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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 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.

The literature on public procurement (PP) has increased significantly in recent years, and, to date, several reviews have been conducted to study this relevant subject. Nevertheless, a bibliometric analysis of the PP knowledge domain is still missing. To fill this knowledge gap, a bibliometric review is carried out to investigate the current state of PP research.

A total of 640 journal articles are selected from the Scopus database for the final analysis. The performance indicators of the literature are identified and explained through bibliometric analysis. Furthermore, the conceptual and intellectual structures are studied through a keyword co-occurrence network and bibliographic coupling.

The results of the review indicate that PP research has increased significantly in recent years. The top ten most productive journals, countries, authors and academic institutions are identified. The findings from the keyword co-occurrence network reveal six main research themes including innovation, corruption and green public procurement (GPP). By applying bibliographic coupling, the focus of PP research revolves around seven thematic areas: GPP, corruption, the role of small and medium-sized enterprises (SMEs) in PP, electronic PP, innovation, labour standards and service acquisition. The research potential of each thematic area is evaluated using a model based on maturity and recent attention (RA).

To the best of the authors' knowledge, this is the first study to successfully organise, synthesise and quantitatively analyse the development of the PP domain amongst a large number of publications on a large time scale.

The authors study how enabling perceptions (flexibility, reparability and internal and global transparency) of a budgetary control system are formed, and whether enabling perceptions empower lower-level managers and make them form less negative attitudes about red tape in the organization. This study research is warranted because of the lack of knowledge on how perceptual variation in flexibility, repairability and transparency of a control system within an organization, where managers experiencing the same control system design, can be explained.

Survey data with answers from 211 managers from a large local government organization in Sweden is analyzed with structural equation modeling.

The extent to which the budget system is perceived as having enabling qualities (being flexible, reparable and transparent) is explained by the safeness of the individual manager's psychological climate. This climate is characterized by trust and fairness perceptions in upper management. In turn, enabling perceptions positively affect a sense of psychological empowerment and reduces attitudes toward red tape in the organization.

The authors contribute by identifying an important factor explaining individual-level variability in enabling perceptions of control systems within organizations. Compared to previous research that has taken an interest in the organizational-level climate, the authors theorize about and investigate (parts of) the individual-level psychological climate as an explanation of within-system variability.

Studies have shown that teachers' perceptions and expectations of their working environment shape their perceived stress. The present study draws upon implicit leadership theory and builds on the job demands-control (JD-C) model to investigate whether there are differences in the implications of participative decision-making and paternalistic leadership for teachers' perceived stress in the Israeli Arab education system.

Data were collected through validated questionnaires returned by a two-stage cluster random sampling of 350 teachers randomly chosen from 70 Israeli Arab elementary schools. Paternalistic leadership and participative decision-making were considered as group-level variables to lower the risk of common method variance. The proposed model was tested through hierarchical regression analysis. Finally, to test the hypothesis that paternalistic leadership and participative decision-making standardized beta weights were statistically significantly different from each other, their corresponding 95% confidence intervals were estimated via bias corrected bootstrap (1000 re-samples).

The findings indicated differences in the levels of the principal's paternalistic leadership and participative decision-making as perceived by the Israeli Arab teachers. Also, the results indicated that participative decision-making was negatively correlated with teachers' perceived stress beyond the influence of paternalistic leadership.

Examining teachers' working conditions and resources can be important since they affect teachers' perceived stress, which may in turn affects school results in the Arab education system in Israel. This study can contribute to the development of training programs for teachers to improve and adapt principals' leadership practices to the sociocultural context of the Arab education system in Israel.

This study employs survey methods to statistically examine the internationalization of quality assurance (IQA) in Taiwanese higher education. The data collected were analyzed to assess the associations between administrators' opinions of the importance of IQA and their evaluations of its implementation, as well as the relationship between implementation and opinions on seven measures of international quality. The study also explores the mediating effect of implementation assessments on the relationship between opinions of the importance of IQA and opinions of international quality.

This study targeted higher education administrators from universities in Taiwan, including presidents, vice presidents, deans, section chiefs, directors, and heads of schools in various departments. Using systematic sampling methods, 80 universities were selected from a population of 159 higher education institutions in Taiwan, with 17-40 potential participants each in 2015. A total of 2,377 questionnaires were distributed to all the administrators of those institutions, and ultimately, 65 institutions and 337 valid questionnaires were analyzed.

The importance of IQA directly and positively influenced implementation of it on higher education institutions. The implementation directly and positively influenced the level of international quality of the institutions and the importance of IQA had an indirect positive influence on international quality through implementation. The aggregated institution-level results were similar to but much stronger than the individual-level results.

This study examined the IQA of higher education in Taiwan, which is increasingly important to institutions' competitiveness in the global higher education market. The data were analyzed using multilevel structural equation modeling at the individual-level and the aggregate-level. The analysis revealed direct and indirect associations between opinions about IQA and institutional quality. This study makes a significant contribution to the literature because it clarifies the role of administrators (individually and collectively) regarding their institutions' educational quality, and it provides useful information that institutions could apply to improve their international competitiveness.

The COVID-19 pandemic significantly increased demand for medical and protective equipment by frontline health workers, as well as the general community, causing the supply chain to stretch beyond capacity, an issue further heightened by geographical and political lockdowns. Various 3D printing technologies were quickly utilised by businesses, institutions and individuals to manufacture a range of products on-demand, close to where they were needed. This study gathered data about 91 3D printed projects initiated prior to April 1, 2020, as the virus spread globally. It found that 60% of products were for personal protective equipment, of which 62% were 3D printed face shields. Fused filament fabrication was the most common 3D print technology used, and websites were the most popular means of centralising project information. The project data provides objective, quantitative insight balanced with qualitative critical review of the broad trends, opportunities and challenges that could be used by governments, health and medical bodies, manufacturing organisations and the 3D printing community to streamline the current response, as well as plan for future crises using a distributed, flexible manufacturing approach.