Impact of artificial intelligence on branding: a bibliometric review and future research directions

Volume and growth trajectory

Figure 2 illustrates the volume and growth trajectory of 592 articles indexed in Scopus from 1982 to 2023. Overall, there has been a rapid increase in research on this topic over the past four decades.

Number of articles and growth trajectory.

During the period from 1982 to 2013, the number of articles focusing on the impact of AI on branding was quite limited, accounting for only 9.1% (with 54 articles over 22 years, averaging 2.45 studies per year). The first publication related to this topic was relatively early, in 1982. Over the next two decades, this topic did not receive significant attention from scholars, with the number of articles remaining in single digits.

The period from 2014 to 2018 witnessed an increase in articles on this topic. In 2014, the number of publications reached double digits for the first time and continued to steadily increase each year. However, the number of documents during this period was still limited, accounting for 14.4% (with 85 articles over five years, averaging 17 studies per year).

The period from 2019 to 2023 saw a significant increase in scholarly interest in the impact of AI on branding. In 2019, there was a remarkable surge in research output, with 57 articles, more than twice the previous year. Subsequently, the number of studies on this topic increased rapidly each year, peaking in 2023 with 117 articles. The total number of studies during this period accounted for 76.5% (with 453 articles over six years, averaging 75.5 studies per year).

Such discoveries regarding the historical development of the topic can be explained by several reasons. Academic interest in AI in brand management began in the 1980s, with the emergence of perspectives suggesting that AI tools could support decision-making (Collins and Mauritson 1987) and effectively forecast sales (Steinberg and Plank 1987), followed by studies focusing on Early Expert-Systems and robotics (e.g. Gill 1995). After more than two decades of relative quiet, the increased interest of scholars and practitioners in brand management may be related to three main factors: the breakthrough of AI techniques and supporting technology tools, the evolution of big data, and the availability of computational power (Overgoor et al. 2019; Bock et al. 2020).

Countries and international collaborations

Regarding the contributing countries, Fig. 3 displays the global distribution of documents on the topic across 76 countries spanning various continents, highlighting widespread interest in AI’s impact on branding. Node size in Fig. 3 reflects the prominence of this topic among researchers worldwide, with significant attention from scholars in the United States, the United Kingdom, India, and China. Notably, research from Austria, Ireland, Singapore, and Germany emerged earlier, followed by contributions from the United States, the United Kingdom, and more recently, India, South Korea, and China.

(threshold: 1 document per country, 76 countries).

Table 1 provides detailed information on the number and percentage of articles from the top 10 most productive countries in this field. Consistent with Fig. 3, the results show that the United States is the most prolific, with 175 documents, accounting for nearly 20% of the global research output. The United States also has the highest citation count. Following the United States is the United Kingdom with 73 documents and India with 67 documents. Both Fig. 3 and Table 1 indicate that the countries most interested in this topic are primarily the United States, various European nations, several Asian countries, and Australia, while other continents contribute less significantly.

Geographical analysis of AI adoption in branding reveals national and regional trends shaped by cultural and economic factors. In the United States, the global leader in AI application research in branding, the significant productivity of scholars can be attributed to advanced technological infrastructure, strong consumer markets, and high levels of innovation in digital marketing. This can also be traced back to the earliest findings on the role of AI in business originating from the United States, such as Allen (1982), Collins and Mauritson (1987), Steinberg and Plank (1987), and Gill (1995). The United States is also home to major tech companies that lead the way in deploying AI solutions for branding and customer interaction. Additionally, the country boasts strong academic institutions and abundant research funding, facilitating the development of AI across various fields, including branding.

In the United Kingdom, the adoption of AI in branding is likely influenced by a strong digital economy and a well-developed advertising and marketing industry. Research in the United Kingdom often focuses on integrating AI technology to enhance customer experience, personalize brands, and optimize marketing strategies. The tradition of data-driven decision-making and the growing focus on ethical AI in the United Kingdom likely also contribute to the country’s strong position in this field.

India’s high output in this area can be attributed to the rapidly growing information technology and tech industry, coupled with a massive consumer market, creating favorable conditions for the application of AI in branding. Companies in India increasingly leverage AI to serve a diverse, tech-savvy population, particularly in fields like e-commerce, mobile applications, and digital advertising. The Indian government also promotes digital initiatives and AI projects, accelerating research and development in this area.

The rise of China in AI branding research can be directly linked to the government’s strategic focus on AI in its national development plans. China’s rapidly growing digital economy and the widespread use of AI technologies in consumer services and retail have created significant opportunities for applying AI in branding. Major tech companies like Alibaba and Tencent have made substantial investments in AI to transform customer interactions and build brand loyalty. Furthermore, China’s large population and high mobile device usage create an ideal environment for applying AI in branding and customer engagement.

Our findings are somewhat consistent with previous related studies. For example, Varsha et al. (2021) reviewed the literature on the impact of AI on branding from 1982 to 2019 and identified the United States, Germany, and France as the top three countries publishing the most research. Mustak et al. (2021), in examining AI in marketing, noted that the bulk of research primarily comes from three regions: the United States, Central and Southern Europe, and East Asia.

Regarding international collaborations, the number and thickness of lines in Fig. 3 indicate significant links between countries. Particularly noteworthy is the research partnership between the United States and the United Kingdom, demonstrating the highest total link strength of 13. This is followed by collaborations between the United Kingdom and India, with a total link strength of 10, and between the United States and Canada, with a total link strength of 9. Other significant collaborations include those between China and the United States, China and the United Kingdom, and the United States and Australia, each with a total link strength of 8.

The collaboration trends shown in Fig. 3 suggest that cultural and economic dynamics may have influenced these partnerships. For example, the strong link between the United States and the United Kingdom can be attributed to similarities in language, culture, and shared research priorities, while the collaboration between the United Kingdom and India reflects the historical relationship and India’s growing role in global technological innovation. The increase in AI branding research in East Asia, particularly from South Korea and China, reflects the region’s investment in AI technology and digital consumer behaviour.

Influential authors

Our data reveal that there are 1581 authors with at least one study on the impact of AI on branding, and 1,469 of these authors have at least one citation. This indicates that the field has garnered significant attention from researchers worldwide.

Table 2 lists the top 10 most prolific authors and the most frequently cited authors in the Scopus database within this field. Leading the list of the most productive authors are Loureiro Sandra Maria Correia from Portugal and Fronzetti Colladon Andrea from Italy, each with seven documents. They are followed by Paulo Rita, Sérgio Moro, João Guerreiro (all from Portugal), and Silvia Ranfagni from Italy, along with Damianos P. Sakas from Greece, each with five documents. Notably, five out of the top ten authors are from Portugal. This finding aligns with Table 1 and Fig. 3, which show that Portugal ranks among the top 10 most productive countries in this research area.

A deeper look into this reveals a few reasons for these results. Most of these prolific authors are affiliated with the University Institute of Lisbon, which hosts eight diverse research units. Among them, the Business Research Unit (BRU-IUL) stands out as a key unit, receiving substantial funding from the Foundation for Science and Technology (FCT) and other sources. This unit boasts numerous researchers and offers doctoral programs in data analysis, marketing, economics, and management. The institution has developed excellent research groups in marketing, management, and data analysis, creating an environment conducive to interdisciplinary research advancements.

Leading the list of the most cited authors are Goh Khim-Yong and Heng Cheng-Suang from Singapore, and Lin Zhijie from China, each with 1,038 citations. This prominence is understandable as these three scholars co-authored a highly influential paper (Goh et al. 2013), which is a typical study exploring social media brand communities and consumer behaviour, as well as the effects of user- and marketer-generated content. Following them are Raffaele Filieri from France with 781 citations, and Philipp Rauschnabel from Germany with 778 citations.

Notably, no author appears on both the lists of the most productive and the most cited authors. This indicates that while the group of researchers from Portugal and some others have made significant efforts to publish numerous papers, none of their works have achieved substantial influence in the field. Conversely, some authors from Singapore and other countries have not published many papers, yet their research has had a significant impact. Despite the United States being considered the most productive country in terms of the number of publications, it does not have any authors who are among the most influential in terms of citation count.

Influential documents

Table 3 presents the most cited papers in the Scopus database. Leading the list is the study by Goh et al. (2013) with 1038 citations. Following that, Dwivedi et al. (2021), and Lee et al. (2018) occupy the second and third positions with 738 and 527 citations, respectively. Overall, most of these studies primarily focus on the impacts of social media. For example, they examine the relative impact of user-generated versus marketer-generated content on social media brand communities (Goh et al. 2013), consumer engagement on Facebook (Lee et al. 2018), digital marketing (Dwivedi et al. 2021), visual content and social media engagement (Li and Xie 2020), measuring social media influence (Arora et al. 2019; Kiss and Bichler 2008), and real-time co-creation (Buhalis and Sinarta 2019). Additionally, some studies explore the impact of online conversations or e-service chatbots (Chung et al. 2020; Tirunillai and Tellis 2014), indicating that participation in social media brand communities leads to increased spending.

Two notable studies are Goh et al. (2013), Dwivedi et al. (2021). Goh et al. (2013) demonstrated the effects of user-generated content (UGC) and marketer-generated content (MGC) on purchasing behaviour through embedded information, providing evidence that UGC has a stronger impact than MGC on purchasing behaviour, thus playing a positive role for brands. The opinion paper of Dwivedi et al. (2021) has garnered the second-highest total citations, within just three years, averaging about 246 citations per year. Dwivedi et al. (2021) synthesized insights from several leading experts in digital marketing and social media communication, thereby providing detailed information and key insights into this topic, such as AI, mobile marketing and advertising, electronic word-of-mouth, ethical issues in marketing, augmented reality, and digital content management.

Main schools of thought

Bibliographic coupling analysis was employed to explore the main schools of thought on the impacts of AI in branding. Using a threshold of at least one citation per document and a total of 390 documents, we identified six main schools of thought. Figure 4 illustrates these clusters. Table 4 presents detailed information on the number of documents and the most cited studies in each cluster.

(threshold 1 citation, 390 documents).

The first school of thought: the integration of AI in branding through Chatbots, voice assistants, and AI influencers

This school of thought (in red colour) tends to focus on topics such as the effectiveness of Chatbot e-services in retail, the evolution of virtual assistants in marketing, building trust with AI voice assistants, proposing conceptual frameworks for robotics adoption in customer service, and examining consumer engagement with brands through AI influencers. For instance, Chung et al. (2020) find that Chatbots provide interactive and engaging brand or customer service encounters, thus supporting their adoption for virtual assistance. Mustak et al. (2021) identify dominant AI-related research themes in marketing, including the roles of Chatbots and virtual assistants. Pitardi and Marriott (2021) show that social presence and cognition are crucial for trust, illustrating a dynamic between privacy and trust in user interactions. Xiao and Kumar (2021) highlight the impact of robotics on service quality and customer engagement, moderated by firm nature, service characteristics, and brand positioning. Trivedi (2019) reveals that quality dimensions of information systems significantly impact customer experience and brand love, moderated by perceived risk, offering strategic directions for enhancing consumer-brand relationships.

The second school of thought: The intersection of social media and AI in brand management

Scholars in this school of thought (in green colour) are interested in investigating various aspects of the impact of social and digital media on consumer behaviour, brand engagement levels, and marketing strategies. They explore topics such as the transformation of consumer interactions through social media, measuring the impact of influencers, enhancing smart destinations through big data analytics, and differentiating destination branding concepts. The findings highlight the opportunities and challenges posed by digital technology and social media, as well as the effectiveness of data-driven approaches in improving branding strategies. For instance, Dwivedi et al. (2021) highlight opportunities and challenges. Buhalis and Sinarta (2019) conclude that real-time consumer intelligence and data-driven approaches revolutionize service co-creation by enabling dynamic, personalized consumer experiences. Marine-Roig and Clavé (2015) indicate key metrics like engagement and sentiment are crucial for determining influencers, with an ensemble model achieving the highest accuracy in predicting influencer index. Moro et al. (2016) show that automated web content mining effectively extracts destination brand identity and image from online sources, providing valuable insights for branding strategies.

The third school of thought: the influence of UGC and MGC on consumer behaviour and brand development

This school of thought (in blue colour) investigates various aspects such as the influence of UGC and MGC on consumer purchase behaviour, the association between online advertising content and consumer engagement, the extraction of consumer satisfaction dimensions from UGC, and the utilization of big data for brand management insights. Additionally, they delve into the detection and response strategies for negative electronic word of mouth (eWOM) and the role of visual content in driving customer engagement on social media platforms. For instance, Goh et al. (2013) find that UGC has a stronger impact than MGC, particularly in undirected communication modes. Lee et al. (2018) highlight the effectiveness of combining brand personality-related content with informative content to improve engagement. Tirunillai and Tellis (2014) investigate that subjective dimensions dominate horizontally differentiated markets, while objective dimensions are more prevalent in vertically differentiated markets.

The fourth school of thought: leveraging advanced analytical approaches in branding through neural networks, sentiment analysis, and AI

The common aim of these studies (in yellow colour) is to explore innovative methods and technologies for understanding consumer behaviour, assessing brand performance, and managing brand equity. Key findings include the application of neural networks to model consumer choices, the development of measures like the semantic brand score to assess brand importance, the use of sentiment analysis and AI to distinguish between fake and real news content and the investigation of factors influencing the adoption of new products by low-income consumers in emerging markets. For example, Bentz and Merunka (2000) show neural networks capture non-linear preferences and aid logit models. Roy et al. (2019) find smart services impact brand equity and word-of-mouth. Roberts et al. (2014) highlight big data and digital communication’s influence on brand management. Colladon (2018) introduces the Semantic Brand Score for measuring brand importance. Pournarakis et al. (2017) combine topic and sentiment analysis for brand performance insights.

The fifth school of thought: navigating consumer experience, insights, and branding strategies in the AI age

The scholars in this school of thought (in purple colour) aim to investigate various aspects of consumer behaviour, perception, and experience in the context of technological advancements, marketing strategies, and brand dynamics. They provide deep insights into how factors such as the Internet of Things, viral marketing, online advertising, sensory perception, and brand personality influence consumer decision-making and brand engagement. For instance, Hoffman and Novak (2018) identify four types of consumer experiences in the IoT era and advocate for a nonhuman-centric approach. Kiss and Bichler (2008) find central customers enhance viral marketing, but the best centrality measure depends on network topology and diffusion. Liu and Mattila (2017) reveal that advertising appeal effectiveness for Airbnb varies with an individual’s sense of power. Chan and Tung (2019) note that robotic service enhances sensory and intellectual experiences, with varied impact on affective and overall brand experiences across hotel segments. Wu et al. (2017) find that a friend-like interaction style in IoT environments enhances brand warmth, competence, and attachment. Chen et al. (2015) show that brand personality traits are encoded in brain regions associated with reasoning, imagery, and affective processing.

The sixth school of thought: Crafting consumer engagement strategies and ensuring brand authenticity in the AI era

These studies (in light blue colour) focus on the impact of online customer engagement dimensions, emotions, and consumer-generated media stimuli on brand engagement and authenticity. Additionally, the scholars in this cluster tend to focus on the role of machine learning and AI integration in enhancing consumer engagement and brand value. For instance, Bilro et al. (2019) find that cognitive processing and hedonic experience significantly impact online customer reviews, stressing the importance of meeting consumer expectations. Loureiro et al. (2019) reveal that website quality, pleasure, and arousal boost consumer-brand engagement, emphasizing emotionally appealing content. Aluri et al. (2019) show that machine learning excels at identifying customers who value specific promotions, enhancing engagement and loyalty. Ballestar et al. (2019) use machine learning to personalize financial incentives, optimizing digital marketing returns. Schivinski (2021) identifies five consumer engagement subtypes through machine learning, offering tailored strategies for brand-related social media behaviour.