Total Number Of Observations Research Articles

Moderating Effects of Dividend Policy Consistency and ROA on Firm Performance in M&A

The occurrence of a positive abnormal return is the dream of every investor in dealing with the decisions taken by the company, because it can provide its own benefits for stakeholders. Therefore, what can create positive abnormality around the announcement of company decision-making, especially Merger and Acquisition (M&A) decisions? To reveal several factors that can create positive abnormal returns around M&A announcements, this study aims to determine the effect of dividend-paying firms on short-term M&A performance which is moderated by the consistency of dividend policy and Return on Assets (ROA) and controlled by size. company using multiple linear regression model. The data used in this research comprises companies in Indonesia that conducted M&A during the period 2002-2021. The total number of observations in this study is 150, and the research employs the Ordinary Least Squares (OLS) technique for analysis. The results of this study indicate that dividend-paying firm has a significant positive effect on short-term M&A performance. The consistency of dividend policy cannot strengthen the positive relationship between dividend-paying firms and short-term M&A performance, but the firm's performance as reflected in ROA, strengthen the positive effect of dividend-paying firms on short-term M&A performance.

Role of Human Capital as a Driver for Sustainable Development (SDG) in Western Balkan Countries

Objective: The objective of this study is to investigate the impact of years of education and life expectancy on the economic development of the Western Balkan Countries (Southeast Europe). Theoretical Framework: This study considers the impact of exogenous and endogenous theories of economic growth, with a particular focus on the role of human capital (human resources) in driving economic development. Method: We have proposed a panel data regression methodology in which the dependent variable is the GDP per capita. The countries analyzed are covered from 2012 to 2021 and the total number of observations in the panel is 50. This study used secondary data from the WorldBank databases. Findings: The results reveal a positive correlation between the Education Index and Labor Force Participation, while the Gross Enrollment Rate in Tertiary School and Life Expectancy show a negative correlation. Research Implications: The demographic, economic, and educational conditions in the Western Balkan countries required immediate action by policymakers to avoid a situation that cannot be sustained over the medium or long term. The governments of these nations must take action to shift the focus of higher education to a more qualitative approach.

Role of Human Capital as A Driver For Sustainable Economic Growth in Western Balkan Countries

Objective: The objective of this study is to investigate the impact of years of education and life expectancy on the economic development of the Western Balkan Countries (Southeast Europe). Theoretical Framework: This study considers the impact of exogenous and endogenous theories of economic growth, with a particular focus on the role of human capital (human resources) in driving economic development. Method: We have proposed a panel data regression methodology in which the dependent variable is the GDP per capita. The countries analyzed are covered from 2012 to 2021 and the total number of observations in the panel is 50. This study used secondary data from the WorldBank databases. Findings: The results reveal a positive correlation between the Education Index and Labor Force Participation, while the Gross Enrollment Rate in Tertiary School and Life Expectancy show a negative correlation. Research Implications: The demographic, economic, and educational conditions in the Western Balkan countries required immediate action by policymakers to avoid a situation that cannot be sustained over the medium or long term. The governments of these nations must take action to shift the focus of higher education to a more qualitative approach.

Pengaruh Intervensi Pemerintah dalam Penanganan Covid-19 Terhadap Return Saham

Purpose – This research aims to obtain empirical evidence regarding the influence of government intervention in handling Covid-19 on stock returns.Design/methodology/approach – This research was conducted on companies listed on the LQ45 index of the Indonesia Stock Exchange for 2020-2021. The total number of observations was 43 companies obtained using the purposive sampling method. This research is quantitative using multiple linear regression analysis techniques. The dependent variable in this research is stock returns. The independent variable used is government intervention consisting of social restriction policies, prevention and health policies, and economic support. Three control variables are also used, namely company size, profitability (ROA), and leverage (DER).Findings – The research results show that social restriction policies and economic support have a positive effect on stock returns, while prevention and health policies have a negative effect on stock returns.Originality/value – This research is initial research that provides evidence of the influence of government intervention in handling Covid-19 on stock returns in Indonesia by utilizing the OxCORT index.Research limitations/implications – The implication of this research is that investors can anticipate increases and decreases in company share prices by considering several indices related to government intervention. The coverage area used is only limited to Indonesia.

A Provably Accurate Randomized Sampling Algorithm for Logistic Regression

In statistics and machine learning, logistic regression is a widely-used supervised learning technique primarily employed for binary classification tasks. When the number of observations greatly exceeds the number of predictor variables, we present a simple, randomized sampling-based algorithm for logistic regression problem that guarantees high-quality approximations to both the estimated probabilities and the overall discrepancy of the model. Our analysis builds upon two simple structural conditions that boil down to randomized matrix multiplication, a fundamental and well-understood primitive of randomized numerical linear algebra. We analyze the properties of estimated probabilities of logistic regression when leverage scores are used to sample observations, and prove that accurate approximations can be achieved with a sample whose size is much smaller than the total number of observations. To further validate our theoretical findings, we conduct comprehensive empirical evaluations. Overall, our work sheds light on the potential of using randomized sampling approaches to efficiently approximate the estimated probabilities in logistic regression, offering a practical and computationally efficient solution for large-scale datasets.

Abstract 2422: Liquid biopsy approaches for monitoring metastatic pancreatic cancer in immunotherapy treated patients

Abstract Introduction: Pancreatic cancer has a poor prognosis especially when identified at advanced stages. Globally in 2020, >450,000 people died from the disease. For those with locally advanced or metastatic cancer, the standard of care treatment is chemotherapy. Preliminary studies show that some patients with advanced-stage disease respond to immune checkpoint blockade treatment. Determining response to therapy using imaging techniques can be challenging. There is a clinical unmet need for noninvasive approaches that can provide a real-time assessment of response to therapy in these patients. We evaluated two methods for assessing response to therapy using circulating cell-free DNA (cfDNA) in patients with metastatic pancreatic cancer treated with immune checkpoint inhibition and radiation as part of the CheckPAC study (NCT02866383). Methods: For all patients with available samples (n=40), we evaluated one pre-treatment blood draw and one on-treatment blood draw (26-65 days after treatment start). We extracted cfDNA from each sample and performed low-coverage 1-2x whole genome sequencing (WGS). We evaluated tumor and matched buffy coat for the 34 patients who had > 10% tumor purity using WGS with at least 60x and 30x coverage, respectively. We compiled the mutated bases in the tumor genome and examined these positions in the cfDNA. We counted the number of mutant observations from cfDNA WGS and divided them by the total number of observations at these bases to calculate whole-genome mutant allele frequency (MAF). We categorized responders based on >30% drop in MAF from baseline. In a tissue independent approach, we detected changes in genome-wide cfDNA fragmentation patterns using the DNA evaluation of fragments for early interception (DELFI) approach. We evaluated survival using DELFI scores of on-treatment samples. Samples were designated DELFI high (above median) or DELFI low (below median.) Log-Rank tests were used to compare survival curves. Results: For the mutation based approach, molecular responders (n=7) had a median progression free survival (PFS) of 485 days compared to 57 days for non-responders (n=9) (p=0.02). Molecular responders had a median overall survival (OS) of 694 days compared to 200 days for non-responders (p=0.062). Patients with DELFI low scores after therapy (n=14) had a longer median PFS than those with DELFI high scores (n=14) (307 days versus 70 days, respectively) (p=0.013). DELFI low patients also had a longer median OS than DELFI high patients (627 vs 158 days, respectively) (p<0.0001). Conclusions: These analyses suggest that WGS mutation-based and fragmentation cfDNA approaches can identify individuals with metastatic pancreatic cancer who respond to immune checkpoint inhibition. Incorporation of these molecular methods for evaluation of tumor burden may provide information for patient-physician decision making to improve patient quality of life. Citation Format: Carolyn Anna Hruban, Inna Chen, Daniel C. Bruhm, Shashikant Koul, Jennie Yao, Susann Theile, Kavya Boyapati, Nicholas A. Vulpescu, Akshaya Annapragada, Leonardo Ferreira, Stephen Cristiano, Zachariah H. Foda, Vilmos Adleff, Julia Johansen, Jillian Phallen, Robert B. Scharpf, Victor E. Velculescu. Liquid biopsy approaches for monitoring metastatic pancreatic cancer in immunotherapy treated patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2422.

Variation in social media sensitivity across people and contexts

Social media impacts people’s wellbeing in different ways, but relatively little is known about why this is the case. Here we introduce the construct of “social media sensitivity” to understand how social media and wellbeing associations differ across people and the contexts in which these platforms are used. In a month-long large-scale intensive longitudinal study (total n = 1632; total number of observations = 120,599), we examined for whom and under which circumstances social media was associated with positive and negative changes in social and affective wellbeing. Applying a combination of frequentist and Bayesian multilevel models, we found a small negative average association between social media use AND subsequent wellbeing, but the associations were heterogenous across people. People with psychologically vulnerable dispositions (e.g., those who were depressed, lonely, not satisfied with life) tended to experience heightened negative social media sensitivity in comparison to people who were not psychologically vulnerable. People also experienced heightened negative social media sensitivity when in certain types of places (e.g., in social places, in nature) and while around certain types of people (e.g., around family members, close ties), as compared to using social media in other contexts. Our results suggest that an understanding of the effects of social media on wellbeing should account for the psychological dispositions of social media users, and the physical and social contexts surrounding their use. We discuss theoretical and practical implications of social media sensitivity for scholars, policymakers, and those in the technology industry.

The Global Hidradenitis Suppurativa Atlas Methodology: Combining Global Proportions in a Pooled Analysis

Introduction: Data concerning the global burden of hidradenitis suppurativa (HS) are limited. Reported prevalence estimates vary between 0.0003% and 4.1%, and data from various geographical regions are still to be collected. Previously reported prevalences have been limited by the methodological approach and source of data. This has resulted in great heterogeneity as prevalence data from physician-diagnosed cases poorly match those of self-reported apparent HS disease. Methods: The Global Hidradenitis Suppurativa Atlas (GHiSA) introduces an innovative approach to determine the global prevalence of HS. This approach involves using a previously validated questionnaire to screen apparently healthy adults accompanying a patient to a non-dermatological outpatient clinic visit in a hospital or a private/family medicine clinic. The screening questionnaire (i.e., the index test) is combined with a subsequent physician-based in-person validation (i.e., the reference standard) of the participants who screen positive. Approximately ten percent of the screen-negative participants are also clinically assessed to verify the diagnostic precision of the test. The local prevalence (pi) will be estimated from each country that submits the number of patients who are HS positive according to the index test and clinical examination (n), and the corresponding total number of observations (N). Conclusion: The GHiSA Global Prevalence studies are currently running simultaneously in 58 countries across six continents (Africa, Europe, Australia, North America, South America, and Asia). The goal of the combined global proportion is the generation of a single summary (i.e., proportional meta-analysis), which will be done after a logit transformation and synthesized using a random-effects model. The novel standardization of the Global Prevalence Studies conducted through GHiSA enables direct international comparisons, which were previously not possible due to substantial heterogeneity in past HS prevalence studies.

Legacy effects of crop sequencing on biomass and their variability on farmers' fields in Finland are shaped by weather, farm conditions and rationales for land use

CONTEXTThe legacy effect of a previous crop on a subsequent crop in rotation is a valuable and often underutilized ecosystem service which can provide benefits when designing cropping systems, especially in the case of cereal-based land use. OBJECTIVETo support future diversification of crop sequencing in Finland, this study was carried out with the aims to identify the variation in the pre-crop values of relevant crop combinations in farmers' fields in Finland, depending on the weather and farm conditions. METHODSWe used a method developed to estimate on-farm realized pre-crop value from Sentinel − 2 images available annually for 120,174–711,828 field parcels in Finland during 2016–2020 (the total number of observations was >1,818,000). RESULTS AND CONCLUSIONSBoth the weather and farming conditions explained the variation in the pre-crop values, which might range from positive to negative. For example, a higher mean temperature and fewer rainy days during the growing season of a previous crop often reduced the legacy effects of crop sequencing, however, this was dependent on the crop choices. A higher number of rainy days and elevated temperatures during the subsequent crop's season often increased the pre-crop value, while prolonged droughts in either of the growing seasons tended to reduce these. The farmers' current land use did not support the realization of pre-crop value – especially in the case of potential benefits provided by grasslands for croplands. SIGNIFICANCEThis study highlights the challenges that weather variation and complexity of the production systems may cause for a farmer when aiming to identify the impacts of crop sequencing on yields, ecosystem services, and farm economy. Hence, there is an evident need to develop a decision support system that recognizes regional farming systems, available crop choices, crop management, weather conditions, and the farmers' rationale concerning decision making.

The actual issues of improvement of organization of screening of skin neoplasms in out-patient conditions

The skin malignant neoplasms constitute one of significant problems of public health. The increasing of morbidity and mortality of melanoma and other types of skin cancers is registered. The early detection of disease assures mortality decreasing and increases survival of patients. The purpose of the study is to develop optimal model of organizational and preventive measures of early diagnostic of malignant skin tumors. The comprehensive study was carried out, which included assessment of survey of patients using questionnaire "Melanoma Diagnosis Day". Also it included identification of level of awareness of physicians about screening, medical records of patients during COVID-19 pandemic, risk groups identification and effectiveness of selecting patient for screening. The total number of observations is 21 581. The selective and continuous observation method was applied and parametric and non-parametric methods as well. The study determined that during COVID-19 pandemic in 2020, there was significant reliable uncompensated decrease in the number of patients with diagnosis of ICD D23, L82, C44 and decrease in the number of detected diseases. The melanoma, basalioma and other skin malignant neoplasms are reliably more common in patients of medium-high risk group, aged 60 years and older and in persons sunburned at the age of 18 years. The assignation of separate specialist to diagnose skin neoplasms allows statistically reliably increase rate of morphological confirmation of malignant neoplasms as compared to common reception by dermatologist. The sociological survey of physicians revealed statistically reliable difference in knowledge between dermatovenereologists, oncologists and physicians of other specialties concerning screening issues. The results of the study permitted to develop complex of organizational measures to improve screening. The model of organizational and preventive measures for early diagnostic of skin malignant tumors in outpatient conditions was proposed.

The Moderating Role of Sustainability Disclosure in the Association between Tax Avoidance and Cost of Debt

Company funding can derive from debt and shares. Managers' funding decisions have an impact on the costs that the company must bear. Even though it has high risks and many requirements, managers use much debt in company funding. The company must bear the debt costs associated with this funding. This research examines the effect of tax avoidance on the cost of debt. This research also uses sustainability disclosure as a moderating variable. This research uses manufacturing company financial report data from www.idnfinancials.com from 2016 to 2020. Based on purposive sampling, the total number of observations can be used in this research is 276. Hypothesis testing was carried out using multiple linear regression analysis for cross-section data. This research concludes that tax avoidance positively affects the cost of debt. This research also finds that sustainability disclosure strengthens the positive influence of tax avoidance on the cost of debt. This research contributes to providing literature on the role of sustainability implemented in companies in one of the developing countries. This implementation can be associated with managerial decisions that may not always align with the interests of company owners.

Cluster Analysis of Personality Types Using Respondents’ Big Five Personality Traits

This study utilized a mixed model approach, incorporating k-means clustering analysis for data examination, discriminant analysis for classification, and multilayer perceptron neural network analysis for prediction. After removing inadequate samples and outliers, the total number of observations was 19,692 for this study, which was collected through an interactive online personality test (i.e., Big Five Personality Traits) in 2012. The empirical results based on the k-means clustering analysis identified four different personality clusters using the total score of Big Five Personality Traits (Extraversion, Neuroticism, Agreeableness, Conscientiousness, and Openness to Experience). The empirical results obtained from the k -means clustering analysis revealed the presence of four distinct personal clusters, determined by the total scores of the Big Five Personality Traits. The accuracy of the clustering analysis was further tested using discriminant analysis, which indicated significant difference among the cluster means and correctly classified 95.5% of the original grouped cases. For predictive modeling, a multilayer perceptron neural network framework was used. The network had a 5-6-4 structure and was employed to determine the personality classification of participants. Notably, the model achieved 99.4% accuracy in correctly classifying the training grouped cases and 99.2% accuracy for the testing grouped cases. The results of this study offer valuable insights into understanding the personalities of participants, with implications for various domains such as psychology, social sciences, cultural studies, and economics.

Workers' emotional exhaustion and mental well-being over the COVID-19 pandemic: a Dynamic Structural Equation Modeling (DSEM) approach.

The COVID-19 pandemic has presented significant challenges to the workforce, particularly concerning emotional and mental well-being. Given the prolonged periods of work-related stress, unexpected organizational changes, and uncertainties about work faced during the pandemic, it becomes imperative to study occupational health constructs under a dynamic methodological perspective, to understand their stable and unstable characteristics better. In this study, drawing on the Dynamic Structural Equation Modeling (DSEM) framework, we used a combination of multilevel AR(1) models, Residual-DSEM (RDSEM), multilevel bivariate VAR(1) models, and multilevel location-scale models to investigate the autoregression, trend, and (residual) cross-lagged relationships between emotional exhaustion (EmEx) and mental well-being (MWB) over the COVID-19 pandemic. Data were collected weekly on 533 workers from Germany (91.18%) and Italy (8.82%) who completed a self-reported battery (total number of observations = 3,946). Consistent with our hypotheses, results were as follows: (a) regarding autoregression, the autoregressive component for both EmEx and MWB was positive and significant, as well as it was their associated between-level variability; (b) regarding trend, over time EmEx significantly increased, while MWB significantly declined, furthermore both changes had a significant between-level variability; (c) regarding the longitudinal bivariate (cross-lagged) relationships, EmEx and MWB negatively and significantly affected each other from week to week, furthermore both cross-lagged relationships showed to have significant between-level variance. Overall, our study pointed attention to the vicious cycle between EmEx and MWB, even after controlling for their autoregressive component and trend, and supported the utility of DSEM in occupational health psychology studies.

The Mean May Not Mean What You Think It Means: The Use and Misuse of Measures of Central Tendency

Analysis of business studies often involves the quantification of qualitative data to derive meaningful insights and making informed decisions. One such challenge is the inappropriate use of the arithmetic mean in economic and financial modeling. The arithmetic mean is a widely used statistical measure of central tendency that sums up a set of values and divides it by the total number of observations. While the arithmetic mean is simple and intuitive, its appropriateness in financial and economic modeling highly depends upon the nature of the data and the specific research question being addressed. This creates a dilemma. Despite the business community traditionally emphasizing quantitative research modeling, the growth of artificial intelligence and big data make qualitative research more desirable, particularly in areas such as ESG scorecards and financial literacy surveys. This paper discusses the challenges presented with analyzing studies after quantifying qualitative data and provides examples of how ordinal regression and other techniques could be used to analyze qualitative variables. This is especially applicable in undergraduate education.

Improvement of the metod of protein content prediction in wheat kernels

The study was conducted in order to expand the possibilities of using the developed multiple nonlinear regression equation, re ecting the dependence of the protein content in wheat grain (Y, %) on the content of raw gluten (X1, %) and the mass of 1000 grains (X2, g). The possibility of practical use of the developed and three improved equations for predicting the protein content of wheat grain is shown. An algorithm and results of checking the prognostic capabilities and accuracy of predict equations from independent data are presented. Generalization of experimental data from 288 literature sources of domestic and foreign authors with a total number of observations n = 4604 on more than three hundred varieties of winter and spring soft and durum wheat grown in the period from 1959 to 2020 in Russia and abroad (Albania, Belarus, Bulgaria, Egypt, Kazakhstan, Lithuania, Poland, Slovakia and Ukraine) showed that the number of values that go beyond to limits regulated by GOST 10846-91 «Protein determination method» was 755 or 16.4 %. At the same time, the accuracy of prediction the protein content reached 83.6 %. The developed equations can be used to predict of protein content in almost all cases: when the results of the analysis of protein content and raw gluten in wheat kernel, as well the 1000-kernels weight are given at actual or fxed moisture content, or in terms of dry matter

Analisis Determinan Faktor-Faktor yang Mempengaruhi Opini Audit Going Concern pada Perusahaan-Perusahaan di Sektor Energi yang Listing di Bursa Efek Indonesia

This research aims to empirically test the factors that influence the Going Concern Audit opinion. The determining factors that influence the Going Concern Audit Opinion in this research are leverage, company size, institutional ownership and managerial ownership. Research was conducted on companies in the Energy sector listed on the Indonesian Stock Exchange in 2019-2022. The sample used was 10 companies with observations for 4 years, so the total number of observations was 40. This research data uses secondary data, namely annual reports of companies in the energy sector listed on the Indonesia Stock Exchange for 2019-2022. Data were analyzed using Logistic Regression Analysis. The results of this research show that leverage has a positive and significant effect on going concern audit opinion, company size has no effect on going concern audit opinion, institutional ownership has a positive and significant effect on going concern audit opinion, managerial ownership has a positive and significant effect on going concern audit opinion.

Determinant Factors of Infrastructure Firm’s Value in Indonesia Stock Exchange

This study will test whether profitability, solvency and firm size have influenced to the firm value for the infrastructure sector companies listed in IDX. Using the purposive sampling, this study obtains 30 companies from the 2018-2021 so that the total number of observations is 120 firm-years. The analysis result conducted by data panel regression shows that profitability and firm size have a significant effect on the firm value at level 10% and 5% respectively. The other finding is that solvency does not affect firm value. From this result, the study will imply that infrastructure sector firms in IDX have considered profitability and size in the effort of maximizing the company value and they actually tend not to use debt as the major regular financing sources.

Determinant Factors of Infrastructure Firm’s Value in Indonesia Stock Exchange

Determinant Factors of Infrastructure Firm’s Value in Indonesia Stock Exchange

Determinant Factors of Infrastructure Firm’s Value in Indonesia Stock Exchange

This study will test whether profitability, solvency and firm size have influenced to the firm value for the infrastructure sector companies listed in IDX. Using the purposive sampling, this study obtains 30 companies from the 2018-2021 so that the total number of observations is 120 firm-years. The analysis result conducted by data panel regression shows that profitability and firm size have a significant effect on the firm value at level 10% and 5% respectively. The other finding is that solvency does not affect firm value. From this result, the study will imply that infrastructure sector firms in IDX have considered profitability and size in the effort of maximizing the company value and they actually tend not to use debt as the major regular financing sources.

Indonesia's Capital Structure And Company Profitability Before And After The Implementation Of PSAK 73

Statement of Financial Accounting Standards (PSAK) 73 regarding leases has been in effect since 2020 in Indonesia. The standard adopts International Financial Reporting Standards (IFRS) 16, which requires companies to record all lease transactions as finance leases. This study examines differences in capital structure and company profitability before and after implementing PSAK 73 in Indonesia. This study uses financial report data for retail and transportation sub-sector companies listed on the Indonesia Stock Exchange (IDX) in 2019 and 2020 with a population of 160 observations. The secondary data is sourced from the Indonesia Stock Exchange website. Based on purposive sampling, the total number of observations that can be used in this study is 104 observations. Hypothesis testing was carried out using a paired sample t-test. This study found that in the first year after the implementation of PSAK 73, the capital structure from debt is higher than that from equity. This condition is different from the period before the implementation of PSAK 73. Then the level of profitability in the first year of implementation of PSAK 73 decreased compared to the period before the implementation of PSAK 73. This research recommends that the Financial Services Authority implement risk mitigation rules for companies registered on the IDX regarding the financial impact of Indonesia's new financial accounting standards.