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The purpose of this work is to explore the new possibilities enabled by the recent introduction of RDF-star, an extension that allows for statements about statements within the Resource Description Framework (RDF). Alongside Named Graphs, this approach offers opportunities to leverage a meta-level for data modeling and data applications.

In this extended paper, the authors build onto three modeling use cases published in a previous paper: (1) provide provenance information, (2) maintain backwards compatibility for existing models, and (3) reduce the complexity of a data model. The authors present two scenarios where they implement the use of the meta-level to extend a data model with meta-information.

The authors present three abstract patterns for actively using the meta-level in data modeling. The authors showcase the implementation of the meta-level through two scenarios from our research project: (1) the authors introduce a workflow for triple annotation that uses the meta-level to enable users to comment on individual statements, such as for reporting errors or adding supplementary information. (2) The authors demonstrate how adding meta-information to a data model can accommodate highly specialized data while maintaining the simplicity of the underlying model.

Through the formulation of data modeling patterns with RDF-star and the demonstration of their application in two scenarios, the authors advocate for data modelers to embrace the meta-level.

With RDF-star being a very new extension to RDF, to the best of the authors’ knowledge, they are among the first to relate it to other meta-level approaches and demonstrate its application in real-world scenarios.

While the Chinese securities market is booming, the phenomenon of listed companies falling into financial distress is also emerging, which affects the operation and development of enterprises and also jeopardizes the interests of investors. Therefore, it is important to understand how to accurately and reasonably predict the financial distress of enterprises.

In the present study, ensemble feature selection (EFS) and improved stacking were used for financial distress prediction (FDP). Mutual information, analysis of variance (ANOVA), random forest (RF), genetic algorithms, and recursive feature elimination (RFE) were chosen for EFS to select features. Since there may be missing information when feeding the results of the base learner directly into the meta-learner, the features with high importance were fed into the meta-learner together. A screening layer was added to select the meta-learner with better performance. Finally, Optima hyperparameters were used for parameter tuning by the learners.

An empirical study was conducted with a sample of A-share listed companies in China. The F1-score of the model constructed using the features screened by EFS reached 84.55%, representing an improvement of 4.37% compared to the original features. To verify the effectiveness of improved stacking, benchmark model comparison experiments were conducted. Compared to the original stacking model, the accuracy of the improved stacking model was improved by 0.44%, and the F1-score was improved by 0.51%. In addition, the improved stacking model had the highest area under the curve (AUC) value (0.905) among all the compared models.

Compared to previous models, the proposed FDP model has better performance, thus bridging the research gap of feature selection. The present study provides new ideas for stacking improvement research and a reference for subsequent research in this field.

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.

In recent years, the proliferation of social commerce (s-commerce) has attracted many researchers to investigate the drivers of individuals' intentions. However, the empirical results reported in these studies were fragmented and inconsistent. This has led various meta-analyses to synthesize these findings, but without including a large number of s-commerce studies. In addition, investigating meta-analytically the effects of moderators such as the six dimensions of Hofstede's national culture is still lacking.

Drawing on nine theories and models, this meta-analysis aims to summarize the findings reported in 109 s-commerce studies published between 2011 and 2021 and to examine the moderating role of national culture. The correlation coefficient (r) has been used as the main effect size for this study. Based on the random-effects method, the CMA V3 software has been employed to calculate the weighted mean effect sizes.

The meta-analysis results showed that all the 11 hypothesized direct relationships are positive and significant. The moderator results also revealed that five out of six cultural dimensions significantly moderate the examined associations.

This research serves to enrich the existing s-commerce literature by addressing contradictory and mixed results reported in the empirical studies. This study is one of the first of its kind to investigate the role of Hofstede's six cultural dimensions as moderators in the field of s-commerce using the meta-analytic techniques.

Achieving swift and even flow of cargo through the border, the ultimate objective of cross-border logistics (CBL) requires the close coordination and collaboration of a multitude of stakeholders, as well as optimally configured systems. To achieve and sustain competitiveness in a dynamic international trade environment, CBL processes must undergo periodic analysis, improvement and optimization. This study aims to develop a modelling framework to capture CBL processes for analysis and improvement.

Relying on the extant literature, a meta-model is developed incorporating significant perspectives required to model CBL processes. Popular process modelling notations are evaluated against the meta-model and their ease of comprehension is also evaluated. The selected notation through evalution is augmented with addendums for a comprehensive depiction of CBL processes.

The capacity of role activity diagrams (RADs) to depict all perspectives, including interactions in a single diagram, makes them particularly suitable for modelling CBL processes. RADs have been complemented with physical flow diagrams and methods to capture temporal dimension, enabling a comprehensive view of CBL processes laying the foundation for insightful analysis.

The meta-model developed in this paper paves the way to develop an analysis framework which requires further research.

The lack of well-accepted modelling notations for studying CBL processes prompts researchers to search and adapt different formalisms. This study has filled this gap by proposing a comprehensive modelling framework able to capture CBL processes at different granularities in rich detail. Not only does the developed meta-model aid in selecting the notation, it is also useful in analysing the constituent elements of CBL processes.

The relationships between integrated reporting quality (IRQ) and corporate governance characteristics have been studied extensively, but the results are still inconclusive and, sometimes, contradictory. The purpose of this paper is to systematize the results of previously published studies on the relationship between corporate governance and IRQ.

This paper uses several complementary theoretical perspectives (agency, stakeholder and signaling theory). The relevant aspects of the corporate governance system are the attributes and composition of the board, the existence of a social responsibility committee, the quality of the audit committee, integrated report assurance and ownership structures. The sample consisted of 61 papers published in top journals between 2015 and 2021. Meta-analytic procedures were applied on bivariate and partial correlations between IRQ and the identified corporate governance characteristics.

The results confirm that director independence, the existence of a social responsibility committee, institutional ownership and the hiring of a Big 4 auditor are significantly correlated with IRQ. On the other hand, board gender diversity, audit committee independence and dedicated assurance have a positive but nonsignificant impact on IRQ. Chairperson-chief executive officer duality does not seem to impact report quality, while ownership concentration has a negative but nonsignificant impact on IRQ.

Future research can improve the measurement of focal indicators by using a common set of variables for comparability, favoring disaggregate measures of corporate governance and updating the measurement of some indicators. Future research could also propose new indicators in the area of corporate governance and expand the theoretical domain of IRQ research.

The findings emphasize the need to explicitly consider the role of corporate governance structures and arrangements in improving IRQ. Through meta-analysis, the paper aims to provide a comprehensive and generalizable set of findings, suggesting that corporate governance indicators cannot be overlooked as predictors of integrated reporting.

In the last 3 decades, organization-wide programs and practices based on the Total Quality Management (TQM) philosophy have become central to continuous improvement (CI) strategy in both public and private enterprises. However, there is paradoxical evidence of TQM-firm performance linkage in non-Japanese contexts. This study presents a meta-analysis of empirical research on TQM-firm performance linkage and investigates the moderating influence of national cultural (NC) values on this relationship.

Meta-analytical procedures are adopted to analyse 364 effects accumulated from 135 independent samples across 31 nations, for 30,015 firm observations. Additionally, weighted least square (WLS) meta-regression is used to test the moderation effects of four NC dimensions based on the Global Leadership and Organizational Behavior Effectiveness (GLOBE) model.

The meta-analysis results reveal that the strengths of the association varied across five soft and hard TQM dimensions and three firm performance dimensions Meta-regression indicate that the effectiveness of the TQM program is high in cultures which reward collectivist behaviours, equity of power distribution and avoidance of ambiguity in rules/structures.

The study contributes to international operational management theory on cultural influences on the effectiveness of operations strategies and decisions.

Evolved from Toyota’s shop floor in Japan, lean bundles are universally accepted for boosting manufacturing performance. However, extant literature shows mixed findings on the effectiveness of lean bundles in non-Japanese settings. This meta-analysis is aimed at understanding the influence of national culture (NC) differences on the lean bundles-performance relationships.

A total of 224 effects from 12,569 observations across 48 empirical studies from 14 countries are meta-analyzed. Also, weighted least squares (WLS) meta-regression using NC scores from the Global Leadership and Organizational Behavior Effectiveness (GLOBE) study are conducted to test the moderating effect of NC dimensions.

The meta-analysis reveals a positive association between the lean bundles and firm performance; though, the strength of the association varies across the individual lean bundles. The meta-regression results show that lean practices are more effective in countries that value high future orientation, high collectivism, low-performance orientation and high assertiveness.

The results contribute to the literature on the role of local cultural influence on strategies and decisions related to the implementation of continuous improvement (CI) programs in cross-cultural settings.

This study aims to integrate the theory of planned behavior (TPB) and the value-belief-norm (VBN) theory into a meta-analytic framework to synthesize green consumption literature.

By integrating the findings from 173 studies, a meta-analysis was performed adopting several analytical methods: bivariate analysis, moderation analysis and path analysis.

VBN- and TPB-based psychological factors (adverse consequences, ascribed responsibility, personal norms, subjective norms, attitude and perceived behavioral control) mediate the effects of altruistic, biospheric and egoistic values on green purchase intention. Further, inconsistencies in the proposed relationships are due to cultural factors (i.e. individualism-collectivism, power distance, uncertainty avoidance, masculinity–femininity, short- vs long-term orientation and indulgence-restraint) and countries’ human development status.

The authors selected papers published in English; hence, other relevant papers in this domain published in other languages might have been missed.

The findings are useful to marketers of green offerings in designing strategies, i.e. specific messages, targeting different customers based on countries’ cultural score and human development index, to harvest positive customer responses.

This study is the pioneering attempt to synthesize the TPB- and VBN-based quantitative literature on green consumer behavior to resolve the reported inconsistent findings.

This paper aims to identify the income and price elasticities of demand for residential electricity in Latin America and the Caribbean (LAC) and to verify their main determinants.

Meta-analysis and meta-regression methods were applied. After collecting and filtering journal articles, the authors obtained a sample composed of 76 studies covering 1979–2020.

The results show that the LAC's income elasticity is approximately 0.20 and 0.92 for the short and long term, respectively. The LAC's price elasticity is approximately −0.37 and −0.46 for the short and long term, respectively. Furthermore, the estimates are affected by the data structure, the estimation method used and the sampling period.

The authors close a gap in the literature by analyzing the price and income elasticities of demand through meta-analysis and meta-regression.