Visual analytics dashboards are widely used for decision-making, yet persistent usability problems, including information overload, graph literacy demands, and limited discoverability of functionality, can limit their effectiveness. Surface-level customisation, such as filtering, view switching, and chart selection, can address these issues, but users often do not engage with these customisations because they perceive them as time-consuming or fail to discover them. We investigate nudging as a lightweight intervention to promote beneficial customisation during task execution. Drawing on evidence that Just-in-Time Prompts (JITP) primarily improve efficiency while Enabling Social Comparison (ESC) improves effectiveness, we evaluate their combination. In a within-subjects study (N = 41) across three dashboards, the combined intervention increased task correctness from 34% to 93% and reduced completion time by 52% compared to an unassisted control. The benefit of guidance is not proportional to its amount: tighter alignment (more pre-configured settings matched to the task) yields the highest absolute performance, while looser alignment (fewer pre-configured settings) delivers greater improvement per unit of help. Overall, transparent, user-controlled nudging can substantially enhance dashboard usability while preserving autonomy.

Visual analytics dashboards have become key tools for decision-making. Yet, information overload and high expectations of the actual graph literacy of users hinder their effective use. Customisation features such as global filters (filters that update all views), visualisation hiding, and graph switching have been proposed to improve dashboard usability and accommodate user diversity. However, time constraints and a lack of awareness of the available customisation functionalities are known to be barriers to engaging with customisations. In this paper, we examine how visual analytics dashboard customisation affects usability and identify existing risks and barriers. We found that short-term surface customisations improve the usability of dashboards, both objectively (users are quicker and more correct) and subjectively (users report a lower cognitive load). For this to happen, the engagement with customisations must not be exploratory but meaningful (i.e., longer than 5 seconds). We also identified a set of customisations that, if not implemented carefully, are detrimental to cognitive load and completion times. Informed by our findings, we provide insights for building user models to capture the usability of dashboards-in-use and associated adaptive interaction techniques to assist users when needed.

Visual analytics dashboards have become essential tools for decisionmaking.However, information overload and mismatches between designers' expected graph literacy and users' actual graph literacy can limit their effectiveness.Customisation has been proposed to mitigate these challenges and accommodate diverse user needs.Yet, customising dashboards is often time-consuming; users may not be aware of existing customisation features, or they may not have sufficient technical skills to use them.In this paper, we conduct an experiment (N=50) to examine if we can use nudging techniques to promote short-term surface customisations while not sacrificing the usability of interactive visual analytics dashboards.We found that while nudges do not necessarily increase the use of customisation functionalities, they benefit usability.Specifically, the Social Comparisons nudge supports decision-making, while the Just-in-Time Prompts nudge reduces task completion time.Our findings suggest that nudges should be tailored to graph literacy as users with moderate graph literacy can benefit the most from nudges.

Information presentation problems on interactive dashboards are known to hinder decision-making. Since a traditional user-centred approach to designing usable dashboards cannot fully satisfy user demands, needs and skills, we isolate behavioural indicators of usability when users conduct typical information-seeking and comparison tasks. In a first study (N = 50), we identified strategies derived from 486,435 interaction events logged in a controlled setting with synthetic dashboards. User models consisting of these user strategies and graph literacy produced strong signals indicating that usability was predictable. In a second study (N = 65), we tested the initial insights on real-world dashboards. While most of our hypotheses were confirmed, graph literacy emerged as the best predictor of usability. Usability was better predicted in dashboards with problems, suggesting promising opportunities for automated usability evaluation and real-time support for users struggling with visual analytics dashboards.

An interactive information dashboard is a data visualisation tool that aggregates and displays one or multiple data sources to support decision making.An effective dashboard provides accurate and meaningful human-readable visualisations from raw data.However, due to information overload, differences in graph literacy and task and user requirements, current dashboards hinder effective decision making.Dashboard customisation functionalities may help to mitigate the aforementioned challenges and address user diversity.However, customisation is difficult and especially challenging for lay users due to timing demands, a lack of motivation, and a lack of awareness of the existence of customisation features.To inform the design of better tailored dashboards, we will examine the challenges that users encounter when customising dashboards, and investigate different methods for improving the lay user experience and overcoming customisation challenges.To address the challenges of dashboard customisation for lay users, possible solutions include implementing nudging technology and promoting different customisation functionalities.Nudging is a design approach that employs elements to encourage users to customise their dashboards based on their unique requirements and preferences.The aim of this approach is to promote positive behaviour change by guiding users towards choices that align with their desired outcomes.Our