Values Of Independent Variables Research Articles

Behavioral Economics Concepts and the Q.E. Method

In Behavioral Economics, the notion of perfect rationality is replaced by a more nuanced understanding that recognizes the limits of human decision-making, such as bounded rationality, willpower, self-interest, and attention. Instead of expecting individuals to always act in perfectly rational ways, models in Behavioral Economics are judged by the accuracy of their predictions rather than the realism of their assumptions. This perspective aligns well with the Quantification of Quality Data (Q.E.) method, which also prioritizes the accuracy and integrity of the model's outcomes over the simplicity of its assumptions. The Q.E. method begins with a solid theoretical framework, where a hypothesis is carefully crafted to define the scope and objectives of the analysis. This initial step is crucial because it sets the foundation for the mathematical determination that follows, ensuring that the analysis is rooted in a clear theoretical context rather than being driven solely by empirical data. By generating values for independent variables within a defined range through randomization, the Q.E. method allows for a comprehensive exploration of how these variables influence the model. This process typically involves the use of multiple mathematical equations to fully capture the complex behavior of the model. A key feature of the Q.E. method is its feedback loop, which plays a critical role in maintaining consistency between the mathematical model and the underlying theoretical principles. Through iterative hypothesis testing, model adjustment, and continuous feedback, the Q.E. method ensures that the mathematical model not only aligns with the theory but also accurately reflects the behavioral nuances that are central to Behavioral Economics. This rigorous approach allows for the refinement of the initial hypothesis and the confirmation of the model’s predictive accuracy, ensuring that the quantification of quality data is both theoretically sound and empirically valid. In this way, the Q.E. method supports the development of Behavioral Economic models that are robust, reliable, and reflective of real-world decision-making processes.

Combination of 3D printing and knitting: the loop stitch design and its effects on tensile strength and elongation at break

ABSTRACT This study explored the creation of 3D printing knit-based structures. Furthermore, the study investigated the relationship between the mechanical performance of these structures and the changes in loop stitch design and 3D printing material. Effects on the mechanical properties of the 3D printing loop structure were evaluated with respect to loop stitch, loop head, loop leg and loop foot. This study analysed the mean differences between the values of independent variables, namely, loop stitch and print material and the values of two dependent variables (i.e. tensile strength and elongation at break) using a multivariate analysis of variance. The results revealed that the mechanical properties of the loop structure vary significantly depending on the loop stitch shape and printing material. Tensile strength is mainly affected by material, whereas elongation property is significantly affected by loop stitch shape.

The Effect of Tax Planning on Company Value in Manufacturing Companies in the Food and Beverage Sector Listed on the IDX

This study aims to determine the influence of Tax Planning on Company Value. Tax Planning is used as an independent variable and Company Value as a dependent variable. The population in this study is food and beverage companies listed on the Indonesia Stock Exchange in 2018-2012. The sample consists of 6 companies with a period of 5 years. namely: Wilmar Cahaya Indonesia Tbk, Nippon Indosari Corporindo Tbk, Sekar Bumi Tbk, Sekar Laut Tbk, Siantar Top Tbk, and Ultrajaya Milk Industry and Trading Company, Tbk. The sample selection method is purposive sampling. The type of data used is quantitative data and data sources using secondary data. The analysis method used to test the hypothesis is to use simple linear regression analysis. The results show that tax planning does not have a significant effect on the Company's Value.

Pengaruh Kebijakan Hutang, Keputusan Investasi dan Free Cash Flow Terhadap Nilai Perusahaan

This research aims to analyze the influence of debt policy, investment decisions and free cash flow on company value. This research was conducted by analyzing the financial reports of companies in the transportation and logistics sector listed on the Indonesia Stock Exchange (BEI) during the 2018-2022 period. The sample used in the research was 11 companies or 55 observation data from the transportation and logistics sector listed on the Indonesia Stock Exchange (BEI) during 2018-2022 using quantitative methods with a sampling technique using purposive sampling. The data used in this research is secondary data in the form of financial reports from each company that has been used as a research sample. The variables used in this research are Debt Policy (X1) as the first independent variable, Investment Decision (X2) as the second independent variable, and Free Cash Flow (X3) as the third independent variable and Company Value (Y) as the dependent variable. The panel data regression method was used as the research methodology in this study. Analysis of research results using the EViews 11 Student Version Lite device. The results of this research show that the best model is the Fixed Effect Model (FEM). The results of this research t test (partial) show that Debt Policy partially has no effect on Company Value, Investment Decisions partially have an effect on Company Value

Synergizing social sustainability and technological innovation for carbon neutrality in OECD economies

AbstractEconomies around the globe are pushing toward a society's sustainability perspectives and upgradation in technology to protect the environment from global warming and the depletion of energy supplies. This shift in perception is primarily intended to alter people's attitudes toward posterity. By integrating the theories of interactions between social and ecological systems and the environment Kuznets curve (EKC) framework, this study explores the impact of nonrenewable energy consumption, social sustainability, and technological innovation on CO₂ emissions across the Organization for Economic Co‐operation and Development (OECD) countries from 2001 through 2020. Based on the estimates of FMOLS, PARDL, and QARDL, the 10‐year CO2 emissions and gross domestic product projections are traced using the average growth rate for each country and an average value of independent variables. The findings of this study highlight the detrimental effect of nonrenewable energy consumption on CO₂ emissions, underscoring the urgency for sustainable energy alternatives. However, the positive role of social sustainability and technological innovation in curbing CO₂ emissions within OECD countries is equally significant. Accentuating the importance of social sustainability within this context, the study sheds light on the integral role that societal factors, such as education, public awareness, and social well‐being, play a substantial role in shaping sustainable practices. It is found that Brazil, China, Malaysia, Russia, South Africa, and Thailand need to focus on higher economic growth to experience decarbonizing economic growth.

Spatial Analysis of the Effect of Halal Product Export and Import on Current Accounts in OIC Countries

OIC countries are countries that have the highest development of the halal industry. Most of the export and import activities carried out by OKI are halal products. Exports and imports of the country’s halal products will describe the condition of the current account surplus or deficit. This study aims to determine the spatial dependence on the current account and the effect of exports and imports of halal products on the current account. The method used descriptive quantitative techniques with spatial analysis. This research was conducted in 51 OIC countries in 2021 using secondary data. The independent variable values are obtained from ITC and the dependent variable is obtained from WDI. The results showed that there is a spatial autocorrelation in the current account, and the spatial autoregressive regression (SAR) results show that exports of halal products have a significant positive effect on the current account. Meanwhile, imports of halal products have a negative but not significant effect on the current account. Keywords: spatial, halal export, halal import, current account balance

R&D investment and innovation performance under vertical partner concentration

ABSTRACT Vertical exchange partners, both customers and suppliers, are key sources of knowledge about new components, technologies, and markets, exerting a critical influence on firms’ innovation performance. While firms’ reliance on a few suppliers and/or a few customers has become a regularly occurring phenomenon, there is only an insufficient understanding of the effect of vertical partner concentration on the returns to firms’ R&D investments. Building on transaction cost and evolutionary economics theories, we conjecture that vertical partner concentration negatively moderates the relationship between R&D investments and innovation performance. Our analysis is built on a dataset of 768 R&D-active French manufacturing firms derived from two independent and temporally separated surveys, enabling the use of lagged values of the key independent variables. The results reveal that customer concentration, whether accompanied or not by supplier concentration, weakens the positive relationship between R&D investment and innovation performance, but this is not observed in dynamic industry settings.

Efektivitas Pelaksanaan Pemungutan Pajak Kendaraan Bermotor di Kantor Bersama SAMSAT Sibolga

In general, this study aims to find out the effectiveness of motor vehicle tax collection in offices with Sibolga samsat. This study uses quantitative descriptive research, which is to find out the value of independent variables, either one or more variables (independent) without making comparisons, or connecting with other variables. As far as data collection techniques are concerned are documentation, interviews and observations. The results of the research showed the effectiveness of motor vehicle tax collection in the Joint Office of Samsat Sibolga has been effective in the collection of motor vehicles tax. Although, there are still many obstacles facing the lack of public awareness to pay taxes on time, which leads to a lot of taxpayers waiting. However, in the process of tax payment procedures is good because it does not take a long time in the management of motor vehicle tax payment.

Pengaruh Pertumbuhan Perusahaan, Keputusan Pendanaan, dan Kebijakan Dividen Terhadap Nilai Perusahaan

This research aims to analyze the influence of company growth, funding decisions and dividend policy on company value. This research was conducted by analyzing the financial reports of companies in the transportation and logistics sector listed on the Indonesia Stock Exchange (BEI) during the period 2018 to 2022. The sample used in this research was 9 technology sector companies listed on the Indonesia Stock Exchange during the period 2018 to 2022 using purposive sampling techniques. The data used in this research is secondary data in the form of financial reports from each company that has been used as a research sample. The variables used in this research are Company Growth (X1) as the first independent variable, Funding Decisions (X2) as the second independent variable, and Dividend Policy (X3) as the third independent variable and Company Value (Y) as the dependent variable. The panel data regression method was used as the research methodology in this study. Analysis of research results using EViews 12 Student Version Lite software. The research results show that the best model is the Fixed Effect Model (FEM). The results of this study show that partial company growth has no effect and is not significant on company value, partial funding decisions have a significant effect on company value, partial dividend policy has no effect and is not significant on company value, and simultaneously company growth and funding decisions and Dividend Policy influences Company Value.

Divided Ideals: Exploring the Relationship Between Political Color and the Importance of Core Political Values in Butuan City, Philippines

The theory of values by Shalom H. Schwartz posits that people’s values serve as the foundation and guiding principle when making decisions. This has been applied in many contexts to aid in the explanation of the various and unique values influencing political behavior including voting. However, studies also claim that an individual’s values may change over time depending on the context or the people’s experiences. While considered generally stable, studies would say that outside factors such as political color can impact voters as they serve as a “visual shorthand” that helps voters quickly identify the party or candidate that best aligns with their beliefs and values. The objective of this paper is to know if political color influences the importance of core political values of the Butuanon voters. A simple linear regression was employed to determine how the two variables used in the study – political color as the independent variable and core political values as the dependent variable – were related to each other. The result showed a statistically significant and positive relationship between the two variables. Political color does affect the importance of core political values which implies that voters in Butuan City who support certain political colors are more likely to give high importance to their identified core political values. Future research may explore longitudinal dynamics and comparative analyses to further elucidate the complex relationship between political color preferences and value prioritization.

Analisis Pasar Dan Referensi Konsumen Terhadap Produk Mie Sedaap

Competition among product brands is crucial since a strong brand would discourage customers from considering making a purchase. Consumer perception of a brand is shaped by their thoughts and emotions upon hearing or seeing its name. The process of identification will enable the brand to stand out from the competition and serve as the primary factor when consumers are making judgments about what to buy. Businesses must determine which aspects of brand equity affect consumers' confidence while making purchases.This study employed a quantitative descriptive research design. The purpose of this study was to ascertain the independent variable's value, whether it be one or multiple variables, without doing comparisons or drawing connections between them. Quantitative descriptive research uses data in the form of numerical results from measurements to characterize variables as they actually exist. One of the things that might persuade people to buy instant noodle products is brand recognition. The majority of consumers who claim that they opt to purchase instant noodles due to their well-known brand demonstrate this beneficial impact. Customers think that their assessment of a product's quality will assist them decide which one to purchase.

Modelling and forecasting India's electricity consumption using artificial neural networks

AbstractPrecise electricity forecasting is a pertinent challenge in effectively controlling the supply and demand of power. This is due to the inherent volatility of electricity, which cannot be stored and must be utilised promptly. Thus, this study develops a framework integrating canonical cointegrating regressions (CCR), time series artificial neural network (ANN) and a multilayer perceptron ANN model for analysing and projecting India's gross electricity consumption to 2030. Annual data for the years 1961–2020 have been collected for variables like gross domestic product (GDP), population, inflation GDP deflator (annual %), annual average temperature and electricity consumption. The study was conducted in three phases. In the first phase of the study, the CCR method was used to check the significance of the selected variables. In the second phase, the projected values of independent variables (GDP, population, inflation GDP deflator [annual %] and annual average temperature) were predicted using the time series ANN model. Finally, a multilayer perceptron ANN model with independent variables was used to forecast the gross electricity consumption in India by 2030. The result shows that the electricity consumption in India will increase by around 50% in the next 10 years, reaching over 1800 TWh in 2030. The proposed approach can be utilised to effectively implement energy policies, as an accurate prediction of energy consumption can help capture future demand.

Digital-based Learning in Social Science Cluster Subjects in High School with Curriculum Merdeka

This study aims to evaluate digital-based learning in Social Sciences cluster subjects (sociology, geography, economics, history and civic education) in Senior High Schools with an independent curriculum. The research method used is descriptive with a quantitative approach. This descriptive research is used with the aim of knowing the value of independent variables without making comparisons or correlating with other variables. Data were collected through questionnaires developed based on key indicators of digital learning variables. The respondents of the study were teachers who taught social subjects and students who were taught. The results of the study found: The type of ICT used was 66.1%; the utilization rate of technology in learning is 82.6%; the level of student proficiency in the use of ICT technology is 73.4%; the level of teacher commitment to use ICT technology in learning is 76.1%; digital learning success rate is 77.0%. The teacher satisfaction rate was 74.9% and the student satisfaction rate was 88.8%. From this indicator data, it can be concluded that digital-based learning in social science cluster subjects in Senior High Schools in Makassar City is 77.0% or is in the very high category.
 Keywords: digital learning, social sciences

EXPLORING THE NEXUS OF MACROECONOMIC FACTORS AND FINANCIAL RATIOS: A HOLISTIC ANALYSIS OF COMPANY PROFITABILITY IN DEVELOPING MARKETS

Purpose of the Study; The primary objective of this document is to find out the patterns among macroeconomic factors, related indexes (as external variables), financial ratio indicators (as internal drivers) that had impact on company’s profit with a holistic approach. This research also aims to clarify the threshold values and the margin of these variables to achieve profit for the listed manufacturing companies which are registered to BIST (Istanbul Stock Exchange) and operating in food, chemistry and metalware sectors. The companies which have net profit margin greater than zero are taken into the pool of investigation for the period from June 2007 to December 2022. Methodology; The study utilized supervised machine learning algorithms on KNIME Analytics Platform (The Konstanz Information Miner). A successful model has been achieved by using Random Forest Learner and Gradient Boosted Trees Learner Algorithms. Findings; Ten prominent rules have been extracted by Random Forest algorithm to predict profit/loss probability of companies. Practical implications; The findings derived from this study have direct relevance for decision makers by formulating the values of variables in different combinations to earn profit. Besides, the threshold values of the financial indicators which deepens our knowledge of the internal and external factors is expected to provide a better insight on the markets of developing countries. Originality/Value; Previous studies are mostly concentrated on the relationship of two or three macro variables with the chosen financial ratios of the companies. Besides a few studies were conducted on the markets of developing countries and if not none of them, very few of them have employed machine learning algorithms. This study aims to show what direction the variables play a role on company’s profit with a holistic approach. The diverse combination of the values of independent variables to generate profit will be evaluated with their threshold values under the country specific conditions of the markets.

How do stand features shape deadwood diversity?

Saproxylic species from different taxonomic groups often occur only on certain types of deadwood with specific qualitative characteristics. The various types of deadwood are very dynamic elements of forest ecosystems, associated with many site and stand features, as well as with the type of forest management. Using a pool of 29,098 sample plots spread across Poland, we analyzed 30 different deadwood types defined on the basis of three characteristics: position (standing, lying), degree of decomposition, and size. Statistical hurdle models were used to assess changes in the volume of individual deadwood types based on a broad range of independent variables. Depending on the type of management, terrain, site fertility, stand volume, tree density, and stand age, the models revealed substantial differences in the volume of different deadwood types, ranging from 0 to approx. 4 m3 ha−1. It was found that the volume of most deadwood types (except for a few, mostly with diameters under 15 cm) increases with stand age or stand volume. In managed forests at all stages of stand development there is a deficiency of thick deadwood. Both standing and lying deadwood at different decay stages is available continuously, irrespective of the values of individual independent variables, but considerable differences exist. While most lying deadwood exhibits higher levels of decomposition, in standing deadwood the proportions of different decay stages are strongly associated with tree diameter at breast height. The developed models make it possible to predict the volume of individual deadwood types for a broad range of independent variables. The current work presents several examples, with the results showing extremely complex relationships between deadwood diversity and site and stand features at every stage of forest development, with continuous changes in the volume and proportions of different deadwood types. In general, at the landscape level Polish forests contain both standing and lying deadwood at all decay stages in more or less equal proportions. However, in forest management one should pay special attention to the dimensions of retained deadwood. The absence of thick deadwood is particularly conspicuous in lowland managed forests.

Toward empirical correlations for estimating the specific heat capacity of nanofluids utilizing GRG, GP, GEP, and GMDH

When nanoparticles are dispersed and stabilized in a base-fluid, the resulting nanofluid undergoes considerable changes in its thermophysical properties, which can have a substantial influence on the performance of nanofluid-flow systems. With such necessity and importance, developing a set of mathematical correlations to identify these properties in various conditions can greatly eliminate costly and time-consuming experimental tests. Hence, the current study aims to develop innovative correlations for estimating the specific heat capacity of mono-nanofluids. The accurate estimation of this crucial property can result in the development of more efficient and effective thermal systems, such as heat exchangers, solar collectors, microchannel cooling systems, etc. In this regard, four powerful soft-computing techniques were considered, including Generalized Reduced Gradient (GRG), Genetic Programming (GP), Gene Expression Programming (GEP), and Group Method of Data Handling (GMDH). These techniques were implemented on 2084 experimental data-points, corresponding to ten different kinds of nanoparticles and six different kinds of base-fluids, collected from previous research sources. Eventually, four distinct correlations with high accuracy were provided, and their outputs were compared to three correlations that had previously been published by other researchers. These novel correlations are applicable to various oxide-based mono-nanofluids for a broad range of independent variable values. The superiority of newly developed correlations was proven through various statistical and graphical error analyses. The GMDH-based correlation revealed the best performance with an Average Absolute Percent Relative Error (AAPRE) of 2.4163% and a Coefficient of Determination (R2) of 0.9743. At last, a leverage statistical approach was employed to identify the GMDH technique’s application domain and outlier data, and also, a sensitivity analysis was carried out to clarify the degree of dependence between input and output variables.

Geospatial Land Evaluation of Medinapur Sub-watershed for Crop Suitability and Sustainable Crop Plan

A study was conducted to reveal the land capability and its suitability to crops in the semi-arid region of North-Eastern Karnataka state, India. Alternate crop plan was proposed with suitable interventions at soil phase level, based on the prevailing climatic regimes and soil-land limitations. Cadastral parcels of Medinapur sub-watershed overlaid on IRS-P6 LISS-IV merged Cartosat-1 satellite imagery was used for interpreting soil units. Soil profiles and morphological studies were made to classify entire sub-watershed (covering 4890.46 ha) into ten soil series and these soil series, further classified into 23 soil phase/management units. The results revealed that major area of 1163 ha (23.79%) covers the soil phase unit “KMLmC2” with deep (100-150 cm) clay textured, gently sloping (3-5 %) and moderately eroded (e2) lands. Two land capability classes (IIIes and IVes) were found in the study area with topography, soil erosion, texture, drainage and soil fertility as major limitation factors. Red gram (59.64%) and Sorghum (18.86%) covering maximum area in the sub watershed were assessed for crop suitability to land. To estimate the significance of crop suitability criteria to land, linear regression analysis was performed with assigned rank values of independent variables. Suitability of these crops was found that the 77.84 % of land was moderately suitable (S2) to redgram with limitations of rooting condition, erosion and topography, only depth showed significant contribution to redgram suitability with R2 = 0.744. Sorghum was highly suitable (S1) to 21.12 % of land and soil depth and pH were significantly contributing to suitability of sorghum with R2 = 0.746. The estimation of criteria for land suitability to Sorghum and Redgram was significant at 5 per cent level. In common soil depth resulted as major contributing factor in deciding land suitability to crops.

Unleashing potential of Pseudomonas aeruginosa RNC3 and Stenotrophomonas maltophilia RNC7 for chlorpyrifos biodegradation by genome analysis and kinetic studies

Chlorpyrifos (CP) is an extensively used organophosphate (OP) pesticide that inhibits the biogeochemical cycle with subsequent loss of soil fertility. In this view, indigenous soil bacteria with significant CP degradation capacity were identified as Pseudomonas aeruginosa RNC3 and Stenotrophomonas maltophilia RNC7 through 16 S rRNA. The optimum values of independent variables for CP degradation were found to be 30 ℃, pH 7, 100 mgL−1 of CP, and 1 OD600 cell culture. RNC3 and RNC7 showed 82.5 mgL−1 and 77.1 mgL−1 CP degradation within 5 days. A Michaelis-Menten kinetic model estimated the degradation rate (Vmax) and substrate binding affinity (Ks) for RNC3 were 1.23 mgL−1h−1 and 123 mgL−1 whereas for RNC7 as 1.19 mgL−1h −1 and 124.3 mgL−1, respectively. The major metabolites 3,5,6-trichloro-2-pyridinol (TCP) and 2-hydroxy pyridine were identified during CP degradation by RNC3 whereas, only TCP by RNC7 using GC-MS. Key enzymes encoded by opd and opch2 genes were annotated in the genomes of RNC3 and RNC7 along with the set of putative degradation genes (tcp, yieH, pho, prp). Protein-ligand docking between OPCH2 and CP found − 7.9 kcal mol −1 as a high binding affinity with the conserved catalytic triad (Ser155-Asp251-His281) in the active site. The study suggests that RNC3 can completely mineralize CP, whereas both strains have shown robust degradation ability of OP group of pesticides. The potential of rapid acclimatization to natural soil environment and non-virulent nature of the selected strains are beneficial for in situ application. Thus, selected indigenous strains can be applied for the bioremediation of OP-contaminated soil.

Pengaruh Kompensasi, Lingkungan Kerja Dan Kepemipinan Terhadap Kepuasan Kerja Guru Pada Sekolah Madrasah Yogyakarta

This research was conducted to analyze the influence of compensation, work environment and leadership on teacher job satisfaction at Madrasah Yogyakarta, which aims to manage superior and advanced human resources in facing this era of globalization, one of which is by paying attention to employee satisfaction. the role of human resources is the planner and implementer in the organization's achievement.This research uses quantitative descriptive methods with data analysis techniques using validity tests, reliability tests, multiple linear regression analysis, t tests, f tests and tests carried out manually using the SPSS 22.0 for Windows application. The population in this study were teachers at Madrasah Yogyakarta, while the sample included was 57 samples.The results of the research show that, in the validation test each variable was smaller than the significance of 0.05 (valid), the reliability test value was 0.946, it was greater than 0.60 (reliable), the multiple linear regression analysis value of job satisfaction was 8.344, the compensation value was 0.053, the work environment value was 0.333 , and leadership 0.297, partial t test compensation has no effect, but it does influence the work environment and leadership on job satisfaction, the f test simultaneously has an influence on teacher job satisfaction. The greatest level of influence is based on multiple linear analysis, the highest beta value of the work environment with a valueof 0.333 compared to the values of other independent variables

The mediating role of engagement in the relationship between equal opportunities and employee performance

This study aimed to explore the relationship between employee engagement, equal opportunities, and employee performance in Bahraini retail businesses. Using a cross-sectional survey, data were collected from 231 employees and analyzed via SPSS and Smart-PLS, uncovering significant results. Out of the 249 participants invited to the survey, 231 responded. All the variables of the study showed different effects, with equal opportunity and employee engagement (EO → EE) considered the strongest hypotheses in this study at the significance level of 0.01 (β = 0.722, = 0.724, SD = 0.070, t = 10.324, p = 0.000), with f2 = 1.087 above 0.15 and less than 0.35, which was considered a high effect size and strongly supported the hypothesis proposed by the researcher. All hypotheses were supported. The R2 values of independent variables were above 38% for the variance of employee performance, which is considered an excellent fit to the data as evidenced by the squared multiple correlation R2 values for the dependent variables. Finally, EO → EE → JP mediating role indirectly affects JP through EO = 24%, which is considered an excellent effect size.The recent study confirmed a strong positive link between equal opportunities and employee performance, with employee engagement acting as a significant mediator. This suggests that promoting equal opportunities can boost performance, especially when coupled with employee involvement. These findings are important for HRM experts and executives, as they indicate that prioritizing fairness and fostering participation can enhance productivity.