Shaping E-commerce Experiences: Unraveling the Impact of Service Quality on Youth Customer Behavior in a Developing Nation

Firms of all stripes recognize that retaining satisfied customers in the current e-commerce cutthroat and digital market relies on delivering exceptional customer service. This longitudinal study aims to examine the relationship between service quality, customer satisfaction, and behavior in an online shopping environment, emphasizing the youth of Pakistan. A research model was developed and tested using two phases of customer data ( n = 331). The results show that in the online shopping environment, the dimension of service quality significantly enhances customer satisfaction, which leads to the development of customer loyalty. The mediating role of customer complaints has been identified in the relationship between customer satisfaction and customer loyalty. Furthermore, it was found that the significant connection between customer loyalty, repurchase intention, and recommendation intention was moderated by emotional intensity. The findings will help firms in Pakistan and other developing countries to establish long-term growth strategies by understanding the complex nexus between service quality that leads to customer satisfaction and boosts their repurchases and recommendations. This research also directs online platform marketers and practitioners to develop marketing and retailing strategies.

Impact of offline experiences on consumer decisions: Balanced vs. unbalanced social exchange in switching to online health services

Objective

The diffusion of online health consultations (OHCs) depends on consumers switching from offline to online channels; consumers' offline experience on health servicer determines their decisions. Our research aims to address this gap in the literature by examining how offline experiences of doctor-patient interactions influence online switching intentions.

Methods

We conducted online surveys, and 662 samples were collected and analysed the data using structural equation model via SmartPLS. Drawing on social exchange theory, we divided offline experience into balanced (discrete and relational social exchange) and unbalanced social exchange (doctor power and patient compliance). In accordance with the dedication-constraint model, perceived value and offline habit were defined as dedication and constraint factors, respectively; the influence of offline experience on both dedication and constraint factors was revealed. Moreover, we introduced the moderation effect to reveal the boundary of the effect of offline experience.

Results

Our findings showed that balanced social exchange increases consumers' perceived value towards OHC, whereas unbalanced social exchange increases offline habit. Additionally, we observed a positive association between perceived value and the intention to switch, whereas offline habits had a negative impact. Online trust, as a moderator, strengthens the relationship between balanced exchange and perceived value and weakens the relationship between unbalanced exchange and offline habits. These results establish a theoretical framework for comprehending cross-channel switching in online health service marketing.

Conclusions

Since offline experience of doctor-patient interactions influences consumers' decisions, it is critical for online health service platforms to apply cross-channel integration strategy rather than depend on online channels alone.

Psychological Determinants of Health Behavior

This review provides a critical overview of current evidence on psychological health behavior determinants and its value in informing intervention and future determinants research. The review begins with work labeling and classifying the myriad of determinants available in the extant research to arrive at core groups of determinants. Next, the conceptual bases of these determinant groups are identified, and the weight of the evidence for their purported effects on health behavior, including belief-based determinants (e.g., outcome expectancies, capacity beliefs), determinants representing self-regulatory capacity (e.g., planning, action control) and nonconscious processes (e.g., habit, implicit cognition), and dispositional determinants (e.g., personality, regulatory control), is critically evaluated. The review also focuses on the theory-based mechanisms underpinning determinant effects and moderating conditions that magnify or diminish them. Finally, the review recommends a shift away from research on determinants as correlates, outlines how determinants can inform intervention development and mechanisms of action tests, suggests alternatives to predominant individualist approaches, and proposes future research directions.

Health information technology to advance care in accountable care organizations: Implications for Medicare patients

BACKGROUND

Prior research has explored the link between health information technology (HIT) and performance of accountable care organizations (ACOs). However, the challenges of HIT use in ACOs for the management of chronic diseases among Medicare beneficiaries remain less examined.

PURPOSE

Given the high costs of implementing HIT and the occurrence of multiple chronic conditions (MCC) among elderly individuals, it is important to understand the extent to which HIT capabilities enable chronic disease management among the Medicare population.

METHODOLOGY/APPROACH

Regression analysis was conducted using data from multiple sources for the year 2017, including Leavitt Partners data, the ACO Public Use File published by the Shared Savings Program of the Centers for Medicare & Medicaid Services (CMS), and the CMS hospital referral region data. The sample consisted of 470 ACOs.

RESULTS

Findings revealed that health information exchange (HIE)- and HIT-enabled patient engagement reduced unplanned admissions for Medicare patients with MCC. When primary care services were utilized, HIE- and HIT-enabled patient engagement and medication reconciliation further decreased unplanned admissions.

CONCLUSION

This study provides empirical support for HIT's role in reinforcing the applicability of the chronic care model to improve health outcomes.

PRACTICE IMPLICATIONS

From the managerial perspective, adopting HIT functions that support care management may be important for ACOs to improve patient outcomes.

A process for conducting mixed methods data analysis

The process of mixed methods data analysis has long been understudied and needs clear guidance for researchers. This review article honors Michael D. Fetters' pioneering work on mixed methods data analysis, building on his concepts of integration, joint displays, and metainferences. This review summarizes existing mixed methods data analysis discussions and proposes advanced steps for the analysis process. Our approach involves identifying a research problem, collecting diverse data, selecting a mixed methods design, integrating the data within the design, using joint displays for analysis, and drawing metainferences. We illustrate our methods with a Japanese empirical research study from family medicine. This article contributes to the field of mixed methods research by detailing a practical process approach to mixed methods analysis combining recent procedures in the field of mixed methods research.

Ridesharing and substance use disorder treatment

We examine whether ridesharing provides a meaningful transportation alternative for those who require ongoing healthcare. Specifically, we combine variation in UberX entry across the U.S. with the Treatment Episode Data Set to estimate the effect of ridesharing on admissions to substance use disorder treatment. People needing such treatment report transportation as a barrier to receiving care. We find that UberX entry into a Core Based Statistical Area has no effect on the overall number of treatment admissions. However, we find a decline in non-intensive outpatient treatment which is fully offset by an increase in intensive outpatient treatment. Given the required relative frequency of non-intensive and intensive outpatient treatment in terms of visits per week, our findings indicate that UberX helps to reduce transportation barriers to accessing healthcare. Event-studies show parallel trends in outcomes before UberX entry and results are robust to numerous sensitivity checks.

Brief Review and Primer of Key Terminology for Artificial Intelligence and Machine Learning in Hypertension

Recent breakthroughs in artificial intelligence (AI) have caught the attention of many fields, including health care. The vision for AI is that a computer model can process information and provide output that is indistinguishable from that of a human and, in specific repetitive tasks, outperform a human's capability. The 2 critical underlying technologies in AI are used for supervised and unsupervised machine learning. Machine learning uses neural networks and deep learning modeled after the human brain from structured or unstructured data sets to learn, make decisions, and continuously improve the model. Natural language processing, used for supervised learning, is understanding, interpreting, and generating information using human language in chatbots and generative and conversational AI. These breakthroughs result from increased computing power and access to large data sets, setting the stage for releasing large language models, such as ChatGPT and others, and new imaging models using computer vision. Hypertension management involves using blood pressure and other biometric data from connected devices and generative AI to communicate with patients and health care professionals. AI can potentially improve hypertension diagnosis and treatment through remote patient monitoring and digital therapeutics.

The mechanism of word-of-mouth learning on chronic disease patients' physician choice in online health communities: Latent Dirichlet allocation analyses and cross-sectional study

Background

Word-of-mouth learning (WOML) plays a substantial role in patients' physician choice behavior. However, there is still a research gap in analyzing the mechanism of WOML on chronic disease patients' physician choice in online health communities (OHCs) considering individual differences.

Objective

This study aims to develop a physician choice mechanism research model to reveal the influence of WOML on chronic disease patients' physician choice decision process from external interaction to internal cognition and emotion in OHCs based on social learning theory (SLT). The moderating effects of reasons for consultation and patients' demographic characteristics on the model's relationships were also explored.

Methods

Guided by SLT, this study identified the external interaction factors and internal cognitive and emotional factors by analyzing 72,123 patients' online reviews based on a Latent Dirichlet Allocation model and developed the physician choice mechanism research model. The model was validated using structural equation modeling based on an online questionnaire survey of 526 valid Chinese patients with chronic disease. The moderating effect of reasons for medical consultation and demographic characteristics was examined using multi-group analysis.

Results

Status capital (SC), decisional capital (DC), and price value (PV)) were the main external interaction factors to initiating chronic disease patients' internal cognition and emotion (perceived convenience (PC), perceived health benefits (PH), and patients' physician choice intention (CI)). PH and PC significantly mediated the relationship between SC, DC, PV, and CI. Reasons for medical consultation, district, and sex significantly moderated the relationships in the model.

Conclusions

Considering individual differences, the results of this study advance a comprehensive understanding of how chronic disease patients interact with the environment through WOML to make physician choice decisions. OHCs can recommend suitable physician information to chronic disease patients considering individual differences to match patients' demands and improve service quality.

Managing medical knowledge flow: Physicians' social media actualisation practices

Physicians have extensive clinical knowledge and are thus uniquely positioned to offer public health education through social media (SM) platforms. However, it may be challenging for them to effectively communicate complex medical information to lay people with varying levels of related knowledge. There is limited research on how physicians navigate these challenges. This study uses affordance actualisation theory to investigate how a prominent physician adapts SM features to effectively disseminate medical and scientific information. The study identifies two key SM actualisation practices in this process. First, the process involves balancing knowledge brevity to improve accessibility while maintaining information fidelity to ensure accuracy. Second, the process involves providing valuable clinical insights while adhering to legal constraints in the open SM environment through the optimisation of SM features as a compensatory mechanism. The current findings contribute to a theoretical framework on how physicians manage medical knowledge flow online, which involves not only advancing knowledge but also carefully regulating knowledge to maintain accuracy and professional standards within the open nature of SM.

Digital health interventions for non-older individuals at risk of frailty: A systematic review and meta-analysis

Objective

Frailty is a significant health problem that results in adverse outcomes, reduced quality of life, and increased medical and nursing care costs. This study aimed to review the effect of digital health interventions on improving physical activity, physical function, and social function in non-older individuals at risk of frailty.

Methods

Randomized controlled trials of digital health interventions reporting frailty-related outcomes on physical activity, physical function, and social function in non-older individuals at risk of frailty were collected. The literature was reviewed, and the risk of bias was assessed. Meta-analyses were conducted for each outcome, and the certainty of evidence was assessed using Grading of Recommendations Assessment, Development, and Evaluation.

Results

The review included a total of 63 records. The meta-analysis showed that digital health interventions positively affected the volume of physical activity, daily steps, walking test, walking speed, VO2peak, dynamic gait index, timed up and go test, and MOS 36-Item Short-Form Health Survey Social Functioning. However, no significant effect on the time of physical activity was found. Despite the absence of fatal bias, the overall certainty of the evidence was not high because of the small number of studies and participants included in the analysis.

Conclusions

Digital health interventions can improve physical activity, physical function, and social function in non-older individuals at risk of frailty. However, the evidence had limited certainty, and the evidence supporting the effect of digital health intervention is insufficient. Thus, more studies are needed to clarify the effects of these interventions.

The impacts on population health by China's regional health data centers and the potential mechanism of influence

Background

China recently established a series of pilot regional health data centers with a mandate to acquire, consolidate, analyze, and translate data into evidence for health policy decision-making. This experiment with "big data" has the potential to influence population health and is the focus of this study.

Methods

This study used national longitudinal survey data from the China Family Panel Studies over the period 2014-2020 to empirically assess the impact of China's establishment of pilot regional health data centers on population health and health inequality. A difference-in-differences model was employed to investigate the policy effect on population health, with additional exploration of possible mechanisms of influence. The corrected concentration index was used to measure health inequality, while Wagstaff decomposition method was applied to examine the marginal influence of the policy effect on health inequality.

Results

Overall health status of local residents has improved after the establishment of the pilot regional health data centers. Using mechanism analysis, the findings demonstrated that improvements to population health were driven by promoting healthy lifestyles and innovations in medical practices. Furthermore, due to differences in individual e-health literacy, the pilot centers produced "pro-rich" health inequality where high-income groups benefited more from the establishment of the pilot centers in terms of health than low-income groups.

Conclusions

This study has highlighted the potential to improve population health, in general, with the advent of big data centers, but for these benefits be unevenly distributed among the resident population.

With big data comes big responsibility: Strategies for utilizing aggregated, standardized, de-identified electronic health record data for research

Electronic health records (EHRs), though they are maintained and utilized for clinical and billing purposes, may provide a wealth of information for research. Currently, sources are available that offer insight into the health histories of well over a quarter of a billion people. Their use, however, is fraught with hazards, including introduction or reinforcement of biases, clarity of disease definitions, protection of patient privacy, definitions of covariates or confounders, accuracy of medication usage compared with prescriptions, the need to introduce other data sources such as vaccination or death records and the ensuing potential for inaccuracy, duplicative records, and understanding and interpreting the outcomes of data queries. On the other hand, the possibility of study of rare disorders or the ability to link apparently disparate events are extremely valuable. Strategies for avoiding the worst pitfalls and hewing to conservative interpretations are essential. This article summarizes many of the approaches that have been used to avoid the most common pitfalls and extract the maximum information from aggregated, standardized, and de-identified EHR data. This article describes 26 topics broken into three major areas: (1) 14 topics related to design issues for observational study using EHR data, (2) 7 topics related to analysis issues when analyzing EHR data, and (3) 5 topics related to reporting studies using EHR data.

How digital paywalls shape news coverage

The internet has significantly transformed how news is produced, consumed, and distributed. As a result, the news industry has transitioned from ad-supported to subscription-based models regulated by digital paywalls. In the light of this disruption, it is crucial to investigate not only how news consumers adapt to this change but also how economic incentives shape content coverage. We analyzed the staggered adoption of digital paywalls by 17 regional US newspapers over 17 years in a difference-in-difference framework to examine the impact of paywall adoption on topical news content coverage. Our results reveal a small but significant decrease in local and soft news coverage, with varying effects across different urban contexts. Specifically, local news coverage experienced a more substantial decline in smaller cities ( population < 500,000 ) and regions experiencing an influx of younger residents (age < 40 years). Conversely, soft news coverage increased in areas with a younger demographic influx, indicating a strategic shift by newspapers to cater to digital-savvy audiences and adapt to changing consumption patterns. Our findings underscore the delicate balance between financial imperatives and editorial choices in the newspaper industry and highlight the need for ongoing research into the effects of digital monetization strategies on journalistic content creation, media plurality, and civic accountability.

Trust (in)congruence, digital technological innovation, and firms' ESG performance: A polynomial regression with response surface analysis

We examine whether and how the different types of digital technology innovation (DTI) affect corporate ESG performance. Based on patents' information, we classify DTI into symbolic DTI and substantive DTI. Specifically, symbolic DTI is the innovation driven by firms' legitimacy strategy, while substantive DTI is the type of innovation that helps strengthen firms' competitive advantages. Using a sample of Chinese A-share-listed firms from 2009 to 2020, we use polynomial regression and response surface analysis to examine how firms balance the tension between competitiveness and legitimacy through digital technological innovations in order to achieve optimal corporate ESG performance. We find that there is an inverted U-shaped relationship between symbolic DTI and corporate ESG performance. Meanwhile, there is a simple positive correlation between substantive DTI and corporate ESG performance. Moreover, the incongruence between the two types of DTI significantly hurts corporate ESG performance and is mainly concentrated in firms with high symbolic-low substantive DTI strategy. Further analyses show that corporate managerial myopia positively (negatively) affects the relationship between symbolic (substantive) DTI and ESG performance. Our paper expands the literature on the relationship between DTI and firms' ESG performance. It provides managerial insights for the Chinese government to encourage digital economy development and promotes firms' substantive DTI to enhance their ESG performance.

Experimental economics for machine learning-a methodological contribution on lie detection

In this paper, we investigate how technology has contributed to experimental economics in the past and illustrate how experimental economics can contribute to technological progress in the future. We argue that with machine learning (ML), a new technology is at hand, where for the first time experimental economics can contribute to enabling substantial improvement of technology. At the same time, ML opens up new questions for experimental research because it can generate previously impossible observations. To demonstrate this, we focus on algorithms trained to detect lies. Such algorithms are of high relevance for research in economics as they deal with the ability to retrieve otherwise private information. We deduce that most of the commonly applied data sets for the training of lie detection algorithms could be improved by applying the toolbox of experimental economics. To illustrate this, we replicate the "lies in disguise-experiment" by Fischbacher and Föllmi-Heusi with a modification regarding monitoring. The modified setup guarantees a certain level of privacy from the experimenter yet allows to record the subjects as they lie to the camera. Despite monitoring, our results indicate the same lying behavior as in the original experiment. Yet, our experiment allows an individual-level analysis of experimental data and the generation of a lie detection algorithm with an accuracy rate of 67%, which we present in this article.

The reciprocal relationship between social exclusion and basic psychological needs through cross-lagged analysis

Social exclusion has wide-ranging and detrimental effects. This study recruited 771 Chinese college students (Mage = 19.65 years, SDage = 1.04, 74.19% females). The findings of two cross-lagged panel models, administered at three different points in time, indicated a negative association between social exclusion (including being ignored and rejected) and basic psychological needs (autonomy, competence, and relatedness). Specifically, both being ignored and rejected significantly predicted decreases in autonomy, competence, and relatedness needs. Moreover, relatedness needs significantly predicted decreases in being ignored, while autonomy needs predicted decreases in being ignored. Additionally, competence needs at T1 positively predicted being ignored but negatively predicted being rejected at T2. This study was based on Self-Determination Theory (SDT) and expanded its application within the realm of social exclusion. The findings offer novel intervention strategies for reducing social exclusion and promoting well-being.

Relationship between official visits, industry chain coordination and digital transformation in China

With digital transformation becoming a global consensus, governments around the world are vigorously promoting the implementation of enterprise digital transformation. However, there are some uncertainties in the process of digital transformation, and companies have overlooked the role of formal access. To better understand the role official visits play in digital transformation, based on signal theory, the study examines the influence of official visits on corporate digitalization using data from listed SMEs in China from 2017 to 2021. Our study found that Official visits are positively correlated with the digital transformation of SMEs; the coordination of the industrial chain plays a partial mediating role in the positive relationship between official visits and the digital transformation of SMEs; the pressure of official promotion positively moderates the impact of official visits on the digital transformation of small and medium-sized enterprises. As a signaling party, this is because the government will enhance the signal strength that firms will receive resources to shelter and further reduce the risk of digitalization strategies. Based on the perspective of signal theory, the study investigates the underlying mechanism of official visits influencing digital transformation through industry chain coordination, which expands the relevant literature on political connections and digital transformation.

Diversifying configurational paths for athlete data protection

In the digital era, data has become a core resource driving the development of the global sports industry. With the increasing awareness of athlete data protection, ensuring the security of this sensitive information worldwide has become a significant issue. This study employs configurational theory and Fuzzy-set Qualitative Comparative Analysis to investigate the various factors affecting athlete data protection. The research reveals three key configurational pathways: "Single Organization Support Type," "Organization-Environment Support Type," and "Organizational Support Absent Type." The "Single Organization Support Type" pathway emphasizes the role of a single organizational condition in athlete data protection, while the "Organization-Environment Support Type" considers the interaction between organizational and external environmental factors. In contrast, the "Organizational Support Absent Type" pathway reveals viable paths for data protection in the absence of effective organizational support. These findings not only provide valuable insights for policymakers to construct a more comprehensive framework for athlete data protection but also offer strategic support for the sustainable development of the global sports industry. They promote the safe use of sports data and the overall progress of the industry. Moreover, with the worldwide focus on sustainable development goals, the sports industry, as an essential component, plays an increasingly prominent role in promoting coordinated development of society, economy, and environment. The rational protection and utilization of sports data will contribute to achieving this global goal, especially in promoting healthy lifestyles, gender equality, and inclusive social construction. Therefore, the conclusions of this study are not only instructive for data governance within the sports domain but also provide practical contributions to global sustainable development.

Unveiling the enigma of blind box impulse buying curiosity: The moderating role of price consciousness

This study explores the causes of curiosity-driven impulse buying in blind boxes using the Stimulus-Organism-Response (S-O-R) model and adaptation level theory. It examines how store environment and product factors contribute to customer curiosity, incorporating price consciousness into the overall framework. Insights from an online survey of 306 Chinese respondents indicate that environmental factors and specific product characteristics positively influence consumer curiosity, while price consciousness has a negative moderate effect. The findings also show that customer curiosity stimulates impulse buying behavior and mediates the relationship between store atmosphere, surprise, and perceived novelty. This study identifies both objective and subjective factors behind blind box impulse buying and offers relevant suggestions for governments and consumers on managing impulse buying.

Explainable AI improves task performance in human-AI collaboration

Artificial intelligence (AI) provides considerable opportunities to assist human work. However, one crucial challenge of human-AI collaboration is that many AI algorithms operate in a black-box manner where the way how the AI makes predictions remains opaque. This makes it difficult for humans to validate a prediction made by AI against their own domain knowledge. For this reason, we hypothesize that augmenting humans with explainable AI improves task performance in human-AI collaboration. To test this hypothesis, we implement explainable AI in the form of visual heatmaps in inspection tasks conducted by domain experts. Visual heatmaps have the advantage that they are easy to understand and help to localize relevant parts of an image. We then compare participants that were either supported by (a) black-box AI or (b) explainable AI, where the latter supports them to follow AI predictions when the AI is accurate or overrule the AI when the AI predictions are wrong. We conducted two preregistered experiments with representative, real-world visual inspection tasks from manufacturing and medicine. The first experiment was conducted with factory workers from an electronics factory, who performed [Formula: see text] assessments of whether electronic products have defects. The second experiment was conducted with radiologists, who performed [Formula: see text] assessments of chest X-ray images to identify lung lesions. The results of our experiments with domain experts performing real-world tasks show that task performance improves when participants are supported by explainable AI with heatmaps instead of black-box AI. We find that explainable AI as a decision aid improved the task performance by 7.7 percentage points (95% confidence interval [CI]: 3.3% to 12.0%, [Formula: see text]) in the manufacturing experiment and by 4.7 percentage points (95% CI: 1.1% to 8.3%, [Formula: see text]) in the medical experiment compared to black-box AI. These gains represent a significant improvement in task performance.

Does smartphone addiction impact happiness? exploring the mediating roles of belongingness and fear of missing out: a cross-sectional survey from Turkey

The aim of the study is to determine whether the fear of missing out (FoMO) has a mediating role in the effect of general belongingness (acceptance, exclusion) and happiness on smartphone addiction. The study consists of 656 university students from Turkey and agreeing to participate in the study. Of the students participating in the study, 77.7% were female. The participants were between the ages of 17 and 34, with a mean age of 21.32 ± 2.08 years. In this cross-sectional research design, the relationship between smartphone addiction, fear of missing out, general belonging and happiness variables was examined. Descriptive statistical methods were used to analyze the data and partial least squares path analysis (PLS-SEM) was used to analyze the research model. FoMO has a mediating effect on the effect of acceptance, exclusion, happiness on smartphone addiction. These findings highlight the significant role of FoMO in mediating the relationship between fundamental social needs (belongingness, happiness) and smartphone addiction. This suggests that interventions aimed at reducing smartphone addiction should consider addressing not only the technological dependence but also the underlying social and emotional needs that drive it. Promoting healthy social connections and fostering a sense of belonging among young adults may be crucial in mitigating the risk of smartphone addiction.

Exploring pathways to digital transformation: fsQCA analysis based on the AMO framework

In recent years, China has significantly increased its global competitiveness in digital technologies, emphasizing the importance of the digital economy during the high-quality development stage. The question of how firms in traditional industries can achieve digital transformation, which is critical for participating in the digital economy, is still understudied. Using the ability-motivation-opportunity (AMO) framework, this research developed a model and identified six factors' ability, motivation, and opportunity dimensions. It used fuzzy-set qualitative comparative analysis (fsQCA) to investigate their synergistic effect on digital transformation. With manufacturing firms in China as examples, the findings were as follows. First, no single factor could provide the necessary conditions for digital transformation, implying that the synergistic effect of multiple conditions must be considered. Second, four configurations with three paths for high digital transformation, namely motivation-opportunity-oriented, total factor-oriented, and motivation-oriented, showed different approaches to digital transformation under different conditions. These findings shed light on the complex causal relationships among antecedents of digital transformation and provide theoretical and practical recommendations for businesses looking to implement the digital process.

Research surveys and their evolution: Past, current and future uses in healthcare

Research surveys are believed to have originated in antiquity with evidence of them being performed in ancient Egypt and Greece. In the past century, their use has grown significantly and they are now one of the most frequently employed research methods including in the field of healthcare. Modern validation techniques and processes have allowed researchers to broaden the scope of qualitative data they can gather through these surveys such as an individual's views on service quality to nationwide surveys that are undertaken regularly to follow healthcare trends. This article focuses on the evolution and current utility of research surveys, different methodologies employed in their creation, the advantages and disadvantages of different forms and their future use in healthcare research. We also review the role artificial intelligence and the importance of increased patient participation in the development of these surveys in order to obtain more accurate and clinically relevant data.

How Does Gamification Improve Purchase Intention? Through the Lens of Perceived Brand Coolness and Time Poverty

Gamification has been extensively employed in marketing practices to meet the diverse needs of consumers. Previous research suggests that gamification marketing plays a pivotal role in influencing customer purchase intention. However, the precise mechanism through which gamification marketing impacts purchase intention requires further investigation. Drawing on the self-determination theory (SDT), this study explores the relationship between gamification marketing and purchase intention, with customers' perceived brand coolness as a mediating variable and time poverty as a moderating variable. Using data collected from 184 participants in the experiment, our research demonstrates that, in comparison to non-gamification marketing, gamification marketing significantly influences purchase intention. Furthermore, perceived brand coolness emerges as a mediating factor in this relationship, providing new insights into the gamification mechanism. Customers who are in low time poverty exert more perceived brand coolness and purchase intentions compared with high time poverty in the context of gamification marketing. This study expands the research of gamification by introducing perceived brand coolness to the relationship between gamification marketing and purchase intention. It also contributes to the study of time poverty under the context of gamification marketing.

How Users' Personality Traits Predict Sentiment Tendencies of User-Generated Content in Social Media: A Mixed Method of Configuration Analysis and Machine Learning

OBJECTIVE

Social media content created by users with different personality traits presents various sentiment tendencies, easily leading to irrational public opinion. This study aims to explore the relationships between users' personality traits and sentiment tendencies of user-generated content (UGC).

METHOD

We crawled 18,686 tweets of 1, 215 users from Twitter to figure out the relationships between personality traits and sentiment tendencies. This study utilizes Essays and Sentiment datasets to train machine learning models for the identification of personality traits and sentiment tendencies and then explores the configuration effect of personality traits on sentiment tendency via crisp-set Qualitative Comparative Analysis (csQCA).

RESULT

The findings suggest that (1) one-dimensional personality trait is not a necessary condition for the sentiment tendencies of UGC. (2) There are multiple equivalent configurations that lead to the sentiment tendencies of UGC.

CONCLUSION

The study suggests that the sentiment tendencies pattern of UGC can be discovered via the configurations of various dimensions of personality traits.

Heuristics Identified in Health Data-Sharing Preferences of Patients With Cancer: Qualitative Focus Group Study

BACKGROUND

Evaluating precision oncology outcomes requires access to real-world and clinical trial data. Access is based on consent, and consent is based on patients' informed preferences when deciding to share their data. Decision-making is often modeled using utility theory, but a complex decision context calls for a consideration of how heuristic, intuitive thought processes interact with rational utility maximization. Data-sharing decision-making has been studied using heuristic theory, but almost no heuristic research exists in the health data context. This study explores this evidence gap, applying a qualitative approach to probe for evidence of heuristic mechanisms behind the health data-sharing preferences of those who have experienced cancer. Exploring qualitative decision-making reveals the types of heuristics used and how they are related to the process of decision-making to better understand whether consent mechanisms should consider nonrational processes to better serve patient decision-making.

OBJECTIVE

This study aimed to explore how patients with cancer use heuristics when deciding whether to share their data for research.

METHODS

The researchers conducted a focus group study of Canadians who have experienced cancer. We recruited participants through an online advertisement, screening individuals based on their ability to increase demographic diversity in the sample. We reviewed the literature on data-sharing platforms to develop a semistructured topic guide on concerns about data sharing, incentives to share, and consent and control. Focus group facilitators led the open-ended discussions about data-sharing preferences that revealed underlying heuristics. Two qualitative analysts coded transcripts using a heuristic framework developed from a review of the literature. Transcripts were analyzed for heuristic instances which were grouped according to sociocultural categories. Using thematic analysis, the analysts generated reflexive themes through norming sessions and consultations.

RESULTS

A total of 3 focus groups were held with 19 participants in total. The analysis identified 12 heuristics underlying intentions to share data. From the thematic analysis, we identified how the heuristics of social norms and community building were expressed through altruism; the recognition, reputation, and authority heuristics led to (dis)trust in certain institutions; the need for security prompted the illusion of control and transparency heuristics; and the availability and affect heuristics influenced attitudes around risk and benefit. These thematic relationships all had impacts on the participants' intentions to share their health data.

CONCLUSIONS

The findings provide a novel qualitative understanding of how health data-sharing decisions and preferences may be based on heuristic processing. As patients consider the extent of risks and benefits, heuristic processes influence their assessment of anticipated outcomes, which may not result in rational, truly informed consent. This study shows how considering heuristic processing when designing current consent mechanisms opens up the opportunity for more meaningful and realistic interactions with the complex decision-making context.

Enhancing risk and crisis communication with computational methods: A systematic literature review

Recent developments in risk and crisis communication (RCC) research combine social science theory and data science tools to construct effective risk messages efficiently. However, current systematic literature reviews (SLRs) on RCC primarily focus on computationally assessing message efficacy as opposed to message efficiency. We conduct an SLR to highlight any current computational methods that improve message construction efficacy and efficiency. We found that most RCC research focuses on using theoretical frameworks and computational methods to analyze or classify message elements that improve efficacy. For improving message efficiency, computational and manual methods are only used in message classification. Specifying the computational methods used in message construction is sparse. We recommend that future RCC research apply computational methods toward improving efficacy and efficiency in message construction. By improving message construction efficacy and efficiency, RCC messaging would quickly warn and better inform affected communities impacted by current hazards. Such messaging has the potential to save as many lives as possible.

Exploring the Potential and Challenges of Digital and AI-Driven Psychotherapy for ADHD, OCD, Schizophrenia, and Substance Use Disorders: A Comprehensive Narrative Review

PURPOSE OF THE REVIEW

Psychotherapy remains a fundamental component of mental health treatment, yet it encounters significant barriers to accessibility and efficacy. The integration of digital therapies, encompassing e-Health, m-Health, and artificial intelligence (AI) offers the transformative potential to enhance therapeutic processes through automated systems that improve access and personalize treatment. This integration, however, involves complex considerations, including health-related quality of life (HRQoL) and the dynamics of the therapeutic alliance. This narrative review synthesizes current literature on the application of digital therapies in psychotherapy, with a focus on attention-deficit/hyperactivity disorder (ADHD), obsessive-compulsive disorder (OCD), schizophrenia, and substance use disorders (SUDs). The review evaluates the effectiveness of these interventions, their impact on HRQoL, therapeutic relationships, and the associated ethical considerations.

COLLECTION AND ANALYSIS OF DATA

Following Green's checklist for narrative reviews, a systematic literature review was conducted on studies published from January 2009 to May 2024. Inclusion criteria centered on digital therapies, including AI-based, Internet-based, and mobile-based interventions in psychotherapy. A total of 23 studies were analyzed.

CONCLUSIONS

The findings indicate that digital therapies hold substantial promise for enhancing therapeutic interventions across various mental health disorders, particularly through chatbots and Internet-based psychotherapy. However, successful integration requires careful attention to the therapeutic alliance, HRQoL, and ethical implications. This review highlights the significant potential of digital therapies, including AI-driven psychotherapy, in managing diverse mental health disorders. A balanced approach to integration, prioritizing patient well-being and addressing ethical complexities, is essential for optimizing treatment outcomes.

Site climate more than soil properties and topography shape the natural arbuscular mycorrhizal symbiosis in maize and spore density within rainfed maize (Zea mays L.) cropland in the eastern DR Congo

Rhizosphere microorganisms, particularly arbuscular mycorrhizal fungi (AMF), play a vital role in enhancing sustainable maize production. However, uncertainty persist regarding the influence of climate variables and soil properties on mycorrhizal colonization (MC) of maize and the abundance of AM fungal spores in the field. This study aimed to explore the environmental factors such as site climate variables, soil physicochemical properties and topography and vegetation variable, affecting the natural MC of maize and the density of AMF spores. The study hypothesizes that natural maize mycorrhizal colonization and AMF spore density vary significantly across different sites and agroecological zones. It further posits that climatic and edaphic variables predominantly explain the observed variation in mycorrhizal parameters. To assess the impact of these factors, a field study was conducted in 32 sites across three territories in the province of South Kivu, namely Kabare, Walungu, and Uvira. Rhizospheric soil and maize roots were collected from different sites. Maize MC varied significantly among sites, with Kabare and Walungu showing high colonization rates (52.1% and 44.7%, respectively) compared to Uvira (26.40%). Meanwhile, spore density was significantly higher in Uvira (1331.7 spores g-1 soil) than in Kabare (518.9 spores g-1 soil) and Walungu (468.58 spores g-1 soil). Correlation analysis indicated that maize MC was influenced by site climate and soil properties. The PLS-SEM model demonstrated that 76.5% (R2) of the total variance in maize root MC was explained by climatic variables and soil chemical properties. Compared to soil chemical properties, climate characteristics had a more pronounced impact on maize MC. Maize MC was inversely correlated with temperature, C and available P content, while being directly and positively correlated with altitude, rainfall, and base saturation rate. Furthermore, 68.5% (R2) of the spore density variability of AMF was explained by climatic variables and soil physical properties. Spore density was inversely correlated with sand and clay content, field capacity, rainfall, and altitude, while being positively correlated with temperature. The results of this study indicate that climatic conditions exert a more pronounced influence on the mycorrhizal colonization of maize and the density of AMF spores than soil characteristics.

Research of digital management on sport: An analysis of bibliometrics using CiteSpace Software

Digital management on sport (DMS) has become a hot topic around sports management. However, a more comprehensive and in-depth investigation into the structure and progression of this field is necessary. We used CiteSpace software to conduct a bibliometric analysis, revealing dynamic patterns in the evolution of co-citation, collaboration, and keyword co-occurrence within this field. Data gathers from the Web of Science Core Collection, which includes Social Sciences Citation Index (SSCI), Arts & Humanities Citation Index (A&HCI), Emerging Sources Citation Index (ESCI) and Science Citation Index Expanded (SCI-EXPANDED), and SCOPUS. Published works in this field have shown a consistent upward trend since 2013. England institutions are leading the way in cooperation, while the US is the most collaborative region with different priorities for collaboration in various topics. The most influential author is the one who receives the most citations, but unfortunately, the most frequently co-cited author shows "ANONYMOUS." Highly cited journals typically exert a substantial influence. Additionally, further study is expected to concentrate on efficacy and validation. Since 2018, the growth in publications on DMS reflects increasing academic interest. Australia's institutions lead in collaboration, though international cooperation is limited. High co-citation authors hold significant influence and research focuses on management, social media, and physical activity.

Physicians' acceptance and adoption of mobile health applications during the COVID-19 pandemic in Saudi Arabia: Extending the unified theory of acceptance and use of technology model

Background: The COVID-19 pandemic has highlighted the critical role of mobile health applications in the management of health crises. Despite the promising outcomes of these technologies, however, their acceptance and use among physicians in the developing world such as Saudi Arabia are notably low. Objective: The study aimed to explore the factors influencing the acceptance and adoption of mobile health applications by physicians in Saudi Arabia during the COVID-19 pandemic. Method: The study employed a qualitative research method, guided by the Unified Theory of Acceptance and Use of Technology (UTAUT). The study collected data through semi-structured interviews with 16 physicians to delve into the determinants of their readiness to adopt m-health technologies. Data were analysed using template analysis to identify key themes and patterns. Results: In line with the UTAUT, the study identified performance expectancy, effort expectancy, social influence and facilitating conditions as significant influencing factors of the acceptance and adoption of mobile health applications by physicians in Saudi Arabia during the pandemic. This study also inquired into context-specific determinants, such as data privacy concerns, patient engagement, organisational support and compatibility with religious and cultural norms, which are especially relevant in Saudi Arabia and similar developing countries, where these factors, alongside the exigencies arising from the COVID-19 pandemic, have shaped the landscape of mobile health applications utilisation. Conclusions: This study enriches the literature by expanding the UTAUT model to include context-specific drivers of acceptance and adoption. It highlights the need for tailored adoption frameworks to fit local contexts for successful m-health integration. Implications: This research broadens the UTAUT model by including cultural compatibility and data privacy concerns, offering deeper insights into mHealth adoption during crises. It highlights the need for policies and practices that support culturally sensitive app design, strengthen data privacy measures and provide improved training and patient engagement to enhance mHealth adoption.

Navigating resistance on digital platforms: delivery and transportation labor in Türkiye

Digital platforms are transforming the world of work. However, platforms operating in similar fields of activity encounter varying mechanisms of opposition, as a result of different degrees of professional institutionalization and their relations with the state. This study examines the diversified labor/capital struggle processes on platforms operating at different points of urban mobility in Istanbul and makes an evaluation between delivery and transportation platforms. Therefore, the actions of workers against digital platforms, news reports and public statements of relevant actors in both fields were systematically analyzed. As a result of the study, it is seen that the labor struggle in delivery services includes demands for the regulation of work in parallel with traditional working class reflexes. On the other hand, the resistance in the urban transport platforms is formed in the center of the rent and turns into an institutional struggle as a result of the public activity of the actors. Thus, the inter-class struggle in delivery services operating on digital platforms transforms into an intra-class rent-sharing struggle in urban transport.

The role of AI-enabled health systems in the Jordanian context: the case of digital contact tracing apps

PURPOSE

Digital health or digital healthcare, is a broad, multidisciplinary concept that includes concepts from an intersection between technology and healthcare. Digital health applies digital transformation to the healthcare field, incorporating software, hardware and services. The usefulness of digital health technologies such as digital contact tracing apps (CTAs) depends deeply on the acceptance to use them. Accordingly, the main purpose of this study is to explore the usage of CTAs by integrating the protection motivation theory (PMT) and technology acceptance model (TAM) in the Jordanian context.

DESIGN/METHODOLOGY/APPROACH

Partial least squares structural equation modelling was employed to test empirically the integrated research model against data collected from 420 respondents, leading to the identification of the important determinants for the usage of CTAs.

FINDINGS

The results reveal that perceived severity and perceived vulnerability have a significant positive impact on the perceived need for CTAs, which, in turn, affects the individuals' intention to use CTAs. It also reveals that perceived trust in government, perceived privacy and perceived usefulness have a significant positive impact on individuals' intention to use CTAs, which, in turn, affects the usage of CTAs. In contrast, perceived ease of use did not find any significant relationship, and hence hypothesis H7 was not supported. Lastly, social media awareness has a significant positive impact on perceived usefulness and ease of use.

ORIGINALITY/VALUE

This study is considered among the first studies in the Jordanian context that integrate the PMT and TAM to gain a better understanding of the critical determinants for the acceptance and usage of CTAs in the context of pandemics.

Impact of Providing a Personalized Data Dashboard on Ecological Momentary Assessment Compliance Among College Students Who Use Substances: Pilot Microrandomized Trial

BACKGROUND

The landscape of substance use behavior among young adults has observed rapid changes over time. Intensive longitudinal designs are ideal for examining and intervening in substance use behavior in real time but rely on high participant compliance in the study protocol, representing a significant challenge for researchers.

OBJECTIVE

This study aimed to evaluate the effect of including a personalized data dashboard (DD) in a text-based survey prompt on study compliance outcomes among college students participating in a 21-day ecological momentary assessment (EMA) study.

METHODS

Participants (N=91; 61/91, 67% female and 84/91, 92% White) were college students who engaged in recent alcohol and cannabis use. Participants were randomized to either complete a 21-day EMA protocol with 4 prompts/d (EMA Group) or complete the same EMA protocol with 1 personalized message and a DD indicating multiple metrics of progress in the study, delivered at 1 randomly selected prompt/d (EMA+DD Group) via a microrandomized design. Study compliance, completion time, self-reported protocol experiences, and qualitative responses were assessed for both groups.

RESULTS

Levels of compliance were similar across groups. Participants in the EMA+DD Group had overall faster completion times, with significant week-level differences in weeks 2 and 3 of the study (P=.047 and P=.03, respectively). Although nonsignificant, small-to-medium effect sizes were observed when comparing the groups in terms of compensation level (P=.08; Cohen w=0.19) and perceived burden (P=.09; Cohen d=-0.36). Qualitative findings revealed that EMA+DD participants perceived that seeing their progress facilitated engagement. Within the EMA+DD Group, providing a DD at the moment level did not significantly impact participants' likelihood of completing the EMA or completion time at that particular prompt (all P>.05), with the exception of the first prompt of the day (P=.01 and P<.001).

CONCLUSIONS

Providing a DD may be useful to increase engagement, particularly for researchers aiming to assess health behaviors shortly after a survey prompt is deployed to participants' mobile devices.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/57664.

What drives Chinese youth to use fitness-related health information on social media? An analysis of intrinsic needs, social media algorithms, and source credibility

Introduction

The role of social media in providing fitness-related health information has been widely discussed; however, there is a notable lack of research on fitness-related health information behaviors among youth within the social media context. This study aims to address this gap by integrating Self-Determination Theory (SDT)-based internal factors and external factors (social media algorithms and source credibility).

Methods

A voluntary sample of 600 participants, aged 15 to 29, was recruited. Data were analyzed using Partial Least Squares-Structural Equation Modeling (PLS-SEM) to examine the relationships between variables.

Results

The analysis revealed that all three intrinsic needs-competence, autonomy, and relatedness-along with social media algorithms and source credibility, positively correlated with fitness-related health information use behaviors among youth. Additionally, social media algorithms moderated the relationship between the need for relatedness and fitness-related health information behavior.

Discussion

These findings provide new insights into developing health communication strategies on social media, particularly targeted toward the youth demographic, enhancing our understanding of effective health information dissemination in digital environments.

Understanding collaborative consumption in the technology-driven sharing economy: A cost-benefit analysis perspective

Driven by the extensive implementation of information communication technology, collaborative consumption has become more popular. Historically, people have always thought that the best way to get something is to obtain the ownership of it. However, collaborative consumption has recently seen a meteoric rise in popularity due to that obtaining the right to use rather than own. More research into this emerging phenomenon is necessary, notwithstanding the huge impact that collaborative consumption activities have had on companies and individuals. Existing research indicates a lack of knowledge on the factors that motivate or impede user engagement in collaborative consumption. Building on the cost and benefit framework, this research presents a model that examines the effects of perceived benefits (enjoyment and economic reward), perceived costs (privacy risk and security risk) and perceived platform quality (system quality, service quality and information quality) on the intention to engage in the collaborative economy. Using a structural equation modelling approach, 524 active users with experience in car sharing evaluated the research model.The results show that perceived benefits and platform quality positively influence CC participation, the perceived cost reveals a partial support relationship to participate in CC, where security risks are supported but privacy risks are not. This research results will contribute to the research and practice on sharing economy.

From Customer Intimacy to Digital Customer Intimacy

Customer intimacy plays a crucial role in shaping customer relationships and guiding organizational strategies. As the digital business environment has grown, the concept of customer intimacy has evolved, leading to significant changes in its dimensions and implications. This systematic literature review and synthesis comprehensively analyzes the current research on customer intimacy, reexamines it within the context of e-commerce and digital marketing, and introduces the concept of “Digital Customer Intimacy”. The paper explores the unique characteristics of digital customer intimacy, how it differs from traditional customer intimacy, and identifies areas for future research. By synthesizing insights from various studies, this review deepens our understanding of customer intimacy in both traditional and digital contexts, offering practical insights for modern marketing and laying the groundwork for future research.

From trendsetters to second movers: Commitments as catalysts for social change

This paper examines the dual role of commitment in accelerating the mitigation of maladaptive norms. By drawing on the literature on social norms and commitment, I analyze how commitments (i) explain the deviant behavior of trendsetters and (ii) expedite social change by prompting counterpreferential choices among second movers. Specifically, the paper explores two key aspects of this dynamic: (1) the importance of moral norms, particularly in the relationship between trendsetters' commitments and their unconditional preferences, and (2) the unique role of second movers, whose commitment to trendsetters can lead them to make choices that contradict their conditional preferences in favor of trendsetters' behavior. This suggests that second movers, despite their tendency to conform to established social norms, may act against their conditional preferences due to their commitment to trendsetters. Understanding the role of commitments in shaping the behaviors of trendsetters and second movers is essential to overcoming psychological barriers that impede social change. Nonetheless, further exploration is needed to determine which maladaptive norms are most responsive to commitments and under what circumstances counterpreferential choices are likely to prevail.

Enhancing Digital Health Engagement in Chronic Disease: The Role of Doctor-Patient Communication and Media Use in Acceptance of Digital Health Care Among End-Stage Kidney Disease Patients

This study examines digital health challenges among end-stage kidney disease (ESKD) patients, a population characterized by older age, lower socioeconomic status, and limited access to modern technologies. Drawing from the Mere Exposure Effect, the Technology Acceptance Model, and insights from doctor-patient communication literature, our study implemented a month-long intervention across three distinct groups. The Digital Media Exposure Group watched doctor-recommended videos on YouTube using a tablet PC twice weekly for four weeks. The Digital Media Exposure with Doctor-Patient Communication Group engaged in physician-led discussions about the viewed content during their medical visits in addition to the activities in the first group. The Control Group received printed medical information that mirrored the content of the videos. Participants in this study, all of whom were diagnosed with ESKD, were recruited from a university hospital in South Korea (n = 88, Mage = 64.8). Their perceptions, attitudes, and behavioral intentions regarding digital health care were measured and compared between groups. The results unveiled significant group differences [Wilk's Λ = 0.829, F(8, 164) = 2.02, p = 0.047, partial η2 = 0.090], with variations in attitudes, perceived ease of use, and intentions among groups, and effect sizes ranging from 0.069 to 0.096.These findings underscore the importance of tailored interventions to address digital health disparities, particularly among underserved demographic groups. Strategies that prioritize user-friendly interfaces and clear communication between doctors and patients are advocated to promote digital health engagement, ensuring equitable access and improved outcomes for patients with chronic disease.

The association between internet addiction and sleep quality among medical students in Saudi Arabia

BACKGROUND

Sleep is one of the fundamental human needs, essential for maintaining a high quality of life and mental and physical well-being across all age groups. Poor sleep quality often stems from negative lifestyle habits, including excessive internet usage. Therefore, it is important to determine the prevalence of internet gaming disorder among youth in Saudi Arabia and to examine the relationship between internet addiction levels and sleep quality.

METHODS

Data were collected from 338 medical students in the southern region of Saudi Arabia (mean age = 21.2 years, standard deviation = 3.29 years). Participants completed an online questionnaire comprising the Internet Addiction Test (IAT) and the Pittsburgh Sleep Quality Index (PSQI). Data analysis employed iterations, chi-square tests, Pearson correlation coefficients, and ANOVA.

RESULTS

The results revealed that 21% of the participants exhibited severe internet addiction, while 31% displayed moderate internet addiction. Furthermore, the findings indicated a positive correlation between sleep quality and the severity of internet addiction. Sleep quality symptoms explained 75% of the variance in Internet addiction scores, even after controlling for demographic variables. Additional bivariate analyses revealed that individuals who spent six or more hours online daily were more likely to experience symptoms of poor sleep quality and exhibit a higher severity of internet addiction. Additionally, Men were more susceptible to developing internet addiction compared to women. Moreover, students with internet addiction tended to have lower academic achievements.

CONCLUSION

These findings, while exploratory, offer valuable insights into potential interventions, strategies, and programs for mitigating internet addiction and enhancing sleep quality among medical college students.

Ideaflow and Ambidexterity in Anesthesiology-Quality and Safety in an Era of Disruptive Innovation

The concept of ambidexterity as a strategic advantage has been discussed extensively in the business literature. While the specialty of anesthesiology has mastered exploitative/variance-reducing tactics as well as any specialty in health care, a new perspective of exploratory/variance-increasing strategies will be necessary to remain relevant in the perioperative space. We offer a framework of "idea flow" as a means for anesthesiology groups and departments to balance ongoing clinical mastery with innovative exploration.

Designing and testing clinical simulations of an early warning system for implementation in acute care settings

Objectives

Conducting simulation testing with end-users is essential for facilitating successful implementation of new health information technologies. This study designed a standardized simulation testing process with a system prototype prior to implementation to help study teams identify the system's interpretability and feasibility from the end-user perspective and to effectively integrate new innovations into real-world clinical settings and workflows.

Materials and Methods

A clinical simulation model was developed to test a new Clinical Decision Support (CDS) system outside of the clinical environment while maintaining high fidelity. A web-based CDS prototype, the "CONCERN Smart Application," which leverages clinical data to measure and express a patient's risk of deterioration on a 3-level scale ("low," "moderate," or "high"), and audiovisual-integrated materials, were used to lead simulation sessions.

Results

A total of 6 simulation sessions with 17 nurses were held to investigate how nurses interact with the CONCERN Smart application and how it influences their critical thinking, and clinical responses. Four themes were extracted from the simulation debriefing sessions and used to inform implementation strategies. The strategies include how the CDS should be improved for practical real-world use.

Discussion and Conclusions

Standardized simulation testing procedures identified and informed the necessary CDS improvements, the enhancements needed for real-world use, and the training requirements to effectively prepare end-users for system go-live.

Let's Talk About It in the Morning: How Circadian Rhythms Impact Information Sharing on Social Media

This study investigated the role of circadian rhythms in online information sharing. We gathered 416,914 posts from the social media platform X (formerly Twitter). We identified daily patterns of collective positive and negative affect in these posts, consistent with previous research on social media and circadian rhythms. We created predicted values of positive and negative affect for each post, based on the time a post was created. We then used these predicted values for each post to estimate that post's likelihood of being reshared. We controlled for a range of possible confounders, such as the actual positive and negative affect expressed in a specific post and the number of existing followers and previous posts of the user who created the post, as well as whether the post contained hashtags, mentions, and quotes. The results support a strong relationship between the predicted positive and negative affect of a post-based on circadian patterns of collective positive and negative affect-and the likelihood of a post being shared. We further examine seasonal changes and design a natural experiment, in which we compare patterns of positive and negative affect and information sharing before and after the clocks change, i.e., "spring forward" and "fall back." The results suggest that these daily collective patterns of positive and negative affect on social media are influenced, at least partly, by hormonal influences and not only collective daily routines.

The impact of perceived vaginal and urinary symptoms and treatment burden in predicting sexual functioning among perimenopausal and postmenopausal women

OBJECTIVE

This study aimed to explore the relationship between urogenital symptom frequency and severity, perception of vaginal treatment burden, and female sexual desire, arousal, and satisfaction.

METHODS

A cross-sectional study was conducted with a sample of 326 patients from three tertiary care hospitals in the United Arab Emirates. The frequency and severity of urogenital symptoms, emotional and physical functioning, and treatment burden were assessed using the validated genitourinary syndrome of menopause symptoms and vaginal treatments acceptability questionnaire (GSM-SVATQ). To examine the mediating roles of emotional and physical functioning, as well as the perceived treatment burden on sexual functioning, a partial least squares-structural equation model was developed using the SmartPLS 4 Software.

RESULTS

The measurement model was successfully established. All constructs had a reliability of > 0.70 and discriminant validity of < 0.90. Emotional, physical and sexual functioning showed an adjusted R2 values of 0.377, 0.282 and 0.169, respectively.The multistep multiple mediator model revealed a full mediation effect of both emotional and physical functioning between symptom, treatment burden and sexual functioning. The model showed high predictive performance with all manifest variables showing lower mean absolute errors compared to the naiive benchmark model.

CONCLUSION

This study enhances our understanding of the relationships between urogenital symptoms, perceived treatment burden, emotional functioning, and sexual well-being. The findings emphasize the importance of addressing emotional well-being in managing urogenital symptoms and in addressing emotional factors associated with the use of vaginal treatments.

Knowledge, perceptions, and readiness of telepharmacy (KPR-TP) questionnaire among pharmacists: Development and psychometric evaluation

INTRODUCTION

The pandemic increased pressure on healthcare services and forced limited care in all health facilities to ensure the care of all patients. Telepharmacy appears as an alternative to the remote pharmacy practice approach through information and communication technologies, but there are no comprehensive tools to measure pharmacists' knowledge, perception, and readiness to implement telepharmacy. This study developed and validated a questionnaire version of the Knowledge, Perceptions, and Readiness of Telepharmacy (KPR-TP) for pharmacists.

METHODS

The KPR-TP assessed three domains: knowledge, perception, and readiness. Its factor structure, reliability, and validity were assessed using 7730 pharmacists from 34 Indonesian provinces. The validity of the model's three-factor structure was assessed using confirmatory factor analysis. Cronbach's alpha and composite reliability were used to validate the consistency of our factors, whereas convergent and discriminant validity established significant relationships between them.

RESULTS

The goodness-of-fit index indicated that the model was economical and reasonable. Furthermore, the correlation between the three domains revealed a significant positive relationship. KRP-TP is a viable instrument for assessing pharmacists' perceptions of telepharmacy in Indonesia.

CONCLUSION

Overall, we discovered that our questionnaire contains critical constructs for assessing a pharmacist's knowledge, perception, and level of readiness to implement telepharmacy. This study will help pharmacists identify appropriate strategies for skill improvement.

A secure blockchain framework for healthcare records management systems

Electronic health records are one of the essential components of health organizations. In recent years, there have been increased concerns about privacy and reputation regarding the storage and use of patient information. In this regard, the information provided as a part of medical and health insurance, for instance, can be viewed as proof of social insurance and governance. Several problems in the past few decades regarding medical information management have threatened patient information privacy. In intelligent healthcare applications, the privacy of patients' data is one of the main concerns. As a result, blockchain is a severe necessity as it can enhance transparency and security in medical applications. Accordingly, this paper uses the design science method to propose a secure blockchain framework for healthcare records management systems. The proposed framework comprises five components: a blockchain network, smart contracts, privacy key management, data encryption, and integration with healthcare information technology. In the proposed framework, healthcare organizations can manage healthcare information securely and privately. Additionally, a secure storage system for electronic records is proposed to meet these organizations' needs. It provides security and privacy for healthcare organizations, especially when managing healthcare information, and also proposes a secure storage system for electronic records to meet the needs of the organizations.