Data processing and analysing techniques in UX research
Айгерим Айтим, Muslima Abdulla · Procedia Computer Science · 2024
Nowadays, it's evident that a system's, product's, or service's User Experience (UX) is a crucial component of its success. User Experience (UX) is a pivotal factor in the success of systems, products, and services, yet UX research often faces challenges in processing the vast amounts of structured and unstructured data involved. Prolonged analysis times result from the complexity and variability of unstructured data and the lack of optimized tools for its processing. This paper addresses these issues by conducting a systematic literature review to assess current methodologies and tools for data collection and analysis in UX research. A total of 120 peer-reviewed publications, sourced from IEEE Xplore, ScienceDirect, and other databases between 2018 and 2023, were reviewed. Through detailed analysis, 17 key papers were selected based on their empirical results, relevance, and focus on AI-driven data processing techniques. The findings indicate that models such as LSTM, GRU, and NLP are increasingly effective in handling both structured and unstructured data, enabling more efficient analysis and insight extraction. These AI-driven techniques not only streamline the research process but also offer scalable, flexible solutions that cater to various UX research contexts, allowing for more accurate decision-making and improved user experiences.