Search results

Traditional multivariable grey prediction models define the background-value coefficients of the dependent and independent variables uniformly, ignoring the differences between their physical properties, which in turn affects the stability and reliability of the model performance.

A novel multivariable grey prediction model is constructed with different background-value coefficients of the dependent and independent variables, and a one-to-one correspondence between the variables and the background-value coefficients to improve the smoothing effect of the background-value coefficients on the sequences. Furthermore, the fractional order accumulating operator is introduced to the new model weaken the randomness of the raw sequence. The particle swarm optimization (PSO) algorithm is used to optimize the background-value coefficients and the order of the model to improve model performance.

The new model structure has good variability and compatibility, which can achieve compatibility with current mainstream grey prediction models. The performance of the new model is compared and analyzed with three typical cases, and the results show that the new model outperforms the other two similar grey prediction models.

This study has positive implications for enriching the method system of multivariable grey prediction model.

This study compares the motives of holding cash between developed (Australian) and developing (Malaysian) financial markets.

For the period 2006–2020, the t-test, fixed-effect and generalised method of moment (GMM) model have been applied to a sample of 1878 (1,165 Australian and 713 Malaysian) firms.

The empirical results reveal that firms in developed financial markets hold higher cash compared to the developing financial markets. The findings confirm that motives to hold cash differ between developed and developing financial markets. The GMM findings further show that cash holdings (CH) in Australia are higher due to higher ratios of cash flow, research and development (R&D) and return on assets (ROA), and lower due to larger dividend payments. In the Malaysian market, however, cash flows and R&D are ineffectual, ROA falls and dividend payments rise CH.

The study helps managers, practitioners and investors understand that firms' distinct economic, institutional, accounting and financial environments are important. To attain the desired outcomes, they must thus comprehend and consider these considerations while developing suitable liquidity strategies.

To the authors' best knowledge, this is the initial research demonstrating how varied cash motives and their ramifications are in developed and developing financial markets. Therefore, this study identifies the importance that CH motives varied among financial markets and that findings from a particular market cannot be generalised to other markets because of the market and financial structural variations.

With the rapid advancement in the automotive industry, the friction coefficient (FC), wear rate (WR) and weight loss (WL) have emerged as crucial parameters to measure the performance of automotive braking systems, so the FC, WR and WL of friction material are predicted and analyzed in this work, with an aim of achieving accurate prediction of friction material properties.

Genetic algorithm support vector machine (GA-SVM) model is obtained by applying GA to optimize the SVM in this work, thus establishing a prediction model for friction material properties and achieving the predictive and comparative analysis of friction material properties. The process parameters are analyzed by using response surface methodology (RSM) and GA-RSM to determine them for optimal friction performance.

The results indicate that the GA-SVM prediction model has the smallest error for FC, WR and WL, showing that it owns excellent prediction accuracy. The predicted values obtained by response surface analysis are closed to those of GA-SVM model, providing further evidence of the validity and the rationality of the established prediction model.

The relevant results can serve as a valuable theoretical foundation for the preparation of friction material in engineering practice.

This study aims to contribute to the academic disciplines of entrepreneurship and management by developing a new theory that explains Founder-CEOs’ succession in family and non-family firms. Many scholars failed to generate a specific theory to describe the succession of Founder-CEOs. Family firms remain complex enterprises comprising interconnectedness of cultural interests in which corporate governance occurs by families, Founder-CEOs and sometimes a board of directors.

This study’s design/methodology/approach reflects post-modernist epistemological and ontological perspectives for conducting systematic literature reviews. To identify relevant studies in the review, the several databases (Australian Business Dean’s Council Journal Quality List; EBSCO Database, including PsycINFO and Psych studies; Web of Science) and a mix of ranked journals from entrepreneurship, management and psychology were used.

The findings and results in this paper reflect the purpose, methodology and literature analysis culminating in 1,582 peer-reviewed studies. A total of 182 peer-reviewed studies met the criterion for review. Throughout the research process, a systematic literature review uncovered management literature gaps overlooked for decades during the theory-building process. Hence, developing a theory of Founder-CEOs succession used a combination of systematic, inductive, comparative and interactive approaches.

A Theory of Founder-CEOs Succession explains the strategic process of replacing a founder systematically. The promotion of, and incentives for, internal executives have been topics of great interest and deliberation among scholars and practitioners for a long time. This study contributes research implications for theory building in the academic disciplines of entrepreneurship and management by offering scholars and practitioners a theory that does not exist to describe Founder-CEOs’ succession encompassing both strategic successes and failures. By incorporating successes and failures, this study provides realistic reflections of Founder-CEOs.

This study aimed to use enrollment information including demographic, family background and financial status, which can be gathered before the first semester starts, to construct early prediction models (EPMs) and extract crucial factors associated with first-year student dropout probability.

The real-world samples comprised the enrolled records of 2,412 first-year students of a private university (UNI) in Taiwan. This work utilized decision trees (DT), multilayer perceptron (MLP) and logistic regression (LR) algorithms for constructing EPMs; under-sampling, random oversampling and synthetic minority over sampling technique (SMOTE) methods for solving data imbalance problems; accuracy, precision, recall, F1-score, receiver operator characteristic (ROC) curve and area under ROC curve (AUC) for evaluating constructed EPMs.

DT outperformed MLP and LR with accuracy (97.59%), precision (98%), recall (97%), F1_score (97%), and ROC-AUC (98%). The top-ranking factors comprised “student loan,” “dad occupations,” “mom educational level,” “department,” “mom occupations,” “admission type,” “school fee waiver” and “main sources of living.”

This work only used enrollment information to identify dropout students and crucial factors associated with dropout probability as soon as students enter universities. The extracted rules could be utilized to enhance student retention.

Although first-year student dropouts have gained non-stop attention from researchers in educational practices and theories worldwide, diverse previous studies utilized while-and/or post-semester factors, and/or questionnaires for predicting. These methods failed to offer universities early warning systems (EWS) and/or assist them in providing in-time assistance to dropouts, who face economic difficulties. This work provided universities with an EWS and extracted rules for early dropout prevention and intervention.

This study aims to investigate the relationship between boardroom gender diversity (BoGD) and risk-taking by property-liability (P-L) stock insurers from an analytical framework that control for organizational form and ownership structure. It relies on the behavioral agency model, the resource dependency theory and the concept of socioemotional wealth (SEW).

This study builds on an unbalanced panel of 2,285 firm-year observations from 232 European and US P-L stock insurers covering the period 2010–2019 and measure risk-taking by using four proxies: total risk (TR), upside risk (UpR), downside risk (DwR) and default risk (DR). Reverse causality and endogeneity concerns are treated by applying different approaches.

Findings suggest that BoGD mitigates the TR, DwR and DR but does not interfere with the UpR, which conceptualizes firm expectations to enhance patrimony and safeguard SEW for heirs, especially in family-owned insurers. The findings hold in various robustness checks including endogeneity and alternative specifications of BoGD and risk-taking.

This study contributes to practice by contrasting the role of female directors’ bevahior when assuming risk, which seems significantly different depending on the risk-taking specification and the organizational form. The author advises policyholders and policymakers to look at closely on BoGD and ownership structure as they affect insurance company risk-taking.

This study takes a more direct approach to highlight the BoGD’s effect on corporate risk-taking by focusing on the insurance sector which is characterized by risk and uncertainty bearing. To the best of the author’s knowledge, this is the first study to consider the full range of the stock organizational forms and the degree of family control in displaying this effect in both widely traded and closely traded insurers and to assess risk-taking from both market-based and accounting-based aspects.

The crux of this paper is to unveil efficient features and practical tools that can predict credit default.

Annual data of non-financial listed companies were taken from 2000 to 2020, along with 71 financial ratios. The dataset was bifurcated into three panels with three default assumptions. Logistic regression (LR) and k-nearest neighbor (KNN) binary classification algorithms were used to estimate credit default in this research.

The study’s findings revealed that features used in Model 3 (Case 3) were the efficient and best features comparatively. Results also showcased that KNN exposed higher accuracy than LR, which proves the supremacy of KNN on LR.

Using only two classifiers limits this research for a comprehensive comparison of results; this research was based on only financial data, which exhibits a sizeable room for including non-financial parameters in default estimation. Both limitations may be a direction for future research in this domain.

This study introduces efficient features and tools for credit default prediction using financial data, demonstrating KNN’s superior accuracy over LR and suggesting future research directions.

This study investigates the observed resurgence in religious beliefs seen across many societies during the COVID-19 pandemic. Using the economic theory of religious clubs, the author models religious participation during the pandemic as a mechanism for alleviating the financial distress associated with the health distress from the pandemic.

Using data from the COVID-19 National Longitudinal Phone Survey (NLPS) in Nigeria, the author investigates the economic motivation for religious intensity during the COVID-19 pandemic. To address endogeneity concerns, the author exploits geographic variables of temperature and longitudes as sources of COVID-19 risk.

Overall, health distress stimulates religious intensity. Consistent with the economic theory of religious clubs, adverse health shocks stimulate financial distress, and the effect is stronger among religious participants. Similarly, people see God and not the government as a source of protection against COVID-19.

The study’s model sees religious organizations as public goods providers, especially when governments and markets are inefficient.

The study’s recommendations support an expanded role for religious networks in healthcare delivery and more public funding to attenuate the post-pandemic resurgence of social violence in economically distressed regions.

Despite the research interest in the COVID-19 pandemic, the long-term implications, many of which relate to social behavior adjustments that cause individuals to identify more closely with their social group, need greater understanding. Suppose religious intensity is linked to economic distress. In that case, this is a major source of worry for countries whose economies are subject to higher fluctuations and where the governments and markets are inefficiently organized. These regions may be more susceptible to a resurgence in religious fundamentalism associated with the economic shocks from the pandemic. Consequently, these regions would require more public funding to attenuate the potential for costly activities like organized violence, suicide attacks and terrorist activities in the aftermath of the pandemic.

Prompted by the observation of the increase in religious identity through religious intensity during the pandemic, the author contributes by developing theoretically-based hypotheses that are incentive-compatible to provide a rational justification for the observation. The author empirically validates the hypothesis by taking advantage of the COVID-19 National Survey in Nigeria by specifically using survey rounds 4 and 7 which have more comprehensive religious items included.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-11-2022-0719

Tribological research is complex and multidisciplinary, with many parameters to consider. As traditional experimentation is time-consuming and expensive due to the complexity of tribological systems, researchers tend to use quantitative and qualitative analysis to monitor critical parameters and material characterization to explain observed dependencies. In this regard, numerical modeling and simulation offers a cost-effective alternative to physical experimentation but must be validated with limited testing. This paper aims to highlight advances in numerical modeling as they relate to the field of tribology.

This study performed an in-depth literature review for the field of modeling and simulation as it relates to tribology. The authors initially looked at the application of foundational studies (e.g. Stribeck) to understand the gaps in the current knowledge set. The authors then evaluated a number of modern developments related to contact mechanics, surface roughness, tribofilm formation and fluid-film layers. In particular, it looked at key fields driving tribology models including nanoparticle research and prosthetics. The study then sought out to understand the future trends in this research field.

The field of tribology, numerical modeling has shown to be a powerful tool, which is both time- and cost-effective when compared to standard bench testing. The characterization of tribological systems of interest fundamentally stems from the lubrication regimes designated in the Stribeck curve. The prediction of tribofilm formation, film thickness variation, fluid properties, asperity contact and surface deformation as well as the continuously changing interactions between such parameters is an essential challenge for proper modeling.

This paper highlights the major numerical modeling achievements in various disciplines and discusses their efficacy, assumptions and limitations in tribology research.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-03-2023-0076/

This study aims to examine the extent to which external auditors (EAs) use the work of the internal audit function (IAF) based on the purpose of its primary activities. The authors rely on attribution theory, which suggests that individuals search for meaning when an event occurs. In this setting, the authors explore how the overall (assurance vs advisory) or specific (e.g. risk management and evaluating internal controls) focus of IAF activities influences perceived EA reliance on the IAF’s work.

The authors first explore the research question with data extracted from a broad, longitudinal survey conducted triennially by the national chapters of the Institute of Internal Auditors in Austria, Germany and Switzerland. The data includes responses from 2014, 2017 and 2020 administrations of the survey. The authors conduct a parallel survey with practicing EAs attending two training sessions of a European office of a global network firm. Hypotheses were tested using ordered logistic regression.

Among the chief audit executive (CAE) participants, the authors observe that a balanced or primarily assurance-related purpose of the IAF, relative to a primarily advisory-related purpose, is associated with higher perceived EA reliance. The authors observe similar perceptions of the extent of reliance among the EA participants.

With a unique data set of practicing internal auditors from three countries, coupled with a sample of EAs, to the best of the authors’ knowledge, this study is the first to examine differences in EA reliance across the IAF’s primary roles. The study relies on data from three European countries, which differs from prior EA reliance literature with a largely North American focus. Further, comparison between perceptions of EAs and CAEs is a novel approach and this paper’s findings suggest that perceptions of CAEs could be a reliable proxy for EA-intended behavior.