Regression Analysis Of Data Research Articles

How social and environmental investments attract and repel foreign investors: Insights on shareholder and stakeholder engagement

AbstractThis study investigates the diversifying impact of social and environmental investment (SEI) to attract and repel foreign investors (FIs) while determining to invest in environmental friendly firms. By analyzing SEI strategies within the Chinese context and employing panel data regression analysis, this study reveals numerous major insights. First, firms implementing average SEI strategies tend to attract greater investments from FI. On the other hand, above average SEI do not yield similar benefits, indicating that over‐investments in social and environmental projects could repel potential investors, and result in a transfer of wealth from shareholders to stakeholders. Furthermore, the present study reveals that firm performance strengthens the positive association between average SEI and FI. Finally, this study finds that the negative relationship between above‐average SEI and FI is intensified when firm performance is considered as a moderating factor. These insights are crucial for policymakers in formulating strategies to enhance foreign investments.

The impact of poverty, malnutrition, and household income on human development in Central Sulawesi, Indonesia: A panel data analysis

Central Sulawesi has been grappling with significant challenges in human development, as indicated by its Human Development Index (HDI). Despite recent improvements, the region still lags behind the national average. Key issues such as high poverty rates and malnutrition among children, particularly underweight prevalence, pose substantial barriers to enhancing the HDI. This study aims to analyze the impact of poverty, malnutrition, and household per capita income on the HDI in Central Sulawesi. By employing panel data regression analysis over the period from 2018 to 2022, the research seeks to identify significant determinants that influence HDI and provide evidence-based recommendations for policy interventions. Utilizing panel data regression analysis with a Fixed Effect Model (FEM), the study reveals that while poverty negatively influences with HDI, underweight prevalence is not statistically significant. In contrast, household per capita income significantly impacts HDI, with lower income levels leading to declines in HDI. The findings emphasize the need for comprehensive policy interventions in nutrition, healthcare, and economic support to enhance human development in the region. These interventions are crucial for addressing the root causes of underweight prevalence and poverty, ultimately leading to improved HDI and overall well-being. The originality of this research lies in its focus on a specific region of Indonesia, providing localized insights and recommendations that are critical for targeted policy making.

Over-education rates and predictors of entry-level jobs in Türkiye

This research aims to determine the proportion of overeducated individuals with higher education levels compared to their colleagues who are graduates of associate, undergraduate, and postgraduate education but work at the same status in entry-level jobs. Overeducation rates in entry-level jobs in Türkiye were determined using the Turkish Statistical Institute (TUIK) Household Labor Force Surveys (2014-2019) microdata set. The job analyst measure was used to determine the rate of overeducation. Logistic regression data analysis was conducted to classify the variables that predict the state of being overeducated with the TUIK 2019 Household Labor Force Survey. According to the findings, overeducation rates increased gradually over the years by 8.02% in 2014, 8.98% in 2015, 9.78% in 2016, 10.43% in 2017, 11.00% in 2018, and 12.5% in 2019. For the state of being overeducated, various demographic variables were analyzed and predicted, such as income, age, region, gender, ISCED, marital status, firm size, place of work, additional job searches, ISCO 08 classification, and employment status.

Horizon problem and capital expenditures: evidence from the public sector in Indonesia

PurposeThis study investigates whether horizon problems affect the allocation of capital budgets and their implementation in a government setting.Design/methodology/approachWe use data from 2005 to 2020 for local governments in Indonesia, which apply a limit of two five-year terms for mayors. We use regression analyses for panel data with total observations of 4,541 local government years from 448 unique local governments. We also use graphical analyses and t-tests to provide robustness to our results.FindingsMayors allocated lower capital expenditures in the second term than in the first. Capital budget allocation is lower for local governments whose mayors are older than 60. Our additional analysis shows that incumbents seeking re-election allocate more capital expenditure than those not seeking re-election.Research limitations/implicationsThis study contributes to the literature on the behavioral effect of term limits on local government's allocation and implementation of capital budgets. Limiting elected government officials to a certain number of terms will prevent the monopoly of power. However, it may negatively affect budget allocation on capital programs in their last term. Our findings should interest public policymakers in discerning the costs and benefits of term limits for elected offices.Originality/valueMost studies on horizon problems have focused on the corporate setting. This study provides evidence of the effects of horizon problems in the government setting, especially in Asia.

Economic Performance: Impact of Implementing Environment Cost and Disclosure Carbon Emissions Fiqh Al Bi'ah (Environment) Analysis Approach

The development of environmental issues is of concern to the public in encouraging companies' awareness to carry out environmental management in their business activities, where companies can plan environmental cost budgets and make disclosures of the impact of carbon emissions produced in order to achieve their goal, namely the sustainability of the Company, namely the Company's economic performance will increase. This research aims to see the impact of environmental costs and carbon emission disruption on economic performance and is analyzed using environmental fiqh (bi'ah). This research is quantitative research. The type of data used is secondary data obtained from the Company's official website. The population in this study are manufacturing companies listed on the Jakarta Islamic Index for 2019-2021. The research sample consisted of 8 companies from the 2019-2021 period, totaling 32 samples. The sampling technique used in this research was purposive sampling. The data analysis used is panel data regression analysis using the SEM PLS data modeling program. The results of the research show that environmental cost and carbon emission disclosure variables have a significant negative influence on economic performance and the results of environmental fiqh (al bi'ah) analysis are in accordance with the verses of the Koran which discuss environmental damage which is something that is absolutely not justified because it will bring negative impact on the sustainability of living creatures or organizations and companies. These limitations suggest that future research could expand the sample size, consider a wider range of sustainability practices, and explore the relevance of environmental performance in different cultural and industry contexts.

Investigating the Influence of Gender Diversity and Management Practices on the Introduction of Innovation in CEE countries

The role of management in any organizational context is to identify and exploit positive influences and opportunities that lead to the achievement of business objectives and competitive advantage. Those involved in effective management should be able to define the factors that have a decisive influence on the innovative activity of the enterprise. The identification of these influences is a key source of knowledge for defining or adjusting corporate strategies, resulting in long-term benefits and effective business development. As the market is constantly evolving, businesses must adapt to it. In recent years, researchers and experts have paid considerable attention to the study of gender issues in various areas of management. Much of the existing research has shown a positive correlation between gender diversity and innovation in developed countries. However, empirical studies in transition or developing countries remain limited. Therefore, we focus on Central and Eastern European countries. Based on regression analysis of data from the large-scale WBES survey, the aim of the study is to identify the relationships between gender structure of management, management practices and innovation. Unlike previous studies, this study uses a management practices index that combines data from eight indicators of management practices and assesses how well they are structured. This study thus enriches the ongoing research on the impact of gender and the structure of management practices on innovation in the EU member states of Central and Eastern Europe. The contribution of this study lies in the analysis of several different types of gender diversity and a composite index of managerial practices in relation to firms' innovation performance, and the specific context of the analysis in Central and Eastern Europe countries may provide new insights, as different factors affect innovation performance in these countries than in their Western counterparts. The results of this research provide a deeper understanding of the dynamics between gender diversity in management and innovation, which could provide a basis for the development of policies and corporate strategies to promote diversity and innovation in the business environment of Central and Eastern Europe countries.

Additive Hazards Regression Analysis of Massive Interval-Censored Data via Data Splitting

With the rapid development of data acquisition and storage space, massive datasets exhibited with large sample size emerge increasingly and make more advanced statistical tools urgently need. To accommodate such big volume in the analysis, a variety of methods have been proposed in the circumstances of complete or right censored survival data. However, existing development of big data methodology has not attended to interval-censored outcomes, which are ubiquitous in cross-sectional or periodical follow-up studies. In this work, we propose an easily implemented divide-and-combine approach for analyzing massive interval-censored survival data under the additive hazards model. We establish the asymptotic properties of the proposed estimator, including the consistency and asymptotic normality. In addition, the divide-and-combine estimator is shown to be asymptotically equivalent to the full-data-based estimator obtained from analyzing all data together. Simulation studies suggest that, relative to the full-data-based approach, the proposed divide-and-combine approach has desirable advantage in terms of computation time, making it more applicable to large-scale data analysis. An application to a set of interval-censored data also demonstrates the practical utility of the proposed method.

Exploring the Relationship Between Very-High-Resolution Satellite Imagery Data and Fruit Count for Predicting Mango Yield at Multiple Scales

Tree- and block-level prediction of mango yield is important for farm operations, but current manual methods are inefficient. Previous research has identified the accuracies of mango yield forecasting using very-high-resolution (VHR) satellite imagery and an ’18-tree’ stratified sampling method. However, this approach still requires infield sampling to calibrate canopy reflectance and the derived block-level algorithms are unable to translate to other orchards due to the influences of abiotic and biotic conditions. To better appreciate these influences, individual tree yields and corresponding canopy reflectance properties were collected from 2015 to 2021 for 1958 individual mango trees from 55 orchard blocks across 14 farms located in three mango growing regions of Australia. A linear regression analysis of the block-level data revealed the non-existence of a universal relationship between the 24 vegetation indices (VIs) derived from VHR satellite data and fruit count per tree, an outcome likely due to the influence of location, season, management and cultivar. The tree-level fruit count predicted using a random forest (RF) model trained on all calibration data produced a percentage root mean squared error (PRMSE) of 26.5% and a mean absolute error (MAE) of 48 fruits/tree. The lowest PRMSEs produced from RF-based models developed from location, season and cultivar subsets at the individual tree level ranged from 19.3% to 32.6%. At the block level, the PRMSE for the combined model was 10.1% and the lowest values for the location, seasonal and cultivar subset models varied between 7.2% and 10.0% upon validation. Generally, the block-level predictions outperformed the individual tree-level models. Maps were produced to provide mango growers with a visual representation of yield variability across orchards. This enables better identification and management of the influence of abiotic and biotic constraints on production. Future research could investigate the causes of spatial yield variability in mango orchards.

Impact of Urban Form in the Yangtze River Delta of China on the Spatiotemporal Evolution of Carbon Emissions from Transportation

The impact of urban form on carbon emissions has become a crucial issue for sustainable socioeconomic development and the advancement of low-carbon cities. Transportation is a significant source of urban carbon emissions, highlighting the need for comprehensive research to aid China in achieving its carbon peak and neutrality goals. Currently, there is a lack of quantitative studies exploring the effects of urban form on transportation-related carbon emissions. This paper seeks to quantify the effect of urban form on the spatial and temporal patterns of transportation carbon emissions, utilizing panel data from 27 cities in the Yangtze River Delta (YRD) region of China, covering the years 2000 to 2020. First, CO2 emissions from transportation are estimated following IPCC guidelines, with Moran’s I utilized to analyze spatial autocorrelation. Next, urban form indicators are quantified based on landscape ecology theory. Finally, econometric models are employed for regression analysis of the panel data. The findings reveal that urban complexity, compactness, and expansion influence transportation carbon emissions to varying degrees, with urban expansion and complexity associated with increased emissions, while compactness contributes to their reduction. This study offers theoretical support and a scientific basis for low-carbon urban spatial planning and development, underscoring the importance of urban form in emissions reduction strategies.

THE INFLUENCE OF GREEN BANKING IMPLEMENTATION, ISLAMIC SOCIAL REPORTING AND COVID-19 CRISIS ON PROFITABILITY WITH BANK SIZE AS A MODERATING VARIABLE IN ISLAMIC GENERAL BANK PERIOD 2016-2023

This study uses bank size as a moderating variable to examine the effects of the Covid-19 crisis, Islamic social reporting, and the deployment of green banking on profitability in Indonesian Islamic commercial banks. In this work, panel data regression and moderated regression analysis are used as data analysis methods. Eleven Indonesian Islamic commercial banks that were registered with the OJK between 2016 and 2023 used as the study's sample. The study's findings show that Islamic commercial banks' profitability is significantly and favorably impacted by ISR. Profitability (ROE) is significantly impacted negatively by the Covid-19 situation, but not ROA. In the meantime, Islamic commercial banks' profitability is unaffected by green banking. The association between green banking and profitability may be moderated by the moderating variable of bank size. This research highlights how crucial risk management, responsibility, and openness are in a period of unpredictable economic times. In the context of Islamic banking, the impact of ISR procedures and reactions to financial crises like COVID-19 is quite significant. Islamic banks should see an improvement in their financial performance in the future by strengthening their ISR management and comprehending the implications of green banking.

Analisis Pengaruh Penerapan Strategi Bauran Pemasaran terhadap Keputusan Pembelian pada UMKM Serba Nanas Alam Sari Subang Jawa Barat

UMKM One of the producers in Subang offering processed pineapples is Serba Nanas Alam Sari. Processed pineapple sales are erratic. Businesses must create a strong marketing mix strategy in order to overcome consumer preferences and market rivalry. The purpose of this study is to examine how purchase decisions at Serba Nanas Alam Sari UMKM in Subang, West Java, are affected by the application of a marketing mix approach. In order to collect data for this study, 100 UMKM customers were given questionnaires. Mixed methods were used in this research. The techniques employed for gathering data included surveys, interviews, documentation, and observation. A Likert scale is used for variable measurement, and MSI is used to alter it. Multiple linear regression data analysis was used to ascertain the impact of place, promotion, price, and product variables on consumer purchase decisions. The study's findings indicate that the significant value is less than 0.05, indicating that purchase decisions are influenced by product, price, place, and promotion all at the same time. With a result of 1,042, the product variable has the biggest impact. This is due to the product's variety, good taste, and guaranteed product quality. The variables that affect promotion, pricing, and location are next in line. Based on the study's findings, Serba Nanas Alam Sari UMKM can benefit from an efficient marketing plan that combines quality products, competitive pricing, effective distribution, and eye-catching promotions to influence more customers' purchases.

The roles of task-specific self-efficacy and enjoyment in EFL learners’ development of speaking CALF: A longitudinal study

This longitudinal study adopts a person-centred approach to examine EFL learners' development of speaking CALF; the fluctuating correlations of affective ID variables (self-efficacy and enjoyment), as well as their impacts on oral task performance over time. Thirty-one Chinese EFL learners completed eight oral tasks bi-weekly over six months, followed by post-task scales to measure task-specific self-efficacy and enjoyment. Data were subjected to group-based multi-trajectory modelling (GBMM) to discern the development of speaking CALF, moving correlation analysis to explore the interactions between task-specific self-efficacy and enjoyment, and panel data regression to examine the longitudinal impact of task-specific self-efficacy and enjoyment on oral task performance. GBMM results revealed variability at both the group and individual levels, despite similar educational backgrounds and learning contexts, as evidenced by differing speaking CALF patterns. Moving correlation analyses revealed dynamic, non-linear trends between self-efficacy and enjoyment. Panel data regression analyses suggested that higher levels of affective ID variables would generate better oral performance, as evidenced by speaking CALF patterns. This study successfully integrated ID variables into TBLT research within a CDST framework for enhancing our understanding of the complex nature of EFL learners’ speaking competence and underscoring the interconnectivity between task-specific self-efficacy and enjoyment.

Unlocking sustainability: exploring the nexus of green banking, digital transformation, and financial performance with foreign ownership moderation

The purpose of this research is to empirically test and analyze the impact of green banking, digital transformation, and asset quality on the financial performance of commercial banks with foreign ownership as a moderator. Additionally, it aims to test and analyze the financial performance of sustainable business practices for commercial banks. This study was conducted on commercial banks, specifically focusing on bank groups categorized by core capital levels 3 and 4, listed on the Indonesia Stock Exchange, covering the period from 2017 to 2022. This research employed a judgment sampling technique, encompassing a total of 47 conventional commercial banks. Panel data regression analysis, a data analysis technique, used three variables: the dependent variable, the independent variable, and the moderation variable. The research unveiled that green banking does not appear to have a significant positive impact on financial performance, whereas digital transformation shows a clear positive effect. Non-performing loans, a measure of asset quality, also affect financial performance. Additionally, foreign ownership strengthens the impact of green banking on the financial performance of commercial banks in Indonesia. However, foreign ownership does not enhance the effect of digital transformation. Finally, foreign ownership plays a moderating role in the relationship between asset quality and financial performance in these banks. Ultimately, financial performance has a significant positive influence on sustainable business, suggesting that stronger financial performance contributes to the long-term sustainability of a bank’s operations. The implications of these findings are, first and foremost, that financial institutions should prioritize incorporating sustainable and environmentally friendly practices into their operations to improve their overall financial performance and emphasize the critical role of digital transformation in enhancing financial performance. Consequently, financial institutions are required to allocate resources towards information technology infrastructure and cultivate digital capabilities to adapt to evolving customer demands, such as offering 24/7 banking services from any location. Secondly, financial institutions should not stay far away from digital disruption, but rather embrace it and adapt their strategies accordingly, which includes allocating an adequate budget for IT infrastructure investment and accessing digital services that meet the banking industry’s demands, resulting in effective communication of sustainable practices.

PENGARUH CORPORATE SOCIAL RESPONSIBILITY (CSR), FINANCIAL DISTRESS, CORPORATE’S GROWTH, DAN FIRM SIZE TERHADAP EARNINGS RESPONSE COEFFICIENT (ERC)

The purpose of this study is to determine the influence of corporate social responsibility (CSR), financial distress, corporate's growth and firm size on earnings response coefficient (ERC). The data analysis method used in this study is a quantitative method and uses panel data regression analysis techniques. The population in this study is manufacturing companies listed on the Indonesia Stock Exchange in 2015-2022. The results show that financial distress has an effect on earnings response coefficient (ERC), corporate social responsibility (CSR), corporate's growth, and firm size has no effect on earnings response coefficient (ERC).

Panel Data: Study Of Income Inequality In West Sumatra

This research aims to analyze the factors that influence income inequality in West Sumatra Province. Some of the variables that are taken into consideration are: unemployment, education and poverty. The research uses secondary data from the Central Statistics Agency (BPS), using panel data regression analysis in 19 Regencies/Cities in West Sumatra Province, during 2017-2023. The selected research model uses the Fixed Effect Model (FEM). The research results show that: (1) the unemployment variable has a positive and insignificant effect on income inequality, (2) the education variable has a negative and significant effect on income inequality, (3) the poverty variable has a positive and significant effect on income inequality.

An Examination of Secondary English Language Arts and Social Studies’ Teachers’ Perceptions of Literacy Integration in Their Content Area Classrooms

Secondary students continue to struggle with achievement gains in literacy. Content area teachers have the responsibility of providing literacy integration to support struggling students and help close the achievement gap in their content area classrooms. With this responsibility, it is important to examine content area teachers’ perspectives and instructional needs related to the role of literacy integration in their content area. Using a correlational research design and multiple regression analysis of survey data, this study examined the relationships between secondary English Language Arts and Social Studies teachers’ perceptions of ease of use, usefulness, and attitude toward literacy integration of effective literacy practices in grades 6-12. Results from this study showed a significant relationship between ease of use and teacher attitude, as well as teacher attitude and integration of literacy instructional practices. The results also showed the significance of ease of use when integrating literacy practices into secondary content area classrooms. Findings from this study may provide much needed insight to school- and district-based literacy professionals and administrators about how to support the instructional decisions of English Language Arts and Social Studies related to integrating literacy practices in their secondary content area classrooms.

Cox regression model with doubly truncated and interval-censored data

Interval sampling is an efficient sampling scheme used in epidemiological studies. Doubly truncated (DT) data arise under this sampling scheme when the failure time can be observed exactly. In practice, the failure time may not be observed and might be recorded only within time intervals, leading to doubly truncated and interval censored (DTIC) data. This article considers regression analysis of DTIC data under the Cox proportional hazards (PH) model and develops the conditional maximum likelihood estimators (cMLEs) for the regression parameters and baseline cumulative hazard function of models. The cMLEs are shown to be consistent and asymptotically normal. Simulation results indicate that the cMLEs perform well for samples of moderate size.

Advancing Interpretable Regression Analysis for Binary Data: A Novel Distributed Algorithm Approach.

Sparse data bias, where there is a lack of sufficient cases, is a common problem in data analysis, particularly when studying rare binary outcomes. Although a two-step meta-analysis approach may be used to lessen the bias by combining the summary statistics to increase the number of cases from multiple studies, this method does not completely eliminate bias in effect estimation. In this paper, we propose a one-shot distributed algorithm for estimating relative risk using a modified Poisson regression for binary data, named ODAP-B. We evaluate the performance of our method through both simulation studies and real-world case analyses of postacute sequelae of SARS-CoV-2 infection in children using data from 184 501 children across eight national academic medical centers. Compared with the meta-analysis method, our method provides closer estimates of the relative risk for all outcomes considered including syndromic and systemic outcomes. Our method is communication-efficient and privacy-preserving, requiring only aggregated data to obtain relatively unbiased effect estimates compared with two-step meta-analysis methods. Overall, ODAP-B is an effective distributed learning algorithm for Poisson regression to study rare binary outcomes. The method provides inference on adjusted relative risk with a robust variance estimator.

Determinan Pertumbuhan Ekonomi Negara Asean-10: Model Data Panel

This study aims to analyze the effect of investment, exchange rate, labor, and human development index (HDI) variables on economic growth in ASEAN countries. This study uses panel data regression analysis with time series data from 2011-2022 and cross-section data from 10 ASEAN countries. The results showed that the Fixed Effect Model was selected as the best model in the study. The results of the analysis concluded that investment and labor have a positive and significant effect on economic growth and HDI has a negative and significant effect on economic growth. Meanwhile, the exchange rate does not have a significant effect on economic growth. In other words, increasing investment and labor will increase economic growth, conversely if the HDI increases it will not increase economic growth. In addition, the exchange rate has no effect on economic growth in 10 ASEAN countries.

Pengaruh Solvabilitas, Profitabilitas, Kepemilikan Institusional, Dewan Komisaris Independen, dan Kepemilikan Publik terhadap Pengungkapan Manajemen Risiko: Studi pada Perusahaan Sub-sektor Perbankan yang Terdaftar di Bursa Efek Indonesia Tahun 2018-2022

The object of this research is a banking sub-sector company listed on the Indonesia Stock Exchange for the period 2018-2022. This study aims to determine the effect of leevrage, profitability, institutional ownership, independent board of commissioners, and public ownership as independent variables and risk management disclosure as the dependent variable. This study used 35 companies, so there were a total of 175 samples used. The study used purposive sampling as a sample selection method used for the selection of certain criteria. This study applied descriptive statistical analysis method and panel data regression analysis. The results of this study show that all variables simultaneously influence risk management disclosure. Meanwhile, leverage, profitability, and independent board of commissioners each have no effect. Meanwhile, institutional ownership and public ownership positively affect risk management disclosure.