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We propose a risk factor for idiosyncratic entropy and explore the relationship between this factor and expected stock returns.

We estimate a cross-sectional model of expected entropy that uses several common risk factors to predict idiosyncratic entropy.

We find a negative relationship between expected idiosyncratic entropy and returns. Specifically, the Carhart alpha of a low expected entropy portfolio exceeds the alpha of a high expected entropy portfolio by −2.37% per month. We also find a negative and significant price of expected idiosyncratic entropy risk using the Fama-MacBeth cross-sectional regressions. Interestingly, expected entropy helps us explain the idiosyncratic volatility puzzle that stocks with high idiosyncratic volatility earn low expected returns.

We propose a risk factor of idiosyncratic entropy and explore the relationship between this factor and expected stock returns. Interestingly, expected entropy helps us explain the idiosyncratic volatility puzzle that stocks with high idiosyncratic volatility earn low expected returns.

With the rise in wine consumption, accurate wine price forecasts have significantly impacted restaurant and hotel purchasing decisions and inventory management. This study aims to develop a precise and effective wine price point and interval forecasting model.

The proposed forecast model uses an improved hybrid kernel extreme learning machine with an attention mechanism and a multi-objective swarm intelligent optimization algorithm to produce more accurate price estimates. To the best of the authors’ knowledge, this is the first attempt at applying artificial intelligence techniques to improve wine price prediction. Additionally, an effective method for predicting price intervals was constructed by leveraging the characteristics of the error distribution. This approach facilitates quantifying the uncertainty of wine price fluctuations, thus rendering decision-making by relevant practitioners more reliable and controllable.

The empirical findings indicated that the proposed forecast model provides accurate wine price predictions and reliable uncertainty analysis results. Compared with the benchmark models, the proposed model exhibited superiority in both one-step- and multi-step-ahead forecasts. Meanwhile, the model provides new evidence from artificial intelligence to explain wine prices and understand their driving factors.

This study is a pioneering attempt to evaluate the applicability and effectiveness of advanced artificial intelligence techniques in wine price forecasts. The proposed forecast model not only provides useful options for wine price forecasting but also introduces an innovative addition to existing forecasting research methods and literature.

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.

The purpose of the study is to comprehend medical service quality, information quality and system quality toward actual use of Tele-Health in rural India. The study further validates the impact of the actual use of Tele-Health on sustainable development, thus providing implications to improve upon the Tele-Health penetration in India.

Data was collected from 326 healthcare practitioners practicing Tele-Health in North Indian states and Structural Equation Modeling was applied to validate the conceptual framework.

The results indicated that medical service quality, information quality and system quality influence Tele-Health behavioral intentions which in turn impact actual use and sustainable development. This research draws upon a conceptual framework to deepen our understanding of Tele-Health by providing an all-inclusive overview.

The massive topography of India with a prime rural populace instills the need for timely healthcare facilities. Tele-Health is a solution to all these problems but is at a nascent stage. Therefore, there is a vital need to study the factors which improve the penetration of Tele-Health in the Indian context. The model that emerged from the study may be validated by other Indian sub-continental countries so that Tele-Health may be implemented hassle-free.

The study aims to develop a multilayer high-effective ensemble of ensembles predictive model (stacking ensemble) using several hyperparameter optimized ensemble machine learning (ML) methods (bagging and boosting ensembles) trained with high-volume data points retrieved from Internet of Things (IoT) emission sensors, time-corresponding meteorology and traffic data.

For a start, the study experimented big data hypothesis theory by developing sample ensemble predictive models on different data sample sizes and compared their results. Second, it developed a standalone model and several bagging and boosting ensemble models and compared their results. Finally, it used the best performing bagging and boosting predictive models as input estimators to develop a novel multilayer high-effective stacking ensemble predictive model.

Results proved data size to be one of the main determinants to ensemble ML predictive power. Second, it proved that, as compared to using a single algorithm, the cumulative result from ensemble ML algorithms is usually always better in terms of predicted accuracy. Finally, it proved stacking ensemble to be a better model for predicting PM_2.5 concentration level than bagging and boosting ensemble models.

A limitation of this study is the trade-off between performance of this novel model and the computational time required to train it. Whether this gap can be closed remains an open research question. As a result, future research should attempt to close this gap. Also, future studies can integrate this novel model to a personal air quality messaging system to inform public of pollution levels and improve public access to air quality forecast.

The outcome of this study will aid the public to proactively identify highly polluted areas thus potentially reducing pollution-associated/ triggered COVID-19 (and other lung diseases) deaths/ complications/ transmission by encouraging avoidance behavior and support informed decision to lock down by government bodies when integrated into an air pollution monitoring system

This study fills a gap in literature by providing a justification for selecting appropriate ensemble ML algorithms for PM_2.5 concentration level predictive modeling. Second, it contributes to the big data hypothesis theory, which suggests that data size is one of the most important factors of ML predictive capability. Third, it supports the premise that when using ensemble ML algorithms, the cumulative output is usually always better in terms of predicted accuracy than using a single algorithm. Finally developing a novel multilayer high-performant hyperparameter optimized ensemble of ensembles predictive model that can accurately predict PM_2.5 concentration levels with improved model interpretability and enhanced generalizability, as well as the provision of a novel databank of historic pollution data from IoT emission sensors that can be purchased for research, consultancy and policymaking.

This study aims to examine the impact of board characteristics on firm performance while also exploring the influential mechanisms that help Chinese listed companies establish effective boards of directors and strengthen their corporate governance mechanisms.

This paper uses machine learning methods to investigate the predictive ability of the board of directors' characteristics on firm performance based on the data from Chinese A-share listed companies on the Shanghai and Shenzhen stock exchanges in China during 2008–2021. This study further analyzes board characteristics with relatively strong predictive ability and their predictive models on firm performance.

The results show that nonlinear machine learning methods are more effective than traditional linear models in analyzing the impact of board characteristics on Chinese firm performance. Among the series characteristics of the board of directors, the contribution ratio in prediction from directors compensation, director shareholding ratio, the average age of directors and directors' educational level are significant, and these characteristics have a roughly nonlinear correlation to the prediction of firm performance; the improvement of the predictive ability of board characteristics on firm performance in state-owned enterprises in China performs better than that in private enterprises.

The findings of this study provide valuable suggestions for enriching the theory of board governance, strengthening board construction and optimizing the effectiveness of board governance. Furthermore, these impacts can serve as a valuable reference for board construction and selection, aiding in the rational selection of boards to establish an efficient and high-performing board of directors.

The study findings unequivocally demonstrate the superiority of nonlinear machine learning approaches over traditional linear models in examining the relationship between board characteristics and firm performance in China. Within the suite of board characteristics, director compensation, shareholding ratio, average age and educational level are particularly noteworthy, consistently demonstrating strong, nonlinear associations with firm performance. Within the suite of board characteristics, director compensation, shareholding ratio, average age and educational level are particularly noteworthy, consistently demonstrating strong, nonlinear associations with firm performance. The study reveals that the predictive performance of board attributes is generally more robust for state-owned enterprises in China in comparison to their counterparts in the private sector.

Since crude oil is crucial to the nation's economic growth, crude oil futures are closely related to many other markets. Accurate forecasting can offer investors trustworthy guidance. Numerous studies have begun to consider creating new metrics from social networks to improve forecasting models in light of their rapid development. To improve the forecasting of crude oil futures, the authors suggest an integrated model that combines investor sentiment and attention.

This study first creates investor attention variables using Baidu search indices and investor sentiment variables for medium sulfur crude oil (SC) futures by collecting comments from financial forums. The authors feed the price series into the NeuralProphet model to generate a new feature set using the output subsequences and predicted values. Next, the authors use the CatBoost model to extract additional features from the new feature set and perform multi-step predictions. Finally, the authors explain the model using Shapley additive explanations (SHAP) values and examine the direction and magnitude of each variable's influence.

The authors conduct forecasting experiments for SC futures one, two and three days in advance to evaluate the effectiveness of the proposed model. The empirical results show that the model is a reliable and effective tool for predicting, and including investor sentiment and attention variables in the model enhances its predictive power.

The data analyzed in this paper span from 2018 through 2022, and the forecast objectives only apply to futures prices for those years. If the authors alter the sample data, the experimental process must be repeated, and the outcomes will differ. Additionally, because crude oil has financial characteristics, its price is influenced by various external circumstances, including global epidemics and adjustments in political and economic policies. Future studies could consider these factors in models to forecast crude oil futures price volatility.

In conclusion, the proposed integrated model provides effective multistep forecasts for SC futures, and the findings will offer crucial practical guidance for policymakers and investors. This study also considers other relevant markets, such as stocks and exchange rates, to increase the forecast precision of the model. Furthermore, the model proposed in this paper, which combines investor factors, confirms the predictive ability of investor sentiment. Regulators can utilize these findings to improve their ability to predict market risks based on changes in investor sentiment. Future research can improve predictive effectiveness by considering the inclusion of macro events and further model optimization. Additionally, this model can be adapted to forecast other financial markets, such as stock markets and other futures products.

The authors propose a novel integrated model that considers investor factors to enhance the accuracy of crude oil futures forecasting. This method can also be applied to other financial markets to improve their forecasting efficiency.

Since there is lack of studies in determine factors that affecting enjoyment sentiment when using online learning system, this study aims to explore the antecedents of perceived online learning enjoyment by using extended technology acceptance model (TAM) and its effect on behavioral intentions (BIN) among higher education institutions students.

The research framework was empirically evaluated using a cross-sectional research design and the data was collected from 715 undergraduate students from public higher education institutions in Malaysia using an online survey method. A structural equation modeling using partial least square method was used to examine the hypothesized model.

The results of partial least squares structural equation modeling indicated that the main predictive variables of TAM along with the extended variables were significantly influence the perceived online learning enjoyment. Meanwhile, the analysis also identified that perceived online learning enjoyment can significantly generate positive BIN for using online learning platforms as well as it also plays as a significant mediator role.

This study has significant implications for higher education institutions that wish to develop online learning environment for their students by providing answers to higher education institutions on how to successfully use the learning management system to assist students' learning performance from the aspect of online learning enjoyment sentiment.

This study is remarkable because it is the first attempt to explore the effect of these five predictors on students' learning enjoyment toward online learning platforms and subsequently on BIN to use this learning platforms, especially in the context of Malaysian higher education system. It is also unique in the way to extend the use of TAM predictive variables with others variables to produce more informative results about the study. Hence, this study also has a new contribution in the literature in the domain of digital learning.

This study aims to develop and test an original conceptual framework that examines the role of various factors borrowed from three theories (i.e. Institutional Theory, Resource-Based View and Diffusion of Innovation) in adopting Human Resource Analytics (HRA).

A new conceptual framework (research model) is developed based on previous research and coherent theoretical arguments. Its factors are classified using the Technology–Organization–Environment (TOE) framework. Research hypotheses are tested using primary data collected from 152 managers of Greek organizations. Empirical data are analyzed using the “Structural Equation Modelling” (SEM) technique.

The technological and organizational context proved extremely important in enhancing Organizational Analytics Maturity (OAM) and HRA adoption, while the environmental context did not. Relative advantage and top management support were found to significantly impact the adoption of HRA, while Information Technology (IT) infrastructure, human resource capabilities and top management support are crucial for increasing OAM. Overall, the latter is the most important factor in enhancing HRA adoption.

This study contributes to the limited published research on HRA adoption while at the same time it can be used as a guideline for future research. The novel findings offer insights into the factors impacting OAM and HRA adoption.

The building sector is vital to a nation’s economy, as it has a major influence on economic activity and growth, job creations and the advancement of infrastructure. Intricate challenges that are inherent in crises such as the COVID-19 outbreak lead to material scarcities, project delays, labor shortages, escalated expenses, funding challenges, regulatory obstacles and dwindling investment funds, all of which culminate in costs that are in excess of those budgeted. While numerous studies have explored the ramifications of COVID-19 on project budgets, there is little, if any, data available on forecasting the magnitude of this impact.

This investigation seeks to bridge this knowledge deficiency by devising a predictive tool grounded in an ordinal logistic regression method. An online survey was designed and disseminated to gauge the views of construction field experts about the diverse contributors to excessive costs during the viral outbreak, and a predictive tool, crafted from the survey participants’ feedback.

Findings showed that smaller-scale enterprises and contractor-centric establishments faced greater adversities than medium-to-large ones and consultancy-or-owner-type entities.

The insights from this research shed light on the amplified risk of higher project costs amid health crises or analogous events, underlining the imperative need for fortified risk management approaches to bolster project outcomes. By factoring in demographics, this research offers policymakers a refined lens through which to customize interventions and promote balanced and enduring advancement in the construction industry.