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As of now, the digitization of corporate finance presents a paradigm shift in business strategy, innovation, financing and managerial capability around the globe. However, the prevailing finance scholarly works hardly document the impact of the digitalization of corporate finance on firm performance with global evidence and analysis. Hence, the contemporary debate on whether firm performance is genuinely stimulated because of the digitalization of corporate finance or not has been a pressing issue in the relevant literature. Therefore, the purpose of this study is to identify a data-driven, concise response to an unaddressed finance issue if the performance of high-digitalized firms (HDFs) outperforms that of their counterpart peers for wealth maximization.

The first stage test models examine the firm performance of relatively high-digitalized firms as opposed to low-digitalized firms based on the system GMM. The second stage test of the probabilistic (logit) model infers that the probability of being HDFs explores because of better performance. Then, the authors execute robust checks based on the different quantile regressions and Z-score-based system GMM. In addition, the authors recheck and present the test results of the fixed effect and random effect to capture time-invariant individual heterogeneity. Finally, the supplementary test findings of firms’ credit strength by using Altman five- and four-factor Z-score models are presented.

By using cross-country panel analysis as 15 years’ test bed for HDFs and low digitalized firms (LDFs), the test results indicate that the overall firm performance of a digitalized firm is significantly better than that of a non-digitalized firm. The global evidence documents that HDFs are exposed to higher values and are financially more persistent as compared to their counterparts. The finding is remarkably concomitant across several possible subsample analysis, such as country–industry–size–period analysis.

This study can be remarkably effective in encouraging managers, policymakers and investors to acknowledge the need for adopting the required digitalization. Overall, this original study addresses a core research gap in the corporate finance literature and remarkably provides further direction to rethink the assumptions of firm digitalization on additive value and thereby identify optimal decisions for wealth maximization. The findings also imply that investors require an additional risk premium if they invest in relatively LDFs, which have relatively lower market value and weaker firm performance.

From an investors point of view, the academic novelty contributes to an innovative and unsettled issue on the impact of digitization of corporate finance on firm performance because there is a new question of high or low digitization of corporate finance in the global market. Hence, this academic novelty contributes to sharing global evidence of the digitalization of corporate finance and its effect on firm performances. In addition, an intensive critical review analysis is conducted based on the most recent and relevant scholarly works published in the top-tier journals of finance and business stream to fix the hypothesis. Overall, this study addresses a core research gap in the corporate finance literature; notably provides further direction to rethink firm digitalization; and thereby identifies optimal decisions for shareholders’ wealth maximization.

The study’s objective was to ascertain the connection between secondary school students' test anxiety, academic self-concept, motivation and academic performance in mathematics. The difference between the academic performances of male and female secondary school students who exhibit high and low test anxiety, academic self-concept and motivation levels in mathematics.

Four hypotheses and four research questions were adopted. The design is a correlation. 42,299 mathematics students in senior school year two (SS2) made up the research population. A sample of 1,650 students was selected through a multi-stage sampling procedure. The main instruments used were the Mathematics Test Anxiety Questionnaire (MTAQ), Academic Self-Concept Questionnaire (ASQ) and Academic Motivation Questionnaire (AMQ) and students’ math scores. These instruments were validated by three experts and the reliability coefficients of 0.69, 0.68 and 0.68 were obtained for MTAQ, ASQ and AMQ, respectively, using Cronbach alpha. Pearson product moment correlation was used to analyze the data.

The study’s results showed a correlation between secondary school students' academic performance in mathematics and test anxiety, academic self-concept and motivation. There was a significant difference between secondary school male and female students' test anxiety; there was a significant difference between secondary school male and female students' self-concept and academic performance in mathematics, and there was a significant difference between secondary school male and female students' motivation and academic performance in mathematics.

The major contribution of this study is to investigate the connection between test anxiety, academic self-concept motivation and students’ mathematics performance. There is a difference between psychological variables, gender and mathematics performance.

This study examines the test's predictive validity of English language performance and compares test constructs to identify the most effective predictors of English language performance.

Data were collected and analysed from test scores of students enrolled in the foundation year (N = 84) and level 2 (N = 127) in the faculty of English at a Saudi university using correlation and regression tests.

The findings revealed that the General Aptitude Test (GAT) is effective in predicting English performance for students in level 2 and that the error detection task is the most effective predictor of performance in English reading.

The study provides support for the validity of the GAT as a university admission requirement for English language courses in the Arabic-speaking world.

This study examines the GAT's power using a fine-grained approach by deriving scores from its breakdown constructs to predict the performance of English skills at the university level.

The purpose of this study is to generate knowledge about assessment methods in blended business education, which have become increasingly important to establish sustainable assessment practices that support knowledge acquisition for undergraduate students in business administration at a Colombian university.

For the analysis, a two groups comparison was performed using a nonequivalent control group design with a sample of 420 students. As this study wants to find insights to improve the knowledge on assessment topics in marketing research (MR) education, it was focused on the students from the business administration program. This study also uses individual scores from the state test as prior cognitive scores and the high school classification provided by the National Ministry of Education in Colombia (2012).

It was found that the variables that best predict performance on the MR course examinations were the mathematics skills and reading comprehension scores on the state test. The study also showed a better performance of female students on both assessment methods. There were no significant differences between the assessment methods or among the high school levels.

One of the limitations of this study is the limited number of items on the tests. Additionally, the authors recommend conducting an analysis of the differences between the testing items to provide a detailed explanation of students’ performance when comparing computer-based testing and paper-and-pencil testing.

Further design of teaching material and assessments online and offline, based on local and regional marketing problems, is suggested. As the current text and readings are more oriented to the English-speaking contexts, most of the problems presented are oriented to multinational companies and brands.

Insights into the skills required for future jobs provide valuable guidance (World Economic Forum, 2020). Essential skills for emerging roles, like data scientists, can find robust support within the MR course. To further enrich in-class and online exercises with Excel and SPSS, Colombian educators can leverage data sets obtained from sources like the national statistics office and international market intelligence databases available through the university’s library, including Passport and Statista. Engaging with authentic data sets provides students with a more profound understanding of practical applications in MR.

This approach facilitates the identification of key variables, such as assessment and cognitive abilities in math and reading, which predict students’ knowledge acquisition in MR. It not only offers insights into the relevant factors influencing learning in MR but also provides valuable feedback. Additionally, it suggests potential avenues for future research in this field.

This pilot study aims to examine the prevalence of test anxiety and its interplay with attitudes, confidence, efficacy, academic performance and socio-demographic factors within the domain of science, technology, engineering and mathematics (STEM) courses.

The authors employed a quantitative, cross-sectional design with 549 sixth-grade students from public lower secondary schools in Prishtina, Kosovo, using the Student Attitudes Toward STEM Survey (S-STEM) for middle/high schools and the test anxiety questionnaire.

Over 70% of Kosovo's sixth-grade students reported moderate to severe test anxiety. The age of students was found to be inversely related to academic performance in STEM. The father's employment was associated with favorable STEM attitudes, confidence, efficacy and academic performance. Having a personal study environment was connected with favorable STEM attitudes, confidence and efficacy in STEM, whereas access to technology was associated with positive academic performance. Test anxiety, academic performance and personal study space predicted students' attitudes, confidence and efficacy in STEM and 21st-century learning.

Educational institutions should prioritize student well-being. By addressing test anxiety, these institutions can create supportive learning environments that improve attitudes, confidence and efficacy in STEM fields. These efforts are crucial for STEM career development and student success in the 21st-century workforce.

The current study findings contribute to a deeper understanding of the factors influencing STEM student engagement and performance, highlighting the importance of addressing test anxiety for positive learning outcomes while emphasizing the need to consider socio-economic and contextual factors in education.

This study aims to dissect the multifaceted impact of ISO/IEC 17025 accreditation, specifically within civil engineering testing and calibration laboratories. To achieve this, it intends to explore several key objectives: identifying the prominent benefits of accreditation to laboratory performance, understanding the advantages conferred through participation in proficiency testing schemes, assessing the role of accreditation in enhancing laboratory competitiveness, examining the primary challenges encountered during the accreditation process, investigating any discernible adverse effects of accreditation on laboratory performance and evaluating whether the financial cost of accreditation justifies the resultant profitability.

This study employs a qualitative approach through semi-structured interviews with 23 industry professionals—including technical managers, quality managers, external auditors and clients. Thematic analysis, guided by Braun and Clarke’s six-stage paradigm, was utilized to interpret the data, ensuring a comprehensive understanding of the accreditation’s impact.

Findings reveal that accreditation significantly enhances operational processes, fosters quality awareness and facilitates continuous improvement, contributing to greater client satisfaction. In addition, standardized operations and rigorous quality controls further result in enhanced performance metrics, such as staff capability and measurement accuracy. However, the study also uncovers the challenges of accreditation, including high resource costs and bureaucratic hurdles that can inhibit innovation and slow routine operations. Importantly, the research underscores that the impact of accreditation on profitability is not universal, but contingent upon various factors like sector-specific regulations and market demand. The study also highlights sector-specific variations in the role of accreditation as a marketing tool and differing perceptions of its value among clients. It further emphasizes the psychological stress of high-stakes evaluations during audits.

This study represents the first in-depth investigation into the impact of ISO/IEC 17025 accreditation on civil engineering testing and calibration laboratories, directly contributing to the enhancement of their quality and operational standards. Providing actionable insights for laboratories, it underscores the importance of weighing accreditation costs and benefits and the necessity for a tailored approach to the unique market and regulatory landscapes they operate in.

Restrictions imposed on freedom of movement and interaction with others due to the COVID-19 pandemic have had the effect of causing many people, especially students, to become addicted to social media. This study aims to investigate the effect of social media addiction on the academic performance of Sri Lankan government university students during the COVID-19 pandemic.

A convenience sampling technique was used to conduct a quantitative cross-sectional survey. The survey involved 570 respondents from nine state universities in Sri Lanka. The raw data from the completed questionnaires were coded and processed using SPSS for descriptive and inferential statistical analysis.

The findings of this study indicated that the overall time spent on social networking increased dramatically during COVID-19. Based on the results, this study found that there was no association between the time spent on social media and the academic performance of students before COVID-19 came on the scene. However, a significant association was found between the time spent on social media and students’ performance during the pandemic. The authors concluded that overblown social media use, leading to addiction, significantly negatively affects academic performance.

This study helps to understand the impact of social media use on the academic performance of students during COVID-19. Restrictions imposed by COVID-19 have changed the typical lifestyle of the students. Therefore, social media usage should be reassessed during the COVID-19 pandemic. The findings of the study will comprise these new insights, and they may well show how to adapt social media to contribute to academic work in meaningful ways.

The purpose of this research is to achieve multi-task autonomous driving by adjusting the network architecture of the model. Meanwhile, after achieving multi-task autonomous driving, the authors found that the trained neural network model performs poorly in untrained scenarios. Therefore, the authors proposed to improve the transfer efficiency of the model for new scenarios through transfer learning.

First, the authors achieved multi-task autonomous driving by training a model combining convolutional neural network and different structured long short-term memory (LSTM) layers. Second, the authors achieved fast transfer of neural network models in new scenarios by cross-model transfer learning. Finally, the authors combined data collection and data labeling to improve the efficiency of deep learning. Furthermore, the authors verified that the model has good robustness through light and shadow test.

This research achieved road tracking, real-time acceleration–deceleration, obstacle avoidance and left/right sign recognition. The model proposed by the authors (UniBiCLSTM) outperforms the existing models tested with model cars in terms of autonomous driving performance. Furthermore, the CMTL-UniBiCL-RL model trained by the authors through cross-model transfer learning improves the efficiency of model adaptation to new scenarios. Meanwhile, this research proposed an automatic data annotation method, which can save 1/4 of the time for deep learning.

This research provided novel solutions in the achievement of multi-task autonomous driving and neural network model scenario for transfer learning. The experiment was achieved on a single camera with an embedded chip and a scale model car, which is expected to simplify the hardware for autonomous driving.

This study aims to compare machine learning models, datasets and splitting training-testing using data mining methods to detect financial statement fraud.

This study uses a quantitative approach from secondary data on the financial reports of companies listed on the Indonesia Stock Exchange in the last ten years, from 2010 to 2019. Research variables use financial and non-financial variables. Indicators of financial statement fraud are determined based on notes or sanctions from regulators and financial statement restatements with special supervision.

The findings show that the Extremely Randomized Trees (ERT) model performs better than other machine learning models. The best original-sampling dataset compared to other dataset treatments. Training testing splitting 80:10 is the best compared to other training-testing splitting treatments. So the ERT model with an original-sampling dataset and 80:10 training-testing splitting are the most appropriate for detecting future financial statement fraud.

This study can be used by regulators, investors, stakeholders and financial crime experts to add insight into better methods of detecting financial statement fraud.

This study proposes a machine learning model that has not been discussed in previous studies and performs comparisons to obtain the best financial statement fraud detection results. Practitioners and academics can use findings for further research development.

This study aims to test mutual fund superiority, comparing the performance of 646 Islamic mutual funds with 475 ethical funds and conventional proxies.

This study uses statistical methods including paired t-statistics of independent samples, one-way Bonferroni test–analysis of variance–F-statistic for testing means equality, the chi-squared test for median equality and regression models corrected for heteroscedasticity. These methods are used to identify superiority of mutual funds and to validate the significance of the results.

The findings confirm the superiority of conventional funds over ethical funds and ethical funds over Islamic funds. Both ethical and Islamic funds, however, outperform conventional proxies during some recessionary periods. Moreover, stronger performance is recorded for Islamic funds in Europe and North America regions and across age and asset allocation categories, but limited support for reversal fund size, composition focus and reversed price effect.

These findings should assist investors when deciding to invest and motivate Islamic and ethical funds to improve their portfolio formation and asset allocation strategies set by their professional managers.

The originality of this study is in its comprehensive approach in that it compares the performance of funds after accounting for such characteristics as fund objectives, size, age, asset allocation, geographical investment focus, fund composition focus, share price levels and the effect of global crises. This study approach is not only original and productive in documenting Islamic funds’ performance for the past three decades (1990–2022) but can also update the literature on these characteristics collectively and individually.