Exploring fishers' pro-environmental behavioral intention and support for policies to combat marine litter in Vietnam

This study applies Value-Belief-Norm (VBN) theory to investigate fishers' pro-environmental behavioral intention and their support for policies to reduce marine litter. While pro-environmental behavioral intention is often associated with personal environmental intention at the household level, policy support represents their support for government action at the political level. Therefore, we examine whether fishers are willing to engage in both of these dimensions. Data from 369 Vietnamese fishers are analyzed using structural equation modeling with FIMIX-PLS and PLS-POS. The results show that the sequence of values, beliefs, and personal norms can explain the fishers' pro-environmental behavioral intention and their support for policies addressing marine litter. Two segments of the fishers are detected from the sample with different perspectives of engagement intention. Fishers in segment 1 placed great emphasis on the consequences of marine litter on fishing activities as a motivator for their responsible actions. Meanwhile, fishers in segment 2 express pro-environmental engagement intention driven by the concern about responsibility and moral obligation. A binary logistic regression indicates that the risks of marine litter on fishing activity can provide the explanations for the differences between them. Our findings highlight both fishers' pro-environmental behavior and policy support as important strategies for dealing with marine litter in fisheries. If implemented simultaneously, they hold promise to alleviate the environmental and economic impact of marine litter in Vietnam.

Research on the dynamic mechanism of digital economy system coupling to enhance urban ecological resilience

Urban ecological environment resilience is an important characteristic that should be possessed in the process of urban development. It is conducive to coping with the challenges of multiple risks and disturbances such as climate change, resolving chronic pressure, improving the ability to resist disaster risk, self-adjustment, and recovery, to maintain the structure and function stability of the urban system. The digital economy is a new economic form caused by the new technological revolution, which may effectively promote economic ecology and ecological economization. We clarify the elements of the digital economic system, construct the coupling evaluation index system of "digital infrastructure-industrial digitization-digital industrialization," and establish the coupling degree model to analyze the characteristics of the integration interaction, coordination, and self-organization of the digital economy subsystem. Based on emergency management theory, adaptive management concept, and resilient city theory, an evaluation index system is constructed from four levels of prevention, resistance, adaptation, and recovery to measure urban ecological resilience. Taking 278 cities in China from 2011 to 2021 as the research object, we established a spatial econometric model to explore the dynamic mechanism of digital economy system composition and coupling coordination to enhance urban resilience and summarize the theoretical model form. Based on this, we further propose countermeasures and suggestions for improving urban ecological resilience by using a digital economic system.

Prospective associations of technostress at work, burnout symptoms, hair cortisol, and chronic low-grade inflammation

BACKGROUND

Working conditions in the age of digitalization harbor risks for chronic stress and burnout. However, real-world investigations into biological effects of technostress, that is stress in the context of digital technology use, are sparse. This study prospectively assessed associations between technostress, general work stress, burnout symptoms, hair cortisol, and chronic low-grade inflammation.

METHODS

Hospital employees (N = 238, 182 females, Mage = 28.5 years) participated in a prospective cohort study with two follow-ups six months apart (T2, T3). Participants answered standardized questionnaires on general job strain (job demand-control ratio), technostressors (work interruptions, multitasking, information overload), burnout symptoms (exhaustion, mental distance), and relevant confounders. Moreover, they provided capillary blood samples for C-reactive protein (CRP) and hair strands for hair cortisol concentration (HCC) analysis. Structural equation modelling was performed.

RESULTS

The factorial structure of survey measures was confirmed. Burnout symptoms (MT2 = 2.17, MT3 = 2.33) and HCC (MT2 = 4.79, MT3 = 9.56; pg/mg) increased over time, CRP did not (MT2 = 1.15, MT3 = 1.21; mg/L). Adjusted path models showed that technostress was negatively associated with HCC (β = -0.16, p =.003), but not with burnout and CRP. General work stress in contrast, was not significantly associated with burnout, HCC or CRP. Furthermore, there were reciprocal effects of CRP on HCC (β = 0.28, p =.001) and of HCC on CRP (β = -0.10, p ≤.001). Associations were robust in additional analyses including further confounders.

CONCLUSION

This is the first study on prospective effects of technostress on employees' endocrine and inflammatory systems. Results suggest differential effects of technostress on the hypothalamic-pituitary-adrenocortical axis activity. Given its key role for long-term health, the findings have important implications for occupational health and safety in digitalized work environments.

Anatomy in the metaverse: Exploring student technology acceptance through the UTAUT2 model

The impact of technology on educational domains has been a subject of research for many years. Therefore, understanding how students perceive and utilize technologies for educational purposes is crucial. Especially in a critical subject like anatomy education, it is essential to employ various models to determine students' technology acceptance and usage levels. One of these models is the unified theory of acceptance and use of technology-2 (UTAUT2). In this study, the acceptance and usage levels of metaverse technology by students in the context of anatomy education in the metaverse environment have been investigated within the framework of the UTAUT2 model. The study was conducted with students from the Department of Midwifery at the Faculty of Health Sciences during the fall semester of the 2022-2023 academic year. After 6 weeks of anatomy education in the metaverse environment, the student's acceptance and usage levels of metaverse technology were assessed using the UTAUT2 scale. The collected data were analyzed using the partial least squares structural equation modeling method. The research has revealed the technology acceptance and usage levels of students who utilized metaverse technology in anatomy education. According to the obtained results, it has been observed that social influence affects behavioral intention, facilitating conditions influence behavioral intention, and habit variable affects behavioral intention, while habit, in turn, influences usage. Other variables explain behavioral intention with a proportion of 75.30%, whereas other variables explain usage behavior with 54.90%. Upon analyzing the responses to the UTAUT2 scale, it has been concluded that there is the highest level of participation in hedonic motivation. In contrast, the habit variable has the lowest participation level. The findings of this study have enabled the identification of the impacts of metaverse technologies on anatomy and health education. These results will likely contribute to improving the utilization of metaverse technologies in anatomy and health education.

The policy case for designating EMS teams for vulnerable patient populations: Evidence from an intervention in Boston

This study documents more than five years of analysis that drove the policy case, deployment, and retrospective evaluation for an innovative service model that enables Boston Emergency Medical Services (EMS) to respond quickly and effectively to investigation incidents in an area of heavy need in Boston. These investigation incidents are typically calls for service from passers-by or other third-party callers requesting that Boston EMS check in on individuals, often those who may appear to have an altered mental status or to be unhoused. First, this study reports the pre-intervention analytics in 2017 that built the policy case for service segmentation, a new Community Assistance Team designated "Squad 80" that primarily responds to investigation incidents in one broad area of the city with high rates of substance abuse and homelessness, helping patients who often refuse ambulance transport connect to social services. Second, this study reports a post-intervention, observational evaluation of its operational advantages and trade-offs. We observe that incidents involving the Community Assistance Team have significantly shorter response times and result in fewer transports to emergency departments than investigation incidents not involving the unit, leading to fewer ambulance unit-hours utilized across the system. This study documents the descriptive analytics that built the successful policy case for a substantive change in the healthcare-delivery supply chain in Boston and how this change offers operational advantages. It is written to be an accessible guide to the analysts and policy makers considering emergency services segmentation, an important frontier in equitable public-service delivery.

Examination of the adoption intention of new energy vehicles from the perspective of functional attributes and media richness

Drawing on the theory of media richness, this paper aims to explore the impact of media richness on consumers' adoption intention through their perception of new energy vehicle (NEV) function attributes, and assess the moderation roles of brand familiarity and locus of control. A structural equation model is applied to analyze the data collected from 427 respondents. Empirical results demonstrate that consumers' perception of an electric attribute (i.e., charging efficiency) and two intelligent attributes (i.e., car networking and self-driving) are determinants of their adoption intention of NEVs. The other electric attribute (range) is trivial in consumers' perception. We also find that low, medium, and high-richness media significantly affect consumers' perception of NEVs' functional attributes. Compared to the high-richness, medium-richness correlates significantly with two types of NEV functional attributes. Regarding moderating effects, consumer familiarity with NEV's brand negatively impacts the relationship between media richness and adoption intention. Furthermore, low and medium-richness media effectively stimulate individuals with external control to adopt NEV, while high-richness media adversely influence individuals with internal control.

Adoption of digital twins as a sustainable energy solution: Determinants to adoption in household

Digital Twin (DT) consists of a recent technology that can enable sustainability. However, Digital Twins are still in early stages of adoption, especially in households, and so the determinants to this adoption have not yet been determined. The aim of this study is to fill this research gap through providing a conceptual model of the drivers to the adoption of Digital Twins in households and it's relation to well-being. This study is developed as a mixed-methods research. The model is produced qualitatively, based on literature discoveries and key findings from interviews with experts and possible consumers. Afterwards, the model was validated with data collected through a questionnaire with 149 respondents. Results show that a set of informational, social, environmental and utility factors can influence the intention to adopt Digital Twins as a sustainable energy solution, and consequently the perceived well-being.

Designing and evaluating tasks to measure individual differences in experimental psychology: a tutorial

Experimental psychology is witnessing an increase in research on individual differences, which requires the development of new tasks that can reliably assess variations among participants. To do this, cognitive researchers need statistical methods that many researchers have not learned during their training. The lack of expertise can pose challenges not only in designing good, new tasks but also in evaluating tasks developed by others. To bridge the gap, this article provides an overview of test psychology applied to performance tasks, covering fundamental concepts such as standardization, reliability, norming and validity. It provides practical guidelines for developing and evaluating experimental tasks, as well as for combining tasks to better understand individual differences. To further address common misconceptions, the article lists 11 prevailing myths. The purpose of this guide is to provide experimental psychologists with the knowledge and tools needed to conduct rigorous and insightful studies of individual differences.

Mindfully and confidently digital: A mixed methods study on personal resources to mitigate the dark side of digital working

A growing body of research demonstrates the potential of mindfulness to reduce employee stress. However, with work increasingly migrating from the physical to the digital workplace, evidence is lacking on how mindfulness might help employees live healthy digital working lives. In addition, employees' confidence when using the digital workplace is seen as important for productivity but may also play a role in reducing well-being impacts from digital working. Using the Job-Demands Resources model as a theoretical foundation, 142 workers were surveyed regarding their levels of trait mindfulness and digital workplace confidence, along with their experiences of the dark side effects (stress, overload, anxiety, Fear of Missing Out and addiction) and well-being outcomes (burnout and health). 14 workers were also interviewed to provide qualitative insights on these constructs. Results from regression analyses indicated that more digitally confident workers were less likely to experience digital workplace anxiety, while those with higher mindfulness were better protected against all of the dark side of digital working effects. Interview data indicated ways in which digital mindfulness helps protect well-being, as well as how digital workplace confidence enables healthier digital habits.

Identifying the Risk Factors of Allergic Rhinitis Based on Zhihu Comment Data Using a Topic-Enhanced Word-Embedding Model: Mixed Method Study and Cluster Analysis

BACKGROUND

Allergic rhinitis (AR) is a chronic disease, and several risk factors predispose individuals to the condition in their daily lives, including exposure to allergens and inhalation irritants. Analyzing the potential risk factors that can trigger AR can provide reference material for individuals to use to reduce its occurrence in their daily lives. Nowadays, social media is a part of daily life, with an increasing number of people using at least 1 platform regularly. Social media enables users to share experiences among large groups of people who share the same interests and experience the same afflictions. Notably, these channels promote the ability to share health information.

OBJECTIVE

This study aims to construct an intelligent method (TopicS-ClusterREV) for identifying the risk factors of AR based on these social media comments. The main questions were as follows: How many comments contained AR risk factor information? How many categories can these risk factors be summarized into? How do these risk factors trigger AR?

METHODS

This study crawled all the data from May 2012 to May 2022 under the topic of allergic rhinitis on Zhihu, obtaining a total of 9628 posts and 33,747 comments. We improved the Skip-gram model to train topic-enhanced word vector representations (TopicS) and then vectorized annotated text items for training the risk factor classifier. Furthermore, cluster analysis enabled a closer look into the opinions expressed in the category, namely gaining insight into how risk factors trigger AR.

RESULTS

Our classifier identified more comments containing risk factors than the other classification models, with an accuracy rate of 96.1% and a recall rate of 96.3%. In general, we clustered texts containing risk factors into 28 categories, with season, region, and mites being the most common risk factors. We gained insight into the risk factors expressed in each category; for example, seasonal changes and increased temperature differences between day and night can disrupt the body's immune system and lead to the development of allergies.

CONCLUSIONS

Our approach can handle the amount of data and extract risk factors effectively. Moreover, the summary of risk factors can serve as a reference for individuals to reduce AR in their daily lives. The experimental data also provide a potential pathway that triggers AR. This finding can guide the development of management plans and interventions for AR.

Predicting online privacy protection for Facebook users with an extended theory of planned behavior

The current research uses an extended theory of planned behavior (TPB) model to predict Facebook users' (N = 376) intentions to protect their privacy online. It aims to replicate and extend Saeri et al. (2014) who found partial support for an extended TPB model that included descriptive norms, perceived risk, and trust. Facebook users completed an online questionnaire assessing attitudes, norms (subjective and group), perceived behavioral control (PBC), perceived risk, trust, privacy concerns, and intentions to protect their privacy online. Results revealed that attitudes, subjective norms, and PBC (i.e. the TPB) predicted online privacy intentions, as well as descriptive group norms and privacy concerns. However, perceived risk, trust, and injunctive group norms were not significant unique predictors of online privacy intentions. The implications for understanding influences on individuals' willingness to protect their privacy online are discussed.

Two-Way FDI assists agricultural sustainable development: Based on digitalization and greening perspectives

With the new challenges and crises facing agriculture, digitalization and green transformation have become important ways to solve the problems. This paper uses an international economics perspective to chart a new path for sustainable agricultural development. Specifically, it analyzes whether two-way international direct investment (FDI) can facilitate agricultural digital-green fusion(DGF)? Using a sample of 31 provinces (autonomous regions) from 2012 to 2021, this study finds: (1) Two-way FDI can significantly contribute to agriculture's DGF. (2) In the mechanism test, it is proved that two-way FDI can promote agriculture's DGF level by promoting green technology innovation capacity and overall regional technology innovation capacity. (3) The positive effects of two-way FDI are prominent in the eastern and central regions, coastal regions, and economically developed areas. (4) In the spatial Durbin model, the local two-way FDI growth improves agriculture's DGF level in the surrounding areas to a certain extent. The government is advised to prioritize openness, foster an environment for technological innovation, leverage spatial radiation for agricultural DGF, and advance digitally empowered agricultural modernization.

Decision support framework for IT project manager recruitment

Information technology project managers (IT PM) have a critical influence on IT project success while attracting and selecting the right IT PM is challenging. We followed a four-level research design and firstly developed a taxonomy as an input for a cluster analysis to identify patterns in IT PM job advertisements. Based hereon, we developed a decision support framework for IT PM recruitment. We evaluated our findings in an online survey. We identified multiple design elements for IT PM job advertisements within five perspectives and deduced five IT PM archetypes. The decision support framework uses five questions to assist IT PM recruitment. We expand the knowledge base and consider not only IT PM requirements but also benefits. Our decision support framework is the first to holistically support IT PM recruitment, supports recruitment managers in structuring job interviews, identifies potential matches between applicants and recruiters, and assists in the final selection.

Why are there differences across German states in student achievement and cognitive ability?

Background

For more than twenty years, large and generally stable differences in academic achievement and cognitive ability have been reported within Germany. In such studies, the southern regions lead in the west and east, while city-states lag behind. Expressed in school learning time, the students in Bavaria are 14 months ahead of the students in Bremen. It is striking that there are no or only marginally received studies on causes and consequences.

Purpose

This study attempts to explore the causes and consequences of the differences within Germany and what can be learned in general about their development.

Materials and methods

We use data from student assessment and other studies (e.g., PISA, IQB) and apply correlational and path analyses, controlled for various background factors.

Results

There are no stable correlations with evolution (genes), educational level of society (adult school years) and wealth (GDP per capita). However, there are high correlations, robust across indicators, with "burgher-conservative" education policies, e.g., central exit examinations, early tracking, grades at a young age (around r ≈ .65); with measures of students' quantity of education (hours of instruction, no teacher shortage; r ≈ .40); with measures of tertiary educational quality and appreciation of education (university quality, short duration of studies, professors' salaries; r ≈ .50); with student native/immigrant ratio (r ≈ .50); with middle-class burgher lifestyle (less private debt, less welfare dependency and less crime; r ≈ .60); and with burgher-conservative-right politics (share of votes for CDU/CSU and non-left parties, non-left state governments; r ≈ .80). Longitudinal analyses over four decades reveal interaction effects, i.e., more burgher policies statistically lead to more cognitively competent students (β ≈ .45) and more cognitively competent populations vote for burgher parties (β ≈ .30).

Conclusions

The results, which support the efficacy a bourgeois-conservative education policy and of lower immigration rates, are delicate for the practice of student achievement research and for the political milieu that dominates the social sciences.

Match experiences affect interest: Impacts of matchmaking and performance on churn in a competitive game

This study assessed how matchmaking and match results affect player churn in a multiplayer competitive game. In competitive games, matchmaking is crucial in gathering players with similar skills and creating balanced player-versus-player matches. Players are highly motivated when they win matches, whereas losing matches is demotivating, leading to churn. We performed a two-way fixed effects estimation using our panel data to analyze the relationship between players' churn and match experience. The panel data retrieved 42 days of server-side in-game logs, comprising approximately six million matches played by more than 262k players in the casual commercial game "Everybody's Marble." The experimental results indicate that churn is positively influenced by being matched with stronger opponents. Interestingly, being matched with weaker opponents decreases the possibility of churn more than fair matches (being matched with equally skilled opponents). Furthermore, large differences in opponents' skill levels positively influence churn, while more frequent and consecutive wins negatively influence it. The results also reveal that consecutive losses can affect churn differently, depending on the players' level. This study provides theoretical and practical implications for researchers who want to understand the factors that affect user churn and game developers who want to maximize user retention rates in commercial games.

Effect of leadership styles on turnover intention among staff nurses in private hospitals: the moderating effect of perceived organizational support

Leadership styles have often been proven to support employees in performing their duties better and with more efficiency while enabling them to have extended organizational tenures. Staff nurses are an essential resource of hospitals to ensure proper administration and quality patient health care. The study aims to determine how transformational and authentic leadership styles affect the staff nurses' turnover intention in private hospitals. In addition, it also finds the moderating effect of perceived organizational support. An explanatory quantitative research design with a cross-sectional investigation and a stratified sampling strategy was used for the study. Data from 296 nurses from the eight chosen private hospitals in the Kingdom of Bahrain were gathered using a questionnaire with 24 items. Smart-PLS was employed to conduct PLS-SEM (partial least squares structural equation modeling) to measure direct and indirect effects. The result indicates that transformational, authentic leadership styles and perceived organizational support significantly negatively affect nurses' turnover intention. The study confirms the negative moderating effect of perceived organizational support between transformational leadership and turnover intention and the positive moderating effect of perceived organizational support between authentic leadership and turnover intention. Managers should concentrate on the leadership style to avoid its impact on turnover intention. By considering human resource practices such as communication and training strategies to cope with the negative effect of turnover intention, organizations can enhance employee engagement, improve job satisfaction, and foster a more stable and productive work environment. The present research revealed the adverse impact of turnover intention within hospitals by examining its association with leadership styles. The research made a significant contribution to the existing literature by delving into the impact of leadership styles on turnover intention, focusing on the moderating effect of perceived organizational support. The study's findings shed light on the intricate relationship between leadership practices and employee turnover, providing valuable insights for both scholars and practitioners in the field. The study used a cross-sectional design to collect data and ensured the absence of standard method variance. The research enhanced the social dominance theory (SDT) by examining how perceived organizational support moderates the relationship between leadership styles and turnover intention.

A machine learning model to predict privacy fatigued users from social media personalized advertisements

The increasing use of social media platforms as personalized advertising channels is a double-edged sword. A high level of personalization on these platforms increases users' sense of losing control over personal data: This could trigger the privacy fatigue phenomenon manifested in emotional exhaustion and cynicism toward privacy, which leads to a lack of privacy-protective behavior. Machine learning has shown its effectiveness in the early prediction of people's psychological state to avoid such consequences. Therefore, this study aims to classify users with low and medium-to-high levels of privacy fatigue, based on their information privacy awareness and big-five personality traits. A dataset was collected from 538 participants via an online questionnaire. The prediction models were built using the Support Vector Machine, Naïve Bayes, K-Nearest Neighbors, Decision Tree, and Random Forest classifiers, based on the literature. The results showed that awareness and conscientiousness trait have a significant relationship with privacy fatigue. Support Vector Machine and Naïve Bayes classifiers outperformed the other classifiers by attaining a classification accuracy of 78%, F1 of 87%, recall of 100% and 98%, and precision of 78% and 79% respectively, using five-fold cross-validation.

Systematic design of health monitoring systems centered on older adults and ADLs

BACKGROUND

Older adults face unique health challenges as they age, including physical and mental health issues and mood disorders. Negative emotions and social isolation significantly impact mental and physical health. To support older adults and address these challenges, healthcare professionals can use Information and Communication Technologies (ICTs) such as health monitoring systems with multiple sensors. These systems include digital biomarkers and data analytics that can streamline the diagnosis process and help older adults to maintain their independence and quality of life.

METHOD

A design research methodology is followed to define a conceptual model as the main artifact and basis for the systematic design of successful systems centered on older adults monitoring within the health domain.

RESULTS

The results include a conceptual model focused on older adults' Activities of Daily Living (ADLs) and Health Status, considering various health dimensions, including social, emotional, physical, and cognitive dimensions. We also provide a detailed instantiation of the model in real use cases to validate the usefulness and feasibility of the proposal. In particular, the model has been used to develop two health systems intended to measure the degree of the elders' frailty and dependence with biomarkers and machine learning.

CONCLUSIONS

The defined conceptual model can be the basis to develop health monitoring systems with multiple sensors and intelligence based on data analytics. This model offers a holistic approach to caring for and supporting older adults as they age, considering ADLs and various health dimensions. We have performed an experimental and qualitative validation of the proposal in the field of study. The conceptual model has been instantiated in two specific case uses, showing the provided abstraction level and the feasibility of the proposal to build reusable, extensible and adaptable health systems. The proposal can evolve by exploiting other scenarios and contexts.

Exploring the role of professional identity in the implementation of clinical decision support systems-a narrative review

BACKGROUND

Clinical decision support systems (CDSSs) have the potential to improve quality of care, patient safety, and efficiency because of their ability to perform medical tasks in a more data-driven, evidence-based, and semi-autonomous way. However, CDSSs may also affect the professional identity of health professionals. Some professionals might experience these systems as a threat to their professional identity, as CDSSs could partially substitute clinical competencies, autonomy, or control over the care process. Other professionals may experience an empowerment of the role in the medical system. The purpose of this study is to uncover the role of professional identity in CDSS implementation and to identify core human, technological, and organizational factors that may determine the effect of CDSSs on professional identity.

METHODS

We conducted a systematic literature review and included peer-reviewed empirical studies from two electronic databases (PubMed, Web of Science) that reported on key factors to CDSS implementation and were published between 2010 and 2023. Our explorative, inductive thematic analysis assessed the antecedents of professional identity-related mechanisms from the perspective of different health care professionals (i.e., physicians, residents, nurse practitioners, pharmacists).

RESULTS

One hundred thirty-one qualitative, quantitative, or mixed-method studies from over 60 journals were included in this review. The thematic analysis found three dimensions of professional identity-related mechanisms that influence CDSS implementation success: perceived threat or enhancement of professional control and autonomy, perceived threat or enhancement of professional skills and expertise, and perceived loss or gain of control over patient relationships. At the technological level, the most common issues were the system's ability to fit into existing clinical workflows and organizational structures, and its ability to meet user needs. At the organizational level, time pressure and tension, as well as internal communication and involvement of end users were most frequently reported. At the human level, individual attitudes and emotional responses, as well as familiarity with the system, most often influenced the CDSS implementation. Our results show that professional identity-related mechanisms are driven by these factors and influence CDSS implementation success. The perception of the change of professional identity is influenced by the user's professional status and expertise and is improved over the course of implementation.

CONCLUSION

This review highlights the need for health care managers to evaluate perceived professional identity threats to health care professionals across all implementation phases when introducing a CDSS and to consider their varying manifestations among different health care professionals. Moreover, it highlights the importance of innovation and change management approaches, such as involving health professionals in the design and implementation process to mitigate threat perceptions. We provide future areas of research for the evaluation of the professional identity construct within health care.

Personality traits and their influence on Echo chamber formation in social media: a comparative study of Twitter and Weibo

The echo chamber effect on social media has attracted attention due to its potentially disruptive consequences on society. This study presents a framework to evaluate the impact of personality traits on the formation of echo chambers. Using Weibo and Twitter as platforms, we first define an echo chamber as a network where users interact solely with those sharing their opinions, and quantify echo chamber effects through selective exposure and homophily. We then employ an unsupervised personality recognition method to assign a personality model to each user, and compare the distribution differences of echo chambers and personality traits across platforms and topics. Our findings show that, although user personality trait models exhibit similar distributions between topics, differences exist between platforms. Among 243 personality model combinations, over 20% of Weibo echo chamber members are "ynynn" models, while over 15% of Twitter echo chamber members are "nnnny" models. This indicates significant differences in personality traits among echo chamber members between platforms. Specific personality traits attract like-minded individuals to engage in discussions on particular topics, ultimately forming homogeneous communities. These insights are valuable for developing targeted management strategies to prevent the spread of fake news or rumors.

A three-player game model for promoting enterprise green technology innovation from the perspective of media coverage

Objective

The objective of this study was to explore the game relationship among enterprise, the government, and the public under the new media environment, so as to provide decision-making reference for improving enterprise green technology innovation and promoting economy high-quality development with new media participation.

Methods

This study constructs a three-subject evolutionary game model of enterprise, government, and public based on multi-agent relationship analysis and evolutionary game theory. In addition, the derivation of an evolutionary equilibrium strategy and numerical simulation analysis is carried out to comprehensively explore the evolution trajectory of green technology innovation system under the new media environment.

Findings

(1) The system may have four stable evolutionary strategies: (1,0,0), (0,0,1), (1,0,1), and (1,1,1). (2) The initial strategy probability of various actors would affect the system evolution speed but not the evolution result, and the authenticity of new media reports is an important factor determining the system evolution of green technology innovation. (3) Numerical simulation results show that a fair and just new media environment can effectively constrain the traditional production behavior of enterprise, actively guide the public to participate in supervision, and play an alternative role to government regulation to a certain extent.

Value

This study explores the evolutionary balance strategy of green technology innovation system under the new media environment, which not only enriches relevant theories of media environment governance but also has important reference value for promoting enterprises' green technology innovation and establishing an environmental governance system jointly governed by government, enterprise, public, and media.

Does AI-Driven Technostress Promote or Hinder Employees' Artificial Intelligence Adoption Intention? A Moderated Mediation Model of Affective Reactions and Technical Self-Efficacy

PURPOSE

The increasing integration of Artificial Intelligence (AI) within enterprises is generates significant technostress among employees, potentially influencing their intention to adopt AI. However, existing research on the psychological effects of this phenomenon remains inconclusive. Drawing on the Affective Events Theory (AET) and the Challenge-Hindrance Stressor Framework (CHSF), the current study aims to explore the "black box" between challenge and hindrance technology stressors and employees' intention to adopt AI, as well as the boundary conditions of this mediation relationship.

METHODS

The study employs a quantitative approach and utilizes three-wave data. Data were collected through the snowball sampling technique and a structured questionnaire survey. The sample comprises employees from 11 distinct organizations located in Guangdong Province, China. We received 301 valid questionnaires, representing an overall response rate of 75%. The theoretical model was tested through confirmatory factor analysis and regression analyses using Mplus and the Process macro for SPSS.

RESULTS

The results indicate that positive affect mediates the positive relationship between challenge technology stressors and AI adoption intention, whereas AI anxiety mediates the negative relationship between hindrance technology stressors and AI adoption intention. Furthermore, the results reveal that technical self-efficacy moderates the effects of challenge and hindrance technology stressors on affective reactions and the indirect effects of challenge and hindrance technology stressors on AI adoption intention through positive affect and AI anxiety, respectively.

CONCLUSION

Overall, our study suggests that AI-driven challenge technology stressors positively impact AI adoption intention through the cultivation of positive affect, while hindrance technology stressors impede AI adoption intention by triggering AI anxiety. Additionally, technical self-efficacy emerges as a crucial moderator in shaping these relationships. This research has the potential to make a meaningful contribution to the literature on AI adoption intention, deepening our holistic understanding of the influential mechanisms involved. Furthermore, the study affirms the applicability and relevance of Affective Events Theory (AET) and the Challenge-Hindrance Stressor Framework (CHSF). In practical terms, the research provides actionable insights for organizations to effectively manage employees' AI adoption intention.

Unveiling the affecting mechanism of digital transformation on total factor productivity of Chinese firms

The importance of digital transformation (DGT) for increasing productivity cannot be negated and Chinese firms are rapidly embracing the digital transformation for their sustainability. But the mechanism and impact of digital transformation on total factor productivity (TFP) of firms is still unclear and this study is intended to fill this gap using the data of 3112 listed firms of China during 2011 to 2022. We applied various econometric techniques like stepwise regression analysis, instrumental variable approach, differences in difference approach, and mediating analysis to determine the relationship between digital transformation and TFP and robustness of estimated findings. The findings indicate that DGT has a positive impact on overall TFP of firms in China while operating efficiency, cheaper costs, and a stronger capacity for innovation mediates this relationship. Moreover, it is explored that conventional information and communication technologies have not significant impact on TFP of firms. The findings of the study remain valid even applying many robustness checks and attempts to control the issue of endogeneity. To fully leverage the potential benefits of digital transformation on TFP, it is essential to focus on enhancing digital literacy and skills among the workforce. Governments and relevant stakeholders should prioritize and invest in comprehensive digital literacy and skills training programs to empower the workforce with the knowledge and expertise needed to navigate the digital age effectively.

The impact of digital infrastructure on provincial green innovation efficiency-empirical evidence from China

In the epoch of the digital economy, technological innovation and energy conservation are significantly facilitated by digital infrastructure, leading to substantial improvements in green innovation efficiency at the provincial level. This study employed the feasible generalized least square (FGLS) method to examine the effects of digital infrastructure on the green innovation efficiency across 30 provinces in the Chinese mainland, utilizing panel data from 2011 to 2020. Additionally, this investigation delves into the intervening role of industrial structure upgrading and the amplifying effects of environmental regulation and human capital on the process. Findings indicate that, to begin with, digital infrastructure contributes to the meaningful enhancement of green innovation efficiency within provinces. Subsequently, the industrial structure upgrading partially mediates the impact of digital infrastructure on the efficiency of provincial green innovation. Lastly, both human capital and environmental regulations amplify the beneficial influence of digital infrastructure on the effectiveness of green innovation at the provincial level. This study provides valuable insights into the mechanisms through which digital infrastructure boosts green innovation efficiency, aiding policymakers in formulating appropriate policies to augment digital infrastructure, thereby promoting provincial green innovation efficiency.

How artificial intelligence is reshaping the autonomy and boundary work of radiologists. A qualitative study

The application of artificial intelligence (AI) in medical practice is spreading, especially in technologically dense fields such as radiology, which could consequently undergo profound transformations in the near future. This article aims to qualitatively explore the potential influence of AI technologies on the professional identity of radiologists. Drawing on 12 in-depth interviews with a subgroup of radiologists who participated in a larger study, this article investigated (1) whether radiologists perceived AI as a threat to their decision-making autonomy; and (2) how radiologists perceived the future of their profession compared to other health-care professions. The findings revealed that while AI did not generally affect radiologists' decision-making autonomy, it threatened their professional and epistemic authority. Two discursive strategies were identified to explain these findings. The first strategy emphasised radiologists' specific expertise and knowledge that extends beyond interpreting images, a task performed with high accuracy by AI machines. The second strategy underscored the fostering of radiologists' professional prestige through developing expertise in using AI technologies, a skill that would distinguish them from other clinicians who did not pose this knowledge. This study identifies AI machines as status objects and useful tools in performing boundary work in and around the radiological profession.

Health literacy needs and preferences for a technology-based intervention to improve college students' sexual and reproductive health

Objective: To explore health literacy needs and preferences for a technology-based intervention (app) to improve sexual and reproductive health (SRH) among college students. Participants: In Spring 2019, in-depth interviews were conducted with 20 participants (10 male, 10 female) from a large, public university. Methods: Interview guide was developed based on Integrated Model of Health Literacy domains and Diffusion of Innovation constructs. Data were analyzed in MaxQDA using applied thematic analysis. Results: Dominant themes included accessing health information and services, evaluating options to make decisions, intervention utility and characteristics, and the emergent theme of credibility. Specific topics included accessing STI testing, contraceptive decision making, information on human papillomavirus (HPV) and the HPV vaccine, patient-provider communication, app design and function elements, and modifying the app to meet the SRH needs of diverse college students. Conclusions: Findings identified areas where an app could address college students' SRH literacy, ultimately improving SRH outcomes among this population.

Framing doctor-managers' resilience during Covid-19 pandemic: A descriptive analysis from the Italian NHS

With the aim of providing evidence about doctor-managers' resilience during the Covid-19 pandemic, this study analyzes the characteristics of 114 doctor-managers operating within the Italian National Health Service (NHS). During the emergency, doctor-managers had to show adaptive capacities to deal with unexpected situations and develop new paradigms, procedures, and quick responses to patients' needs. This is in line with resilience, and in this perspective, it is crucial to investigate resilience determinants. The paper, therefore, provides an identikit of the resilient doctor-manager. The research was conducted between November and December 2020. Primary data were collected through an online questionnaire consisting of six sections. Participation was voluntary and anonymous. Data were analyzed using quantitative techniques and employing Stata 16. Confirmatory Factor Analysis was employed to test construct validity and scale reliability. Results show that increasing levels of individual resilience are related to increasing levels of managerial identity. Moreover, physicians' individual resilience has a positive association with commitment, knowledge diffusion, and Evidence-Based Medicine adoption. Finally, physicians' individual resilience has a negative association with their role in the university, their specialty, and their gender. The study suggests some practical implications for healtcare organizations. In general, career paths are decided primarily on competency assessment, while an important role should be devoted to behavioral characteristics. Furthermore, organizations should take care of the levels of individual commitment and encourage professional networking because both help doctor-managers cope with uncertainty. The originality of the study relies on a fresh look at all previous work. There are currently few contributions in the literature to explore and investigate resilience elements in doctor-managers during the pandemic era.

Automated Natural Language Processing-Based Supplier Discovery for Financial Services

Public procurement is viewed as a major market force that can be used to promote innovation and drive small and medium-sized enterprises growth. In such cases, procurement system design relies on intermediates that provide vertical linkages between suppliers and providers of innovative services and products. In this work we propose an innovative methodology for decision support in the process of supplier discovery, which precedes the final supplier selection. We focus on data gathered from community-based sources such as Reddit and Wikidata and avoid any use of historical open procurement datasets to identify small and medium sized suppliers of innovative products and services that own very little market shares. We look into a real-world procurement case study from the financial sector focusing on the Financial and Market Data offering and develop an interactive web-based support tool to address certain requirements of the Italian central bank. We demonstrate how a suitable selection of natural language processing models, such as a part-of-speech tagger and a word-embedding model, in combination with a novel named-entity-disambiguation algorithm, can efficiently analyze huge quantity of textual data, increasing the probability of a full coverage of the market.

Social influence in the darknet market: The impact of product descriptions on cocaine sales

BACKGROUND

The rise of the darknet market, supported by technologies such as the Tor Browser and cryptocurrencies, has created a secure environment in which illicit transactions can occur. However, due to the lack of government oversight in this hidden online domain, darknet markets face significant challenges in upholding social order. Hence, this study explores the social dynamics that promote social order in a darknet market, focusing on the impact of item descriptions on sales. In particular, the study examines how text contained in product listings can influence sales and contribute to social order.

METHOD

To conduct this analysis, we examined 4160 cocaine listings on AlphaBay, which was active from December 2014 to July 2017 and is one of the largest darknet markets in history. Using generalised additive models (GAMs), we assessed the impact of various listing description features, including content and semantic structure, on cocaine sales.

RESULTS

The results showed that sales increased by 61.6 % when listings included delivery information in their description, compared to offers that did not. In addition, the standardised sentiment score (ranging 0,1) of the product description increased positively, and estimated sales increased by 260.5 %. We also found that international shipping reduced sales by 28.3 %. Finally, we found that listings stating the product origin increased sales for all continents except Asia.

CONCLUSION

The study sheds light on the characteristics of product advertising that facilitate social order within a darknet market. Listings that include delivery details in the description reduce uncertainty about a critical stage of the transaction process while using positive language increases trust. This study makes both an empirical and a theoretical contribution by demonstrating the influence of ad descriptions on sales and the intricate role of social influences in shaping market order.

Evaluation in design science: A framework to support project studies in the context of University Research Centres

This paper proposes a discussion of the evaluation of an artefact developed under the Design Science paradigm using the Delphi method. It evaluates the Ecosystem framework of University Research Centres in Project Studies, considering a set of criteria pre-established in the literature. The Delphi method is an evaluation implemented in an electronic platform involving twenty-one participants, among whom were academics, practitioners, and PhD candidates in the field of project management. It reached consensus and stability in two rounds: the results indicate a consensus among the participants in the applicability, novelty, simplicity, completeness, fidelity to modelled phenomena, consistency and internal coherence, scalability, flexibility, interest, elegance, and reusability criteria. Usability was the only criterion that did not attain the predefined percentage of consensus among the participants (70%). Given the framework's characteristics, Delphi participants indicated the need to produce complementary guidelines for its implementation.

Beyond Intrinsic Motivation: Why Researchers Should Consider the Full Motivation Continuum in Games for Health Research

Research within the domain of games for health has predominantly focused on individuals' intrinsic motivation characterized by an inherent enjoyment of the activity. Despite the apparent benefits of intrinsic motivation, we argue that it is imperative to adopt a more nuanced and refined perspective on motivation. Relying on the motivation continuum as outlined within Self-Determination Theory, research within this domain needs to distinguish between both intrinsic and extrinsic (i.e., external, introjected, identified, and integrated regulation) types of motivation. Researchers should, therefore, embrace instruments that assess a broader continuum of motivation rather than just intrinsic motivation alone. By doing so, future research can yield more insight into what fosters autonomous forms of motivation in the field of health-related games, including intrinsic and identified and integrated regulation.

Deep learning-aided decision support for diagnosis of skin disease across skin tones

Although advances in deep learning systems for image-based medical diagnosis demonstrate their potential to augment clinical decision-making, the effectiveness of physician-machine partnerships remains an open question, in part because physicians and algorithms are both susceptible to systematic errors, especially for diagnosis of underrepresented populations. Here we present results from a large-scale digital experiment involving board-certified dermatologists (n = 389) and primary-care physicians (n = 459) from 39 countries to evaluate the accuracy of diagnoses submitted by physicians in a store-and-forward teledermatology simulation. In this experiment, physicians were presented with 364 images spanning 46 skin diseases and asked to submit up to four differential diagnoses. Specialists and generalists achieved diagnostic accuracies of 38% and 19%, respectively, but both specialists and generalists were four percentage points less accurate for the diagnosis of images of dark skin as compared to light skin. Fair deep learning system decision support improved the diagnostic accuracy of both specialists and generalists by more than 33%, but exacerbated the gap in the diagnostic accuracy of generalists across skin tones. These results demonstrate that well-designed physician-machine partnerships can enhance the diagnostic accuracy of physicians, illustrating that success in improving overall diagnostic accuracy does not necessarily address bias.

'Is your accuser me, or is it the software?' Ambiguity and contested expertise in probabilistic DNA profiling

What happens when an algorithm is added to the work of an expert group? This study explores how algorithms pose a practical problem for experts. We study the introduction of a Probabilistic DNA Profiling (PDP) software into a forensics lab through interviews and court admissibility hearings. While meant to support experts' decision-making, in practice it has destabilized their authority. They respond to this destabilization by producing alternating and often conflicting accounts of the agency and significance of the software. The algorithm gets constructed alternately either as merely a tool or as indispensable statistical backing; the analysts' authority as either independent of the algorithm or reliant upon it to resolve conflict and create a final decision; and forensic expertise as resting either with the analysts or with the software. These tensions reflect the forensic 'culture of anticipation', specifically the experts' anticipation of ongoing litigation that destabilizes their control over the deployment and interpretation of expertise in the courtroom. The software highlights tensions between the analysts' supposed impartiality and their role in the courtroom, exposing legal and narrative implications of the changing nature of expertise and technology in the criminal legal system.

Telemedicine experiences and perspectives of healthcare providers in long-term care: A scoping review

OBJECTIVE

To consolidate existing evidence on experiences and perspectives of healthcare providers involved in telemedicine services in long-term residential care.

METHODS

A scoping review was conducted. A systematic search for articles published in 2000-2021 was performed in CINAHL, Web of Science, PubMed, EMBASE and Scopus; further, relevant journals and grey literature websites were hand searched. Key search terms included 'telemedicine', 'telehealth' and 'nursing homes'.

RESULTS

Twenty-six articles were included. A narrative synthesis of evidence was conducted. The review identified four themes: (1) Presence of multidisciplinary care, (2) perceived usefulness of telemedicine, (3) perceived ease of use and (4) expanded role of nursing home staff. The presence of multidisciplinary care providers provided a wide range of telemedicine services to residents and promoted interprofessional collaboration between acute and long-term care. Telemedicine was perceived to increase timely onsite management by remote specialists, which enabled care quality improvement. However, technical problems associated with equipment usage reduced the ease of use of telemedicine. Concerns emerged from the expanded role of nursing home staff, which could negatively affect clinical decision-making and create medico-legal risks.

CONCLUSION AND IMPLICATIONS

Telemedicine is valuable in distance-based care, especially in the current 2019 coronavirus pandemic, for supporting continuity of care to nursing home residents. This review provided evidence from multiple healthcare providers' perspectives. Further research can elucidate their specific roles and responsibilities in telemedicine and challenges in work processes, which will facilitate developing evidence-based competencies and improving technical infrastructure, thus contributing to personal and organisational readiness for telemedicine integration.

Applying the Affective Events Theory to Explore the Effect of Daily Micro-Interruptions on Mental Health: The Mediating Role of Affect and the Moderating Role of Pets at Work

This study relied on the affective events theory and the social exchange theory to develop a framework that explains how situational factors (daily micro-interruptions) enhance affective reactions (negative affect) and, in turn, impair health conditions (mental health) at work. We further delineate theoretical arguments to propose the pet-human's health effect by demonstrating that pets are boundary conditions that attenuate this relation, and as such are protective conditions for employees' mental health. We conducted a 5-day diary study with two groups of participants, one with participants who owned pets (N = 82 x 5 = 410), and the other who did not own pets (N = 87 x 5 = 435). The multilevel results showed an indirect effect of daily micro-interruptions on individuals' mental health through negative affect, with a daily backdrop of poorer mental health for those who did not own a pet (compared to those who owned a pet). These results evidence the benefits of owning a pet for individuals' mental health, even at work, and as such provide recommendations for teleworking practices. Moreover, this study resorts to an innovative and robust data collection method to demonstrate the pet-human' health effect. This study expands knowledge on the role of pets in working daily routines and shows that pets may be a personal resource for individuals while working.

Digital transformation and corporate financialization in emerging markets: Evidence from China

Corporate financialization poses serious challenges to the development of the real economy. In the context of promoting the deep integration of the digital economy and the real economy, it is crucial to explore whether digital transformation can inhibit corporate financialization. Using data from Chinese listed companies from 2009 to 2021, we construct a fixed effects model and find that digital transformation significantly reduces the level of corporate financialization, a conclusion that still holds after a series of robustness tests such as propensity score matching and adding control variables. Channel analysis shows that that digital transformation inhibits corporate financialization by enhancing the information mobility and operational capability of corporations. In addition, this effect is more pronounced at higher levels of industry competition as well as marketization. Finally, we also find structural differences in the impact of digital transformation on corporate financialization. Our study explores the determinants of corporate financialization in terms of a firm's mode of operation and type of strategy, and the findings provide a theoretical basis for the active development of digital technologies in emerging markets that are undergoing economic transitions, as well as for guarding against the shift of the economy from the real to the virtual.

An Environmental Uncertainty Perception Framework for Misinformation Detection and Spread Prediction in the COVID-19 Pandemic: Artificial Intelligence Approach

BACKGROUND

Amidst the COVID-19 pandemic, misinformation on social media has posed significant threats to public health. Detecting and predicting the spread of misinformation are crucial for mitigating its adverse effects. However, prevailing frameworks for these tasks have predominantly focused on post-level signals of misinformation, neglecting features of the broader information environment where misinformation originates and proliferates.

OBJECTIVE

This study aims to create a novel framework that integrates the uncertainty of the information environment into misinformation features, with the goal of enhancing the model's accuracy in tasks such as misinformation detection and predicting the scale of dissemination. The objective is to provide better support for online governance efforts during health crises.

METHODS

In this study, we embraced uncertainty features within the information environment and introduced a novel Environmental Uncertainty Perception (EUP) framework for the detection of misinformation and the prediction of its spread on social media. The framework encompasses uncertainty at 4 scales of the information environment: physical environment, macro-media environment, micro-communicative environment, and message framing. We assessed the effectiveness of the EUP using real-world COVID-19 misinformation data sets.

RESULTS

The experimental results demonstrated that the EUP alone achieved notably good performance, with detection accuracy at 0.753 and prediction accuracy at 0.71. These results were comparable to state-of-the-art baseline models such as bidirectional long short-term memory (BiLSTM; detection accuracy 0.733 and prediction accuracy 0.707) and bidirectional encoder representations from transformers (BERT; detection accuracy 0.755 and prediction accuracy 0.728). Additionally, when the baseline models collaborated with the EUP, they exhibited improved accuracy by an average of 1.98% for the misinformation detection and 2.4% for spread-prediction tasks. On unbalanced data sets, the EUP yielded relative improvements of 21.5% and 5.7% in macro-F1-score and area under the curve, respectively.

CONCLUSIONS

This study makes a significant contribution to the literature by recognizing uncertainty features within information environments as a crucial factor for improving misinformation detection and spread-prediction algorithms during the pandemic. The research elaborates on the complexities of uncertain information environments for misinformation across 4 distinct scales, including the physical environment, macro-media environment, micro-communicative environment, and message framing. The findings underscore the effectiveness of incorporating uncertainty into misinformation detection and spread prediction, providing an interdisciplinary and easily implementable framework for the field.

A survey of deep learning applications in cryptocurrency

This study aims to comprehensively review a recently emerging multidisciplinary area related to the application of deep learning methods in cryptocurrency research. We first review popular deep learning models employed in multiple financial application scenarios, including convolutional neural networks, recurrent neural networks, deep belief networks, and deep reinforcement learning. We also give an overview of cryptocurrencies by outlining the cryptocurrency history and discussing primary representative currencies. Based on the reviewed deep learning methods and cryptocurrencies, we conduct a literature review on deep learning methods in cryptocurrency research across various modeling tasks, including price prediction, portfolio construction, bubble analysis, abnormal trading, trading regulations and initial coin offering in cryptocurrency. Moreover, we discuss and evaluate the reviewed studies from perspectives of modeling approaches, empirical data, experiment results and specific innovations. Finally, we conclude this literature review by informing future research directions and foci for deep learning in cryptocurrency.

Knowledge Transfer Within Organizations: Mechanisms, Motivation, and Consideration

Knowledge transfer-the extent to which one unit learns from or is affected by the experience of another-has the potential to improve the performance of organizations. Through knowledge transfer, developments made in one unit of an organization can benefit others. Studies have found, however, considerable variation in the extent to which knowledge transfers across organizational units. In some cases, knowledge transfers seamlessly, whereas in others, knowledge transfer is far from complete. This article reviews research with the aim of explaining the variation observed in knowledge transfer. Key factors identified as explaining the variation include knowledge transfer opportunities, knowledge characteristics, mechanisms for knowledge transfer, motivation for transfer, and the depth of consideration of knowledge. These factors are integrated into a theoretical framework that predicts when knowledge transfer will be successful. The article concludes with a discussion of directions for future research to increase our understanding of knowledge transfer in organizations.

The impact of environmental regulatory instruments on agribusiness technology innovation-A study of configuration effects based on fsQCA

This paper investigates the complex causal relationships between various types of environmental regulatory instruments (ERI) and agri-firms' technological innovation employing fuzzy set qualitative comparative analysis (fsQCA). The study finds a well-designed set of ERI can promote technological innovation in agribusiness; control-command ERI cannot promote technological innovation in agribusiness solely, market-incentivized ERI is indispensable in promoting firms' innovation performance, implicit ERI plays an important role in promoting firms' innovation and voluntary ERI does not play a significant role in promoting firms' technological innovation. The government should coordinate among various types of ERI and improve the design of ERI to achieve a win-win situation for both economic and environmental performance in the agriculture sector.

Systematic review using a spiral approach with machine learning

With the accelerating growth of the academic corpus, doubling every 9 years, machine learning is a promising avenue to make systematic review manageable. Though several notable advancements have already been made, the incorporation of machine learning is less than optimal, still relying on a sequential, staged process designed to accommodate a purely human approach, exemplified by PRISMA. Here, we test a spiral, alternating or oscillating approach, where full-text screening is done intermittently with title/abstract screening, which we examine in three datasets by simulation under 360 conditions comprised of different algorithmic classifiers, feature extractions, prioritization rules, data types, and information provided (e.g., title/abstract, full-text included). Overwhelmingly, the results favored a spiral processing approach with logistic regression, TF-IDF for vectorization, and maximum probability for prioritization. Results demonstrate up to a 90% improvement over traditional machine learning methodologies, especially for databases with fewer eligible articles. With these advancements, the screening component of most systematic reviews should remain functionally achievable for another one to two decades.

Intra- and Inter-Individual Associations of Family-to-Work Conflict, Psychological Distress, and Job Satisfaction: Gender Differences in Dual-Earner Parents during the COVID-19 Pandemic

The COVID-19 pandemic changed the work-family interface dynamics in some families. For couples who kept earning a double income during the pandemic, their family demands may entail a loss of psychological resources that affect the work domain. This study explored the intra-individual and inter-individual (crossover) direct and indirect effects of family-to-work conflict (FtoWC) on psychological distress and job satisfaction in a non-probabilistic sample of 860 different-sex dual-earner parents with adolescent children from Temuco and Rancagua, Chile. Mothers and fathers answered an online questionnaire measuring FtoWC, the Depression, Anxiety, and Stress Scale, and the Overall Job Satisfaction Scale. The data were analyzed using the actor-partner interdependence model with structural equation modeling. Results showed that a higher FtoWC is linked to greater psychological distress and lower job satisfaction in both parents. In contrast, psychological distress is directly linked to lower job satisfaction in fathers. In both fathers and mothers, they and their partners' FtoWC were indirectly linked to lower job satisfaction via the fathers' psychological distress. These findings indicate the need for gender-sensitive social and labor policies aimed at reducing the conflict between family and work to increase job satisfaction in both parents and reduce psychological distress, particularly in fathers.

Research on the evaluation and configuration path of China's rural common Prosperity-NCA and fsQCA based on provincial panel data

Based on 2013-2019 panel data covering 31 provinces and cities in China, this paper uses the entropy weight technique for order of preference by similarity to ideal solution (TOPSIS) method to measure rural common prosperity (CP). Based on the global and local Moran's I methods, we analyze the dynamic evolutionary characteristics of China's rural CP and the regional differences. Additionally, we use the necessary condition analysis (NCA) and fuzzy-set qualitative comparative analysis (fsQCA) methods to explore how six antecedents at the technology-organization-environment (TOE) level interact to affect CP. This research finds that, first, China's rural CP showed a fluctuating upward trend, with the highest level of CP in rural areas in the eastern region, followed by the central and western regions. Additionally, the gap between the three gradually narrowed. Second, China's rural CP had "high-high" and "low-low" agglomeration characteristics, with positive spatial autocorrelation, no transition changes, and strong spatial stability. Third, individual digital economic elements and organizational and environmental elements were not necessary conditions for promoting rural CP. Fourth, the multiple concurrent factors of the digital economy, organizations and the environment constituted three diversified configurations of rural CP, showing that the driving path of rural CP was characterized by "different paths that lead to the same goal". Moreover, "perfect digital facilities" and "high entrepreneurial activity" had a universal role in promoting rural CP. The conclusions of this research hold important theoretical and practical significance for improving China's rural CP.

From atoms to bits: Resource mobilization of non-digital, hybrid, and digital cleantech startups

Climate change is one of the most critical challenges to tackle in this century, where innovations developed and commercialized by cleantech startups are crucial contributors to achieve emission reduction targets. Entrepreneurship scholars have long presumed that resource mobilization is essential for startups to transit successfully through the conception and commercialization life cycle stages. Yet, we have a limited understanding of how resource mobilization varies across the three startups types of non-digital, hybrid - an intermediate type of non-digital and digital startups -, and digital cleantech startups. Drawing on insights from 16 semi-structured interviews with startups, investors, and industry experts in the U.S. cleantech industry, as well as secondary data, this study provides a novel framework that identifies the resource mobilization approaches of cleantech startups disentangled by the three startup types. The findings indicate that non-digital cleantech startups face the most severe resource mobilization challenges, followed by hybrid and digital cleantech startups, respectively. The study contributes to the literature on resource mobilization of cleantech startups and digital entrepreneurship. It also outlines implications for startups and venture capital investors as practitioners as well as for policymakers.

Social media use and social capital: Social media usage habits and perceptions of school administrators

Social media allows people to spend time together online and interact and connect. The relationship between well-being and social media usage is being studied more and more because it affects many areas and is discussed from different aspects. In this study, a complete count was made, and an online questionnaire was applied to all 1282 school administrators working at Antalya Provincial National Education public schools. Results reveal how the social media usage habits and social media perceptions of school administrators are, and accordingly how social media may help fertilize their social capital and establishing a healthy work environment. In light of the findings, social media usage habits are gathered under four dimensions: research/information, entertainment, recognition, and development. These gratified needs are the key elements of building one's social capital.

Can Positive Emotions Predict Consumer Satisfaction in Adverse Services?

Background

Managing emotions during hospital visits is important to enhance patient satisfaction. The purpose of this paper is to explore the relationship between patients' happiness and satisfaction through patients' mood and perceived service quality at a healthcare setup.

Methods

This study was conducted in a tertiary care hospital located in coastal Karnataka during the period from November to December 2021. Primary data was collected through a structured questionnaire from 227 respondents. "Statistical Package for the Social Sciences (SPSS) 27.0" and "SmartPLS 3.0" software was used for data analysis.

Results

Hypotheses proposed in this study were examined by comparing the direct effect of patients' happiness level on perceived service quality and the indirect effect of the level of patients' happiness on patient satisfaction. The influence of all the exogenous latent variables namely, happiness, mood, perceived service quality, on the endogenous latent variable of patient satisfaction is estimated to be moderate (R 2=62.5%).

Conclusion

This study empowers hospital managers to recognize how patient satisfaction is dependent on patients' happiness. In order to enhance patient satisfaction, the quality of care provided by health services, human resources, and infrastructure must be improved. As a result, the entire service encounter can be made enjoyable to the customers by reducing the distress caused by adverse services. Managers can utilize the outcomes of the study to develop marketing strategies to influence patients' emotions in the healthcare setup by modifying the servicescape elements.

Predictive Model of the Relationship between Positive and Negative Affect, Self-Consciousness of Appearance, and Positive Body Image in Physical Exercise Practice

The biopsychosocial development of individuals is influenced by body image and physical exercise. This study aimed to evaluate seven hypotheses regarding the impact of positive affect, negative affect, self-consciousness of appearance, and positive body image on the practice of physical exercise. The data were processed using the statistical package SmartPLS 4.0.9.5. Using an exploratory methodology based on structural equation modeling (SEM) of structural equations applied to small sample sizes, a tentative model has been generated to define the drivers of physical activity. The survey used the Body Mass Index, Body Appreciation Scale-Revised, and Derriford Appearance Scale-14 items. The study involved 129 men and 279 women (N = 408). The model demonstrated a distinct internal consistency in that, out of the seven hypotheses analyzed, only one was rejected. The findings of the multigroup analysis indicate that there are no statistically significant discrepancies between the constructed measures and the practice of physical activity. The conclusions of this study showed that, although positive and negative affect, and self-consciousness influence positive body image, there were no statistically significant differences between those who exercise and those who do not exercise. Various factors influence biopsychosocial development. Future studies should explore the influence of psychological and social variables on understanding body image and physical exercise.

Privacy Concerns About Sharing General and Specific Health Information on Twitter: Quantitative Study

BACKGROUND

Twitter is a common platform for people to share opinions, discuss health-related topics, and engage in conversations with a wide audience. Twitter users frequently share health information related to chronic diseases, mental health, and general wellness topics. However, sharing health information on Twitter raises privacy concerns as it involves sharing personal and sensitive data on a web-based platform.

OBJECTIVE

This study aims to adopt an interactive approach and develop a model consisting of privacy concerns related to web-based vendors and web-based peers. The research model integrates the 4 dimensions of concern for information privacy that express concerns related to the practices of companies and the 4 dimensions of peer privacy concern that reflect concerns related to web-based interactions with peers. This study examined how this interaction may affect individuals' information-sharing behavior on Twitter.

METHODS

Data were collected from 329 Twitter users in the United States using a web-based survey.

RESULTS

Results suggest that privacy concerns related to company practices might not significantly influence the sharing of general health information, such as details about hospitals and medications. However, privacy concerns related to companies and third parties can negatively shape the disclosure of specific health information, such as personal medical issues (β=-.43; P<.001). Findings show that peer-related privacy concerns significantly predict sharing patterns associated with general (β=-.38; P<.001) and specific health information (β=-.72; P<.001). In addition, results suggest that people may disclose more general health information than specific health information owing to peer-related privacy concerns (t165=4.72; P<.001). The model explains 41% of the variance in general health information disclosure and 67% in specific health information sharing on Twitter.

CONCLUSIONS

The results can contribute to privacy research and propose some practical implications. The findings provide insights for developers, policy makers, and health communication professionals about mitigating privacy concerns in web-based health information sharing. It particularly underlines the importance of addressing peer-related privacy concerns. The study underscores the need to build a secure and trustworthy web-based environment, emphasizing the significance of peer interactions and highlighting the need for improved regulations, clear data handling policies, and users' control over their own data.

Motivation crowding effects on the intention for continued use of gamified fitness apps: a mixed-methods approach

It has become an emerging idea for fitness apps to be gamified to intrinsically and extrinsically motivate user's usage intention or behavior. For the long-term success of gamified fitness apps, understanding what and how various motivations affect continued use is critical. A combination of qualitative and quantitative methodologies was used to explore two research questions concerning gamified fitness applications. Specifically, the research questions focused on understanding the impacts of intrinsic and extrinsic motivations on continued usage. Additionally, the study aimed to investigate whether extrinsic motivations enhance or diminish the influence of intrinsic motivations. Results from qualitative study identified three intrinsic motivations (self-development, self-control and hedonic motivation) and two extrinsic motivations (social recognition and financial reward) in gamified fitness apps. Results from quantitative study indicated that intrinsic motivations (formed by self-development, self-control and hedonic motivation), financial reward and social recognition could significantly improve intention for continued use; and further, both financial reward and social recognition could crowd-in intrinsic motivations. This research offers insights into the phenomenon of motivation crowding effects on the intention to continue using gamified fitness apps.

A Sustainable Model for Healthcare Systems: The Innovative Approach of ESG and Digital Transformation

In recent years, the globe has faced a series of topics of growing concern, such as the COVID-19 pandemic, the international financial crisis, rising socio-economic inequalities, the negative outcomes of greenhouse gas emissions, which resulted in climate change, and many others. Organizations worldwide have confronted these new challenges of sustainable finance by incorporating environmental, social, and corporate governance (ESG) factors and digital transformation (DT) in their innovation business strategies. The healthcare sector represents a large share of the global economy (about 10% of global economic output), employs a large number of workers, and needs to rely more on an open innovation model where interested parties, especially patients, are going to have a say in their own well-being. Thus, it is imperative that healthcare providers be efficient, effective, resilient, and sustainable in the face of significant challenges and risks. At the same time, they must offer sustainable development goals and digital transformation to healthcare users through limited governmental resources. This study investigates the role, importance, and correlation of ESG factors and digital transformation to the sustainable finance of healthcare systems through an innovative model. The main purpose of the paper is to present the already implemented ESG and DT factors in the healthcare sector and to propose a mutual and combined implementation strategy based on common evaluation tools, methods, and actions. A set of proposed actions and strategies are presented for the sustainability and resilience of the healthcare sector.