West Science Social and Humanities Studies Vol.
No.
March 2026, pp.
Bibliometric Analysis of Conversational Marketing Loso Judijanto IPOSS Jakarta.
Indonesia
Article Info
ABSTRACT
Article history:
This study aims to map and analyze the intellectual structure, thematic evolution, and research trends in conversational marketing using a bibliometric approach.
Data were collected from the Scopus database using relevant keywords related to conversational marketing, artificial intelligence, chatbots, and digital interaction.
The analysis was conducted using bibliometric techniques, including co-occurrence, coauthorship, and thematic mapping, supported by visualization tools such as VOSviewer.
The findings reveal that conversational marketing has developed as an interdisciplinary field, with artificial intelligence serving as the central foundation linking marketing, communication, and user experience.
The results also indicate a clear evolution of research themes, from early communication-focused studies to the integration of machine learning and natural language processing, and more recently toward advanced conversational AI, personalization, and customer experience.
Furthermore, the study identifies emerging topics such as anthropomorphism, large language models, and conversational commerce as key directions for future research.
This study contributes by providing a comprehensive overview of the field and highlighting potential avenues for further theoretical and empirical development in conversational marketing.
Received Mar, 2026 Revised Mar, 2026 Accepted Mar, 2026 Keywords:
Conversational Marketing Artificial Intelligence Chatbots Customer Experience Bibliometric Analysis This is an open access article under the CC BY-SA license.
Corresponding Author:
Name: Loso Judijanto Institution: IPOSS Jakarta.
Indonesia Email: losojudijantobumn@gmail.
INTRODUCTION
The rapid evolution of digital businesses interact with consumers.
Over the past decade, marketing strategies have shifted from one-way communication models toward more interactive, personalized, and real-time engagement approaches.
Among these marketing has gained significant attention as a method that leverages dialogue-driven interactions to build relationships, enhance customer experience, and drive conversions.
Powered by advancements in artificial platforms, conversational marketing enables customers in a more human-like, responsive, and context-aware manner .
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The conversational marketing is closely linked to the increasing adoption of messaging Consumers responses and personalized solutions, which traditional marketing channels often fail to Conversational tools such as live chat Journal homepage: https://wsj.
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AI-driven chatbots, and voice assistants provide businesses with the capability to engage users instantly, products, and guiding purchasing decisions in real time.
This shift reflects a broader trend toward customer-centric marketing, where the focus lies on creating meaningful interactions rather than simply delivering promotional messages .
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In addition, the integration of big data analytics and machine learning technologies has significantly enhanced the effectiveness of conversational marketing.
By analyzing user behavior, preferences, and interaction patterns, companies can tailor conversations to individual needs, thereby improving engagement and satisfaction.
This datadriven approach not only increases conversion rates but also strengthens brand As a result, conversational marketing is increasingly viewed as a strategic tool for achieving competitive advantage in the digital marketplace .
Despite its growing importance, the academic literature on conversational marketing remains fragmented and dispersed communication studies, and computer Researchers have explored different aspects of conversational marketing, such as chatbot design, customer engagement, user experience, and technological adoption.
However, there is still a lack of comprehensive understanding regarding the development, trends, and intellectual structure of this research field.
This fragmentation makes it difficult for scholars and practitioners to identify key themes, influential works, and emerging research directions .
Bibliometric systematic and quantitative approach to examining the evolution of a research field by analyzing patterns in academic publications, citations, and collaborations.
Through techniques such as co-citation analysis, visualization, bibliometric studies can reveal the underlying structure and dynamics of scholarly knowledge.
Applying bibliometric analysis to conversational marketing research can provide valuable insights into its growth trajectory, major contributors, dominant themes, and future opportunities.
Such an analysis is essential for advancing theoretical applications in this rapidly evolving domain .
Although conversational marketing has emerged as a prominent topic in both academic and practical contexts, the existing body of literature lacks a comprehensive and systematic synthesis.
Current studies tend to focus on specific technologies or applications, resulting in a fragmented understanding of the field.
There is limited knowledge about the overall development patterns, key research clusters, influential authors, and Without a structured overview, it becomes challenging for researchers to identify research gaps and for practitioners to apply insights effectively.
Therefore, a bibliometric analysis is needed to map the intellectual landscape of conversational marketing and provide a clearer, evidencebased understanding of its progression and future directions.
The primary objective of this study is to conduct a comprehensive bibliometric analysis of conversational marketing literature in order to identify its intellectual structure, research trends, and key
METHODS
This study employs a bibliometric analysis approach to systematically examine the body of literature on conversational Bibliometric analysis is a quantitative research method used to evaluate academic publications through statistical and mathematical techniques, enabling the identification of patterns, trends, and relationships within a specific research field .
The method is particularly suitable for this study because it allows for a comprehensive and objective assessment of large volumes of scholarly data.
By analyzing publication outputs, citation structures, and keyword Vol.
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West Science Social and Humanities Studies occurrences, this study aims to uncover the intellectual structure and developmental trajectory of conversational marketing The data for this study are collected from reputable academic databases such as Scopus and Web of Science, which are widely recognized for their extensive coverage of peer-reviewed literature.
A systematic search strategy is applied using relevant keywords.
Auconversational marketing,Ay Auchatbots,Ay AuAI marketing communication,Ay and Auinteractive marketing,Ay within titles, abstracts, and keywords.
Inclusion criteria are established to ensure data quality, such as selecting only journal articles and conference papers published in English within a defined time frame.
Duplicate records and irrelevant publications are removed through a screening The final dataset is then exported in compatible formats for further analysis using bibliometric software tools such as VOSviewer and .
RESULTS AND DISCUSSION
1 Keyword Co-Occurrence Network Figure 1.
Network Visualization Source: Data Analysis Result, 2026 Figure 1 reveals that conversational marketing is a highly interdisciplinary field, positioned at the intersection of artificial intelligence, marketing, and communication The most central node appears to be artificial intelligence, which connects strongly with marketing, user experience, and chatbotrelated concepts.
This indicates that the literature is anchored in technological advancements, with AI serving as the backbone that enables conversational interactions between firms and customers.
The density of connections suggests that research in this domain is rapidly expanding and increasingly integrated.
The red cluster, centered around artificial intelligence, user experience, and digital marketing, highlights a dominant research stream focused on customer-centric Studies in this cluster emphasize how conversational technologiesAisuch as chatbots and virtual assistantsAienhance user experience, personalize interactions, and influence customer journeys.
The presence of Vol.
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West Science Social and Humanities Studies terms like anthropomorphism suggests a growing interest in making AI interactions feel more human-like, which is critical in conversational marketing contexts.
The green cluster reflects the technological foundation of conversational marketing, with keywords such as machine learning, natural language processing, deep learning, and computational linguistics.
This cluster indicates that a significant portion of the literature is rooted in technical disciplines, focusing on how conversational agents are developed and optimized.
The strong linkage between these terms and chatbot or advancements in language processing technologies directly influence the evolution of marketing applications.
The blue cluster, centered around marketing, communication, advertising, and large language models, represents the communication-oriented perspective of the field.
This cluster shows how conversational marketing is increasingly being integrated into broader marketing strategies, including advertising and social media engagement.
The inclusion of emerging concepts like large language models and ChatGPT signals a recent shift toward more advanced AI systems, which are transforming how brands communicate at scale while maintaining personalization.
The purple and yellow clusters illustrate the commercial and platform-based applications of conversational marketing.
Terms e-commerce, conversational commerce, and social media indicate that research is moving toward These clusters demonstrate that conversational marketing is not only a theoretical construct but also a strategic tool for driving sales, enhancing customer interaction, and leveraging platforms like social media.
Figure 2.
Overlay Visualization Source: Data Analysis Result, 2026 Figure 2 highlights the temporal evolution of conversational marketing research, where color intensity represents the average publication year of each keyword.
Earlier studies .
hown in darker blue tones, around 2018Ae2.
are concentrated on foundational concepts such as marketing, communication, advertising, and human Vol.
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West Science Social and Humanities Studies This indicates that initial research framed conversational marketing within paradigms, focusing on how digital channels mediate interaction between firms and customers rather than on advanced technologies themselves.
As the field progresses into the middle period .
reen tones, around 2020Ae 2.
, the focus shifts toward technological integration, particularly around artificial intelligence, machine learning, natural language processing, and chatbots.
During this phase, research begins to bridge technical capabilities with marketing applications, emphasizing how conversational agents can enhance customer engagement and automate service processes.
The emergence of terms like conversational agent and user experience reflects a growing interest in aligning technological sophistication with usercentered design.
In the most recent period .
ellow tones, around 2023Ae2.
, the literature shows a clear movement toward advanced AI-driven and experience-oriented themes, including conversational AI, customer experience, anthropomorphism, and the appearance of large language models and ChatGPT.
This suggests that the field is entering a more mature stage, where the emphasis is not only on deploying conversational technologies but also on optimizing their human-like qualities, personalization, and strategic impact on customer relationships.
Figure 3.
Density Visualization Source: Data Analysis Result, 2026 Figure 3 highlights the core concentration of research themes in conversational marketing, where brighter .
areas indicate higher frequency and stronger co-occurrence of keywords.
The most intense region centers around artificial intelligence, marketing, and sales, suggesting that the field is heavily anchored in the integration of AI technologies within marketing and commercial activities.
This central density reflects that the majority of studies focus on how AI-driven toolsAisuch as chatbots and conversational agentsAiare applied to enhance marketing performance, customer interaction, and ultimately sales Surrounding this core, moderately dense areas .
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West Science Social and Humanities Studies as user experience, digital marketing, social media, and customer experience, indicating understanding the experiential and relational aspects of conversational marketing.
More peripheral and less dense areas .
lue tone.
, such as natural language processing, deep learning, and computational linguistics, show that while technical foundations are important, they are less dominant compared to application-oriented discussions.
3 Citation Analysis Table 1.
Top Cited Research Citations Authors and year Title AuSo what if ChatGPT wrote it?Ay Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy On the Design of and Interaction with Conversational Agents: An Organizing and Assessing Review of Human-Computer Interaction Research ChatGPT and consumers: Benefits.
Pitfalls and Future Research Agenda Knowledge Graphs: Methodology.
Tools and Selected Use Cases Spreading Social Media Messages on Facebook: An Analysis of Restaurant Business-to-Consumer Communications Cutting through Content Clutter: How speech and image acts drive consumer sharing of social media brand messages Unleashing the power of word of mouth: Creating brand advocacy to drive growth Adoption and impacts of generative artificial intelligence:
Theoretical underpinnings and research agenda Learning from the Dark Web: leveraging conversational agents in the era of hyper-privacy to enhance marketing The effect of social presence and chatbot errors on trust Source: Scopus, 2026 Discussion The findings of this bibliometric marketing has evolved as a convergence marketing strategy, and customer interaction intersect in a tightly integrated knowledge The network and density visualizations consistently position artificial intelligence as the intellectual core, closely linked with marketing and sales-oriented This pattern reflects a shift in marketing thought, where interaction is no longer mediated solely through static channels but is increasingly shaped by adaptive, real-time communication systems.
Rather than treating technology as a supporting tool, the literature places AI at the center of value creation, redefining how firms initiate, sustain, and scale customer At the same time, the prominence of user experience, customer experience, and digital marketing signals a strong movement The field is no longer limited to technological deployment but is increasingly concerned with how conversational interfaces shape perceptions, trust, and engagement.
The emergence of themes such as anthropomorphism suggests that researchers are paying closer attention to the human-like qualities of AI agents, particularly in how these qualities influence emotional responses and interaction quality.
This indicates a conceptual shift from efficiency-driven experience-driven conversational marketing depends on its Vol.
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West Science Social and Humanities Studies ability to simulate meaningful human The temporal overlay further reveals a clear trajectory in the development of the Early research was rooted in communication and advertising frameworks, emphasizing digital channels and human This was followed by a phase characterized by the integration of machine learning, natural language processing, and More recent contributions highlight the rise of advanced systems such as large language models and generative AI, accompanied by a growing emphasis on This progression illustrates how the field has matured from foundational exploration to more sophisticated.
AI-enabled understanding and adaptive engagement.
Despite these advances, the analysis also uncovers several gaps that warrant further investigation.
Much of the existing technological capabilities and firm-level applications, with limited attention to contextual variations such as cultural settings, small and medium-sized enterprises, and emerging markets.
In addition, while customer experience is frequently discussed, there is still a need for deeper theoretical integration with broader frameworks such as customer journey mapping and relationship Future research may also benefit from examining the ethical and psychological dimensions of conversational marketing, particularly in relation to data privacy, transparency, and user trust.
These directions open space for developing a more context-sensitive understanding of conversational marketing as both a technological and social phenomenon.
CONCLUSION
This study provides a comprehensive bibliometric mapping of conversational marketing, revealing its evolution into an interdisciplinary field anchored in artificial intelligence and increasingly oriented toward customer experience and strategic marketing The findings show a clear communication-based technology-enabled interaction, and more recently toward AI-driven emphasize personalization and human-like The dominance of themes related to artificial intelligence, marketing, and user experience underscores the central role of conversational technologies in reshaping how firms interact with customers.
At the same time, the study highlights the need for broader contextual and theoretical development, particularly in integrating experiential, ethical, and market-specific REFERENCES