International Journal of Human–Computer Interaction

Hua, Luhui; Jing, Yuan; Sun, Xiaocui; Tian, Lujia; Apuke, Oberiri Destiny

doi: 10.1080/10447318.2024.2352927pmid: N/A

Abstract We, the Editors and Publisher of the International Journal of Human–Computer Interaction have retracted the following article: Hua, L., Jing, Y., Sun, X., Tian, L., & Apuke, O. D. (2024). Developing a Stressor Strain Outcome Model That Predicts Fake News Sharing Behaviour on Social Media: The Mediating Role of Social Media Exhaustion. International Journal of Human–Computer Interaction, 1–14. https://doi.org/10.1080/10447318.2024.2352927 Following publication, the journal was made aware that this article was still under consideration by another journal when it was submitted to the International Journal of Human–Computer Interaction, thereby constituting a duplicate submission in breach of our editorial policies. Upon further assessment, while the study design and timeframe as well as a very significant proportion of the data remained the same across both articles, the authorship lists on both articles differed significantly. We have contacted the authors for a full explanation, but we have not received a satisfactory response regarding the article content overlaps and the discrepancies in the authorship. As determining the integrity of the published content and verifying authorship is core to the integrity of published work, we are therefore retracting the article. The corresponding author listed in this publication has been informed. 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 The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as ‘Retracted’.

Chinchilla, P.; Kim, Jihyun

doi: 10.1080/10447318.2024.2390263pmid: N/A

Abstract The popularity of live streaming has continuously grown in recent years, with streamers sharing various aspects of their lives with audiences. This study explores the impact of streamers’ self-disclosure, including visual cues and personal life stories on audiences’ perceived parasocial interaction (PSI). This study used a 2 × 2 online experiment, using a between-subjects design: presence of a streamer’s face (no face vs. face) and type of shared stories (professional vs. personal). Key findings underscore the significant role of social presence. Specifically, sharing personal life stories enhances the perceived social presence of the streamer, leading to heightened perceptions of parasocial interaction. Moreover, the study highlights that parasocial interaction predicts increased enjoyment. Overall, the research underscores the importance of fostering social presence in livestreaming contexts.

Zhuo, Shuer; Oh, Jeeyun

doi: 10.1080/10447318.2024.2390755pmid: N/A

Abstract Delving into corner shapes in interface design (i.e., circular vs. angular), we experimentally delineated two routes whereby users with strong privacy risk beliefs can be persuaded into adopting a mobile payment app through a bill-split use case. By introducing cognitive load as an ability factor and perceived privacy risk as a motivation force, this study bridges literature on user interface design with social psychology to unpack how subtle design cues impact app adoption through the lens of privacy assurance. The interplay among shapes, tasks, and user traits demonstrates the contextual efficacy of circular design to evoke social presence and the benefit of angular design to enhance processing fluency, which tap into users’ affective and cognitive needs in face of privacy risks under varied cognitive demands to encourage adoption.

Abbas, Ammar N.; Amazu, Chidera W.; Mietkiewicz, Joseph; Briwa, Houda; Perez, Andres Alonso; Baldissone, Gabriele; Demichela, Micaela; Chasparis, Georgios C.; Kelleher, John D.; Leva, Maria Chiara

doi: 10.1080/10447318.2024.2391605pmid: N/A

Abstract In complex industrial and chemical process control rooms, effective decision-making is crucial for safety and efficiency. The experiments in this paper evaluate the impact and applications of an AI-based decision support system integrated into an improved human-machine interface, using dynamic influence diagrams, a hidden Markov model, and deep reinforcement learning. The enhanced support system aims to reduce operator workload, improve situational awareness, and provide different intervention strategies to the operator adapted to the current state of both the system and human performance. Such a system can be particularly useful in cases of information overload when many alarms and inputs are presented all within the same time window, or for junior operators during training. A comprehensive cross-data analysis was conducted, involving 47 participants and a diverse range of data sources such as smartwatch metrics, eye-tracking data, process logs, and responses from questionnaires. The results indicate interesting insights regarding the effectiveness of the approach in aiding decision-making, decreasing perceived workload, and increasing situational awareness for the scenarios considered. Additionally, the results provide insights to compare differences between styles of information gathering when using the system by individual participants. These findings are particularly relevant when predicting the overall performance of the individual participant and their capacity to successfully handle a plant upset and the alarms connected to it using process and human-machine interaction logs in real-time which resulted in a 95.8% prediction accuracy using hidden Markov model. These predictions enable the development of more effective intervention strategies.

Atashpanjeh, Hanieh; Paudel, Rizu; Al-Ameen, Mahdi Nasrullah

doi: 10.1080/10447318.2024.2392064pmid: N/A

Abstract Users’ mental models influence secure and privacy-preserving behavior in a computing environment. Prior studies on users’ mental models of Internet, security tools, and digital privacy show that there is no one-size-fits-all solution when it comes to security and privacy design. However, little study to date has explored the ways to translate users’ mental models into interactive security and privacy designs. As we begin to address this gap, we focus on privacy policy in this paper. The typical text-based privacy policy suffers from poor readability and usability. A recent study proposed a Visual Interactive Privacy Policy (VIPP), showing promise to offer a better user experience as compared to prior designs – we used VIPP as a control condition and compared that with our mental model (MM)-based designs, inspired by users’ privacy mental models explored in the existing literature. We iteratively improved our MM-based designs through a series of user studies in the lab setting. We evaluated our updated designs in an online study with 182 participants over Amazon Mechanical Turk. The participants rated MM-based designs significantly better than the control in most of our evaluation parameters. Furthermore, we found that when a design is centered around the mental model of participants, study participants rated it higher in terms of personal connection to the design, perspicuity, attractiveness, being stimulated towards privacy protection, as well as the propensity for real-life adoption. Based on our findings, we discussed the successes and challenges of MM-based designs and provided guidelines on the scope of leveraging mental models in the broader area of privacy and security designs.

Xu, Zheng; Jiang, Tanghan; Xiao, Dong; Fang, Yihai; Zheng, Nan

doi: 10.1080/10447318.2024.2392069pmid: N/A

Abstract The necessity of human supervision and intervention during autonomous driving has long been a topic of controversial discussion. From a developer’s perspective, it is expected that users will readily adapt to well-calibrated autonomous driving systems (ADS) due to their superior performance in dynamic driving tasks (DDT) compared to conventional human-driven vehicles. However, when passengers experience an autonomous vehicle (AV), there may be an adjustment period during which they modify their behavior to accommodate the driving patterns of the ADS. Additionally, some passengers might not adapt to autonomous driving at all, highlighting potential limitations in the current ADS development strategy. This work studies the dynamics of human-automation interaction and introduces an “objective method”, which employs a Virtual Reality (VR)-enabled simulation approach for in-depth behavioral analysis concerning riders’ behavioral adaptation to autonomous driving. Specifically, we examined how participants interacted with and intervened in Level 4 ADS operating under conservative, moderate, and aggressive driving patterns in a fully autonomous environment. A realistic urban road network was recreated in VR, integrated with traffic microsimulation to generate various driving scenarios. Twenty-seven participants completed driving tasks across different AV modes, with their intervention behaviors analyzed in relation to traffic conditions and AV aggressiveness. Key findings include: (1) Participants showed higher intention to intervene but lower actual intervention rates under aggressive AV modes compared to moderate and conservative modes, suggesting quicker adaptation to more challenging driving scenarios. (2) Interventions generally proved unnecessary and sometimes detrimental to overall traffic performance in a full-AV environment. (3) Aggressive AV modes significantly improved traffic efficiency, with a 40% increase in average travel speed and a 53% reduction in waiting time. However, human interventions posed the greatest challenge to achieving optimal traffic conditions. This research provides insights into the complex dynamics of human-AV interaction and adaptation, offering valuable implications for AV interface design, implementation strategies, and public acceptance of autonomous driving technologies.

Demirel, Sadettin; Kahraman-Gokalp, Elif; Gündüz, Uğur

doi: 10.1080/10447318.2024.2392964pmid: N/A

Abstract Artificial intelligence (AI) technologies, as a product of processes aimed at imitating human intelligence through computers and software, affect our daily lives in various aspects, including cultural, technological, and economic. The advent of ChatGPT, developed by OpenAI, signifies a pivotal AI advancement in human language comprehension and generation, heralding profound implications across societal, economic, and cultural dimensions. However, a notable gap exists in scholarly literature concerning the examination of perceptions and discussions surrounding ChatGPT. This study aims to address this gap by analyzing Twitter conversations, comprising 1.1 million tweets containing “ChatGPT” or “#ChatGPT” between December 1 2022 and June 1 2023, with geolocation data. Employing a text as data approach encompassing text, sentiment, and semantic network analyses, sentiment polarity and lexical patterns were explained, while semantic network analysis revealed central expressions and prominent themes in Twitter discussions. The study highlights the social effects of AI technologies from the perspective of Twitter users and reveals the sentimental tendency and themes in tweets with geographical and economic dimensions. The findings reflect the prevalence of positive sentiment and hype toward ChatGPT while there are also concerns regarding privacy, cybersecurity, and misuse of AI tools. More importantly, content generation, technical aspects, business applications, educational use, and competitors are among the main themes of ChatGPT-related discussions on Twitter.

Wei, Jingzhu; Zhang, Tongrui; Lu, Xiang

doi: 10.1080/10447318.2024.2392965pmid: N/A

Abstract Patent information platforms have become crucial for innovators to stay informed about technological advancements. These platforms, distinguished by their function-oriented nature, differ significantly from traditional systems, highlighting the need for research on user behavior impact. This study proposes a research model to investigate how information, system, and interface quality influence continuous use intention, with perceived pleasure and needs satisfaction as mediators. A survey with 468 users was conducted, followed by regression and structural equation modeling for path and mediation analyses. The findings indicate that information and system quality have positive but distinct impacts on perceived pleasure and needs satisfaction, while interface quality affects only perceived pleasure. Needs satisfaction mediates the impact of information and system quality on continuous use intention, whereas perceived pleasure does not. This study introduces a function-oriented scenario into information systems research and identifies the unique impact paths of information and system quality. For these function-oriented platforms, the influence of interface quality on needs satisfaction or perceived pleasure on continuous use intention is insignificant, establishing a new context relevant to pragmatism and push–pull–mooring theories. Practical insights are provided for managers to improve system efficiency and enhance user loyalty.

Zhang, Jiaxin; Li, Qingchuan

doi: 10.1080/10447318.2024.2392966pmid: N/A

Abstract Social cues play a critical role in the exchange of information during patient–physician communication. Although previous research has recognized the impact of social cues on patients’ perceptions and behaviors in mobile medical consultations, little is known about how different combinations of social cues affect the interactions. Employing a within-subject factorial design experiment, this study examined the effects of three social cues—empathic cues, interactivity cues, and social validation—on patients’ perceptions of interactivity, social presence, and trust. A total of 103 participants was recruited. The results revealed the various significant impacts of empathic cues, interactivity cues, and social validation on perceived interactivity, and social validation affected trust, as well as the relationships among these perceptions. The findings suggested strategies for integrating various social cues to enhance patients’ positive perceptions, offering potential benefits to patient–physician communication in mobile medical platforms.

Qin, Fang; Guo, Xiaochai

doi: 10.1080/10447318.2024.2392969pmid: N/A

Abstract With the development and integration of artificial intelligence (AI) and virtual reality technology, metahuman powered by AI is gradually becoming an important force in service marketing. Considering the novel characteristics of metahuman services, this study aims to identify and validate the core dimensions of the metahuman service quality. Online review analysis, multiple regression analysis, and fuzzy-set Qualitative Comparative Analysis (fsQCA) were employed to analyze data in this study. The results show that nine dimensions of metahuman service quality, including stability, complementary, efficiency, continuous improvement, empathy, accessibility, personalization, cultural sociability, and fault tolerance, significantly contribute to customer satisfaction. Moreover, the results suggest that there are five multiple and equally effective combinations of metahuman service quality dimensions that can achieve high consumers’ satisfaction. This study contributes to the avatar literature by unveiling the complexity behind the adoption of AI-powered avatar. It also offers meaningful practical implications for marketing managers to optimize the combination of metahuman service quality.