Pub Date : 2025-02-18DOI: 10.1016/j.chb.2025.108609
Seung Hwan "Shawn" Lee (Associate Professor) , Kyoung-Nan Kwon (Professor)
This study investigates the effects of restricting user engagement on fairness perceptions within social media platforms. Using experimental methods, we find that limiting user participation, particularly in providing negative feedback, significantly reduces perceptions of procedural and overall fairness. Notably, users who highly value self-expression are especially sensitive to these restrictions, showing heightened concern when their ability to contribute is curtailed. Our research establishes a direct link between changes in engagement policies and user perceptions of fairness and trust, suggesting that prioritizing content creators over users may undermine trust and future usage intentions. Furthermore, procedural fairness is affected by the opportunity to provide feedback, irrespective of its influence on outcomes. These insights underscore the importance of tailoring engagement policies to the platform's primary function, whether for socializing, entertainment, or information sharing.
{"title":"Restrictions in social media engagement and User's fairness perception","authors":"Seung Hwan \"Shawn\" Lee (Associate Professor) , Kyoung-Nan Kwon (Professor)","doi":"10.1016/j.chb.2025.108609","DOIUrl":"10.1016/j.chb.2025.108609","url":null,"abstract":"<div><div>This study investigates the effects of restricting user engagement on fairness perceptions within social media platforms. Using experimental methods, we find that limiting user participation, particularly in providing negative feedback, significantly reduces perceptions of procedural and overall fairness. Notably, users who highly value self-expression are especially sensitive to these restrictions, showing heightened concern when their ability to contribute is curtailed. Our research establishes a direct link between changes in engagement policies and user perceptions of fairness and trust, suggesting that prioritizing content creators over users may undermine trust and future usage intentions. Furthermore, procedural fairness is affected by the opportunity to provide feedback, irrespective of its influence on outcomes. These insights underscore the importance of tailoring engagement policies to the platform's primary function, whether for socializing, entertainment, or information sharing.</div></div>","PeriodicalId":48471,"journal":{"name":"Computers in Human Behavior","volume":"167 ","pages":"Article 108609"},"PeriodicalIF":9.0,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143464077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-14DOI: 10.1016/j.chb.2025.108598
Chen Li , Yixin Dai , Guang Chen , Jing Liu , Ping Li , Horace Ho-shing Ip
Collaborative virtual environments (CVEs) facilitate avatar-mediated communication (AMC), where users interact using human-like virtual characters in shared virtual worlds, enhancing the attractiveness, attentiveness, and connectedness of remote social experiences and thus becoming extremely popular nowadays in various application domains such as education and healthcare. Understanding how different aspects of avatar behaviours influence various types of social interactions is crucial for improving the design of CVEs. Grounded in a theoretical framework based on avatar anthropomorphic realism, nonverbal social cues, eye-mind hypothesis, and interaction process analysis, this study investigates the impact of avatars’ gaze behaviours on users’ attention allocation and perceptions during AMC in CVEs. A two-arm randomised controlled trial (RCT) with 60 participants (29 males and 31 females) compared static gaze and natural gaze avatars during socio-emotional and task interactions. Three-dimensional eye-tracking data revealed distinct attention patterns across three primary nonverbal social cues: eye gaze, head orientation, and pointing gesture. Furthermore, avatars’ gaze type and interaction type were both found to significantly affect participants’ attention allocation; natural gaze behaviour and task interactions mitigated the general gaze-avoidance pattern observed in previous studies. However, avatars’ gaze type did not impact participants’ perceptions of social presence and anxiety. This research provides a nuanced understanding of attention allocation across nonverbal social cues during AMC and underscores the importance of avatars’ gaze and interaction types, highlighting important implications for the future design of CVE to enhance attention coordination and communication. Additionally, it calls for more comprehensive studies to explore avatars’ anthropomorphic realism and its effects on user perceptions and overall experience during AMC.
{"title":"Avatar-mediated communication in collaborative virtual environments: A study on users’ attention allocation and perception of social interactions","authors":"Chen Li , Yixin Dai , Guang Chen , Jing Liu , Ping Li , Horace Ho-shing Ip","doi":"10.1016/j.chb.2025.108598","DOIUrl":"10.1016/j.chb.2025.108598","url":null,"abstract":"<div><div>Collaborative virtual environments (CVEs) facilitate avatar-mediated communication (AMC), where users interact using human-like virtual characters in shared virtual worlds, enhancing the attractiveness, attentiveness, and connectedness of remote social experiences and thus becoming extremely popular nowadays in various application domains such as education and healthcare. Understanding how different aspects of avatar behaviours influence various types of social interactions is crucial for improving the design of CVEs. Grounded in a theoretical framework based on avatar anthropomorphic realism, nonverbal social cues, eye-mind hypothesis, and interaction process analysis, this study investigates the impact of avatars’ gaze behaviours on users’ attention allocation and perceptions during AMC in CVEs. A two-arm randomised controlled trial (RCT) with 60 participants (29 males and 31 females) compared static gaze and natural gaze avatars during socio-emotional and task interactions. Three-dimensional eye-tracking data revealed distinct attention patterns across three primary nonverbal social cues: eye gaze, head orientation, and pointing gesture. Furthermore, avatars’ gaze type and interaction type were both found to significantly affect participants’ attention allocation; natural gaze behaviour and task interactions mitigated the general gaze-avoidance pattern observed in previous studies. However, avatars’ gaze type did not impact participants’ perceptions of social presence and anxiety. This research provides a nuanced understanding of attention allocation across nonverbal social cues during AMC and underscores the importance of avatars’ gaze and interaction types, highlighting important implications for the future design of CVE to enhance attention coordination and communication. Additionally, it calls for more comprehensive studies to explore avatars’ anthropomorphic realism and its effects on user perceptions and overall experience during AMC.</div></div>","PeriodicalId":48471,"journal":{"name":"Computers in Human Behavior","volume":"167 ","pages":"Article 108598"},"PeriodicalIF":9.0,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143445779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-14DOI: 10.1016/j.chb.2025.108600
Lucas Kohnke , Benjamin Luke Moorhouse
This qualitative study investigates the impact of generative artificial intelligence (GenAI) on the emotional engagement, motivation and well-being of first-year university students in Hong Kong. We conducted semi-structured interviews with 21 students and three instructors to explore their perceptions of how GenAI influences the affective dimensions of language learning. The data were analyzed using manual coding and inductive thematic analysis to identify key themes. The findings revealed that GenAI generally enhances students’ motivation, reduces anxiety and stress, and fosters an emotionally supportive learning environment. However, challenges related to cultural context and technical issues were also identified. The study highlights the pivotal role of instructors in shaping students’ experiences with GenAI and underscores the need for ongoing support and professional development. It also demonstrates the importance of cultural sensitivity, technological infrastructure and balance. The study is valuable for those who aim to harness GenAI while preserving the irreplaceable human elements of teaching. It contributes to the growing body of knowledge on integrating AI in language learning.
{"title":"Enhancing the emotional aspects of language education through generative artificial intelligence (GenAI): A qualitative investigation","authors":"Lucas Kohnke , Benjamin Luke Moorhouse","doi":"10.1016/j.chb.2025.108600","DOIUrl":"10.1016/j.chb.2025.108600","url":null,"abstract":"<div><div>This qualitative study investigates the impact of generative artificial intelligence (GenAI) on the emotional engagement, motivation and well-being of first-year university students in Hong Kong. We conducted semi-structured interviews with 21 students and three instructors to explore their perceptions of how GenAI influences the affective dimensions of language learning. The data were analyzed using manual coding and inductive thematic analysis to identify key themes. The findings revealed that GenAI generally enhances students’ motivation, reduces anxiety and stress, and fosters an emotionally supportive learning environment. However, challenges related to cultural context and technical issues were also identified. The study highlights the pivotal role of instructors in shaping students’ experiences with GenAI and underscores the need for ongoing support and professional development. It also demonstrates the importance of cultural sensitivity, technological infrastructure and balance. The study is valuable for those who aim to harness GenAI while preserving the irreplaceable human elements of teaching. It contributes to the growing body of knowledge on integrating AI in language learning.</div></div>","PeriodicalId":48471,"journal":{"name":"Computers in Human Behavior","volume":"167 ","pages":"Article 108600"},"PeriodicalIF":9.0,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143487116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-14DOI: 10.1016/j.chb.2025.108603
Eran Bar , Marcela Radunz , Christina R. Galanis , Blake Quinney , Tracey D. Wade , Daniel L. King
Background
There has been a global trend to ban mobile phones in schools, with the aim of reducing distraction, improving focus on learning, and increasing prosocial behaviour. Survey evidence suggests tentatively that bans may increase academic performance and reduce bullying. However, an understudied but important aspect of understanding the impact of phone bans is students' personal views on, and experiences of, these policies. To address this gap, this study investigated students' perspectives on the benefits and challenges related to phone bans in schools.
Methods
This study was a preregistered policy experiment conducted across five secondary schools in South Australia. A total of 1549 students provided 7188 responses to open-ended survey questions.
Results
Thematic analysis of 69,589 words identified five categories with 16 themes. In terms of undesired effects of the bans, students reported: (i) feeling less independent and trustworthy, (ii) losing access to digital learning tools, and (iii) difficulties in regulating emotional distress without phones. However, students also reported benefits in areas of: (i) face-to-face social interaction, (ii) personal health and safety, and (ii) classroom engagement. Some students expressed a desire for education on responsible phone use, as well as approaches to managing digital devices with flexibility and personal agency, as an alternative to banning phones outright.
Conclusions
These findings underscore the urgent need to monitor and address students’ overreliance on phones for socialising, emotion regulation, and coping with mental health issues. Students contribute valuable insights to inform policies and guidelines at the nexus of digital technology and student learning and well-being.
{"title":"Student perspectives on banning mobile phones in South Australian secondary schools: A large-scale qualitative analysis","authors":"Eran Bar , Marcela Radunz , Christina R. Galanis , Blake Quinney , Tracey D. Wade , Daniel L. King","doi":"10.1016/j.chb.2025.108603","DOIUrl":"10.1016/j.chb.2025.108603","url":null,"abstract":"<div><h3>Background</h3><div>There has been a global trend to ban mobile phones in schools, with the aim of reducing distraction, improving focus on learning, and increasing prosocial behaviour. Survey evidence suggests tentatively that bans may increase academic performance and reduce bullying. However, an understudied but important aspect of understanding the impact of phone bans is students' personal views on, and experiences of, these policies. To address this gap, this study investigated students' perspectives on the benefits and challenges related to phone bans in schools.</div></div><div><h3>Methods</h3><div>This study was a preregistered policy experiment conducted across five secondary schools in South Australia. A total of 1549 students provided 7188 responses to open-ended survey questions.</div></div><div><h3>Results</h3><div>Thematic analysis of 69,589 words identified five categories with 16 themes. In terms of undesired effects of the bans, students reported: (i) feeling less independent and trustworthy, (ii) losing access to digital learning tools, and (iii) difficulties in regulating emotional distress without phones. However, students also reported benefits in areas of: (i) face-to-face social interaction, (ii) personal health and safety, and (ii) classroom engagement. Some students expressed a desire for education on responsible phone use, as well as approaches to managing digital devices with flexibility and personal agency, as an alternative to banning phones outright.</div></div><div><h3>Conclusions</h3><div>These findings underscore the urgent need to monitor and address students’ overreliance on phones for socialising, emotion regulation, and coping with mental health issues. Students contribute valuable insights to inform policies and guidelines at the nexus of digital technology and student learning and well-being.</div></div>","PeriodicalId":48471,"journal":{"name":"Computers in Human Behavior","volume":"167 ","pages":"Article 108603"},"PeriodicalIF":9.0,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143437035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-13DOI: 10.1016/j.chb.2025.108601
Ahmad Al-Qerem , Ali Mohd Ali , Ahmad Nabot , Issam Jebreen , Mohammad Alauthman , Someah Alangari , Ammar Almomani , Vinay Chamola , Amjad Aldweesh
The proliferation of Internet of Things (IoT) devices has led to edge-cloud computing paradigms where resource-constrained edge devices connect to cloud servers. However, traditional concurrency control methods like two-phase locking (2 PL) and optimistic concurrency control (OCC) are inefficient in these heterogeneous environments. This paper presents adaptive transaction management protocols for edge-cloud systems. We propose EC-Lock which transitions between non-blocking and blocking phases, and EC-OCC which distinguishes edge and cloud transactions during timestamp validation. These hybrid techniques reduce unnecessary blocking and restarts. Simulation studies demonstrate that EC-Lock and EC-OCC provide substantial performance gains over traditional protocols under diverse workloads. By balancing consistency and efficiency, the proposed protocols enable scalable edge-cloud transaction processing. Our results show EC-Lock and EC-OCC better utilize scarce edge resources while minimizing cloud transaction impact. This work delivers innovative adaptive concurrency control optimized for emerging IoT-based edge-cloud computing architectures.
{"title":"Balancing consistency and performance in edge-cloud transaction management","authors":"Ahmad Al-Qerem , Ali Mohd Ali , Ahmad Nabot , Issam Jebreen , Mohammad Alauthman , Someah Alangari , Ammar Almomani , Vinay Chamola , Amjad Aldweesh","doi":"10.1016/j.chb.2025.108601","DOIUrl":"10.1016/j.chb.2025.108601","url":null,"abstract":"<div><div>The proliferation of Internet of Things (IoT) devices has led to edge-cloud computing paradigms where resource-constrained edge devices connect to cloud servers. However, traditional concurrency control methods like two-phase locking (2 PL) and optimistic concurrency control (OCC) are inefficient in these heterogeneous environments. This paper presents adaptive transaction management protocols for edge-cloud systems. We propose EC-Lock which transitions between non-blocking and blocking phases, and EC-OCC which distinguishes edge and cloud transactions during timestamp validation. These hybrid techniques reduce unnecessary blocking and restarts. Simulation studies demonstrate that EC-Lock and EC-OCC provide substantial performance gains over traditional protocols under diverse workloads. By balancing consistency and efficiency, the proposed protocols enable scalable edge-cloud transaction processing. Our results show EC-Lock and EC-OCC better utilize scarce edge resources while minimizing cloud transaction impact. This work delivers innovative adaptive concurrency control optimized for emerging IoT-based edge-cloud computing architectures.</div></div>","PeriodicalId":48471,"journal":{"name":"Computers in Human Behavior","volume":"167 ","pages":"Article 108601"},"PeriodicalIF":9.0,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143437036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-12DOI: 10.1016/j.chb.2025.108606
Cuicui Wang , Liangting Ni , Bei Yuan , Momo Tang
Artificial intelligence (AI) has been widely used across diverse service sectors, including tourism and hospitality. However, service failures with AI engagement are inevitable. How to provide service recovery for AI service failures to increase consumer reuse intention is an important issue. The current study investigated AI service recovery, examining the remediation effect of AI warm response on service failure. Through four experiments, the results suggested that AI high warm response increased consumer reuse intention in service recovery, and social presence mediated this relationship between AI warm response and consumer reuse intention. Moreover, the positive recovery effect of AI warm response was significant only under conditions of low service failure severity. Additionally, the compensatory effect of human intervention in service recovery was significant exclusively when following an AI low warm response, rather than a high warm response. The findings not only extend the knowledge regarding the reuse of AI agents after service failure but also reveal the potential of AI warm response in service recovery strategies. Furthermore, they also encourage AI service providers to elevate the warmth level of AI agents’ response to recover general service failure and minimize the reliance on human intervention.
{"title":"Exploring the effect of AI warm response on consumer reuse intention in service failure","authors":"Cuicui Wang , Liangting Ni , Bei Yuan , Momo Tang","doi":"10.1016/j.chb.2025.108606","DOIUrl":"10.1016/j.chb.2025.108606","url":null,"abstract":"<div><div>Artificial intelligence (AI) has been widely used across diverse service sectors, including tourism and hospitality. However, service failures with AI engagement are inevitable. How to provide service recovery for AI service failures to increase consumer reuse intention is an important issue. The current study investigated AI service recovery, examining the remediation effect of AI warm response on service failure. Through four experiments, the results suggested that AI high warm response increased consumer reuse intention in service recovery, and social presence mediated this relationship between AI warm response and consumer reuse intention. Moreover, the positive recovery effect of AI warm response was significant only under conditions of low service failure severity. Additionally, the compensatory effect of human intervention in service recovery was significant exclusively when following an AI low warm response, rather than a high warm response. The findings not only extend the knowledge regarding the reuse of AI agents after service failure but also reveal the potential of AI warm response in service recovery strategies. Furthermore, they also encourage AI service providers to elevate the warmth level of AI agents’ response to recover general service failure and minimize the reliance on human intervention.</div></div>","PeriodicalId":48471,"journal":{"name":"Computers in Human Behavior","volume":"166 ","pages":"Article 108606"},"PeriodicalIF":9.0,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143428030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-12DOI: 10.1016/j.chb.2025.108607
Deming Li , Nie Tang , Meredith Chandler , Emilio Nanni
Research background
Knowledge tracking in educational data mining has become increasingly important for identifying students' knowledge gaps and enhancing individualized instruction. Traditional exercise recommendation algorithms often overlook students' forgetting behaviour, hindering effective learning.
Purpose
This study aims to develop a novel approach to integrating the law of forgetting into a deep learning-based knowledge-tracking model, improving exercise recommendations and effectively addressing students' learning gaps.
Methods
The proposed knowledge probability prediction model incorporates the forgetting curve theory alongside a dynamic key-value memory mechanism for tracking students' knowledge mastery levels. This model continuously adapts to students' interactions, allowing personalized exercise recommendations considering mastered and forgotten knowledge points.
Results
The proposed model was evaluated using ASSISTment 2009, Statics 2011, and DouDouYun datasets. The results indicate that our approach significantly outperforms traditional recommendation algorithms regarding novelty and concept coverage, effectively accommodating students' forgetting behaviour.
Conclusion
Integrating the forgetting law into knowledge-tracking systems leads to more effective and personalized exercise recommendations, ultimately facilitating improved learning outcomes. This approach enhances students' acquisition of new knowledge and more efficiently addresses their existing knowledge gaps.
{"title":"An optimal approach for predicting cognitive performance in education based on deep learning","authors":"Deming Li , Nie Tang , Meredith Chandler , Emilio Nanni","doi":"10.1016/j.chb.2025.108607","DOIUrl":"10.1016/j.chb.2025.108607","url":null,"abstract":"<div><h3>Research background</h3><div>Knowledge tracking in educational data mining has become increasingly important for identifying students' knowledge gaps and enhancing individualized instruction. Traditional exercise recommendation algorithms often overlook students' forgetting behaviour, hindering effective learning.</div></div><div><h3>Purpose</h3><div>This study aims to develop a novel approach to integrating the law of forgetting into a deep learning-based knowledge-tracking model, improving exercise recommendations and effectively addressing students' learning gaps.</div></div><div><h3>Methods</h3><div>The proposed knowledge probability prediction model incorporates the forgetting curve theory alongside a dynamic key-value memory mechanism for tracking students' knowledge mastery levels. This model continuously adapts to students' interactions, allowing personalized exercise recommendations considering mastered and forgotten knowledge points.</div></div><div><h3>Results</h3><div>The proposed model was evaluated using ASSISTment 2009, Statics 2011, and DouDouYun datasets. The results indicate that our approach significantly outperforms traditional recommendation algorithms regarding novelty and concept coverage, effectively accommodating students' forgetting behaviour.</div></div><div><h3>Conclusion</h3><div>Integrating the forgetting law into knowledge-tracking systems leads to more effective and personalized exercise recommendations, ultimately facilitating improved learning outcomes. This approach enhances students' acquisition of new knowledge and more efficiently addresses their existing knowledge gaps.</div></div>","PeriodicalId":48471,"journal":{"name":"Computers in Human Behavior","volume":"167 ","pages":"Article 108607"},"PeriodicalIF":9.0,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143529665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-12DOI: 10.1016/j.chb.2025.108605
Allen S. Brown, Christopher R. Dishop, Andrew Kuznetsov, Ping-Ya Chao, Anita Williams Woolley
Contemporary managemenet practices are often designed with the needs of knowledge-based workers in mind, but an increasingly pressing challenge today is how to manage and effectively handle non-routine work. This paper revisits the job characteristics model through the lens of self-determination theory, specifically in the context of algorithmic performance management. Non-routine work is inherently unpredictable, and individuals often struggle with prolonged uncertainty. However, automated interventions that help individuals make sense of their work in uncertain conditions may help overcome the challenges of non-routine work and increase worker performance. In a randomized, controlled experiment delivered in a novel online task environment, we find that automated, real-time feedback increases the perceived trustworthiness of an algorithmic performance rating under conditions of high task uncertainty. Our research demonstrates the potential of artificial intelligence to automate certain tasks in non-routine work environments that positively augment human work performance while simultaneously enhancing trust in these automated work systems.
{"title":"Beyond efficiency: Trust, AI, and surprise in knowledge work environments","authors":"Allen S. Brown, Christopher R. Dishop, Andrew Kuznetsov, Ping-Ya Chao, Anita Williams Woolley","doi":"10.1016/j.chb.2025.108605","DOIUrl":"10.1016/j.chb.2025.108605","url":null,"abstract":"<div><div>Contemporary managemenet practices are often designed with the needs of knowledge-based workers in mind, but an increasingly pressing challenge today is how to manage and effectively handle non-routine work. This paper revisits the job characteristics model through the lens of self-determination theory, specifically in the context of algorithmic performance management. Non-routine work is inherently unpredictable, and individuals often struggle with prolonged uncertainty. However, automated interventions that help individuals make sense of their work in uncertain conditions may help overcome the challenges of non-routine work and increase worker performance. In a randomized, controlled experiment delivered in a novel online task environment, we find that automated, real-time feedback increases the perceived trustworthiness of an algorithmic performance rating under conditions of high task uncertainty. Our research demonstrates the potential of artificial intelligence to automate certain tasks in non-routine work environments that positively augment human work performance while simultaneously enhancing trust in these automated work systems.</div></div>","PeriodicalId":48471,"journal":{"name":"Computers in Human Behavior","volume":"167 ","pages":"Article 108605"},"PeriodicalIF":9.0,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143452958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-11DOI: 10.1016/j.chb.2025.108604
Chia-Hui Hou , Yi-Hui Liu
With the rapid advancement of artificial intelligence and information technology, big data analytics have become increasingly applied in healthcare and older adult care. However, in Taiwan, awareness of dementia remains limited and participation in dementia-related programs is low. As the older adult population grows and long-term care budgets become strained, enhancing the awareness of dementia in communities is vital. This study developed a "Dementia Awareness Prediction Model using machine learning to predict the need for dementia education among adult learners, thereby improving resource allocation efficiency. A total of 229 survey responses were collected, and three machine-learning algorithms—Decision Trees, Decision Forests, and Logistic Regression—were used to build predictive models. The results show that all three models effectively predict dementia awareness, with Decision Forests and Logistic Regression demonstrating superior accuracy. Using a reduced set of attributes, the models achieved an average accuracy of over 95.90%, indicating high predictive performance. These findings provide valuable insights for enhancing dementia awareness and optimizing resource distribution in both public and private sectors.
{"title":"Using artificial intelligence for predictive analysis of dementia awareness among community adult learners and evaluation of dementia-friendliness in community environments","authors":"Chia-Hui Hou , Yi-Hui Liu","doi":"10.1016/j.chb.2025.108604","DOIUrl":"10.1016/j.chb.2025.108604","url":null,"abstract":"<div><div>With the rapid advancement of artificial intelligence and information technology, big data analytics have become increasingly applied in healthcare and older adult care. However, in Taiwan, awareness of dementia remains limited and participation in dementia-related programs is low. As the older adult population grows and long-term care budgets become strained, enhancing the awareness of dementia in communities is vital. This study developed a \"Dementia Awareness Prediction Model using machine learning to predict the need for dementia education among adult learners, thereby improving resource allocation efficiency. A total of 229 survey responses were collected, and three machine-learning algorithms—Decision Trees, Decision Forests, and Logistic Regression—were used to build predictive models. The results show that all three models effectively predict dementia awareness, with Decision Forests and Logistic Regression demonstrating superior accuracy. Using a reduced set of attributes, the models achieved an average accuracy of over 95.90%, indicating high predictive performance. These findings provide valuable insights for enhancing dementia awareness and optimizing resource distribution in both public and private sectors.</div></div>","PeriodicalId":48471,"journal":{"name":"Computers in Human Behavior","volume":"167 ","pages":"Article 108604"},"PeriodicalIF":9.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143452957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-07DOI: 10.1016/j.chb.2025.108597
Michael Wadsley, Niklas Ihssen
Checking social networking site (SNS) accounts periodically has become a quintessential daily habit for billions of people. The present study tracked the psychophysiological impact of brief periods of SNS use and subsequent use cessation, designed to mimic natural usage patterns. It specifically aimed to identify markers of problematic/compulsive use during these periods in 54 Instagram users varying in problematic SNS behaviors. Heart rate, galvanic skin response (GSR) and affective/motivational ratings were recorded across three 15-min phases consisting of a baseline reading task, Instagram exposure and Instagram cessation phase. Participants reported increased stress, anxiety and SNS cravings following Instagram cessation. Instagram exposure resulted in a large decrease in heart rate and increase in GSR compared to baseline, indicating increased appetitive arousal and a state of deep attentional engagement. Instagram cessation resulted in an increase in heart rate and GSR compared to exposure, indicating increased aversive (stress-related) arousal. Importantly, changes in physiology were not associated with problematic use symptoms. Our findings indicate that brief engagement with SNSs elicits reward-driven arousal and attentional immersion while ending such states can induce aversive physiological and subjective stress in both problematic and regular SNS users.
{"title":"The psychophysiology of Instagram – Brief bouts of Instagram use elicit appetitive arousal and attentional immersion followed by aversive arousal when use is stopped","authors":"Michael Wadsley, Niklas Ihssen","doi":"10.1016/j.chb.2025.108597","DOIUrl":"10.1016/j.chb.2025.108597","url":null,"abstract":"<div><div>Checking social networking site (SNS) accounts periodically has become a quintessential daily habit for billions of people. The present study tracked the psychophysiological impact of brief periods of SNS use and subsequent use cessation, designed to mimic natural usage patterns. It specifically aimed to identify markers of problematic/compulsive use during these periods in 54 Instagram users varying in problematic SNS behaviors. Heart rate, galvanic skin response (GSR) and affective/motivational ratings were recorded across three 15-min phases consisting of a baseline reading task, Instagram exposure and Instagram cessation phase. Participants reported increased stress, anxiety and SNS cravings following Instagram cessation. Instagram exposure resulted in a large decrease in heart rate and increase in GSR compared to baseline, indicating increased appetitive arousal and a state of deep attentional engagement. Instagram cessation resulted in an increase in heart rate and GSR compared to exposure, indicating increased aversive (stress-related) arousal. Importantly, changes in physiology were <em>not</em> associated with problematic use symptoms. Our findings indicate that brief engagement with SNSs elicits reward-driven arousal and attentional immersion while ending such states can induce aversive physiological and subjective stress in both problematic and regular SNS users.</div></div>","PeriodicalId":48471,"journal":{"name":"Computers in Human Behavior","volume":"166 ","pages":"Article 108597"},"PeriodicalIF":9.0,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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