Modeling Analysis Of Data Research Articles

Limitations and Future Directions in Studying Organisational Justice in Academia: A Call for Longitudinal and Multivariate Approaches

This study delves into the complex dynamics of organizational justice within academic settings, acknowledging its significant role in shaping faculty perceptions and institutional effectiveness. Despite offering valuable insights, the study is constrained by its cross-sectional design, which limits the ability to infer causality. To address this limitation, future research should employ longitudinal designs, enabling the exploration of how perceptions of organisational justice evolve over time. Additionally, investigating other potential modifiers, such as organisational subcultures and individual differences, could provide a more nuanced and comprehensive understanding. Emphasizing these approaches will enhance the depth of knowledge regarding organisational justice, facilitating more effective strategies to foster fair and equitable academic environments. Through Partial Least Squares Structural Equation Modeling (PLS-SEM) analysis of data from 276 MBA faculty members, the research identifies leadership behavior, organisational policies and procedures, and interpersonal relationships as key antecedents of organisational justice. The study further explores how these antecedents, through organizational justice, affect job satisfaction and organizational commitment, and how academic rank moderates these relationships.

Off-shell pion properties: Electromagnetic form factors and light-front wave functions

The off-shell pion electromagnetic form factors are explored with corresponding off-shell light-front wave functions modeled by constituent quark and antiquark. We apply the Mandelstam approach for the microscopic computation of the form factors relating the model parameters with the pion decay constant and charge radius. Analyzing the existing data on the cross sections for the Sullivan process, H1(e,e′,π+)n [H. P. Blok (Jefferson Lab Fπ Collaboration), Charged pion form factor between Q2=0.60, and 2.45 GeV2. I. Measurements of the cross section for the H1(e,e′π+)n reaction, .], we extract the off-shell pion form factor using the relation derived from the generalized Ward-Takahashi identity for the pion electromagnetic current. They are compared with our previous results [H.-M. Choi , Pion off-shell electromagnetic form factors: Data extraction and model analysis, .] from exactly solvable manifestly covariant model of a (3+1)-dimensional fermion field theory. We find that the adopted constituent quark model reproduces the extracted off-shell form factor F1(Q2,t) from the experimental data within a few percent difference and matches well with our previous theoretical simulation which exhibits a variation of about 10% for the extracted off-shell pion form factor g(Q2,t). We also identify the pion valence parton distribution function (PDF) and transverse momentum distribution (TMD) in terms of the light-front wave function and discuss their off-shell properties. Published by the American Physical Society 2024

Enhancing Financial Advisory Services with GenAI: Consumer Perceptions and Attitudes Through Service-Dominant Logic and Artificial Intelligence Device Use Acceptance Perspectives

Financial institutions are currently undergoing a significant shift from traditional robo-advisors to more advanced generative artificial intelligence (GenAI) technologies. This transformation has motivated us to investigate the factors influencing consumer responses to GenAI-driven financial advice. Despite extensive research on the adoption of robo-advisors, there is a gap in our understanding of the specific contributors to, and differences in, consumer attitudes and reactions to GenAI-based financial guidance. This study aims to address this gap by analyzing the impact of personalized investment suggestions, human-like empathy, and the continuous improvement of GenAI-provided financial advice on its authenticity as perceived by consumers, their utilitarian attitude toward the use of GenAI for financial advice, and their reactions to GenAI-generated financial suggestions. A comprehensive research model was developed based on service-dominant logic (SDL) and Artificial Intelligence Device Use Acceptance (AIDUA) frameworks. The model was subsequently employed in a structural equation modeling (SEM) analysis of survey data from 822 mobile banking users. The findings indicate that personalized investment suggestions, human-like empathy, and the continuous improvement of GenAI’s recommendations positively influence consumers’ perception of its authenticity. Moreover, we discovered a positive correlation between utilitarian attitudes and perceived authenticity, which ultimately influences consumers’ responses to GenAI’s financial advisory solutions. This is manifested as either a willingness to engage or resistance to communication. This study contributes to the research on GenAI-powered financial services and underscores the significance of integrating GenAI financial guidance into the routine operations of financial institutions. Our work builds upon previous research on robo-advisors, offering practical insights for financial institutions seeking to leverage GenAI-driven technologies to enhance their services and customer experiences.

Stress in social and health care professionals working with people with intellectual disabilities: risk and protective factors

Objectives Those working in the field of intellectual disabilities are confronted with a multitude of stressors, which can give rise to considerable psychological difficulties. The objective of this study was to identify the key predictors of perceived and relational stress among professionals working in intellectual disability centers. Methods A cross-sectional design was employed, with 726 professionals participating (mean age = 43.35 years, SD = 11.19), predominantly women (78.6%). The variables assessed included perceived stress, relational stress, emotional balance, life satisfaction, social support, empathy, and counseling training. The analysis was conducted using structural equation modeling and qualitative-comparative analysis of fuzzy data (QCA). Results The study revealed that the stress levels of the study participants were moderate to low. The relationship between emotional balance and stress was mediated by life satisfaction. In both structural equation models, negative affect and life satisfaction were identified as primary predictors, significantly influencing perceived and relational stress. Additionally, QCA highlighted that empathy and social support, along with these factors, contribute to explaining stress levels. Conclusions Our findings highlight the necessity of implementing preventive strategies and support systems to mitigate stress among healthcare professionals who work with individuals with intellectual disabilities. These findings contribute to the existing body of knowledge regarding stress management in this specialized healthcare context.

Examining Diversity and Equity in LIS Education: A Syllabus to Reading List Analysis

To determine how effectively the Master of Library and Information Science curriculum at Indiana University - Indianapolis is addressing the new ALA core competency related to social justice, we analyzed all the course syllabi and are now in the process of analyzing all course readings, using various text analysis methods. We found the initial review of syllabi to lack the level of granularity that would have revealed the different kinds of diversity and equity issues in the curriculum. Additionally, courses we know for certain that covered topics related to social justice, the syllabus alone indicated otherwise. For these reasons, we decided to examine the readings that comprised the entire curriculum. With 68 courses and over 3000 readings to date, gathering the data for analysis has taken several months of time. Metadata and PDFs of course readings were stored in Zotero and extracted from there for analysis. Terminology, as identified from the study that examined all ALA-accredited LIS program course descriptions, will be used to frame the textual analysis (Dill, Grote, & Hardin, 2023). We will present the topics and trends identified through bibliometric analysis, topic modeling, and visual analysis of the citation data. This analysis will shed light on areas of strength and for improvement within curriculum with the goal of preparing students to effectively work in socially just library environments.

Digital Immigrants Versus Digital Natives: Decoding Their E-commerce Adoption Behavior

It is easier to assume that educated older adults will find digital gadgets or the Internet as simple to use as the young generation does. However, it is not as simple as that. The generation that was not born into the digital world but has had to make an effort to learn to use digital technologies during their middle or late middle age is referred to as Digital Immigrants (DIs). Most of these individuals were forced to adapt to information technologies due to environmental pressure to survive and thrive at their workplace. The objective of this study is to investigate if the proposed “digital divide” that differentiates digital immigrants from digital natives (DNs) exists among e-commerce users in India, and if so, are digital immigrants less likely to adopt and use e-commerce services? Data was collected through a self-administered survey questionnaire from 432 Indian Internet users aged 19 to 65. Multigroup structural equation modeling analysis (M-SEM) of data revealed that DIs and DNs perceive e-commerce services differently. Though digital immigrants find e-commerce services challenging to use, their higher perception of its usefulness propels them to adopt and use e-commerce. This study contributes to the existing body of literature by extending our understanding of the technology adoption behavior of digital immigrants. The study’s implications and the scope for future research are discussed at the end of the article.

Insight into Limited Recoveries in Printed Circuit Board Leaching: Evidence of Gold Adsorption Loss to Polymeric Particles

AbstractSustainable resource utilization ultimately depends on total recovery during recycling operations. PCB leach pulp for target metal recovery contains ceramic and polymeric particles resistant to reagent attack. The investigation herein shows the physicochemical behavior of five polymeric particles namely green, red, black, white, and blue in gold chloride complex [AuCl4]− solution. Gold concentration change was monitored over time to study how the gold solution interacted with the polymeric particles and how the interaction conforms to known adsorption isotherm and kinetic models. The black and green polymeric particles have the highest adsorption per unit mass to about 1.81 mg/g and 2.18 mg/g, respectively. Four adsorption kinetic models evaluated for insight on the adsorption mechanism observed show Elovich and pseudo-second-order (PSO) models fit the kinetic data best compared to other models with correlation coefficients of 0.9867 and 0.9863, respectively. Of the seven adsorption isotherm models evaluated, Sips model shows best fit for the isotherm data with correlation coefficients of 0.9727 and 0.9328 for green and black particles, respectively. Isotherm and kinetic model analyses of the experimental data indicated physisorption and chemisorption as driving mechanism of the interaction between PCB polymeric particles and gold chloride complex aliquot. Graphical Abstract

Offline data processing system of the BESIII experiment

The BESIII experiment has been operating since 2009 and has received several upgrades, to study the τ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ au $$\\end{document}-charm physics utilizing the BEPCII accelerator. The BEPCII is a double-ring multi-bunch collider with a luminosity of 1×1033cm-2s-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1 \ imes 10^{33} \\, {\ ext {cm}}^{-2} \\, {\ ext {s}}^{-1}$$\\end{document} optimized at a center-of-mass (cms) energy of 2×1.89\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$2 \ imes 1.89$$\\end{document} GeV. The rich physics topics, long lifetime and the large volume of data present challenges to offline data processing. This paper describes the design and implementation of the offline software system to meet the requirements and challenges of BESIII, including offline software architecture, geometry management, database management, event data model and tag-based analysis.

New training simulator for lumbar puncture base on magnetorheological

In response to the difficulties in accurately reproducing the resistance drop generated by puncturing key tissue layers with a needle and the poor experience in existing simulators, based on the continuous controllability and rapid response of magnetorheological fluid under the influence of a magnetic field, this paper proposes a lumbar puncture training simulator(LPTS) that can accurately simulate the puncture feedback force within tissues such as the skin, subcutaneous fat, and supraspinous ligament throughout the entire process. By using a dual rod structure and reasonably arranging the damping channel gap, the influence of mechanical friction and zero-field damping force on the feedback force during tissue progression is minimized. This paper introduces the acquisition and modeling analysis of raw data, and based on this, the design, simulation, and mechanical testing of the simulator are carried out. Finally, a performance testing platform for the simulator is established to evaluate its tracking performance of the expected puncture strength and the reproducibility of the puncture sensation. The results show that the experimental puncture strength deviates from the expected puncture strength by 0.35 N to 0.61 N in the crucial steps of breaking through the supraspinous ligament, interspinous ligament, ligamentum flavum, and dura mater, with a relative error below 10 %.

Influence of Technology Readiness in the Adoption of Augmented Reality Applications in Retailing

Forget crowded aisles and unending product comparisons. Imagine yourself trying on clothes virtually, visualizing furniture in your living room before making a purchase, or even discovering information about products that are hidden behind interactive labels. Augmented reality (AR) promises to revolutionize the shopping experience, but widespread adoption depends on understanding user motivations. This study explores what motivates consumers to interact with this emerging technology to determine the “why” behind AR retail adoption. Although AR has the potential to transform many industries, consumer technology readiness is a critical factor that will determine how widely adopted AR becomes. This research investigates how a person’s ability, willingness, and confidence in their ability to use technology serves as a vital link between the potential for innovation and its practical implementation. The study investigates the effects of performance expectancy, price, effort expectancy, hedonic motivation, and technological readiness on adoption behavior using a partial least squares-structural equation modeling analysis of data from 306 online survey respondents. The results show that price and performance expectancy are significant drivers, but hedonic motivation and effort expectancy have no impact on adoption behavior. Notably, one factor that strongly influences the adoption of AR apps is technological readiness. These findings provide retailers with insightful advice, telling them to prioritize app value, affordability, and user-friendly design while also realizing how crucial it is to accommodate different degrees of technological comfort. Retailers can effectively use AR technology to improve customer engagement and accelerate adoption in the retail space by knowing these key drivers.

Overdispersion in gate tomography: Experiments and continuous, two-scale random walk model on the Bloch sphere

Noisy intermediate-scale quantum computers (NISQ) are computing hardware in their childhood, but they are showing high promise and growing quickly. They are based on so-called qubits, which are the quantum equivalents of bits. Any given qubit state results in a given probability of observing a value of zero or one in the readout process. One of the main concerns for NISQ machines is the inherent noisiness of qubits, i.e. the observable frequencies of zeros and ones do not correspond to the theoretically expected probability, as the qubit states are subject to random disturbances over time and with each additional algorithmic operation applied to them. Models to describe the influence of this noise exist. In this study, we conduct extensive experiments on quantum noise. Based on our data, we show that existing noise models lack important aspects. Specifically, they fail to properly capture the aggregation of noise effects over time (or over an algorithm’s runtime), and they are underdispersed. With underdispersion, we refer to the fact that observable frequencies scatter much more between repeated experiments than what the standard assumptions of the binomial distribution would allow for. Based on these shortcomings, we develop an extended noise model for the probability distribution of observable frequencies as a function of the number of gate operations. The model roots back to a known continuous random walk on the (Bloch) sphere, where the angular diffusion coefficient can be used to characterize the standard noisiness of gate operations. Here, we superimpose a second random walk at the scale of multiple readouts to account for overdispersion. Further, our model has known, explicit components for noise during state preparation and measurement (SPAM). The interaction of these two random walks predicts theoretical, runtime-dependent bounds for probabilities. Overall, it is a three-parameter distributional model that fits the data much better than the corresponding one-scale model (without overdispersion), and we demonstrate the better fit and the plausibility of the predicted bounds via Bayesian data-model analysis.

Online opinion leadership styles and purchase intention in livestreaming e-commerce

ABSTRACT As online opinion leaders and persuaders, livestreamers enable the widespread success of livestreaming e-commerce by substantially impacting viewers’ online purchase intention. Yet, extant studies have overlooked online opinion leadership styles exhibited by livestreamers and how these influence viewers’ online purchase intention. We explore this issue by developing a comprehensive framework of livestreamers’ online opinion leadership styles, comprising authentic, ethical, servant, and participative dimensions, based on both livestreaming and opinion leadership literature. Then, drawing on regulatory focus theory, the partial least square-structural equal modeling analysis of survey data collected from 321 Chinese viewers revealed that livestreamers with authentic and ethical online opinion leadership styles promote viewers’ prevention focus. Next, livestreamers with the servant and participative styles promote viewers’ promotion focus. Finally, viewers’ prevention and promotion focus propel their online purchase intention. Our findings contribute to the development of a comprehensive framework of online opinion leadership styles in computer-mediated livestreaming e-commerce.

Predictive Models of Long-Term Outcome in Patients with Moderate to Severe Traumatic Brain Injury are Biased Toward Mortality Prediction.

The prognostication of long-term functional outcomes remains challenging in patients with traumatic brain injury (TBI). Our aim was to demonstrate that intensive care unit (ICU) variables are not efficient to predict 6-month functional outcome in survivors with moderate to severe TBI (msTBI) but are mostly associated with mortality, which leads to a mortality bias for models predicting a composite outcome of mortality and severe disability. We analyzed the data from the multicenter randomized controlled Continuous Hyperosmolar Therapy in Traumatic Brain-Injured Patients trial and developed predictive models using machine learning methods and baseline characteristics and predictors collected during ICU stay. We compared our models' predictions of 6-month binary Glasgow Outcome Scale extended (GOS-E) score in all patients with msTBI (unfavorable GOS-E 1-4 vs. favorable GOS-E 5-8) with mortality (GOS-E 1 vs. GOS-E 2-8) and binary functional outcome in survivors with msTBI (severe disability GOS-E 2-4 vs. moderate to no disability GOS-E 5-8). We investigated the link between ICU variables and long-term functional outcomes in survivors with msTBI using predictive modeling and factor analysis of mixed data and validated our hypotheses on theInternational Mission for Prognosis and Analysis of Clinical Trials in TBI (IMPACT) model. Based on data from 370 patients with msTBI and classically used ICU variables, the prediction of the 6-month outcome in survivors was inefficient (mean area under the receiver operating characteristic 0.52). Using factor analysis of mixed data graph, we demonstrated that high-variance ICU variables were not associated with outcome in survivors with msTBI (p = 0.15 for dimension 1, p = 0.53 for dimension 2) but mostly with mortality (p < 0.001 for dimension 1), leading to a mortality bias for models predicting a composite outcome of mortality and severe disability. We finally identified this mortality bias in the IMPACT model. We demonstrated using machine learning-based predictive models that classically used ICU variables are strongly associated with mortality but not with 6-month outcome in survivors with msTBI, leading to a mortality bias when predicting a composite outcome of mortality and severe disability.

Role of Social Demography Factors in Economic Growth and Sustainability Models: Evidence from selected emerging economies

A country’s social demography characteristics are described by population dynamics and technology-based lifestyle changes. Such unique elements may correlate with economic growth and sustainability. In this study, a static panel data model analysis for economic growth and sustainable development of ten emerging economies was presented. Estimation results revealed that social demography factors exhibited a negative significant impact in both economic models, relative to the technological influence. Therefore, effective macroeconomic policies should be meticulously designed against changing demographic trends. Conclusively, by delivering focused public services to the urban area, more liveable cities will emerge to empower human capital development.

Dinamika Pembangunan Sumber Daya Manusia (SDM) terhadap Perekonomian dengan Kontribusai Pendapatan Asli Daerah (PAD) sebagai Pemoderasi

The vulnerability of Bali's economy, which only depends on the tourism sector during the Covid-19 pandemic, has given rise to the idea of economic transformation to develop other sectors. In this effort, human development is a key factor as a major economic actor. Human Development Index (HDI) as an indicator of human resource quality is thought to have a positive influence on the economy as seen from Gross Regional Domestic Product (GRDP) at constant prices. Original Local Government Revenue (OLGR) as financial capital is considered capable of strengthening empowerment programs and policies that directly touch the community and downstream can strengthen the economy. The data used is secondary data for the 2013-2022 period obtained from Regencies and Cities in Bali Province (panel data). This research was tested using Econometric Views (Eviews) software (panel data regression). The analysis techniques used include descriptive statistics, panel data model analysis, classical assumption tests, interaction tests or Moderated Regression Analysis (MRA) and hypothesis testing with analysis of the coefficient of determination and t test. The aim of the research is to determine the effect of HDI on GRDP and to find out whether OLGR is able to moderate the influence of HDI on GRDP. The results show that HDI has a significant positive effect on GRDP. This means that increasing HDI will have a positive impact on increasing GDP. OLGR strengthens the positive influence of HDI on GRDP. This means that OLGR is able to strengthen and have an impact on human resource development and ultimately increase GDP growth which is an indicator of regional economic growth. Suggestions that can be given from the results of human resource investment research can be a priority so that regional communities can become the driving force of the regional economy. It is hoped that the use of OLGR for empowerment programs and policies that touch the community can be implemented more optimally so that it can improve the quality of human resources and have an impact on positive economic growth.

Rigorous assessment of a unique tiger recovery in Southeast Asia based on photographic capture-recapture modeling of population dynamics

Tigers and other large predatory carnivores have suffered population extirpations and range contractions. This is particularly true for tiger populations in southeastern Asia, which harbours one-third of their remaining habitats. In stark contrast, a sustained recovery of a wild tiger population has occurred between 2007 and 2023, in three reserves of Thailand: Huai Kha Khaeng (HKK), Thung Yai East (TYE) and Thung Yai West (TYW), which together cover 6470 km2 (36%) of the larger Western Forest Complex (WEFCOM). We quantitatively monitored this recovery employing closed and open model analyses of data from photographic capture-recapture sampling. The resulting estimates of tiger population dynamic parameters showed: mean (±SE) tiger abundance annually varied from 36 (1.0) to 79 (1.53) in HKK, 2 (0.26) to 20 (4.45) in TYE and 3 (0.26) to 44 (2.11) in TYW, driven by mean annual survival rates of 0.79 (0.02) in HKK, 0.72 (0.05) in TYE, and 0.69 (0.05) in TYW. The annual numbers of recruits fluctuated from 0 (1.69) to 33 (1.93) tigers in HKK, 0 (0.47) to 13 (0.57) in TYE and 0 (1.13) to 36 (2.28) in TYW. Overall, the mean tiger population densities/100 km2 ranged between 1.3 (0.19) and 2.9 (0.29) in HKK, 0.2 (0.08) and 1.8 (0.34) in TYE, and 0.2 (0.07) and 3.1 (0.56) in TYW. Generally, the tiger population trended upward, with reserves protected over longer periods leading the tiger recovery. Our results are further backed by ancillary records on births of 67 cubs, 47 tiger dispersal events, as well as the recovery corresponding with incremental spatio-temporal coverage by the patrols. Cumulatively, our results provide evidence that effective law enforcement should be a critical component for achieving tiger population recoveries in Asia. Alternative conservation strategies that ignore this component do not appear to be evidence-based. Our results also demonstrate the utility of the independent collaborative monitoring framework adopted by the Thailand Government.

Forecasting Macro-Economic Indicators Based on Text Information from Strategic Management Field in Russia

The current situation in economic activity is notable for exponential increase in accessible e-information and serious interest in its use in order to get competitive advantages. The article studies influence of information published in mass media on the essential social and economic indicators, which show key concepts of the system of strategic management in Russia. Methodological basis of the research was formed by theories of cognitive, topical modeling and regressive analysis. In the investigation the author used methods of topical modeling, machine education and statistical analysis of data. The author put forward the procedure of automated plotting of the cause-and-effect diagram based on qualitative and quantitative data. The system of causal links between key concepts of the system gave a chance to build forecast models of high accuracy. Findings of the research showed that topics being highlighted in mass media can influence social and economic indicators. Unfortunately, accidental events can make mathematic models, relying on system inertia, inadequate. In theoretical aspect the article proposes the procedure of automated building of the cause-and-effect diagram based on heterogeneous data, which can eliminate the problem of subjectivity of expert estimations in plotting cognitive maps. In applied aspect models of forecasting the most important social and economic indicators based on mass media publications were worked out that lean against cause-and-effect diagram and can support well-grounded managerial decisions and in case of necessity can affect the situation.

Japanese National Basketball Association Fans and Social Media Acceptance: Exploring the Role of Brand Respect

This study explores the factors influencing satellite sport fans’ social media engagement, focusing on Japanese National Basketball Association (NBA) fans. Using the technology acceptance model and Brand Respect Scale, we analyzed the effects of perceived usefulness, perceived enjoyment, and perceived trustworthiness on social media stickiness and investigated the mediating roles of brand trust, brand performance, brand acceptance, and brand reputation. A partial least squares structural equation modeling analysis of data from 813 Japanese NBA fans revealed that perceived usefulness and enjoyment significantly affected social media stickiness, while perceived trustworthiness indirectly influenced this stickiness through brand trust and acceptance. The study advances the theoretical understanding of social media behavior among satellite sport fans and provides practical implications for optimizing social media strategies to enhance fan engagement. Our findings offer valuable insights for sport teams and organizations seeking to strengthen their connection with geographically distant fans. In addition, this research paves the way for future studies to encompass diverse age groups, sports, and regions to offer a more comprehensive understanding of the factors influencing satellite sport fans’ social media engagement.

HOW IMPORTANT IS CSR COMMITMENT IN SHAPING CORPORATE REPUTATION?

Objective: The study investigates the relationship between CSR and corporate reputation, given the trend of companies integrating CSR to meet stakeholders’ expectations and enhance reputation. Theoretical Framework: The study employs stakeholder theory to emphasize positive stakeholder relationships' impact on corporate reputation, the institutional approach advocating strategic societal engagement, signal theory highlighting CSR's role in signaling commitment to stakeholders, and legitimacy theory emphasizing active CSR participation to maintain societal legitimacy. Method: We use a panel data model on a sample of the world's most admired companies between 2015 and 2019. The proxy considered for corporate reputation includes eight firms’ attributes. Results and Discussion: Findings provide the positive relationship between CSR and corporate reputation firstly the stakeholders’ theory which suggests that companies engage in CSR in order to improve their image and meet stakeholders’ expectations suggesting that ethical CSR practices can create a belief that the organization upholds ethical standards and cares about the well-being of society, which, therefore, positively influences corporate reputation. Research Implications: The study has direct implications for academia and business, emphasizing CSR’s importance in enhancing corporate reputation, urging transparent implementation of CSR initiatives, and highlighting competitive advantages associated with responsible business practices. Originality/Value: The study provides novel insights into the relationship between CSR commitment and corporate reputation by employing a comprehensive corporate reputation proxy and a panel data model analysis of globally renowned companies across various industries. It offers new insights into CEO motivations for CSR adoption.

Association between pre-diagnostic serum uric acid levels in patients with newly diagnosed epilepsy and conversion rate to drug-resistant epilepsy within 5 years: A common data model analysis

PurposeDrug-resistant epilepsy (DRE) poses a significant challenge in epilepsy management, and reliable biomarkers for identifying patients at risk of DRE are lacking. This study aimed to investigate the association between serum uric acid (UA) levels and the conversion rate to DRE. MethodsA retrospective cohort study was conducted using a common data model database. The study included patients newly diagnosed with epilepsy, with prediagnostic serum UA levels within a six-month window. Patients were categorized into hyperUA (≥7.0 mg/dL), normoUA (<7.0 and >2.0 mg/dL), and hypoUA (≤2.0 mg/dL) groups based on their prediagnostic UA levels. The outcome was the conversion rate to DRE within five years of epilepsy diagnosis. ResultsThe study included 5,672 patients with epilepsy and overall conversion rate to DRE was 19.4%. The hyperUA group had a lower DRE conversion rate compared to the normoUA group (HR: 0.81 [95% CI: 0.69–0.96]), while the hypoUA group had a higher conversion rate (HR: 1.88 [95% CI: 1.38–2.55]). ConclusionsSerum UA levels have the potential to serve as a biomarker for identifying patients at risk of DRE, indicating a potential avenue for novel therapeutic strategies aimed at preventing DRE conversion.