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Research Article | | Peer-Reviewed

Exploring the Impact of Generative AI-driven Advertising on Generation Z's Consumer Behavior in China: A Grounded Theory Approach

Nie Yifan,  Liu Zhengyuan,  Jiang Yi*
Published in American Journal of Applied Psychology (Volume 14, Issue 4)
Received: 2 July 2025     Accepted: 14 July 2025     Published: 31 July 2025
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Abstract

As a new and significant force of consumption in contemporary China, Generation Z showcases distinct characteristics. Their penchant for uniqueness and personalized psychology are especially evident in their daily consumption behaviors. In the current digital age, generative AI has emerged as a powerful tool in the advertising realm. Thus, exploring the influence mechanism of generative AI-driven advertising on the consumption behavior of Generation Z holds great significance. This exploration is crucial for the further integration and application of artificial intelligence technology in the development and reform of the advertising industry. Based on in-depth qualitative interviews with 24 Chinese university students, this study employs programmatic grounded theory. By doing so, it aims to uncover the intricate black box of the influence mechanism between generative AI-driven advertising elements and consumer behavior. The research ultimately discovers the pathways through which four dimensions-corporate cognition, value cognition, emotional cognition, and risk cognition-impact the consumption behavior of Generation Z. This offers valuable insights for advertisers targeting this demographic.

Published in American Journal of Applied Psychology (Volume 14, Issue 4)
DOI 10.11648/j.ajap.20251404.11
Page(s) 113-128
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2025. Published by Science Publishing Group
Previous article
Keywords

Consumption Behavior, Generation Z, Generative AI-driven Advertising, Influence Mechanism

1. Introduction
The report to the 19th National Congress of the Communist Party of China requires that great attention should be paid to the in-depth integration of big data technology and the real economy, and to the construction of cyber power. The world is in the midst of the fourth industrial revolution. The rapid development of artificial intelligence technology has become the core force driving social change, and the process of digitalization and informatization is sweeping across all fields at an unprecedented speed. Artificial intelligence-generated content (AIGC) technology, as an emerging frontier technology in this era, has triggered subversive changes in many traditional industries by virtue of its excellent creation ability and efficient processing efficiency
[10, 18]
.
In the field of e-commerce, Taobao, Jingdong and other online shopping platforms widely use intelligent algorithms to conduct in-depth analysis of massive consumer data and deduce consumer consumption characteristics and preferences, so as to achieve accurate product recommendation and personalized services.
[32]
According to relevant statistics, this personalized recommendation strategy based on AIGC technology can increase the sales volume of the platform by 20%-30%
[17]
. When consumers browse e-commerce platforms, they will receive product recommendations based on their purchase history, browsing preferences and real-time behavioral data. These recommended products are often more in line with consumers' potential needs, thus effectively improving the purchase conversion rate. At the same time, the influence of AIGC technology is not only reflected in the logical calculation of back-end data, but also has a profound reshaping effect on the front-end design and creation. With the emergence of advanced Sora AIGC technology in 2024, the advertising design industry ushered in the great change, originally relies on artificial creation and design of advertisement production process by AI generated gradually infiltrated by ads. Video generated by painting, AI and AI generates emerging forms, such as advertising, with its unique creative and efficient production capacity, quietly into People's Daily life and consumption scenarios, changed the way consumers access to information and brand perception.
Although some studies have discussed the theory of consumer behavior, the influence of advertising on consumer behavior, and the characteristics and applications of AI-generated advertising, there are still some research gaps. However, there are few relevant studies on AI-generated advertising at home and abroad, and no in-depth exploration of its influence mechanism on the consumption behavior of Generation Z youth. Overall, there is a certain degree of research gap. Based on the in-depth qualitative interview results of 24 Chinese Generation Z individuals, this article employs the procedural grounded theory method for qualitative analysis. Through coding design, it explores the inner cognition and contact of the Generation Z group regarding AI-generated ad scenarios, aiming to reveal the influencing path of AI-generated ads on the consumption behavior mechanism of Generation Z youth. Finally, the results expand and deepen the current research on the influence mechanism of the booming AIGC on the consumer behavior of Generation Z. It provides theoretical guidance for the transformation and application of artificial intelligence technology in the advertising industry. How AI-generated ads can conform to the personality cognition of Generation Z to strengthen relevant consumer behavior has important practical significance.
Based on the above, this study put forward the following problems: AI how emergent advertising affect the consumer behavior of Chinese youth Generation Z? What is the influencing mechanism? What are the problems and challenges faced by AI-generated advertising in the process of influence? How to promote the healthy development of AI-generated advertising through effective strategies and measures, and realize its benign interaction with the consumption behavior of Generation Z youth? This study will use grounded theory, in-depth interviews, observation and case analysis methods to systematically study and discuss these issues, in order to provide useful reference for the development of related fields.
2. Research Background
2.1. Development of Consumer Behavior Theory
The theory of consumer behavior has experienced the evolution from traditional theory to modern theory. Traditional consumer behavior theories mainly focus on the rational decision-making process of consumers like utility theory and consumer decision-making process model. These theories assume that consumers are rational when making purchase decisions. They make choices based on objective factors-product function and price. However, with the change of market environment, the modern theory of consumer behavior gradually realize the complexity and diversity of consumer behavior, the influence factors of start into more, such as consumers' emotional, cognitive, social and cultural background, etc
[20]
.
For Generation Z youth consumer behavior research, scholars have found that this group has a unique consumption characteristics. They grew up in the digital age, information access to rich, the acceptance of new technology and new products and pay more attention to individuation, self-expression and experiential consumption
[33, 35]
. When purchasing consumer goods of interest, they not only pay attention to the function of the product, but also pursue the added value such as emotion and culture contained in the product. The products that are "good-looking, fun and easy to use" are more likely to attract them
[33]
. Simultaneously, their consumption behavior is significantly shaped by social influences. They obtain product information through social media, refer to others' comments, and buy products that can integrate into the social circle or show their personal taste.
2.2. Relevant Research on the Influence of Advertising on Consumer Behavior
Advertising has consistently been recognized as a pivotal factor influencing consumer behavior. Traditional advertising mainly influences consumers' cognition and attitude by disseminating product information and building brand image, thus promoting their purchase behavior
[3, 39]
. With the development of advertising technology, precise marketing concept gradually rise, emphasized by the analysis of consumer data, precision push, advertising, improve the effect of advertising
 [12, 34, 37]
.
In the process of advertising influencing consumers' purchase intention, consumers' perceived value plays a key mediating role
[28]
. Consumers will be based on their perception of a product or service value to decide whether to buy, and advertising can be passed through the function of the product information, quality, and emotional value, affect consumers' perceived value
 [11, 34]
. In addition, advertising can be controlled by shaping the brand image, create a consumption atmosphere, stimulate consumers' desire to buy and promote the formation of the purchase decision
[39]
.
2.3. Multi-dimensional Review of AIGC Technology
AIGC technology, as an emerging force in the field of artificial intelligence, is changing the pattern of content creation and dissemination at an unprecedented speed. As technology keeps evolving non-stop, AIGC technology is being applied more and more extensively across numerous fields. This situation has caught the intense attention of both the academic community and the industry. AIGC technology marks an important leap from perceptual intelligence to cognitive intelligence
[38, 43]
. Its development has traversed numerous stages, evolving from the nascent phase of computational intelligence, through the phase characterized by perceptual capabilities. And culminating in the present AIGC stage, which exhibits a degree of active thinking and comprehension abilities. This process reflects the trend of continuous evolution and breakthrough of technology, lays the foundation for the subsequent wide application.
AIGC's text generation covers structured, unstructured and interactive text for applications, include news, financial reports, marketing and virtual interaction. Audio generation encompasses technologies for instance text-to-speech and voiceprint recognition. These aspects are super crucial for intelligent customer service and audio creation. When it comes to image generation technologies, they range from GANs (Generative Adversarial Networks) to diffusion models. These advancements have not only enhanced the stability but also streamlined the data-processing procedures. As a result, they've made a really significant contribution to the fields of art and design. Despite inherent challenges, video generation technology has found applications in editing and shows potential for producing longer videos. Cross-modal generation technology plays a facilitative role in enabling the conversion among diverse media formats. Through this functionality, it effectively expands the range of content creation possibilities and enhances the level of understanding regarding such content. Virtual life applications combine multiple technologies and have potential values such as entertainment and education.
AIGC technology has greatly changed the ecology of media content industry. On one hand, AIGC significantly cuts down the cost of content production. It boosts production efficiency via automatic news generation and intelligent video editing. On the other hand, AIGC enhances the quality and innovation of content. It lends a hand to creators, enabling them to generate more captivating and personalized content. In the world of social media, AIGC-powered tools can analyze user preferences and generate customized video scripts or graphic designs
[29]
. This, in turn, promotes the innovation and development of the media industry
[43]
.
The appearance of virtual host is an important application result of AIGC in the field of media, which improves the intelligent level of communication link and expands the media form and content presentation mode
[30, 43]
. In marketing communication, AIGC empowers communication subjects, enabling them to use intelligent algorithms and big data analysis to realize human-computer collaboration, accurately grasp consumer needs, optimize marketing programs, and improve marketing effects. In terms of communication content, AIGC attracts audience attention, triggers topic discussion, and improves the influence of marketing activities by efficiently generating text, image, video and other content. In terms of communication forms, AIGC integrates with new technologies to bring immersive experience to consumers, enhance communication effects and promote the development of full-link marketing
[44]
.
2.4. Characteristics and Development Status of AI-generated Advertising
AI-generated advertising is an innovative application of artificial intelligence technology in the field of advertising. It uses big data, machine learning, natural language processing and other technologies to automate the generation of advertising content, including AD copy, images, and videos. AI-generated ads are highly personalized, real-time generated, and interactive. It customizes personalized AD content for each consumer based on their interests, preferences, behavior habits and other factors, and achieves accurate push through in-depth analysis of consumer data. At the same time, AI-generated advertisements can adjust advertising strategies and content according to consumer feedback and market changes in real time to improve the timeliness and adaptability of advertisements
[40]
.
Currently, AI-generated advertising is being applied more and more extensively in the market. Many enterprises and advertising platforms have begun to adopt AI technology to optimize the AD delivery and creation process. They use it to improve the effectiveness of advertising. Some e-commerce platforms use AI-generated ads to recommend personalized products to consumers. Some social media platforms generate customized AD content based on users' interests and behaviors. However, as AI-generated advertising continues to develop, it also encounters several problems and challenges. For example, algorithmic bias, the dissemination of false information, and privacy leakage have drawn extensive attention from all sectors of society
[41]
. As such, it is crucial to find effective solutions to address these problems and ensure the healthy development of AI-generated advertising
[9]
.
2.5. Research at Home and Abroad
With the rise of generative intelligence technology, the field of advertising has undergone profound changes, and the impact of AI-generated advertising on consumer behavior has become a research hotspot. The following will describe the relevant research from two aspects: foreign and domestic.
2.5.1. Domestic Research
Chinese scholars have paid attention to the significant uniqueness of Generation Z group affected by the Internet wave. In terms of consumption trends, they have diversified interests, pursue self-pleasing experience, pay attention to social attributes, and pay attention to value expression
[13]
. From the perspective of consumption power, according to the Boston Consulting Group, Generation Z will create a large amount of consumption in the future and has gradually become the main force of Chinese consumption
[19]
. Releasing their consumption potential is of great significance to economic growth.
Domestic research on the basis of learning from foreign achievements, combined with the characteristics of local Generation Z in-depth analysis. However, the research on the relationship between generative intelligence technology and the consumption behavior of Generation Z is relatively lacking. Specifically, in aspects such as the specific influence mechanism and process of AI-generated advertising on the consumption behavior of Generation Z youth, and how to guide their active consumption, there is a need for further in-depth and systematic exploration. This area of study is still in its developing stage, and more comprehensive research efforts are essential to fill the existing knowledge gaps. As Generation Z becomes an increasingly important consumer group, understanding the impact of generative intelligence technology on their consumption behavior is of great significance for both academic research and market practice
[38, 42]
.
2.5.2. Foreign Research
Foreign research on consumer behavior started early. In the early 20th century, consumer psychology research was carried out in the field of advertising and promotion
[20]
, and formal academic research began in the mid-20th century
[16]
. In recent decades, scholars in many fields have participated in the exploration of the motivations of consumer behavior. Taking consumer decision-making as the core issue, they have constructed a variety of decision-making models from a quantitative perspective to quantitatively explain consumer purchasing behavior
[14]
. With the popularization of the Internet and mobile terminals in the 21st century, the concept of digital consumer behavior is born, and the research tends to be intelligent
[21]
.
In terms of the influencing factors of consumer behavior, the existing research shows that advertising and advertising effect account for a large proportion (about 30%), and marketing communication and persuasion also have a certain impact on consumers' purchasing attitude. For Generation Z, foreign scholars started their research early, calling them "digital natives"
[1]
. As the most tech-savvy generation, intelligence is their important pursuit
[2]
, and they are quick to accept AI-generated advertising. However, in the context of generative intelligence technology, there are relatively few studies on the influence mechanism of the consumption behavior of Generation Z youth. Existing studies have been carried out from the aspects of moral concept, environmental protection and green consumption awareness, and found that they are more sensitive to AI-generated advertising content
[5, 6]
.
After conducting comprehensive research at home and abroad, it can be observed that although certain achievements have been attained in the study of consumer behavior and Generation Z, there are still numerous gaps in the research on the mechanism of how AI-type advertisements affect Generation Z's consumer behavior. In future research, it is essential to further enhance interdisciplinary cooperation and comprehensively utilize the knowledge of multiple disciplines, including psychology, sociology, marketing, and computer science, to delve deeper into this mechanism. Simultaneously, researchers should take into account the characteristics of Generation Z youth, such as their high acceptance of intelligence and unique consumption values
[4, 25]
. They should conduct further research on how to optimize AI-generated advertising strategies to better meet consumer demands, promote the development of the consumer market, guide Generation Z youth to consume healthily, and achieve a balance between commercial value and social value. Moreover, attention must be paid to the potential negative impacts of AI-generated ads, such as information-related misguidance, privacy issues, etc., and corresponding countermeasures should be explored
[31]
.
3. Research Design
3.1. Research Method
The basic idea of grounded theory is that the research design and data collection should adopt qualitative methods, while it also absorbs quantitative research ideas in data analysis. The theory advocates for the extraction of concepts from first-hand data gathered through actual observation, without preconceived assumptions. These concepts are then categorized, coded through successive layers of refinement, and constructed into a new theoretical system from the bottom up
 [8, 22]
. This approach seeks to build theory based on original materials and empirical judgments through continuous comparison, reflection, and analysis
[3, 7]
. The research process consists of three main steps: open coding, axial coding, and selective coding. Specifically:
Conceptualize and categorize the phenomenon or event.
Explore the logical relationship between the initial categories and establish the main categories.
Identify the core categories that dominate all the categories and build a generalized grounded theory until the theory is saturated.
3.2. Data Source
Table 1. Respondents’ Information.

Statistic

Property

Quantities

Distinguishing between the sexes

male

10

female

14

Year of birth

1995-2000

7

2001-2005

14

2006-2010

3
Grounded theory utilizes theoretical sampling, which means purposive selection of samples to propose a concept or construct a theory
[7]
. The selected samples must be closely related to the research purpose and represent a typical group that reflects certain category phenomena
[24]
. Therefore, this study conducted in-depth interviews with 24 Gen Z youths of different age groups who have a certain understanding and awareness of generative AI-driven advertising. The basic information of the interviewees is presented in Table 1.
In addition to the primary interview data, this paper also collected a large amount of secondary data from various sources to supplement the primary interview information data, including media news reports, academic articles from journals such as CNKI and Wanfang database, in order to obtain comprehensive and comprehensive materials and form a triangular verification.
Table 2. Data sources.

Data type

Data source

Data acquisition method

Acquired content

Interview duration / min

Word count of data

Primary data

Gen Z who have a certain understanding of generative AI-driven advertising

Semi-structured interview

Their attitudes towards generative AI-driven advertising

30-40

40k

Secondary data

Media news reports, CNKI, Wanfang database and other journals research articles
3.3. Quality Control
Based on the grounded theory, this study uses Nvivo12.0 software to conduct step-by-step coding of interview data and establish category relationships. To ensure the reliability and validity of the research conclusions, a preliminary analysis was conducted against the coding table. Software was used to statistically analyze the textual data and perform consistency tests, maintaining a consistency coefficient above 0.8 to minimize the influence of personal subjectivity and experience on theoretical research
[6]
. The methodology for maintaining a consistency coefficient above 0.8 originates from the classical conventions of reliability standards in social science research methods. Its core foundations include Nunnally's classification of internal consistency, Landis & Koch's interpretation of inter-rater agreement, and the practical synthesis of the "reliability-feasibility" balance in cross-disciplinary research. The essence of this threshold lies in ensuring the stability of textual data analysis results through quantitative standards, thereby reducing the interference of personal subjective experience in theoretical research. This represents a long-established scientific norm in the academic community. Secondly, after revising the category relationships, two-thirds of the interview samples (16 cases) were selected for coding analysis and model construction. This process facilitated the understanding of the reach channels, content presentation, corporate cognition, value cognition, emotional cognition, risk cognition, and consumer behavior related to generative AI-driven advertising. It also explore the attitudes of Gen Z towards generative AI-driven advertising and the impact of these elements on their consumption behavior. Before formal coding, a portion of the data needs to be reserved for theoretical saturation testing
[15]
. Therefore, one-third of the samples (8 cases) were randomly selected for theoretical saturation testing. A continuous comparative analysis approach was employed throughout the process, constantly refining and adjusting the theory until theoretical saturation was achieved
[23]
.
4. Analysis of Result
4.1. Open Coding
Open coding is the process of decomposing, comparing, and refining the initial concepts and discovering the categories of the original statements in the interview data. In this study, the open coding process is mainly divided into two steps. The first step is the conceptualization of the original sentences. The representative words and sentences that occur frequently in the interview data are summarized and sorted out, which are abstracted as concepts and named as far as possible after the original words of the interviewees, to minimize the influence of the researchers' orientation and subjective bias on the research results. This process results in the abstraction of 52 initial concepts. The second step is to categorize similar concepts, reclassify and integrate concepts with high similarity, divide similar concepts into the same category or class, and finally form a total of 26 initial categories after categorizing 52 initial concepts. The individuals responsible for the coding of this article are the authors Yifan Nie and Yi Jiang. They each carried out independent coding and made revisions to the category items. Throughout the coding process, this article utilized the Delphi method. Additionally, experts in the field of consumer psychology were invited to make the final determinations regarding the coding categories
[45]
.
Table 3. Open Coding Results.

Initial categories

Initial concepts

Raw statement

Channel type

Print ads; social media; short videos

When on social media and other online platforms, it is common to see generative AI-driven advertising; On Double 11, brands such as Tmall and Jingdong used AI-generated poster ads.

Delivery accuracy

Precise targeting of audiences; precise placement; precise pushing

Ability to target audiences more accurately; Deliver and push precisely.

Validity

Errors of logic; truthfulness and clarity

The splicing of images is unreasonable, the movement of characters is hard, and the composition of scenes is unnatural; The internal logic of advertisements is incoherent and disjointed.

Naturalness

Raw; strange; aesthetically pleasing

Each part of the advertisement is not coordinated together; It stiffly combines many elements into a whole; The generative AI-driven advertising that mimic real people makes me uncomfortable, and the character's face and muscles are in a strange state of motion; The graphics should be in line with mainstream aesthetics.

Personalized

Individuality; customization; conformity to preference

AI can analyze viewers' preferences based on algorithms, and I think generative AI-driven advertising can deliver more "personalized ads" to me.

Degree of recognition

To recognize; to perceive

It's impossible to tell which ads are generative AI-driven advertising; I don't know if I can tell whether it is generated by AI or man-made in the future.

Creativity

Innovation; novelty

Creative and unique, it can generate new and interesting ad content; Likely to be less creative than human-designed ads.

Standardization

Mechanical; Patterned; Similar

Generative AI-driven advertising are similar in style, wording, typography, etc.; The kernels are all the same; Too templated.

Secondary innovation

Human instructions; based on human data

Nothing more than instructions that humans describe in words; They mimic existing advertisements and carry out the instructions entered by humans; Generative AI-driven advertising themselves are derived from keywords supplied by human creativity and from existing relevant design works on the web.

Brand image

Perfunctory; sincere

Reflects the image of the enterprise lazy opportunism; I think people who use generative AI-driven advertising do not pay attention and are lazy; I do not see the sincerity of the marketer.

Product awareness

Authenticity; guarantees

Makes people question the authenticity of the product; Generative AI-driven advertising don't usually have celebrity endorsements, and I think a product with a recognizable celebrity as an endorser would be more secure.

High efficiency

Time; cost; output efficiency

Shorter ad generation cycles; More labor costs; More efficient ad outputs.

Impact on post

Replacement jobs; creation of emerging jobs

There is a negative impact on advertising design, art related staff, they are likely to be replaced by AI; There will be the birth of new jobs, applicable to the benefit of AI technology for advertising design professionals.

Miscellaneous function

Ancillary

Combining generative AI-driven advertising with traditional ads led by human ideas; AI can't replace marketers for now.

Needs & wants

Demand

More in line with the user's needs; It depends on whether the product is what I need.

Consumer preference

Interest

Some of the generative AI-driven advertising can pique my interest; When I like the style of ads.

Emotional link

Empathy; humanization

I feel that AI advertising is a little impersonal, can not arouse my emotional resonance, although it looks very advanced and cool.

Dislike

Discomfort; disgust

Generative AI-driven advertising that mimic real people make me feel uncomfortable; Be resistant to it.

Neutral

Neutral

Not resistant or appreciative; Not resistant, but overuse of AI is not supported either.

Ethics

Morality; ethics; law

Generative AI-driven advertising should uphold good moral and ethical guidelines; They cannot go beyond legal or moral limits.

Privacy security

Information security; privacy

Strengthen information security and privacy protection to ensure privacy of users’ data.

Information cocoon

Information cocoon

Over-precise push is easy to make consumers trapped in the information cocoon.

Willingness to watch

Skim over

Scratch that; I couldn't finish the ad video.

Willingness to buy

Buying

After watching the advertisements of KFC and Rainbow Candy, I feel that the images look a little bit weird compared with traditional advertisements, and it is hard to generate consumer interest; It is hard for consumers to gain the desire to consume.

Willingness to share

Share (joys, benefits, privileges etc) with others

If the picture is better, I will be attracted by new and creative ads and willing to share it with my friends to appreciate the subtleties of its production!

Willingness to explore

Curiosity; in-depth knowledge

Pleasantly surprised, very curious, choose to be a looker on ads; I'm open to the development of this emerging technology; I may explore it further via the links in the advertisement.
4.2. Axial Coding
Axial coding is a process of logical connection of 26 initial categories extracted from open coding, with the goal of developing the categories and establishing the main categories. This study is based on the paradigm model, which is an important analytical tool of grounded theory, namely "causal conditions-phenomenon-action line-mediating condition-action strategy -results". We compare and analyze the correlation and logicality between the initial categories, resulting in the establishment of seven main categories, which are reach channel, content presentation, corporate cognition, value cognition, emotion cognition, risk cognition, and consumption behavior.
Table 4. Axial Coding Results.

Dimension

Main category

Initial categories

Categorical connotation description

Generative AI-driven advertising elements

Reach channels

channel type

Generative AI-driven advertising communicate, interact and engage with potential users in a variety of ways (online and offline).

delivery accuracy

Generative AI-driven advertising are targeted to select delivery channels to increase ad exposure and click-through rates.

Content presentation

validity

The gap between the content and picture presentation generated by generative AI-driven advertising and real objects.

naturalness

Generative AI-driven advertising with graphics and sound vs. real life.

personalized

Generative AI-driven advertising uses big data algorithms to learn consumers' personal preferences and generate ads for different consumers.

degree of recognition

Differences between generative AI-driven advertising and regular ads.

creativity

Generative AI-driven advertising creativity in the design of ad content, graphics, etc.

standardization

Homogenization of generative AI-driven advertising in terms of content and form.

secondary innovation

Levels of generative AI-driven advertising created independently.

Corporate cognition

brand image

The impression that businesses using generative AI-driven advertising make on consumers.

product awareness

The impression that products using generative AI-driven advertising leave in the minds of consumers.

Value cognition

high efficiency

Generative AI-driven advertising in terms of cost, time, labor, and input to output ratio.

impact on post

The role of the creation of generative AI-driven advertising in the replacement of bottom-level manual jobs and the promotion of high-tech talent employment.

miscellaneous function

What generative AI-driven advertising bring to the ad industry and beyond.

needs & wants

Generative AI-driven advertising present content in line with consumers' desires and willingness to acquire various consumer materials in order to fulfill their requirements for survival, enjoyment and development.

consumer preference

Generative AI-driven advertising match consumers’ preferences and interests in content or style.

emotional link

Generative AI-driven advertising in emotional, humanity aspects of communication.

Emotional cognition

dislike

Consumer resistance to generative AI-driven advertising.

neutral

Neutral consumer sentiment toward generative AI-driven advertising.

Risk cognition

ethics

Generative AI-driven advertising are ethically, legally sound.

privacy security

Generative AI-driven advertising on the protection of consumer information and privacy security.

information cocoon

The information areas that consumers focus on are habitually guided by their own interests.

Consumer behavior

willingness to watch

Consumer perceptions and ideas generated by watching generative AI-driven advertising.

willingness to buy

Whether consumers make purchase decisions about products after viewing generative AI-driven advertising.

willingness to share

Consumer perceptions and thoughts on sharing generative AI-driven advertising.

willingness to explore

Consumer perceptions and thoughts on exploring generative AI-driven advertising.
4.3. Selective Coding
Selective coding excavates the core category from the main category extracted by axial coding, deeply analyzes the logical relationship between the main categories and between the core category and the main category, and describes the relationship in the way of "story line", so as to build a theoretical framework or model, as indicated in Figure 1. With reference to the SOR stimulus response theory, this study conducts in-depth research and analysis on the seven main categories formed by the axial coding, including reach channel, content presentation, corporate cognition, value cognition, emotional cognition, risk cognition, and consumption behavior, and finally refines the core category of "Influence Factors of generative AI-driven advertising on the Consumption Behavior of Gen Z Consumers". The "story line" surrounding the core category can be summarized as follows: each element of generative AI-driven advertising has a significant impact on consumers' corporate cognition, value cognition, emotional cognition, and risk cognition, and will further stimulate consumers' consumption behavior. The typical relation structure of the main categories of selective coding is shown in Table 5.
Figure 1. Model of Generative AI-driven Advertising Influencing Gen Z Consumer Behavior Mechanism.
Table 5. Structure of Selective Coding Relationships.

Typical Relationship Structures

Structural Implications

Representative Exemplar Statements

Generative AI-driven advertising elements → Consumer behavior

Consumers' exposure to generative AI-driven advertising through multiple channels will drive consumer behavior

When I see generative AI-driven advertising on various social media and online platforms, I'm still happy to watch them if the images aren't that raw; if I see high-quality, innovative ads, I'm more than happy to check them out.

Generative AI-driven advertising elements → Corporate cognition→ Consumer behavior

Consumers’ viewing generative AI-driven advertising influence the perceptions formed about a company and thus consumer behavior

The generative AI-driven advertising side-step the image of lazy and speculative companies, making me feel perfunctory and that the company is not sincere in any way, making me doubt the authenticity of its products, and in turn, not buying any of their products.

AI-generated advertising elements → Value cognition → Consumer behavior

Consumers' perceived value of generative AI-driven advertising after viewing them influences spending behavior

Generative AI-driven advertising are convenient, efficient, and can push more personalized ads to me, which are more likely to capture my interest. When ads match my preferences and needs, I'm more likely to be interested in making a purchase.

Generative AI-driven advertising elements → Emotional cognition → Consumer behavior

Generative AI-driven advertising influence consumers' emotional cognition and thus influence consumption behavior

After watching the generative AI-driven advertising for KFC and Rainbow Sugar, I feel that the images look a little weird compared to traditional ads, and it is hard to generate consumer interest, and it is hard for consumers to gain the desire to consume. If these kinds of ads can exclude the basic problematic issues of aesthetics, logic and emotional resonance mentioned above, and personalize the presentation based on a basically up-to-date ad, I think it can stimulate my interest.

Generative AI-driven advertising elements → Risk cognition → Consumer behavior

Consumers' perceived risk after viewing generative AI-driven advertising affects their spending behavior

Generative AI-driven advertising themselves are based on big data, there is a risk of violating my private information, if I can circumvent the ethical and moral risks arising from the use of AI, under the premise of protecting private information, I do not reject the reasonable application of these technologies.
5. Research Conclusions
The main purpose of this paper is to explore the influence path mechanism of AI-generated advertising on the consumption behavior of Chinese generation Z youth. Based on the analysis results of grounded theory and referring to SOR model theory, the semi-structured in-depth interviews were extracted and summarized through three-level coding, and seven main categories and five core categories were extracted.
5.1. Stimulus Factor (S) Level
5.1.1. Access Channels
Touch of channels as the primary part of consumer contact AI generation type advertisement, the diversity of its type and precision of advertising has a decisive influence on advertising effectiveness. Among them, the type of the channel and advertising precision will influence the Z generation consumers channels of contact advertising effect. In terms of AD content presentation, the high creativity and strong naturalness of AI-generated ads are more likely to attract consumers' attention and stimulate their emotions. Under the background of the current digital media, social media platforms, video sharing sites, mobile applications, and so on a variety of channels to offer a wide range of advertising communication way. Each type of channel, has its own characteristics of user groups and the propagation mode.
By big data analysis technique, advertisers can precisely target their audience by analyzing multidimensional data such as age, gender, interests, and consumer behavior. This enables them to accurately position their advertising, thereby increasing ad visibility and click-through rates, and enhancing the effectiveness of advertising channels.
5.1.2. Content Rendering
The presentation of content is one of the key factors for artificial intelligence-driven generative advertising to stimulate consumer interest. Artificial intelligence technology gives the ads in a highly innovative and natural affinity, make it in the form of numerous traditional advertising is particularly prominent. Creativity in advertising shows through its capacity to shatter the traditional mindset. It captivates consumers by employing distinct creative concepts, expressive styles, and storytelling methods. Immersive advertising experiences can be fabricated by technologies like virtual reality (VR) and augmented reality (AR). Consumers will then seem to be right in the heart of a product or brand's universe. Additionally, artificial intelligence algorithms can be utilized to develop personalized advertising concepts. By tailoring content to each consumer's tastes and behavioral patterns, it effectively enhances the allure and visibility of advertising.
The concept of natural affinity emphasizes unifying advertising content with how consumers perceive reality, taking into account visual impressions, the way language is used, and the delivery of emotions. It aims to avoid any form of presentation that is blunt, untrue, or overly hyped up. When the contents of advertisements can naturally when the environment is seamlessly integrated into the consumer information more easily arouse consumers' emotional resonance, enhance their interest in advertising and goodwill, to lay a solid foundation for the subsequent consumption decisions.
5.2. Organism Factor (O) Level
5.2.1. Corporate Cognition
AI-generated advertising plays an important role in shaping consumer cognitions of a business. Through the corporate image, brand concept, corporate culture and other information conveyed by the advertisement, Generation Z consumers form the initial impression and cognitive evaluation of the enterprise. Positive and consistent advertising can help enterprises to set a good brand image in consumers' mind, enhance the consumer to enterprise's trust and recognition; Fuzzy, contradiction, or conversely, if the advertising information inconsistent with the enterprise actual situation, may cause consumer confusion, doubt and even negative evaluation on enterprise, affect the enterprise reputation and competitiveness in the market.
5.2.2. Value Cognition
Value cognition is defined as consumers' subjective assessment and perception regarding the value of products or brands when they come across advertisements generated by AI. Advertising guides consumers to form cognitions of product or brand value by showing product functional features, quality advantages, price rationality and added value contained in the brand. For Gen Z consumers, they not only pay attention to the practical value of products, but also pay more attention to the multiple value connotation represented by the brand, such as personality, fashion, environmental protection and social responsibility. Therefore, AI generates ads need to accurately grasp the value orientation of Z generation consumers, in the form of attractive product or brand value proposition, and stimulate the consumers desire to buy and value identity, enhance the brand value in the eyes of consumers.
5.2.3. Emotional Cognition
In the realm of emotion cognition, investigations have demonstrated that Generation Z consumers typically display repulsive or neutral emotional responses to advertisements generated by AI. Firstly, a portion of consumers harbor skepticism or concerns regarding the involvement of AI technology. They worry that it might have an impact on the authenticity and reliability of advertisements, thereby giving rise to a feeling of rejection. Secondly, in light of the deficiency in creativity, the blunt manifestation of emotions, or the inability to strike a chord with consumers, consumers adopt a neutral stance towards these advertisements and find it challenging to evoke positive emotional reactions.
However, this phenomenon also points out the direction for advertising practitioners to improve. Brands must dig deep into what consumers' emotional requirements are, pinpoint their psychological sensitivities, and leverage AI tech to precisely communicate emotional messages. Only in this way can they produce works that can truly stir consumers' profound emotions.
5.2.4. Risk Cognition
Risk cognition entails consumers' cognition and evaluation of diverse risks. These could be brought by advertisements generated through AI. Among them, the bad value orientation of advertising may mislead consumers' values, especially for Generation Z youth whose values are not fully mature. The encroachment upon intellectual property rights not only damages the legitimate rights and interests of creators. It also upsets the fair competition environment in the market.
Simultaneously, information leakage poses a threat to consumers' personal privacy and property safety. These risk factors play a crucial role in consumers' risk cognition framework. Additionally, they impact their decision-making concerning consumer behavior, either directly or indirectly. For example, if consumers realize an advertisement carries a high risk of information leakage, they may avoid the product or brand advertised and refrain from purchasing it or sharing it with others.
5.3. Response Factor (R) Level
Consumer behavior in response to AI-generated advertisements is reflected through four key intentions: viewing, purchasing, sharing, and exploring. AI-generated advertising elements lead Gen Z consumers to experience different cognitive responses, which significantly influence their consumption behavior. When an ad effectively grabs consumers' attention through its reach channel and sparks their interest and emotional resonance in its content presentation, they are more likely to watch it. As they view the ad, the information it conveys about the enterprise, value, emotion, and risk further shapes their cognitive evaluation, prompting a comprehensive assessment of the product or brand. If consumers positively perceive the ad's message, recognize the product/brand's value and image, and their risk cognition stays within acceptable limits, they tend to develop a purchase intention and convert this intent into actual buying behavior
[36]
.
Following the purchase, depending on their satisfaction and recognition of the product or brand, consumers may possess the willingness to share. Their targets include friends, family members, and members of social network groups. The dissemination is carried out via channels such as social media and word-of-mouth communication, along with other means. Through these recommendation behaviors, the influence of the brand is expanded, and its market coverage is broadened. In addition, some consumers may also because advertising has inspired their curiosity and desire to explore on the product or brand, further insight into the product, the brand's history and culture, for more information on the related product or service, for the long-term development of the brand and customer loyalty cultivation.
Based on the above, this study provides a theoretical framework for clarifying the influencing mechanism by establishing a process mechanism model of the influence of AI-generated advertising on the consumption behavior of Generation Z group. At the same time, it also provides some practical enlightenment for current advertising design enterprises to further effectively utilize the comparative advantages of AI-generated advertising and promote the formation of a benign ecosystem of the whole industry.
6. Outlook and Shortage
6.1. Content Natural Degree of Optimization
6.1.1. Technology Upgrading and Audit Strengthening
The lack of naturalness of content presentation, the core element of AI-generated advertising, is a major reason why consumers are not really stimulated effectively. Due to the immature artificial intelligence technology, the picture of the advertisement occasionally has flaws and logical errors, which makes it difficult for consumers to integrate into the scene during the viewing process. In the face of such problems, the enterprise should strengthen in images and content under the audit of promote human-machine cooperation to improve AD quality, so as to improve enterprise competitiveness and industry influence.
6.1.2. Deep Mining and Dynamic Adjustment of Emotional Resonance
To foster profound emotional resonance with Gen Z consumers, enterprises must thoroughly delve into their emotional needs and psychological traits. Enterprises can leverage big data analysis and consumer insight techniques. By doing so, they are able to construct precise emotional description models. These models enable enterprises to comprehensively grasp the emotional inclinations and pain points of diverse market segments under various circumstances. Enterprises should pay full attention to the emotional communication ability of AI-generated advertising content, realizing that this is the key factor that truly acts on people's spirit to form a long-term brand impression and move people's hearts. By taking advantage of AI-generated advertising to directly hit users' pain points, enterprises can create advertising content full of stories and novel. For instance, enterprises can create a sequence of meticulously designed short advertisements. These advertisements are meticulously arranged to progressively reveal a comprehensive brand narrative. Concurrently, leveraging user feedback and interactions, the frequency and strategy of advertising can be flexibly tailored. This approach guarantees that users remain consistently engaged and profoundly immersed in the brand's narrative.
6.2. Improving the Accuracy of Content
Data-driven accurate creation and verification. Generation Z consumers' attitudes towards brands largely depend on the accuracy of advertising information, and the shallow use of AI-generated advertising will make consumers feel that they are not valued. With the rapid progress of science and technology, the surge of information leads to the challenge of serious information overload. Once customers find that the advertising content pushed by a company is vague, distorted or lacks substantive value, their favorable impression on the brand and its advertising products will be significantly reduced, and even have a negative impression on the company as a whole.
After the ads are created, a data-validated model is used to evaluate and improve the accuracy of the ads. By way of A/B testing contrast different style of advertising in the degree of accuracy of information transmission, consumers understand the existing status and acceptance level difference, the ideal version to pick out the large-scale delivery operation. At the same time, build up the advertising effectiveness of monitoring and feedback mechanism, real-time tracking advertising after consumer behavior data and feedback market information content, quickly detect and correct the deviation or misleading information may lurk in ads, To ensure that the advertising information can always maintain a high degree of consistency with the actual needs of consumers and the real situation of the market.
To enhance the accuracy of AI-generated advertising content, both internal cross-departmental collaboration and external expert support are essential. The advertising team should establish communication and collaboration with departments such as market research, product development, and data analysis to share resources and knowledge. Market research provides insights into consumer demand and market trends, product development introduces product features and innovations, and data analysis offers data-driven support. Meanwhile, industry experts should be invited to participate in consultations. By leveraging their experience and knowledge to assess the content of advertisements and recommend changes, we can ensure that the content is accurate, professional, and authoritative.
6.3. Prevention of Ethical Risks
6.3.1. Integration of Smart Technology Innovation and Ethical Norms
In the realm of risk cognition, the ethical dimensions of AI-generated advertising are of significant concern to consumers and directly influence their consumption behavior. Among these concerns, the most prominent issues include the poor value alignment of advertisements, intellectual property infringement, and data leakage. AI-generated advertising materials can readily produce misleading content and inherent biases, which in turn impact the objectivity and rationality of the advertising content. Consequently, companies should proactively enhance the application of intelligent recognition technologies and establish a positive advertising creative ecosystem to ensure that the content of advertisements aligns with societal morality and ethics. Additionally, companies must prioritize fostering advertising creativity and creative capabilities to ensure that the content of advertisements achieves a harmonious integration of technological and artistic quality and value, thereby exerting a positive social impact.
6.3.2. The Cooperative Governance Mechanism of Multiple Subjects Has Been Deepened
Consumers have voiced worries regarding the possible intellectual property rights infringement in AI-generated ads. Particularly those that might incorporate copyrighted material. To tackle these issues, the involvement of the government, enterprises, universities, and research institutions in research and discussion is essential. A clear copyright protection framework needs to be established. This framework should fully safeguard intellectual property rights. By clearly defining the author's rights of authorship and specifying the citation sources of AI-generated advertising works, potential copyright infringements can be averted.
Moreover, the use of artificial intelligence technology to generate advertising works to collect user's personal sensitive information, such as name, address, telephone number, and once the information was leaked, will bring serious inconvenience to the user or damage to property. More seriously, the super-large pre-training model of AI-generated advertising also involves sensitive data such as medical and financial data. If there is no effective protection, the audience may be secretly manipulated, which may lead to civil and even criminal risks
 [26, 27]
. In order to meet the challenges of the privacy and data protection, enterprises should set out from the origin of technology innovation, extensively solicit the public opinions and suggestions, to establish and improve the related specification and guiding principles. The government should play a leading role in implementing hierarchical and classified management of the AI-generated advertising industry, and encourage the active participation of advertising industry organizations, media organizations and consumers, so as to build a governance system with multi-actor coordination and joint supervision.
6.4. Privacy Protection Technology Innovation and Public Education Should Go Hand in Hand
In the field of privacy protection, enterprises should continue to increase investment in the research and development of privacy protection technology. Actively explore and use new encryption methods, block chain and other cutting-edge technologies to ensure the confidentiality and integrity of users' personal key information in the process of AD generation and delivery. With the help of the distributed record characteristics of block chain technology, the traceability and tamper-proof effect of user data can be achieved, and the data can be effectively prevented from being illegally stolen or maliciously modified.
Build a user data authorization control system, clearly define the user's right to control and know personal data, ensure that advertisers have obtained clear permission from users when collecting and using user data, and can only be applied to legal and compliant advertising business scope. To prevent data leakage, companies should heighten their emphasis on data security within the workforce. This entails strengthening training programs specifically designed for data security among employees. By taking this approach, it is feasible to elevate employees' awareness of data protection and ensure their compliance with stringent operational standards. These measures are essential in preempting data leakage incidents that might stem from either inadvertent errors or intentional breaches of rules within the organization. At the level of public education, the government, enterprises and social organizations should cooperate to strengthen the publicity and education of public privacy protection awareness and advertising literacy. We should promote and popularize privacy protection knowledge, advertise-related laws and regulations, as well as the operation principle and potential hidden dangers of AI-generated advertising through various publicity activities such as public service advertising, thematic seminars and online and offline training courses. Enhance the public's attention to personal privacy protection, guide the public to correctly understand AI-generated advertising, so that they can enjoy the information and convenience brought by advertising, and effectively safeguard their privacy rights and interests.
Looking forward to the future, with the continuous development of artificial intelligence technology and the continuous changes in consumer demand and market environment, AI-generated advertising will encounter more opportunities and problems. Through in-depth analysis of its mechanism of action on consumer behavior, continuous improvement of advertising creation and operation strategies, and active response to various risk tests, it is hoped to achieve accurate fit and benign communication between AI-generated advertising and consumer demand, and promote the innovation, reform and sustainable development of the advertising industry in the digital era. And contribute to the construction of a more prosperous, healthy and orderly market ecosystem.
The research in this study mainly uses one-on-one semi-structured interviews for qualitative research, without using large-scale questionnaires for corroboration, which is yet to be confirmed, and the coding process is susceptible to the influence of subjective factors. Because the research topic of the impact of generative AI-driven advertising on the consumer behavior of Gen Z is relatively small and the current development is rapid, the relevant literature is relatively scarce, and the conclusions may not be comprehensive enough to analyze the limited literature and data.
In the era of big data, the rapid development of technology makes personalization gradually become the norm in people's lives, and personalized generative AI-driven advertising have gained rapid development and widespread attention in this context
[9, 12]
. This study has some limitations of the research object because the sample focuses on the youth group of Gen Z, who are the driving force of consumption. It is suggested that in the future, scholars can divide the market group into age groups to study the impact of generative AI-driven advertising on the attitudinal cognition and responsive behaviors of different consumers, or divide the research object according to the other traits of the consumer population, so as to further enhance the comprehensiveness and scientificity of the study. Meanwhile, this study is a qualitative study, using grounded theory and inductive analysis to draw conclusions, and to a certain extent there is subjective bias on the part of the researcher. Future research can make full use of or develop scientific questionnaires and scales to explore how generative AI-driven advertising influence the attitudinal cognitions and consumer behaviors of Gen Z, for sake of enhancing the objectivity and accuracy of the study. Finally, this study found that the application and production content of generative AI-driven advertising may, to a certain extent, lead to concern, resentment and loss of some consumers, and it is significant to explore the important factors that influence them for future theoretical research and practical guidance.
Abbreviations

AIGC

Artificial Intelligence Generated Content

AI

Artificial Intelligence

Gen Z

Generation Z

AI-generated

Artificial Intelligence Generated

GANs

Generative Adversarial Networks

Ad/AD

Advertisement
Author Contributions
Yifan Nie: Conceptualization, Data curation, Formal Analysis, Investigation, Methodology, Resources, Software, Supervision, Validation, Visualization, Writing – original draft
Zhengyuan Liu: Conceptualization, Data curation, Formal Analysis, Investigation, Resources, Software, Visualization, Writing – original draft
Yi Jiang: Data curation, Investigation, Methodology, Supervision, Validation, Writing – original draft, Writing – review & editing
Data Availability Statement
The data supporting the outcome of this research work has been reported in this manuscript.
Funding
This work is not supported by any external funding.
Conflicts of Interest
The authors declare no conflicts of interest.
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    Yifan, N., Zhengyuan, L., Yi, J. (2025). Exploring the Impact of Generative AI-driven Advertising on Generation Z's Consumer Behavior in China: A Grounded Theory Approach. American Journal of Applied Psychology, 14(4), 113-128. https://doi.org/10.11648/j.ajap.20251404.11

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    Yifan, N.; Zhengyuan, L.; Yi, J. Exploring the Impact of Generative AI-driven Advertising on Generation Z's Consumer Behavior in China: A Grounded Theory Approach. Am. J. Appl. Psychol. 2025, 14(4), 113-128. doi: 10.11648/j.ajap.20251404.11

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    Yifan N, Zhengyuan L, Yi J. Exploring the Impact of Generative AI-driven Advertising on Generation Z's Consumer Behavior in China: A Grounded Theory Approach. Am J Appl Psychol. 2025;14(4):113-128. doi: 10.11648/j.ajap.20251404.11

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  • @article{10.11648/j.ajap.20251404.11,
  author = {Nie Yifan and Liu Zhengyuan and Jiang Yi},
  title = {Exploring the Impact of Generative AI-driven Advertising on Generation Z's Consumer Behavior in China: A Grounded Theory Approach
},
  journal = {American Journal of Applied Psychology},
  volume = {14},
  number = {4},
  pages = {113-128},
  doi = {10.11648/j.ajap.20251404.11},
  url = {https://doi.org/10.11648/j.ajap.20251404.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajap.20251404.11},
  abstract = {As a new and significant force of consumption in contemporary China, Generation Z showcases distinct characteristics. Their penchant for uniqueness and personalized psychology are especially evident in their daily consumption behaviors. In the current digital age, generative AI has emerged as a powerful tool in the advertising realm. Thus, exploring the influence mechanism of generative AI-driven advertising on the consumption behavior of Generation Z holds great significance. This exploration is crucial for the further integration and application of artificial intelligence technology in the development and reform of the advertising industry. Based on in-depth qualitative interviews with 24 Chinese university students, this study employs programmatic grounded theory. By doing so, it aims to uncover the intricate black box of the influence mechanism between generative AI-driven advertising elements and consumer behavior. The research ultimately discovers the pathways through which four dimensions-corporate cognition, value cognition, emotional cognition, and risk cognition-impact the consumption behavior of Generation Z. This offers valuable insights for advertisers targeting this demographic.},
 year = {2025}
}

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  • TY  - JOUR
T1  - Exploring the Impact of Generative AI-driven Advertising on Generation Z's Consumer Behavior in China: A Grounded Theory Approach

AU  - Nie Yifan
AU  - Liu Zhengyuan
AU  - Jiang Yi
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PY  - 2025
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DO  - 10.11648/j.ajap.20251404.11
T2  - American Journal of Applied Psychology
JF  - American Journal of Applied Psychology
JO  - American Journal of Applied Psychology
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EP  - 128
PB  - Science Publishing Group
SN  - 2328-5672
UR  - https://doi.org/10.11648/j.ajap.20251404.11
AB  - As a new and significant force of consumption in contemporary China, Generation Z showcases distinct characteristics. Their penchant for uniqueness and personalized psychology are especially evident in their daily consumption behaviors. In the current digital age, generative AI has emerged as a powerful tool in the advertising realm. Thus, exploring the influence mechanism of generative AI-driven advertising on the consumption behavior of Generation Z holds great significance. This exploration is crucial for the further integration and application of artificial intelligence technology in the development and reform of the advertising industry. Based on in-depth qualitative interviews with 24 Chinese university students, this study employs programmatic grounded theory. By doing so, it aims to uncover the intricate black box of the influence mechanism between generative AI-driven advertising elements and consumer behavior. The research ultimately discovers the pathways through which four dimensions-corporate cognition, value cognition, emotional cognition, and risk cognition-impact the consumption behavior of Generation Z. This offers valuable insights for advertisers targeting this demographic.
VL  - 14
IS  - 4
ER  -

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Author Information

  • Nie Yifan

    School of Public Health, Sun Yat-sen University, Guangzhou, China

    Biography: Yifan Nie is a postgraduate student at School of Public Health, Sun Yat-sen University, ORCID0009-0001-0240-6878

    Contact Email

    http://orcid.org/0009-0001-0240-6878

  • Liu Zhengyuan

    The Chinese University of Hong Kong, Hongkong, China

    Biography: Zhengyuan Liu is a postgraduate student at The Chinese University of Hong Kong. Her current research interests focus on cross-cultural management.

    Contact Email

    http://orcid.org/0009-0002-8779-8539

  • Jiang Yi

    College of Economics and Business Administration, Beijing University of Technology, Beijing, China

    Biography: Yi Jiang is an undergraduate student at Beijing University of Technology. Her major is a double degree program in Business Administration and Law.

    Contact Email

    http://orcid.org/0009-0003-0534-542X

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    1. 1. Introduction
    2. 2. Research Background
    3. 3. Research Design
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