Search results

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.

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.

The purpose of this study is to investigate the adoption of waqf technology (Waqftech) using blockchain smart contracts for corporate waqf crowdfunding. Despite the growing interest in Waqftech, Malaysian enterprises have not fully embraced this emerging technology because of uncertainty regarding the benefits it offers to contributors. The research incorporates two theoretical frameworks: the electronic data interchange (EDI) model for firms’ technology adoption, and the triple bottom line theory (TBL) for corporate social responsibility.

A quantitative method using a cross-sectional survey design with a five-point Likert scale questionnaire was used. Data was collected from 210 decision-makers representing small and medium-sized enterprises and analyzed using partial least squares-structural equation modeling.

The findings from this research suggest that Malaysian enterprises are influenced by both corporate and social predictive benefits when using blockchain crowdfunding, but not by environmental benefits. The adoption of blockchain smart contracts does not correlate with predictive environmental benefits because of misconceptions about the disruptive technology’s impact on biological and digital environmental preservation.

This research focuses on organizational behavior rather than individual users of waqf crowdfunding, and it is limited, primarily focusing within Malaysia and regions with similar waqf structures.

The Waqftech framework allows innovative mechanisms for executing corporate waqf investment returns to the intended beneficiaries through the smart contracts’ platform. In addition, this study supports relevant corporate social responsibility and creating shared value technology adoption theories, including EDI and TBL. Aside from this, the study provides empirical implications for waqf management using fintech platforms.

This groundbreaking study focuses on creating a Waqftech model for corporate waqf crowdfunding. The results of this study are important for the development of government policies that support the use of Waqftech in charitable fundraising. More research on biological and digital environmental perspectives is proposed to foster investors’ confidence in the visibility of digital tracking and lead to swift investments in future metaverse fundraising platforms.

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.

Companies allowing employees to self-report business expenses face the risk of expense fraud because some occasionally engage in dishonest behavior to receive reimbursements for their use. Drawing on the technology acceptance model, this study aims to investigate the effects of perceived usefulness, perceived ease of use and perceived security on the trust in e-reimbursement systems and the relationship with honest disclosure intention.

A self-administered questionnaire was distributed to 254 respondents, with the partial least squares structural equation modeling used to analyze the data.

The findings showed that perceived security and perceived usefulness explained trust in e-reimbursement systems, whereas perceived ease of use had no significant effect on it. Corporate governance and trust in e-reimbursement systems have a positive relationship with whistleblowing intention. At the same time, corporate governance mediates the relationship between trust in e-reimbursement systems and honest disclosure intention.

This study sheds light on using e-reimbursement systems within organizations to prevent fraudulent reimbursements and offers recommendations to management on enhancing employees’ intention to engage in honest disclosure behavior through e-reimbursement systems.

This study aims to conceptualize the dimensions of resilient leadership and develop the resilient leadership scale (RLS) through three studies.

In Study 1, based on interviews with 77 leaders and 8 junior employees, a seven-factor resilient leadership model was constructed. In Study 2, exploratory factor analysis (n = 237) was conducted to refine the initial items. In Study 3, confirmatory factor analysis (n = 610) was performed to validate the dimensional structure identified in Study 2, and different types of validity of the RLS were assessed.

The validated RLS composed of seven dimensions: contingency planning, improvisation, adaptive instructing, contingency control, emergency care, adjustment recovery and mutual growth. The scale showed desirable measurement qualities in terms of reliability and validity. Resilient leadership and its dimensions significantly impact employee turnover intentions and employee resilience.

This research contributes to the literature on the resilience of hospitality and tourism enterprises and enriches the research scope and theoretical framework of resilient leadership.

This research revealed the resilient leadership responses to crisis in hospitality and tourism enterprises with practical implications for tourism enterprise leaders to deal with major crisis.

This study examined the determinants of mobile health (M-Health) application, adoption, usage and discontinuation among corporate workers diagnosed with hypertension and diabetes in Ghana.

The diffusion innovation and reasoned action theories were employed using an exploratory design. Three hundred corporate workers diagnosed with diabetes and hypertension from three health facilities for the past six months were sampled for the study using a multi-stage sampling technique and administered questionnaires. Descriptive statistics and logistic regression tools were employed in the analysis of data.

The study found a significant number of factors influencing m-health applications adoption, usage and discontinuity. These factors include nature and demand of job, perceived advantage, compatibility, complexity, triability, aesthetics and trust. Aesthetics emerged as the strongest predictive factor for the adoption, usage and discontinuity of use among diabetic and hypertensive corporate workers. With the adoption of M-Health applications, compatibility, as well as nature and demand of job, were significant predictors. With the usage of M-Health applications, complexity, triability, aesthetics and trust were significant predictors. Moreover, perceived advantage, compatibility, complexity and triability influenced significantly the choice to discontinue using M-Health applications. The study concluded that M-Health application functionalities play a valuable role in patients’ intention to adopt, use and discontinue the use of an M-Health application in Ghana.

This exploratory study offers in-depth insight into how major M-Health application features affect its adoption, usage and discontinuity, providing crucial information for future research and the improvement of chronic condition healthcare delivery.

The theory of planned behaviour (TPB) and its extensions are often used to explain intentions to perform sustainable behaviours. Emotions can provide the impetus for action and should be considered in high-involvement situations, such as sustainable food purchase decisions. Therefore, the aim of this research work was to investigate whether the addition of different types of emotions (self-related, social and pro-active) to the TPB main constructs – attitude (ATT), subjective norm (SN) and perceived behavioural control (PBC) – improves the explanation of intention to make two sustainable food purchase choices: purchase Fairtrade (FT) products and purchase through solidarity purchasing groups (SPGs).

The present investigation was conducted by two studies using online questionnaires and focussing on purchasing FT food products (Study 1) and purchasing food through SPGs (Study 2). Stepwise regression models were run to explain the intention to purchase FT products (Study 1; N = 240) and the intention to purchase through SPGs (Study 2; N = 209).

Results show that emotions increase the predictive validity of the TPB model. The study also highlights the importance to distinguish between different types of emotions. Amongst those considered in both studies, only pro-active emotions play a significant role in predicting food purchasing intention. Results encourage further investigation of the role of emotions in TPB-based models for predicting sustainable food purchase choices.

The measurement of the three classical antecedents of the TPB model (ATT, SNs and PBC) performed by single items can be considered the main limitation of this work. In future research, instruments made up of more items measuring these three theoretical dimensions should be used. In addition, it would be important to conduct an analysis of the beliefs that determine ATTs, norms and perceived control.

Different from previous studies that considered emotions in extension of the TPB model to explain sustainable behaviours, the present work separately analysed the role of three different kinds of emotions (self-related, social and pro-active) in explaining sustainable food behaviours.