Chatbots hold promise as a technology to aid in sustained weight management. However, determining the optimal way for chatbots to deliver advice to effectively change user behaviors remains a significant hurdle. This research investigates the effects of different chatbot communication styles and expressions of care on user satisfaction, misinterpretation, and intent to adhere to the advice in weight-related conversations. A mixed method study with 97 participants classified as overweight was conducted, dividing them into four groups based on explicit/implicit communication styles and the presence or absence of caring language. Surprisingly, the study found that most participants in the explicit communication groups viewed the chatbot as non-offensive. These participants also reported higher levels of enjoyment and a greater intention to follow the chatbot's recommendations. Utilizing caring language may diminish users' perception of the chatbot as a marketing tool, thereby increasing their willingness to interact. The article discusses the implications for the design of healthcare chatbots.

AI conversational agents have demonstrated efficacy in social contact interventions for stigma reduction at a low cost. However, the underlying mechanisms of how interaction designs contribute to these effects remain unclear. This study investigates how participating in three human-chatbot interactions affects attitudes toward mental illness. We developed three chatbots capable of engaging in either one-way information dissemination from chatbot to a human or two-way cooperation where the chatbot and a human exchange thoughts and work together on a cooperation task. We then conducted a two-week mixed-methods study to investigate variations over time and across different group memberships. The results indicate that human-AI cooperation can effectively reduce stigma toward individuals with mental illness by fostering relationships between humans and AI through social contact. Additionally, compared to a one-way chatbot, interacting with a cooperative chatbot led participants to perceive it as more competent and likable, promoting greater empathy during the conversation. However, despite the success in reducing stigma, inconsistencies between the chatbot's role and the mental health context raised concerns. We discuss the implications of our findings for human-chatbot interaction designs aimed at changing human attitudes.

Previous research has shown that workplace social norms influence employee well-being. However, such norms vary based on the cultures in which workplaces are embedded, suggesting that cultural differences may influence perceived norms about when and where work should occur. These differences, in turn, could impact employee well-being. Accordingly, through the lenses of cultural tightness-looseness and individualism-collectivism, this paper investigates cultural differences in perceived social norms, and the relationship between those norms and hybrid workers’ well-being. We conducted a survey of 1,000 Japanese and 1,000 American hybrid workers. Results indicated that American respondents perceived stronger norms and demonstrated a higher willingness to conform to norms compared to Japanese respondents. Additionally, strong injunctive norms were positively associated with well-being among Americans but not among Japanese. Interviews (N = 24) showed that Japanese perceived injunctive norms negatively, while Americans saw them positively. We discuss implications for future remote-collaboration technologies in hybrid-work settings.

Discussions about project values are important for engineering software that meets diverse human needs and positively impacts society. Because value-related discussions involve deeply held beliefs, they can lead to conflicts or other outcomes that may affect motivations to continue contributing to open source projects. However, it is unclear what kind of value-related discussions are associated with significant changes in turnover. We address this gap by identifying discussions related to important project values and investigating the extent to which those discussions predict project turnover in the following months. We collected logs of GitHub issues and commits from 52 projects that share similar ethical commitments and were identified as part of the DWeb (Decentralized Web) community. We identify issues related to DWeb's core values of respectfulness, freedom, broadmindedness, opposing centralized social power, equity & equality, and protecting the environment. We then use Granger causality analysis to examine how changes in the proportion of discussions related to those values might predict changes in incoming and outgoing turnover. We found multiple significant relationships between value-related discussions and turnover, including that discussions about respectfulness predict an increase in contributors leaving and a decrease in new contributors, while discussions about social power predicted better contributor retention. Understanding these antecedents of contributor turnover is important for managing open source projects that incorporate human-centric issues. Based on the results, we discuss implications for open source maintainers and for future research.

Chatbots are increasingly being used in mental healthcare - e.g., for assessing mental-health conditions and providing digital counseling - and have been found to have considerable potential for facilitating people's behavioral changes. Nevertheless, little research has examined how specific chatbot designs may help reduce public stigmatization of mental illness. To help fill that gap, this study explores how stigmatizing attitudes toward mental illness may be affected by conversations with chatbots that have 1) varying ways of expressing their interpretations of participants' statements and 2) different styles of self-disclosure. More specifically, we implemented and tested four chatbot designs that varied in terms of whether they interpreted participants' comments as stigmatizing or non-stigmatizing, and whether they provided stigmatizing, non-stigmatizing, or no self-disclosure of chatbot's own views. Over the two-week period of the experiment, all four chatbots' conversations with our participants centered on seven mental-illness vignettes, all featuring the same character. We found that the chatbot featuring non-stigmatizing interpretations and non-stigmatizing self-disclosure performed best at reducing the participants' stigmatizing attitudes, while the one that provided stigmatizing interpretations and stigmatizing self-disclosures had the least beneficial effect. We also discovered side effects of chatbot's self-disclosure: notably, that chatbots were perceived to have inflexible and strong opinions, which undermined their credibility. As such, this paper contributes to knowledge about how chatbot designs shape users' perceptions of the chatbots themselves, and how chatbots' interpretation and self-disclosure may be leveraged to help reduce mental-illness stigma.

Previous studies have suggested that organizational social norms can positively affect employee well-being. However, such social norms have not been well developed during the post-COVID-19 transition to hybrid work, which combines office and remote work, and it is unclear how employees’ perceptions of social norms for hybrid work affect their well-being. In this study, we investigated the impact of social norms for hybrid work on the well-being of hybrid workers living in Japan through a mixed-method approach consisting of an online survey (n = 212) and semi-structured interviews (n = 20). The results indicate that hybrid workers who feel subject to strong social norms have lower well-being. Conversely, those who are more willing to conform to social norms have higher well-being. Given our findings, we discuss implications for the design of systems to help hybrid workers conform to organizational social norms and to improve their well-being.

Chatbots have been designed to provide interventions in mental healthcare. However, how chatbot-based social contact can mitigate social stigma in mental illness remains under-explored. We designed two chatbots that deliver either first-person or third-person narratives about mental illness and evaluated them using a mixed methods study. Compared to a web survey group, participants in both chatbot groups decreased their beliefs that individuals are personally responsible for their mental illnesses, and increased their intentions to help. Additionally, participants in the first-person chatbot group showed a reduced level of fear, and a lower desire for social distance from people with mental illness. Many in the first-person chatbot group also reported a feeling of relationship with the chatbot, and chose to phrase their responses empathetically. Results demonstrated that chatbot-based social contact has promising potential for mitigating mental illness stigma. Implications for designing chatbot-based social contact are discussed.

Academic writing in English can be challenging for non-native English speakers (NNESs). AI-powered rewriting tools can potentially improve NNESs’ writing outcomes at a low cost. However, whether and how NNESs make valid assessments of the revisions provided by these algorithmic recommendations remains unclear. We report a study where NNESs leverage an AI-powered rewriting tool, Langsmith, to polish their drafted academic essays. We examined the participants’ interactions with the tool via user studies and interviews. Our data reveal that most participants used Langsmith in combination with other tools, such as machine translation (MT), and those who used MT had different ways of understanding and evaluating Langsmith’s suggestions than those who did not. Based on these findings, we assert that NNESs’ quality assessment in AI-powered rewriting tools is influenced by the simultaneous use of multiple tools, offering valuable insights into the design of future rewriting tools for NNESs.

Data portability--the capability to transfer one's data from one platform to another--has been described as an important tool for giving individuals more control over their data. It is defined in significant regulations such as the GDPR, and implemented by major online platforms. Unfortunately, there is a lack of research investigating internet users' perceptions and expectations of this technology in specific contexts, which is vital for building effective designs. One particularly important context for studying user perspectives is social media, since it is deeply embedded into daily life and is particularly complex regarding the value and portability of user data. This paper addresses that gap through a survey and interviews of social media users in the United States. We identify current attitudes and practices toward controlling their social media data, and examine participants' impressions about the extent to which data portability may enhance their control. Participants had generally favorable impressions, but had differing opinions about what forms of control are important and the extent to which those could be served by data portability. Based on the results, we propose future directions for improving users' control in the context of social media, such as fine-tuned filtering of data to be transferred and ways to coordinate transfers alongside social contacts.

Discussions about polarizing topics are essential to have, yet they can easily become hostile, aggressive, or distressing on current social media platforms. Content moderation interventions aim to mitigate this issue, though such approaches are reactionary, removing harmful content only after it has been posted. We conducted a mixed-methods experiment with 40 participants to investigate how a design friction that manipulates the temporal flow during a contentious conversation can foster interpersonal mindfulness, a trait critical for productive communication. Dyads were randomly assigned into the Control Group which received no intervention, and the Experiment Group where participants were limited to sending one message per two-minute interval. Triangulating quantitative and qualitative data from conversation logs, questionnaires, interviews, and computational text analysis, our findings revealed a two-fold effect: Experiment Group participants felt simultaneously frustrated by the intervention as it disrupted the pacing of their conversation and interfered with rapport-building, and appreciative of the intervention as it nudged them towards writing thoughtful and task-focused messages. We discuss implications of these findings for future investigation into the design of temporal interventions to influence interpersonal mindfulness during polarizing online conversations.

COVID-19 has demonstrated the importance of digital contact tracing apps in reducing the spread of disease. Despite people widely expressing interest in using contact tracing apps, actual installation rates have been low in many parts of the world. Prior studies suggest that decisions to use these apps are largely shaped by pandemic beliefs, social influences, perceived benefits and harms, and other factors. However, there is a gap in understanding what factors motivate intention, but not subsequent behavior of actual adoption. Reporting on a survey of 290 U.S. residents, we disentangle the intention-behavior gap by investigating factors associated with installing a contact tracing app from those associated with intending to install, but not actually installing. Our results suggest that social norms can be leveraged to span the intention-behavior gap, and that a privacy paradox may influence people’s adoption decisions. We present recommendations for technologies that enlist individuals to address collective challenges.

Communication technologies have significant social impacts, and it is important to consider how designers' and developers' values shape their design. Increasingly, these technologies are released as continually evolving platforms and services, so their development involves ongoing discussions and debates about unforeseen problems and future directions. However, there is a gap in research about how designers, developers, and other stakeholders engage with values during later stages of development. We investigate discussions about values in the context of open source software development, focusing on projects related to the Decentralized Web. We conducted a large-scale analysis of GitHub issues among diverse yet ideologically-related projects. We show that the percentage of discussions about values increases later in development, and we identify features and outcomes of conflicts related to open source participants' values. Finally, we propose suggestions to improve upon existing discussion practices by supporting common ground among collaborators with diverse goals, perspectives, and experiences.

Contact tracing apps have been suggested as a promising approach towards containing viral spread during pandemics, yet their actual use in the COVID-19 pandemic has been low. While researchers have examined reasons for or against installing contact tracing apps, we have less understanding of their ongoing use and how they interact with everyday pressures related to work, communities, and mental well-being. Through a survey of 153 working people in Japan and 15 follow-up interviews, we investigated attitudes toward installing and using Japan's national contact tracing app, COCOA, and how these related to respondents' daily lives, work structures, and general attitudes about the pandemic. We found that motivations about installing the app differed from those related to ongoing usage. Specifically, we identified ways that people navigate uncertain norms of behaviour during the pandemic, and how people consider individual risks such as COVID-related stigmas, anxiety, and financial precarity when deciding if and how to use COCOA. In light of these, we discuss the tension between COCOA's design and desires to protect oneself by selective controlling disclosures. We note that perceived risks are closely tied to respondents' local contexts, and based on our analysis, we identify ways to address these challenges and tensions through design interventions at multiple scales.