Digital health competences and AI beliefs as conditions for the practice of evidence-based medicine: a study of prospective physicians in Canada

BACKGROUND

The practice of evidence-based medicine (EBM) has become pivotal in enhancing medical care and patient outcomes. With the diffusion of innovation in healthcare organizations, EBM can be expected to depend on medical professionals' competences with digital health (dHealth) and artificial intelligence (AI) technologies.

OBJECTIVE

We aim to investigate the effect of dHealth competences and perceptions of AI on the adoption of EBM among prospective physicians. By focusing on dHealth and AI technologies, the study seeks to inform the redesign of medical curricula to better prepare students for the demands of evidence-based medical practice.

METHODS

A cross-sectional survey was administered online to students at the University of Montreal's medical school, which has approximately 1,400 enrolled students. The survey included questions on students' dHealth competences, perceptions of AI, and their practice of EBM. Using structural equation modeling (SEM), we analyzed data from 177 respondents to test our research model.

RESULTS

Our analysis indicates that medical students possess foundational knowledge competences of dHealth technologies and perceive AI to play an important role in the future of medicine. Yet, their experiential competences with dHealth technologies are limited. Our findings reveal that experiential dHealth competences are significantly related to the practice of EBM (β = 0.42, p < 0.001), as well as students' perceptions of the role of AI in the future of medicine (β = 0.39, p < 0.001), which, in turn, also affect EBM (β = 0.19, p < 0.05).

CONCLUSIONS

The study underscores the necessity of enhancing students' competences related to dHealth and considering their perceptions of the role of AI in the medical profession. In particular, the low levels of experiential dHealth competences highlight a promising starting point for training future physicians while simultaneously strengthening their practice of EBM. Accordingly, we suggest revising medical curricula to focus on providing students with practical experiences with dHealth and AI technologies.

Enhancing privacy in mHealth Applications: A User-Centric model identifying key factors influencing Privacy-Related behaviours

BACKGROUND

Mobile health (mHealth) applications have revolutionized healthcare by offering accessible and efficient services through mobile devices. However, privacy concerns regarding the protection of sensitive health information remain a significant barrier to widespread adoption. This study investigates factors influencing privacy-related behaviours among mHealth users and proposes a user-centric model to address these privacy concerns.

OBJECTIVE

This research aims to identify strategies that enhance privacy protections in mHealth applications while maintaining usability. It focuses on transparency, user autonomy, and trust as key factors in fostering responsible data-sharing practices and encouraging broader adoption.

METHODS

An online survey targeting mHealth users in Australia was conducted to assess the relationships between privacy policy effectiveness, perceived benefits and risks, autonomy, trust, and privacy-enhancing behaviours. The data were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM) to validate the proposed research model and test key hypotheses.

RESULTS

The findings highlight that clear and transparent privacy policies increase trust and enhance perceived benefits, such as personalized health insights. However, detailed policies may also increase user's awareness of risks, making them more cautious. Autonomy emerged as a critical factor for building trust; users who feel empowered to control their data are more likely to positively engage with mHealth platforms. Trust significantly drives privacy-enhancing behaviours, such as active management of data-sharing settings. However, risks and privacy concerns alone did not deter users from engaging with these applications. Effective communication, robust security measures, and user-friendly features were identified as factors that mitigate privacy concerns and enhance adoption.

CONCLUSION

The study highlights the need for a balanced, user-centric approach to privacy in mHealth applications. Transparent policies, robust security features, and empowering user controls are essential to fostering trust and encouraging adoption, ensuring secure and efficient healthcare delivery.

The benefits and dangers of anthropomorphic conversational agents

A growing body of research suggests that the recent generation of large language model (LLMs) excel, and in many cases outpace humans, at writing persuasively and empathetically, at inferring user traits from text, and at mimicking human-like conversation believably and effectively-without possessing any true empathy or social understanding. We refer to these systems as "anthropomorphic conversational agents" to aptly conceptualize the ability of LLM-based systems to mimic human communication so convincingly that they become increasingly indistinguishable from human interlocutors. This ability challenges the many efforts that caution against "anthropomorphizing" LLMs, attaching human-like qualities to nonhuman entities. When the systems themselves exhibit human-like qualities, calls to resist anthropomorphism will increasingly fall flat. While the AI industry directs much effort into improving the reasoning abilities of LLMs-with mixed results-the progress in communicative abilities remains underappreciated. In this perspective, we aim to raise awareness for both the benefits and dangers of anthropomorphic agents. We ask: should we lean into the human-like abilities, or should we aim to dehumanize LLM-based systems, given concerns over anthropomorphic seduction? When users cannot tell the difference between human interlocutors and AI systems, threats emerge of deception, manipulation, and disinformation at scale. We suggest that we must engage with anthropomorphic agents across design and development, deployment and use, and regulation and policy-making. We outline in detail implications and associated research questions.

Consumer welfare of the state-level franchising processes for multi-channel video programming distributors

Internet protocol television (IPTV) that telecom carriers convey has recently appeared as a competitor of cable television service in the multi-channel video programming distributor (MVPD) market. A carrier must be granted franchise for multi-channel video programs from the local government in the US, which becomes an issue for delaying service launching, hampering fair competition, and leading to inefficient resource allocation of carriers. Therefore, a state-level franchising process has been suggested as an alternative to shorten the launching period, to improve competition in the MVPD market, and as a result, to increase consumer welfare. In this regard, this study proposes a new approach to estimate the gain in consumer welfare from changing the MVPD franchising mechanism from local-level to state-level. Results show that the state-level process will grant a gain in consumer welfare ranging from $1.8 to $3.1 billion dollars for ten years. This result should encourage regulators and legislators to consider a state-level process as a viable option for improving competition in the MVPD, as this change will increase consumer welfare.

Smart Home Technologies for Enhancing Independence of Living and Reducing Care Dependence in Older Adults: A Systematic Review

AIM

To systematically review the potential of smart home technology to enhance the independence of older adults and reduce their dependence on care. Additionally, it sought to examine the positive impacts of such technology on their golden years.

DESIGN

A systematic review based on Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA).

DATA SOURCES

The search was conducted on 8 April 2024. Peer-reviewed studies in PubMed, Embase, Web of Science, IEEE Xplore, Scopus, The Cochrane Library, CINAHL, CNKI, WANFANG DATA and VIP from 1 January 2000 to 8 April 2024 were searched.

METHODS

The methodological quality assessment used the Mixed Methods Appraisal Tool (MMAT). Positive findings relevant to this study were extracted from the literature and analysed using thematic synthesis.

RESULTS

After meticulously examining 3404 studies, we identified 21 relevant sources for in-depth analysis, including qualitative studies (n = 10), experimental studies (n = 9) and mixed method studies (n = 2). These sources were grouped into five core themes based on the pivotal role of smart home technologies in enabling ageing in place: daily monitoring, assisted living activities, life reminders, functional improvement and emotional companionship. The study found that smart home technology offers numerous benefits to the lives of older adults, including increased independence, psychological support, improved cognitive functioning, enhanced self-management, increased mobility, support for caregivers, promoted social engagement and enhanced quality of life.

CONCLUSION

Smart home technology can enhance the independence of older adults' lives, reduce their dependence on care, alleviate the burden on caregivers and promote home-based elderly care.

IMPACT

This systematic review contributes to understanding the capability of smart home technology to promote elderly care at home and help better utilise smart home technology to benefit older adults. Older adults and their caregivers should be encouraged to adopt this technology to improve older adults' quality of life.

PATIENT OR PUBLIC CONTRIBUTION

No patient or public contribution.

The impact of technology frustration and consumer passion on consumer evaluation shift in case of mobile apps

Online consumer ratings play a crucial role in determining a product's long-term success. This study examines the direct impact of consumer technology frustration and consumer passion on app ratings and evaluations over time. Beyond these direct effects, we propose that consumer passion moderates the relationship between technology frustration and evaluations-specifically, passion amplifies the negative impact of frustration, making a double-edged sword. Using data from the Google Play Store, we tracked the top 20,000 Android apps over three months (September 29, 2023-January 1, 2024), constructing a panel dataset with 11,568 panels and 13,810 observations. Our disaggregated analysis-considering download volume, app age, price, and publisher rating-confirms the hypothesized effects. Remarkably, established apps and publishers are particularly vulnerable to the intensified negative substitution effect. Our findings suggest that new app developers should prioritize a functional, intuitive platform and enhance it over time to foster consumer passion. Meanwhile, established apps must actively minimize technological frustration, as passionate users' negative experiences can significantly impact ratings and future downloads.

Crash injury severity for ride-hailing drivers: a questionnaire study in China

The development of ride-hailing service has also led to an increase in the frequency of traffic accidents. The present study investigated the causes and accident severity for the ride-hailing accidents attributed to drivers-related factors in the context of China. From an online, self-reported survey of 1356 ride-hailing drivers across the country, we collected data about the drivers' demographic characteristics, working conditions, fatigue, risky driving behaviors and accident records between 2021 and 2023. We turned the data into insights through a two-step approach: using a Random Forest (RF) model to identify the most significant factors influencing accident severity, followed by building a Bayesian Network (BN) model to analyze the relationships between the identified factors and accident severity. With 16 top factors according to the RF model, results from the BN model showed that the main risk factors differ between different levels of accident severity. Among all the factors, nine proved to be directly related to accident severity, mostly involving drowsiness, using smartphones in inappropriate situations and risky driving behaviors; the drivers' demographic and working conditions otherwise influence accident severity in indirect ways. The findings from this study are useful for proposing more targeted policies to mitigate the accident severity among ride-hailing drivers.

Impact of online negative and positive reviews on app innovation

Past studies have suggested that online reviews positively impact app innovation. However, extant research has not yet explored the distinct impacts of online negative and positive reviews on app innovation. Based on signaling theory and negative bias, this study empirically examines the effects of online negative reviews versus online positive reviews on app innovation by using panel data from the iOS App Store in China. The findings demonstrate that online negative reviews have a more positive influence on app innovation than online positive reviews. Additionally, compared with online positive reviews, app performance more effectively weakens the promoting effect of online negative reviews on app innovation. Moreover, both app history and platform owner's entry play a positive moderating role in the impact of online negative reviews on app innovation, while no positive moderating effect is observed in the impact of online positive reviews on app innovation. These results demonstrate the different effects of online negative reviews and online positive reviews on app innovation, expand the contingent value of online reviews and app innovation.

Mapping the Cyberthreat Landscape in Healthcare Using GDELT: A Multimethod Approach

Cyberattacks that target critical national infrastructure, such as hospitals, pose a significant threat to the safety and wellbeing of individuals, as evidenced by incidents like the WannaCry worldwide ransomware attack. To better understand vulnerabilities within the healthcare sector and develop preventive measures, it is crucial to examine the evolving nature of cyberthreats and the types of attacks occurring. In this article, we describe a multimethod approach comprising social networks analysis, natural language processing, and machine learning, using data from GDELT (Global Database of Events, Language, and Tone), to identify the prevalence of attacks on hospitals while considering the type of attack and its date. Through this approach, meaningful patterns in the evolution of cyberattacks are revealed by analyzing the relationships between emerging cyberattacks mentioned in news reports. Findings show that the number of attacks from 2017 to 2023 increased substantially, with hospitals being more prone to critical attacks such as cyberterrorism/state actor-sponsored criminal activities, advanced persistent threats, and distributed denial of service. Mapping real-time data from diverse sources using a multimethod approach, such as the framework proposed in this article, can lead to better understanding of the threat landscape. This is a crucial step in determining necessary cyberdefenses and informing the development of policy interventions to ensure the cybersecurity of critical national infrastructure.

The effects of virtual reality and augmented reality technologies on students' story retelling performance

This study aims to compare the retelling performance of two groups that engaged in reading activities with virtual reality and augmented reality texts. Furthermore, the results of the interventions using these technologies were compared with the results of the printed text reading activity. The study participants comprised 100 students aged 12-13 years studying in a secondary school. The researchers evaluated the students' story-retelling performance through a rubric in the study. In the pre-test stage, the students performed a paper-based reading activity on the texts in the coursebook and their retelling performance was evaluated. In the post-test stage, the reading activities of the two groups were carried out with the intervention of virtual reality and augmented reality. While the pre-test results showed no significant difference between the groups, the post-test results indicated that the augmented reality intervention better supported the students' retelling performance than virtual reality. However, there was no significant difference between the two groups in the sub-categories of setting and characters. Additionally, the virtual reality intervention did not create a significant difference in the sub-categories of characters, event/plot, problem, solution, and total score compared to the printed text reading activity. However, it produced better results in the setting sub-category than the printed text. A positive difference was observed in all sub-categories when the augmented reality intervention was compared to the printed text reading activity. AR showed greater benefits for retelling performance in this study, but further research is needed on long-term retention.

Older Adults' Information Use on Social Media: The Role of Psychological Needs and Personality Traits

This study explores how basic psychological needs and personality traits relate to the types of information that older adults use on social media platforms. Relying on a nationally representative survey of 1100 German Facebook and Instagram users aged 60+, it examines the relationship between three psychological needs-autonomy, competence, and relatedness-and four types of information use: undirected, topic-related, group-related, and problem-related. The study also explores associations with fear of missing out (FOMO), political interest, and openness to experience. Findings indicate that the need for relatedness and FOMO are key predictors of older adults' information use, emphasizing the social motivations behind their social media engagement. Notably, Facebook is more frequently used than Instagram across all types of information use. These insights provide a deeper understanding of the interactions between psychological needs, personality traits, and information behaviors among older adults, informing future research on their social media usage.

Context-Contingent Privacy Concerns and Exploration of the Privacy Paradox in the Age of AI, Augmented Reality, Big Data, and the Internet of Things: Systematic Review

BACKGROUND

Despite extensive research into technology users' privacy concerns, a critical gap remains in understanding why individuals adopt different standards for data protection across contexts. The rise of advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), augmented reality (AR), and big data has created rapidly evolving and complex privacy landscapes. However, privacy is often treated as a static construct, failing to reflect the fluid, context-dependent nature of user concerns. This oversimplification has led to fragmented research, inconsistent findings, and limited capacity to address the nuanced challenges posed by these technologies. Understanding these dynamics is especially crucial in fields such as digital health and informatics, where sensitive data and user trust are central to adoption and ethical innovation.

OBJECTIVE

This study synthesized existing research on privacy behaviors in emerging technologies, focusing on IoT, AI, AR, and big data. Its primary objectives were to identify the psychological antecedents, outcomes, and theoretical frameworks explaining privacy behavior, and to assess whether insights from traditional online privacy literature, such as e-commerce and social networking, apply to these advanced technologies. It also advocates a context-dependent approach to understanding privacy.

METHODS

A systematic review of 179 studies synthesized psychological antecedents, outcomes, and theoretical frameworks related to privacy behaviors in emerging technologies. Following established guidelines and using leading research databases such as ScienceDirect (Elsevier), SAGE, and EBSCO, studies were screened for relevance to privacy behaviors, focus on emerging technologies, and empirical grounding. Methodological details were analyzed to assess the applicability of traditional privacy findings from e-commerce and social networking to today's advanced technologies.

RESULTS

The systematic review revealed key gaps in the privacy literature on emerging technologies, such as IoT, AI, AR, and big data. Contextual factors, such as data sensitivity, recipient transparency, and transmission principles, were often overlooked, despite their critical role in shaping privacy concerns and behaviors. The findings also showed that theories developed for traditional technologies often fall short in addressing the complexities of modern contexts. By synthesizing psychological antecedents, behavioral outcomes, and theoretical frameworks, this study underscores the need for a context-contingent approach to privacy research.

CONCLUSIONS

This study advances understanding of user privacy by emphasizing the critical role of context in data sharing, particularly amid ubiquitous and emerging health technologies. The findings challenge static views of privacy and highlight the need for tailored frameworks that reflect dynamic, context-dependent behaviors. Practical implications include guiding health care providers, policy makers, and technology developers toward context-sensitive strategies that build trust, enhance data protection, and support ethical digital health innovation.

TRIAL REGISTRATION

PROSPERO CRD420251037954; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251037954.

The relevancy of the relevance: Different aspects of relevancy during emotional episodes evaluations

The present research investigates the effect of relevance on our subjective experience while generating alternative events in our mind. To accomplish this aim, we asked participants to read and visualize 280 situations outlined in concise scripts. During each trial, they evaluated the relevance of the situation and other aspects of their metacognitive and emotional experience using a Likert scale ranging from 1 to 9. We employ PLS-SEM (partial least squares structural equation modelling) to investigate a potential causal sequence among three tiers of latent components. Specifically, the situational level consists of relevancy and plausibility factors, the metacognitive level consists of an imaginability factor, and the subjective experience level consists of feeling and thought suppression factors. Our analysis indicates that the effect of the plausibility of alternative scenarios on subjective experience is mediated by both the relevance and imageability factors, regardless of whether events are negative or positive. Furthermore, we successfully replicated the negativity rejection bias shown in our prior work. This bias refers to the tendency of participants to perceive bad events as less likely to occur than good events, independent of the precise details of those events.

Examining the Effects of the Protection Motivation Theory-Based Online Intervention on Improving the Cognitive Behavioral Outcomes of Caregivers of Children With Atopic Diseases: Quasi-Experimental Study

BACKGROUND

The increasing prevalence of pediatric atopic diseases in China poses substantial risks to children's physical health, mental well-being, and quality of life. Cognitive behavioral interventions for caregivers are effective in managing pediatric atopic diseases. Existing interventions are typically siloed and lack integration across the comorbidities of the atopic march. The protection motivation theory (PMT) could provide an integrated cognitive behavioral intervention framework for addressing shared pathophysiological mechanisms and unifying management strategies across atopic diseases, while online interventions offer advantages in accessibility, cost-effectiveness, and scalability, particularly for caregiver-mediated pediatric care.

OBJECTIVE

This study aimed to develop and evaluate a PMT-based cognitive behavioral online (PMT-CBO) intervention for caregivers of children with atopic diseases, assessing its effects on caregivers' protective motivation, behavioral intentions, preventive practices, and children's atopic disease outcomes.

METHODS

A quasi-experimental design was conducted in 3 health care institutions in Hangzhou, China, where 2 health care institutions were assigned to the PMT-CBO group (127/243, 52.3%) and 1 health care institution was assigned to the control group (116/243, 47.7%). Caregivers in the PMT-CBO group received a 4-week structured course comprising 16 online modules delivered via a WeChat mini-program, whereas controls received routine care with verbal education. Primary outcomes included caregivers' PMT dimensions (threat appraisal and coping appraisal), behavioral intentions, and preventive behaviors, and secondary outcomes involved children's symptom severity and medication adherence. The primary outcome scales or questionnaires were designed by the research team, while the secondary outcome scales were derived from established studies. All scales demonstrated good reliability and validity. Intention-to-treat analysis was used.

RESULTS

Compared to the control group, the PMT-CBO group demonstrated significant improvements in overall PMT scores (Z=-6.289; P<.001) and most subdimensions (response efficacy, self-efficacy, threat severity, and response cost, with P<.05), except susceptibility (Z=-1.321; P=.19) and reward appraisals (Z=-0.989; P=.32). In the intervention group, caregivers exhibited stronger intentions and partial behavioral optimization (eg, environmental allergen control, with Z=-3.025; P=.002) and children showed improved medication adherence (Z=-4.457; P<.001) and alleviated eczema (Z=-3.112; P=.002) and allergic rhinitis symptoms (Z=-3.277; P<.001), although no significant differences emerged in asthma control (Z=-.830; P=.41) or food allergy-related caregiver burden (Z=-1.693; P=.09).

CONCLUSIONS

The PMT-CBO intervention enhanced caregivers' motivation and intentions and children's medication adherence and eczema and rhinitis outcomes, with a 91.3% (116/127) completion rate via WeChat's scalable platform. Limited improvements in asthma control and food allergy management implied the future need for additional condition-specific plug-ins, beyond the core PMT-CBO modules. Moreover, merging this PMT-CBO intervention with implementation techniques or ecological frameworks could help address intention-behavior gaps and external barriers, thereby promoting equitable and precision-based allergy care.

The mediating role of financial well-being in the relationship between housing affordability and mental health

This study examines the mediating role of financial well-being in the relationship between housing affordability and mental health across the European Union. Utilizing data from the 2018 EU Statistics on Income and Living Conditions, the analysis incorporates both objective and subjective measures of housing affordability and financial well-being. The findings reveal that financial well-being acts as a mechanism that links housing pressure to mental health, proxied by the MHI-5 Mental Health Inventory. Notably, the effect is stronger for subjective indicators, which exhibit a more pronounced mediating effect than do objective financial well-being indicators. The results underscore the importance of considering both objective and subjective dimensions in understanding the complex interplay between housing affordability, financial well-being, and mental health. The study contributes to the literature by providing insights into the underlying mechanisms through which housing affordability impacts mental health, with implications for policy interventions aimed at alleviating the negative impacts of housing affordability issues on mental health and overall well-being.

Digital innovation for circular supply chain sustainability and resilience for achieving carbon neutrality: An empirical study

This study investigates the role of digital innovation within the Circular Economy (CE) by addressing two critical research gaps: conceptualizing digital innovation in the CE as a multidimensional construct and understanding its effects on circular supply chain performance under varying levels of social uncertainty. Unlike prior research that treats digitalization and circular economy initiatives separately, this study integrates the two perspectives by conceptualizing digital innovation in the CE as a second-order construct encompassing innovation capability, innovation focus, innovation collaboration, and innovation governance. Drawing on the dynamic capability view and contingency theory, the proposed model is empirically tested using data from 279 South African firms operating in high-carbon sectors. The findings reveal that heightened digital innovation in the CE significantly enhances both sustainability and resilience in circular supply chains aimed at achieving carbon neutrality, with a stronger impact observed on sustainability. Additionally, the study examines the moderating role of social uncertainties and finds that, although digital innovation remains beneficial, the moderating effects are not supported. This research contributes to the CE and digital supply chain literature by offering a new integrated framework, identifying key drivers of sustainable and resilient circular supply chains, and providing empirical insights from a dynamic emerging economy context.

Multi-agent norm perception and induction in distributed healthcare

This paper presents a Multi-Agent Norm Perception and Induction Learning Model aimed at facilitating the integration of autonomous agent systems into distributed healthcare environments through dynamic interaction processes. The nature of the medical norm system and its sharing channels necessitates distinct approaches for Multi-Agent Systems to learn two types of norms. Building on this foundation, the model enables agents to simultaneously learn descriptive norms, which capture collective tendencies, and prescriptive norms, which dictate ideal behaviors. Through parameterized mixed probability density models and practice-enhanced Markov games, the multi-agent system perceives descriptive norms in dynamic interactions and captures emergent prescriptive norms. We conducted experiments using a dataset from a neurological medical center spanning from 2016 to 2020. The descriptive norm-sharing experiment results demonstrate that the model can effectively perceive the descriptive collective medical norms - which embody the current best clinical practices - across medical communities of varying scales. By contrasting this with the fact that the real descriptive diagnostic practice patterns in the neurological medical center dataset gradually converged over a period of 5 years, we find that the model, through prolonged learning and sharing processes, progressively mirrors the actual descriptive diagnostic trends and collective behavioral tendencies present within the medical community. In the experiment where multiple agents infer prescriptive norms within a dynamic healthcare environment, the agents effectively learned the key clinical protocols within the norm space H, which includes control norms, without developing high belief in invalid norms. Furthermore, the agents' belief update process was relatively smooth, avoiding any discontinuous stepwise updates.

Simulating mismatch between calibration and target population in AI for mammography the retrospective VAIB study

AI cancer detection models require calibration to attain the desired balance between cancer detection rate (CDR) and false positive rate. In this study, we simulate the impact of six types of mismatches between the calibration population and the clinical target population, by creating purposefully non-representative datasets to calibrate AI for clinical settings. Mismatching the acquisition year between healthy and cancer-diagnosed screening participants led to a distortion in CDR between -3% to +19%. Mismatching age led to a distortion in CDR between -0.2% to +27%. Mismatching breast density distribution led to a distortion in CDR between +1% to 16%. Mismatching mammography vendors lead to a distortion in CDR between -32% to + 33%. Mismatches between calibration population and target clinical population lead to clinically important deviations. It is vital for safe clinical AI integration to ensure that important aspects of the calibration population are representative of the target population.

The role of digitization: an empirical study on the prediction and impact of changes in waste separation behavior in China

Digitalization has subtly affected all aspects of people's lives, including waste separation. This study explores how the role of digitization affects the behavior of mass waste separation, establishes the behavioral role of waste separation in the context of digitization through the theory of planned behavior, further subdivides the role of digitization into digitization practices and digitization quality, and constructs a research model of digitization's impact on mass waste separation behavior. Structural equation modeling was used to analyze the data and fit the model to the questionnaires of the 611 respondents. The results show that digital practice and digital quality have a positive and significant relationship with waste behavioral intention. In contrast, digital practice has a considerable mediating and chain-mediating effect on the relationship between digital quality and waste separation behavioral intention. These findings provide good theoretical support for the role of digitization in mass waste separation and practical guidance for developing waste separation in the context of digitization.

Exploring the drivers of digital transformation in Chinese port and shipping enterprises: A machine learning approach

With the transition to a global green low-carbon economy, the urgency for digital transformation in the port and shipping industry has become increasingly prominent in making enterprises more efficient and sustainable. This study focuses on how Chinese port and shipping enterprises, which are key carriers for global containerized trade, can attain digital transformation as a means to tackle environmental challenges and improve competitiveness. Using a representative sample of 83 A-share-listed companies (2008-2023) and employing several modeling techniques, such as Ridge regression, LightGBM, and XGBoost, we investigate a data-driven approach with the support of the Technology-Organization-Environment (TOE) framework. We find that nonlinear models (LightGBM, XGBoost) outperform linear models and emphasize the importance of a supportive environment for green finance. We further perform a number of sensitivity and robustness checks toensure the validity of our findings. These insights provide actionable guidance for policymakers and industry leaders seeking to harmonize digital innovations with green development.

Examining the Changes in Bullying Discourse on Reddit: A Comparative Analysis Before and After the COVID-19 Pandemic

Bullying, a type of power abuse, deserves to be addressed, and this study examines bullying-related discussions on Reddit before and after the pandemic to better understand its dynamics during this time. We analyzed 8,720 posts and 21,607 comments from the r/Bullying subreddit using static and dynamic topic modeling (DTM) to understand the major topics discussed in the subreddit. Based on static topic modeling, we discovered that before the COVID-19 pandemic, the topics surrounding bullying focused on bullying in the school context, cyberbullying, and help-seeking, but changed to bullying against minority groups, workplace bullying, relationships and communication, and coping strategies. The long-term impact of bullying has emerged for both periods, implying that more efforts to prevent and combat bullying are needed to reduce the negative impacts throughout an individual's lifecycle. We also discovered that the proportion of cyberbullying/antibullying, negative emotions, and self-esteem increased following the COVID-19 pandemic, according to DTM. Our findings suggest that following the pandemic, victims and places of bullying became more distinct. In addition to the widely studied and disseminated bullying research and policies concerning children at school, more assistance is needed to prevent and assist bullying victims who are racial and religious minorities in various settings.

Institutional influences on healthcare service innovation: Lessons from German rehabilitation clinics

In view of the growing need for rehabilitation worldwide due to demographic changes and health trends, healthcare organizations are facing increasing pressure to innovate. This study focuses on the institutional dynamics shaping service innovation in orthopedic rehabilitation settings in Germany. Using Scott's framework of institutional pillars and carriers, we conduct a multiple case study analysis. Based on interviews with physicians and managers from six different clinics, conducted in two rounds and supplemented by secondary data analysis, we investigate the influence of regulative, normative, and cultural-cognitive institutions on healthcare service innovation. By distinguishing between the various institutional barriers and facilitators, our research provides valuable insights for healthcare practitioners and managers, equipping them with the necessary knowledge to effectively navigate and utilize the institutional environment.

Impact of patient engagement types on doctors' service provision in online health communities

OBJECTIVES

Online Health Communities (OHCs) contributes to patient access to health information and doctor-patient communication. However, how patient engagement types influence doctor service provision in OHCs remains unclear.

METHODS

With the PACE system, this study employs content analysis and clustering methods to examine the relationship between patient engagement types and doctor service provision. A qualitative study utilizing discourse analysis of online doctor-patient communication was conducted. A total of 300 consultation records from the Chinese OHC, Ding Xiang Doctor (DXY), were collected between August 1 and October 31, 2023.

RESULTS

The study identifies four patient engagement types, including emotional disclosure, decision-making engagement, consumption-oriented, and doctor-driven types. Discourse analysis reveals that doctors adjust their communication strategies according to patient engagement types, prioritizing service provision and communication tasks differently. Specifically, doctors turn to 1) respond with more empathy to emotional disclosure patients, providing more comforting words; 2) discuss more with decision-making engagement patients on treatment plan and provide more medical explanations; 3) focus more on information provision by answering patient inquiries with less interaction with consumption-oriented patients; and 4) demonstrate more authority with higher frequency of use of closed questions, outlining possible conditions and providing concise diagnostic advice with doctor-driven patients.

CONCLUSIONS

This study explores the impact of patient engagement in OHCs on doctor service provision. It reveals that doctors adapt their communication approaches based on the patient engagement types.

PRACTICE IMPLICATIONS

The results indicate that doctors adjust their service provision based on patient engagement online, emphasizing the importance role of patients in self-report health conditions and concerns. Patients are encouraged to adopt proactive engagement to facilitate the effective online doctor- patient communication, and ultimately improve the quality of online health consultations. In addition, the doctors are expected to be more patient in OHCs compared to offline, and help patients to align their online engagement types accordingly.

Occupational therapists' perspectives on welfare technologies for dementia: exploring recommendation frequencies - a pilot study

BACKGROUND

Occupational therapists have long been recognized for their expertise in welfare technology use, particularly in the context of dementia care. Despite this advanced training, however, there is often a gap between their knowledge of available technologies and the frequency with which they recommend these tools to individuals with dementia.

AIMS/OBJECTIVES

This study explores the perspectives of occupational therapists on the recommendations of welfare technologies for individuals with dementia.

MATERIALS AND METHODS

A survey was created with the assistance of an expert group in a Swedish municipality. The survey consisted of 26 questions (demographics, background/workload, perceptions of welfare technologies, accessibility/information and knowledge on how and when to use, and initiation of contact and recommendation frequency) and was distributed to 60 occupational therapists within the selected municipality. We received a total of 29 responses. The responses were then analyzed qualitatively for meaningful insights across various themes. Using an encoding scheme, a statistical analysis of the responses was performed to uncover potential trends in the distribution of the responses.

RESULTS

Results indicate positive perceptions of welfare technologies by occupational therapists, but a low frequency of recommendations for individuals with dementia.

CONCLUSIONS AND SIGNIFICANCE

Based on the results, we find it important to advocate for more educational opportunities for occupational therapists regarding welfare technologies and improved organizational structures that promote their understanding and ultimately benefits of recommending welfare technologies to individuals with dementia.

Activation or Rigidity: The Dual‐Channel Mechanism of Responsibility on Consumer Citizenship Behavior Beneficial to Community Members

In recent years, private domain communities have become key hubs for connecting businesses and users. However, many private domain communities remain “rigid” rather than “activated,” limiting their marketing potential. This paper, grounded in psychological reactance theory, examines how dual‐channel responsibility affects consumer citizenship behavior beneficial to community members. Using surveys, experiments, and regression analysis across seven studies, the research shows that taking charge boosts freedom and promotes citizenship behavior beneficial to community members. In contrast, responsibility diffusion limits such behavior by imposing constraints. Community warmth moderates the effects of responsibility diffusion and taking charge on these behaviors.

Differences in the Effectiveness of Three OHS Training Delivery Methods

BACKGROUND

Methods of delivering occupational safety and health (OSH) training have shifted from in-person to online. Widespread delivery of a standardized OSH training course in three modalities in the province of Ontario, Canada allowed measurement of differences in their effectiveness.

METHODS

Learners (N = 899) self-selected into face-to-face (F2F) instructor-led learning, online instructor-led synchronous distance learning, or online self-paced e-learning. Pre- and post-training surveys collected information on knowledge and other measures. Multiple regression analyses compared modalities on knowledge achievement (0%-100% scale; the primary outcome), engagement, perceived utility, perceived applicability, self-efficacy, and intention-to-use.

RESULTS

F2F learners achieved a statistically significant 2.5% (95% CI: 0.3%, 4.7%) higher post-training knowledge score than distance learners (Cohen's d = 0.23, which is considered small). A statistically insignificant difference of 0.4% (95%: -1.4%, 2.3%) was seen between e-learners and distance learners. Collaborating training providers regarded these differences as not meaningful in practice. Statistically significant differences between modalities were seen for engagement, perceived utility, and self-efficacy. Scores of F2F learners were more favorable than scores of distance learners, which were, in turn, more favorable than scores of e-learners.

CONCLUSIONS

This study provides evidence that there are small to no differences among F2F, distance and e-learning in their ability to ensure knowledge achievement among learners. This finding is likely generalizable to other well-designed short-term OSH training aimed at acquiring new knowledge. More research is needed to understand whether there are important differences across these modalities in basic OHS skill acquisition and transfer of learning to the workplace.

Predicting Agitation Events in the Emergency Department Through Artificial Intelligence

Importance

Agitation events are increasing in emergency departments (EDs), exacerbating safety risks for patients and clinicians. A wide range of clinical etiologies and behavioral patterns in the emergency setting make agitation prediction difficult in this setting.

Objective

To develop, train, and validate an agitation-specific prediction model based on a large, diverse set of past ED visit data.

Design, Setting, and Participants

This cohort study included electronic health record data collected from 9 ED sites within a large, urban health system in the Northeast US. All ED visits featuring patients aged 18 years or older from January 1, 2015, to December 31, 2022, were included in the analysis and modeling. Data analysis occurred between May 2023 and September 2024.

Exposures

Variables that served as potential exposures of interest, encompassing demographic information, patient history, initial vital signs, visit information, mode of arrival, and health services utilization.

Main Outcomes and Measures

The primary outcome of agitation was defined as the presence of an intramuscular chemical sedation and/or violent physical restraint order during an ED visit. A clinical model was developed to identify risk factors that predict agitation development during an ED visit prior to symptom onset. Model performance was measured using area under the receiver operating characteristic curve (AUROC) and area under the precision recall curve (PR-AUC).

Results

The final cohort comprised 3 048 780 visits. The cohort had a mean (SD) age of 50.2 (20.4) years, with 54.7% visits among female patients. The final artificial intelligence model used 50 predictors for the primary outcome of predicting agitation events. The model achieved an AUROC of 0.94 (95% CI, 0.93-0.94) and a PR-AUC of 0.41 (95% CI, 0.40-0.42) in cross-validation, indicating good discriminative ability. Calibration of the model was evaluated and demonstrated robustness across the range of predicted probabilities. The top predictors in the final model included factors such as number of past ED visits, initial vital signs, medical history, chief concern, and number of previous sedation and restraint events.

Conclusions and Relevance

Using a cross-sectional cohort of ED visits across 9 hospitals, the prediction model included factors for detecting risk of agitation that demonstrated high accuracy and applicability across diverse patient populations. These results suggest that clinical application of the model may enhance patient-centered care through preemptive deescalation and prevention of agitation.

The baby duck syndrome as cognitive bias in user-interface interaction

As technological interfaces are relatively new cultural tools, regular interaction can lead to new psychological phenomena, like the baby duck syndrome, where users favor old interfaces over updates. This syndrome is seen as a cognitive bias in human-computer interaction. Two studies were conducted: Study 1 on an Airbnb app (old vs. new versions), and Study 2 on a self-developed website (blue vs. purple design). Subjects completed sequential five usability tasks in two blocks with the resting between. Experimental groups engaged with different interface versions, while control groups used the same one. After completing tasks, subjects filled questionnaires about interface interaction. Study 1 showed significantly higher preference for the old Airbnb version, though scores varied by group. Study 2 revealed a trend of reduced scores in experimental groups on second assessment, not seen in controls. Version-based rating differences were insignificant. Overall, Studies 1 and 2 indicated baby duck syndrome is challenging to study in labs, suggesting it emerges when users are genuinely engaged with the interface. This article has both theoretical and experimental significance for studying the emergence of psychological phenomena in human-digital interaction. As a practical application, the obtained results can be useful for correct development updates for interfaces.

Promoting elderly wellbeing through indigenous music and choral performance

This paper explores the impact of indigenous instrumental music and choir performances on the wellbeing of elderly individuals who perform their music at the Hlanganani Malamulele Society for the Aged centre in Giyani, Limpopo Province, South Africa. It examines how these musical activities provide emotional nourishment and promote happiness and wellbeing among the elderly. Through interviews, performances, music transcription, participation, and observation, the study finds that engagement in music activities, such as choir singing, performing and listening to indigenous instruments, significantly promotes the wellbeing and health of the elderly. The findings suggest that these musical activities contribute to the reduction of stress and depression while encouraging a fulfilling lifestyle for the elderly participants. This paper argues that indigenous instrumental and choral music play a crucial role in fostering a healthy, wellbeing and happy life for the elderly members.

The impact of AI-driven sentiment analysis on patient outcomes in psychiatric care: A narrative review

This article addresses the pressing question of how advanced analytical tools, specifically artificial intelligence (AI)-driven sentiment analysis, can be effectively integrated into psychiatric care to enhance patient outcomes. Utilizing specific search phrases like "AI-driven sentiment analysis," "psychiatric care," and "patient outcomes," a comprehensive survey of English-language publications from the years 2014-2024 was performed. This examination encompassed multiple databases such as PubMed, PsycINFO, Google Scholar, and IEEE Xplore. Through a comprehensive analysis of qualitative case studies and quantitative metrics, the study uncovered that the implementation of sentiment analysis significantly improves clinicians' ability to monitor and respond to patient emotions, leading to more tailored treatment plans and increased patient engagement. Key findings indicated that sentiment analysis improves early mood disorder detection, personalizes treatments, enhances patient-provider communication, and boosts treatment adherence, leading to better mental health outcomes. The significance of these findings lies in their potential to revolutionize psychiatric care by providing healthcare professionals with real-time insights into patient feelings and responses, thereby facilitating more proactive and empathetic care strategies. Furthermore, this study highlights the broader implications for healthcare systems, suggesting that the incorporation of sentiment analysis can lead to a paradigm shift in how mental health services are delivered, ultimately enhancing the efficacy and quality of care. By addressing barriers to new technology adoption and demonstrating its practical benefits, this research contributes vital knowledge to the ongoing discourse on optimizing healthcare delivery through innovative solutions in psychiatric settings.

Feasibility and Acceptability of Smartwatches for Use by Nursing Home Residents

Smartwatch wearables are a promising health information technology to monitor older adults with complex chronic care needs. Pilot and feasibility studies have assessed smartwatch use with community-dwelling older adults, but less is known about their use in nursing homes. The purpose of this study was to test the feasibility and acceptability of smartwatch technology in a real-world nursing home setting to generate initial evidence about potential use. Using a qualitative descriptive approach, we conducted a pilot feasibility and acceptability study of smartwatch technology: Phase 1, pretrial semistructured interviews and focus groups with nursing home leaders, staff, and residents/families; Phase 2, a 7-day smartwatch trial deployment with residents; and Phase 3, posttrial semistructured interviews and focus groups. Themes related to feasibility findings included a part of the workflow and making the technology work . Themes related to acceptability findings included it's everywhere anyway , how will you protect me , knowing how you really are , more information = more control , and knowing how they are doing . These findings have important implications for the design of technology-supported interventions incorporating these devices within the unique context of residential nursing homes to best meet the needs of older adult residents, families, and staff caretakers.

Time in hand: Temporal focus in risk discourse and audience emotions on knowledge-sharing platforms

Online knowledge-sharing platforms construct risk knowledge and provide the audience with risk-related scientific facts. We study how speakers organize narratives in past, present, and future foci to influence the audience's emotions through the audience's appraisal of motive congruency and coping potential. Empirical evidence from 210 Technology, Entertainment, Design talks about disasters from 2002 to 2018 demonstrates that emphasizing the past, present, and future in risk narrative leads to the audience's comments with more negative, less positive, and more positive emotions, respectively. Concrete (vs. abstract) portrayal of the risk narrative improves the audience's situational awareness, enhances their risk appraisal, and intensifies the impact of temporal focus on emotions, providing evidence of how temporal focus impacts. These findings demonstrate that temporal focus can effectively reduce risk overreaction or ignorance and facilitate emotion regulation in risk communication.

Digital transformation in the healthcare sector: a novel strategic perspective

PURPOSE

This study aims to fill the gap in digital transformation (DT) literature, particularly within the healthcare sector, by investigating the effect of strategic reconfiguration (SREC), as an antecedent, on DT. Further, it also aims to investigate the effect of DT on strategic renewal (SR) as a strategic outcome of DT. Thereby, the current study explores the drivers and outcomes of DT from a new strategic perspective.

DESIGN/METHODOLOGY/APPROACH

The structural model is tested via the partial least squares structural equation modeling using a sample of 264 private Egyptian hospitals.

FINDINGS

SREC directly and positively affects SR. Besides, the SREC-SR relationship is partially mediated by DT. Accordingly, this study introduces a novel strategic perspective model of DT that depicts how Egyptian private hospitals could reconfigure themselves to transform toward digitalization, which ultimately enabled them to deliver new value propositions and diversified services.

RESEARCH LIMITATIONS/IMPLICATIONS

The sample is restricted to Egyptian private hospitals; thereby, the results may differ in other sectors and other countries. This study ignores the boundary conditions that may accelerate organizations' movement toward digitalization.

PRACTICAL IMPLICATIONS

Managers of private hospitals can leverage the findings of this study to manage their strategic resources through SREC and foster a culture of DT to enhance their renewal in an increasingly digitalized healthcare landscape.

SOCIAL IMPLICATIONS

By demonstrating the positive effects of DT on SR, this study underscores the role of technology in improving healthcare delivery, patient outcomes and overall quality of care.

ORIGINALITY/VALUE

To the best of the author's knowledge, this is the first empirical study to introduce a model of the strategic antecedents and consequences of DT within the healthcare sector. Unlike the existing DT literature, the current study goes beyond the traditional technological perspective for studying DT by concentrating on the strategic perspective. Therefore, the current study contributes to the existing DT literature by being the first empirical study to investigate the non-technological strategic antecedents that enable successful DT while propping the potential strategic outcome of DT.

Are women more likely to engage in extra green behaviors in the workplace? Gender differences in the spillover effect from employee in-role to extra-role green behavior

Introduction

With the growing importance of green development, organizational research pointed that employee green behavior is an important micro foundation to addressing the environmental challenge. While past studies have categorized the in-role and extra-role green behavior as two dimensions of employee green behavior from the job performance perspective, they have overlooked the potential interaction between the two types of green behavior. This oversight may hamper organizations' green efforts since deeper engagement in green behavior needs more psychological motivations compared to compliance with green management policies. According to cognitive consistency theory, this study explores employees' psychology reactions to in-role green behaviors, and how these psychological changes induce extra-role green behaviors.

Methods

Survey data collected in two times from 351 employees from 8 companies in China to assess the proposed hypothesis. SPSS 25 and Smart PLS 3.0 were used to test the theoretical model.

Results

Results show that reflective moral attentiveness and person-organization fit mediate the spillover between in-role to extra-role green behavior. Moreover, these effects were moderated by employee gender: reflective moral attentiveness plays a more important role for women in the green behavior spillover process; conversely, in-role green behaviors lead to stronger person-organization fit for man than for woman.

Discussion

This study provides unique insights into the potential interaction between the two types of green behavior. Furthermore, the psychological mechanisms of this behavioral change are different for male and female employees. Companies should take these differences into account when developing green management policies.

Executives' IT background and corporate digital technology innovation: Evidence from Chinese microenterprises

This study examines how executives' IT backgrounds affect corporate digital technology innovation, using panel data from listed companies between 2011 and 2021. Findings indicate that executives' IT backgrounds significantly enhance corporate digital technology innovation, particularly in digital technology invention patents, where the effect is especially pronounced, and the results are robust. Mechanistic analysis suggests managerial myopia and executives' attention to digital technology are key factors that allow executives' IT backgrounds to promote corporate digital technology innovation. Further analysis shows executives' IT backgrounds have a greater promotional effect on digital technology innovation in state-owned enterprises, non-IT sectors, firms with influential executives, and organizations with long-serving executives. Economic analysis reveals that executives' IT backgrounds can improve corporate financial performance by enhancing digital technology innovation. This paper highlights the importance of executive IT human capital and provides insights into government and corporate talent recruitment policies.

Investigating Continuance Intention for Telehealth Visits in Children's Hospitals: Survey-Based Study

BACKGROUND

Telehealth visits are remote health care consultations conducted using digital technologies, such as video calls, phone calls, or web-based platforms. This type of service offers numerous benefits for both health care users and health care providers. Users save time and money by avoiding traveling to health care facilities. At the same time, health care providers can expand access to care for users in remote areas and enhance the continuity of care. These advantages are even more evident in pediatric settings, where attending in-person services must align with the commitments of the patient (eg, school activities) and the caregiver. Although the potential benefits of telehealth visits for users and health care providers were already known before the COVID-19 pandemic, its widespread adoption only occurred during it. Having experienced its benefits, hospitals are now, in the postpandemic phase, determined to maintain and strengthen their remote service offerings. It has, therefore, become crucial for them to understand the factors influencing users' intention to continue using telehealth visits (or "continuance intention"), even now after the access restrictions to health care facilities imposed during the COVID-19 pandemic have been lifted. However, the literature lacks comprehensive, valid, and reliable models explaining users' continuance intention toward telehealth visit services.

OBJECTIVE

This study aims to investigate the variables impacting users' continuance intention toward telehealth visits and identify suggestions for improvement.

METHODS

Two models of variables impacting users' continuance intention toward telehealth visits were developed. The first model applied to all users undergoing telehealth visits, while the second one applied only to patients who received a telehealth visit using videoconferencing tools. The models were created based on the literature and a qualitative study comprising interviews with physicians with extensive experience in telehealth visits. The models were then tested using partial least squares structural equation modeling on 477 responses obtained by administering a survey to guardians of patients who had received at least 1 telehealth visit in a major European children's hospital.

RESULTS

Both models showed that the variable information quality positively influenced the variables continuance intention and perceived usefulness and that perceived usefulness positively influenced continuance intention. The first model was robust to the medical specialty and the channel used to deliver the visit. The second model also showed that systems quality positively influenced information quality.

CONCLUSIONS

This study has identified and tested 2 comprehensive, valid, and reliable models on the variables influencing users' continuance intention toward telehealth visits. Moreover, the study's results provide insights for hospitals to improve telehealth visit services.

How the care workforce navigates the digital 'skills gap': problems and opportunities from policy to practice

Introduction

Care systems and services across the globe are under pressure, with challenges related to the recruitment and retention of the care workforce identified as a particular issue. In England, digital technologies are presented in policy discourse and strategy as a potential way to navigate these complexities by delivering faster, cheaper and better care. The workforce, meanwhile, tends to be defined as requiring better digital skills to enable the full potential of digital technologies to be realised.

Methods

We carried out qualitative case study research of seven social care provider organisations, involving interviews with a total of 62 people from a range of roles across the care workforce and observations of work-based practices. Drawing on this data, we explore in-depth the workforce's experiences of and perspectives on using new technologies, and the requisite skills.

Results

The results show how the issue of maximising the adoption of technologies is (1) affected less by a deficit in worker skills, and more by the type of digital technologies in use, the job role of the worker, and the type of care provider, (2) can be facilitated by a supportive learning environment, and (3) can be impeded by issues in the functionality of systems and devices.

Discussion

We show a disconnect between the assumptions made in policy discourse and the practicalities and variations in how workers adapt, apply, and develop skills. We also explore the importance of peer support, albeit hindered by time constraints and sometimes overly relying on individual workers. In addition, the paper highlights the importance of understanding how new technology adoption can be stymied by the design of the technology itself, rather than the result of the workforce's lack of digital skills per se. An unintended consequence of defining the problem as a skills mismatch and the solution as skilling the workforce is that the abilities of the workforce to creatively and flexibly manage the short-comings of digital devices and systems are overlooked and under-utilised - reflecting a wider failure to acknowledge and compensate care workers' skills.

The impact of the rural digital economy on China's new-type urbanization

The Chinese government is vigorously implementing the rural revitalization strategy and accelerating the process of new-type urbanization. The rapid development of the rural digital economy has emerged as a new driving force for new-type urbanization. This study aims to explore how the rural digital economy impacts China's new-type urbanization from direct, heterogeneous, and indirect perspectives. Using the provincial-level panel data in China from 2014 to 2022, a mixed-methods approach is employed for the empirical research. The CRITIC and Entropy TOPSIS are used to assess the comprehensive development level and temporal characteristics of the rural digital economy and new-type urbanization. Moreover, a global-local auto-correlation analysis is carried out to depict the spatial distribution of the two variables. Subsequently, a two-way fixed effects model is constructed to verify the direct impact of the rural digital economy on new-type urbanization, as well as its structural and spatial heterogeneity characteristics. Finally, an mediating effect model is established to explore the impact paths through which the rural digital economy impacts new-type urbanization. The results show that the rural digital economy has significantly promoted new-type urbanization. Specifically, rural digital infrastructure, digital transformation of agriculture, agricultural production service informatization have a significant positive effect, while the role of rural life digitization is not significant. The rural digital economy has more significant positive impact on population agglomeration and economic growth, followed by social public service, but has no significant impact on ecological environmental protection and urban-rural coordination. Additionally, the qualitative analysis identifies geographical region, poverty, demographic structure and social equality as notable influencing factors in this impact. Further mechanism analysis result indicates that the rural digital economy impacts new-type urbanization through rural human capital improvement, agricultural economic growth and rural industrial structure upgrading. This research contributes to the existing body of knowledge by providing the practical path of rural development to promote new-type urbanization in the context of the digital economy, also clarifies the weak points and key links in this process. It also highlights the need for further research into the institutional factors that influence this relationship to enhances the policy applicability.

Digital transformation and the choice of management control modes in enterprise groups

Digital transformation has a significant impact on the choice of management control modes within enterprise groups. This study uses data from publicly listed companies in China from 2010 to 2022 to empirically examine the effect of digital transformation on the management control modes of enterprise groups. It further explores the mechanism and moderating effects of digital transformation in influencing the selection of management control modes. The findings indicate that, under the impact of digital transformation, enterprise groups are more inclined to adopt decentralized management control modes. The mechanism analysis suggests that digital transformation can mitigate principal-agent problems between parent and subsidiary companies by improving internal control quality, thus promoting a decentralized management control mode. The moderating effects reveal that the facilitative impact of digital transformation on choosing a decentralized control mode is more pronounced in state-owned enterprise groups and those operating in environments with higher uncertainty. Moreover, as the level of digital transformation or corporate governance improves, enterprise groups are more likely to adopt decentralized management control modes. This study extends the measurement indicators for the choice of management control modes in enterprise groups across four dimensions: personnel authority, operational authority, investment authority, and financial authority, and constructs a research framework to reveal the practical effects of China's digital transformation strategy.

The Impact of Technostress Generated by Artificial Intelligence on the Quality of Life: The Mediating Role of Positive and Negative Affect

In the era of Artificial Intelligence, the magic of achieving results at the "speed of light" for tasks that until recently required a lot of work and effort shocks, arouses enthusiasm and generates fears at the same time. Therefore, starting from this reality of our days, we proposed within the current research to study the relationship between the factors of technostress (techno-overload, techno-invasion, techno-complexity, techno-insecurity, techno-uncertainty) perceived as a result of the implementation of AI at the societal level and the quality of life, filtering the relationship through the "lens" of the positive and negative affect mediators. The mediation analyses, conducted on a sample of 217 adult Romanian citizens (18-62 years old), suggested that although AI-related technostress does not directly influence quality of life, it has a significant indirect impact through affective traits-general tendencies to frequently experience positive or negative emotions. This indicates that technostress contributes to variations in quality of life by influencing emotional experiences, which mediate the relationship. These findings emphasize not only the absence of a direct effect, but also the importance of the indirect pathway in understanding how individuals are affected by AI-related stress. We believe that the results of the current study can be equally useful in raising awareness of the psychological mechanisms responsible for the quality of life and in understanding the importance of implementing official programs, both technically, regarding the development of skills to understand and work with AI, and psychological support programs, considering the management of emotions, with reference to this technology.

Smartwatch-Based Tailored Gamification and User Modeling for Motivating Physical Exercise: Experimental Study With the Maximum Difference Scaling Segmentation Method

BACKGROUND

Smartwatch-based gamification holds great promise for enhancing fitness apps and promoting physical exercise; however, empirical evidence on its effectiveness remains inconclusive, partly due to "one-size-fits-all" design approaches that overlook individual differences. While the emerging research area of tailored gamification calls for more accurate user modeling and better customization of game elements, existing studies have relied primarily on rating scale-based measures and correlational analyses with methodological limitations.

OBJECTIVE

This study aimed to improve smartwatch-based gamification through an innovative user modeling approach to better motivate physical exercise among different user groups with tailored solutions. It incorporated both individual preferences and needs for game elements into the user segmentation process and used the maximum difference scaling (MaxDiff) technique, which can overcome the limitations of traditional methods.

METHODS

With data collected from 2 MaxDiff experiments involving 378 smartwatch users and latent class statistical models, the relative power of each of the 16 popular game elements was examined in terms of what users liked and what motivated them to exercise based on which distinct user segments were identified. Prediction models were also proposed for quickly classifying future users into the right segments to provide them with tailored gamification solutions on smartwatch fitness apps.

RESULTS

We identified 3 segments of smartwatch users based on their preferences for gamification. More importantly, we uncovered 4 segments motivated by goals, immersive experiences, rewards, or social comparison. Such user heterogeneity confirmed the susceptibility of the effects of gamification and indicated the necessity of accurately matching gamified solutions with user characteristics to better change health behaviors through different mechanisms for different targets. Important differences were also observed between the 2 sets of user segments (ie, those based on preferences for game elements vs those based on the motivational effects of the elements), indicating the gap between what people enjoy using on smartwatches and what can motivate them for physical exercise engagement.

CONCLUSIONS

To our knowledge, this study is the first to investigate MaxDiff-based user segmentation for tailored gamification on smartwatches promoting physical exercise and contributes to a detailed understanding of preferences for, and the effectiveness of, different game elements among different groups of smartwatch users. As existing tailored gamification studies continue to explore ways of user modeling with mostly surveys and questionnaires, this study supported the adoption of MaxDiff experiments as an alternative method to better capture user heterogeneity in the health domain and inform the design of tailored solutions for more application types beyond smartphones.

Unlocking innovation: the dynamics of user engagement in an open-source 3D printer community

User innovation lies at the core of a growing body of research, with a predominant focus on how individual user attributes can be associated with observed contributions to innovation. However, framing user innovation in terms of individual attributes risks neglecting important changes in behavior at the individual and community level over time. Here, our focus is on how the time that individual users spend in that community relates to innovation outcomes. We study innovation in a firm-hosted online user community for a 3D printer company, marked by their commitment to open-source hardware. We analyze a unique longitudinal dataset of 38,277 observations recording user posts to an open-access user forum from October 2011 to March 2015. We find that time spent in a user community is positively associated with contributions to innovation, as users migrate from identifying problems and needs, to contributing innovation-related modifications and hacks. Our finding emphasizes the value of user retention in open-source communities as our analysis shows that typical users can add value to firms' innovation activities over time. Our focus on open-hardware complements many existing studies on open-source software and advances our understanding of open innovation more generally.

Life against algorithmic management: a study on burnout and its influencing factors among food delivery riders

Background

With the rapid development of global digital economy, burnout among food delivery riders has become an important public health issue. Although burnout has been widely studied, research on burnout among food delivery riders, particularly the impact of algorithmic management on riders' burnout remains limited. This study adopts a novel perspective on the intersection of algorithmic management and burnout, offering an in-depth examination of the burnout levels of food delivery riders under the strict control of algorithmic management and identifying its influencing factors.

Methods

A survey of 953 food delivery riders was conducted using the Maslach Burnout Inventory-General Survey (MBI-GS). SPSS was used to conduct independent sample t-tests, one-way ANOVA, Pearson correlation, and multiple linear regression to investigate burnout status and identify factors affecting riders' burnout.

Results

The findings indicate that food delivery riders are experiencing moderate level of burnout, with Emotional Exhaustion, Depersonalization, and Reduced Personal Accomplishment as the primary dimensions. In the context of algorithmic management, key factors affecting riders' burnout include gender, age, working years, ranking system, Punishment system, work rules, Work monitoring mechanism, workflow design, customer feedback, and restaurant preparation time.

Conclusion

Under algorithmic management, burnout is prevalent among China's food delivery riders and influenced by multiple factors. Individualized support, humane organizational systems, satisfied work mechanism, and supportive social environment can help lessen algorithmic management's negative effects on food delivery riders and reduce their burnout. This study provides theoretical recommendations to protect occupational health of gig workers in platform economy, and offers valuable guidance for practical application.

The bright side of social media information overload for anti-COVID-19 behaviors: a stimulus-organism-response framework

Social media has an important impact on social management by communicating information that influences individual behavior; however, too much social media information can cause user information overload. The coronavirus disease 2019 (COVID-19) pandemic context provides an opportunity to analyze the impact of social media information overload on individuals' prosocial behaviors in emergency events. In this study, the impact of social media information overload on people's anti-COVID-19 behaviors, that is, behaviors that limit the transmission of COVID-19, was investigated by linking norm activation theory and information overload theory. Data were collected from 242 Chinese participants, and structural equation modeling was used for data analysis. The results showed that social media information overload positively influenced the awareness of consequences and the ascription of responsibility in anti-COVID-19 behaviors and that ascription of responsibility and awareness of consequences could activate people's personal norms and eventually motivate people to engage in prosocial behaviors for epidemic prevention. These findings could extend the information overload concept to a public health emergency context and yield useful insights for world pandemic control.

Development of a sperm morphology assessment standardization training tool

Training to improve the standardization of subjective assessments in biological science is crucial to improve and maintain accuracy. However, in reproductive science there is no standardized training tool available to assess sperm morphology. Sperm morphology is routinely assessed subjectively across several species and is often used as grounds to reject or retain samples for sale or insemination. As with all subjective tests, sperm morphology assessment is liable to human bias and without appropriate standardization these assessments are unreliable. This proof-of-concept study aimed to develop a standardized sperm morphology assessment training tool that can train and test students on a sperm-by-sperm basis. The following manuscript outlines the methods used to develop a training tool with the capability to account for different microscope optics, morphological classification systems, and species of spermatozoa assessed. The generation of images, their classification, organization, and integration into a web interface, along with its design and outputs, are described. Briefly, images of spermatozoa were generated by taking field of view (FOV) images at 40× magnification on DIC optics, amounting to a total of 3,600 FOV images from 72 rams (50 FOV/ram). These FOV images were cropped to only show one sperm per image using a novel machine-learning algorithm. The resulting 9,365 images were labelled by three experienced assessors, and those with 100% consensus on all labels (4821/9365) were integrated into a web interface able to provide both (i) instant feedback to users on correct/incorrect labels for training purposes, and (ii) an assessment of user proficiency. Future studies will test the effectiveness of the training tool to educate students on the application of a variety of morphology classification systems. If proven effective, it will be the first standardized method to train individuals in sperm morphology assessment and help to improve understanding of how training should be conducted.

Antecedents and Outcomes of Physician Coworker Conflict: A Differential Occupational Model for Health Care Managers

OBJECTIVES

Interpersonal conflict between physicians and their coworkers hinders health care organizations today on an unprecedented scale. Most research on physician conflict has been restricted to 2 occupational groups, nurses and administrators. Yet as health care delivery becomes more complex and interdependent, physician interpersonal conflict impacts all occupational groups. Thus, we seek to provide health care managers with specific guidance about how they might eliminate negative effects of physician-related interpersonal conflict for each occupation.

METHODS

We examined antecedents and outcomes of physician conflict across 4 occupational groups (office clerical and support staff, professional and technical, nursing, and managerial) in a survey of 1451 US health care employees. Using Multigroup Structural Equations Modeling (MSEM) analysis we estimated each relationship in our model across the 4 occupational groups.

RESULTS

We found that workload, perceived HR climate and patient-related incentives predicted physician conflict, and that physician conflict related to burnout, intention to turn over and psychological safety. Most notably, these antecedents and outcomes varied meaningfully across occupational groups.

CONCLUSIONS

These observed differential effects in our results suggest that managers should carefully consider the needs of different occupational groups separately when designing and implementing interventions to prevent and ameliorate physician conflict. In short, the antecedents and outcomes of physician conflict are different for office/clerical, nurses, professional/technical employees, and management groups and therefore require different solutions. In short, it behooves health care organizations to avoid a one size fits all approach to improving workplace relationships.

Promoting Health Literacy With Human-in-the-Loop Video Understandability Classification of YouTube Videos: Development and Evaluation Study

BACKGROUND

An estimated 93% of adults in the United States access the internet, with up to 80% looking for health information. However, only 12% of US adults are proficient enough in health literacy to interpret health information and make informed health care decisions meaningfully. With the vast amount of health information available in multimedia formats on social media platforms such as YouTube and Facebook, there is an urgent need and a unique opportunity to design an automated approach to curate online health information using multiple criteria to meet the health literacy needs of a diverse population.

OBJECTIVE

This study aimed to develop an automated approach to assessing the understandability of patient educational videos according to the Patient Education Materials Assessment Tool (PEMAT) guidelines and evaluating the impact of video understandability on viewer engagement. We also offer insights for content creators and health care organizations on how to improve engagement with these educational videos on user-generated content platforms.

METHODS

We developed a human-in-the-loop, augmented intelligence approach that explicitly focused on the human-algorithm interaction, combining PEMAT-based patient education constructs mapped to features extracted from the videos, annotations of the videos by domain experts, and cotraining methods from machine learning to assess the understandability of videos on diabetes and classify them. We further examined the impact of understandability on several dimensions of viewer engagement with the videos.

RESULTS

We collected 9873 YouTube videos on diabetes using search keywords extracted from a patient-oriented forum and reviewed by a medical expert. Our machine learning methods achieved a weighted precision of 0.84, a weighted recall of 0.79, and an F1-score of 0.81 in classifying video understandability and could effectively identify patient educational videos that medical experts would like to recommend for patients. Videos rated as highly understandable had an average higher view count (average treatment effect [ATE]=2.55; P<.001), like count (ATE=2.95; P<.001), and comment count (ATE=3.10; P<.001) than less understandable videos. In addition, in a user study, 4 medical experts recommended 72% (144/200) of the top 10 videos ranked by understandability compared to 40% (80/200) of the top 10 videos ranked by YouTube's default algorithm for 20 ramdomly selected search keywords.

CONCLUSIONS

We developed a human-in-the-loop, scalable algorithm to assess the understandability of health information on YouTube. Our method optimally combines expert input with algorithmic support, enhancing engagement and aiding medical experts in recommending educational content. This solution also guides health care organizations in creating effective patient education materials for underserved health topics.

Exploring social influences and values in promoting sustainable food consumption using hybrid three stage methods

Although social media is widely recognized as necessary for promoting sustainable food consumption, the theoretical essence of its social influence has yet to be clarified. This research establishes a mutually beneficial model for sustainable food consumption, drawing on Social Influence Theory and Value Theory. The study collected data from an online survey of 15 experts in sustainable food and 311 consumers and analyzed it through a hybrid three-stage approach of fuzzy Delphi, PLS-SEM, and ANN. The results indicate that subjective norms, group norms, and social identity positively influence egoistic and altruistic values, promoting sustainable food consumption. In comparison, social identity has a more important impact on sustainable food consumption, followed by subjective norms and group norms. Usage and value barriers are the main obstacles to sustainable consumption in China, with value barriers moderating the relationship between altruistic value and sustainable food consumption. This study offers innovative approaches to leveraging information technology for achieving sustainable development goals. It holds substantial practical value for stakeholders, including food marketers, government policymakers, and social advocates, providing actionable insights into optimizing marketing strategies, implementing policy incentives, and promoting a culture of sustainable food consumption.