Overcoming the Interpretive Gap in Scientific Library Web Analytics Through a Cyclical Model of Synthesizing Quantitative and Qualitative Data

The article is devoted to the current challenges facing scientific libraries in the context of digitalization and high competition. The aim of the study is to develop and methodologically justify the cyclical model for synthesizing quantitative and qualitative data to overcome the interpretive gap in web analytics. It analyzes the problem of the interpretation gap that arises when relying solely on quantitative data from web analytics, which do not reveal the motives and deep information needs of users. As a solution, the author’s cyclical model is proposed, based on the system integration of quantitative web analytics data (the answer to the question “what?”) and qualitative research methods, such as surveys and usability testing (the answer to the question “why?”). The model is presented as a four-stage management framework (data collection, analysis, optimization, evaluation) for the continuous improvement of digital services. The main results are the creation of a holistic methodology that allows libraries the motion from analyzing actions to a deep understanding of users, as well as presenting a specific framework for its practical implementation. The use of the model allows one the transformation of a library into an adaptive ecosystem that effectively meets the complex information needs of the scientific community.