International Journal of Human-Computer Interaction

Yan, Yiping

doi: 10.1080/10447318.2024.2320981pmid: N/A

We, the Editors and Publisher of International Journal of Human Computer Interaction, have retracted the following article: Yan, Y. (2024). Analysis and Research of Psychological Crisis Behavior Model Based on Improved Apriori Algorithm. International Journal of Human–Computer Interaction, 1–13. https://doi.org/10.1080/10447318.2024.2320981 Following publication, significant concerns have been raised regarding discrepancies between the data reported in the article and the underlying dataset that is stated as having been used for the research. There are additional concerns regarding Figure 2, Figure 5 and Figure 6; the description of these in the body of the article does not correlate with the figure captions. Despite numerous attempts to contact the author, they have not responded to requests for an explanation. As the editorial team no longer have confidence in the reported conclusions and validity of the article, the decision has been made to retract the article. We have been informed in our decision-making by our editorial policies and the COPE guidelines. The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as ‘Retracted’.

Lee, Minha; Contreras Alejandro, Jessica; IJsselsteijn, Wijnand

doi: 10.1080/10447318.2023.2231277pmid: N/A

Abstract Gratitude is a moral emotion that demonstrates our appreciation of altruism. In psychology, feeling grateful is linked to an increase in well-being, yet there is a lack of HCI research on if gratitude can be cultivated through and with conversational agents. We quantitatively studied whether a chatbot can increase people’s gratitude (N = 133), as well as its influence on people’s positive and negative emotions. Compared to the control condition, a chatbot that shared gratitude interventions significantly enhanced people’s gratitude and positive emotions, while lowering negative emotions. Interestingly, people’s experience of gratitude differed from other positive emotions: Simple positive emotions, like joy, can go up while reported gratitude decreases. We share qualitative observations on how gratitude can be a complex emotional experience, encompassing positive and negative emotions, such as finding relief in admitting to a chatbot about one’s sadness over friendship during the COVID-19 pandemic.

Følstad, Asbjørn; Bjerkreim-Hanssen, Nina

doi: 10.1080/10447318.2023.2238355pmid: N/A

Abstract Chatbots are increasingly taken up by the public sector, as a means to efficient provision of information and services. However, there is a lack of knowledge on how users interact with such chatbots. To address this knowledge gap, we have conducted an analysis of user interactions with a chatbot for citizens of Norwegian municipalities. We analyzed a total of 2663 user-chatbot dialogues from six municipalities, using the framework of Følstad and Taylor. The analysis showed that most user input was characterized by brief messages and a utility-oriented dialogue style whereas chatbot responses were characterized by substantial response relevance (68% of chatbot responses categorized as relevant) and helpfulness (66% of dialogues categorized as help being offered and likely used). Furthermore, message brevity and a utility-oriented dialogue style was found to be positively associated with users receiving relevant chatbot responses and helpful dialogue outcomes. Variation in chatbot design, specifically how the chatbot was presented to users, was found to impact user message brevity and dialogue style, and, by extension, response relevance and dialogue outcome. On the basis of the findings, we summarize lessons learnt and suggest directions for future research.

Xu, Cheng; Liang, Xueji; Sun, Yanqi; He, Xudong

doi: 10.1080/10447318.2023.2238367pmid: N/A

Abstract This study seeks to investigate the potential of utilizing the metaverse environment to enhance the ability of financial analysts in detecting financial fraud. A preregistered randomized controlled experiment was conducted, which involved two control groups of participants discussing financial statements onsite and via video conference, and an experimental group utilizing a metaverse environment. The results revealed that the experimental group’s performance in terms of the accuracy of fraud detection surpassed that of the two controlled groups. This outcome may be attributed to the enhanced usage of data visualization and more proactive participation of female participants. This study provides valuable insights into the potential benefits of employing metaverse technology in corporate finance and informs future collaboration strategies for financial analysts in fraud detection.

Long, Yonghao; Luo, Xiapu; Zhu, Yujie; Lee, Kun Pyo; Wang, Stephen Jia

doi: 10.1080/10447318.2023.2228997pmid: N/A

Abstract Data transparency plays a critical role in understanding IoT privacy practices and making informed decisions. To gain a comprehensive understanding of transparency in the IoT environment, a systematic literature review of 58 academic articles is conducted to investigate the progress and status of existing data transparency studies from a design perspective. Data transparency was identified as a signifier to bridge the connection between user behavior and privacy risks. The level of transparency achieved was shaped by users’ privacy perceptions, which in turn influenced their privacy behavior. GUI-based transparency design has been widely used in IoT, but it is not sufficient to provide users with accessible, understandable, and unified transparency information. A conceptual transparency design is proposed based on the extracted design opportunities and practices. This paper provides an important resource on transparency issues in the IoT environment, and will benefit the design and computer science communities.

Tandon, Urvashi; Ertz, Myriam; Shashi,  

doi: 10.1080/10447318.2023.2232977pmid: N/A

Abstract Optimal healthcare provision for the elderly is increasingly possible via real-time health indicators’ data generated by mHealth care applications. Yet, these apps require continuous utilization, which remains problematic. This research examines gamification, usability, as well as empathetic cooperation and social interaction (ESCI) as enablers whereas inertia, sunk cost, transition cost, perceived risk, and technological anxiety are validated as inhibitors of mHealth care applications continued usage intention. Drawing on self-determination theory (SDT) and the Health IT Usability Evaluation Model (Health-ITUEM), the study also validates engagement as an influencer of continued intention. The sample comprised 643 older adults using mHealth care applications and residing in North Indian states. Structural Equation Modelling (SEM) was applied to assess and validate the hypothesized relationships. The results confirmed that usability strongly impacted engagement, followed by gamification and ESCI. Conversely, perceived risk emerged as the strongest inhibitor, followed by sunk cost, technological anxiety, and transition cost. Interestingly, Inertia had a positive and significant impact on engagement. This research is an initial endeavor to understand enablers and inhibitors of mHealth care applications (mHealth care apps) concerning older adults. The model that emerged from this study would provide valuable insights by validating various significant issues to generate engagement of the elderly towards mHealth care apps.

Kim, Minseong; Baek, Tae Hyun

doi: 10.1080/10447318.2023.2233125pmid: N/A

Abstract This empirical study investigates the structural relationships among virtual influencer attributes, perceived creepiness, perceived authenticity, emotional attachment, and word-of-mouth intentions of social media users. To be specific, this study conceptually identifies the roles of the virtual influencer attributes of language similarity, interest similarity, social attractiveness, physical attractiveness, and attitude homophily on perceived creepiness and perceived authenticity of the virtual influencer, as these in turn affect emotional attachment and word-of-mouth. This study collected data from social media users in the United States via a survey approach and found that creepiness was significantly influenced by attitude homophily while perceived authenticity was significantly affected by language similarity, interest similarity, physical attractiveness, and attitude homophily. In addition, this study found significant relationships among perceived creepiness, perceived authenticity, emotional attachment, and word-of-mouth intention. This empirical study has both theoretical and practical implications for virtual influencer marketing.

Fang, Le; Xing, Sark Pangrui; Ma, Zhengtao; Zhang, Zhijie; Long, Yonghao; Lee, Kun-Pyo; Wang, Stephen Jia

doi: 10.1080/10447318.2023.2228983pmid: N/A

Abstract Human-computer interaction has seen growing interest in emotion detection. To gain deeper insights into the physiological indicators of emotions, researchers have delved into utilizing electroencephalography (EEG) and micro-gestures (MGs). This study assesses the efficacy of EEG and MG features in emotion detection by recruiting 15 participants to gather EEG and MG data in response to diverse figure-based emotional stimuli. To incorporate these features, this article introduces Emo-MG, a multimodal interface that integrates EEG and MG features and employs a long short-term memory (LSTM) model to predict emotional states within the valence-arousal-dominance (VAD) space. This study presents an in-depth analysis of feature importance and correlation results based on EEG and MG features for feature selection in emotion detection tasks. Through accuracy and F1-score metrics, Emo-MG achieves outstanding performance in emotion detection by comparing it to baseline and deep learning models, validating the efficacy of integrating EEG and MG features

Chua, Yvonne; Cooray, Sankha; Cortes, Juan Pablo Forero; Denny, Paul; Dupuch, Sonia; Garbett, Dawn L.; Nassani, Alaeddin; Cao, Jiashuo; Qiao, Hannah; Reis, Andrew; Reis, Deviana; Scholl, Philip M.; Sridhar, Priyashri Kamlesh; Suriyaarachchi, Hussel;

Wang, Yu-Min; Wei, Chung-Lun; Chen, Wen-Jing; Wang, Yi-Shun

doi: 10.1080/10447318.2023.2231278pmid: N/A

Abstract The COVID-19 pandemic brought about significant changes in educational delivery methods and student learning. E-learning systems, which previous research had found to be effective in voluntary contexts, suddenly became mandatory but proved to be less effective for students worldwide. Hence, there is a need for academics and practitioners to revisit the e-learning systems success model in the post-COVID-19 era. Building upon previous e-learning and information systems success models, this study aimed to re-specify and validate the e-learning systems success model by examining the role of monitoring quality. Data were collected from 191 college students and analyzed using the partial least squares approach. The results indicated that information quality, system quality, and service quality had a positive influence on both user satisfaction and communication quality, which, in turn, positively impacted loyalty intention and subsequently enhanced learning effectiveness. Notably, the newly added construct of monitoring quality had the strongest effect on communication quality compared to information quality, system quality, and service quality. However, it had no impact on user satisfaction. The findings of this study provide several important theoretical and practical implications for e-learning systems success models in the post-COVID-19 era.