This study experimented with mobile devices to investigate the effects of tones and time pressure on the voice input experience for Mandarin Chinese. Voice input for Mandarin Chinese is gaining popularity, and its implementation on mobile devices has recently developed very rapidly. Previous studies have focused mainly on technical considerations for designing a voice input system and have not investigated the user experience of voice input. Two experiments were conducted to test single-character and two-character phrases. Four measurements, the number of errors, user satisfaction, mental workload, and skin conductance, were gathered from sixty-four participants for each experiment. The results indicated that Mandarin tones had a significant influence on the number of errors and user satisfaction, and time pressure significantly influenced all measurements. Specifically, the falling-rising tone produced the highest number of errors, and the rising tone led to the lowest satisfaction. A high level of time pressure resulted in low satisfaction, a high mental workload, and high skin conductance. The results indicated that (a) Mandarin tones significantly affected error rates and user satisfaction, and (b) time pressure had a significant effect on user experience. The findings have design implications for voice input systems in terms of user experience.

Attaining mutual trust between humans and agents is considered pivotal in designing effective human-AI interaction methodologies. This research aims to develop and validate a questionnaire to measure people’s perception of AI agents’ trust in them. Two studies with 1373 participants were conducted. Study 1 focused on human-vehicle cooperation, using exploratory factor analysis (EFA) to develop the Perception of Being Trusted (PBT) scale, and the mediation analyses were performed to explore the mediation effect between the human’s propensity to trust and trust in the agent by PBT. Study 2 extended the scenario to human-AI cooperation, using confirmatory factor analysis (CFA) to validate the structure of PBT scale and path analysis were performed to explore the impact of PBT on behavioral intentions toward AI agents. Results confirm the PBT scale’s validity and predictive power, offering valuable insights into human psychological responses and promoting mutual trust in human-AI relationships.

Voice interaction is a user-friendly interaction method for Mandarin Chinese. Voice perception has been widely implemented on smart devices for Mandarin voice interaction. However, previous studies gave little attention to how language features, such as the text type and text length affect the voice perception experience. Moreover, how the device type influences Mandarin voice perception is unclear, as the device type has been shown to have a significant effect on other interaction methods, such as interactions with keyboard entry and screen displays. Thus, the aim of this study was to investigate the effects of the text type, text length, and device type on the voice perception experience. This study invited seventy-two participants (36 females and 36 males) for an empirical experiment. Three text types, i.e., classical Chinese text, professional text, and daily text, three text lengths, namely, short (i.e., 10 characters), medium (i.e., 20 characters), and long (i.e., 30 characters), and two device types, i.e., mobile phones and smartwatches, were considered. Four measurements were used to evaluate the voice perception experience, namely, performance (i.e., self-reported performance and correct ratio), the satisfaction rating, mental workload, and skin conductance. The results indicate that the text type, text length, and device type had significant effects on the four measurements. Specifically, the voice perception experience was best for daily text and worst for classical Chinese text, shorter text led to a better voice perception experience, and users of smartwatches had a better voice perception experience than mobile phones. Furthermore, the design implications for improving the voice perception experience that consider the text type, text length, and device type were discussed.

Conversational agents (CAs) have recently become ubiquitous. Smart speakers, mobile phone voice assistants, and in-car voice assistants have entered our lives. Studies have examined some factors influencing the user experience (UX) of CAs. However, there is little research on CAs’ reply design, especially when the information the users need is uncertain, which is regarded as an uncertain information scenario. The current research mainly focuses on measuring the UX with CAs’ replies in uncertain information scenarios. We designed two reply strategies of CAs, namely, a further inquiry strategy and a list-style reply strategy, to improve UX in this kind of uncertain information scenario. Two studies were designed and conducted based on the E-prime platform to verify the effect of the two reply strategies on UX. In Study 1, we verified the influence of inquiry strategy with different address terms for users on UX. In Study 2, we verified the influence of the list-style reply strategy (explicit and implicit) on UX. The gender differences in the evaluation of the two reply strategies were also examined. The results showed that, in the uncertain information scenarios, the reply strategy of further inquiries received a higher UX evaluation than direct replies in Study 1. Moreover, male participants preferred the “master” address to the “nin” address. However, male participants had no significant preference for further inquiries. For male participants, there was no significant difference in the UX evaluations for the “master” address and “nin” address. List-style replies with ranking information received the highest UX ratings, followed by list-style replies without ranking information and direct replies in Study 2, indicating that users preferred the explicit reply design of CAs. In addition, we found that male participants tended to have a higher rating of CA replies for all reply methods than female participants in our studies, suggesting that women have higher expectations for the reply design of CAs. In general, these results may contribute to the design of CA replies and highlight the importance of personalizing CA language.

Vast existing user experience (UX) studies have considered expectation and disconfirmation/confirmation in predicting UX satisfaction and behavioral intention. Moreover, both theoretical and empirical findings indicate the asymmetrical effect of positive and negative disconfirmation. However, few studies have compared the effect of different UX aspects or moderators of these effects. To make further exploration, the current article proposes hypotheses based on the expectation-confirmation model and expectation-confirmation theory. The hypotheses are tested in two mobile e-banking app UX studies. A retrospective questionnaire was used to collect data in the first study. The analyzing results confirmed the impact of expectation and the asymmetrical effect of disconfirmation on UX satisfaction and behavioral intention. The second experimental study manipulated user expectation levels by information priming, and expectation level was found to show a moderating effect in predicting user satisfaction and behavioral intention. The conclusions from the current article provide both theoretical and practical implications for UX study.

There is a growing tendency for users to expect conversational agents (CAs) to recognise social cues and follow interpersonal communication principles to enhance their subjective evaluation. Therefore, this paper studies how personal pronouns should be used by CAs in response to users. We conducted a 3 (CAs’ personal pronoun) × 3 (users’ personal pronoun) × 2 (participants’gender) mixed design. this study used mixed methods based on an experimental design, including ratings, forced choices and interviews, for mutual confirmation. The findings indicate that first, users prefer that CAs use second-person pronouns. Second, there is also turn-taking and convergence tendency between users and CAs in personal pronoun use. Third, there are gender differences in personal pronoun preferences and relationship positions toward CAs. These results can inform personalised voice interaction and humanlike design and help build closer relationships between users and CAs in future human–computer interactions.

This study presents a web survey investigating the effects of gender, age, prior usage behaviours, and closed-ended answers on response behaviour for open-ended questions in a user satisfaction or experience evaluation. Two types of open-ended questions were measured: general comment-specific questions designed to collect comments and explanation-specific questions designed to determine the reason for a higher or lower score for the closed-ended questions. Using an online structured questionnaire on an e-service telecom website, 13,346 valid responses (73.1% male; all aged 19 or above) were analysed. More than 75% of the respondents did not answer any of the open-ended questions. Although respondents tended to answer comment-specific questions (19.9%) more often than explanation-specific questions (11.9%), personal characteristics emerged as significant predictors of participants’ response behaviour in both types of open-ended questions. Males, younger participants, and those who had more e-service usage behaviours answered more often than other participants. Regarding the relationship between answers in closed-ended and open-ended questions, respondents’ scaled scores were significant predictors of responses to open-ended questions, particularly comment-specific questions. This study suggests that a score of four on a five-point scale may indicate an interesting answer, with negative responses (5.5%) more likely than positive responses (3.9%).

This paper is the first time to report the results of a recent survey of user-centered design (UCD) practice in China, conducted in 2007. The survey involved over four hundred respondents who were at the User Friendly conference or attended other UCD related activities. The survey identified, e.g. practitioners' demographics and experience, the type of organization, the usage frequency of methods that are commonly used in usability community, and UCD process and assessment on it. The results indicate, UCD practice in China industries is very young, and growing fast; currently almost all UCD practice are applied in IT related business; practitioners need improvement on UCD knowledge. These findings have implications for solving the problem that how to use and integrate UCD methods and techniques into product development process.