Sage Journals: Discover world-class research

Abstract

The cultures of China’s ethnic minorities, with their rich histories, languages, customs, and artistic expressions, form a vital part of Chinese civilization and global cultural heritage. In the digital era, preserving and revitalizing these traditions is increasingly important. This study investigates how Artificial Intelligence Generated Content (AIGC) influences consumer engagement with minority cultures. A questionnaire survey was conducted with 1,005 Chinese respondents to examine three core aspects: (1) the effects of different content production modes (User-Generated Content, Professionally-Generated Content, and AIGC) on consumer behavioral intention and cultural experience; (2) the influence of AIGC content types (functional, emotional, and entertainment) and application scenarios (entertainment, service, and commercial); and (3) the mediating role of flow experience in these relationships. Data were analyzed using PLS-SEM, Pearson correlation, multiple regression, and bootstrapped mediation analysis. Results show that AIGC in service and commercial contexts significantly enhances engagement, with emotional and entertainment content, particularly in PGC formats, enriching cultural experiences. Flow experience mediates most of these relationships. These findings highlight the potential of AIGC to support the digital preservation and interactive transmission of ethnic minority traditions.

Keywords

AIGC technology integration application consumer behavior mind theory cultural dissemination

Introduction

Artificial Intelligence refers to “programs, algorithms, systems, and machines that display intelligence” (Shankar, 2018). Driven by advances in deep learning, computing power, and big data, AI technologies are being increasingly integrated into various sectors. Among them, AIGC (AI generated content) technology has recently emerged as a transformative force in creative industries (Bengesi et al., 2023). AIGC not only enables the reconfiguration and digital enhancement of traditional artworks, but also significantly heightens audience engagement and attention (B. Gao et al., 2023), signifying its growing influence in content production and presentation.

Culture represents the most fundamental, enduring, and intrinsic force in the development of a nation. In today’s globalized world, approximately 3,000 ethnic groups exist across more than 200 countries and regions, with the vast majority of nations composed of multiple ethnic communities (State Council Information Office, 2009). In China, academic studies on ethnic minorities have yielded abundant results and have made notable contributions to the development of minority cultures. However, study on the protection and inheritance of ethnic minority culture is still limited (Z. Zhang, 2023), the methods of cultural protection are relatively simple (Farjami & Türker, 2021), mainly through traditional methods such as written records, audio or video interviews, and video recordings, only collect the actual situation of physical objects (J. Lu et al., 2024).

More structured approaches, including the renovation of ethnic museums, the incorporation of ethnic culture into school curricula, and the promotion of culturally themed tourism, have been partially implemented. Nevertheless, China’s traditional cultural heritage continues to face a critical risk of loss and disconnection from modern life (14th Five-Year Plan for the Protection of Intangible Cultural Heritage, 2021). In this context, it becomes imperative to keep up with the development of the times and constantly explore new ways and means to enable members of the nation and the general public to actively and spontaneously develop the nation’s unique culture.

With the iterative development of global technology, the digital content consumption has become widely embraced by consumers. In response, industries have actively adapted to evolving demands, placing increasing emphasis on technologies that effectively capture user acceptance and engagement (Wang et al., 2025). The emergence of AI technology on the basis of User-Generated Content (UGC) and Professionally-Generated Content (PGC) provides a new choice model for consumers (C. Wang, 2023).

These content production modes UGC, PGC, and now AI-generated content (AIGC) have demonstrated distinct influences on consumer perceptions and behavioral responses (Watts et al., 2021). Compared with traditional media formats, technologically integrated content offers more dynamic forms of interaction and fulfills consumers’ growing preference for personalized experiences (Meier et al., 2024), Furthermore, the application of AI in scene-based services enhances user immersion by embedding cultural content within interactive and contextual environments (Zhao et al., 2022).

Both tangible and intangible aspects of traditional culture continue to resonate with consumers. Cultural experiences, however, are no longer perceived as passive or symbolic engagements. They also carry significant potential for economic development, heritage preservation, and industry innovation. Despite these developments, existing research on consumer purchase behavior and experience with ethnic minority cultural products, especially those embedded in digital or AIGC formats, remains scarce. The literature lacks both theoretical integration and systematic empirical analysis, particularly in cross-cultural contexts (H. Zhang, 2010). Addressing this research gap is essential for understanding how technology mediates cultural participation and consumer decision-making in the digital age.

The protection and transmission of minority cultures are essential for preserving cultural diversity at both national and global levels. These cultures enrich the collective heritage of humanity and play a vital role in promoting cross-cultural exchange, mutual understanding, and trust among diverse communities. Such interactions are fundamental to achieving harmonious coexistence in a pluralistic world. Technological development has enriched the presentation of traditional content, but at the same time, the factors influencing audience consumption behavior have become more complex.

Against this backdrop, the present study explores how consumers experience and respond to AIGC, based on representations of ethnic minority cultures.

The research focuses on three key dimensions: (1) the mode of content production and dissemination (User-Generated Content, Professionally-Generated Content, and Artificial Intelligence-Generated Content); (2) the type of AIGC communication content, distinguishing between functional and entertainment-oriented formats; and (3) the application scenarios in which AIGC content is delivered, including entertainment, service, and commercial contexts. Secondly, based on previous research and actual conditions, this study proposes a mediating variable of flow theory. To investigate whether this factor moderates the cultural experience of ethnic minorities and the consumption intention of AIGC consumers. Accordingly, the study proposes the following research questions: First, does the mode of content production namely User-Generated Content (UGC), Professionally-Generated Content (PGC), and Artificial Intelligence-Generated Content (AIGC) influence consumers’ cultural experience of ethnic minority content and their intention to engage with AIGC-based products?

Second, does the type of AIGC communication content (i.e., functional vs. entertainment-oriented) affect consumers’ cultural experience and their behavioral intention to consume AIGC?

Third, do different application scenarios of AIGC such as entertainment, service, and commercial contexts impact consumers’ cultural experience and their intention to engage with AIGC content?

Fourth, does Flow Theory play a moderating role in the relationship between consumers’ cultural experience and their intention to consume AIGC?

This study argues that the dissemination of China’s minority cultures in the digital age is not only about cultural preservation and ethnic identity but is increasingly reflected in consumers’ behavioral preferences toward cultural products and content in both real-world and digital settings. Especially with the advancement of AIGC (artificial intelligence-generated content) technology, minority cultures can be digitally reconstructed through visual, audio, and interactive forms, making them more appealing and immersive (X. Gao et al., 2024). This re-creation not only enhances the efficiency of cultural dissemination but also influences consumers’ cognitive, emotional, and behavioral responses when encountering related cultural products.

In response to these challenges, this study defines “consumer behavioral intent” as: whether consumers are willing to experience, purchase, or actively disseminate content or products related to minority cultures with the assistance of AIGC technology. “Minority cultures” refer to cultural expressions such as ethnic art, language, beliefs, and festivals that are recreated or interpreted in AIGC media. By integrating content type, technological medium, and psychological mechanisms (immersive Flow experience), this study constructs a comprehensive pathway: cultural content → AIGC re-creation → Flow immersive experience → consumer behavioral intent, to reveal new mechanisms in cultural dissemination within contemporary expression and market interaction (M. Li et al., 2024).

To deepen the theoretical understanding, this paper systematically extends the “immersive experience” theory (Flow Theory) to the context of digital cultural dissemination, filling the interdisciplinary gap in research on the interactive mechanisms between AIGC technology and cultural content. The study constructs a “content generation, user experience behavioral intent” pathway model to reveal how AIGC plays a key role in promoting the acceptance, re-identification, and behavioral transformation of minority cultures (Xinyu et al., 2024).

Practically, the study offers multi-dimensional recommendations for cultural creators, AI developers, and policymakers, such as prioritizing the deployment of emotional or entertainment-oriented AIGC content in commercial and service scenarios to enhance user immersion and stimulate cultural participation. This audience-centric strategy not only facilitates the digital preservation and dissemination of intangible cultural heritage but also provides theoretical support and empirical evidence for cross-cultural exchange and the transformation and upgrading of the cultural and creative industry (Wu, 2025).

Theoretically, this study enriches the interdisciplinary understanding of digital cultural dissemination by integrating Flow Theory with AIGC application scenarios, thereby bridging the gap between consumer psychology and technology-driven cultural production. Practically, the research offers a decision-making framework for stakeholders to design immersive and culturally sensitive digital experiences, which can enhance cultural identity, drive user engagement, and foster the sustainable development of minority cultural industries in both domestic and international markets.

Literature Review

Technology Iteration Reconstructs New Ideas for the Consumption of Traditional Culture of Ethnic Minorities

The development of the digital technology era has not only continuously promoted the cultural industry, but also injected innovative vitality into traditional culture. The advent of the PGC (Professionally Generated Content) era marks a major shift between content creation and traditional communication methods. The emergence of PGC has made the dissemination of content more accurate, and the ability to understand audience preferences and behaviors through data analysis can improve the relevance of content (Malthouse et al., 2013), which in turn establishes a closer relationship with consumers, enhances consumers’ sense of identity and engagement with information, and increases their willingness to consume (Burgess & Green, 2009; Jenkins, 2006). The emergence of UGC (User Generated Content) has given ordinary consumers greater voice and creativity (Kaplan & Haenlein, 2010). Compared with PGC, UGC is characterized by being more personalized, emotional, reflecting real life and personal experience, enhancing consumer resonance. Its stronger interaction mode and engagement experience promote direct communication between the product and consumers and enhance consumers’ willingness to spend and brand loyalty (Bruns, 2008; Z. Ma et al., 2022; A. A. Pathak & Kaushik, 2024). The rise of AIGC has brought revolutionary changes to the content creation model, and at the same time, it has also reshaped the landscape of content dissemination. AIGC (Artificial Intelligence Generated Content) is significantly different from UGC and PGC in terms of communication and content presentation. Through advanced algorithms and machine learning techniques, AIGC is able to quickly generate high-quality content, outpacing human creators in productivity and scale (Anantrasirichai & Bull, 2022). This kind of automatically generated content is not only diverse in form, but can also be personalized to meet the needs of different consumers (Lei & Xing, 2024). However, at present, there are relatively few studies on AICG and consumer consumption intentions and behaviors in China, and more research on the impact of emerging technologies in a large field such as AI on consumers (Han, 2019). However, there are limited studies on AI technology from the perspective of consumer acceptance technology (Wang, Zhang, et al., 2024). Moreover, some studies have found that the application of AI in a specific environment may not have the same beneficial effect (S. J. Shi et al., 2024), so if we want to examine the argument of AIGC for consumers, we can only rely on previous research, indirect and preliminary understanding (L. Ma, 2024).

The organic combination of UGC and PGC solves the problem of one-way reception of content by the audience in traditional communication, and promotes the acceptance and inheritance of traditional culture by consumers in different ways in the dissemination of traditional culture. Bennett (2011) found that UGC provides a platform for ordinary users to share their personal understanding of traditional culture, making traditional culture more accessible and acceptable among the younger generation, stimulating interest, and promoting a sense of participation, thereby promoting the modern re-creation of traditional culture (Zhang et al., 2025). PGC, on the other hand, has professionally interpreted and promoted traditional culture through high-quality documents, documentaries, and cultural products, thereby enhancing its authority and influence (Koutromanos et al., 2023). The emergence of these two modes of production has enabled consumers to not only engage with traditional culture in an entertaining way, but also to understand its value and significance at a deeper level (Y. Li & Zhao, 2016). The advent of AI technology can analyze user preferences and generate personalized cultural content, especially virtual reality experiences, interactive applications, and digital art, which can effectively attract young people’s interest in traditional culture (Karterouli et al., 2021). AI lowers the barrier to cultural participation by automating and integrating large amounts of cultural data, making traditional culture easier to disseminate and share. Making complex traditional cultures more visible and understandable enhances consumers’ sense of identity and emotional connection . By analyzing consumer behavior, the data constructs consumer interest portraits and pushes personalized content for them, so that consumers’ experience of content can change from “let me see” to “I want” and finally “I recognize” (Zhang et al., 2024). Not only that, the interactive nature of AI technology enables consumers to actively participate in the creation and transmission of culture, encouraging them to share their personal stories and experiences, further promoting the active transmission of traditional culture (N. Wang et al., 2019). It not only injects new vitality into the dissemination of traditional culture, but also promotes cultural diversification and sustainable development.

National culture is the result of the long-term accumulation of national history. It forms the foundation of a nation’s current experience and serves as a pillar for its future development. In this context, the digital age introduces transformative potentials for cultural expression and communication. UGC, PGC, and AIGC represent three distinct modes of content production. Investigating their respective effects on public acceptance of ethnic minority culture and cultural consumption behavior is both theoretically significant and practically relevant (X. Huang et al., 2023; Niu, 2025). These modes differ in technical architecture, interactivity, and narrative engagement, all of which affect consumer cognition, emotional involvement, and identity formation (R. C. Pathak et al., 2025; Yu et al., 2024).

First, exploring the structural characteristics of each content mode helps us understand how to enhance the dissemination efficiency of minority cultures in an era of information overload. Studies show that while PGC ensures credibility and professional appeal (X. Huang et al., 2023), UGC fosters authenticity and emotional proximity among audiences (Atf et al., 2024). AIGC, with its algorithm-driven personalization and scalability, can dynamically align cultural narratives with user values and behavioral profiles (Ding et al., 2025; J. H. Han & Bae, 2022).

Second, the analysis of cultural acceptance and consumption willingness across these three production modes reveals not only technological influence but also the socio-psychological mechanisms of cultural experience (Babadoğan, 2024; Bögel & Upham, 2018). This perspective provides an empirical basis for market-oriented cultural communication strategies and supports adaptive policymaking in cultural industries.

Findings in recent studies affirm that when technologies are appropriately integrated into cultural narratives, they strengthen emotional resonance and collective memory, and can thus promote sustainable preservation and intergenerational transmission of intangible cultural heritage (Kasemsarn & Nickpour, 2025; Perera et al., 2024). Digital tools also provide cultural workers and policymakers with new paradigms to design more inclusive, diversified communication channels for ethnic minorities (Higgins et al., 2023).

Ultimately, when supported by participatory technology frameworks and consumer-centered content strategies, traditional cultures can evolve and thrive in contemporary society. This digitally mediated reactivation of cultural practices not only revitalizes ethnic identity but also contributes to the broader goal of cultural pluralism and national cohesion. Based on this, the study proposes Hypothesis 1:

H1: Different content production modes (UGC, PGC, AIGC) significantly impact consumers’ traditional cultural experience and willingness to engage in cultural consumption.

The Impact of AIGC Application on Traditional Cultural Acceptance Behavior

In the era of digital media, consumers receive more and more diverse types of information and are more and more interactive (Chen & Zhi, 2021). The type of market has shifted from audience demand to the market of ideas, and the pursuit of spiritual content has become an inevitable (C. Wang, 2023). Studies have found that different types of information have an impact on consumer behavior (Behnke et al., 2024). Different scholars have different definitions of content types, and content types are roughly divided into functional, emotional, entertaining, social, and informational (Sardar et al., 2024). Functional information often provides practical knowledge or guidance that can effectively meet consumers’ information needs and facilitate decision-making and action . The accuracy and clarity of this information tends to increase consumer trust, making them more inclined to act on advice. In contrast, entertainment information focuses on attracting attention and providing pleasure, and it increases the rate of sharing information by enhancing consumer engagement through humor, storyline, or visuals (M. Eisend, 2017). Emotional messages promote emotional connection and brand loyalty by elicit emotional resonance with the audience, such as sympathy, happiness, or nostalgia (Ladhari et al., 2017). The study also found that informational content boosts consumer engagement in the pre-consumer stage, while entertainment content increases consumer engagement in the post-consumer stage (Kim et al., 2019). Sardar et al. (2024) pointed out that different content attributes affect consumers’ purchase intention, and consumer participation plays a mediating role in content characteristics and purchase intention. Information often strengthens consumers’ sense of identity and makes them more willing to engage with relevant content or social activities. Therefore, it is of great significance to understand the communication characteristics of these different types of information and their impact on consumers to develop effective communication strategies. Therefore, the study further proposes Hypothesis 2:

H2: The information type of AIGC content (functional, emotional, entertainment) significantly influences consumers’ cultural acceptance and behavioral intentions.

On the other hand, the different presentation methods of scenes not only affect consumers’ sense of experience and participation, but also affect the way consumers process information, so that they may rely more on emotional, practical or commercial interests when making decisions (Yin & Qin, 2024). The scene is a situation constructed in a specific spatio-temporal environment for specific participation themes to interact and connect in a specific way, and the spatiotemporal environment, participating subjects, and interaction methods are indispensable participation elements (Z. Ma & Lv, 2023). O. Duralia (2024) proposes that in the entertainment scene, the content is usually interesting and interactive, aiming to attract the audience’s attention and provide a pleasant experience, thereby promoting consumers’ active participation and information sharing. This kind of scene can enhance emotional resonance and increase consumers’ identification with the brand or content. In contrast, the service scenario emphasizes practicality and functionality, and provides an efficient service experience while meeting the specific needs of consumers (Bitner, 1992). Compared with the first two, commercial scenarios are also one of the types that have long emerged, influencing consumers’ purchase decisions through advertising, promotion and other means (Guo et al., 2020), and the paradigm research of application scenarios is helpful to standardize their personalized application in various fields. Understanding the differences in the application of different scenarios is essential to developing an effective content distribution strategy. Based on this scenario analysis, the study proposes Hypothesis 3:

H3: AIGC application scenarios (entertainment, service, commercial) significantly affect consumers’ perception, immersion, and cultural engagement.

The linkage between content and scene innovation can promote the deep integration of consumer sensory experience and communication content (Zhan et al., 2023). With the support of artificial intelligence technology, it can help to enhance the sense of presence of content and bring consumers a new interactive experience (Sun et al., 2024). In China, many studies on AIGC focus on AIGC-related technological innovations, application fields, and its risk and ethical challenges, but ignore consumers’ consumption willingness and propensity in the process of AIGC development and application (Li et al., 2024). In the era of digital economy, AI has become an important driving force to promote the transformation and productivity of enterprises, and unlike traditional computers, AI has powerful algorithms and interaction mechanisms, which can conduct deeper interaction with audiences in different application scenarios. There may be new breakthroughs in AIGC’s capabilities in the future. At this stage, the dimension of China’s exploration of the impact of AIGC communication content and scenario application on consumers should be further expanded, and the impact on consumers’ behavioral willingness needs to be further verified.

Application of Flow Theory in the Presentation of AIGC Technology

The term “Flow Theory” was first coined in 1975 by United States psychologist Csikszentmihalyi. It refers to a state of mind in which oneself is naturally caught up in what one is doing like a stream of water. That is, when people are engaged in a certain daily activity, they are fully engaged and focused on the corresponding situation, filtering out other feelings that are not relevant (Biasutti, 2011).

Flow theory is studied in many fields such as psychology, art, sports, science, sociology, etc., and with the development of computer technology, flow theory has expanded to the study of human interaction and influenced the development of virtual reality (Zeng, 2015). Such technology is also widely used and researched in different spaces, such as museums, media art spaces, exhibition spaces, theme parks, healing spaces, and actively studies the value and model of flow theory input in space design. Flow-oriented design means making great use of space and stimulating the imagination. The introduction of flow theory design can greatly improve the sense of authenticity and experience, and the virtual reality presented in this way will coordinate the elements of reality and art to provide users with a realistic and perceptual experience (Z. Zhou & Yoon, 2024). Characterized by its deep immersion and full engagement with the character, immersive experiences are highly recognized for their positive psychological effects (Hang et al., 2024), and the significant effects of flow theory on user experience have also been found in the field of design in the field of artificial intelligence (Bae & Park, 2022).

Flow experience functions not only as a direct psychological outcome of high-quality digital content interaction but also as a mediating mechanism that connects external digital stimuli such as content format, informational richness, and contextual delivery with users’ cognitive and emotional responses. Recent research demonstrates that users in a flow state engage in deeper information processing and experience heightened emotional resonance, which strengthens content identification and significantly enhances behavioral engagement (W. W. Dong et al., 2023; Fang, 2024; Qin et al., 2023). As such, the flow mechanism offers a theoretical lens to understand how various cultural and technological variables may indirectly influence consumption intentions and cultural identity formation.

Empirical evidence further supports that flow states, particularly within contexts like livestreaming e-commerce or immersive virtual reality, amplify perceived enjoyment, time distortion, and attentional focus. These factors in turn predict users’ emotional and cognitive alignment with content stimuli (Song & Lu, 2024). Moreover, users experiencing flow are more likely to derive perceived value and satisfaction, two key predictors of sustained engagement and loyalty across digital platforms (Park et al., 2023). These findings reinforce the indirect effect role of flow experience and validate its function as a conduit through which content design and digital features impact downstream psychological and behavioral outcomes.

Recent scholarship also reveals that the degree and nature of flow experience modulate how users interpret and respond to digital content. D. Zhou et al. (2025), for example, emphasized that variations in cognitive immersion and emotional resonance within flow states significantly influence how users evaluate content quality and cultural relevance. Similarly, Z. Zhang et al. (2024) demonstrated that flow experience mediates the relationship between perceived algorithmic evaluation and service performance in the gig economy. Their study underscores how differentiated cognitive and affective processes, shaped by contextual factors such as viability challenges, impact user engagement and task outcomes.

Furthermore, studies have found that differences in the degree and type of flow experience will also have an impact on the experiencer’s perception of receiving the content (Samira et al., 2023), and Thomas and Baral (2023) also found that the flow theory design is conveyed through different paths, and the experiencer’s actions, cognition, and emotions will have different impacts. This leads to Hypothesis 4, Hypothesis 5:

H4: Flow experience mediates the relationship between content production modes and consumers’ willingness to participate in traditional culture.

H5: Flow experience mediates the relationship between information types and perceived cultural identity.

The introduction of AIGC technology has even been continuously upgraded, providing users with a completely different flow experience from the past. It is of great value to investigate the application of flow theory in the design of AIGC technology, and to mediate the influences on the contact, feeling and effect of experiencers, and to design AIGC technology-related applications in the future (Figure 1).

H6: Flow experience mediates the relationship between scenario presentation and consumers’ behavioral intention regarding traditional culture.

Figure 1.

Conceptual model of AIGC influence via mind theory.

Methodology

Population and Sampling Method

The population targeted in this study comprised Chinese digital content consumers who have been exposed to cultural representations of ethnic minorities, particularly in digital contexts involving Artificial Intelligence-Generated Content (AIGC). To enhance the representativeness of the findings and reduce sampling bias, a stratified random sampling method was employed. This approach stratified the population by four key demographic variables: geographical region, gender, age group, and ethnicity, ensuring proportional representation of diverse subgroups within the population. Such a sampling strategy is particularly suitable for studies aiming to generalize findings to a broader heterogeneous population (Alawadh et al., 2024).

The sample size consisted of 1,005 valid respondents, drawn from a widely used Chinese online survey platform during the period from September 1 to September 10, 2024. The final sample included 134 individuals identifying as ethnic minorities, with gender representation comprising 453 males and 552 females. The sample skewed younger, with 69.8% of respondents aged 18 to 25 and 73.0% possessing a university degree or higher. Additionally, to account for geographical diversity, the sample included participants from both urban and rural regions, with a notably high proportion from Sichuan Province, a region known for its ethnic heterogeneity.

The data collection adhered to ethical research standards. Participants were informed of the purpose and potential risks of the study and provided written informed consent. The principles of voluntary participation, anonymity, and confidentiality were strictly followed throughout the data collection process, which was previously detailed in the results section and is here appropriately relocated under methodology in accordance with reviewer feedback.

The high internal consistency and construct validity of the survey instrument (Cronbach’s α = .935; KMO = 0.951) further confirm the robustness of the data collection process.

Measures

To assess Chinese consumers’ perceptions, behaviors, and psychological engagement with ethnic minority cultural content in AI-generated digital contexts, the study employed a structured questionnaire composed of six key constructs. All scales were scored on a 5-point Likert scale (1 = strongly disagree; 5 = strongly agree). Unless otherwise noted, all items were adapted from existing literature or developed based on validated conceptual frameworks. Cronbach’s alpha values for all subscales exceeded the .85 threshold, indicating high internal consistency.

Content Production Mode

This 9-item scale was adapted from prior studies on digital content typologies and user engagement patterns (Leng et al., 2024; Ntoa et al., 2021; Zhang, 2024). It measured participants’ attitudes toward three content production models: Professionally Generated Content (PGC), User Generated Content (UGC), and Artificial Intelligence Generated Content (AIGC). Items assessed browsing habits, engagement (like/comment/share), and trust in different sources.

AIGC Communication Content

Based on information classification theory, this 9-item scale measured how users perceive AIGC-generated content across three dimensions: functional, emotional, and entertainment (Cover et al., 2024; Tang, 2023). Items were specifically contextualized to content related to ethnic minority intangible cultural heritage (ICH). Sample items include beliefs about value, relevance, resonance, and engagement.

Application Scenarios of AIGC

This 9-item scale categorized users’ willingness to use AIGC in entertainment (e.g., gaming, museums), service (e.g., government services, writing tools), and commercial (e.g., virtual live-streaming, AI endorsements) contexts. Based on use case taxonomies in emerging AI adoption literature (Kim, 2024; C. Wang, 2023; Zhan et al., 2023).

Flow Theory

Flow theory refers to the immersive experience in which the audience’s attention is highly invested in a certain thing, and the time perception is unbalanced (Doğan et al., 2024), referring to the “Flow State Scale” (FSS) compiled by Australia scholars Jackson and Marsh in 1996. The adapted 3-item version focused on immersion, time distortion, and attentional absorption while engaging with ethnic minority content online.

AIGC Consumption Intention

Adapted from Silva-Paz et al. (2024), this 3-item scale assessed users’ willingness to purchase, experience, or follow products/services that apply AIGC. It captures consumer purchase behavior in the context of AI-enhanced digital environments.

Ethnic Minority Cultural Experience

This 3-item scale was developed based on the framework by R. Lu and Li (2024) to evaluate how exposure to ethnic minority ICH via digital platforms fosters interest, engagement, and recognition of cultural distinctiveness. It captured the cognitive and emotional engagement of users in online cultural interaction.

Data Analysis Procedures

To systematically examine the proposed research hypotheses and validate the conceptual framework, a multi-step data analysis strategy was employed. Each step was theoretically driven and methodologically justified to ensure robustness, coherence, and empirical rigor across all stages of the study.

Step 1: Reliability and Sampling Adequacy Evaluation

To establish the foundational quality of the dataset and measurement instruments, descriptive statistics were used to summarize demographic characteristics of the sample, ensuring representativeness and diversity. Internal consistency of all constructs was evaluated through Cronbach’s alpha, while the Kaiser-Meyer-Olkin (KMO) test was used to confirm sampling adequacy for factor-based analyses. This step was essential to ensure the psychometric robustness of the scales before proceeding to structural modeling.

Step 2: Measurement Model Assessment via PLS-SEM

To assess the reliability and validity of latent constructs, the measurement model was evaluated using Partial Least Squares Structural Equation Modeling (PLS-SEM). Metrics including Composite Reliability (CR), Average Variance Extracted (AVE), Cronbach’s alpha, and Dillon-Goldstein’s rho were computed. The use of PLS-SEM at this stage was particularly appropriate due to its suitability for exploratory models and complex constructs involving psychological and behavioral dimensions. This analysis ensured that all measurement indicators reliably reflected their underlying theoretical constructs, thus reinforcing the construct validity of the research model.

Step 3: Correlation and Regression Analyses for Hypothesis Testing

To preliminarily understand the linear associations between the key variables, Pearson correlation analysis was conducted. This was followed by multiple linear regression analyses to evaluate the predictive effects of content production modes (UGC, PGC, AIGC), AIGC content types (functional, emotional, entertainment), and application scenarios (entertainment, service, commercial) on two key outcomes: AIGC consumption intention and ethnic minority cultural experience. The regression approach allowed for the isolation of unique effects within multivariate settings and addressed potential confounding relationships, thus providing clarity on the direct influence of independent variables on outcome variables.

Step 4: Mediation Analysis to Examine Psychological Mechanisms

To investigate the internal psychological process linking external stimuli (content type, production mode, scenario) to consumer response, flow experience was tested as a mediating variable. Mediation was assessed using bootstrapping (5,000 samples, 95% CI) as recommended by Hayes, which allowed for non-parametric estimation of indirect effects without assuming normal distribution. This step was critical to uncover the cognitive-affective pathways through which immersive digital content influences cultural engagement and consumption behavior, thereby deepening the theoretical contribution of the study.

Taken together, these four steps ensured a methodologically rigorous, theoretically grounded, and empirically validated examination of how AIGC-based digital content influences consumer behavior and cultural perception through psychological immersion.

Results

Measurement Model Evaluation

The reliability and validity of the measurement model were evaluated using several statistical indicators, including Composite Reliability (CR), Average Variance Extracted (AVE), Cronbach’s alpha, Dillon-Goldstein’s rho (DG.rho), and eigenvalue decomposition. As presented in Table 1, all constructs surpassed the commonly accepted thresholds CR and Cronbach’s alpha values above .70, AVE values above .50, and DG.rho values above .80 indicating strong internal consistency and convergent validity. Additionally, principal component analysis confirmed the unidimensionality of each construct, as the first eigenvalue (Eig.1st) was substantially larger than the second eigenvalue (Eig.2nd), eliminating concerns of multidimensionality. Collectively, these results affirm that the measurement model possesses robust psychometric properties and provides a sound basis for the subsequent structural model analysis.

Table 1.

Measurement Model Reliability and Validity.

Construct		CR	AVE	Cronbach α	DG.rho	Eig.1st	Eig.2nd
PGC	A1-A3	0.92	0.793	.869	0.92	2.38	0.341
UGC	B1-B3	0.938	0.834	.9	0.938	2.5	0.273
AIGC	C1-C3	0.901	0.753	.835	0.901	2.26	0.5
Functional	D1-D3	0.941	0.841	.906	0.941	2.53	0.281
Emotional	E1-E3	0.939	0.837	.903	0.939	2.51	0.296
Entertainment	F1-F3	0.926	0.805	.879	0.926	2.42	0.354
Entertainment_scene	G1-G3	0.929	0.813	.885	0.929	2.44	0.304
Service_scene	H1-H3	0.893	0.735	.821	0.893	2.21	0.446
Commercial_scene	I1-I3	0.935	0.827	.895	0.935	2.48	0.333
Mind_theory	N1-N3	0.95	0.862	.92	0.95	2.59	0.233
Consumer_behavioral_intention	J1-J3	0.942	0.843	.907	0.942	2.53	0.285
Cultural_experience	O1-O3	0.953	0.87	.926	0.953	2.61	0.199

The structural model was evaluated using path coefficients, factor loadings, and reliability statistics. Standardized path coefficients (β) were analyzed to assess direct relationships among constructs. As shown in Figure 2. flow experience (“Mind_theory”) significantly predicted both AIGC consumption intention (β = .512) and ethnic minority cultural experience (β = .890), indicating a strong mediating effect.

Figure 2.

PLS-SEM path model.

Among the antecedents, entertainment information had the strongest influence on flow experience (β = .339), followed by functional information (β = .171), emotional information (β = .034), and entertainment scenarios (β = .215). Service and commercial scenarios had relatively weaker effects (β = .031 and β = .030, respectively).

Interestingly, no significant direct paths were found from UGC, PGC, or AIGC production modes to behavioral or cultural outcomes. These results reinforce the central role of psychological immersion in mediating the effects of content attributes on behavioral intention and cultural engagement.

Correlation Analysis of Content Production Mode, Information Type of AIGC Communication Content, Application Scenarios, AIGC Consumption Intention, and Ethnic Minority Cultural Experience

In this study, the Pearson analysis method was used to determine the relevance of each index in the dimension of content production mode to AIGC consumption intention and ethnic minority cultural experience according to the data results. The results of the analysis are shown in the following table:

From the results of Table 2, it can be seen that the correlations between UGC, PGC and AIGC and AIGC consumption intention were 0.488, 0.537, and 0.727, respectively, and p < .01, indicating that the correlation between the variables was significant. The correlations between functional, affective and entertainment information and AIGC consumption intention were 0.403, 0.543, and 0.526, respectively, and p < .01, indicating that the correlation between each variable and AIGC consumption intention was significant in the dimension of AIGC communication content. At the same time, the correlation coefficients between entertainment scenes, service scenarios, and commercial scenarios and AIGC consumption intention were .583, .674, and .844, respectively, and p < .01, indicating that each variable in the AIGC application scenario dimension had a significant correlation with AIGC consumption intention. Based on this, it can be seen that the variables in three dimensions, including content production mode (UGC, PGC, AIGC), AIGC dissemination content (functional information, emotional information, entertainment information), and application scenarios (entertainment scenarios, service scenarios, commercial scenarios), have an impact on AIGC consumption intention.

Table 2.

Correlation Analysis of Content Production Mode, AIGC Content, and Application Scenarios with Consumption Intention and Cultural Experience.

	AIGC consumption intention	Ethnic minority cultural experience
UGC	.488**	.563**
PGC	.537**	.655**
AIGC	.727**	.572**
Functional information	.403**	.652**
Entertainment information	.543**	.726**
Emotional information	.526**	.738**
Entertainment scenarios	.583**	.678**
Service scenarios	.674**	.608**
Commercial scenarios	.844**	.528**

At the level of .01 (two-tailed), the correlation is significant.

Meanwhile, the correlations between UGC, PGC, and AIGC and ethnic minority cultural experience were 0.563, 0.655, and 0.572, respectively, and p < .01, indicating that the correlation between the variables was significant. Secondly, the correlation between functional, affective and entertainment information and ethnic minority cultural experience were .652, .726, and .738, respectively, and p < .01, indicating that the correlation between each variable and ethnic minority cultural experience was significant in each AIGC communication content dimension. Finally, the correlation coefficients between the entertainment scene, the service scenario, and the commercial scene and the cultural experience of ethnic minorities were .678, .608, and .528, respectively, and the p was <.01, indicating that the variables in the application scenario dimension of AIGC had a significant correlation with the cultural experience of ethnic minorities. Based on this, it can be seen that the variables have an impact on the cultural experience of ethnic minorities in three dimensions: content production mode (UGC, PGC, AIGC), AIGC dissemination content (functional information, emotional information, entertainment information) and application scenarios (entertainment scenarios, service scenarios, commercial scenarios).

From the data results, it can be seen that each variable has an impact on the consumption intention of AIGC and the cultural experience of ethnic minorities in three dimensions: content production mode, AIGC communication content, and application scenarios, which effectively verifies research questions 1, 2, and 3. The correlation coefficients were all in the range of .4 to .9, indicating that each variable had a positive impact on the consumption intention of AIGC and the cultural experience of ethnic minorities.

Regression Analysis of Content Production Mode, Information Type of AIGC Communication Content, Application Scenarios, AIGC Consumption Intention, and the Cultural Experience Ethnic Minorities

In this part, multiple linear regression method will be used to analyze the causal relationship between the consumers’ AIGC consumption intention and ethnic minority cultural experience from the content production mode, information type of AIGC communication content, and application scenarios.

As can be seen from Table 3, the multiple regression analysis results of AIGC consumption willingness as the dependent variable are meaningful with the content production mode, AIGC dissemination content, and application scenarios as independent variables. and R² = .755, good goodness of fit. F = 340.756, p < .001, indicating that the regression equation is significant.

Table 3.

Regression Analysis of Content Production Mode, AIGC Information Type, Application Scenarios, and Consumption Intention.^a

Model	Unnormalized coefficient		Normalized coefficient	t	Significance	Collinearity diagnosis
Model	B	Standard error	Beta	t	Significance	Tolerance	VIF
(Constant)	0.077	0.086		0.904	0.366
AIGC	0.163	0.028	.151	5.749	0.000	0.357	2.801
Service scenarios	0.133	0.034	.112	3.853	0.000	0.294	3.402
Commercial scenarios	0.566	0.023	.608	24.446	0.000	0.397	2.516

R² = .755. F = 340.756. p < .001.

Dependent variable: consumption intention.

The VIF was less than 5 and the tolerance was greater than 0.2, indicating that there was no multicollinearity between the variables. The significance of the three variables was less than 0.05, indicating that the AIGC (β = .151, p < .001) in the dimension of content production mode, the service scenario (β = .112, p < .001) and the commercial scenario (β = .608, p < .001) had a significant positive impact on the public’s AIGC consumption intention.

Table 4 shows multiple regression analysis with content production mode, AIGC communication content, and application scenario as independent variables, and AIGC consumption intention as dependent variables are meaningful. R² = .623, indicating that the regression model had good goodness of fit, and F = 340.756, p < .001, and the regression equation was significant.

Table 4.

Regression Analysis of Content Production Mode, AIGC Information Type, Application Scenarios, and Ethnic Minority Cultural Experience.

Model	Unnormalized coefficient		Normalized coefficient	t	Significance	Collinearity diagnosis
Model	B	Standard error	Beta	t	Significance	Tolerance	VIF
(Constant)	0.346	0.091		3.804	0.000
PGC	0.108	0.034	.109	3.217	0.001	0.328	3.047
Emotional information	0.167	0.046	.165	3.622	0.000	0.182	5.497
Entertainment information	0.279	0.047	.269	5.877	0.000	0.181	5.534
Entertainment scenarios	0.147	0.040	.140	3.690	0.000	0.263	3.801
Commercial scenarios	0.122	0.025	.154	4.971	0.000	0.397	2.516

R² = .623. F = 182.704. p < .001.

Dependent variable: the cultural experience ethnic minorities.

The significance of the five variables in the three dimensions was less than 0.05, indicating that PGC (β = .109, p < .001) in the dimension of content production mode, emotional information (β = .165, p < .001) and entertainment information (β = .269, p < .001) in the dimension of content dissemination by AIGC, as well as entertainment scene (β = .140, p < .001) and business scene (β = .154, p < .001) in the dimension of application scenario < .001) The five variables had a significant positive impact on the cultural experience of ethnic minorities.

In summary, AIGC and its application in service scenarios and commercial scenarios have a significant positive impact on the consumption intention of the public’s AIGC, while the variables such as PGC, emotional information, entertainment information, entertainment scene and commercial scene have a significant positive impact on the cultural experience of ethnic minorities.

The Mediating Effect of Flow Theory

From the analysis of correlation data, it can be found that there are significant correlations between the variables under the three dimensions of content generation mode, AIGC communication content and application scenarios, and the public’s intentions to consume AIGC and the experience of ethnic minority culture. However, flow theory may affect the final decision-making behavior of users during the user experience process, so flow theory is introduced as a mediating variable. The Bootstrap method recommended by Hayes was used to test the mediating effect of flow theory, and the number of bootstrap sessions was set to 5,000, and the confidence interval was 95%, and the data results were as follows.

Based on the data results(Table 5), the indirect effects of flow theory are 0.0941 and 0.2650, respectively, and the 95% confidence intervals of Bootstrap are [0.0251, 0.1626] and [0.1557, 0.3654], respectively. However, if the confidence interval is [−0.1017, 0.0176] and the interval includes 0, the mediating effect is not established.

Table 5.

Analysis of the Mediating Effect of Flow Theory on AIGC Consumption Intention.

Independent variable	Indirect effect	Bootstrap 95%CI
Independent variable	Indirect effect	Upper limit	Lower limit
Content production mode	0.0941	0.0251	0.1626
AIGC communication content	0.2650	0.1557	0.3654
Application scenarios	−0.0414	−0.1017	0.0176

Table 6 shows that the indirect effects of flow theory are 0.4888, 0.5242, and 0.4754, respectively, when the content production mode, AIGC communication content, and application scenarios affect the cultural experience path of ethnic minorities. And the 95% confidence intervals of Bootstrap are [0.4349, 0.5247], [0.4438, 0.5994], [0.4181, 0.5382], respectively, and the interval does not include 0, indicating that the intermediary is established.

Table 6.

Analysis of the Mediating Effect of Flow Theory on the Cultural Experience of Ethnic Minorities.

Independent variable	Indirect effect	Bootstrap 95%CI
Independent variable	Indirect effect	Upper limit	Lower limit
Content production mode	0.4888	0.4349	0.5247
AIGC communication content	0.5242	0.4438	0.5994
Application scenarios	−0.4754	0.4181	0.5382

The results of this data solve the fourth research question, in the content production mode, AIGC communication content, and application scenarios on the public’s AIGC consumption intention, and the cultural experience of ethnic minorities, flow theory has a mediating role in the content production mode, AIGC communication content, and AIGC consumption intention, and ethnic minority cultural experience. However, in the application scenario influence path, flow theory only mediates the cultural experience of ethnic minorities, but does not mediate the consumption intention of AIGC.

Discussion and Conclusion

Summary of Findings

This study empirically examined how AIGC technology influences consumer behavioral intention and ethnic minority cultural experience within digital environments. Drawing from a sample of 1,005 Chinese respondents obtained through stratified random sampling, the research analyzed the effects of three core dimensions content production mode (UGC, PGC, AIGC), AIGC content types (functional, emotional, entertainment), and application scenarios (entertainment, service, commercial) on two outcome variables: behavioral intention and cultural experience. The analytical strategy employed advanced methods, including PLS-SEM path modeling, multiple linear regression, Pearson correlation analysis, and bootstrap-based mediation testing. This methodological rigor ensured both statistical reliability and theoretical coherence.

The findings show that AIGC is dominant in influencing behavioral intention, particularly in service and commerce-oriented digital scenarios. This suggests algorithmic content is most persuasive when embedded in goal-driven or transactional contexts. Conversely, PGC had a more substantial effect on enhancing users’ engagement with ethnic minority culture, underscoring its value in conveying depth, authenticity, and cultural nuance (Xinyu et al., 2024). Among AIGC content types, emotionally expressive and entertainment-focused content were most effective in fostering both intention and experience, reflecting the importance of affective engagement in digital cultural interfaces (Zhang et al., 2024).

Notably, the study identified flow experience as a key mediating mechanism explaining how content configurations shape cognitive-cultural and behavioral responses. This mediation effect was consistently significant across most pathways, particularly for content type and production mode. Still, it was absent in the application scenario-to-intention pathway, where direct situational cues appeared to override immersive psychological processing. However, the study also recognizes that a high level of technological provision does not always yield better outcomes. The mere application of emerging technologies or interactive features does not inherently promote positive user attitudes (Choi et al., 2022). The efficacy of flow experience in such learning environments is intricately tied to the quality of interaction (Doğan et al., 2024). Furthermore, while AIGC technology continues to evolve, its novel deployment in specific environments does not automatically translate into more favorable learning effects (W. Shi et al., 2025). Collectively, these results offer empirical validation for all proposed hypotheses while also cautioning against over-reliance on technological novelty without contextual alignment.

Theoretical Contributions

This study contributes to the theoretical literature in three major ways. First, it extends Flow Theory beyond its conventional domains of marketing and gaming into the relatively underexplored territory of digital cultural engagement. By positioning flow as a mediating cognitive-affective mechanism, the study confirms that immersion not only enhances user enjoyment but also deepens cultural interpretation and behavioral activation. This application marks a significant theoretical expansion of flow’s domain, suggesting its relevance in socially significant and identity-forming contexts such as ethnic cultural heritage (Z. Huang et al., 2024).

Second, the study proposes and validates an integrative model that connects digital content production paradigms (UGC, PGC, AIGC) with layered psychological responses and behavioral outcomes. Unlike earlier models that examined these elements in isolation, this framework foregrounds the interplay between production logic, content perception, and user cognition. In doing so, it clarifies how different forms of digital mediation influence affective immersion and behavioral response through distinct psychological routes. This theoretical positioning fills a critical gap in literature concerning AIGC’s influence on cultural re-identification and digital heritage consumption (A. Gao, 2024).

Third, the research reconceptualizes AIGC not merely as a technological innovation but as a cultural interface, a platform through which values, traditions, and identities are rearticulated digitally. It highlights AIGC’s role in shaping cross-cultural narratives and user experiences by linking digital communication technologies with the affective and cognitive dimensions of cultural psychology. This reconceptualization provides a novel theoretical bridge between AI systems, human engagement, and cultural meaning-making, contributing to a broader understanding of how technological environments reshape human interaction with intangible cultural heritage (H. Dong, 2025; Yi, 2025).

Practical Implications

The study yields a set of actionable recommendations for digital content developers, AI system designers, cultural institutions, and policy stakeholders. First, emotionally and entertainment-centered AIGC content should be prioritized in campaigns and platforms aimed at promoting minority cultures, as this type of content most effectively triggers user engagement and immersive response. Incorporating adaptive narrative elements, esthetic enhancements, and emotionally resonant imagery into AI-generated cultural content can elevate the user’s experiential quality, making intangible cultural expressions more relatable and memorable (Liu et al., 2024).

Second, hybrid models that combine the narrative authenticity of PGC with the generative flexibility and scalability of AIGC are particularly promising. Such configurations allow for culturally rich storytelling while benefiting from the efficiency and personalization capabilities of AI. Policymakers and funding agencies should actively support these collaborative models, especially initiatives led by ethnic minority creators or community representatives. These efforts would not only enhance representation but also promote digital inclusivity and equity (Chiang et al., 2025).

Third, AIGC platforms should be designed with user-centered and culturally adaptive principles. To enhance flow and cultural resonance, systems must consider user segmentation by age, region, ethnicity, and technological literacy. To optimize engagement, features like personalized content delivery, multilingual support, interactive learning modules, and feedback loops should be integrated. However, as highlighted by recent literature, technological enhancement alone does not guarantee user satisfaction or learning efficacy. The quality and relevance of interaction play a pivotal role (Choi et al., 2022; Doğan et al., 2024). For digital natives in particular who play a central role in shaping and disseminating digital culture, such inclusive and context-aware design approaches can significantly strengthen the emotional and cultural impact of AIGC experiences (Liang & Ren, 2025).

Despite the study’s contributions, several limitations must be acknowledged. The sample was limited to Chinese digital users, which, while appropriate for the research context, constrains the generalizability of findings to other cultural or national settings. With the rise of technologies like AIGC, prior research has emphasized the importance of cross-cultural sampling to enhance the validity of consumer behavior models in distribution contexts (Nguyen et al., 2021; Rai et al., 2023; Yoon-Joo & Ahn, 2021). The reliance on self-reported survey data may also introduce common method bias, suggesting the value of incorporating behavioral or physiological measures in future work. Additionally, the cross-sectional design prevents the examination of temporal shifts in AIGC engagement or cultural perception over time. Finally, although the study highlights flow experience as a key mediator, it does not explore potential moderators such as digital literacy, prior cultural exposure, or AI trust, which could provide deeper insight into user variability. Future research should consider cross-cultural comparisons, longitudinal tracking, and extended models to address these gaps and build on the present study’s framework.

Footnotes

ORCID iD

Zhongzheng Fang

Ethical Considerations

The procedures used in this study adhere to the tenets of the Declaration of Helsinki.

Consent to Participate

The potential risks associated with this study were clearly explained to all participants prior to obtaining written informed consent. The study was conducted in accordance with the ethical standards of the Declaration of Helsinki and the APA Ethical Principles of Psychologists (Section 8.05). Given the nature of the research, which involved an anonymous online questionnaire with no sensitive or intrusive questions, formal ethical approval was not required by Xihua University. Participants were informed of the study’s purpose and their right to withdraw at any time. Informed consent was obtained before participation. No personally identifiable information was collected, and all responses were treated with strict confidentiality. The study followed the principles of voluntary participation, data anonymity, and protection of participant welfare throughout the research process.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Li Bing Research Center, Sichuan Provincial Key Research Base of Social Sciences: Ethnic exchanges, exchanges, and integration from the perspective of cyberspace in the era of intelligent media. LBYJ2024-015. The Digital Culture and Media Research Base: Research on the application of AIGC technology and the communication of ethnic culture: focusing on the experience, consumption and inheritance of ethnic minority culture. SC24DCM15.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

“Study on the Impact of AIGC Technology Integration Application on Consumer Behavioral Intentions of Chinese Ethnic Minority Cultures,”, Harvard Dataverse, V3.

References

14th Five-Year Plan for the Protection of Intangible Cultural Heritage. (2021). Notice of the Ministry of Culture and Tourism on the issuance of the “14th Five-Year Plan for the Protection of Intangible Cultural Heritage.”mct.gov.cn.

Alawadh

Rafi

Kalyar

M. N.

Abrudan

D. B.

(2024). The role of motivation and knowledge in promoting green consumer behavior in Saudi Arabia and Pakistan. Journal of Infrastructure, Policy and Development, 8(13), 9152.

Anantrasirichai

Bull

(2022). Artificial intelligence in the creative industries: A review. Artificial Intelligence Review, 55(1), 589–656.

Atf

Lewis

P. R.

Lloyd

(2024, September). Evaluating the trustworthiness of user-generated content on social media [Conference session]. 2024 IEEE International Symposium on Technology and Society (ISTAS) (pp. 1–8). IEEE.

Babadoğan

(2024). Unveiling the power of AI-driven personalization: Transforming consumer behavior in the age of digital marketing. Next Frontier for Life Sciences and AI, 8(1), 61.

Bae

B. J.

Park

H. S.

(2022). Effect of artificial intelligence virtual assistant service characteristics on consumer intention to use : Mediating effect of immersion. The Journal of Next-generation Convergence Technology Association, 6(9), 1539–1553.

Behnke

Jung

Bai

(2024). Writing restaurant menu descriptions: The influence of word choice on consumer behavior. International Journal of Hospitality Management, 123, 103923.

Bengesi

El-Sayed

Sarker

M. K.

Houkpati

Irungu

Oladunni

(2023). Advancements in Generative AI: A Comprehensive Review of GANs, GPT, Autoencoders, Diffusion Model, and Transformers. arXiv preprint, arXiv:2311.10242.

Bennett

(2011). Culture, class, distinction. Routledge.

10.

Biasutti

(2011). Flow and optimal experience. In Runco

M. A.

Pritzker

S. R.

(Eds.), Encyclopedia of creativity (pp. 522–528). Academic Press.

11.

Bitner

M. J.

(1992). Servicescapes: The impact of physical surroundings on customers and employees. Journal of Marketing, 56, 57–71.

12.

Bögel

P. M.

Upham

(2018). Role of psychology in sociotechnical transitions studies: Review in relation to consumption and technology acceptance. Environmental Innovation and Societal Transitions, 28, 122–136.

13.

Bruns

(2008). Blogs, Wikipedia, Second Life, and Beyond: From production to produsage. Peter Lang.

14.

Burgess

Green

(2009). YouTube: Online video and participatory culture. Polity.

15.

Chen

Zhi

(2021). Discussion on user-centered content production in the era of new media communication. Journal of Jiangxi Normal University (Philosophy and Social Science), (06), 25–30.

16.

Chiang

Lin

P. H.

Lin

(2025). AIGC-driven innovative design education based on material experience and cultural context [Conference session]. International Conference on Human-Computer Interaction (pp. 3–17). Springer.

17.

Choi

Hickerson

Lee

Choe

(2022). Digital tourism and wellbeing: Conceptual framework to examine technology effects of online travel media. International Journal of Environmental Research and Public Health, 19(9), 5639.

18.

Cover

Parker

Young

Ostapets

(2024). Entertaining information: Third-party influencers’ role in COVID-safety health communication. Media Information Australia, 192(1), 150–164.

19.

Ding

Antonucci

Venditti

(2025). Unveiling user responses to AI-powered personalised recommendations: A qualitative study of consumer engagement dynamics on Douyin. Qualitative Market Research An International Journal, 28(2), 234–255.

20.

Doğan

Şahin

Y. L.

Kobak

Okur

M. R.

(2024). Enhancing clinical law education through immersive virtual reality: A flow experience perspective. Learning and Instruction, 94, 101989.

21.

Dong

(2025). AIGC empowers the “belt and road” initiative: New pathways for cross-cultural Communication. International Journal of Asian Social Science Research, 2(2), 9–16.

22.

Dong

W. W.

Wang

Y. Q.

Qin

(2023). An empirical study on impulse consumption intention of livestreaming e-commerce: The mediating effect of flow experience and the moderating effect of time pressure. Frontiers in Psychology, 13, 1019024.

23.

Duralia

(2024). The impact of digital marketing on consumer behaviour. Studies in Business and Economics, 19(2), 96–109.

24.

Eisend

(2017). Explaining the use and effects of humour in advertising: An evolutionary perspective. International Journal of Advertising, 37(4), 526–547.

25.

Fang

Y. M.

(2024). Exploring usability, emotional responses, flow experience, and technology acceptance in VR: A comparative analysis of freeform creativity and goal-directed training. Applied Sciences, 14(15), 6737.

26.

Farjami

Türker

Ö. O.

(2021). The extraction of prerequisite criteria for environmentally certified adaptive reuse of heritage buildings. Sustainability, 13(6), 3536.

27.

Gao

(2024). From PGC to UGC to AIGC: Change of content paradigm [Conference session]. In SHS Web of Conferences (Vol. 199, p. 03017). EDP Sciences.

28.

Gao

Xie

Wang

Zuo

Zeng

(2023, August). Exploring user acceptance of Al image generator: Unveiling influential factors in embracing an artistic AIGC software [Conference session]. In International Conference on AI-generated Content (pp. 205–215). Springer Nature.

29.

Gao

Noh

L. M.

Huang

(2024). Chinese opera character painting style transfer: Using AI to generate and preserve art. International Journal of Research and Innovation in Social Science, 8(3s), 5724–5731.

30.

Guo

Liao

Liu

Zhang

(2020). Consumer preference analysis: A data-driven multiple criteria approach integrating online information. Omega, 96, 102074.

31.

Han

J. H.

Bae

S. Y.

(2022). Roles of authenticity and nostalgia in cultural heritage tourists’ travel experience sharing behavior on social media. Asia Pacific Journal of Tourism Research, 27(4), 411–427.

32.

Han

(2019). Instant messaging chat bot: Your new best friend? In Barbosa

Filipe

Santos

C. A.

Simões

(Eds.), Smart marketing with the Internet of Things (pp. 199–220). IGI Global.

33.

Hang

Unenbat

Tang

Wang

Lin

Zhang

(2024). Exploring the neural correlates of flow experience with multifaceted tasks and a single-channel prefrontal EEG recording. Sensors, 24(6), 1894. https://doi.org/10.3390/s24061894

34.

Higgins

Ferri

Donnellan

(2023). Enhancing access to digital culture for vulnerable groups: The role of public authorities in breaking down barriers. International Journal for the Semiotics of Law-Revue internationale de Sémiotique juridique, 36(5), 2087–2114.

35.

Huang

Bentaleb

Zimmermann

Zhai

(2023, July). XGC-VQA: A unified video quality assessment model for user, professionally, and occupationally-generated content [Conference session]. In 2023 IEEE International Conference on Multimedia and Expo Workshops (ICMEW) (pp. 434–439). IEEE.

36.

Huang

Zhan

Gao

(2024, May). Research on the mechanism of cultural identity impact under the integration of red tourism and cultural tourism empowered by digital technology [Conference session]. In Proceedings of the 2024 International Conference on Digital Society and Artificial Intelligence (pp. 451–456).

37.

Jenkins

(2006). Convergence culture: Where old and new media collide. NYU Press.

38.

Kaplan

A. M.

Haenlein

(2010). Users of the world, unite! The challenges and opportunities of social media. Business Horizons, 53, 59–68.

39.

Karterouli

Batsaki

Oaks

(2021). AI and cultural heritage image collections: Opportunities and challenges. In EVA.

40.

Kasemsarn

Nickpour

(2025). Digital Storytelling in cultural and heritage tourism: A review of social media integration and youth engagement frameworks. Heritage, 8(6), 200.

41.

Kim

(2024). Unveiling the e-Servicescape of ChatGPT: Exploring user psychology and engagement in AI-powered chatbot experiences. Behavioral Sciences, 14(7), 558.

42.

Kim

Lee

J. K.

Lee

K. Y.

(2019). Interplay of content type and product type in the consumer response to native advertising on social media. Asian Journal of Communication, 29(6), 464–482. https://doi.org/10.1080/01292986.2019.1679852

43.

Koutromanos

Koukopoulos

Mouzakis

(2023). Cultural heritage content development and dissemination through a participatory platform: Lessons learned from in-service teachers’ perception. Education and Information Technologies, 28(3), 3513–3536.

44.

Ladhari

Souiden

Dufour

(2017). The role of emotions in utilitarian service settings: The effects of emotional satisfaction on product perception and behavioral intentions. Journal of Retailing and Consumer Services, 34, 10–18.

45.

Lei

Xing

(2024). AIGC reconstructs behavior: Study on influencing factors of intention to continue use. Journal of Technoeconomics and Management, 06, 152–158.

46.

Leng

Zhang

Xiong

Y. J.

Chen

(2024). CODP-1200: An AIGC based benchmark for assisting in child language acquisition. Displays, 82, 102627.

47.

Zhao

Zou

(2024). Exploring the phenomenon of “breaking the circle” of intangible cultural heritage under the flow theory. Journal of Social Science Studies, 11(2), 137.

48.

Wang

Feng

(2024). A review of AI-generated content: Applications, risks and governance. Library and Information Work. (17), 136–149. https://doi.org/10.13266/j.issn.0252-3116.2024.17.011

49.

Zhao

(2016). Understanding the role of social media in cultural transmission: A comparative study of UGC and PGC. Social Media + Society.

50.

Liang

Ren

(2025). Research on the application of AIGC-based exhibition space design and cross-cultural communication in nature museums. International Communication of Chinese Culture, 12, 633–650.

51.

Liu

Chen

Xie

Zhang

(2024, May). “Hyper Photography” Artifact: An interactive aesthetic education experience device designed based on AIGC [Conference session]. In 2024 IEEE World AI IoT Congress (AIIoT) (pp. 435–443). IEEE.

52.

Lai

I. K. W.

Zhang

Liu

(2024). How can tourism be made engaging and meaningful? A measurement scale for ritual interaction. Tourism Management, 103, 104900.

53.

(2024). On cultural identity in intangible cultural heritage in the digital age. Jiangsu Social Sciences, 05, 232–240. https://doi.org/10.13858/j.cnki.cn32-1312/c.20240920.003

54.

(2024). The impact of human-computer interaction quality on consumers’ purchase intention on virtual e-commerce live streaming platforms. Journal of Business and Economics, 06, 84–87.

55.

(2023). Leading enterprises lead the establishment of innovation consortiums’ application scenario. Scientific Management Studies (5), 125–130. https://doi.org/10.19445/j.cnki.15-1103/g3.2023.05.016

56.

Song

Zhou

Chong

Xiong

(2022). How do comeback Korean pop performers acquire audience empathetic attachment and sustained loyalty? Parasocial interactions through live stream shows. Frontiers in Psychology, 13, 865698.

57.

Malthouse

E. C.

Haenlein

Skiera

Wege

Zhang

(2013). Managing customer relationships in the social media era: Introducing the social CRM house. Journal of Interactive Marketing, 27(4), 270–280.

58.

Meier

Maier

Thatcher

J. B.

Weitzel

(2024). Chatbot interactions: How consumption values and disruptive situations influence customers’ willingness to interact. Information Systems Journal, 34(5), 1579–1625.

59.

Nguyen

Tran

Nguyen

(2021). The effects of perceived risks on food purchase intention: The case study of online shopping channels during COVID-19 pandemic in Vietnam. Journal of Distribution Science, 19–9.

60.

Niu

(2025). The impact of user-generated content on consumer trust and brand loyalty. Advances in Economics, Management and Political Sciences, 161, 141–146.

61.

Ntoa

Margetis

Rivera

Evans

Adami

Mathioudakis

Weerakkody

Metaxakis

Markopoulos

Mrak

Stephanidis

(2021). User generated content for enhanced professional productions: A mobile application for content contributors and a study on the factors influencing their satisfaction and loyalty. Multimedia Tools and Applications, 80(25), 33679–33699.

62.

Park

(2023). The perceived value of digital fashion product and purchase intention: The mediating role of the flow experience in Metaverse platforms. Asia Pacific Journal of Marketing and Logistics, 35(11), 2645–2665.

63.

Pathak

A. A.

Kaushik

(2024). Driving consumer engagement for digital payment brands: Content strategy on Instagram. Journal of Retailing and Consumer Services, 79, 103821.

64.

Pathak

R. C.

Agarwal

Gehlot

Singh

Akram

S. V.

Gupta

L. R.

Thakur

A. K.

Priyadarshi

Twala

(2025). Advanced digital technologies for promoting Indian culture and tourism through cinema. Recent Advances in Computer Science and Communications, 18.

65.

Perera

Samarahewa

Jinasena

Thilakarathna

Kasthurirathna

Rupasinghe

(2024, December). Optimizing user experience in mobile travel applications: A study on personalized routing, thematic content, and cultural engagement [Conference session]. In 2024 6th International Conference on Advancements in Computing (ICAC) (pp. 97–102). IEEE.

66.

Qin

Musetti

Omar

(2023). Flow experience is a key factor in the likelihood of adolescents’ problematic TikTok use: The moderating role of active parental mediation. International Journal of Environmental Research and Public Health, 20(3), 2089.

67.

Rai

Dahal

R. K.

Shahi

B. J.

Ghimire

(2023). Impulse buying behavior in distribution centers of Kathmandu. Journal of Distribution Science, 21(5), 19–29.

68.

Samira

Saeed

Mohammadtaghi

(2023). Investigating the effect of task type and modality on flow experience among intermediate Persian EFL learners. Journal of Psycholinguistic Research, 52(6), 2835–2862.

69.

Sardar

Tata

S. V.

Sarkar

(2024). Examining the influence of source factors and content characteristics of influencers’ post on consumer engagement and purchase intention: A moderated analysis. Journal of Retailing and Consumer Services, 79, 103888.

70.

Shankar

(2018). How artificial intelligence (AI) is reshaping retailing. Journal of Retailing, 94(4), vi–xi.

71.

Shi

S. J.

J. W.

Zhang

(2024). A study on the impact of Generative Artificial Intelligence supported Situational Interactive teaching on students’‘flow’ experience and learning effectiveness — A case study of legal education in China. Asia Pacific Journal of Education, 44(1), 112–138. https://doi.org/10.1080/02188791.2024.2305161

72.

Shi

Zhang

(2025). Research on driving factors of consumer purchase intention of artificial intelligence creative products based on user behavior. Scientific Reports, 15(1), 17400–17423.

73.

Silva-Paz

R. J.

Prada-Linarez

T. A.

Rivera-Ashqui

T. A.

Apaza-Humerez

C. R.

Eccoña-Sota

Verde-Lujan

H. E.

(2024). Effect of label elements in bottled water: Impact on consumer preferences, purchase intentions and health perception through affective sensory tests. Heliyon, 10(15), e35106.

74.

Song

(2024). The effect of virtual tourism experience on tourist responses: The lens from cognitive appraisal theory. Asia Pacific Journal of Tourism Research, 29(7), 885–899.

75.

State Council Information Office. (2009). White Paper on China’s Ethnic policy and the common prosperity and development of all ethnic groups. Information Office of the State Council of the People’s Republic of China (scio.gov.cn)

76.

Sun

(2024). Public Perceptions and attitudes towards the application of artificial intelligence in Journalism: From a China-based survey. Journalism Practice, 18(3), 548–570.

77.

Tang

(2023). The influence of short video content marketing on consumers’ online purchase intentions based on the grounded theory. China Business Review (16), 103–109. https://doi.org/10.19699/j.cnki.issn2096-0298.2023.16.103

78.

Thomas

N. J.

Baral

(2023). Mechanism of gamification: Role of flow in the behavioral and emotional pathways of engagement in management education. International Journal of Management in Education, 21(1), 100718.

79.

Wang

(2023). Cultural audio and video content dissemination under the theory of consumer culture. Sound and Screen World, 07, 70–72.

80.

Wang

Zhao

Wang

Zou

(2019). Novel system for rapid investigation and damage detection in cultural heritage conservation based on deep learning. Journal of Infrastructure Systems, 25(3), 04019020.

81.

Wang

Zhang

Jia

Chai

Guo

(2024). The impact of digital economy development on household consumption: Effects and mechanisms. Journal of Econometrics, 4(3), 805–834.

82.

Wang

Zhang

Jia

Chai

Guo

(2024). The impact of Digital Economy Development on Household Consumption: Effects and Mechanisms. Journal of Econometrics,(3), 805–834

83.

Wang

Zhao

Tian

Yang

Luo

(2025). The influence of subjective knowledge, technophobia and perceived enjoyment on design students’ intention to use artificial intelligence design tools. International Journal of Technology and Design Education, 35, 333–358. https://doi.org/10.1007/s10798-024-09897-3

84.

Watts

E. R.

Koban

Bowman

N. D.

(2021). Digital gaming audiences: Awareness, without closeness. Entertainment Computing, 36, 100384.

85.

(2025). A study on the empowerment of traditional Chinese time-honored brands through digital intelligence technology in branding communication representation and pathways. Communications in Humanities Research, 61, 105–117.

86.

Xinyu

Lei

Bingbing

Menghan

(2024). The advanced path of research on digital display empowered by AIGC technology for intangible cultural heritage. Journal of Tourism Management, 1, 45–50.

87.

(2025). Application of AIGC technology in intangible cultural heritage vocal music from the perspective of cross-cultural communication. Journal of International Education and Development, 9(3), 204–207.

88.

Yin

Qin

(2024). The triple dimensions of production of space of the National Cultural Park’s Cultural Tourism Metaverse. Tourism Science, 38(8), 46–59. https://doi.org/10.16323/j.cnki.lykx.2024.08.005

89.

Yoon-Joo

Ahn

(2021). Retail distribution strategies for train tickets: The extended UTAUT model. Journal of Distribution Science, 19(9), 5–17.

90.

Dickinger

K. K. F.

Egger

(2024). Artificial intelligence-generated virtual influencer: Examining the effects of emotional display on user engagement. Journal of Retailing and Consumer Services, 76, 103560.

91.

Zeng

(2015). Interactive image design based on immersive experience. Big Stage, 2015(08), 68–69.

92.

Zhan

Sun

(2023). Application scenarios and development opportunities of AIGC in the digital intelligence integration environment. Documentation, Information and Knowledge, (1), 75–85. https://doi.org/10.13366/j.dik.2023.01.075

93.

Zhang

Yazheng

Zhengyi

(2024). From OGC to AIGC: The transformation and prospect of media content production models. Communication and Copyright, (02), 1–4.

94.

Zhang

(2010). Cultural learning and experience: The core demands of cultural heritage tourists. Tourism Journal, 04, 10.

95.

Zhang

Cai

Xiang

Sun

(2024). Reconstruction and integration: The impact of artificial intelligence generated content on news production. Lecture Notes in Education Psychology and Public Media, 50, 162–170.

96.

Zhang

Kong

(2024). From OGC to AIGC: The transformation and prospect of media content production models. Communication and Copyright, (2), 1–4. https://doi.org/10.16852/j.cnki.45-1390/g2.2024.02.001

97.

Zhang

Niu

Zhao

E. Y.

(2025). User language and cultural product innovation: Insights from the global mobile gaming industry. Journal of International Business Studies, 56(8), 1050–1068.

98.

Zhang

(2023). Localization of ethnic minority Confucian temples and the narrative of the community of the Chinese national community: A case study of Confucian Temple in Jianshui, Yunnan. Guangxi Ethnic Studies, (3), 103–112.

99.

Zhang

Liu

Pei

Zhang

Liu

(2024). Perceived algorithmic evaluation and app-workers’ service performance: The roles of flow experience and challenges of gig work. Journal of Organizational Behavior, 46, 997–1017.

100.

Zhao

Song

(2022). Research on the impact of service scenarios on consumer purchasing behavior of intangible cultural heritage products. JAC Forum, (2), 53–58. https://doi.org/10.16064/j.cnki.cn34-1003/g0.2022.02.008

101.

Zhou

Huang

Tang

Yang

(2025). Enhancing user experience on social media platforms: A flow theory-based approach. International Journal of Human–Computer Interaction, 41, 8280–8295.

102.

Zhou

Yoon

J. E.

(2024). Research on analyzing the reality of virtual space through Immersion theory. Journal of the Korea Institute of the Spatial Design, 19(4), 89.