Child helpline training often relies on human-led roleplay, which is both time-and resource-consuming.To address this, rule-based interactive agent simulations have been proposed to provide a structured training experience for new counsellors.However, these agents might suffer from limited language understanding and response variety.To overcome these limitations, we present a hybrid interactive agent that integrates Large Language Models (LLMs) into a rule-based Belief-Desire-Intention (BDI) framework, simulating more realistic virtual child chat conversations.This hybrid solution incorporates LLMs into three components: intent recognition, response generation, and a bypass mechanism.We evaluated the system through two studies: a script-based assessment comparing LLM-generated responses to human-crafted responses, and a within-subject experiment (𝑁 = 37) comparing the LLM-integrated agent with a rule-based version.The first study provided evidence that the three LLM components were non-inferior to human-crafted responses.In the second study, we found credible support for two hypotheses: participants perceived the LLM-integrated agent as more believable and reported more positive attitudes toward it than the rule-based agent.Additionally, although weaker, there was some support for increased engagement (posterior probability = 0.845, 95% HDI [-0.149, 0.465]).Our findings demonstrate the potential of integrating LLMs into rule-based systems, offering a promising direction for more flexible but controlled training systems.

Agent-based training systems can enhance people's social skills. The effective development of these systems needs a comprehensive architecture that outlines their components and relationships. Such an architecture can pinpoint improvement areas and future outlooks. This paper presents ARTES: a general architecture illustrating how components of agent-based social training systems work together. We studied existing systems and architectures for training and tutoring to design ARTES and identify its essential components and interaction characteristics. ARTES comprises two core components: the agent simulation of social situations, and educational elements to provide guided learning. We link ARTES's crucial components to four primary learning theories (behaviourism, cognitivism, social cognitive theory, and constructivism) to illustrate the role of agent simulation and tutoring elements in establishing desired learning outcomes. Furthermore, we map ARTES's components against eight architectures, 43 systems and three tools to indicate the components' relevance, completeness, generalisation, and deployment potential across contexts. In addition to ARTES, the paper also contributes by identifying future improvements and research directions, such as the agent's thinking, tutoring methods, knowledge transfer, and ethical implications. We believe ARTES can help bridge the gap between virtual human simulations and impactful educational learning, offering training system developers desirable features like understandability and adaptability.

Validating claims and replicating findings on the impact of artificial social agents (ASA), such as virtual agents, conversational agents, and social robots, requires a standardised measurement instrument that researchers can employ in different settings and for various agents. Such an instrument would allow researchers to evaluate their agents and establish insights beyond their specific study context. Therefore, we present the long and short versions of the ASA questionnaire (ASAQ) for evaluating human-ASA interaction on 19 constructs, such as the agent’s believability, sociability, and coherence. It has been developed by an international workgroup with more than 100 ASA-researchers over multiple years who identified community-relevant constructs and associated questionnaire items and examined the questionnaire’s reliability, validity, and interpretability. The result is a questionnaire that can capture more than 80% of the constructs that studies in the intelligent virtual agent community investigate, with acceptable levels of reliability, content validity, construct validity, and cross-validity. We suggest that ASA-researchers use the ASAQ short version to report their agent’s psychographic information and the ASAQ long version to analyse any constructs in-depth that are specifically relevant to their agent or study. Finally, this paper gives instructions for practical use, such as sample size estimations, and how to interpret and present results. • The Artificial-Social-Agent Questionnaire (ASAQ) is a validated measure used to evaluate the human experience of interacting with an artificial social agent (ASA). • There are two versions of the ASAQ. The short version is used to report the ASAs psychographic information, while the long version allows for in-depth analysis of constructs relevant to a specific ASA. • More than 100 ASA researchers were involved in the development of ASAQ. • The 19 ASAQ constructs capture over 80% of the constructs investigated in the Intelligent Virtual Agent community between 2013-2018. • The ASAQ has demonstrated acceptable levels of reliability, content validity, construct validity, and cross-validity. • We present the ASAQ representative set 2024 of 29 agents. • Instructions for the practical use of ASAQ are given, including guidance on choosing the ASAQ version, estimating sample sizes, and interpreting and presenting the results (e.g., ASAQ chart).

Social interactions form an essential aspect of people’s life, however, it is quite challenging for individuals to handle a wide range of social situations. Therefore, a variety of training systems have been developed to improve their skills. This literature review seeks to give an overview of the state of the art of technology-supported systems for social skills training. The studies eligible for inclusion described a technology-supported system with the purpose of training social skills and included an experimental or observational study to evaluate the efficacy of the system. 225 studies (224 publications) with 216 systems were identified, characterized, and analyzed in this literature review. Using the taxonomy as put forward in this study, the analysis shows that the majority of these systems were screen-based applications, with virtual reality technology being the most frequently observed. The systems most often targeted communication skills that focus on transferring information to produce greater understanding, i.e. mending general communication impairments in children with autism. In terms of functions, support for learning-by-doing was the most observed function, while focusing on job interviews provided the largest number of functions. Finally, the studies reported overwhelmingly positively regarding the systems’ impact, including 76 studies with a randomized controlled trial design. Still, most studies only used a quasi-experimental design based on self-report measures. We anticipate the proposed taxonomy to be a starting point for researchers to position their work and that the review will help them with gaining inspiration for the design and evaluation of social skills training systems.

Enabling the widespread utilization of the Artificial-Social-Agent (ASA) Questionnaire, a research instrument to comprehensively assess diverse ASA qualities while ensuring comparability, necessitates translations beyond the original English source language questionnaire. We thus present Dutch and German translations of the long and short versions of the ASA Questionnaire and describe the translation challenges we encountered. Summative assessments with 240 English-Dutch and 240 English-German bilingual participants show, on average, excellent correlations (Dutch ICC M = 0.82, SD = 0.07, range [0.58, 0.93]; German ICC M = 0.81, SD = 0.09, range [0.58, 0.94]) with the original long version on the construct and dimension level. Results for the short version show, on average, good correlations (Dutch ICC M = 0.65, SD = 0.12, range [0.39, 0.82]; German ICC M = 0.67, SD = 0.14, range [0.30, 0.91]). We hope these validated translations allow the Dutch and German-speaking populations to evaluate ASAs in their own language.

Child helplines offer a safe and private space for children to share their thoughts and feelings with volunteers. However, training these volunteers to help can be both expensive and time-consuming. In this demo, we present Lilobot, a conversational agent designed to train volunteers for child helplines. Lilobot’s reasoning is based on the Belief-Desire-Intention (BDI) model, which simulates, for example, a bullied child who contacts the helpline through text. Users engage with Lilobot in a role-play format, taking on the volunteer’s role. Through this system, volunteers can practice applying the Five Phase Model, a conversational strategy helplines use. The training tool includes a trainer interface for monitoring and modifying Lilobot’s interactions. Trainers can also create new conversational scenarios through an authoring tool. An initial evaluation led to enhancements in Lilobot’s knowledge base and intent recognition, addressing the main issues encountered by participants. The components used to implement the system were Java Spring for the BDI model and the authoring tool, Rasa for Natural Language Understanding, PostgreSQL for the database, and Vue.js for the front-end. This tool aims to provide volunteers with consistent, interactive training, enhancing their counselling skills in a controlled environment.

Abstract Goal-setting is commonly used in behavior change applications for physical activity. However, for goals to be effective, they need to be tailored to a user’s situation (e.g., motivation, progress). One way to obtain such goals is a collaborative process in which a healthcare professional and client set a goal together, thus making use of the professional’s expertise and the client’s knowledge about their own situation. As healthcare professionals are not always available, we created a dialog with the virtual coach Steph to collaboratively set daily step goals. Since judgments in human decision-making processes are adjusted based on the starting point or anchor, the first step goal proposal Steph makes is likely to influence the user’s final goal and self-efficacy. Situational factors impacting physical activity (e.g., motivation, self-efficacy, available time) or how users process information (e.g., mood) may determine which initial proposals are most effective in getting users to reach their underlying previous activity-based recommended step goals. Using data from 117 people interacting with Steph for up to five days, we designed a reinforcement learning algorithm that considers users’ current and future situations when choosing an initial step goal proposal. Our simulations show that initial step goal proposals matter: choosing optimal ones based on this algorithm could make it more likely that people move to a situation with high motivation, high self-efficacy, and a favorable daily context. Then, they are more likely to achieve, but also to overachieve, their underlying recommended step goals. Our dataset is publicly available.

Abstract Virtual coaches have the potential to address the low adherence common to eHealth applications for behavior change by, for example, providing motivational support. However, given the multitude of factors affecting users’ attitudes toward virtual coaches, more insights are needed on how such virtual coaches can be designed to affect these attitudes in a specific use context positively. Especially valuable are insights that are based on users interacting with such a virtual coach for longer. We thus conducted a study in which more than 500 smokers interacted with the text-based virtual coach Sam in five sessions. In each session, Sam assigned smokers a new preparatory activity for quitting smoking and provided motivational support for doing the activity. Based on a mixed-methods analysis of users’ willingness to continue working and their relationship with Sam, we obtained eight themes for users’ attitudes toward Sam. These themes relate to whether Sam is seen as human or artificial, specific characteristics of Sam (e.g., caring character), the interaction with Sam, and the relationship with Sam. We used these themes to formulate literature-based recommendations to guide designers of virtual coaches for behavior change. For example, letting the virtual coach get to know users and disclose more information about itself may improve its relationship with users.

Smoking tobacco and physical inactivity are key preventable behavioural risk factors of cardiovascular disease (CVD). Computerised coaching systems can help individuals to modify risky behaviours, thereby preventing CVD. However, most reported eHealth or computerized coaching systems are hard to reuse in slightly different settings. To provide an open-source, reusable computer coaching system, we developed Perfect Fit. The reusability is manifested by building around the open-source text- and voice-based contextual assistant framework Rasa. Rasa provides a simple, standard interface to many popular messaging and voice channels, and custom connectors are easily implemented. A set of algorithms have been developed and connected to Rasa to drive and personalize the conversation flow and the coaching process. Such algorithms make use of data stored in a devoted database. Furthermore, Perfect Fit adheres to best practices and standards in software engineering. The modular design of Perfect Fit will allow researchers to connect the virtual coach to any messaging or voice channel with only modest modification. Perfect Fit is available under open-source license in GitHub and is currently in prototype-phase. Concluding, Perfect Fit will deliver a virtual coach that can easily be adapted and reused in different settings. The coach helps individuals to achieve and maintain abstinence from smoking and sufficient physical activity (PA).

We present the ASA Questionnaire, an instrument for evaluating human interaction with an artificial social agent (ASA), resulting from multi-year efforts involving more than 100 Intelligent Virtual Agent (IVA) researchers worldwide. It has 19 measurement constructs constituted by 90 items, which capture more than 80% of the constructs identified in empirical studies published in the IVA conference 2013--2018. This paper reports on construct validity analysis, specifically convergent and discriminant validity of initial 131 instrument items that involved 532 crowd-workers who were asked to rate human interaction with 14 different ASAs. The analysis included several factor analysis models and resulted in the selection of 90 items for inclusion in the long version of the ASA questionnaire. In addition, a representative item of each construct or dimension was selected to create a 24-item short version of the ASA questionnaire. Whereas the long version is suitable for a comprehensive evaluation of human-ASA interaction, the short version allows quick analysis and description of the interaction with the ASA. To support reporting ASA questionnaire results, we also put forward an ASA chart. The chart provides a quick overview of the agent profile.

In this paper, we report on the multi-year Intelligent Virtual Agents (IVA) community effort, involving more than 90 researchers worldwide, researching the IVA community interests and practice in evaluating human interaction with an artificial social agent (ASA). The joint efforts have previously generated a unified set of 19 constructs that capture more than 80% of constructs used in empirical studies published in the IVA conference between 2013 to 2018. In this paper, we present expert-content-validated 131 questionnaire items for the constructs and their dimensions, and investigate the level of reliability. We establish this in three phases. Firstly, eight experts generated 431 potential construct items. Secondly, 20 experts rated whether items measure (only) their intended construct, resulting in 207 content-validated items. Next, a reliability analysis was conducted, involving 192 crowd-workers who were asked to rate a human interaction with an ASA, which resulted in 131 items (about 5 items per measurement, with Cronbach's alpha ranged [.60 -- .87]). These are the starting points for the questionnaire instrument of human-ASA interaction.

Effective psychological interventions for anxiety disorders often include exposure to fearful situations. However, individuals with low self-efficacy may find such exposure too overwhelming. We created a vicarious experience in virtual reality, which enables observation of one’s experience from a first person perspective without actual performance and which might increase self-efficacy. With similarities to both traditional vicarious experiences and direct experiences, the level of self-identification with the experience was hypothesized to affect self-efficacy and its relationship with direct experiences. To test this, vicarious experiences with two distinct levels of self-identification were compared in a between-subjects experiment (n=60). After being exposed to a vicarious experience of giving lectures on elementary arithmetic in front of a virtual audience with either a high or low level of self-identification with the public speaker, participants from both conditions actively gave another lecture. The results revealed that self-identification affected people’s self-efficacy after vicarious experience. They further revealed that self-identification is a moderator of (1) the correlation between perceived performance and self-efficacy, (2) the correlation between self-efficacy measured after the vicarious and the follow-up direct experience; and (3) the correlation between the sense of presence reported in the vicarious and in the follow-up direct experience. We anticipate that the first-person-perspective experiences with high-level of self-identification have the potential to be beneficial for training where changing people’s self-efficacy is desirable.

Abstract Virtual cognitions (VCs) are a stream of simulated thoughts people hear while emerged in a virtual environment, e.g. by hearing a simulated inner voice presented as a voice over. They can enhance people’s self-efficacy and knowledge about, for example, social interactions as previous studies have shown. Ownership and plausibility of these VCs are regarded as important for their effect, and enhancing both might, therefore, be beneficial. A potential strategy for achieving this is the synchronization of the VCs with people’s eye fixation using eye-tracking technology embedded in a head-mounted display. Hence, this paper tests this idea in the context of a pre-therapy for spider and snake phobia to examine the ability to guide people’s eye fixation. An experiment with 24 participants was conducted using a within-subjects design. Each participant was exposed to two conditions: one where the VCs were adapted to eye gaze of the participant and the other where they were not adapted, i.e. the control condition. The findings of a Bayesian analysis suggest that credibly more ownership was reported and more eye-gaze shift behaviour was observed in the eye-gaze-adapted condition than in the control condition. Compared to the alternative of no or negative mediation, the findings also give some more credibility to the hypothesis that ownership, at least partly, positively mediates the effect eye-gaze-adapted VCs have on eye-gaze shift behaviour. Only weak support was found for plausibility as a mediator. These findings help improve insight into how VCs affect people.

In this paper, we report on the multi-year Intelligent Virtual Agents (IVA) community effort, involving more than 80 researchers worldwide, researching the IVA community interests and practises in evaluating human interaction with an artificial social agent (ASA). The effort is driven by previous IVA workshops and plenary IVA discussions related to the methodological crisis on the evaluation of ASAs. A previous literature review showed a continuous practise of creating new questionnaires instead of reusing validated questionnaires. We address this issue by examining questionnaire measurement constructs used in empirical studies between 2013 to 2018 published in the IVA conference. We identified 189 constructs used in 89 questionnaires that are reported across 81 studies. Although these constructs have different names, they often measure the same thing. In this paper, we, therefore, present a unifying set of 19 constructs that captures more than 80% of the 189 constructs initially identified. We established this set in two steps. First, 49 researchers classified the constructs in broad theoretically based categories. Next, 23 researchers grouped the constructs in each category on their similarity. The resulting 19 groups form a unifying set of constructs, which will be the basis for the future questionnaire instrument of human-ASA interaction.

Research into artificial social agents aims at constructing these agents and at establishing an empirically grounded understanding of them, their interaction with humans, and how they can ultimately deliver certain outcomes in areas such as health, entertainment, and education. Key for establishing such understanding is the community's ability to describe and replicate their observations on how users perceive and interact with their agents. In this paper, we address this ability by examining questionnaires and their constructs used in empirical studies reported in the intelligent virtual agent conference proceedings from 2013 to 2018. The literature survey shows the identification of 189 constructs used in 89 questionnaires that were reported across 81 papers. We found unexpectedly little repeated use of questionnaires as the vast majority of questionnaires (more than 76%) were only reported in a single paper. We expect that this finding will motivate joint effort by the IVA community towards creating a unified measurement instrument.

Social commitments (SCs) provide a flexible, norm-based, governance structure for sharing and receiving data. However, users of data sharing applications can subscribe to multiple SCs, possibly producing opposing sharing and receiving requirements. We propose resolving such conflicts automatically through a conflict resolution model based on relevant user values such as privacy and safety. The model predicts a user’s preferred resolution by choosing the commitment that best supports the user’s values. We show through an empirical user study ( n = 396) that values, as well as recency and norm type, significantly improve a system’s ability to predict user preference in location sharing conflicts.

In this paper, a user interface paradigm, called Talk-and-Tools, is presented for automated e-coaching. The paradigm is based on the idea that people interact in two ways with their environment: symbolically and physically. The main goal is to show how the paradigm can be applied in the design of interactive systems that offer an acceptable coaching process. As a proof of concept, an e-coaching system is implemented that supports an insomnia therapy on a smartphone. A human coach was replaced by a cooperative virtual coach that is able to interact with a human coachee. In the interface of the system, we distinguish between a set of personalized conversations (“Talk”) and specialized modules that form a coherent structure of input and output facilities (“Tools”). Conversations contained a minimum of variation to exclude unpredictable behavior but included the necessary mechanisms for variation to offer personalized consults and support. A variety of system and user tests was conducted to validate the use of the system. After a 6-week therapy, some users spontaneously reported the experience of building a relationship with the e-coach. It is concluded that the addition of a conversational component fills an important gap in the design of current mobile systems.

Experiencing stress during training is a way to prepare professionals for real-life crises. With the help of feedback tools, professionals can train to recognize and overcome negative effects of stress on task performances. This paper reports two studies that empirically examined the effect of such a feedback system. The system, based on the COgnitive Performance and Error (COPE) model, provides its users with physiological, predicted performance and predicted error-chance feedback. The first experiment focussed on creating stressful scenarios and establishing the parameters for the predictive models for the feedback system. Participants (n=9) performed fire-extinguishing tasks on a virtual ship. By altering time pressure, information uncertainty and consequences of performance, stress was induced. COPE variables were measured and models were established that predicted performance and the chances on specific errors. In the second experiment a new group of participants (n=29) carried out the same tasks while receiving eight different combinations of the three feedback types in a counterbalanced order. Performance scores improved when feedback was provided during the task. The number of errors made did not decrease. The usability score for the system with physiological feedback was significantly higher than a system without physiological feedback, unless combined with error feedback. This paper shows effects of feedback on performances and usability. To improve the effectiveness of the feedback system it is suggested to provide more in-depth tutorial sessions. Design changes are recommended that would make the feedback system more effective in improving performances.

Currently, expressive virtual humans are used in psychological research, training, and psychotherapy. However, the behavior of these virtual humans is usually scripted and therefore cannot be modified freely at runtime. To address this, we created a virtual audience with parameterized behavioral styles. This paper presents a parameterized audience model based on probabilistic models abstracted from the observation of real human audiences (n = 16). The audience's behavioral style is controlled by model parameters that define virtual humans' moods, attitudes, and personalities. Employing these parameters as predictors, the audience model significantly predicts audience behavior. To investigate if people can recognize the designed behavioral styles generated by this model, 12 audience styles were evaluated by two groups of participants. One group (n = 22) was asked to describe the virtual audience freely, and the other group (n = 22) was asked to rate the audiences on eight dimensions. The results indicated that people could recognize different audience attitudes and even perceive the different degrees of certain audience attitudes. In conclusion, the audience model can generate expressive behavior to show different attitudes by modulating model parameters.

Two problems may arise when an intelligent (recommender) system elicits users’ preferences. First, there may be a mismatch between the quantitative preference representations in most preference models and the users’ mental preference models. Giving exact numbers, e.g., such as “I like 30 days of vacation 2.5 times better than 28 days” is difficult for people. Second, the elicitation process can greatly influence the acquired model (e.g., people may prefer different options based on whether a choice is represented as a loss or gain). We explored these issues in three studies. In the first experiment we presented users with different preference elicitation methods and found that cognitively less demanding methods were perceived low in effort and high in liking. However, for methods enabling users to be more expressive, the perceived effort was not an indicator of how much the methods were liked. We thus hypothesized that users are willing to spend more effort if the feedback mechanism enables them to be more expressive. We examined this hypothesis in two follow-up studies. In the second experiment, we explored the trade-off between giving detailed preference feedback and effort. We found that familiarity with and opinion about an item are important factors mediating this trade-off. Additionally, affective feedback was preferred over a finer grained one-dimensional rating scale for giving additional detail. In the third study, we explored the influence of the interface on the elicitation process in a participatory set-up. People considered it helpful to be able to explore the link between their interests, preferences and the desirability of outcomes. We also confirmed that people do not want to spend additional effort in cases where it seemed unnecessary. Based on the findings, we propose four design guidelines to foster interface design of preference elicitation from a user view.

The ever-increasing involvement of computer technology in work and living environments—for training and actual task performances—sets continuously new challenges for cognitive ergonomics in diverse domains like transport, crisis management and healthcare (Brinkman, 2011). A major challenge is to harmonize the technology development to the dynamics and complexity of the social, cognitive and affective processes in these environments, taking into account of the diversity and multiplicity of human needs (cf. Klein et al., 2004). Such a harmonization comprises effective and efficient human-automation collaboration that proves (1) to be resilient for critical situations and (2) to facilitate creative problem solving in such situations. Current research focuses on collaborative artefacts that help to establish these two effects by enhancing work team’s conditions, knowledge and capabilities for acting in a safe and healthy way. For example, studies on flying, driving, and sailing provide requirements for pilot-automation collaboration that brings about adequate recover piloting behaviour after a “failure” (cf. Woods and Hollnagel, 2006; Lenior et al., 2006). As a second example, recent research on shared situation awareness of distributed teams in crisis management provide support that may improve team coordination and corresponding performance (van der Kleij et al., 2009). For the development of such human-automation collaboration, cognitive ergonomics methods are needed for deriving and testing of the “situational” requirements systematically (cf. Neerincx and Lindenberg, 2008). For example, game-based evaluations with Virtual-Reality tools can help to train for new situations or to test specific artefacts (Smets et al., 2011). Furthermore, a combination of scenario-based investigation and controlled lab experiments can, for example, help to study automated assistant functions to support therapists in high demand situations when treating multiple patients simultaneously over the internet (Paping et al., 2011). As a final example, user experience sampling methods that apply advanced interaction events analysis (Brinkman et al., 2005; Brinkman et al., 2007) or sensor technology can improve the insight into situated activities over time (such as heart-rate, eye tracking), but it might prove to be difficult to apply in high-demand environments (e.g. Grootjen et al., 2007).

A situation map that shows the overview of a disaster situation serves as a valuable tool for disaster response teams. It helps them to orientate their location and to make disaster response decisions. It is, however, a complicated task to rapidly generate a complete and comprehensive situation map of a disaster area, particularly due to the centralized organization of disaster management and the limited emergency services. In this study, we propose to let the affected population be utilized as an additional resource that can actively help to make such a situation map. The aim of this study was to investigate the possibility of constructing a shared situation map using a collaborative distributed mechanism. By examining earlier research, a detailed list of potential problems is identified in the collaborative map-making process. These problems were then addressed in an experiment which evaluated a number of proposed solutions. The results showed that more collaboration channels led to a situation map of better quality, and that including confidence information for objects and events in the map helped the discussion process during the map-making.

The use of virtual reality (VR) technology to support the treatment of patients with phobia, such as the fear of flying, is getting considerable research attention. Research mainly focuses on the patient experience and the effect of the treatment. In this paper, however, the focus is on the interaction therapists have with the system. Two studies are presented in which the therapist user interface is redesigned and evaluated. The first study was conducted in 2001 with the introduction of the system into the clinic. The original user interface design was compared with a redesign that was based on interviews with therapists. The results of a user study with five therapists and 11 students showed significant usability improvement. In 2008 a follow-up study was conducted on how therapists were now using the redesigned system. Using a direct observation approach six therapists were observed during a total of 14 sessions with patients. The analysis showed that: 93% of the exposures had similar patterns, therapists triggered 20 inappropriate sound recordings (e.g. the pilot giving height information while taking off), and more complex airplane simulation functions (e.g. roll control to make turns with the airplane) were only used by a therapist who was also a pilot. This resulted in a second redesign of the user interface, which allowed therapists to select flight scenarios (e.g. a flight with extra long taxiing, a flight with multiple taking off and landing sessions) instead of controlling the simulation manually. This new design was again evaluated with seven therapists. Again, results showed significant usability improvements. These findings led to five design guidelines with the main tenet in favour of a treatment-focused user interface (i.e. specific flying scenario) instead of a simulation-focused user interface (i.e. specific airplane controls).

This article discusses the sharing of daily pleasures with lightweight technology. Two mobile applications called PosiPost Me (Mobile internet edition) and PosiPost Be (Bluetooth edition) were developed to understand the potential of remote and proximity-based sharing of positive messages. These implementations are compared through an empirical study of 15 participants in England and The Netherlands who used both applications for a week each. Drawing from interviews and a corpus of 379 shared messages, the study presents an exploration of the types of pleasures people expressed in everyday use. The study addresses the influence of the two applications on the nature of the messages shared. It further points to the importance of the mundaneness of the postings and discusses the role lightweight mobile technology can play in making mundane pleasures visible.

Although software engineers extensively use a component-based software engineering (CBSE) approach, existing usability questionnaires only support a holistic evaluation approach, which focuses on the usability of the system as a whole. Therefore, this paper discusses a component-specific questionnaire for measuring the perceived ease-of-use of individual interaction components. A theoretical framework is presented for this compositional evaluation approach, which builds on Taylor's layered protocol theory. The application and validity of the component-specific measure is evaluated by re-examining the results of four experiments. Here, participants were asked to use the questionnaire to evaluate a total of nine interaction components used in a mobile phone, a room thermostat, a web-enabled TV set and a calculator. The applicability of the questionnaire is discussed in the setting of a new usability study of an MP3 player. The findings suggest that at least part of the perceived usability of a product can be evaluated on a component-based level.

This paper presents the results of a meta-analysis carried out on the results of six experiments to support the claim that component-specific usability measures are on average statistically more powerful than overall usability measures when comparing different versions of a part of a system. An increase in test effectiveness implies the need for fewer participants in usability tests that study different versions of a component. Three component-specific measures are presented and analyzed: an objective efficiency measure and two subjective measures, one about the ease-of-use and the other about the users' satisfaction. Whereas the subjective measures are obtained with a questionnaire, the objective efficiency measure is based on the number of user messages received by a component. Besides describing the testing method, this paper also discusses the underlying principles such as layered interaction and multiple negative-feedback loops. The main contribution of the work described is the presentation of component-based usability testing as an alternative for traditional holistic-oriented usability tests. The former is more aligned with the component-based software engineering approach, helping engineers to select the most usable versions of a component.

Current usability evaluation methods are essentially holistic in nature. However, engineers that apply a component-based software engineering approach might also be interested in understanding the usability of individual parts of an interactive system. This paper examines the efficiency dimension of usability by describing a method, which engineers can use to test, empirically and objectively, the physical interaction effort to operate components in a single device. The method looks at low-level events, such as button clicks, and attributes the physical effort associated with these interaction events to individual components in the system. This forms the basis for engineers to prioritise their improvement effort. The paper discusses face validity, content validity, criterion validity, and construct validity of the method. The discussion is set within the context of four usability tests, in which 40 users participated to evaluate the efficiency of four different versions of a mobile phone. The results of the study show that the method can provide a valid estimation of the physical interaction event effort users made when interacting with a specific part of a device.

Maintenance in production environments is becoming increasingly complex as machines become more technologically advanced and need less maintenance. As a result, maintenance personnel face more difficult tasks. At the same time the maintenance engineers obtain less experience with the tasks. In this context, online multimedia manuals are thought to give better support for searching information and expressing complex interactions with physical objects than paper manuals. Three prototypes were designed and formative evaluated in an industrial environment. The evaluation of the first prototype showed that maintenance engineers encountered serious problems with the online multimedia manual, as they omitted crucial steps in the task. The second prototype addressed this problem. The third prototype addressed a problem observed in the second prototype when subjects switched between the online manual and the machine controls.