International Journal of Human–Computer Interaction

Mordaschew, Viktoria; Latos, Benedikt; Rubart, Jessica; Röcker, Carsten

doi: 10.1080/10447318.2025.2578390pmid: N/A

Abstract Production environments are characterized by an increasingly diverse workforce caused by demographic change, globalization, and the rising demand for inclusion and equality. To ensure employee satisfaction for such a diverse workforce, gamification is a promising method. However, one-size-fits-all approaches are not sufficient, and more user-adaptive and inclusive gamified systems are needed. Therefore, this systematic literature review aims to answer the question of which user-adaptive gamified systems exist for manufacturing and how they provide inclusivity for individuals in their everyday work. Following PRISMA guidelines, a search of five electronic databases retrieved 22 relevant articles. Analysis of the literature revealed a lack of user-centered and inclusive gamified systems. Further, limited empirical evaluations in real production environments, limited application scenarios, and the need for more diverse research were identified. Based on the results, this study identifies key research gaps and provides recommendations for future research.

Pradhan, Mukesh; Gopal, P. R. C.; Thangeda, Rahul

doi: 10.1080/10447318.2025.2579784pmid: N/A

Abstract Conversational Artificial Intelligence (CAI) has become a pivotal tool for businesses to reshape customer service, offering personalized and scalable solutions. This study adopted a bibliometric analysis of 191 scholarly articles and a content analysis under a systematic literature review employing the TCCM framework for 45 articles. The findings from the bibliometric analyses categorize CAI research into three phases of thematic evolution: emergence (2018–2020), development (2021–2022), and maturity (2023–2024). Further, from the systematic review, a comprehensive content analysis of articles reveals that, the most prominent theories and models in the phenomenon, research contexts in terms of countries, industries, various CAI technologies, and characteristics of conceptual models in order to understand different outcomes, antecedents, and mediators/moderators. This study also observed three critical dimensions of CAI research that are adoption, implementation, and monitoring with relevance to organization and consumer. This paper argues that despite considerable evidence from the literature in CAI in recent years, there is a large scope for research to address successful factors to adopt CAI from the perspectives of organizations as well as consumers. It emphasizes the need for researchers to investigate the interaction and engagement between CAI and consumers, as well as the challenges that arise during implementation and the ways in which they can be mitigated in business firms. In the CAI monitoring, issues related to the security and privacy dimensions of CAI technology, aimed at benefiting customers and users, are emphasized.

Esener, Yildiz; Kim, Heejun; Kaya, Ali; Kaz-Onyeakazi, Ijay

doi: 10.1080/10447318.2025.2580545pmid: N/A

Abstract This systematic literature review examines the usability and acceptability of mobile apps developed for individuals with Post-Traumatic Stress Disorder (PTSD). While mobile technology is increasingly used in mental health interventions, evidence regarding the usability and acceptability of PTSD apps remains limited. The primary aim of this review is to identify key factors that influence usability and acceptability, focusing on discovering new factors to extend the Technology Acceptance Model (TAM). By synthesizing existing research, this study proposes an enhanced assessment tool for evaluating PTSD apps, incorporating factors critical for their improvement. The findings reveal significant insights into the strengths and weaknesses of current PTSD apps, highlighting the importance of usability, engagement, and customization for diverse user needs. Additionally, the review identifies essential factors for potential expansion of the TAM model, offering a framework for improving future app development. Ultimately, the study emphasizes the potential of PTSD apps to improve care quality and provides a framework for assessing and enhancing their effectiveness.

Jin, Jia; Wang, Zhongfeng; Wang, Zheng; Pei, Guanxiong

doi: 10.1080/10447318.2025.2581856pmid: N/A

Abstract In the era of human-robot symbiosis, endowing robots with emotional intelligence is essential for creating harmonious human-robot interactions and enabling them to fulfill social functions effectively. Although significant progress has been made in robotic emotion recognition, emotion generation remains underdeveloped, struggling to meet user-centered interaction demands in multi-turn, complex-task, and diverse-scenario settings. This often results in robotic behaviors that appear rigid and lack empathy. To address these challenges, this study proposes an Optimization Model for Robotic Artificial Emotion Generation (OMRAEG). Structured around a “theory–mechanism–method” framework, the model establishes a closed-loop optimization system encompassing method application, effectiveness evaluation, influencing factors, and feedback iteration. Specifically, the research integrates mainstream approaches and key technologies across four dimensions: facial expression synthesis, emotional dialogue generation, emotional speech synthesis, and emotional motion synthesis. Furthermore, it constructs a systematic evaluation indicator system and clarifies the fine-tuning role of application scenarios and development trends guiding technological evolution.

Mumcu, Bayram Berkay; Çebi, Ayça

doi: 10.1080/10447318.2025.2573031pmid: N/A

Abstract With the widespread use of mobile devices, push notifications that replace email- or login-based systems can play an important role in increasing student engagement and improving time management. This study examines the effect of push notifications sent at different times on students' interaction behaviors in online learning environments. The study was conducted using a quasi-experimental design involving university students. During the intervention, push notifications were sent to students on a scheduled basis via the Moodle mobile app, and their interactions with the system were analyzed. The results revealed that push notifications sent at scheduled times, particularly in the morning and evening, increased student engagement, improved reaction times, and strengthened platform interaction. However, the negative outcomes of uncontrolled peer-generated notification intensity emphasize the need for personalized notification systems based on student preferences. It is recommended that the design of notifications in online learning environments should be optimized using a user-centered approach.

Smutny, Zdenek; Sudzina, Frantisek

doi: 10.1080/10447318.2025.2573037pmid: N/A

Abstract Generative artificial intelligence (AI) tools are reshaping individual work performance. While many studies explore the adoption of generative AI tools, few examine mediations and factors influencing performance expectancy and intentions to use AI chatbots like ChatGPT. This study builds on Camilleri’s (2024) framework, integrating enhanced UTAUT and IAM models, and presents extended replication. A survey questionnaire (N=787, aged 18–34 y) was analyzed using SmartPLS4. The results show that performance expectancy significantly mediates the relationship between effort expectancy, source trustworthiness, and information quality to intentions to use AI chatbots. Sex differences were found, with men prioritizing the quality of information, while women emphasize the source trustworthiness. Compared to replicated research, distinct cultural and demographic factors influenced adoption outcomes. Despite user-intuitive control similar to internet-mediated human communication, which facilitates adoption among young people, users remain cautious due to risks like hallucinations, social bias, misinformation, or adversarial prompts. The study contributes theoretically and empirically to understanding how AI chatbots affect work-related behavior and decision-making.

Heijboer, Stefan; Breitschaft, Stefan; Forster, Yannick; Huisman, Gijs; Vink, Peter; Song, Wolf

doi: 10.1080/10447318.2025.2573040pmid: N/A

Abstract As touchscreen interfaces replace many physical controls in vehicles, safe and intuitive interaction becomes increasingly important. This study evaluated piezo-actuated haptic feedback versus conventional audio-only feedback across 11 use cases in parking and driving contexts. In a within-subject experiment with 23 participants in a fixed-based driving simulator, haptic feedback was rated as more satisfying, helpful, and supportive, particularly for scrolling or confirming actions while driving, despite showing no significant differences in objective performance measures. Participants described haptics as more engaging, though some noted habituation effects and inconsistencies. Audio-only feedback was seen as more stimulating but less consistently preferred. Findings suggest that contextual haptics improves perceived usability. Additionally, the study highlights the potential of semantic haptics—enabled by piezo technology—to convey meaning through expressive tactile patterns. Future research could explore adaptive multimodal systems and assess long-term effects under real-world conditions.

Birnstiel, Sandra; Reiss, Sylvia; Fernández Galeote, Daniel; Duemler, Burkhard; Morschheuser, Benedikt

doi: 10.1080/10447318.2025.2573042pmid: N/A

Abstract Following the Quantified Self (QS) movement, sports apps increasingly adopt self-tracking technologies that offer data-driven insights into personal competencies to enhance self-efficacy. However, sustained engagement with QS technology remains challenging, as interpreting data is complex. One potential solution is to make QS data more meaningful to users. To address this, we present a player card feature within a QS sports app, incorporating a personalized avatar to enhance users’ enjoyment, data meaningfulness, and self-efficacy to promote continued use. We evaluate the app through a field experiment in a soccer context to examine the impact of the avatar feature. Results indicate that avatar identification positively affects self-efficacy, data meaningfulness, continued use intention, and enjoyment, though enjoyment did not impact continued use. Our findings suggest that (1) avatar identification can be enhanced through personalization, (2) personalized avatars effectively boost self-efficacy, and (3) sustained engagement may rely more on meaning than on enjoyment alone.

Liu, Liming; Chen, Yiming

doi: 10.1080/10447318.2025.2573044pmid: N/A

Abstract In 2021, China enacted its first Personal Information Protection Law (PIPL), requiring digital platforms to establish formal privacy compliance measures and signifying the PIPL as a distinctive model of global privacy governance. Given the paucity of research focusing on how user-centric perspectives perceive and negotiate privacy on platforms in the post-PIPL era, this study addresses this gap by drawing insights from interviews with 28 active users across five leading Chinese platforms. The findings indicate that users negotiate their personal information through interrelated strategies: privacy knowledge discourses, reflective sharing practices and protection mechanisms, and engagement in dynamics of (in-)visibility games by platform policies. We conceptualize these practices as a dialectic of privacy visibility, advancing human-computer interaction research by foregrounding users’ active role in privacy management. The article highlights practical implications for integrating user experiences into digital privacy literacy initiatives and fostering human-centered approaches to regulatory design.

Hong, Yuhwa; Kim, Haejun; Yu, Jihae; Shin, Heedo; Yu, Xiaoqun; Xiong, Shuping; Kim, Woojoo

doi: 10.1080/10447318.2025.2573502pmid: N/A

Abstract In Fitts’ law research for pointing and selecting tasks, the multi-directional tapping task from ISO/TS 9241-411 has been widely used as a standard task. However, the ISO standard does not describe several task factors in sufficient detail, leading researchers to interpret and apply them independently in various ways. This study aims to investigate the effects of four task factors for multi-directional tapping task: error highlight, hover highlight, target shape, and number of targets. The experimental results indicated that all investigated task factors had significant effects on user performance and behavior, as reflected in both eye movement and mouse movement patterns. Our research findings can be helpful for researchers who want to evaluate the efficiency and effectiveness of interaction techniques and input devices for graphical user interfaces as a multi-directional tapping task protocol.