b)

Future Research Directions

After analyzing 139 articles, a research agenda for Generative AI has been developed. This review of the current TCCM framework aligns with the recommendations by Paul and Rosado-Serrano (2019) and suggests potential research directions across theories, contexts, constructs, and methodologies. It is important to note that, although many recommendations are presented as possible research paths in the upcoming sections, this list is not comprehensive. Researchers should also assess the relevance and accuracy of these identified gaps before exploring them further.

Scope for Theory Development

Out of the 139 articles reviewed, only 37 used a theoretical framework to study the effect of Gen AI on consumer behavior. This creates a chance for future researchers to add studies using a theoretical framework to improve the quality and influence of their work. Among the 16 theories used across different articles, the Technology Acceptance Model (TAM) and its extended version were the most common, appearing in seven articles. Eight theories were used rarely, showing up only once. Therefore, future researchers can test these theories in different settings, such as various countries, cultures, and industries. Additionally, these theories can be applied with different types of variables, such as dependent, independent, mediating, and moderating variables. They can also be used in various studies by adopting different research methods.

Value Exchange Theory can help future researchers better understand the benefits and costs of Gen AI's influence on consumer behavior. Benefits like increased efficiency, time savings, content creation, effective communication, and greater convenience deserve further study. On the other hand, costs related to Gen AI, such as trust issues, reliability, ethics, and privacy concerns, also need investigation. These studies will benefit both academics and practitioners. The Theory of Avatar Marketing and Ambivalence Theory have only been explored in one study each. Future research could examine how the human-like qualities of Gen AI avatars affect consumer behavior. Since Ambivalence Theory has been studied just once, it might also be used to explore consumer mixed feelings toward Gen AI and how these feelings influence their willingness to adopt. The Theory of Choice Overload, which has also been limited to one study, could be explored to see how a wider range of Gen AI-generated options impacts consumers’ cognitive dissonance. Finally, Frustration-Aggression Theory has been used only once, making it a promising area for researching how consumer frustration with Gen AI systems may lead to aggressive responses, such as negative word-of-mouth.

Only one study (Jasper et al., 2025) applied the Elaboration Likelihood Model (ELM). Future researchers might explore this theory to determine whether consumers engage with Gen AI-generated product reviews through the central or peripheral route. Authenticity Theory appeared in just one study (Kirk & Givi, 2025). Subsequent researchers can employ this theory to evaluate if consumers perceive Gen AI-generated reviews as authentic. This exploration could include perceptions of trust and authenticity in Gen AI-generated content across sectors such as retail, education, tourism, and advertising. Self-Verification Theory has been used in only one study (Park & Ahn, 2024). Future investigations could utilize this framework to analyze consumer reactions when Gen AI-generated content conflicts with their self-image. Additionally, only one study has applied the service recovery aspect within justice theory (Koc et al., 2023). Future researchers can use this theory to examine whether a company's service recovery measures are viewed as fair across all three justice dimensions: distributive justice (refunds, replacements, discounts), procedural justice (speed, flexibility, transparency), and interactional justice (courtesy, empathy, respect). Researchers might also consider buyer behavior theory, used in only one study (Madanchian, 2024), to determine if interactions with Gen AI improve consumer perceptions of businesses through personalization. Furthermore, future studies could analyze the source-credibility framework to assess whether consumers see Gen AI as a reliable source for product recommendations and whether Gen AI enhances or reduces the brand’s credibility.

Many studies have used different theories, such as the Technology Acceptance Model, the Extended Technology Acceptance Model, the Theory of Planned Behavior, the Unified Theory of Acceptance and Use of Technology (UTAUT & UTAUT 2), Diffusion of Innovation, and Usage and Gratifications Theory (U&G).

Future researchers can apply these theories in various settings, including different countries, cultures, and industries. The Stimulus-Organism-Response (SOR) Theory was used in two studies (Gu et al., 2024; Alsharhan et al., 2024). It can help future researchers explore how various stimuli, such as Gen AI content personalization, voice or avatar design, and interactivity, affect the organism (trust and satisfaction), which then influences consumer responses like attitude, intent, engagement, and loyalty. The Mind Perception Theory (MPT) was applied in two studies: Tan et al. (2025) and G. Lee & Kim (2024). This theory can assist future researchers in examining whether consumers trust Gen AI more when they perceive it as having emotional intelligence. They can also analyze how consumers' trust in Gen AI suggestions depends on their perceived sense of agency.

Contextual Perspective: Countries

The articles examined in this study mainly focus on 20 countries (Table 8). Most research was conducted within a single country, which limits how well the results can be applied elsewhere. Only two studies used a multi-country approach—one looked at North America and Asia Pacific. The other countries studied included Egypt, Iraq, Lebanon, Palestine, the Philippines, Saudi Arabia, Sudan, Kuwait, India, Jordan, and Thailand. It is recommended that future researchers conduct more studies across multiple countries. This will help them understand how differences in macro factors—such as political, economic, socio-cultural, and technological aspects—across various nations affect the influence of Gen AI on consumer behavior.

There is potential for future research on less-studied countries such as India, Italy, Thailand, Turkey, Brazil, Botswana, Canada, France, Germany, Iran, Ireland, Jordan, Portugal, and the UAE. Future researchers should also examine countries that have not yet been studied, including African nations (Kenya, Nigeria, Ghana, South Africa, Ethiopia), South Asian nations (Bangladesh, Sri Lanka, Pakistan, Nepal), European countries (Norway, Sweden, Finland, Hungary), Middle Eastern countries (Saudi Arabia, Egypt), and Southeast Asian nations (Indonesia, Vietnam, Malaysia, Philippines).

Industry

This paper reviews research articles across ten distinct sectors: hospitality and tourism, digital marketing, education, advertising, e-commerce, retail, fashion and luxury, healthcare, marketing research, and financial services, including fintech (Table 9). Notably, 22 studies focus on the hospitality and tourism sector, followed by 14 in digital marketing, and eight in education and advertising. Future researchers should explore underrepresented industries such as market research, financial services, fintech, healthcare, fashion and luxury, and retail. Additionally, it is important to investigate emerging industries such as automobiles, real estate, logistics, telecommunications, pharmaceuticals, travel aggregators, media and entertainment, sports and recreation, gaming, the airline industry, agriculture, agricultural technology, and food delivery, among others.

Constructs

Most research has focused on the effects of independent variables, such as the features, quality, and performance of Gen AI, as well as anthropomorphism and the interactions and co-creation experiences between Gen AI and consumers, along with perceived risks and ethical issues (Table 11). Future research could examine how different independent factors, including the type of Gen AI system (e.g., ChatGPT, virtual assistants, avatars), various versions of ChatGPT (e.g., 4.5 and 4.0), Gen AI design, customization, and the level of automation (full automation versus AI-assisted interaction) influence different aspects of consumer behavior. Additionally, researchers should explore which ethical principles should guide the use of Gen AI in fields like healthcare and finance. Separate studies might also look into the negative effects of Gen AI on human skills and abilities, as well as how the absence of empathy and emotions in Gen AI impacts industries that rely on emotional intelligence, such as hospitality, education, and healthcare.

Most of the mediating variables studied can be grouped into four categories: consumer characteristics, interactional dynamics, consumer experience, and behavioral factors (Table 12). Future researchers could also explore how the influence of Gen AI on consumer behavior might involve mediating effects through variables such as consumer personality, consumer learning, cultural values, and environmental factors (including political, legal, and technological aspects). Very few studies have examined the impact of moderating variables. These can be divided into four groups: consumer characteristics, product features, content features, and consumer perceptions (Table 13). Future researchers might consider investigating less-explored moderating factors like environmental influences (political, socio-cultural, technological, and legal), consumer personality, the need for uniqueness, high achievement, and different consumer types, including innovators, early adopters, early majority, late majority, and laggards. They can also explore how elements like AI hallucinations, ethics, and trust can affect the impact of Gen AI on consumer behavior.

Most of the dependent variables studied were divided into two main groups: customer experiences and behavioral responses (Table 14). Within customer experiences, key dependent variables relate to cognitive, emotional, and interactional-emotional experiences. Regarding behavioral responses, two categories of dependent variables emerged: consumer attitudes and behavioral intentions, as well as adoption, loyalty, and advocacy. Future researchers can explore the impact of Gen AI on consumer learning, customer relationship management, customer satisfaction, and consumer engagement. The effects of Gen AI on marketing effectiveness and strategy also offer interesting areas for further research. Additionally, they can investigate how Gen AI influences B2B marketing, including customer relationship management and the B2B buying process. Researchers may also examine how Gen AI affects marketing communications, pricing strategies, product development, content creation, and innovation within organizations. Another valuable area to explore is the impact of Gen AI on consumer decision-making.

Methods

We analyzed 139 articles and found that 69 relied on secondary data from reviews, concepts, or theories, while 70 focused on primary data. Among these, 51 articles used quantitative methods, 12 used qualitative methods, and seven employed mixed methods. Future research could examine mixed-methods studies that combine both quantitative and qualitative data for a more comprehensive understanding of the topic. This approach allows both methods to complement each other, effectively addressing research issues and improving overall validity (Biswas et al., 2018; Bragançaa et al., 2018). Survey methods face challenges; respondents may give inaccurate answers due to a tendency to choose socially acceptable responses, leading to data inaccuracies. Future advancements could leverage neuromarketing tools like fMRI (functional Magnetic Resonance Imaging), EEG (Electroencephalography), and eye tracking, which could enhance research effectiveness (Bhardwaj, P. and Kalro; A. D., 2024). Currently, bibliometric studies on this topic are limited. Researchers might consider using bibliometric analysis to identify emerging trends and key research areas. To better assess Gen AI’s impact on consumer behavior, future studies should increase both sample size and diversity for a more representative perspective. Many existing studies are cross-sectional, highlighting the need for longitudinal research. Ultimately, future work should foster interdisciplinary collaboration between academia and industry to develop innovative and practical solutions (McBee-Black, 2021; McBee-Black & Ha-Brookshire, 2022).

Discussion

This review article examines how Gen AI influences consumer behavior, significantly enhancing the theoretical framework of the topic. In Section 6.1, a detailed overview of various theoretical contributions is provided. Additionally, the article highlights practical implications for marketers by sharing insights into how Gen AI affects consumer behavior. These insights assist marketers in integrating Gen AI into their traditional marketing strategies, enhancing customer experience, satisfaction, engagement, and loyalty. Section 6.2 underscores the implications for marketing professionals.

Theoretical Contributions

Contributions can be incremental, revelatory, consolidatory, or replicatory (Nicholson et al., 2018). This review article makes incremental contributions by identifying new gaps in existing scholarly literature and suggesting extensions for research on various theories, contexts, constructs, and methods. It also offers a revelatory contribution by highlighting potential future research areas that have gone unnoticed or unrecognized. Additionally, it uncovers the impact of various factors—whether related to the features of Gen AI, the interaction of Gen AI with consumers, or consumers' characteristics and perceptions—on consumer experiences and behavioral responses. Several theoretical perspectives are proposed to define the research agenda and reveal new aspects of the domain. The current review further makes a consolidatory contribution by advancing knowledge and providing a comprehensive understanding of the impact of Gen AI on consumer behavior using the TCCM framework. It summarizes previous review articles on Gen AI, consumer experiences, and consumer responses, along with the most prominent journals, authors, and countries contributing to the field. Moreover, it highlights all key theories, contexts (including countries and industries), constructs (factors and variables), and methodologies in the literature regarding the influence of Gen AI on consumer experiences and behavioral responses. Furthermore, it outlines directions for future research in this area.

Specifically, the theoretical contributions include a descriptive and performance analysis of articles through bibliometric analysis. This analysis provides an overview of related articles and reviews, offering a broad picture of the number of articles published, the growth rate of publications in the field, leading journals, and the articles published in those journals. The most cited articles are identified, and their findings summarized. Top authors and countries contributing to the field are highlighted to showcase their contributions. By applying the TCCM framework, this review identifies the top 16 theories used. Future researchers can easily understand the theories employed, their usefulness, and their impact. They can incorporate these theories into future research or adapt them to new cultures, countries, or industry contexts. This study also identifies the settings in which studies have been conducted to understand the relationship between Gen AI and consumer behavior and to examine Gen AI's influence on consumer experiences and responses. The most important constructs are identified, including independent, dependent, mediating, and moderating variables. The review discusses how these variables, related directly or indirectly to Gen AI, affect various aspects of consumer behavior, with significant findings explained for consumers, marketers, and future researchers. The methodologies used in all the articles have also been identified and analyzed. The findings will assist future scholars and researchers.

After a thorough review of the articles, the author has developed a conceptual framework (Figure 3) that outlines the independent, mediating, moderating, and dependent variables explored in this review. The model clarifies how Gen AI (including all relevant independent, mediating, and moderating variables) influences consumer experiences and behavioral responses. This framework offers a new contribution to the existing body of knowledge on the topic. Additionally, it will serve as valuable guidance for future researchers.

Managerial Implications

This study has important implications for marketers. They can gain a better understanding of consumers’ needs, preferences, responses, and behaviors. This enhanced insight will help them develop more effective marketing strategies, leading to improved marketing outcomes and increased consumer engagement, satisfaction, and loyalty. Marketers can use insights from this study to ensure that integrating Gen AI into traditional marketing fosters more human-like interactions. They should aim to incorporate human emotions such as empathy and warmth into Gen AI tools. It is essential to understand and influence consumers' perceptions of various aspects of Gen AI and how brands utilize it. Marketers must ensure that recommendations, reviews, and ratings generated by Gen AI tools are reliable, trustworthy, and ethical. Additionally, they should work to reduce or eliminate risks related to consumer interactions (Shi et al., 2024). Addressing three main risks—privacy, accuracy, and overreliance—will enable consumers to use Gen AI confidently in their decision-making, with marketers guiding these choices through Gen AI tools. Exploring how chatbots can provide higher-quality service by being more responsive and empathetic is also beneficial. Marketers should focus on improving the perceived accuracy, convenience, relevance, and personalization of the Gen AI tools they implement (Alghamdi & Abdelwahed, 2024). As consumers begin to see responses from Gen AI as more accurate, their trust will increase, leading to greater reliance. Ensuring that Gen AI tools are user-friendly for consumers is equally important. The information provided by Gen AI should be relevant and help consumers make better decisions. Ultimately, marketers should aim for Gen AI to deliver more personalized information or solutions to enhance customer satisfaction and loyalty

Limitations

Like any literature review, this one has its limitations. First, the articles were exclusively sourced from the Scopus database, which is highly influential. Future researchers should explore additional sources and databases, including Google Scholar, Web of Science, and EBSCOhost. Second, the review's timeframe was relatively short, covering only 2023 to 2025; a longer period is usually preferred. This limited timeframe was chosen because Gen AI technology was released in November 2022, meaning relevant publications became accessible only from 2023 onward. Nonetheless, the review was conducted due to the significant growth of publications in this field, driven by increased interest among scholars and practitioners. In fact, over 500 publications were recorded during this brief period, which is a substantial number. Of these, 139 relevant articles were included in the review. Future researchers should consider extending the timeframe to include more publications for deeper insights. Third, only ChatGPT was examined in the context of Gen AI. Future studies could benefit from investigating other Gen AI tools and technologies, such as Gemini (Bard), DALL-E, Jasper AI, and Canva. Despite these limitations, this review makes a notable contribution to the literature on the impact of Gen AI on consumer experiences and behavioral responses. This is the first comprehensive literature review to combine bibliometric analysis with the TCCM framework. Furthermore, it provides valuable guidance for future research, expanding knowledge for both scholars and practitioners.

Conclusion

Gen AI has transformed the marketing landscape, sparking significant interest among researchers and practitioners regarding its applications, benefits, and potential risks. However, studies on Gen AI's impact on consumer experiences and responses are still emerging. This research seeks to fill that gap by reviewing 139 relevant articles and reviews. Several objectives guided the study: first, to summarize existing research on Gen AI's effect on consumer experiences and responses; second, to gain insights into the theories used in the published studies; third, to analyze the contexts of these studies by country and industry; and fourth, to identify key factors such as independent, dependent, mediating, and moderating variables that influence consumer responses. Additionally, the study aims to outline future research directions, with the ultimate goal of assessing implications for scholars and marketers.

To the best of the author’s knowledge, this is the first review that combines a hybrid approach by integrating bibliometric analysis with the TCCM framework. It examines and reviews 139 articles, offering significant benefits to scholars, marketers, and researchers. The first objective was achieved through a bibliometric analysis, which identified the number of publications, growth trends, leading journals that publish relevant articles, and the most prolific authors in the field. It also highlighted the most cited articles, key findings, and the top contributing countries in research and publications on the topic. The second objective was accomplished using the TCCM framework, which identified the top 20 theories applied across all articles, providing deeper insights into the subject and suggesting future research directions. The study examined the contexts of various countries and industries, highlighting overlooked areas that present opportunities for future researchers. Primary constructs, including independent, dependent, mediating, and moderating variables, were defined. The influence of different independent variables, both directly and indirectly related to Gen AI, on consumer behavior was also examined.

Experiences and behavioral responses (the dependent variables) were examined. The roles of mediating and moderating variables were also explored. The research methods used, including quantitative, qualitative, and mixed methods, were discussed, with a focus on less commonly used methods to guide future research. The third objective was achieved by providing directions for future studies using the TCCM framework. Suggested theories and relevant contexts involving countries and industries that could improve future research were outlined. Additionally, underexplored constructs and variables related to the topic were recommended, along with some less common but effective research methods. The study's fourth objective was met by outlining implications for scholars and marketers. This work clarifies how the findings can help scholars extend and refine existing theories, as well as develop new ones related to the current topic. It also shows how marketers can use these findings to enhance consumer experience and responses. This review advances current understanding by establishing a new conceptual framework that clarifies the topic and serves as a guide for future research.

Acknowledgment

The author gratefully acknowledges Dr. Tata Sai Vijay, Associate Professor at IIM Ranchi, for his valuable support.

Conflict of Interest

The author declares no conflict of interest.

About the Author

Dr. Chandan Kumar Thakur currently serves as Professor of Marketing at the Delhi School of Business in New Delhi, India. He brings over 25 years of experience in academia, research, and executive training. His research interests encompass cause-related marketing, online consumer reviews, consumer behavior, and the applications of generative AI in marketing.

References

Ahmad, M., Alhalaiqa, F., & Subih, M. (2023). Constructing and testing the psychometrics of an instrument to measure the attitudes, benefits, and threats associated with the use of Artificial Intelligence tools in higher education. Journal of Applied Learning and Teaching, 6(2), 114–120.

Indexed at, Google Scholar, Cross Ref

Al Matalka, M., Badir, R., Bani Ahmad, A. Y. A., Al-Said, K., Nassar, H. T. I., Alzoubi, S., & Alzoubi, M. (2024). The adoption of ChatGPT marks the beginning of a new era in educational platforms. International Journal of Data and Network Science, 8(3), 1941–1946.

Alghamdi, Z. K., & Abdelwahed, N. A. A. (2024). Developing consumers’ experience with chatgpt towards customer digital marketing satisfaction strategy. Corporate and Business Strategy Review, 5(3), 36–46.

Alizadeh, H., &Nazarpour Kashani, H. (2024). The Impact of Perceived Experience with ChatGPT on Online Consumers' Information-Searching Behavior: An Empirical Study of Iranian College Students. Asia Pacific Journal of Marketing and Logistics.

Indexed at, Google Scholar, Cross Ref

Al-Kfairy, M. (2025). Strategic Integration of Generative AI in Organizational Settings: Applications, Challenges and Adoption Requirements. IEEE Engineering Management Review.

Alsharhan, A., Al-Emran, M., & Shaalan, K. (2024b). Chatbot Adoption: A Mult perspective Systematic Review and Future Research Agenda. IEEE Transactions on Engineering Management, 71, 10232–10244. https://doi.org/10.1109/TEM.2023.3298360

Argan, M., Gürbüz, B., Dursun, M. T., Dinç, H., & Tokay Argan, M. (2024). Usage Intention of ChatGPT for Health in Turkey: An Extended Technology Acceptance Model. International Journal of Human-Computer Interaction.

Indexed at, Google Scholar, Cross Ref

Ashour, A. F., & Rashdan, W. (2024). Artificial Intelligence: Potentialities and Challenges in Art and Design. International Journal of Design Management and Professional Practice, 18(2), 19–36.

Baek, T. H. (2023). Digital Advertising in the Age of Generative AI. Journal of Current Issues and Research in Advertising, 44(3), 249–251.

Baek, T. H., Kim, J., & Kim, J. H. (2024). Effect of disclosing AI-generated content on prosocial advertising evaluation. International Journal of Advertising.

Indexed at, Google Scholar, Cross Ref

Behrens, R., Kupfer, A.-K., & Hennig-Thurau, T. (2024). There is business like show business! What marketing scholars and managers can learn from 40 years of entertainment science research. Journal of the Academy of Marketing Science.

Belanche, D., Belk, R. W., Casaló, L. V., & Flavián, C. (2024). The dark side of artificial intelligence in services. Service Industries Journal, 44(3–4), 149–172.

Bilgihan, A., Dogru, T., Hanks, L., Line, N., & Mody, M. (2024). The GAI marketing model: A conceptual framework and future research directions. International Journal of Hospitality Management, 123.

Brüns, J. D., &Meißner, M. (2024). Do you create your content yourself? Using generative artificial intelligence for social media content creation diminishes perceived brand authenticity. Journal of Retailing and Consumer Services, 79.

Indexed at, Google Scholar, Cross Ref

Christensen, J., Hansen, J. M., & Wilson, P. (2024b). Understanding the role and impact of Generative Artificial Intelligence (AI) hallucination within consumers’ tourism decision-making processes. Current Issues in Tourism.

Cillo, P., & Rubera, G. (2024). Generative AI in innovation and marketing processes: A roadmap of research opportunities. Journal of the Academy of Marketing Science.

Crisp, D., Newsted, J., Brendon, B., Barnes, D., Hayes, C., &Partner, J. (2024). Customizing generative AI: Harnessing document retrieval and fine-tuning alternatives for dynamic marketing insights. Applied Marketing Analytics, 10(1), 18–31.

Cutler, K. (2024). The Evolution of Digital Marketing in the Era of AI Applied Marketing Analytics, 10(1), 6–17.

De Oliveira, F. G., Belitski, M., Kakabadse, N., & Theodorakopoulos, N. (2024). Unveiling the potential of digital human avatars in modern marketing strategies. International Marketing Review.

Farah, M. F., Ramadan, Z., &Nassereddine, Y. (2024). When digital spaces matter: The influence of uniqueness and place attachment on self-identity expression with brands using generative AI on the metaverse. Psychology and Marketing.

Florido-Benítez, L. (2024). Generative artificial intelligence: a proactive and creative tool to achieve hyper-segmentation and hyper-personalization in the tourism industry. International Journal of Tourism Cities.

Indexed at, Google Scholar, Cross Ref

Foroughi, B., Iranmanesh, M., Yadegaridehkordi, E., Wen, J., Ghobakhloo, M., Senali, M. G., & Annamalai, N. (2025). Factors Affecting the Use of ChatGPT for Obtaining Shopping Information. International Journal of Consumer Studies, 49(1). https

Fosso Wamba, S., Guthrie, C., Queiroz, M. M., & Minner, S. (2024b). ChatGPT and generative artificial intelligence: an exploratory study of key benefits and challenges in operations and supply chain management. International Journal of Production Research, 62(16), 5676–5696.

Graham, C., & Stough, R. (2025). Consumer perceptions of AI chatbots on Twitter (X) and Reddit: an analysis of social media sentiment and interactive marketing strategies. Journal of Research in Interactive Marketing.

Gu, C., Jia, S., Lai, J., Chen, R., & Chang, X. (2024). Exploring Consumer Acceptance of AI-Generated Advertisements: From the Perspectives of Perceived Eeriness and Perceived Intelligence. Journal of Theoretical and Applied Electronic Commerce Research, 19(3), 2218–2238.

Gude, V. (2023). Factors Influencing ChatGPT Adoption for Product Research and Information Retrieval. Journal of Computer Information Systems.

Indexed at, Google Scholar, Cross Ref

Gupta, A. S., & Mukherjee, J. (2024). Framework for the adoption of generative AI for information search of retail products and services. International Journal of Retail and Distribution Management.

Gupta, M., Dheekonda, V., & Masum, M. (2024). Genie: Enhancing information management in the restaurant industry through AI-powered chatbot. International Journal of Information Management Data Insights, 4(2).

Gupta, R., Nair, K., Mishra, M., Ibrahim, B., & Bhardwaj, S. (2024). Adoption and impacts of generative artificial intelligence: Theoretical underpinnings and research agenda. International Journal of Information Management Data Insights, 4(1).

Indexed at, Google Scholar, Cross Ref

Gursoy, D., Li, Y., & Song, H. (2023). ChatGPT and the hospitality and tourism industry: an overview of current trends and future research directions. Journal of Hospitality Marketing and Management, 32(5), 579–592.

Haddud, A. (2024). ChatGPT in supply chains: exploring potential applications, benefits and challenges. Journal of Manufacturing Technology Management, 35(7), 1293–1312.

Han, H., Kim, S. (Sam), Hailu, T. B., Al-Ansi, A., Loureiro, S. M. C., & Kim, J. J. (2024). Determinants of approach behavior for ChatGPT and their configurational influence in the hospitality and tourism sector: a cumulative prospect theory. International Journal of Contemporary Hospitality Management.

Hartmann, J., Exner, Y., &Domdey, S. (2024). The power of generative marketing: Can generative AI create superhuman visual marketing content? International Journal of Research in Marketing.

Hassan, S. A., Omar, A. I., & Ahmed, N. R. (2024). Exploring the ethical implications of ai in public health research: A comprehensive analysis. Southeastern European Journal of Public Health, 25, 108–115.

Indexed at, Google Scholar, Cross Ref

Haupt, M., Freidank, J., & Haas, A. (2024). Consumer responses to human-AI collaboration at organizational frontlines: strategies to escape algorithm aversion in content creation. Review of Managerial Science.

Hermann, E., &Puntoni, S. (2024). Artificial intelligence and consumer behavior: From predictive to generative AI. Journal of Business Research, 180.

Hoffman, D. L., & Novak, T.P. (2024). The evolving consumer IoT: A novel framework for marketing strategy based on assemblage theory. Journal of Product Innovation Management.

Hoffmann, S., Lasarov, W., & Dwivedi, Y.K. (2024). AI-empowered scale development: Testing the potential of ChatGPT. Technological Forecasting and Social Change, 205.

Indexed at, Google Scholar, Cross Ref

Hossain, M. S. (2024). Textual feature engineering for purchase intent and customer satisfaction: Insights from marketing 4.0 and sentiment. Sustainable Futures, 8.

Hussain, K., Khan, M. L., & Malik, A. (2024). Exploring audience engagement with ChatGPT-related content on YouTube: Implications for content creators and AI tool developers. Digital Business, 4(1).

Indrawati, Letjani, K. P., Kurniawan, K., &Muthaiyah, S. (2024). Adoption of ChatGPT in educational institutions in Botswana: A customer perspective. Asia Pacific Management Review.

Jasper Jia, S., Chi, O. H., & Chi, C. G. (2025b). Unpacking the impact of AI vs. human-generated review summaries on hotel booking intentions. International Journal of Hospitality Management, 126.

Indexed at, Google Scholar, Cross Ref

Jeong, N., & Lee, J. (2024). An Aspect-Based Review Analysis Using ChatGPT for the Exploration of Hotel Service Failures. Sustainability (Switzerland), 16(4).

Joshi, S., Bhattacharya, S., Pathak, P., Natraj, N. A., Saini, J., & Goswami, S. (2025). Harnessing the potential of generative AI in digital marketing using the Behavioral Reasoning Theory approach. International Journal of Information Management Data Insights, 5(1).

Kar, A. K., Varsha, P. S., & Rajan, S. (2023). Unravelling the Impact of Generative Artificial Intelligence (GAI) in Industrial Applications: A Review of Scientific and Grey Literature. Global Journal of Flexible Systems Management, 24(4), 659–689.

Kim, I., Ki, C. W., Lee, H., & Kim, Y. K. (2024). Virtual influencer marketing: Evaluating the influence of virtual influencers’ form realism and behavioral realism on consumer ambivalence and marketing performance. Journal of Business Research, 176.

Kim, J., Kim, J. H., Kim, C., & Park, J. (2023). Decisions with ChatGPT: Reexamining choice overload in ChatGPT recommendations. Journal of Retailing and Consumer Services, 75.

Kirk, C. P., & Givi, J. (2025). The AI-authorship effect: Understanding authenticity, moral disgust, and consumer responses to AI-generated marketing communications. Journal of Business Research, 186.

Kirshner, S. N. (2024). GPT and CLT: The impact of ChatGPT’s level of abstraction on consumer recommendations. Journal of Retailing and Consumer Services, 76.

Koc, E., Hatipoglu, S., Kivrak, O., Celik, C., & Koc, K. (2023). Houston, we have a problem!: The use of ChatGPT in responding to customer complaints. Technology in Society, 74.

Indexed at, Google Scholar, Cross Ref

Kovács, B. (2024). The Turing test of online reviews: Can we tell the difference between human-written and GPT-4-written online reviews? Marketing Letters.

Kumar, A., Gupta, N., & Bapat, G. (2024). Who is making the decisions? How Retail Managers Can Utilize the Power of ChatGPT. Journal of Business Strategy, 45(3), 161–169.

Kumar, A., Shankar, A., Behl, A., Chakraborty, D., &Gundala, R. R. (2025). Anthropomorphic generative AI chatbots for enhancing customer engagement, experience and recommendation. Journal of Consumer Marketing.

Kumar, D., &Ratten, V. (2024). Artificial intelligence and family businesses: a systematic literature review. Journal of Family Business Management.

Kunz, W. H., & Wirtz, J. (2024). Corporate digital responsibility (CDR) in the age of AI: implications for interactive marketing. Journal of Research in Interactive Marketing, 18(1), 31–37.

Lee, G., & Kim, H. Y. (2024a). Algorithm fashion designer? Ascribed mind and perceived design expertise of AI versus human. Psychology and Marketing.

Lee, G., & Kim, H.-Y. (2024c). Human vs. AI: The battle for authenticity in fashion design and consumer response. Journal of Retailing and Consumer Services, 77.

Lee, W. J., Lee, H. S., & Cha, M. K. (2023). AI like chatgpt, users like us: how chatgpt drivers and ai efficacy affect consumer behaviour. Virtual Economics, 6(4), 44–59.

Indexed at, Google Scholar, Cross Ref

Limna, P., &Kraiwanit, T. (2023). The role of chatgpt on customer service in the hospitality industry: an exploratory study of hospitality workers’ experiences and perceptions. Tourism and Hospitality Management, 29(4), 583–592.

Liu, C. C. H., & Chen, C. S. C. (2024). Digital technologies: Transforming home decor experience. Journal of Information Technology Teaching Cases.

LLeslie-Miller, C. J., Simon, S. L., Dean, K., Mokhallati, N., & Cushing, C. C. (2024). The critical need for expert oversight of ChatGPT: Prompt engineering for safeguarding child healthcare information. Journal of Pediatric Psychology, 49(11), 812–817.

Luo, X., Xu, D., Li, Y. (William), & Wan, L. C. (2025). Advancing information search through Gen AI: the roles of search type, travel motive and Gen AI customization level. International Journal of Contemporary Hospitality Management.

Madanchian, M. (2024b). Generative AI for Consumer Behavior Prediction: Techniques and Applications. In Sustainability (Switzerland) (Vol. 16, Issue 22). Multidisciplinary Digital Publishing Institute (MDPI).

Indexed at, Google Scholar, Cross Ref

Markovitch, D. G., Stough, R. A., & Huang, D. (2024a). Consumer reactions to chatbot versus human service: An investigation in the role of outcome valence and perceived empathy. Journal of Retailing and Consumer Services, 79.

Mogaji, E., & Jain, V. (2024). How generative AI is (will) change consumer behavior: Postulating the potential impact and implications for research, practice, and policy. Journal of Consumer Behavior.

Mogaji, E., Viglia, G., Srivastava, P., & Dwivedi, Y. K. (n.d.). Is it the end of the technology acceptance model in the era of generative artificial intelligence?

Morosan, C. (2025a). Rethinking information disclosure to Gen AI in hotels: An extended parallel process model. International Journal of Hospitality Management, 124.

Ng, W., Hao, F., & Zhang, C. (2024). From Function to Relation: Exploring the Dual Influences of Warmth and Competence on Generative Artificial Intelligence Services in the Hospitality Industry. Journal of Hospitality and Tourism Research.

Ngo, V. M. (2024a). Does ChatGPT change artificial intelligence-enabled marketing capability? Social media investigation of public sentiment and usage. Global Media and China, 9(1), 101–125.

Indexed at, Google Scholar, Cross Ref

Oviedo-Trespalacios, O., Peden, A. E., Cole-Hunter, T., Costantini, A., Haghani, M., Rod, J. E., Kelly, S., Torkamaan, H., Tariq, A., David Albert Newton, J., Gallagher, T., Steinert, S., Filtness, A. J., & Reniers, G. (2023). The risks of using ChatGPT to obtain common safety-related information and advice. Safety Science, 167.

Ozuem, W., Ranfagni, S., Willis, M., Salvietti, G., & Howell, K. (2024a). Exploring the relationship between chatbots, service failure recovery and customer loyalty: A frustration–aggression perspective. Psychology and Marketing.

Pantano, E., Serravalle, F., &Priporas, C.-V. (2024). The form of AI-driven luxury: how generative AI (GAI) and Large Language Models (LLMs) are transforming the creative process. Journal of Marketing Management.

Park, J. K., & Ahn, S. (2024a). Traditional vs. AI-generated brand personalities: Impact on brand preference and purchase intention. Journal of Retailing and Consumer Services, 81.

Pasca, M. G., & Arcese, G. (2024). ChatGPT: Opportunities and Challenges in an Empirical Study in Italy. TQM Journal.

Pathak, K., Prakash, G., Samadhiya, A., Kumar, A., & Luthra, S. (2025). Impact of Gen-AI chatbots on consumer services experiences and behaviors: Focusing on the sensation of awe and usage intentions through a cybernetic lens. Journal of Retailing and Consumer Services, 82.

Indexed at, Google Scholar, Cross Ref

Pranatawijaya, V. H., Sari, N. N. K., Rahman, R. A., Christian, E., & Geges, S. (2024). Unveiling User Sentiment: Aspect-Based Analysis and Topic Modeling of Ride-Hailing and Google Play App Reviews. Journal of Information Systems Engineering and Business Intelligence, 10(3), 328–339.

Ramos-Henriquez, J. M., & Morini-Marrero, S. (2024). Airbnb customer experience in long-term stays: a structural topic model and ChatGPT-driven analysis of the reviews of remote workers. International Journal of Contemporary Hospitality Management.

Rienties, B., Domingue, J., Duttaroy, S., Herodotou, C., Tessarolo, F., & Whitelock, D. (2024). What distance learning students want from an AI Digital Assistant. Distance Education.

Sadiq, M. W., Akhtar, M. W., Huo, C., & Zulfiqar, S. (2024). ChatGPT-powered chatbot as a green evangelist: an innovative path toward sustainable consumerism in E-commerce, Service Industries Journal, 44(3–4), 173–217.

Saleh, M. I. (2025). Generative artificial intelligence in hospitality and tourism: future capabilities, AI prompts and real-world applications. Journal of Hospitality Marketing and Management.

Indexed at, Google Scholar, Cross Ref

Sarraf, S., Kar, A. K., & Janssen, M. (2024a). How do system and user characteristics, along with anthropomorphism, impact cognitive absorption of chatbots – Introducing SUCCAST through a mixed methods study. Decision Support Systems, 178.

Sarstedt, M., Adler, S. J., Rau, L., & Schmitt, B. (2024). Using large language models to generate silicon samples in consumer and marketing research: Challenges, opportunities, and guidelines. In Psychology and Marketing (Vol. 41, Issue 6, pp. 1254–1270). John Wiley and Sons Inc.

Schmitt, B. (2024). Transforming qualitative research in phygital settings: the role of generative AI. Qualitative Market Research, 27(3), 523–526.

Shankar, V. (2024). Managing the Twin Faces of AI: A Commentary on “Is AI Changing the World for Better or Worse?” Journal of Micromarketing.

Shi, J., Lee, M., Girish, V. G., Xiao, G., & Lee, C. K. (2024c). Embracing the ChatGPT revolution: unlocking new horizons for tourism. Journal of Hospitality and Tourism Technology, 15(3), 433–448.

Sigala, M., Ooi, K. B., Tan, G. W. H., Aw, E. C. X., Cham, T. H., Dwivedi, Y. K., Kunz, W. H., Letheren, K., Mishra, A., Russell-Bennett, R., & Wirtz, J. (2024). ChatGPT and Service: Opportunities, Challenges, and Research Directions. Journal of Service Theory and Practice, 34(5), 726–737.

Silva, C. A. G. da, Ramos, F.N., de Moraes, R.V., & Santos, E.L. dos. (2024a). ChatGPT: Challenges and Benefits in Software Programming for Higher Education. Sustainability (Switzerland), 16(3).

Indexed at, Google Scholar, Cross Ref

Singh, C. (2025). Is generative AI (artificial intelligence) the next advent in the evolution of finance and navigating financial crime and regulation? Journal of Financial Crime.

Skórnóg, D., & Kmiecik, M. (2023). Supporting the inventory management in the manufacturing company by chatgpt. Log forum, 19(4), 535–554.

Sterne, J., & Davenport, T. H. (2024). Enhancing Voice of Customer Analysis with Generative AI. Applied Marketing Analytics, 10(1), 32–41.

Sun, H., Kim, M. R., Kim, S., & Choi, L. (2025). A methodological exploration of generative artificial intelligence (AI) for efficient qualitative analysis on hotel guests’ delightful experiences. International Journal of Hospitality Management, 124.

Tan, K. P. S., Liu, Y. V., & Litvin, S. W. (2025c). ChatGPT and online service recovery: How potential customers react to managerial responses of negative reviews. Tourism Management, 107.

Thomas, I. (2024). Embracing cookies less advertising with AI. Applied Marketing Analytics, 10(2), 103–115.

Indexed at, Google Scholar, Cross Ref

Thukral, V., Latvala, L., Swenson, M., & Horn, J. (2023). Customer journey optimization using large language models: Best practices and pitfalls in generative AI. Applied Marketing Analytics, 9(3), 281–292.

Vhatkar, M. S., Raut, R. D., Gokhale, R., Kumar, M., Akarte, M., & Ghoshal, S. (2024). Leveraging digital technology in retailing business: Unboxing synergy between omnichannel retail adoption and sustainable retail performance. Journal of Retailing and Consumer Services, 81.

Wang, S., & Zhang, H. (2024). Promoting sustainable development goals through generative artificial intelligence in the digital supply chain: Insights from Chinese tourism SMEs. Sustainable Development.

Wen, Y., & Laporte, S. (2025). Experiential Narratives in Marketing: A Comparison of Generative AI and Human Content. Journal of Public Policy and Marketing.

Yang, Q., & Lee, Y. C. (2024). Enhancing Financial Advisory Services with Gen AI: Consumer Perceptions and Attitudes Through Service-Dominant Logic and Artificial Intelligence Device Use Acceptance Perspectives. Journal of Risk and Financial Management, 17(10).

Yi, J., Oh, Y. K., & Kim, J.-M. (2025). Unveiling the drivers of satisfaction in mobile trading: Contextual mining of retail investor experience through BERTopic and generative AI. Journal of Retailing and Consumer Services, 82.

Indexed at, Google Scholar, Cross Ref

Yin, M., Ma, B., & Pan, X. (2024). Consumer Attitudes Towards Gen AI Advertisements: A Multi-Experiment Comparative Study Based on Engagement. Journal of Cases on Information Technology, 26(1).

Yue, B., & Li, H. (2023). The Impact of Human-AI Collaboration Types on Consumer Evaluation and Usage Intention: A Perspective on Responsibility Attribution. Frontiers in Psychology, 14.

Yue, Y., Ng, S.-I., & Basha, N. K. (2024). Consumption Values, Attitudes and Continuance Intention to Adopt ChatGPT-driven E-Commerce AI Chatbot (LazzieChat). Pakistan Journal of Commerce and Social Sciences, 18(2), 249–284.

Zhang, Y., & Gosline, R. (2023). Human favoritism, not AI aversion: People’s perceptions (and bias) toward generative AI, human experts, and human–GAI collaboration in persuasive content generation. Judgment and Decision Making, 18.

Indexed at, Google Scholar, Cross Ref

Received: 11-Jul-2025, Manuscript No. AMSJ-25-16064; Editor assigned: 12-Jul-2025, PreQC No. AMSJ-25-16064(PQ); Reviewed: 10- Aug-2025, QC No. AMSJ-25-16064; Revised: 26-Aug-2025, Manuscript No. AMSJ-25-16064(R); Published: 07-Sep-2025