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The objective of the paper is to explore and evaluate practical accounting education to find its weaknesses and suggest avenues to build strengths which will provide the market with effective accountants from the universities (the primary source of accountants).

The study uses semi-structured interviews to understand and extract the study problem and build the questionnaire; the final step is to analyse and interpret the questionnaire results based on structured interviews, dividing the research community into professors and market elements, business managers and university graduates.

The market has provided a negative evaluation of practical education. Reasons include a shortage of instructors with professional experience; curriculums that lack the topic of professional and ethical skills; and internships if provided, with unsatisfactory results. The study suggests accounting simulation labs as a reasonable substitution for the placement year (internship) if the labs are qualified and the internship results unsatisfactory.

This article is based on a multiregional research community, making results transferable to any country that faces a lack of professional accounting education. The applied evaluation method is capable of use by any other field in the business industry since accounting is part of this industry.

China has proposed two-stage goals of carbon peaking by 2030 and carbon neutralization by 2060. The carbon emission reduction effect of the power industry, especially the thermal power industry, will directly affect the progress of the goal. This paper aims to reveal the spatial-temporal characteristics and influencing factors of carbon emission efficiency of the thermal power industry and proposes policy suggestions for realizing China’s carbon peak and carbon neutralization goals.

This paper evaluates and compares the carbon emission efficiency of the thermal power industry in 29 provinces and regions in China from 2014 to 2019 based on the three-stage slacks-based measure (SBM) of efficiency in data envelopment analysis (DEA) model of undesired output, excluding the influence of environmental factors and random errors.

Empirical results show that during the sample period, the carbon emission efficiency of China’s thermal power industry shows a fluctuating upward trend, and the carbon emission efficiency varies greatly among the provincial regions. The carbon emission efficiency of the interregional thermal power industry presents a pattern of “eastern > central > western,” which is consistent with the level of regional economic development. Environmental factors such as economic level and environmental regulation level are conducive to the improvement of carbon emission efficiency of the thermal power industry, but the proportion of thermal power generation and industrial structure is the opposite.

This paper adopts the three-stage SBM–DEA model of undesired output and takes CO₂ as the undesired output to reveal the spatial-temporal characteristics and influencing factors of carbon emission efficiency in China’s thermal power industry. The results provide a more comprehensive perspective for regional comparative evaluation and influencing factors of carbon emission efficiency in China’s thermal power industry.

The purpose of the work is to provide a comprehensive review of the digital image correlation (DIC) technique for those who are interested in performing the DIC technique in the area of manufacturing.

No methodology was used because the paper is a review article.

no fundings.

Herein, the historical development, main strengths and measurement setup of DIC are introduced. Subsequently, the basic principles of the DIC technique are outlined in detail. The analysis of measurement accuracy associated with experimental factors and correlation algorithms is discussed and some useful recommendations for reducing measurement errors are also offered. Then, the utilization of DIC in different manufacturing fields (e.g. cutting, welding, forming and additive manufacturing) is summarized. Finally, the current challenges and prospects of DIC in intelligent manufacturing are discussed.

Employees facing challenges in their careers – i.e. female, migrant, elderly and lower-educated employees – might expect job searches to have a low likelihood of success and might therefore more often stay in unsatisfactory positions. The goal of this study is to discover inequalities in job mobility for these employees.

We rely on a large sample of Dutch public sector employees (N = 30,709) and study whether employees with challenges in their careers are hampered in translating job dissatisfaction into job searches. Additionally, we assess whether this is due to their perceptions of labor market alternatives.

Findings show that non-Western migrant, elderly and lower-educated employees are less likely to act on job dissatisfaction than their advantaged counterparts, whereas women are more likely than men to do so. Additionally, we find that although they perceive labor market opportunities as limited, this does not affect their propensity to search for different jobs.

This paper is novel in discovering inequalities in job mobility by analyzing whether employees facing challenges in their careers are less likely to act on job dissatisfaction and therefore more likely to remain in unsatisfactory positions.

Metropolitan areas suffer from frequent road traffic congestion not only during peak hours but also during off-peak periods. Different machine learning methods have been used in travel time prediction, however, such machine learning methods practically face the problem of overfitting. Tree-based ensembles have been applied in various prediction fields, and such approaches usually produce high prediction accuracy by aggregating and averaging individual decision trees. The inherent advantages of these approaches not only get better prediction results but also have a good bias-variance trade-off which can help to avoid overfitting. However, the reality is that the application of tree-based integration algorithms in traffic prediction is still limited. This study aims to improve the accuracy and interpretability of the models by using random forest (RF) to analyze and model the travel time on freeways.

As the traffic conditions often greatly change, the prediction results are often unsatisfactory. To improve the accuracy of short-term travel time prediction in the freeway network, a practically feasible and computationally efficient RF prediction method for real-world freeways by using probe traffic data was generated. In addition, the variables’ relative importance was ranked, which provides an investigation platform to gain a better understanding of how different contributing factors might affect travel time on freeways.

The parameters of the RF model were estimated by using the training sample set. After the parameter tuning process was completed, the proposed RF model was developed. The features’ relative importance showed that the variables (travel time 15 min before) and time of day (TOD) contribute the most to the predicted travel time result. The model performance was also evaluated and compared against the extreme gradient boosting method and the results indicated that the RF always produces more accurate travel time predictions.

This research developed an RF method to predict the freeway travel time by using the probe vehicle-based traffic data and weather data. Detailed information about the input variables and data pre-processing were presented. To measure the effectiveness of proposed travel time prediction algorithms, the mean absolute percentage errors were computed for different observation segments combined with different prediction horizons ranging from 15 to 60 min.

The purpose of this research is to investigate the effects of service recovery experiences on customer engagement in negative word-of-mouth (WOM) in the hotel industry and explore the psychological motives and mediating mechanisms driving consumer behavior.

A scenario-based experimental design on Qualtrics was used, with a pre-test (N = 200). The main study data were collected using Amazon's Mechanical Turk platform.

Findings reveal that negative service experiences lead to higher engagement in negative WOM compared to positive and satisfactory recovery service experiences. Even well-executed recovery efforts may not completely eliminate negative WOM. The mediating role of emotional responses is substantiated, as heightened negative service experiences result in more intense negative emotional responses, leading to increased engagement in negative WOM.

The study emphasizes the importance of service recovery strategies and the need for businesses to consistently strive for exceptional service quality. It also highlights the complexity of customer reactions to service experiences, suggesting that further research is needed to explore the factors that minimize negative WOM across various service contexts.

This paper aims to evaluate the relation between governance and financial efficiency of credit unions in Brazil. The study shows how poor financial efficiency in credit unions may result from undesirable configurations in executive management and other variables related to governance.

The study develops an innovative methodology to classify credit unions according to the level of governance using indicators of representativeness and participation, leadership, management and supervision. This methodology integrates the use of multiple correspondence and cluster analysis. The study then applies stochastic frontier models to analyze how governance affects the indicators of financial efficiency.

The results highlight that better governance substantially increases the efficiency of credit unions in terms of a higher level of credit operations per institution.

The paper uses a pioneering survey applied by the Central Bank to almost the total population of credit unions in Brazil. The results highlight how to operationalize a subjective and broad concept related to cooperative governance to identify the remarkable impacts of good governance practices on the financial efficiency of credit unions.

The purpose of the article is (1) to find out whether students instructed with Computer-Assisted Simulation Learning Games (CASLGs) will improve in their scholarly learning outcomes and (2) to ascertain if the biological construct of sex will affect students scholarly learning outcomes.

The study was experimental. The study sample comprised 120 students from four schools. The study instrument was the Scholarly Learning Outcomes Test (SLOT), drawn from the school syllabus. The study lasted six weeks. Before the experimentation, the students were pre-tested using the Scholarly Learning Outcomes Test (SLOT. After which, students were posttested to ascertain students' scholarly learning outcomes. The statistical mean was employed to analyse data generated from the pretest and posttest to provide answers for the research questions, while analysis of covariance (ANCOVA) statistics was utilised to test the hypotheses.

The results show that CASLGs improved students' scholarly learning outcomes more than the face-to-face instructional approach and the biological construct of sex is of no effect on students' scholarly learning outcomes.

First, in performing the study, the regular instructors/teachers were utilized. The personalities of these instructors were not considered or scrutinized; this might have impacted the research outcomes.

Implications for future studies: One advantage is that it is part of a bigger initiative in which there are extra sources of data to study. This information or data from this study would help to throw further light on the predictors of student scholarly learning outcomes in the near future. One implication of this study rests on the confirmation that activity-based strategies such as CASLGs enhance students' scholarly learning outcomes.

This study is a product of the author’s doctoral thesis. It is the outcome of the investigation carried out by the author for the award of PhD; therefore, it is original. The study’s results are of immense value because they contributed to knowledge in the area of computer-based learning games.

The economic development of Bangladesh is heavily reliant on the banking industry, yet it faces numerous hurdles, including liquidity issues, capital shortages, non-performing loans, inefficiencies and so on. Therefore, this study investigated the performance and efficiency of scheduled banks (state-owned, private commercial, foreign commercial and specialized banks) operating in Bangladesh.

The research was conducted using secondary data from annual reports of banks. The CAMELS rating system and Data Envelopment Analysis (DEA) methods were employed to measure the performance and efficiency of banks, respectively.

In the overall bank rankings, results revealed that foreign commercial Standard Chartered Bank and state-owned Sonali Bank Limited came in first and last position, respectively. Among the four categories of banks, foreign commercial banks were the best performer, while state-owned banks were the worst. Only two banks, i.e. Citibank NA and HSBC Bank, were scale efficient while the remaining banks were inefficient. In terms of performance and efficiency, state-owned and specialized banks were deemed wanting.

This study proposes recommendations to the policymakers that could lead to more effective tactics for improving the banking industry's performance and efficiency.

As far as the authors are concerned, this study presents empirical evidence on the performance and efficiency of different types of banks and explores comparisons among them, which has never been done to this extent in the country before.

CEOs’ (chief executive officer) term of office may explain discretionary accruals as a result of opportunistic behavior arising during certain periods of the term of office. Therefore, CEOs, in their early years of office, have incentives to report results that meet market expectations. In turn, CEOs in their senior year may be motivated to use discretionary accruals to gain private benefits. In this scenario, corporate governance mechanisms play an important role in monitoring relationships. Hence, the purpose of this study is to verify the influence of monitoring mechanisms on the relationship between CEOs’ term of office and discretionary accruals.

Descriptive statistics, multiple cross-sectional regression to estimate the accruals and regression of panel data to test the hypotheses were used. The sample comprised 195 companies listed on BM&FBovespa.

The results indicated that CEOs’ long term of office has a negative impact on the level of discretionary accruals, and thus, Brazilian CEOs with a longer term of office tend to establish a certain reputation in the stock market. On the other hand, it is concluded that CEOs’ intentions, in the first years of term, are positively related to the use of accruals and that the monitoring mechanisms can minimize these CEOs’ opportunistic practices.

The results broaden the literature on corporate governance, pointing that different systems of variable remuneration may influence CEOs’ willingness to manage results in their last year of term.