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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 paper aims to investigate the impact of family involvement in ownership and governance on the quality of internal auditing.

Leveraging a hand-collected data set of listed family firms from 2014 to 2020, this study uses regression analyses to investigate the impact of family ownership, family involvement on the board, family CEO and the generational stage of the family business on the quality of internal auditing.

The results provide evidence that family ownership is positively associated with the quality of internal auditing, while later generational stages of family businesses have the opposite effect. Additional analyses reveal that the presence of a sustainability board sub-committee moderates the relationship between generational stages of family businesses and the quality of internal auditing function.

This paper does not consider country-institutional factors and other potentially family-related antecedents or governance factors that may affect the quality of internal auditing.

The results are informative for investors and non-family stakeholders interested in understanding under which conditions family-related factors influence the quality of internal auditing functions.

This study offers fresh evidence regarding the relationship between family-related factors and the quality of internal auditing and board sub-committees that moderate such a relationship in family businesses.

Due to the miniaturization of electronic devices, the increased current density through solder joints leads to the occurrence of electromigration failure, thereby reducing the reliability of electronic devices. The purpose of this study is to propose a finite element-artificial neural network method for the prediction of temperature and current density of solder joints, and thus provide reference information for the reliability evaluation of solder joints.

The temperature distribution and current density distribution of the interconnect structure of electronic devices were investigated through finite element simulations. During the experimental process, the actual temperature of the solder joints was measured and was used to optimize the finite element model. A large amount of simulation data was obtained to analyze the neural network by varying the height of solder joints, the diameter of solder pads and the magnitude of current loads. The constructed neural network was trained, tested and optimized using this data.

Based on the finite element simulation results, the current is more concentrated in the corners of the solder joints, generating a significant amount of Joule heating, which leads to localized temperature rise. The constructed neural network is trained, tested and optimized using the simulation results. The ANN 1, used for predicting solder joint temperature, achieves a prediction accuracy of 96.9%, while the ANN 2, used for predicting solder joint current density, achieves a prediction accuracy of 93.4%.

The proposed method can effectively improve the estimation efficiency of temperature and current density in the packaging structure. This method prevails in the field of packaging, and other factors that affect the thermal, mechanical and electrical properties of the packaging structure can be introduced into the model.

Corporate social responsibility (CSR) is viewed as a differentiating strategy that wins over stakeholders’ confidence. Due to the potential strategic and positive effects on businesses, the study of CSR and its relationship to competitiveness has gained relevance. While studies have examined the impact of CSR activities on firm competitiveness, the findings so far remain contradictory. Further research on the underlying processes/mechanisms that explain how CSR contributes to competitiveness remains scarce. Accordingly, this study aims to look into the link between CSR and competitiveness with a focus on Asian business and management studies.

By using a bibliometric approach, this paper aims to provide a review of the state-of-the-art research on the linkage between CSR and competitiveness in Asian context. The sample for this research included all 538 studies from the period of 2001–2023 in the Scopus database. A bibliometric study included both co-occurrence and co-citation analysis.

The study’s findings made significant contributions by identifying seven distinct clusters of co-occurrences. Using co-citation, three journals-based co-citation clusters and another three authors-based co-citation clusters are identified. The findings show how processes/mechanisms such as – accountability, multi-stakeholder dialogue/engagement, resource generation, emphasizing sustainable development goals and emerging markets, redefining strategy, cultivating value/vision and CSR leadership – are increasing in importance.

Overall, the authors argue that CSR-led competitiveness is indeed one of the key drivers for improved sustainability performance of a firm.

Based on findings, a conceptual framework has been proposed highlighting different processes and mechanisms that influence the CSR-led competitiveness – outcomes relationship.

This paper aims to deepen the understanding of the relationship between global economic uncertainty and price volatility, specifically focusing on commodity, industrial materials and energy price indices as proxies for global inflation, analyzing data from 1997 to 2020.

The dynamic conditional correlation generalized autoregressive conditional heteroscedasticity model is used to study the dynamic relationship between variables over a while.

The results demonstrated a positive relationship between commodity prices and the global economic policy uncertainty (GEPU). Except for 1999–2000 and 2006–2008, the results of the energy price index model were very similar to those of the commodity price index. A predominant positive relationship is observed focusing on the connection between GEPU and the industrial material price index. The results of the pairwise Granger causality reveal a unidirectional relationship between the GEPU – the Global Commodity Price Index – and the GEPU – the Global Industrial Material Price Index. However, there is bidirectional causality between the GEPU – the Global Energy Price Index. In sum, changes in price indices can be driven by GEPU as a political factor indicating unfavorable economic conditions.

This paper provides a deeper understanding of the role of global uncertainty in the global inflation process. It fills the gap in the literature by empirically investigating the dynamic movements of global uncertainty and the three most important groups of prices.

The construction industry is widely recognized as one of the most hazardous sectors in the world. Despite extensive research on safety management, a critical issue remains that insufficient attention is devoted to safety practices in rural areas. Notably, accidents frequently occur during the construction of rural self-built houses (RSH) in China. Safety management tends to be overlooked due to the perceived simplicity of the construction process. Furthermore, it is essential to acknowledge that China currently lacks comprehensive laws and regulations governing safety management in RSH construction. This paper aims to analyze the behavior of key stakeholders (including households, workmen, rural village committee and the government) and propose recommendations to mitigate safety risks associated with RSH construction.

This paper applies evolutionary game theory to analyze the symbiotic evolution among households, workmen and rural village committee, in situations with or without government participation. Additionally, numerical simulation is utilized to examine the outcomes of various strategies implemented by the government.

Without government participation, households, workmen, and rural village committee tend to prioritize maximizing apparent benefits, often overlooking the potential safety risks. Numerical simulations reveal that while government involvement can guide these parties towards safer decisions, achieving the desired outcomes necessitates the adoption of reasonable and effective strategies. Thus, the government needs to offer targeted subsidies to these stakeholders.

Considering that during the construction phase, stakeholders are the main administrators accountable for safety management. However, there exists insufficient research examining the impact of stakeholder behavior on RSH construction safety. This study aims to analyze the behavior of stakeholders about how to reduce the safety risks in building RSH. Thus, the authors intend to contribute to knowledge in this area by establishing evolutionary game model. Firstly, this study carried out a theoretical by using tripartite evolutionary game to reveal the reasons for the high safety risk during building RSH. Practically, this research points out the important role of households, workmen and rural village committee in improving safety management in rural areas. Besides, some suggestions are proposed to the government about how to reduce construction safety risks in rural areas.

This study aims to identify the decision-making process involved in the purchase of organic wine from consumer values to attitudes to behavioural intention towards organic wine via the value–attitude–behaviour (VAB) model. Involvement in wine is also taken into consideration.

The data were collected using a snowball sampling method and a closed-ended questionnaire. A total of 209 responses were analysed. Linear regression and PROCESS Macro on SPSS were used to perform data analysis.

Both biospheric-altruistic values and egoistic values are positively associated with attitudes towards organic wine. Attitude is found to mediate the relationship between biospheric-altruistic/egoistic values and behavioural intention. Egoistic values are found to significantly predict behavioural intention in the organic wine purchase context. Involvement was found to moderate the relationship between egoistic values and attitudinal loyalty.

This study identifies the decision-making hierarchy from consumer values to attitudes to behavioural intention, theoretically confirming the robustness of the VAB model in the organic wine consumption context. It also makes a practical contribution by indicating the marketing emphasis of organic wine and segmenting potential consumers according to their values and levels of wine involvement.

The expansion of bike-sharing enterprises has led to a series of social problems, and due to the high breakage rate and high recycling cost of bike sharing, enterprises are reluctant to maintain them. Therefore, government regulation does play a leading role in maintaining bike sharing. This study’s purpose is to investigate how the government should regulate the bike-sharing enterprises that maintain bicycles.

Firstly, the authors assume that there is only one bike-sharing enterprise and establish a game model that the government regulates the enterprise. Furthermore, the authors extend the model to the case that there are two competing enterprises in the market. Finally, through numerical analysis, the influence of various factors on the government strategy and revenue is analyzed.

The authors find that the regulatory probability of two enterprises are regulated by the government is larger than that of one enterprise. When two bike-sharing enterprises compete without government regulation, both will choose the non-self-discipline strategy, thus falling into the Prisoner's Dilemma. If the government regulates them, then both enterprises will choose the self-discipline strategy. Finally, through numerical analysis, it is found that the self-discipline behavior of bike-sharing enterprises is related to the government regulatory probability, the cost of self-discipline, the probability of being reported and the penalties. Interestingly, the cost of government regulation will not affect the regulation probability of government.

This research provides a theoretical reference for the government to make the regulation strategies of bike sharing and achieve the sustainable development of bike sharing.

This research provides a theoretical reference for the government to make the regulation strategies of bike sharing and achieve the sustainable development of bike sharing.

The impact of mankind on the environment and the usage of natural resources might be influenced by spirituality, through the consciousness of creating an improved moral sense regarding the consequences of human activities and the necessity to alter these to achieve sustainable development. However, the spiritual element in the form of ecospirituality (ES) has not been sufficiently considered in pro-environmental studies as it relates to the influence of green training (GT) on voluntary workplace green behaviour (VWGB) in the construction sector. This study aims to determine the effect of GT on VWGB and the mediating effect of ES on the relationship between GT and VWGB on construction projects.

This study’s data were gathered through a questionnaire survey of construction site managers and project managers by adopting the probability sampling method. 249 appropriately completed questionnaires were returned. The data obtained were analysed by means of the partial least squares structural equation modelling technique (PLS-SEM).

The outcomes of the study show that GT has a significant positive impact on VWGB, while ES has a significant mediating effect on the relationship between GT and VWGB, both supporting the study’s hypotheses.

These findings point to the fact that the hitherto conflicting results reported in earlier studies on the GT–VWGB relationship can be attributed to the lack of consideration given to ES. Hence, special attention should be given to ES.

This research presents actions to enhance the transformation of GT into VWGB by giving due consideration to ES, which was not taken into account in previous studies.

As the crucial support component of the propeller power system, the reliability of the operation of submersible pumps is influenced by the lubrication performance of water-lubricated thrust bearings. When the water-lubricated thrust bearings are under start-stop or heavy load conditions, the effect of surface morphology is crucial as the mixed lubrication regime is encountered. This paper aims to develop one mixed lubrication model for the water-lubricated thrust bearings to predict the effects of surface skewness, kurtosis and roughness orientation on the loading carrying capacity and tribological behavior.

This paper developed one improved mixed lubrication model specifically for the water-lubricated thrust bearing system. In this model, the hydrodynamic model was improved by using the height of the rough surface and its probability density function, combined with the average flow model. The asperity contact model was improved by using the equation for the Pearson system of frequency curves to characterize the non-Gaussian aspect of surface roughness distribution.

According to the results, negative skewness, large kurtosis and lateral surface pattern can improve the tribological performance of water-lubricated thrust bearings. Optimizing the surface morphology is a reasonable design method that can improve the performance of water-lubricated thrust bearings.

In this paper, one mixed lubrication model specifically for the water-lubricated thrust bearing with the effect of surface roughness into consideration was developed. Based on the developed model, the effect of surface morphology on tribological behavior can be evaluated.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-08-2023-0247/