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Within North American institutions of higher education, the sociopolitical construct of whiteness comprises an often implicit set of lessons that are reflected not only in policy and curricula but also in the teaching practices of faculty. Such lessons perpetuate white centricity and supremacy, at enormous costs to those who have been negatively racialized. Therefore, it is critical for white faculty to engage meaningfully with ongoing processes of self-reflection, self-education, and skill development so that they can contribute positively to the interrogation and disruption of whiteness in higher education. This chapter discusses seven processual considerations for white educators who seek to interrogate and disrupt the problem of whiteness in teaching and learning.

Using a multilevel model, this study examined how paternalistic leadership behaviors, including authoritarianism, morality and benevolence, influence followers' performance.

A sample of 556 leader–follower dyads from 66 groups in a manufacturing firm in China was collected for analysis. Descriptive statistics and multi-level regression analyses were used to analyze the data.

The results indicated that group efficacy mediates the relationship between authoritarian leadership and followers' performance and that self-efficacy mediates the relationship between benevolent leadership and followers' performance. In addition, the positive relationship between self-efficacy and followers' performance is weaker when followers exhibit higher levels of group efficacy.

The data were collected in a manufacturing firm in China, it is difficult to generalize the results to other settings.

Managers should use their abilities and skills to interpret which paternalistic leadership styles their followers prefer, so as to improve their performance.

This study developed a multilevel model to examine the mediating processes of group efficacy and self-efficacy in the effect of PL behaviors, including authoritarianism, benevolence and morality, on followers' performance.

Compared with traditional methods relying on manual teaching or system modeling, data-driven learning methods, such as deep reinforcement learning and imitation learning, show more promising potential to cope with the challenges brought by increasingly complex tasks and environments, which have become the hot research topic in the field of robot skill learning. However, the contradiction between the difficulty of collecting robot–environment interaction data and the low data efficiency causes all these methods to face a serious data dilemma, which has become one of the key issues restricting their development. Therefore, this paper aims to comprehensively sort out and analyze the cause and solutions for the data dilemma in robot skill learning.

First, this review analyzes the causes of the data dilemma based on the classification and comparison of data-driven methods for robot skill learning; Then, the existing methods used to solve the data dilemma are introduced in detail. Finally, this review discusses the remaining open challenges and promising research topics for solving the data dilemma in the future.

This review shows that simulation–reality combination, state representation learning and knowledge sharing are crucial for overcoming the data dilemma of robot skill learning.

To the best of the authors’ knowledge, there are no surveys that systematically and comprehensively sort out and analyze the data dilemma in robot skill learning in the existing literature. It is hoped that this review can be helpful to better address the data dilemma in robot skill learning in the future.

This study aims to examine the influence of reward personalization on financing outcomes in the Industry 5.0 era, where reward-based crowdfunding meets the personalized needs of individuals.

The study utilizes a corpus of 218,822 crowdfunding projects and 1,276,786 reward options on Kickstarter to investigate the effect of reward personalization on investors’ willingness to participate in crowdfunding. The research draws on expectancy theory and employs quantitative and qualitative approaches to measure reward personalization. Quantitatively, the number of reward options is calculated by frequency; whereas text-mining techniques are implemented qualitatively to extract novelty, which serves as a proxy for innovation.

Findings indicate that reward personalization has an inverted U-shaped effect on investors’ willingness to participate, with investors in life-related projects having a stronger need for reward personalization than those interested in art-related projects. The pledge goal and reward text readability have an inverted U-shaped moderating effect on reward personalization from the perspective of reward expectations and reward instrumentality.

This study refines the application of expectancy theory to online financing, providing theoretical insight and practical guidance for crowdfunding platforms and financiers seeking to promote sustainable development through personalized innovation.

The rise of communication software has changed our work style. The objectives of this study are: (1) to explore the effect of supervisors making after-hours work requests using communication software (SWRUCS) on employees’ job stress, quality of life and (2) to examine the moderating effect of personality traits and the cross-level contextual effect of social support.

A questionnaire survey was conducted to obtain information from 357 employees.

The results suggested that SWRUCS exacerbated job stress, which negatively impacted on quality of life and well-being. Moreover, different personality traits can either increase or decrease the positive or negative effect of SWRUCS on job stress. This study also revealed that social support can reduce employees’ job stress in a cross-level fashion. Furthermore, social support, especially organizational and supervisory support, can decrease the negative effect of job stress on employees’ quality of life and well-being.

Theoretically, this study has broadened the research scope of the organizational application of communication software, and practically, this study has demonstrated the reason why organizations should provide social support and select employees with suitable personality traits.

This paper aims to investigate the moderating effect of sustainability reporting on the relationship between the independent variables of board diversity, and earnings management and the dependent variable of readability of financial statements.

The study panel data regression analysis involved 36 Kenyan-listed companies from 2016 to 2020.

Key findings were that increased board diversity was found to significantly improve the readability of financial statements. Discretionary earnings management was found to significantly reduce the readability of financial statements. Sustainability reporting was found to significantly increase the readability of financial statements, and it moderated the relationship between board diversity, earnings management and financial statements readability in Kenya.

The study sample of 36 non-financial listed in the Nairobi Securities Exchange was very small and was affected by the problem of thin trading; hence, caution should be adopted when interpreting the findings.

The Capital Markets Authorities (CMA) as a policymaker should enforce sustainability reporting by Kenyan listed firms as there is evidence that the reporting enhances the readability of financial statements. The Institute of Certified Public Accountants as a policymaker should closely monitor the published financial statements of firms for earnings management and punish the perpetrators, as there is empirical evidence that the practice reduces the readability of financial statements.

Sustainability reporting is successful as a moderating variable between readability of financial statements and determinants of readability of financial statements.

This study contributes to knowledge by studying sustainability reporting as a moderating variable between the independent variables of board diversity and earnings management and the dependent variable of readability of financial statements and measured sustainability reporting using a dummy variable for the period before and after the enactment and release of CMA code of 2016 on corporate governance that required sustainability reporting by Kenyan listed companies.

This study explores the moderated mediation effect, wherein collective mindfulness attenuates the hypothesised relationship between customer incivility, service sabotage and psychological well-being and is supported by the conservation of resources (COR) theory.

Multiwave and multisource data were collected from 315 frontline employees (FLEs) working in 32 Indian bank branches. Using HLM 7.00, the authors tested a multilevel model in which branch-level collective mindfulness moderated the association amongst individual-level customer incivility, psychological well-being and service sabotage.

A higher level of collective mindfulness had a profound cross-level effect on the association between customer incivility and service sabotage through psychological well-being.

Distinct from prior research that focussed on individuals' personal resources as a buffer against customer incivility, the authors' study identified branch-level collective mindfulness as a boundary condition that helps employees experiencing customer incivility decrease service sabotage. By uncovering a branch-level variable that reduces the negative impact of customer incivility on service sabotage, the authors' study offers valuable insights for banks to enhance customer service at their branches.

The study is aimed to compare the prediction success of basic machine learning and ensemble machine learning models and accordingly create novel prediction models by combining machine learning models to increase the prediction success in construction labor productivity prediction models.

Categorical and numerical data used in prediction models in many studies in the literature for the prediction of construction labor productivity were made ready for analysis by preprocessing. The Python programming language was used to develop machine learning models. As a result of many variation trials, the models were combined and the proposed novel voting and stacking meta-ensemble machine learning models were constituted. Finally, the models were compared to Target and Taylor diagram.

Meta-ensemble models have been developed for labor productivity prediction by combining machine learning models. Voting ensemble by combining et, gbm, xgboost, lightgbm, catboost and mlp models and stacking ensemble by combining et, gbm, xgboost, catboost and mlp models were created and finally the Et model as meta-learner was selected. Considering the prediction success, it has been determined that the voting and stacking meta-ensemble algorithms have higher prediction success than other machine learning algorithms. Model evaluation metrics, namely MAE, MSE, RMSE and R², were selected to measure the prediction success. For the voting meta-ensemble algorithm, the values of the model evaluation metrics MAE, MSE, RMSE and R² are 0.0499, 0.0045, 0.0671 and 0.7886, respectively. For the stacking meta-ensemble algorithm, the values of the model evaluation metrics MAE, MSE, RMSE and R² are 0.0469, 0.0043, 0.0658 and 0.7967, respectively.

The study shows the comparison between machine learning algorithms and created novel meta-ensemble machine learning algorithms to predict the labor productivity of construction formwork activity. The practitioners and project planners can use this model as reliable and accurate tool for predicting the labor productivity of construction formwork activity prior to construction planning.

The study provides insight into the application of ensemble machine learning algorithms in predicting construction labor productivity. Additionally, novel meta-ensemble algorithms have been used and proposed. Therefore, it is hoped that predicting the labor productivity of construction formwork activity with high accuracy will make a great contribution to construction project management.