Ordinary Least Squares Research Articles

The Effect of Job Skill Demands Under Artificial Intelligence Embeddedness on Employees' Job Performance: A Moderated Double-Edged Sword Model.

With the widespread application of AI technology, the skills and abilities required by employees in their work are undergoing fundamental changes, redefining the roles of employees. This research aims to explore the effect of job skill demands under AI embeddedness on well-being in organizations and job performance. Based on conservation of resources theory, this research randomly selected 479 employees from 8 companies in China using a time-lag method as samples, and conducted statistical analysis with ordinary least squares (OLS). This research found that, job skill demands under AI embeddedness will both increase employees' competency needs, promoting their well-being in organizations and job performance and decrease employees' job embeddedness, inhibiting their well-being in organizations and job performance. Meanwhile, technological anxiety moderated the impact of job skill demands under AI embeddedness on job embeddedness.

Do US cross-listings, IFRS adoption and domestic investor protection impact earnings management?

PurposeThis paper investigates the impact of International Financial Reporting Standards (IFRS) adoption and domestic investor protection on the relationship between United States (US) cross-listing and earnings management.Design/methodology/approachThe paper applies ordinary least squares (OLS) regression analyses to a matched sample of cross-listed and non-cross-listed firms from 2000 to 2018, covering 38 countries.FindingsWe find that US cross-listed firms have lower real earnings management. The results also show that real earnings management is lower for US cross-listed firms that adopt IFRS and from high domestic investor protection countries. Our results further show that real earnings management is higher when cross-listed firms use Level 1 American Depository Receipts (ADRs) to cross-list but adopting IFRS and high domestic investor protection help reduce real earnings management. Using the SEC’s Rule 12h-6 as a quasi-natural experiment, we document that post-12h-6 Rule, firms in high domestic investor protection countries experience a reduction in both accruals and real earnings management. However, the result is the opposite in countries with low investor protection.Originality/valueOur findings have implications for regulators and policymakers on the impact of ADR levels and Rule 12h-6 on earnings management.

Comparing The Effectiveness Of Multiple-Answer And Single-Answer Multiple-Choice Questions In Assessing Student Learning

ABSTRACT Driven by technological innovations, continuous digital expansion has transformed fundamentally the landscape of modern higher education, leading to discussions about evaluation techniques. The emergence of generative artificial intelligence raises questions about reliability and academic honesty regarding multiple-choice assessments in online education. In this context, this study investigates multiple-answer questions (MAQs) versus traditional single-answer questions (SAQs) in online higher-education assessments. A mixed-methods study involving quantitative field experiments and qualitative interviews was conducted with students enrolled in an online Marketing M.Sc. program. The students were divided randomly and assessed using either SAQs or MAQs, and the impacts on test performance of variables such as grade averages, study times, perceived workload, and difficulty were evaluated using independent-sample t-tests and ordinary least-squares regression analysis. The results show that although grades were lower and MAQs were perceived as being more difficult, study times and perceived workload did not differ significantly between the two formats. These findings suggest that despite their challenge, MAQs can promote deeper understanding and greater learning retention. Furthermore, even with their higher perceived difficulty and impact on performance, MAQs hold potential for dealing with academic-integrity concerns related to artificial intelligence.

Economic growth and unemployment nexus: empirical test of Okun’s law in Somalia

This paper aims to empirically examine the relationship between GDP and unemployment in Somalia from 2000 to 2021. The study also estimates Okun's coefficient. To evaluate the association between the unemployment rate and economic growth, we employ the Hodrick–Prescott (HP) filter detrending technique, the Augmented Dickey–Fuller (ADF) test, ordinary least squares (OLS), and fully modified OLS. The findings of this study demonstrate that the series is stationary at the level. However, the results confirm a statistically insignificant negative relationship between unemployment and economic growth. Consequently, our findings suggest that Okun's law does not apply in Somalia. For robustness, we employ fully modified ordinary least squares (FMOLS), canonical cointegrating regression (CCR), and dynamic ordinary least squares (DOLS). Nevertheless, the relationship between the GDP gap and unemployment is not strong enough to be considered statistically significant, and other factors may also influence unemployment. Therefore, policies aimed at reducing unemployment should take into account various factors, including education and training, labor market regulations, and social protection measures.

Quantile-based robust Kibria–Lukman estimator for linear regression model to combat multicollinearity and outliers: Real life applications using T20 cricket sports and anthropometric data

The performance of ordinary least squares (OLS) and ridge regression (RR) are influenced when outliers are present in y-direction with multicollinearity among independent variables. The robust RR with ridge parameters provides a biased estimator that has a smaller variance than conventional OLS and RR estimators. The optimal value of the ridge parameter has a vital role in bias-variance tradeoff. This study proposes quantile-based estimation of ridge parameter in robust RR to deal with the joint issue of y-direction outliers and multicollinearity. The effectiveness of the proposed estimators is evaluated using intensive Monte Carlo simulation and two real data sets in terms of mean square error (MSE) and predictions sum of squared error (PSSE) criterion. Simulation findings reveal that the newly developed estimators of ridge parameter in robust RR have better performance than OLS, RR, and existing robust RR estimators when errors are normally and non-normally distributed. The results from two numerical examples of T20 Cricket sports and anthropometric data show that the new estimator with quantile probability 0.50 and 0.99 respectively has winning performance among all competing and proposed estimators.

Assessment of Climate-Induced Rice Yield Using Ordinary Least Squares (OLS) Regression Analysis: A Case Study from Coastal Context

Assessment of Climate-Induced Rice Yield Using Ordinary Least Squares (OLS) Regression Analysis: A Case Study from Coastal Context

The double disadvantage faced by adolescents from low socioeconomic backgrounds with mental health problems affects earnings up to mid-life

Early childhood socioeconomic disadvantage and mental health problems are both important determinants of adult social and economic experiences, but little is known about how they interact in this respect. We aimed to assess whether poor mental health in adolescence exacerbates labour market inequalities originating from low socioeconomic status (SES) in childhood. We use a birth cohort of individuals born in 1958 in England and follow their employment experiences and cumulative earnings up to age 55. We proxy low SES in childhood with father's occupational class at the time the respondent was aged 11, and use caregiver (usually, parent) ratings of the Rutter inventory at age 16 to identify mental health problems in adolescence. We fit ordinary least squares (OLS) models to estimate the effect of growing up in a low-SES family and experiencing mental health problems (conduct or emotional problems) in adolescence on cumulative earnings (log-transformed). We use an interaction term to test whether the association between mental health problems (conduct and emotional separately) and earnings differed by socioeconomic group. Individuals who experienced conduct problems in adolescence had lower cumulative earnings and employment levels up to age of 55. Moreover, the association between mental health problems and cumulative earnings was higher among individuals who also experienced low SES in childhood. Families from a higher socioeconomic group may have more effective means to counteract the adverse impacts of adolescent mental health problems, likely due to broader access to resources, support systems and opportunities. This underscores the role of structural supports in addressing socioeconomic inequalities in mental health outcomes and their long-terms implications.

Leisure sedentary time and elevated blood pressure: evidence from the statutory retirement policy.

The relationship between sedentary behaviors and elevated blood pressure remains inconclusive, and the socioeconomic mechanisms underlying the linkage are rarely discussed. Since retirement is often associated with behavioral changes that impact health, this study aims to provide evidence on changes in leisure sedentary time after the statutory retirement age on elevated blood pressure, along with the socioeconomic mechanisms. We utilized data from five waves (2004-2015) of the China Health and Nutrition Survey (CHNS), focusing on males aged 55-65 employed in the formal sector. Leisure sedentary time, the independent variable, was measured based on self-reported data, while diastolic (DBP) and systolic (SBP) blood pressure were the dependent variables. Using statutory retirement policy as an exogenous variation, we employed a continuous difference-in-differences (DID) framework and a propensity score matching difference-in-differences (PSM-DID) approach to examine the relationship between changes in leisure sedentary time after the statutory retirement age and elevated blood pressure. The analysis was conducted using ordinary least squares (OLS). To address potential endogeneity, we applied the instrumental variable (IV) method via two-stage least squares (2SLS). Our findings indicate an increase in diastolic blood pressure after statutory retirement, attributed to increased leisure sedentary time. However, there was no significant increase in systolic blood pressure. Moreover, physical activity did not appear to offset this rise in blood pressure, while higher educational attainment and having family members employed in the medical field helped mitigate its negative effects. This study highlights the potential adverse impact of increased leisure sedentary time on diastolic blood pressure among middle-aged men in the formal sector, while also exploring the socioeconomic factors that may alleviate these effects. These results provide a foundation for public health initiatives aimed at addressing the rising prevalence of sedentary behavior and its association with blood pressure issues.

The value relevance of deferred tax assets: An empirical analysis of German HDAX-listed companies

Deferred taxes emerge from timing differences in recognizing income and expenses between commercial and tax financial statements. However, the capitalization of deferred tax assets remains contentious, with questions raised about their value relevance to investors. This study aims to investigate the informational value and economic significance of deferred tax assets in financial statements prepared in accordance with both German commercial law (HGB) and International Financial Reporting Standards (IFRS). The analysis is based on financial data from 1,066 firm-year observations of HDAX-listed companies from 2000 to 2022. Using an Ordinary Least Squares (OLS) regression model, the study examines the relationship between deferred tax assets and the market value of equity. Key financial variables, including research and development expenses, deferred tax liabilities, net income, and the market-to-book ratio, are incorporated to provide a comprehensive assessment of deferred tax asset relevance in capital markets. The results demonstrate that deferred tax assets have a negligible impact on the market value of companies, with no statistically significant effect detected. Conversely, research and development costs, as well as net income, exhibit a strong positive influence on firm valuation. These findings suggest that deferred tax assets serve largely as an accounting mechanism, lacking informational value for investors.

More knowledge, more choices? How peer recognition of physicians' knowledge sharing affect patients' consultation in online health communities.

The advent of telemedicine has revolutionized healthcare consultations, primarily due to the digital era and global health concerns. Online healthcare communities (OHCs) have emerged as platforms for physicians to share health-related articles, promoting digital public health awareness and knowledge dissemination. The continuous dissemination of health knowledge by physicians online is considered a crucial driving force in attracting patients to seek online consultations. Based on the elaboration likelihood model and the information overload theory, this study explores how persuasive messages from other patients' peer recognition, including knowledge popularity and attractiveness, affect patients' consultation decisions. Additionally, the study examines the three-way interaction between knowledge popularity, attractiveness, and quantity in shaping patient consultations. Using data collected from 2,676 physicians on haodf.com, this study established an ordinary least squares (OLS) regression model with time and city fixed effects to test the hypothesis. The results show that: (1) peer recognition (knowledge popularity and attractiveness) from other patients positively impacts patients' consultation; (2) knowledge attractiveness positively moderate the relationship between knowledge popularity and patients' consultation; (3) there is a three-way effect of knowledge popularity, knowledge attractiveness, and knowledge quantity on patients' consultation. Our findings offer valuable guidance for platform design and healthcare practitioners, boosting patient-physician engagement in online healthcare communities.

The institutionalization of digital compliance

PurposeThis research paper investigates how companies incorporate digital compliance, particularly data security and protection, as a fundamental aspect of corporate digital responsibility (CDR). We address the gap in understanding the institutionalization of digital compliance as a part of CDR.Design/methodology/approachUsing institutional theory, we assess the impact of coercive, mimetic and normative forces on digital compliance. We used survey data from 162 predominantly large German companies and analyzed it through ordinary least squares (OLS) regressions.FindingsMimetic forces, driven by competitor knowledge, substantially affect digital compliance. Normative forces related to professional knowledge are also influential, while coercive forces exerted by customers have no significant impact on the adoption of digital compliance. In a supplemental analysis, the study highlights the moderating role of organizational agility.Research limitations/implicationsCDR is not limited to digital compliance. Future research should explore higher levels of CDR and consider stakeholders beyond customers. The focus on large German companies may limit generalizability.Practical implicationsThe findings stress the importance of understanding competitive landscapes and professional discourses. Managers should be aware of these institutional forces and incorporate them into strategic planning for digital compliance and CDR.Originality/valueThis research extends institutional theory to digital compliance, offering insights into CDR-related corporate behavior and strategy, emphasizing the importance of competitor awareness and professional norms to manage digital risks.

Financial well-being, anxiety and payment delinquency among student loan holders in the United States: insights from the COVID-19 pandemic

PurposeThe COVID-19 pandemic presented unprecedented challenges, particularly intensifying the financial and psychological burden for individuals with student loans in the United States. Firstly, this study examined the association between student loan ownership and financial well-being during the pandemic. Secondly, among student loan holders, we tested the association of financial anxiety and payment delinquency with COVID-19 shocks and financial knowledge. Lastly, we investigated the associations between the recipients of student loans and their financial well-being, anxiety and behaviors concerning student loans.Design/methodology/approachUtilizing data from the 2021 National Financial Capability Study, we explored how unprecedented economic disruptions have affected student loan holders' financial well-being, levels of debt anxiety and payment delinquency, considering financial knowledge as a critical factor. We conducted Ordinary Least Squares (OLS) and logistic regressions to examine the associations addressed in the purpose of the study.FindingsThe results of regression analyses indicate that individuals with student loans generally experienced lower financial well-being than those without loans. Among student loan holders, COVID-19 shocks were positively associated with student loan anxiety and payment delinquency. Additionally, subjective financial knowledge showed a positive association, while objective financial knowledge displayed a negative association with loan delinquency. Lastly, respondents who secured loans for themselves exhibited lower levels of financial well-being than other student loan holders.Originality/valueThis study represents one of the initial efforts to investigate the issues of financial well-being, debt anxiety and payment delinquency among student loan holders, along with their associations with the potential COVID-19 shocks they experienced. The research shed light on the acute financial stress and mental health challenges faced by student loan holders during global crises, highlighting the significance of effective policy development for student debt management and borrower support during times of economic uncertainty.

BANKRUPTCY RISK AND SHARE PRICES: A CASE STUDY ON ENERGY COMPANIES IN KUWAIT

<p>This study aims to investigate how investors' perceptions of risk impact the stock prices of energy companies listed on the Kuwait Stock Exchange (KSE) from 2016 to 2023. Investors' belief in the future financial stability of the companies they invest in influences their risk perception. Therefore, if investors hold pessimistic views, they will decrease their investment in these companies, causing share prices to drop, and vice versa. The Zmijewski x-score model is utilized in this study as a measure of risk to assess the financial stability of Kuwaiti energy companies and its impact on their stock prices. The study employs ordinary least square regression (OLS) to analyze the correlation between the x-score and share prices. Results from this research indicated that there was no statistically significant relation between Zmijewski's x-score and stock price and that investors look at other factors when choosing their investments. This is evident because the company with the greatest bankruptcy risk in the following two years also had the second-highest share price.</p><p> </p><p><strong>JEL: </strong>G1, G4, M21</p><p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/soc/0699/a.php" alt="Hit counter" /></p>

Factors Affecting the Sustainability of Corporates in Polluting Sectors

Corporate sustainability performance is gaining ever greater importance. The negative impact of climate change is manifested through heavy air, water and soil pollution. Polluting sectors, as the major players, are characterized by large amounts of emissions, waste and consumption of resources, and therefore have a larger negative impact on the environment. Companies operating in polluting sectors are recognized globally as the main sources of greenhouse gas emissions; thus, their performance is widely debated. Despite their character, such companies strive for higher profitability, better financial performance and operational efficiency. However, higher financial resources create the potential for innovation investments in companies. It is widely accepted that research and experimental development (R&D) expenditures enable new business ideas, models, products, services, and processes. However, while pursuing sustainability targets, financial results could be directed towards sustainability performance. The purpose of this paper is to analyze how the financial and innovation results of companies in polluting sectors interact with sustainability performance scores. For it, we have identified three essential pillars of sustainability: environmental, governance, and social. Using ordinary least squares (OLS) regressions, models were developed for each pillar of sustainability, including corporate financial performance indicators and R&D expenditures. The obtained results provide the insights that a company operating in polluting sector size and turnover significantly interacts with all pillars of sustainability. However, we also found that the corporate debt ratio, earnings ratio, and current liquidity have a significant relation only with environmental and social sustainability indicators.

Associations between environmental factors and running performance: An observational study of the Berlin Marathon.

Extensive research has delved into the impact of environmental circumstances on the pacing and performance of professional marathon runners. However, the effects of environmental conditions on the pacing strategies employed by marathon participants in general remain relatively unexplored. This study aimed to examine the potential associations between various environmental factors, encompassing temperature, barometric pressure, humidity, precipitation, sunshine, cloud cover, wind speed, and dew point, and the pacing behavior of men and women. The retrospective analysis involved a comprehensive dataset comprising records from a total of 668,509 runners (520,521 men and 147,988 women) who participated in the 'Berlin Marathon' events between the years 1999 and 2019. Through correlations, Ordinary Least Squares (OLS) regression, and machine learning (ML) methods, we investigated the relationships between adjusted average temperature values, barometric pressure, humidity, precipitation, sunshine, cloud cover, wind speed, and dew point, and their impact on race times and paces. This analysis was conducted across distinct performance groups, segmented by 30-minute intervals, for race durations between 2 hours and 30 minutes to 6 hours. The results revealed a noteworthy negative correlation between rising temperatures and declining humidity throughout the day and the running speed of marathon participants in the 'Berlin Marathon.' This effect was more pronounced among men than women. The average pace for the full race showed positive correlations with temperature and minutes of sunshine for both men and women. However, it is important to note that the predictive capacity of our model, utilizing weather variables as predictors, was limited, accounting for only 10% of the variance in race pace. The susceptibility to temperature and humidity fluctuations exhibited a discernible increase as the marathon progressed. While weather conditions exerted discernible influences on running speeds and outcomes, they did not emerge as significant predictors of pacing.

Common auditor, knowledge transfer and audit quality: international evidence

Purpose Using an international sample of firms affiliated with a business group, this paper aims to investigate the audit quality of common auditors, i.e. auditors shared by multiple firms affiliated with the same business group. Design/methodology/approach This paper utilizes a large sample of group-affiliated firms from 35 regions. Following the frameworks of DeFond and Zhang (2014) and Fung et al. (2017), this paper measures audit quality using the absolute value of abnormal accruals and modified audit opinions. Ordinary least squares (OLS) regression is used to address the research question. Findings The findings demonstrate that audit quality improves for firms that share the same auditor with other group members. Importantly, cross-country analyses reveal that this relationship is stronger when auditors operate in countries with stricter local auditor inspection programs and an overall opaque information environment. Additionally, the impact of common auditors on audit quality is more pronounced for group affiliates that have a more opaque firm-level information environment and are monitored by heightened institutional ownership. Practical implications While affiliates are more likely to select the same audit firms as common auditors, the evidence regarding the effects of common auditors is mixed. The results of this study provide further insights into auditor choices for group-affiliated firms and offer a potential avenue for better protecting shareholders' interests. Originality/value The results of this study add to the ongoing debates regarding the costs and benefits of common auditor choice for group affiliates. To the best of the authors’ knowledge, this study is the first to demonstrate that the effects of common auditors may vary in different external environments.

Predicting refractive index of inorganic compounds using machine learning

Refractive index (RI) is one of the most important optical properties of materials. Due to the high importance of this physical parameter, there has always been a demand to find a method that provides the most optimal estimation. In this research, we utilize experimentally measured RI values of 272 inorganic compounds to build a machine learning model capable of predicting the RI of materials with low computational cost. Considering the significant relationship between the band gap and RI, we select this parameter as a predictor. In addition to the band gap, the atomic properties related to the building elements of the compounds form our data set in this work. To find the most optimal model and set of suitable predictors, we examine our data in four categories with 1, 5, 10, and 21 features. In addition, we compare the predicted RIs of 6 different independent regression methods, namely, ordinary least squares (OLSR), Gaussian process (GPR), support vector (SVR), random forest (RFR), gradient boosted trees (GBTR), and extremely randomized trees regression(ERTR). We notice that ERTR predicts RI with the highest accuracy compared to other regression methods. The prediction strength of our model excels in empirical relations and provides accurate results for a wide range of RIs. Thus, we demonstrate the high potential of machine learning methods for evaluating the RI, especially when it comes to providing an estimation of a desired physical quantity.

The Impact of Foreign Direct Investment, Trade Value on the Economic Growth of Greater Mekong Subregion (GMS)

In the study entitled “The impact of foreign direct investment, trade value on the economic growth of the Greater Mekong Subregion countries” which aims to study the changes of foreign direct investment, trade value on the economic growth of the Greater Mekong Subregion countries (GMS) from 2005–2022, and analyze the impact of foreign direct investment, trade value on the economic growth of the Greater Mekong Subregion countries (GMS), The data used in the study is geographical data. This study uses descriptive statistics to analyze changes and uses the estimation method of ordinary least squares (OLS) through a statistical program. Through the analysis of changes in foreign direct investment, trade value, and gross domestic product of GMS, it was found that, since GMS was established in 1992, the condition of foreign direct investment, trade value, and gross domestic product of GMS has been changing continuously due to the influence of Chinese investment that has come to invest in the region in terms of transportation infrastructure and electricity. In terms of trade, there are trade relations among the group of GMS countries, focusing on facilities for transportation and trade in the region. Cooperation in the region, contributing to economic growth and reducing poverty, as well as responding to the demand for products in the region, developing agriculture in urban areas, promoting GMS as a tourist source, and making the total domestic product of the countries in the region grow. A temporary drop in foreign direct investment, trade value, and gross domestic product of the GMS is due to the Mekong Delta member countries being affected by the financial crisis in Asia, political unrest, the financial crisis in China, and the spread of the COVID-19 disease. Analysis of the effect of foreign direct investment and trade value on the economic growth of the GMS countries by testing the relationship found that both the value of foreign direct investment and the value of international trade are related in the same direction to economic growth: if the value of foreign direct investment in the GMS countries increases by 1%, it will result in an increase in the total domestic product by 0.1225%, and if the value of trade in the GMS countries increases by 1%, it will result in an increase in the total domestic product by 0.7566% through statistical significance of 0.01.

Towards socially equitable public transport systems: The effect of COVID-19 on taxi trip behavior

The COVID-19 pandemic emerged as a very influential occurrence with a profound impact on a global scale. The onset of the pandemic abruptly disrupted the regular course of everyday activities, primarily impacting urban regions. Hence, it is imperative to understand the effects of the COVID-19 pandemic on modern urban areas. This study seeks to analyze the effect of the pandemic on travel behavior by utilizing GPS data obtained from taxis, with a specific focus on spatial socioeconomic features. The M2 metro line in Istanbul has been selected for evaluation. In this analysis, four distinct periods are considered: total, off-peak, morning, and evening peaks. The stations are categorized using K-means clustering. The estimation models are constructed using ordinary least squares (OLS), spatial autoregression (SAR), and geographically weighted regression (GWR) techniques, which are applied to the variation in daily average cab trips and the characteristics of stations. The GWR models provide superior performance in comparison to the other two models, with notable distinctions observed in peak times, particularly morning peak when compared to total and off-peak counts. The findings indicate that factors such as population, population density, socioeconomic status, and the quantity of shopping malls are influential variables in elucidating and forecasting the fluctuations in taxi trip counts.

The Influence Of Total Production, NTP And Grdp On Labor Absorption In The Coconut Plantation Subsector In Riau Province

This research aims to determine the effect of total production, Farmer Exchange Rate (NTP) and Gross Regional Domestic Product (GRDP) on labor absorption in the smallholder coconut plantation subsector in Riau Province in 2012-2023. In this research, multiple linear regression techniques were used using the Ordinary Least Square (OLS) method. The research results show that the simultaneous test (F test) shows that total production, NTP and GRDP together have a significant influence on labor absorption in the coconut plantation subsector in Riau Province in 2012-2023. Meanwhile, the partial regression test (t test) shows that total production has a positive and significant effect on labor absorption, NTP has a positive and significant effect on labor absorption, and GRDP has a negative and insignificant effect on labor absorption. The results of the coefficient of determination (R2) of the independent variable on the dependent variable obtained a value of 0.973238. This means that the contribution of the independent variables (total production, NTP and GRDP) to the dependent variable (demand for employment) is 97.32% and the remaining 2.68% is influenced by other variables outside the regression model in this research.