Pub Date : 2026-01-31DOI: 10.1016/j.chbr.2026.100956
Tuan Hai Nguyen
This study aims to explore the psychological mechanisms that influence brand loyalty behavior in the online food delivery (OFD) sector, with a focus on the roles of brand satisfaction and brand image in shaping brand loyalty. Based on the Stimulus–Organism–Response (S–O–R) framework, the research analyzes how factors such as e-service quality and perceived food quality affect internal psychological responses, thereby leading to brand loyalty. Data was collected in a single phase, with a total of 335 OFD users in Vietnam who had used the service within the past three months. The structural equation modeling (SEM) method was used to test the research hypotheses. The results show that both e-service quality and perceived food quality significantly enhance brand satisfaction and brand image, which subsequently strengthen brand loyalty. Practically, the study provides insights for OFD platforms to improve operational performance, ensure consistent food quality, and build a trustworthy and distinctive brand image. Theoretically, the research applies and empirically supports the S–O–R framework to digital service contexts in developing economies, highlighting the influence of perceptual and psychological factors on non-contact brand loyalty behavior.
{"title":"Click, eat, repeat: Understanding brand loyalty in Vietnam's online food delivery sector","authors":"Tuan Hai Nguyen","doi":"10.1016/j.chbr.2026.100956","DOIUrl":"10.1016/j.chbr.2026.100956","url":null,"abstract":"<div><div>This study aims to explore the psychological mechanisms that influence brand loyalty behavior in the online food delivery (OFD) sector, with a focus on the roles of brand satisfaction and brand image in shaping brand loyalty. Based on the Stimulus–Organism–Response (S–O–R) framework, the research analyzes how factors such as e-service quality and perceived food quality affect internal psychological responses, thereby leading to brand loyalty. Data was collected in a single phase, with a total of 335 OFD users in Vietnam who had used the service within the past three months. The structural equation modeling (SEM) method was used to test the research hypotheses. The results show that both e-service quality and perceived food quality significantly enhance brand satisfaction and brand image, which subsequently strengthen brand loyalty. Practically, the study provides insights for OFD platforms to improve operational performance, ensure consistent food quality, and build a trustworthy and distinctive brand image. Theoretically, the research applies and empirically supports the S–O–R framework to digital service contexts in developing economies, highlighting the influence of perceptual and psychological factors on non-contact brand loyalty behavior.</div></div>","PeriodicalId":72681,"journal":{"name":"Computers in human behavior reports","volume":"21 ","pages":"Article 100956"},"PeriodicalIF":5.8,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-29DOI: 10.1016/j.chbr.2026.100955
Ivy S. Huang , Johan F. Hoorn
Depression is prevalent among young adults, many of whom encounter obstacles to accessing traditional interventions. This study investigated whether the modality of robotic delivery influences outcomes when administering identical psychological health intervention content. We compared three modalities, a text-based chatbot, an audio bot, and a video telepresence robot, each delivering the same imagery-enhanced interpretation bias modification (eiIBM) intervention. Forty-nine young adults with depressive symptoms (Mage = 22.71, SD = 3.30) were randomly assigned to one of the three robot conditions and completed six eiIBM sessions over two weeks. An additional control group (n = 18) received no intervention. User experience was assessed using the I-PEFiC framework, and measures of depression severity and interpretation biases were collected. All three robot modalities yielded comparable outcomes, with substantial reductions in depression symptoms (Hedges' g = 1.11–1.33) and approximately 40 % decreases in negative interpretation biases. Bayesian analyses focusing on the modality provided substantial evidence for the absence of differences between modalities regarding intervention outcomes (BFincl < 0.026). Notably, user experience emerged as a significant predictor of intervention efficacy: participants who reported positive user experiences exhibited markedly greater reductions in interpretation bias (Cohen's d > 3.0) regardless of the robot modality. These findings suggest that, when intervention content is standardized, increasing the sensory richness of the delivery modality does not enhance intervention outcomes. For structured cognitive interventions such as eiIBM, the fidelity of content delivery and the quality of user experience are more critical determinants of effectiveness than the sensory richness.
{"title":"Advanced robot interfaces are unnecessary for effective psychological health interventions","authors":"Ivy S. Huang , Johan F. Hoorn","doi":"10.1016/j.chbr.2026.100955","DOIUrl":"10.1016/j.chbr.2026.100955","url":null,"abstract":"<div><div>Depression is prevalent among young adults, many of whom encounter obstacles to accessing traditional interventions. This study investigated whether the modality of robotic delivery influences outcomes when administering identical psychological health intervention content. We compared three modalities, a text-based chatbot, an audio bot, and a video telepresence robot, each delivering the same imagery-enhanced interpretation bias modification (eiIBM) intervention. Forty-nine young adults with depressive symptoms (<em>M</em><sub>age</sub> = 22.71, <em>SD</em> = 3.30) were randomly assigned to one of the three robot conditions and completed six eiIBM sessions over two weeks. An additional control group (<em>n</em> = 18) received no intervention. User experience was assessed using the I-PEFiC framework, and measures of depression severity and interpretation biases were collected. All three robot modalities yielded comparable outcomes, with substantial reductions in depression symptoms (Hedges' <em>g</em> = 1.11–1.33) and approximately 40 % decreases in negative interpretation biases. Bayesian analyses focusing on the modality provided substantial evidence for the absence of differences between modalities regarding intervention outcomes (BF<sub>incl</sub> < 0.026). Notably, user experience emerged as a significant predictor of intervention efficacy: participants who reported positive user experiences exhibited markedly greater reductions in interpretation bias (Cohen's <em>d</em> > 3.0) regardless of the robot modality. These findings suggest that, when intervention content is standardized, increasing the sensory richness of the delivery modality does not enhance intervention outcomes. For structured cognitive interventions such as eiIBM, the fidelity of content delivery and the quality of user experience are more critical determinants of effectiveness than the sensory richness.</div></div>","PeriodicalId":72681,"journal":{"name":"Computers in human behavior reports","volume":"21 ","pages":"Article 100955"},"PeriodicalIF":5.8,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-29DOI: 10.1016/j.chbr.2026.100947
Mohammad Mulayh Alshammari, Yaser Hasan Al-Mamary
Many assume personal attitudes are the main driver of information security policy (ISP) compliance, but this also rests on stewardship and norms inside organizations. With a focus on Saudi Arabia, a high power-distance, collectivist context, we integrate Protection Motivation Theory and the Theory of Planned Behavior with psychological ownership, trust in management, and social influence, we test a direct path from response efficacy to behavior. We surveyed 628 employees across Saudi organizations and analyzed the data with PLS-SEM (SmartPLS 4). Results show that intention is the strongest predictor of compliance behavior. Trust, psychological ownership, social influence, security awareness, and self-efficacy raise intention. Response efficacy, security awareness, and more weakly, perceived severity, shape attitude; perceived vulnerability does not. Response efficacy also increases compliance directly. Mediation tests indicate that intention carries most effects from stewardship, efficacy, awareness, and norms to behavior. These results fit a context where norms and stewardship weigh more than personal attitudes in forming intentions. The study offers theoretical and practical implications: build ownership and trust, leverage peer norms, as well as strengthen awareness and efficacy to raise compliance.
{"title":"Non-technical determinants of information security policy compliance: An integrated PMT-TPB model with psychological ownership, trust, and social influence","authors":"Mohammad Mulayh Alshammari, Yaser Hasan Al-Mamary","doi":"10.1016/j.chbr.2026.100947","DOIUrl":"10.1016/j.chbr.2026.100947","url":null,"abstract":"<div><div>Many assume personal attitudes are the main driver of information security policy (ISP) compliance, but this also rests on stewardship and norms inside organizations. With a focus on Saudi Arabia, a high power-distance, collectivist context, we integrate Protection Motivation Theory and the Theory of Planned Behavior with psychological ownership, trust in management, and social influence, we test a direct path from response efficacy to behavior. We surveyed 628 employees across Saudi organizations and analyzed the data with PLS-SEM (SmartPLS 4). Results show that intention is the strongest predictor of compliance behavior. Trust, psychological ownership, social influence, security awareness, and self-efficacy raise intention. Response efficacy, security awareness, and more weakly, perceived severity, shape attitude; perceived vulnerability does not. Response efficacy also increases compliance directly. Mediation tests indicate that intention carries most effects from stewardship, efficacy, awareness, and norms to behavior. These results fit a context where norms and stewardship weigh more than personal attitudes in forming intentions. The study offers theoretical and practical implications: build ownership and trust, leverage peer norms, as well as strengthen awareness and efficacy to raise compliance.</div></div>","PeriodicalId":72681,"journal":{"name":"Computers in human behavior reports","volume":"21 ","pages":"Article 100947"},"PeriodicalIF":5.8,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-25DOI: 10.1016/j.chbr.2026.100946
Scott Pelham, Jo Jung, Christopher Kueh
Implementing user experience (UX) methodologies within government settings is both possible and powerful – but fraught with systemic and cultural complexities. This report presents insights from the redesign of a government intranet for a prominent Australian emergency services organisation, using a human-centred design approach grounded in the Double Diamond framework. The research reveals a series of challenges: bureaucracy, opinion bias, overwhelming content volume with minimal organisational value, a governance paradox where design rules are desired but selectively applied, a disconnect in engagement from key stakeholders, and the impact of a design by committee approach. While these challenges were significant, they did not hinder progress; rather, they paved the way for valuable opportunities for UX practitioners. The study demonstrated UX methods can be used to enhance organisational self-awareness, governance can be restructured to support – not hinder – usability, and sustained stakeholder engagement can be achievable with the right methods and framing. The findings also highlight a broader lesson: although government environments often present greater resistance to change, user experience practices can serve as powerful catalysts when thoughtfully aligned with institutional constraints.
{"title":"User experience (UX) meets bureaucracy: Lessons from a government intranet prototype","authors":"Scott Pelham, Jo Jung, Christopher Kueh","doi":"10.1016/j.chbr.2026.100946","DOIUrl":"10.1016/j.chbr.2026.100946","url":null,"abstract":"<div><div>Implementing user experience (UX) methodologies within government settings is both possible and powerful – but fraught with systemic and cultural complexities. This report presents insights from the redesign of a government intranet for a prominent Australian emergency services organisation, using a human-centred design approach grounded in the Double Diamond framework. The research reveals a series of challenges: bureaucracy, opinion bias, overwhelming content volume with minimal organisational value, a governance paradox where design rules are desired but selectively applied, a disconnect in engagement from key stakeholders, and the impact of a design by committee approach. While these challenges were significant, they did not hinder progress; rather, they paved the way for valuable opportunities for UX practitioners. The study demonstrated UX methods can be used to enhance organisational self-awareness, governance can be restructured to support – not hinder – usability, and sustained stakeholder engagement can be achievable with the right methods and framing. The findings also highlight a broader lesson: although government environments often present greater resistance to change, user experience practices can serve as powerful catalysts when thoughtfully aligned with institutional constraints.</div></div>","PeriodicalId":72681,"journal":{"name":"Computers in human behavior reports","volume":"21 ","pages":"Article 100946"},"PeriodicalIF":5.8,"publicationDate":"2026-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Virtual reality (VR) offers new opportunities to promote active behaviors by enhancing engagement and allowing controlled modifications of urban environments. This study investigates whether virtual environments (VEs) can evoke affective responses comparable with real environments (REs), both psychologically and physiologically, by using an immersive VE combined with a walking simulator that replicates walking motion. Forty-nine healthy adults, Luxembourg residents or cross-border commuters, aged 18–65, including students, university staff, and the general public, walked two contrasting street segments, walking-friendly and car-friendly, in both RE and VE in a crossover design. Affective responses were assessed through questions on aesthetics, safety, enjoyment, comfort, relaxation, momentary stress, and real-time physiological data collected using E4 wristband.
Significant differences emerged between the RE and VE across all affective measurements, except for nonspecific skin conductance responses, with the RE consistently eliciting more positive affective responses. Nevertheless, similar affective trends were observed in both the RE and VE across the two segments. Moreover, environmental characteristics significantly influenced affective responses in both the RE and VE, with the walking-friendly segment yielding more positive affective ratings than the car-friendly one. The interactions between environment type (RE vs. VE) and segment type (car-friendly vs. walking-friendly) were not significant for most measurements, indicating that the effect of environment type on affective responses remained consistent across segments. These findings emphasize that VEs can mimic the overall patterns of affective responses observed in REs. This research highlights VR's potential in planning healthier cities, offering insights into its benefits and limitations for future research.
虚拟现实（VR）通过增强参与度和允许对城市环境进行可控修改，为促进积极行为提供了新的机会。本研究通过使用身临其境的虚拟环境与复制步行运动的步行模拟器相结合，研究虚拟环境（VEs）是否能在心理和生理上唤起与真实环境（REs）相当的情感反应。49名健康成年人，卢森堡居民或跨境通勤者，年龄在18-65岁之间，包括学生、大学工作人员和一般公众，在交叉设计中行走在两个不同的街道上，步行友好和汽车友好，在RE和VE。通过美观、安全、享受、舒适、放松、瞬间压力和使用E4腕带收集的实时生理数据等问题来评估情感反应。除了非特异性皮肤电导反应外，RE和VE在所有情感测量中都存在显著差异，RE始终引起更积极的情感反应。然而，在两个部分中，在RE和VE中观察到类似的情感趋势。此外，环境特征显著影响了RE和VE的情感反应，步行友好的情感反应比汽车友好的情感反应产生更积极的情感评价。环境类型（RE vs. VE）和区段类型（car-friendly vs. walking-friendly）之间的交互作用在大多数测量中不显著，表明环境类型对情感反应的影响在区段之间保持一致。这些发现强调，虚拟现实可以模拟在res中观察到的情感反应的整体模式。这项研究强调了虚拟现实在规划更健康城市方面的潜力，为未来的研究提供了对其优点和局限性的见解。
{"title":"Walking experience in real and virtual environments: A comparative study","authors":"Marzieh Ghanbari , Martin Dijst , Reza Aghanejad , Sébastien Claramunt , Camille Perchoux","doi":"10.1016/j.chbr.2026.100950","DOIUrl":"10.1016/j.chbr.2026.100950","url":null,"abstract":"<div><div>Virtual reality (VR) offers new opportunities to promote active behaviors by enhancing engagement and allowing controlled modifications of urban environments. This study investigates whether virtual environments (VEs) can evoke affective responses comparable with real environments (REs), both psychologically and physiologically, by using an immersive VE combined with a walking simulator that replicates walking motion. Forty-nine healthy adults, Luxembourg residents or cross-border commuters, aged 18–65, including students, university staff, and the general public, walked two contrasting street segments, walking-friendly and car-friendly, in both RE and VE in a crossover design. Affective responses were assessed through questions on aesthetics, safety, enjoyment, comfort, relaxation, momentary stress, and real-time physiological data collected using E4 wristband.</div><div>Significant differences emerged between the RE and VE across all affective measurements, except for nonspecific skin conductance responses, with the RE consistently eliciting more positive affective responses. Nevertheless, similar affective trends were observed in both the RE and VE across the two segments. Moreover, environmental characteristics significantly influenced affective responses in both the RE and VE, with the walking-friendly segment yielding more positive affective ratings than the car-friendly one. The interactions between environment type (RE vs. VE) and segment type (car-friendly vs. walking-friendly) were not significant for most measurements, indicating that the effect of environment type on affective responses remained consistent across segments. These findings emphasize that VEs can mimic the overall patterns of affective responses observed in REs. This research highlights VR's potential in planning healthier cities, offering insights into its benefits and limitations for future research.</div></div>","PeriodicalId":72681,"journal":{"name":"Computers in human behavior reports","volume":"21 ","pages":"Article 100950"},"PeriodicalIF":5.8,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-22DOI: 10.1016/j.chbr.2026.100939
Elena Ball, Lara Warmelink, Sophie Nightingale, Trevor Crawford
Humans struggle to notice changes in visual scenes, a limitation called change blindness. Older adults show heightened susceptibility to change blindness, which can affect both decision making and safety. Despite increasing reliance on technology in our environments, change blindness in human-computer interactions is poorly understood. Here we used two experiments to explore change blindness in quick response (QR) codes. Experiment 1 recruited 40 participants who completed a one-shot change detection task incorporating modifications to QR code images. Overall, 65.1% of modifications were identified, and detection performance improved with larger changes (95.9%) compared to smaller changes (34.4%). Experiment 2 recruited 60 younger and 60 older adults to complete the same task, while their eye movements were tracked to measure visual scanning behaviour. We examined whether modifications to QR codes could be detected, and whether detection performance was influenced by age, age-related cognitive factors such as visual working memory, visual scanning behaviour and prior QR-code experience. In total, 64.8% of modified QR codes were identified, with greater detection for larger changes (92.6%) than smaller changes (37.0%). Overall, QR-code experience, visual scanning behaviour, and age significantly predicted detection performance, as older adults (61.5%) detected fewer modified QR codes than younger adults (67.9%). Although larger visual working-memory capacity was a significant predictor of better detection performance when considered in isolation, its effect was no longer significant once QR-code experience, visual scanning, and age were added to the model. This implies change-detection performance is shaped directly by QR-code experience, age, and visual scanning.
{"title":"Spot the difference: Investigating the effects of ageing on change blindness in QR codes with eye tracking","authors":"Elena Ball, Lara Warmelink, Sophie Nightingale, Trevor Crawford","doi":"10.1016/j.chbr.2026.100939","DOIUrl":"10.1016/j.chbr.2026.100939","url":null,"abstract":"<div><div>Humans struggle to notice changes in visual scenes, a limitation called change blindness. Older adults show heightened susceptibility to change blindness, which can affect both decision making and safety. Despite increasing reliance on technology in our environments, change blindness in human-computer interactions is poorly understood. Here we used two experiments to explore change blindness in quick response (QR) codes. Experiment 1 recruited 40 participants who completed a one-shot change detection task incorporating modifications to QR code images. Overall, 65.1% of modifications were identified, and detection performance improved with larger changes (95.9%) compared to smaller changes (34.4%). Experiment 2 recruited 60 younger and 60 older adults to complete the same task, while their eye movements were tracked to measure visual scanning behaviour. We examined whether modifications to QR codes could be detected, and whether detection performance was influenced by age, age-related cognitive factors such as visual working memory, visual scanning behaviour and prior QR-code experience. In total, 64.8% of modified QR codes were identified, with greater detection for larger changes (92.6%) than smaller changes (37.0%). Overall, QR-code experience, visual scanning behaviour, and age significantly predicted detection performance, as older adults (61.5%) detected fewer modified QR codes than younger adults (67.9%). Although larger visual working-memory capacity was a significant predictor of better detection performance when considered in isolation, its effect was no longer significant once QR-code experience, visual scanning, and age were added to the model. This implies change-detection performance is shaped directly by QR-code experience, age, and visual scanning.</div></div>","PeriodicalId":72681,"journal":{"name":"Computers in human behavior reports","volume":"21 ","pages":"Article 100939"},"PeriodicalIF":5.8,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-22DOI: 10.1016/j.chbr.2026.100945
Jiacheng Sun, Katherine Shagalov, Ting Liao
Managing cognitive load is a critical challenge in immersive learning environments, where sensory interference could disrupt attention and information processing. This study developed and evaluated a VR-based adaptive learning system that incorporates real-time physiological monitoring to support cognitive regulation under auditory and visual interference. Cognitive load was assessed using the Cumulative Skin Conductance Response (CSCR), a continuous measure of sustained sympathetic activation, and learning outcomes were evaluated with a post-study quiz. Results showed that auditory interference elicited significantly higher CSCR than visual interference, indicating greater task-evoked cognitive load, although this difference did not directly translate into lower quiz performance. Participants who received real-time feedback exhibited reduced CSCR levels and slightly improved quiz scores, suggesting that feedback helped stabilize cognitive load during the learning task. A significant negative correlation between CSCR and quiz performance further indicated that higher sustained cognitive load was associated with poorer learning efficiency. These findings demonstrate the potential of integrating physiological monitoring into adaptive VR learning systems and motivate future work on multimodal sensing and more individualized feedback strategies. Future research could focus on personalizing feedback mechanisms and integrating multimodal biosignals to further refine these interventions.
{"title":"Examining sensory interference and adaptive feedback in VR-based learning for cognitive load management","authors":"Jiacheng Sun, Katherine Shagalov, Ting Liao","doi":"10.1016/j.chbr.2026.100945","DOIUrl":"10.1016/j.chbr.2026.100945","url":null,"abstract":"<div><div>Managing cognitive load is a critical challenge in immersive learning environments, where sensory interference could disrupt attention and information processing. This study developed and evaluated a VR-based adaptive learning system that incorporates real-time physiological monitoring to support cognitive regulation under auditory and visual interference. Cognitive load was assessed using the Cumulative Skin Conductance Response (CSCR), a continuous measure of sustained sympathetic activation, and learning outcomes were evaluated with a post-study quiz. Results showed that auditory interference elicited significantly higher CSCR than visual interference, indicating greater task-evoked cognitive load, although this difference did not directly translate into lower quiz performance. Participants who received real-time feedback exhibited reduced CSCR levels and slightly improved quiz scores, suggesting that feedback helped stabilize cognitive load during the learning task. A significant negative correlation between CSCR and quiz performance further indicated that higher sustained cognitive load was associated with poorer learning efficiency. These findings demonstrate the potential of integrating physiological monitoring into adaptive VR learning systems and motivate future work on multimodal sensing and more individualized feedback strategies. Future research could focus on personalizing feedback mechanisms and integrating multimodal biosignals to further refine these interventions.</div></div>","PeriodicalId":72681,"journal":{"name":"Computers in human behavior reports","volume":"21 ","pages":"Article 100945"},"PeriodicalIF":5.8,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146022610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-22DOI: 10.1016/j.chbr.2026.100931
Mehdi A. Kamran , Fatma Al Yaarubi , Nooshin Atashfeshan , Reza Babazadeh
Ensuring the sustained use of self-service technologies (SSTs) is essential for companies investing in these innovations. This study investigates key factors shaping human attitudes toward SST adoption in airports by developing a conceptual model that examines the effects of perceived usefulness (PU), perceived ease of use (PEOU), and the need for human interaction (NHI) on utilitarian and hedonic attitudes. Structural Equation Modelling (SEM) is used to test the model, with age and gender assessed as moderating variables. Data were collected from 214 passengers at Muscat International Airport and enriched with interview insights. The model explains 76.2 % of the variance in passengers’ intentions to use SSTs. PU and PEOU significantly influence both hedonic and utilitarian attitudes, while NHI shows no significant effect. Gender moderates the relationship between PU, PEOU, and utilitarian attitudes, while age moderates the link between hedonic attitudes and SST usage intention.
To complement the SEM analysis and address behavioral complexities, five machine learning (ML) models—Decision Tree, Random Forest, Support Vector Machine, Artificial Neural Network, and Extreme Gradient Boosting—are employed to predict SST adoption. These models achieve an average prediction accuracy of approximately 96 %. The integration of SEM and ML provides both explanatory depth and predictive strength, enhancing the understanding of behavioral drivers and supporting more informed SST implementation.
Overall, this research offers practical implications for airport authorities and technology developers, emphasizing the importance of designing SSTs that balance functional efficiency with user engagement, while also considering demographic differences in technology acceptance.
{"title":"Airport self-service technologies: Are we fully engaged? A hybrid SEM-machine learning approach","authors":"Mehdi A. Kamran , Fatma Al Yaarubi , Nooshin Atashfeshan , Reza Babazadeh","doi":"10.1016/j.chbr.2026.100931","DOIUrl":"10.1016/j.chbr.2026.100931","url":null,"abstract":"<div><div>Ensuring the sustained use of self-service technologies (SSTs) is essential for companies investing in these innovations. This study investigates key factors shaping human attitudes toward SST adoption in airports by developing a conceptual model that examines the effects of perceived usefulness (PU), perceived ease of use (PEOU), and the need for human interaction (NHI) on utilitarian and hedonic attitudes. Structural Equation Modelling (SEM) is used to test the model, with age and gender assessed as moderating variables. Data were collected from 214 passengers at Muscat International Airport and enriched with interview insights. The model explains 76.2 % of the variance in passengers’ intentions to use SSTs. PU and PEOU significantly influence both hedonic and utilitarian attitudes, while NHI shows no significant effect. Gender moderates the relationship between PU, PEOU, and utilitarian attitudes, while age moderates the link between hedonic attitudes and SST usage intention.</div><div>To complement the SEM analysis and address behavioral complexities, five machine learning (ML) models—Decision Tree, Random Forest, Support Vector Machine, Artificial Neural Network, and Extreme Gradient Boosting—are employed to predict SST adoption. These models achieve an average prediction accuracy of approximately 96 %. The integration of SEM and ML provides both explanatory depth and predictive strength, enhancing the understanding of behavioral drivers and supporting more informed SST implementation.</div><div>Overall, this research offers practical implications for airport authorities and technology developers, emphasizing the importance of designing SSTs that balance functional efficiency with user engagement, while also considering demographic differences in technology acceptance.</div></div>","PeriodicalId":72681,"journal":{"name":"Computers in human behavior reports","volume":"21 ","pages":"Article 100931"},"PeriodicalIF":5.8,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-21DOI: 10.1016/j.chbr.2026.100941
Ezgi Mansuroğlu, Andrew P. Smith
As technology continues to reshape industries, understanding the effects of technostress on employee well-being becomes imperative. While research on technostress has grown substantially in recent years, existing studies are often fragmented in scope and limited in cross-contextual depth. In this systematic review, we synthesized the findings of 201 (after the double screening) peer-reviewed empirical studies, primarily retrieved from the PubMed, Scopus, and Web of Science databases, to map technostress along the four analytical dimensions: its core components, its impact on well-being, key mediating and moderating variables, and contextual variations. Our findings demonstrated that the relationship between technostress and employee well-being has been most frequently studied in Germany, Italy, and India, with education and healthcare emerging as the most commonly examined sectors. Furthermore, techno-overload and techno-invasion were the most reported technostressors linked to adverse well-being indicators across the studies. Our analysis revealed an underrepresentation of cross-national and cross-cultural comparisons in the existing literature. Drawing on these insights, this review advances the literature by introducing the Demands-Resources-Individual Effects (DRIVE) model as a coherent integrative framework for studying technostress and well-being. The model provides a theoretically grounded explanation of how digital demands, personal resources, and individual differences interact to shape well-being outcomes. Combined with the Well-being Process Questionnaire (WPQ), it also offers a practical, validated approach for assessing these mechanisms in diverse organizational contexts.
随着技术不断重塑行业，了解技术压力对员工幸福感的影响变得势在必行。虽然近年来对技术压力的研究有了很大的发展，但现有的研究往往在范围上是碎片化的，在跨背景的深度上是有限的。在这篇系统综述中，我们综合了201篇（经过双重筛选）同行评议的实证研究的结果，这些研究主要来自PubMed、Scopus和Web of Science数据库，以绘制技术压力在四个分析维度上的图：技术压力的核心成分、对幸福感的影响、关键的中介和调节变量以及环境变化。我们的研究结果表明，技术压力与员工幸福感之间的关系在德国、意大利和印度得到了最频繁的研究，其中教育和医疗保健成为最常见的研究领域。此外，在所有研究中，技术超载和技术入侵是与不良健康指标相关的最常见的技术压力因素。我们的分析显示，在现有文献中，跨国和跨文化比较的代表性不足。基于这些见解，本综述通过引入需求-资源-个体效应（DRIVE）模型作为研究技术压力和幸福感的连贯综合框架来推进文献。该模型从理论上解释了数字需求、个人资源和个体差异如何相互作用，从而形成幸福结果。结合幸福感过程问卷（WPQ），它也提供了一个实用的，有效的方法来评估这些机制在不同的组织环境。
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Pub Date : 2026-01-21DOI: 10.1016/j.chbr.2026.100942
Abdulla Alsharhan , Mostafa Al-Emran , Khaled Shaalan
Despite the growing integration of Artificial Intelligence (AI) in educational settings, there is a notable gap in the literature regarding the role of chatbots in promoting social sustainability in higher education. This study aims to fill this void by developing a model that combines constructs from Task-Technology Fit (TTF), Source Credibility Theory (SCT), Fogg's Model of Web Credibility, and Social Presence Theory (SPT). This research utilizes a hybrid approach of Structural Equation Modeling (SEM) and Artificial Neural Networks (ANN) to evaluate the proposed model based on data collected from 341 students. The results confirmed 13 out of 16 hypotheses, underscoring the pivotal roles of credibility, social presence, and TTF in enhancing chatbot utilization, which, in turn, supports social sustainability. The ANN findings showed that TTF is the most important factor influencing chatbot use, with a normalized importance of 99.1 %. The significance of this research lies in its potential to guide the development of chatbot applications that effectively support universities' educational and social objectives, making a vital contribution to the discourse on technology's role in sustainable educational practices.
{"title":"From interaction to impact: Examining the role of chatbots in enhancing social sustainability using SEM-ANN approach","authors":"Abdulla Alsharhan , Mostafa Al-Emran , Khaled Shaalan","doi":"10.1016/j.chbr.2026.100942","DOIUrl":"10.1016/j.chbr.2026.100942","url":null,"abstract":"<div><div>Despite the growing integration of Artificial Intelligence (AI) in educational settings, there is a notable gap in the literature regarding the role of chatbots in promoting social sustainability in higher education. This study aims to fill this void by developing a model that combines constructs from Task-Technology Fit (TTF), Source Credibility Theory (SCT), Fogg's Model of Web Credibility, and Social Presence Theory (SPT). This research utilizes a hybrid approach of Structural Equation Modeling (SEM) and Artificial Neural Networks (ANN) to evaluate the proposed model based on data collected from 341 students. The results confirmed 13 out of 16 hypotheses, underscoring the pivotal roles of credibility, social presence, and TTF in enhancing chatbot utilization, which, in turn, supports social sustainability. The ANN findings showed that TTF is the most important factor influencing chatbot use, with a normalized importance of 99.1 %. The significance of this research lies in its potential to guide the development of chatbot applications that effectively support universities' educational and social objectives, making a vital contribution to the discourse on technology's role in sustainable educational practices.</div></div>","PeriodicalId":72681,"journal":{"name":"Computers in human behavior reports","volume":"21 ","pages":"Article 100942"},"PeriodicalIF":5.8,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146022608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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