Network analysis empowers researchers to gain deep insights into the advertising landscape, fostering informed advancements in the dynamic field [51]. It allows them to identify key trends, influential players, and the field’s ongoing evolution to aid its progress. To further delve into the network structure and actor influence, we complemented VOS Viewer with NodeXL Pro, another powerful network analysis tool.

NodeXL Pro, a paid service, enables full-scale social network and content analysis of network data. It calculates advanced metrics such as centralities (degree, betweenness, and closeness) and layouts, and automates tasks. This makes it popular among researchers, students, and professionals in various fields like sociology, communication studies, political science, health, business, and marketing [52]. NodeXL Pro helps users explore network structures, identify key nodes and clusters, and uncover patterns of interaction and influence among network actors, which can pinpoint the key players and institutions shaping the field of Generative AI advertising. This combined approach with VOS Viewer and NodeXL Pro enabled a robust and comprehensive exploration of the interconnected landscape within the Generative AI in advertising, revealing valuable insights into its evolution and key players.

 4. Results and Analyzation

4.1. Identifying Trends and Techniques: Word (Keywords and Terms) Co-Occurrence Analysis

4.1.1. Investigating Terms Patterns

Keywords and term co-occurrences are utilized in identifying trends and techniques. Terms are focused on words or phrases in the text, potentially including more general vocabulary not indicative of a specific topic. Keywords often refer to words or phrases specifically relevant to the topic or domain of interest, carrying a more thematic signifi-cance. Combining both approaches provides a holistic perspective. This way, we can glean the big picture from the broader term analysis while simultaneously getting a focused exploration of specific thematic keywords.

Our investigation into the core themes and concepts governing AI in advertising begins with a meticulous analysis of node relationships within the network structure. Figure 5, a rich tapestry of 904 interconnected elements, unveils valuable insights into this dynamic field. This initial exploration, focusing on network analysis and centrality measures, lays the groundwork for understanding the fundamental building blocks of AI advertising research.

Figure 5. Co-Occurrences of Terms Visualization (Binary Counting Method). Source(s): Authors’ Own Work. The colors of nodes in VOS Viewer represent distinct clusters or communities within the network data, aiding in the visualization and interpretation of complex network structures

Leveraging the analytical prowess of Vos Viewer, we delve deeper into the network using NodeXL Pro. This software empowers us to calculate crucial centrality metrics for each node, revealing their significance within the larger network (See Table 1.). Interestingly, “learning” emerges as the undisputed champion in terms of degree centrality, highlighting its pivotal role in this domain. This finding resonates with the very essence of artificial intelligence, as discussed in the literature review—the ability to learn and adapt is ingrained in the core functionality of AI [53]. “Learning” encompasses a multi-faceted spectrum, from acquiring knowledge and developing skills to organizing information and extracting new insights. Since the dawn of the computer age, integrating learning into machines has been a constant pursuit, leading to the co-evolution of AI and machine learning. 

Table 1. Co-occurrences of Terms (Binary)—Network Analysis Data.

Beyond degree centrality, we utilize betweenness centrality to identify critical “bridges” connecting various themes and subtopics. Intriguingly, “learning”, “dataset”, and “accuracy” emerge as key connectors, potentially revealing the foundational components shaping the discourse in this domain. These central terms likely facilitate communication and knowledge flow across different research dimensions, acting as crucial conduits for information exchange.

Furthermore, closeness centrality paints a picture of interconnectedness by high-lighting “accuracy”, “dataset”, and “text” as having strong associations with other key terms. This suggests that these terms not only hold central positions within their immediate thematic clusters but also maintain close connections with other important concepts, potentially serving as core components that contribute to a cohesive knowledge structure.

Finally, link strength analysis sheds light on the robust association between “learning” and “accuracy”, further solidifying their potential foundational roles. Examining term frequency reinforces this notion, with “learning”, “accuracy”, and “dataset” appearing most frequently, solidifying their prevalence in the literature and their prominence in AI research for advertising applications.

Our initial exploration through co-occurrences of terms (binary method) provided a foundational understanding of key concepts in AI advertising research. To further refine and expand on these insights, we delved into a full counting co-occurrence analysis using VOS Viewer. This approach allowed us to capture the richness and interconnectedness of term usage within the Web of Science dataset. After rigorous filtering and thesaurus integration, we analyzed a substantial set of 1486 terms. The resulting network visualization (Figure 6) unveils a captivating tapestry of 10 thematic clusters, intricately woven together by 150,955 links and a total link strength of 772,553. This intricate tapestry underscores the multifaceted and deeply interconnected nature of the AI advertising landscape.

Figure 6. Co-Occurrences of Terms Network Visualization (Full Counting). Source(s): Authors’ Own Work. The colors of nodes in VOS Viewer represent distinct clusters or communities within the network data, aiding in the visualization and interpretation of complex network structures.

Table 2 provides a deeper look into the network’s structural properties, revealing valuable insights into the relative importance and interconnectedness of different terms. The degree centrality metric, represented by the prominence of terms like “technology”, “tweet”, and “knowledge”, highlights the pivotal role of technological advancements and social media discourse in this interdisciplinary field. “Technology” emerges as the undisputed leader, underscoring the fundamental importance of cutting-edge tools and methodologies within AI advertising research. This finding aligns with the observed emphasis on innovation and progress in this dynamic field.

Furthermore, the prevalence of “tweet” and “sentiment” suggests a significant focus on social media dynamics. This finding underscores the integral role of user-generated content and sentiment analysis in understanding and shaping AI applications within the advertising domain.

Table 2. Terms Network Analysis (Full Counting).

Closeness centrality further reinforces the significance of “technology” and “tweet”, emphasizing their proximity to other terms within the network. This proximity signifies their integral position as central hubs that facilitate efficient information flow and connectivity. Additionally, the presence of “sentiment” and “word” highlights the interconnected nature of language, sentiment analysis, and word usage, suggesting a rich interplay of linguistic elements within the AI landscape.

Analyzing tie strength, we observe the prominent connection between “tweet” and “coronavirus”, underscoring the intersection between AI, social media, and real-world events. The strength of ties in these areas suggests a heightened focus on leveraging AI for analyzing sentiments, opinions, and information dissemination, particularly in the context of significant global events like the COVID-19 pandemic. This aligns with the observed trend of utilizing AI for social good and tackling real-world challenges. Furthermore, the substantial tie strength associated with “sentiment analysis” and “technology” further emphasizes the synergy between linguistic analysis and technological advancements. This suggests a landscape where AI is harnessed for understanding and interpreting user sentiments in various applications.

Finally, the total link strength, representing the cumulative strength of connections between terms, coincides with the prominence of “tweet”, “sentiment”, and “technology”. This cumulative strength underscores the collective influence and interdependence of these terms, highlighting their integral role in shaping the discourse and directions within the AI research landscape.

Examining occurrences, the frequency of “tweet” and “sentiment” indicates a sustained scholarly interest in understanding and utilizing social media content for AI applications. The high occurrences of these terms suggest a continuous exploration of the role of user-generated content and sentiment analysis in the development and implementation of AI models. This aligns with the present influence of social media and the value of understanding user sentiment in advertising strategies. Furthermore, the consistent presence of “advertising” and “artificial intelligence” in occurrences aligns with the overarching theme of this study, emphasizing the persistent relevance of AI In advertising and the ongoing exploration of its multifaceted applications.

The combined analysis of centrality, tie strength, and term occurrences within the co-occurrence network paints a compelling picture of the interconnected and dynamic landscape of AI advertising research. Technology, social media, sentiment analysis, and advertising emerge as key interconnected elements, each playing a crucial role in shaping the field’s trajectory. These findings not only enhance our understanding of the interdisciplinary nature of AI research but also provide valuable insights into the emerging frontiers and potential applications of AI in the advertising domain.

4.1.2. Investigating Keyword Patterns

Following the terms analysis, we investigated the intricate network of the keywords governing the landscape of AI advertising. In this analysis, we focused on the full counting method to capture granular details of the strength and frequency in keyword relationships. To ensure focused analysis, we implemented a minimum occurrence threshold of 5 and excluded non-relevant health-related terms, resulting in a refined dataset of 984 keywords. This meticulously pruned dataset then served as input for NodeXL Pro, where centrality measures were calculated to identify pivotal keywords and their interconnectedness within the network. By synergistically combining VOS viewer’s network analysis with NodeXL Pro’s centrality calculations, we were able to achieve a comprehensive understanding of the key thematic clusters and influential terms shaping the discourse in AI advertising research. Network Visualization can be seen in Figure 7, below.

Figure 7. Keyword Co-Occurrence Analysis Network Visualizations. Source: Authors’ Own Work. The colors of nodes in VOS Viewer represent distinct clusters or communities within the network data, aiding in the visualization and interpretation of complex network structures.

Our exploration of keyword co-occurrences within the AI advertising research landscape paints a captivating picture of interconnected themes and prominent actors (See Table 3.). Unsurprisingly, machine learning reigns supreme, dominating centrality and occurrence metrics. This dominance underscores its foundational role as the very “backbone of optimized computer programs powering advanced AI applications”, [54]. Its versatility extends to the advertising realm, where it personalizes content, optimizes targeting, and enhances overall effectiveness [55]. Consider, for instance, how machine learning algorithms can leverage vast amounts of social media data to tailor ads based on user demographics and preferences, a prime example of its transformative impact.

Interestingly, social media and Twitter closely follow, signifying their increasing weight within the research landscape. In this regard, while Twitter is a social media platform, it is regarded as one big entity in this big data because of its undeniable power as a communication platform. It has steadily ingrained itself within the fabric of daily life, transcending geographical boundaries. This widespread adoption is reflected by the impressive figures of 554.7 million active users globally, collectively generating 58 million tweets daily [56]. The dynamic and real-time nature of these two aligns seamlessly with the evolving advertising landscape, prompting researchers to explore the integration of AI, particularly machine learning, for ad dissemination and audience engagement. For instance, AI-powered sentiment analysis tools can extract user opinions from social media discussions, allowing advertisers to refine their message and connect with audiences on an emotional level.

However, a comprehensive understanding necessitates acknowledging emerging trends that might challenge the current dominance. While machine learning, social media, and sentiment analysis hold sway, we observe a growing interest in explainable AI and ethical considerations within the field. Additionally, the inherent limitations of the co-occurrence analysis, such as potential bias and the inability to capture causal relationships, necessitate further investigation using qualitative or experimental methods for a holistic perspective.

Table 3. Keyword Network Analysis.

4.1.3. Identifying Themes and Concepts through Clustering

Our investigation into the ever-evolving landscape of AI in advertising utilizes a rigorous network analysis approach, examining co-occurrence patterns within a vast array of terms and keywords. This meticulous exploration, involving three distinct analyses (binary co-occurrence, term co-occurrence, and keyword co-occurrence), yields three prominent thematic trends, painting a comprehensive picture of this dynamic field (refer to Table 4).

Table 4. AI Advertising Trends: Uncovering Themes Through Clustering

Firstly, “Implementation and Technical Aspects” align with the focus on emerging trends, highlighting the intricate technical nature of AI’s integration into advertising practices. Themes like AI-powered ecosystems, misinformation detection, and deception modeling offer specific insights into the inner workings of AI within this domain. Notably, machine learning advancements play a pivotal role in this cluster, laying the groundwork for understanding the multifaceted evolution of AI in advertising. Further solidifying this, our co-occurrence analysis identified the largest and most relevant cluster, “AI-Enhanced Advertising Ecosystem”. This cluster encapsulates the dynamic fusion of AI technologies with the intricate landscape of advertising practices. It incorporates cutting-edge AI methodologies like learning algorithms and convolutional neural networks, signifying the integration of machine learning and deep learning techniques, including the leveraging of social network services [57].

Additionally, it delves into crucial ethical considerations with terms like “attack”, “blockchain”, and “protection”. The multifaceted nature of this cluster extends to user-centric elements, evident in terms like “customer”, “satisfaction”, and “recommender system”, emphasizing AI’s role in tailoring advertising content to individual preferences. Moreover, the cluster reflects the evolution of advertising strategies with terms like “computational advertising”, “search advertising”, and “contextual advertising”, highlighting the increasing role of AI in targeted and personalized advertising. Beyond traditional boundaries, terms like “IoT”, “smart city”, and “autonomous vehicle” underscore the pervasive influence of AI applications on shaping not only advertising ecosystems but also the broader aspects of technology and society.

Furthermore, “Underlying Technologies and Methodologies” strengthens the analysis by focusing on foundational techniques such as natural language processing (NLP) and machine learning, particularly methods like deep learning, word2vec, and Generative Adversarial Networks (GANs). Examining these techniques is crucial for comprehending the potential of Generative AI in future advertising practices. This cluster aligns seamlessly with the focus on Generative AI by providing the necessary tools and methodologies.

Additionally, “Societal Impact” broadens the perspective by addressing the broader social, ethical, and societal implications of AI advertising. Themes like mental health, surveillance, including trust and privacy concerns [58], and knowledge-sharing emphasize the crucial need for responsible innovation and ethical frameworks, especially when considering the application of Generative AI. While not directly aligned with the focus on emerging trends, this cluster offers a critical counterpoint, reminding us of the broader impact of AI in advertising practices beyond purely technical considerations. This finding is echoed in our network analysis, which revealed 12 distinct clusters, including “AI Impact and Ethical Considerations in Advertising”, “Deep Learning in Advertising and Information Retrieval”, and “Social Media Impact on Mental Health and Public Health Surveillance”. These clusters encapsulate various thematic concentrations within the vast AI and advertising research landscape. By examining co-occurrences and clustering terms, we gained valuable insights into prevalent topics, emerging trends, and the interplay between various concepts within this dynamic field. This combined approach of clustering and co-occurrence analysis enhances our comprehension of the multidimensional facets of research in AI advertising, providing a foundation for a more targeted and insightful investigation into this ever-evolving domain.

In conclusion, our analysis reveals three key trends shaping the landscape of AI in advertising: “Implementation and Technical Aspects”, “Underlying Technologies and Methodologies”, and “Societal Impact”. Understanding these trends is crucial for navigating the complex and rapidly evolving world of AI-powered advertising.

4.1.4. Identifying Emerging Trends through Word Analysis

Following the concepts and themes, we now deconstruct the evolving landscape of artificial intelligence (AI) in advertising, which necessitates a meticulous, multi-pronged approach (See Table 5). This research employs a synergistic examination of three distinct co-occurrence analyses conducted using Vos Viewer software, unveiling established themes, emerging trends, and the captivating potential of Generative AI within this dynamic field.

Table 5. Evolution of Words (Keywords and Terms) Through Publication Years.

Commencing with a granular perspective, the binary co-occurrence analysis of terms acts as a compass, pinpointing potential future directions. Terms like “ChatGPT”, “bi-directional”, and “generative AI” collectively indicate a fervent drive towards advancing conversational AI, natural language understanding, and the creation of captivating content through AI. This discernible shift towards interactions exhibiting greater hu-man-like qualities and personalization powered by AI promises to shatter existing boundaries and obfuscate the lines between human-generated and machine-generated content.

Expanding the lens, the co-occurrence analysis of terms unveils unique perspectives from a broader vantage point. The recent emergence of “tcim” hints at a potentially fertile area of burgeoning research interest, while “neurosurgery awareness month” suggests intriguing evolving approaches in healthcare communication. Further investigation into acronyms like “CPSS”, “MGC”, and “IPV” holds the potential to unearth previously undiscovered research directions. Notably, the enduring presence of “indirect appeal” and “self-competence” underscores the sustained focus on the psychological and per-suasive dimensions of communication, a crucial element for comprehending advertising effectiveness.

Finally, the analysis using keyword co-occurrences with full counting illuminates a vibrant and transformative landscape. Notably, “Generative AI” takes center stage, reflecting the industry’s relentless pursuit of cutting-edge and impactful strategies through the generation of sophisticated content. The burgeoning significance of “social media platforms” and “digital media” emphasizes the strategic shift towards leveraging these channels for broader reach, seamlessly aligning with contemporary consumer behavior and media consumption patterns. Furthermore, the prominent presence of “accessibility”, “inclusion”, and “AI ethics” underscores a heightened awareness of the necessity for responsible innovation and the creation of inclusive advertising content.

We opted for a multi-faceted approach as each employed analytical method possesses distinct strengths. Full counting excels at revealing broader thematic trends, while term analysis dives deep into specific concepts. Binary analysis, on the other hand, serves as a potent tool for identifying potential future trajectories. By meticulously integrating these multifaceted perspectives, this research paints a comprehensive portrait of the evolving trajectory of AI in advertising, effectively highlighting the growing prominence of Generative AI in shaping the field’s future.

4.2. Identifying National Trends in Generative AI via Co-Authorship Analysis (Countries)

To offer a multifaceted understanding of the field, we proceeded with analyzing co-authorship networks in countries. We explored global research collaboration in AI advertising by combining full/fractional counting with a 25-country limit. We pinpointed key collaborations while avoiding dilution and a 5-document minimum focused on substantial partnerships as we performed the analysis. Through this combined analysis, we gained valuable insights into how countries collaborate and lead research efforts in AD advertising. We identified key players, investigated collaborative patterns, and ultimately charted the global landscape of research in this dynamic field.

Analyzing co-authorship networks in AI advertising research, we employed both full and fractional counting methods, revealing complementary perspectives on global collaborations. This approach emphasized the quality of collaborations, showcasing Chinese Mainland and Hong Kong and Macao’s strong ties through their substantial strength ties, particularly in collaboration with industry partners like Alibaba and Tencent, among others (See Figures 8 and 9.).

Figure 8. Co-Authorship Analysis Network Visualization—Full Counting Method. Source(s): Au-thors’ Own Creation. The colors of nodes in VOS Viewer represent distinct clusters or communities within the network data, aiding in the visualization and interpretation of complex network structures.

Figure 9. Co-Authorship Analysis Network Visualization—Fractional Counting Method. Source(s): Authors’ Own Creation. The colors of nodes in VOS Viewer represent distinct clusters or communities within the network data, aiding in the visualization and interpretation of complex network structures.

Interestingly, both methods consistently identified the United States, England, and India as leaders in collaborative research, demonstrating their extensive engagement (See Table 6.). However, the fractional counting method provided a more nuanced view, highlighting Chinese Mainland and Hong Kong and Macao’s unique strength in building high-quality collaborations, especially with industry partners where they transfer knowledge for business application. This comparison underscores the value of using both methods; full counting offers insight into the overall volume of collaboration, while fractional counting sheds light on the quality and strength of collaborative ties. Ultimately, this multi-method approach provides a deeper understanding of the dynamic and multifaceted landscape of global research collaborations in AI advertising.

Table 6. Centrality and Publishing Activities in the Network (Full and Fractional Counting).

Investigating the leadership of these countries, several factors contribute to the leadership role of Generative AI for advertising. Firstly, substantial investments from both public and private sectors in these countries, like the USA, fuel extensive research and advanced technology development. Secondly, top-tier research institutions concentrated in these countries act as hubs for talented researchers, fostering crucial breakthroughs and advancements. Thirdly, a pool of skilled professionals in AI and related fields allows these nations to undertake sophisticated research projects and drive innovation. Finally, global collaboration and knowledge exchange through networks and partnerships between academics, industries, and governments create a collaborative environment that accelerates progress.

Leveraging these factors, leading countries like the USA and Chinese Mainland and Hong Kong and Macao stay at the forefront of Generative AI research, influencing the field’s trajectory. Interestingly, our analysis suggests distinct collaboration patterns; For Chinese Mainland and Hong Kong and Macao, collaborations often involve strong industry partnerships, particularly with leading e-commerce platforms like Alibaba and Tencent, among others. This focus on industry-driven research translates into publications heavily oriented towards computational machine learning. On another note, USA’s collaboration largely occurs within the academic sphere, generating research and innovation publi-cations. While existing partnerships with companies like Facebook, Yahoo, and Microsoft exist, strengthening collaborations with industries and government institutions could foster practical applications and knowledge transfers.

5. Summary of Results

In summary, this study, conducted with the use of WoS data and employing methodologies such as co-occurrence analysis and co-authorship analysis, seeks to unveil key aspects of research in the dynamic intersection of artificial intelligence and advertising. In this research, we aimed to investigate the evolving relationship between advertising and AI through the lens of generative models as we explored five critical dimensions, encompassing research topics, emerging trends, geographical patterns, technological trends, and applications. Through rigorous analysis, the study sheds light on the trajectory of Generative AI in advertising, tracing its development from foundational AI concepts to advanced machine learning techniques. We found that the trajectory of AI advertising is heavily anchored, but not limited to, machine learning, especially in terms of deep learning, predictive modeling, natural language processing (NLP), and techniques related to content recognition and creation. The methodologies employed provide a comprehensive understanding of the landscape, addressing overarching questions and contributing valuable insights to the evolving field of artificial intelligence in advertising.

RQ1: Thematic Trends and Techniques in AI AdvertisingExamining the thematic landscape and technical trends of AI in advertising reveals a captivating interplay between foundational concepts and emerging areas. Keyword co-occurrence analysis and pattern recognition, as captured by our multidimensional analytical framework grounded in scientometric network analysis (see Figure 1), unveils an array of key terms and keywords, like machine learning, sentiment analysis, predictive modeling, deep learning, error detection, natural language processing, and more. These words perfectly coincide with our three key trends, themes, and concepts: Implementation and Technical Aspects, focusing on the intricacies of AI integration; Underlying Technologies and Methodologies, highlighting foundational techniques like NLP and machine learning; and Societal Impact, emphasizing ethical considerations and broader social implications. Understanding these interconnected trends is crucial for navigating the evolving landscape of AI advertising responsibly and ensuring its positive impact on society.

The landscape does not remain static. Emerging trends like deep learning techniques, evidenced by terms like “BERT”, “CNNs”, “LSTM”, “word2vec”, and “real-time systems”, signify a shift towards more sophisticated approaches, including advanced language modeling (e.g., word2vec). Notably, Generative AI models remain an exciting new frontier, actively explored and applied across diverse models, techniques, and tools, as revealed by our framework’s “topic analysis” and “Generative AI adoption” dimensions.

RQ2: National Trends and Generative AI

While the global landscape of AI advertising research reveals a strong collaborative spirit with diverse contributions, delving deeper unveils intriguing national trends impacting Generative AI adoption. Notably, although the USA maintains its position as a leading contributor, Chinese Mainland and Hong Kong and Macao present a unique pattern. Their higher centrality within the network suggests not only active research but also robust internal collaboration and knowledge exchange. Furthermore, Chinese research exhibits a distinct focus on “computational advertising”, likely propelled by partnerships with industry giants like Alibaba and Tencent. This points towards a strong industry-driven approach leveraging Generative AI for potential economic growth, suggesting a possible link between national trends and adoption rates.

However, a holistic understanding requires a nuanced perspective. While Chinese Mainland and Hong Kong and Macao offer a potential model for industry-driven adoption, other approaches hold equal merit. For instance, the USA, despite significant collaborations, prioritizes diverse areas like ethical considerations and evolutionary studies, leading to a broader, albeit potentially slower, commercialization of Generative AI across various sectors. Additionally, India and England also contribute significantly, highlighting a globally distributed effort with potentially diverse motivations and outcomes.

Therefore, understanding national trends within AI advertising research offers valuable context for comprehending Generative AI adoption patterns. Chinese Mainland and Hong Kong and Macao’s case demonstrates a model for industry-driven adoption, but broader societal considerations, emphasized by the USA’s research focus, are equally important. Further research exploring these connections and their impact on the global evolution of Generative AI is crucial to gaining a comprehensive understanding of the complex interplay between national trends and technological adoption.

Zooming in on Generative AI adoption, deep learning techniques like GANs, CNNs, LSTMs, BERT, and word2vec play a prominent role, facilitating content generation and analysis, as identified by our framework’s “Technological Trends and Application Do-mains” dimension. The application domains span various areas like content creation, sentiment analysis, and personalized marketing, showcasing the broad reach and potential of Generative AI techniques.

In conclusion, AI advertising research exhibits a vibrant tapestry of themes, with a growing focus on advanced deep learning and Generative AI techniques. Collaborative efforts across nations shape the adoption and use of these techniques, ultimately paving the way for innovative and impactful advertising strategies. This progress is significantly illuminated by our multidimensional analytical framework (see Figure 1), which transcends the limitations of prior studies by offering a holistic examination of the knowledge domain surrounding Generative AI in advertising. By integrating diverse analytical perspectives, it empowers researchers to navigate the intricate network of relations between AI, advertising, and generative models, ultimately yielding a comprehensive understanding of this dynamic and rapidly evolving field.

6. Conclusions and Future Work

The widespread adoption of artificial intelligence and machine learning is poised to have a profound impact. This study has shed light on the significant themes and subtopics within artificial intelligence in advertising, giving concreteness to this. This encompasses fundamental aspects like “learning”, “neural networks”, and “data”, underscoring the core of AI applications. Additionally, emerging trends indicate a surge in deep learning techniques, signifying a pivotal shift towards more sophisticated approaches. Geo-graphically, the USA, Chinese Mainland and Hong Kong and Macao, India, England, and other countries mentioned above have made substantial contributions, highlighting a global collaborative strength in advancing research on AI in advertising. This collaborative strength merits an opportunity to investigate the relationship and roles of university, industry, government, and possibly more, through the Triple Helix Model. Through this, we can identify if there is the presence of triple, quadruple, and n-tuple helix models, including the sociological theory of communication, which can be utilized to provide insights into the processes that drive scientific and technological change [59].

Regarding technological trends and application domains, Generative AI, mainly through deep learning techniques like GANs, CNNs, LSTM, BERT [60], and word2vec, is pivotal in content generation and analysis across various facets of advertising. This is because machine learning can learn complex patterns [61] and relationships when data is provided, making it ideal for creating creative and realistic outputs. The developmental trajectory of Generative AI in advertising showcases a progression from foundational AI concepts toward advanced deep learning techniques, indicating a forward-looking approach in advertising practices.

As AI and advertising intertwine through the transformative power of generative models, future research must dissect the intricacies of this phenomenon. Deciphering how consumers emotionally respond to AI-generated messaging, how their trust fluctuates with varying degrees of realism, and how these dynamics evolve alongside technological advancements are the keys to unlocking the true potential of this potent partnership. Just as businesses require new skills to navigate the ever-shifting digital landscape, so must we equip ourselves with the tools to decode the complex interplay between AI, advertising, and generative models within the human psyche [62]. The integration of artificial intelligence into society will fundamentally reshape human-AI interactions, necessitating careful consideration of the legal, ethical, and societal implications [63].

So, we must remain mindful of the potential pitfalls associated with centralized data models, as highlighted by the recent Facebook data breach and concerns about “cultural imperialism” in the hands of dominant platforms like GAFA [64]. This underscores the urgency of exploring the potential of decentralized technologies within the AI advertising landscape. Decoupling data ownership and control from centralized entities could offer a promising path toward mitigating privacy risks, fostering a more equitable power distribution, and ensuring culturally sensitive advertising practices in the AI era.

On another note, controlled experiments and surveys can play a crucial role in this endeavor. Researchers can uncover nuanced patterns that inform more effective and emotionally resonant strategies by meticulously measuring emotional responses, purchasing intent, and user satisfaction in response to AI-driven advertising [65]. As Zarifis and Fu highlight in their study on mobile app purchases, trust in AI-powered initiatives remains complex and multifaceted [66]. Future research exploring trust across various digital outlets, particularly in regions with prominent AI adoption and emerging markets, holds immense potential to inform ethical and effective advertising practices in the AI era.

A deep dive into the evolution of machine learning techniques and tools within advertising is crucial, especially their trajectory and how they can work with individuals and organizations for various opportunities. This aligns with Wheeler’s emphasis on transliteracy, the ability to navigate and utilize diverse platforms for content creation, collection, sharing, and interaction [67]. In adapting to the changing landscape of academic publication, it becomes imperative to integrate these insights into a decentralized framework for editorial, production, and readership functions [68].