A sweeping bibliometric analysis of a decade of scholarship has mapped how artificial intelligence has reshaped the study of online customer experience, and the picture it paints is one of a field in mid-transformation. Researchers Yarramsetti Sumanth and G Somasekhar examined 257 Scopus-indexed journal articles published between 2015 and 2024, using the Biblioshiny interface of the R-based Bibliometrix package together with the network-mapping tool VOSviewer. Their review, published in Discover Artificial Intelligence, documents a sharp acceleration of research output since 2018 and a clear thematic migration: early work concentrated on efficiency, automation and recommendation engines, while the most recent studies focus on chatbots, personalization, emotional AI, trust and the ethics of customer-facing systems.
The dataset behind the study was deliberately narrow. The authors queried Scopus for documents containing the terms artificial intelligence or AI alongside customer experience in the title, abstract or keyword fields, restricted to English-language journal articles from 2015 to 2024 in business, management and accounting, social sciences, and computer science. The initial query returned 1,122 records, which step-by-step screening for publication year, subject area, document type, source type and language reduced to the final corpus of 257 peer-reviewed articles. The authors acknowledge that this focused strategy may have excluded relevant studies on constructs such as personalization, service quality or AI-enabled services that never explicitly used the phrase customer experience, so their findings describe the explicit AI-CX literature rather than the full landscape of adjacent research.
On the production side, the numbers tell a story of rapid growth. Publications in the field increased by 53.75 percent across the 2015 to 2024 window, a rise the authors attribute to deepening industry involvement in the digital economy, ongoing technological progress and the growing strategic importance of customer experience as a research area. The most prolific contributors at the country level were India, with 56 articles, and the United States, with 52. Yet productivity and influence did not align neatly. The United States accumulated 2,318 citations from its 52 articles, while the United Kingdom drew 2,993 citations from only 23 papers, a contrast the authors interpret as evidence that citation impact reflects earlier publication dates, broader international collaboration and visibility in prominent journals rather than sheer volume of output.
At the journal level, the Journal of Retailing and Consumer Services dominated the retrieved dataset, contributing eight articles and achieving the highest h-index within the corpus. Other influential outlets included the Journal of Service Management, Psychology and Marketing, the Journal of Services Marketing and the International Journal of Information Management. Among individual researchers, Jochen Wirtz and Anuj Kumar emerged as the most productive authors, each contributing four articles to the dataset over the decade. Wirtz also achieved 41 citations for a single paper, while Tat-Huei Cham and Eugene Cheng-Xi Aw recorded the highest total citations at 122. The authors note that prolific publishing does not always translate into scholarly influence, citing one researcher who has published continuously since 2019 with comparatively modest citation performance.
Collaboration patterns reveal a field that is cooperative but not yet fully globalized. The average document carried 3.11 co-authors, and 32.3 percent of papers involved international co-authorship, which the authors characterize as moderate. The co-authorship network resolved into distinct clusters shaped by shared interests and regional ties. A large red cluster centered on Keng-Boon Ooi, Garry Wei-Han Tan, Tat-Huei Cham and Eugene Cheng-Xi Aw reflects Southeast Asian institutional collaboration, with most of its studies examining how AI operates in digital retail settings. A green cluster linking Werner H. Kunz, Jochen Wirtz and Yogesh K. Dwivedi gravitates toward service innovation and digital transformation. These clusters, the authors argue, show how common research questions drive collaborative knowledge production and strengthen the methodological value of the literature.
The conceptual heart of the study lies in its keyword co-occurrence analysis. From 906 author keywords, 19 met a minimum occurrence threshold of seven and formed four thematic clusters in the VOSviewer map. A red cluster captured the technological dimension, containing chatbot, deep learning, generative AI, machine learning and customer journey, and reflecting rising interest in large language models and more human-like customer communication. A green cluster grouped customer outcomes such as customer satisfaction, service quality, customer support and big data applications. A blue cluster represented strategic and marketing uses, including digital transformation, digital marketing, marketing strategy and customer engagement. A purple cluster flagged emerging themes such as anthropomorphism and retailing, pointing to growing psychological research on how human-like AI features shape user acceptance.
The thematic evolution analysis, which plots themes by centrality and density across three time windows, traces the field’s intellectual journey in detail. Between 2015 and 2018, research concentrated on technologies enabling personalization, decision-making and big data analytics for e-commerce. From 2019 to 2021, attention shifted toward more sophisticated use cases, notably chatbots and real-time analytics. In the most recent period, 2021 to 2024, the fastest-growing topics were omnichannel personalization, generative AI and affective or emotional AI. The authors read this trajectory as a movement away from treating AI as a pure efficiency technology toward examining it as a customer experience issue, with increasing attention to how conversational and emotional systems enhance interaction and raise ethical questions.
To interpret these patterns, the authors drew on three theoretical lenses, while emphasizing that these serve as interpretive frameworks rather than empirically validated models. The Technology Acceptance Model, which explains adoption through perceived usefulness and ease of use, maps naturally onto the technology cluster of chatbots, machine learning and generative AI. The Stimulus-Organism-Response framework, in which external features trigger cognitive and affective reactions that produce behavioral outcomes such as satisfaction and loyalty, fits the customer-outcome cluster. Service-Dominant Logic, which treats AI as an operant resource for value co-creation, illuminates the engagement and service-related themes. The authors also propose a three-layer conceptual synthesis linking AI tools, experiential mechanisms such as personalization and trust, and outcomes including satisfaction, loyalty and value co-creation, while cautioning that these layers are not empirically tested.
The study’s limitations are clearly stated and matter for how its conclusions should be read. Reliance on Scopus alone may introduce database-specific bias compared with Web of Science, Google Scholar or Dimensions, and the focused query design means closely related constructs such as personalization, service quality and customer satisfaction were captured only when they appeared alongside the term customer experience. The analysis is also quantitative, leaving the content of thematic clusters open to deeper qualitative review. The authors call for future work combining bibliometrics with systematic literature analysis, comparative studies across databases, and research into ethical AI and consumer vulnerability beyond business-to-consumer settings. Even so, the overall message is unambiguous: a decade of scholarship has carried AI in online customer experience from back-office automation to the front lines of emotion, trust and responsibility.
Subject of Research: Bibliometric and thematic evolution analysis of artificial intelligence in online customer experience research
Article Title: A bibliometric and thematic evolution analysis of artificial intelligence in online customer experience
Article References: Sumanth, Y., & Somasekhar, G. (2026). A bibliometric and thematic evolution analysis of artificial intelligence in online customer experience. Discover Artificial Intelligence, 6(1), Article 1002. https://doi.org/10.1007/s44163-026-01867-z
Image Credits: AI Generated
DOI: 10.1007/s44163-026-01867-z
Keywords: artificial intelligence, customer experience, bibliometrics, e-commerce, chatbots, personalization, generative AI, Scopus, VOSviewer, thematic evolution, trust, ethics
News Source: Denise Maddox. (October 28, 2026). AI research on online customer experience shifts from automation to emotion, ethics and trust. Scienmag.
