International Journal of Human-Computer Studies最新文献

This paper describes the development of a cartoon animation system usability scale (A-SUS) based on the established text-based SUS questionnaire. We propose a methodology and design a short graphic interchange format (.GIF) animation for each SUS item. Experimental evaluations confirm that the scale has satisfactory psychometric properties (e.g., structural validity, reliability factor structure, concurrent validity, and sensitivity). A second experiment is used to evaluate and compare the questionnaire experiences associated with the SUS, a pictorial SUS (P-SUS), and the developed A-SUS. The results indicate that A-SUS performs well in terms of recommendations, aesthetics, motivations, and completion time. Compared with the SUS and P-SUS, the animated version is more interesting, and the overall questionnaire experience is better.

Smart Home Heating Technologies (SHHT) have been designed to improve demand flexibility and energy conservation. SHHT rely on rational theories of energy use postulating that people will use less energy when the energy cost is higher. The inclusion of AI within SHHT is poised to optimise energy use in the future as the introduction of lower carbon energy sources place new demands on the grid. When SHHT is introduced in the home, however, they become situated in temporal heating practices that are shaped by an interplay of materiality, meanings, and competencies. We report findings from a mixed methods field study involving eleven households utilising an AI-enabled SHHT probe ‘Squid’. Taking a temporal focus throughout, our study contributes a new lens as to why households may not fully engage with SHHT's rational design, given that energy conversation is already embedded in their ongoing socio-material practices with heating. Focusing on the AI-human relation, we articulate the necessity for human agency where heating is involved, whilst also advancing an understanding of the new forms of hidden labour that households incur before they can engage with the AI. Crucially, our research informs the ongoing HCI concern over how humans understand AI, raising the question of who is responsible to assess the appropriateness of AI when the effects of human-AI performance remain opaque. Our findings contribute a new theoretical perspective into the intricate relationship between individuals and AI in the home and raise several new design implications for SHHT.

Animation fluency is an important factor of user experience in smartphone applications. The purpose of this study is to explore the effect of time duration in different sections of application usage on users’ perceived fluency. We manipulated the duration of different phases, including the click-response delay (CD), duration of animation (DA), and duration of loading completion (DL), in an open app task and an in-app page switching task. The participants evaluated the perceived fluency during their smartphone use. The results showed that the CD and DL had a significant effect on perceived fluency and that the CD, DA and DL showed significant interactions in some situations. These results could supply detailed parameters for smartphone design and could be used to build a model to guide manufacturers to provide a better user experience.

This experiment systematically examines whether, in safety-critical environments such as Air Traffic Control, the negative effects of increasing automation associated with static automation concepts can be mitigated by adaptable automation. Adaptable automation is a form of flexible automation in which the human operator (rather than the system as in adaptive automation) can decide when and to what extent to delegate tasks. A special focus is on its effects on human perception in terms of perceived autonomy and competence, satisfaction, and human role perception. We conducted two online studies using the same dual-task paradigm. Study 1 was conducted with a novice sample via Prolific (N = 93) and study 2 with an expert sample of Air Traffic Controllers (N = 126). Participants were either supported by static information automation, static decision automation, or an adaptable solution that allowed them to switch between the two automation stages. The findings of both studies are similar. Results indicated that, even when humans rarely switched, adaptable automation could increase perceived autonomy, led to high satisfaction, and had positive effects on role perceptions without impairing performance or workload. Furthermore, satisfaction was found to correlate with performance. From a human-centered perspective, flexible concepts seem to be particularly suitable when automation increasingly takes over parts of a job task not only at the stage of information analysis but also at the stage of decision-making.

Driver distraction by smartphone use is a major contributor in traffic accidents. Context-sensitive driver (in)attention monitoring and warning systems might mitigate the associated risks. However, few naturalistic studies are yet available on the effects of such systems. In this paper, utility of context-sensitivity in inattention monitoring was studied by a smartphone-based context-sensitive distraction warning system. The warnings were based on driver's phone use and the attentional demands of the upcoming traffic environment. The system's effects on 26 heavy smartphone users’ phone usage and acceptance were analyzed after a within-subject naturalistic study with 12 weeks of control (warnings off) and 12 weeks of interventions (warnings on). The system decreased odds that the drivers would touch their smartphones in reminder areas that were defined a priori as high demanding for attention. Against expectations, the system had no effect in urban road environments. The drivers reported that they had paid more attention to traffic because of the system and that the warnings were acceptable and useful, even if annoying. Similar systems’ safety effects should be further studied. No eye-tracking or driving performance measures were collected and thereby it is questionable if there was a true positive effect on participants’ attention. However, the findings suggest that (in)attention warning systems might benefit from adaptation of the warnings to the upcoming driving demands. The findings can be utilized for the development of proactive and context-sensitive (in)attention monitoring and distraction mitigation systems.

Although many methods currently exist to evaluate a user interface, they have been mainly developed and applied for graphical and vocal user interfaces, leaving aside other modalities such as gesture interaction. As a consequence, the evaluation of the global quality of a gesture user interface most often resorts to these methods, which take little or no explicit and specific account of the gesture modality or which adapt existing methods in a way that jeopardizes their validity. To remedy this situation, this paper introduces, defines, and justifies a method for evaluating explicitly and specifically a gesture user interface based on a conceptual model that consists of: (1) six quantitative measures covering user and system aspects, such as gesture thinking time and recognition rate; (2) three new qualitative scales, i.e., discoverability, learnability, and social acceptability, combined with seven scales in a gesture evaluation scheme, which is formally based on four measures, i.e., subscale score, subscale importance, scale mean score and scale mean importance; (3) a debriefing interview to cross-analyze the results of the quantitative measures and qualitative scales. This method also includes the use of multiple sessions to take into account short and long-term memory, as well as discoverability, and integrates tests specific to each gesture, as well as concrete use cases. For validation and illustration purposes, this method is applied to a gesture user interface serving as a case study. The results are then analyzed, starting with classic methods and gradually adding the particularities of our method to highlight its added value. Based on this experiment, we suggest some implications for evaluating gesture interfaces and for incorporating these scales into UEQ+, a modular user interface evaluation method.

Advances in haptic technologies can alter how real objects feel to our touch and create the experience of haptic augmented reality (AR). However, the definition, use cases, and value to end users of such haptic AR remain unclear. Existing work is concerned with technological implementation and lacks a user-centered perspective. To address these limitations, we analyze haptic AR systems in the literature to understand what constitutes haptic AR, why we would want to alter our sense of touch, and how haptic AR interactions take place. To demonstrate the value of studying haptic AR in the context of real-world tasks and user impressions of the concept itself, we also conducted a small exploratory study with five prototypical applications of different haptic AR systems. Our analysis highlights unexplored areas for haptics and HCI researchers and the need to conduct user evaluations of the overall concept rather than just point examples.

The avatar is a digital character that represents a person in a virtual environment, such as the metaverse. It is widely used in games and is also becoming a reality in other domains. Research indicates that the characteristics of a person’s avatar can influence his/her behavior while using a system, improving the sense of presence, immersion, and self-expression. A gap is how to support developers proposing avatar personalization, considering a user’s context, and understanding how context-awareness applied to avatars influences self-expression. In this article, we investigate how to support the development of self-expressive avatars. We present VISHnu, an approach developed to support the formalization of the use of context for avatar personalization. We conducted three evaluations to investigate the potential and feasibility of the VISHnu approach with specialists and developers: a focus group (n $=$ 6), interviews (n $=$ 6), and a questionnaire (n $=$ 12). In addition to guidelines derived from mixed qualitative methods and data triangulation, including insights from the literature review, we present two tangible artifacts—the checklist and glossary. These artifacts provide a robust initial foundation for developers and enthusiasts aiming to create avatar customization solutions aligned with advancements in immersive worlds like the metaverse. Our results have the potential to contribute to the creation of avatar personalization solutions that are more inclusive and more representative of human diversity.

While numerous studies have examined 3D interaction techniques for Virtual Reality (VR) across various tasks and scenarios, limited research has focused on music-related applications. However, the most common input techniques in consumer VR systems have been developed outside of the musical domain. Therefore they have not been tested in tasks where synchronization with auditory stimuli and timing plays a crucial role. There is a lack of empirical knowledge about performance and user experience. This paper presents a comparison of five selection input techniques for VR employing the tapping paradigm commonly utilized in the research on sensorimotor synchronization. We assess asynchrony and timing variance as well as user experience, encompassing factors such as ease of use, workload, and cybersickness of such techniques. The study involved 30 participants, both with and without musical expertise, and encompassed the examination of all techniques using one and two hands. Our analysis yielded several key findings: (1) different input techniques yielded distinct outcomes regarding timing asynchrony and variance; (2) the choice of interaction metaphor significantly influenced the user experience; (3) tracking stability emerged as a critical factor. Building upon these insights, we identified essential considerations for selecting the most suitable technique for music creation in VR and proposed design guidelines and future research directions in this domain.

The value of personal health data continues to be a debated topic in HCI and society more broadly. We investigate the monetary value people attach to their health data. Using a custom mobile app for 14 days with 55 participants, we collected health data (sleep duration, sleep quality, pain intensity, wake-up times) and a daily monetary data valuation using a reverse second-price auction. Participants bid to sell their data to a for-profit company, the government, or academia. Our findings indicate that people value their data differently based on who is buying. We also show that people are interested in monetizing their personal health data despite privacy and data protection concerns. The presented study helps us understand the data value landscape and paves way to a healthier data-driven future where people may benefit more from their own contributions, either in monetary or other forms.