A Bibliometric Analysis of Eye Tracking in User Experience Research

Abstract

This study employs bibliometric methods, which are methods that use quantitative analysis and statistics to measure the impact of publications, to provide insights into the research progress of ET-UER. The data source is the literature of ET-UER collected from the Web of Science database. VOSviewer and CiteSpace, which are software tools for visualizing bibliometric data, are used to conduct keyword analyses, evolutionary analyses, and co-citation analyses of the literature. The results show that the overall trend of literature volume is increasing. The hotspots of ET-UER include three main clusters: #1, the variables and evaluation content of eye tracking research; #2, the application scenarios of eye tracking; and #3, the indicators of eye tracking research. Evolution analysis reveals four trends in the development of ET-UER: firstly, the expansion of research scenarios; secondly, changes in eye tracking experimental environments and stimulus materials; thirdly, advances in research methods, paradigms, and analytical technologies; and fourthly, user centered design. The other trend is user-centered research. The most frequently co-cited research on ET-UER is divided into three categories: application of eye tracking technology, research on eye tracking technology, and methods and measurement. Based on the analysis of this study, the following three questions are still worth further attention. First, how to optimize the user experience of eye tracking as an interactive input. Second, how to support continuous and reliable research on gaze behavior in real-world experimental environments and for dynamic, 3D, interactive and other experimental materials, which requires more advanced experimental and data analysis techniques. Third, in the field of ET-UER, machine learning has great potential for follow-up research.

Keywords: Eye Tracking, User Experience, Bibliometric

DOI: 10.54941/ahfe1004540