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The importance of metacognitive awareness in learning, on the one hand, and the necessity of considering demographic variables, on the other hand, have encouraged the researchers to conduct this research. This research aims to initially determine the relationship between the level of metacognitive awareness and demographic variables of students from three Lithuanian universities, such as age, gender and area of study.

The quantitative research strategy was applied in this study using the survey with the students scoring the Metacognitive Awareness Inventory (MAI). The research involved 296 students from three universities in Lithuania. Data were analysed using statistical analysis methods to compare different groups of subjects according to selected criteria.

It became evident that two demographic variables of age and the field of study had a relationship with knowledge of cognition. Conditional knowledge had a positive relation with the variables of age and the field of study. Procedural knowledge was the second area which had a relation with the area of this study. Therefore, it maybe be concluded that under specific circumstances, declarative and procedural knowledge is at the same level of performance while conditional knowledge revealed the highest relation with metacognitive awareness. Furthermore, no statistically significant difference was found with gender in all metacognitive subcomponents despite the initial assumption.

One of the limitations of this study is that the research did not address the actual application of metacognitive strategies during teaching and learning. The research would benefit from in-depth class observation and triangulation of data from various sources. The teaching model should be tested in a larger population to obtain aggregated results for a vast population.

Results are significant in identifying students' cognitive abilities which can be attributed to various factors such as creativity, which in turn may efficiently foster students' potential. Metacognitive awareness can be developed by explicitly informing students about the importance of metacognition and life-long learning. Lecturers' role-modelling induce students to continuously assess, monitor, plan and reflect on their own learning process as well as to recognize cognition along with metacognitive prompts, questions, checklists, reports and discussion of topics in the learning process.

Forecasting population movement trends is crucial for implementing effective policies to regulate labor force growth and understand demographic changes. Combining migration theory expertise and neural network technology can bring a fresh perspective to international migration forecasting research.

This study proposes a conditional generative adversarial neural network model incorporating the migration knowledge – conditional generative adversarial network (MK-CGAN). By using the migration knowledge to design the parameters, MK-CGAN can effectively address the limited data problem, thereby enhancing the accuracy of migration forecasts.

The model was tested by forecasting migration flows between different countries and had good generalizability and validity. The results are robust as the proposed solutions can achieve lesser mean absolute error, mean squared error, root mean square error, mean absolute percentage error and R² values, reaching 0.9855 compared to long short-term memory (LSTM), gated recurrent unit, generative adversarial network (GAN) and the traditional gravity model.

This study is significant because it demonstrates a highly effective technique for predicting international migration using conditional GANs. By incorporating migration knowledge into our models, we can achieve prediction accuracy, gaining valuable insights into the differences between various model characteristics. We used SHapley Additive exPlanations to enhance our understanding of these differences and provide clear and concise explanations for our model predictions. The results demonstrated the theoretical significance and practical value of the MK-CGAN model in predicting international migration.

This study aims to identify and explore different risk typologies associated with consumer acceptance of purchase recommendations from voice assistants (VAs).

Drawing on components of perceived risk, consumer trust theory, and consumption value theory, a research model was proposed and tested using structural equation modeling (SEM) with data from 482 voice shoppers.

The results reveal that, unlike risks associated with physical harm, privacy breaches, and security threats, a variety of other concerns—including financial, psychological, social, performance-related risks, time loss, and the overall perceived risks—significantly influence consumers' willingness to accept VAs purchase recommendations. The effect is mediated by trust in VA purchase recommendations and their perceived value. Different types of risk affect various consumption values, with functional value being the most influential. The model explains 58.6% of the variance in purchase recommendation acceptance and significantly elucidates the variance in all consumption values.

This study contributes crucial knowledge to understanding consumer decision-making processes as they increasingly leverage AI-powered voice-based dialogue platforms for online purchasing. It emphasizes recognizing diverse risk typologies associated with VA purchase recommendations and their impact on consumer purchase behavior. The findings offer insights for marketing managers seeking to navigate the challenges posed by consumers' perceived risks while leveraging VAs as an integral component of modern shopping environments.

Destinations to be able to compete with each other need to equip themselves with as many competitive advantages as possible. Tourists' loyalty to a destination is considered as a prominent competitive tool for destinations. Tourists' loyalty manifests itself in recommendation of the destination to others, repeat visit of the destination and willingness to revisit the destination. Although a plethora of studies have tried to define models to show the relation between loyalty and the antecedent factors leading up to it, few of them have tried to integrate these models with mathematical approaches for better understanding of loyalty behavior. The purpose of this paper is to integrate a tourist destination model with Bayesian Network in order to predict the behaviour of destination loyalty and its antecedent factors.

This paper has developed a probability model by the integration of a destination loyalty model with a Bayesian network (BN) which enables to predict and analyze the behavior of loyalty and its influential factors. To demonstrate the application of this framework, Tehran, the capital of Iran, was chosen as a destination case study.

The outcome of this research will assist in identifying the weak key points in the tourist destination area for giving insights to the marketers, businesses and policy makers for making better decisions related to destination loyalty. In the analysis process, the most influential factors were recognized as the travel environment image, natural/historical attractions and, with a lower degree, infrastructure image which help the decision maker to detect and reinforce the weak factors and put more effort in focusing on improving the necessary parts rather than the irrelevant parts.

The research identified all critical factors that have the most influence on destination loyalty while driving the associate uncertainty which is significant for the tourism industry. This resulted in better decision-making which is used to identify the impact of tourism destination loyalty.

The world of work is changing rapidly as a result of technology, with more workers being impacted by automation, the gig economy and temporary work contracts. This study focusses on how employees perceive their disruption knowledge and how this perception impacts their career planning, career satisfaction and training behaviors.

The authors use data from 1,516 employees across a broad range of industries and professions from the United States (n = 505), New Zealand (n = 505) and Australia (n = 506).

The authors find that an employee's knowledge and research into automation positively influence how employees plan their careers, their career satisfaction and their training behaviors. While career planning is positively related to career satisfaction and training behavior, career satisfaction is negatively related to training behaviors. The authors test mediation effects and find consistently significant indirect effects, and these findings are all largely replicated across the three countries.

This study highlights the importance of understanding the processes that employees go through when thinking about disruption knowledge, their careers and the impact on their training behaviors.

This paper examines the extreme dependence and asymmetric risk spillovers between crude oil futures and ten US stock sector indices (consumer discretionary, consumer staples, energy, financials, health care, industrials, information technology, materials, telecommunication and utilities) before and during COVID-19 outbreak. This study is based on the rationale that stock sectors exhibit heterogeneity in their response to oil prices depending on whether they are classified as oil-intensive or non-oil-intensive sectors and the possible time variation in the dependence and risk spillover effects.

The authors employ static and dynamic symmetric and asymmetric copula models as well as Conditional Value at Risk (VaR) (CoVaR). Finally, they use robustness tests to validate their results.

Before the COVID-19 pandemic, crude oil returns showed an asymmetric tail dependence with all stock sector returns, except health care and industrials (materials), where an average (symmetric tail) dependence is identified. During the COVID-19 pandemic, crude oil returns exhibit a lower tail dependency with the returns of all stock sectors, except financials and consumer discretionary. Furthermore, there is evidence of downside and upside risk asymmetric spillovers from crude oil to stock sectors and vice versa. Finally, the risk spillovers from stock sectors to crude oil are higher than those from crude oil to stock sectors, and they significantly increase during the pandemic.

There is heterogeneity in the linkages and the asymmetric bidirectional systemic risk between crude oil and US economic sectors during bearish and bullish market conditions; this study is the first to investigate the average and extreme tail dependence and asymmetric spillovers between crude oil and US stock sectors.

This study aims to analyze the efficient market hypothesis (EMH) of cryptocurrencies on multiple platforms by observing whether there is a discrepancy in the levels of efficiency between different exchanges. Additionally, EMH is tested in a multivariate way: whether the prices of the same cryptocurrencies traded on different exchanges are temporally related to each other. ADF and KPSS tests, whereas the vector autoregression model of order p – VAR(p) – for multivariate system.

Both Bitcoin and Ethereum show efficiency in the weak form on the main platforms in each market alone. However, when estimating a VAR(p) between prices among exchanges, there was evidence of Granger causality between cryptocurrencies in all exchanges, suggesting that EMH is not adequate due to cross information.

It is essential to assess the cryptocurrency market in a multivariate way, not only to favor its maturation process, but also to promote a broad understanding of its inherent risks. Thus, it will be possible to develop financial products that are actively managed in a more sophisticated cryptocurrency market.

There is a possibility of performing arbitrage on different exchanges and market assets through cross-exchanges. Thus, emphasizing the need for regulation of exchanges in the digital asset market, as an eventual price manipulation on a single platform can impact others, which generates various distortions.

This study is the first to find evidence of cross-information for the same (and other) cryptocurrencies among different exchanges.

This study aims to deal with the case adaptation problem associated with continuous data by providing a non-zero base solution for knowledge users in solving a given situation.

Firstly, the neighbourhood transformation of the initial case base and the view similarity between the problem and the existing cases will be examined. Multiple cases with perspective similarity or above a predefined threshold will be used as the adaption cases. Secondly, on the decision rule set of the decision space, the deterministic decision model of the corresponding distance between the problem and the set of lower approximate objects under each choice class of the adaptation set is applied to extract the decision rule set of the case condition space. Finally, the solution elements of the problem will be reconstructed using the rule set and the values of the problem's conditional elements.

The findings suggest that the classic knowledge matching approach reveals the user with the most similar knowledge/cases but relatively low satisfaction. This also revealed a non-zero adaptation based on human–computer interaction, which has the difficulties of solid subjectivity and low adaptation efficiency.

In this study the multi-case inductive adaptation of the problem to be solved is carried out by analyzing and extracting the law of the effect of the centralized conditions on the decision-making of the adaptation. The adaption process is more rigorous with less subjective influence better reliability and higher application value. The approach described in this research can directly change the original data set which is more beneficial to enhancing problem-solving accuracy while broadening the application area of the adaptation mechanism.

The examination of the calculation cases confirms the innovation of this study in comparison to the traditional method of matching cases with tacit knowledge extrapolation.

The algorithm models established in this study develop theoretical directions for a multi-case induction adaptation study of tacit knowledge.

This study designs a multi-case induction adaptation scheme by combining NRS and CBR for implicitly knowledgeable exogenous cases. A game-theoretic combinatorial assignment method is applied to calculate the case view and the view similarity based on the threshold screening.

Collaboration and preference for teamwork play a fundamental role in strengthening practical completion of team tasks. An organizational culture should facilitate learning systems where knowledge creation occurs through socialization. The purpose of this study is to develop a moderated mediation model, investigating the conditional indirect effect of co-worker support on the relationship between preference for teamwork and communities of practice.

Questionnaire survey was conducted via Google Forms to collect data from 210 employees working in the private and public sector in India. Hayes PROCESS macro models were used for analyzing the mediation of personal interaction and moderation of co-worker support.

This study showed evidence regarding the mediating role of personal interaction on the relationship between preference for teamwork and communities of practice. Co-worker support moderated the relationship between personal interaction and communities of practice. It also moderated the conditional indirect effect.

The results approve the substantial role of preference for teamwork in influencing personal interaction and communities of practice. The mediating role of personal interaction on preference for teamwork and communities of practice can lead to creation and sustenance of communities of practice. Furthermore, the moderating role of co-worker support as a conditional indirect effect shows that social support and exchange can lead to social learning.

Theoretical explanations and analytical approaches provide insights into the relationship between the preference for teamwork and communities of practice through a conditional indirect effect, a one of its kind of a study.

This paper aims to examine the effect of conditional conservatism on audit fees and whether the firm’s engagement in sustainable practices moderates the relationship between conditional conservatism and audit fees.

Using a sample of 3,767 firm-year observations from 14 European Union countries over the period of 2006–2019, the authors adopt the ordinary least square estimator to perform a panel data analysis of the effect of conditional conservatism on audit fees, and the moderating role of the environmental, social and governance (ESG) scores on the relationship between conditional conservatism and audit fees.

The authors find that conditional conservatism has a significant negative effect on audit fees, suggesting that auditors charge lower audit fees on more conservative clients. The authors also find that firms engaging in ESG actions, whether combined or individual, pay higher audit fees. More interestingly, the authors provide evidence that the negative effect of conditional conservatism on audit fees is mitigated only when ESG performance is considered in combination. This implies that firms exhibiting less commitment to ESG sustainability practices are prone to paying reduced audit fees when engaged in more conservative reporting. The findings remain robust after conducting a battery of tests.

The findings of this study have practical implications for several parties, including companies, auditors and regulators. This study emphasizes the potential benefit associated with using conservative accounting practices in terms of shaping downward the amount of audit fees. However, it also highlights the importance of considering the additional audit costs associated with higher ESG scores when making decisions about implementing sustainable practices.

Unlike prior studies that investigate the direct impact of sustainable practices on audit fees, the present work contributes to the literature on the benefits and costs of ESG by examining the moderating role of ESG performance in the association between audit fees and conditional conservatism. To the best of the authors’ knowledge, this study is the first to examine this relationship. Theoretically, the research integrates the theories of audit risk and agency to provide a more comprehensive understanding of the drivers of audit fees.