research

HCI has long evaluated qualitative research through a positivist lens, overemphasizing quantifiable metrics and neglecting its interpretive nature. Method: Drawing on epistemological critique, this paper systematically distinguishes positivist and interpretivist paradigms, exposing the fundamental limitations of quantification in understanding human behavior. It then proposes the first non-quantitative quality assessment framework specifically designed for HCI qualitative research. Contribution/Results: The framework introduces five interpretivist quality criteria—credibility, transferability, dependability, confirmability, and resonance—grounded in qualitative logic rather than numerical standards to ensure rigor and contextual appropriateness. It shifts evaluation away from positivist assumptions toward interpretivist principles, enhancing methodological fidelity to qualitative inquiry. The framework has been preliminarily adopted in the ACM Transactions on Computer-Human Interaction (TOCHI) and CHI conference review guidelines, marking a substantive step toward an interpretivist reorientation in HCI methodology.

This study investigates whether large language models (LLMs) can bridge the gap between UX experts and non-experts in authoring user scenarios. In a controlled experiment, both groups authored scenarios with LLM assistance; outputs were evaluated via mixed methods—structured scoring and qualitative coding—assessing structural completeness, expressive clarity, and audience orientation. Results demonstrate, for the first time empirically, that LLMs significantly enhance non-experts’ performance: their scenarios achieve structural and clarity levels comparable to experts’, and—remarkably—surpass experts in articulating user perspectives. The findings validate LLMs as effective, democratized tools for requirements analysis and reveal their unique capacity to augment empathic user-centered expression. This work advances accessible UX practice by lowering barriers to rigorous scenario-based design.

This study addresses the practical challenge of aligning cross-functional teams around dual objectives: enhancing user value and driving business growth. We propose a hybrid methodology integrating the User Experience Research (UXR) Point-of-View (POV) pyramid with growth hacking practices, synthesizing qualitative insights (e.g., contextual interviews, ethnographic observation) and quantitative analytics (e.g., behavioral log analysis, A/B testing, data science modeling) into a reusable collaborative decision-making framework. Our key contribution is the first systematic integration of UXR’s strategic-level POV with the growth funnel metrics framework, enabling empirical alignment and validation between user needs and business goals. Deployed in a product serving over one million users, the approach guided core feature iterations and yielded a 18% improvement in key UX metrics, alongside a 12% increase in DAU and a 9.5% uplift in conversion rate—demonstrating a positive coupling mechanism between user value creation and commercial growth.

This study addresses the challenge of systematically integrating data, evidence, and strategic insights in enterprise User Experience Research (UXR). To this end, it proposes three AI-augmented UXR paradigms: (1) intelligent multimodal analysis—leveraging computer vision (CV) and natural language processing (NLP) to extract insights from video and textual content; (2) an evaluable code editor—embedding real-time AI feedback to accelerate researcher skill development; and (3) an opportunity mapping model—aligning technical capabilities, user needs, and strategic priorities to enable cross-level opportunity discovery. It is the first work to unify cross-modal understanding, real-time feedback mechanisms, and strategic-level modeling within a cohesive UXR methodology. All three paradigms have been deployed in industry settings, yielding measurable improvements in product decision quality, team capability development efficiency, and precision in innovation opportunity identification—demonstrating the feasibility and sustained impact of AI-driven UXR in closed-loop value creation.

Traditional survey methods for early-stage feature prioritization in consumer products lack scalability for million-scale engineering decisions and suffer from low response rates and poor data quality—particularly among sensitive populations such as persons with disabilities. Method: This study proposes a lightweight adaptation of MaxDiff, reducing questionnaire length by 50% while preserving statistical power and user experience. Integrated with the UXR Point-of-View (PoV) framework and stratified sampling, it constructs an interpretable multinomial logistic regression preference model. Contribution/Results: The method is first validated in a tablet feature study with users with disabilities, significantly improving completion rates (+32%) and data reliability (Cronbach’s α > 0.92). It enables high-accuracy, reproducible feature ranking with a small sample (N = 120), establishing a novel, user-driven paradigm for product decision-making.

This study addresses the persistent challenges faced by User Experience Research (UXR) teams—namely, stakeholder bias, reactive engagement, and fragmented insights—that hinder their ability to exert strategic influence. To overcome these limitations, the authors innovatively integrate structured strategic thinking into UXR function development, proposing an organizational maturity model grounded in a UXR Point-of-View (POV) framework. Complementing this model is a practical playbook that combines “offensive” and “defensive” strategies to guide implementation. This integrated approach systematically enables UXR teams to transition from tactical execution to strategic impact, significantly enhancing their capacity to forge strategic partnerships, generate actionable insights, and contribute meaningfully to long-term corporate strategy formulation.

This study addresses the challenges in user experience research (UXR)—notably, the lack of methodological rigor and protracted workflows that hinder timely support for product decisions, compounded by the added burdens of prompt engineering, data preprocessing, and result validation when applying generative AI. To overcome these limitations, this work proposes a structured approach that systematically integrates generative AI with a UXR point-of-view (PoV) construction framework. Leveraging Google NotebookLM, the method introduces five context-specific, phased prompts designed to collaboratively distill evidence-driven strategic insights across the four stages of PoV development. Evaluation on eleven UXR reports demonstrates that this approach effectively positions AI as an efficient collaborator, significantly enhancing both the efficiency and feasibility of UXR practices.

This work addresses the lack of an efficient experimental platform for comparative user studies across diverse information access systems—such as retrieval-augmented generation (RAG) and autonomous agents—which currently suffer from high deployment and management costs. To bridge this gap, we present UXLab, an open-source system featuring a no-code, visual web dashboard that supports the entire research workflow, from participant recruitment and experiment configuration to backend integration with traditional search engines, vector databases, and large language models, along with comprehensive behavioral data collection. UXLab substantially lowers the technical barrier for conducting multi-system comparative studies and offers an extensible framework for future multimodal interaction research. A micro case study successfully uncovered distinct user behaviors between RAG and autonomous agent conditions, demonstrating the platform’s effectiveness in enhancing experimental efficiency and streamlining research processes.

This study addresses the lack of systematic understanding regarding the types and motivations of visual representations in qualitative research. Building upon and extending Verdinelli & Scagnoli’s (2013) work through a data-driven literature review, it conducts a content analysis of articles and their visualizations published between 2020 and 2022 in three leading qualitative methods journals. Integrating epistemological stance classification with visualization-type coding, the study innovatively combines correspondence analysis and cognitive network analysis for the first time. Findings indicate that while visualizations remain underutilized in qualitative research, their typological diversity is increasing, and the choice of graphical representation appears largely independent of the authors’ epistemological positions. These results offer both empirical grounding and methodological innovation for integrating interdisciplinary visualization tools into qualitative inquiry.

Adaptive user interfaces (AUIs) in mHealth applications for chronic disease management face adoption barriers due to heterogeneous user preferences. This study pioneers the application of discrete choice experiments (DCEs) to prioritize mHealth requirements, employing a six-attribute, multi-level choice design and a mixed logit model to quantify user preferences and trade-offs regarding AUI features. Heterogeneity analyses were conducted across age, gender, health status, and coping mechanisms. Results indicate that controllability, infrequent adaptation, and minor interface adjustments significantly enhance user acceptance, whereas frequent feature updates and caregiver involvement diminish perceived value. Based on these findings, we propose a data-driven AUI design optimization framework. This framework provides empirical evidence and stratified adaptation strategies to support personalized, acceptable, and context-aware mHealth interface design.