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The purpose of this paper is to explore meta‐models that pertain to information behaviour. It seeks to highlight the possibilities they offer to researchers wishing to develop their own and to readers more generally interested in information behaviour literature.

Various frameworks that may be regarded as information behaviour meta‐models were examined and three separate types were identified. These are discussed in turn, with particular characteristics of individual meta‐models used to illustrate the types.

A meta‐model is considered here to be a model that has been derived from one or more existing models. Information behaviour meta‐models fall into three categories: those that unify, within one framework, disparate models/theories from a number of areas; those that integrate the fundamentals of several models which share common strands; and those that recast an established model for a particular purpose.

The extent of the typology presented in the paper is bound by the limits of the authors' endeavours in uncovering relevant meta‐models. Should further meta‐models be traced, it is possible that other types would also come to light.

The work will aid the reader's understanding of how theoretical frameworks in information behaviour are developed. It will help those who study the field's literature to grasp variations in the origin of the individual models they see, by demonstrating that models may be derived from others in different ways. It will also enable readers intent on constructing their own models to understand some of the courses of action open to them.

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 purpose of this paper is to illustrate how international business (IB) researchers can benefit from meta-analytic structural equation modeling (MASEM) by introducing a statistically rigorous approach (i.e. two-stage meta-analytic structural equation modeling or TSSEM) and comparing it with a conventional approach (i.e. the univariate-r approach). The illustration and comparison present a methodological overview of MASEM that will assist IB researchers in selecting an optimal method.

In this paper, the MASEM method is elaborated upon, and methodological issues are addressed, by comparing the TSSEM and the univariate-r approaches using an empirical illustration. In this illustrative example, which is based on transaction cost economics, the effects of a firm’s internal factors on its levels of commitment in an international entry strategy are examined.

The MASEM method can help IB researchers to test and build on IB theories by synthesizing findings in the extant literature because this method reflects the theoretical complexity of IB (e.g. intercorrelationships among factors). Comparing the two approaches of MASEM, it is found in this study that due to its statistical rigorousness TSSEM has methodological advantages in helping IB researchers test theoretical models.

This is the first study to introduce MASEM into the discipline of IB strategies. In this paper, the authors introduce an advanced research method and illustrate two ways of using it.

The aim of this paper is to develop, test and improve a method that draws upon business process improvement literature and combines it with knowledge management approaches for modeling and analyzing knowledge‐intensive business processes.

Analyzing and integrating previous meta models served in knowledge oriented business process researches, a preliminary meta model was developed for modeling knowledge‐intensive business processes. Then an initial version of Proper Arrangement of Knowledge Management Processes (PAKMP) framework was developed according to the knowledge management processes approaches. Third round of interviews with process 137 members were conducted in order to test applicability and completeness of both preliminary meta model and initial version of PAKMP framework in order to improve them. In addition, a five‐steps analysis method achieved through case study which is based on the application of both final Meta model and PAKMP framework. In fact this five‐steps method was applied in Tehran's Municipality which redounded to improve preliminary meta model and initial version of PAKMP framework and endorsed the applicability of the proposed method in real world.

This paper has a contribution in enriching the literature related to integrating KM efforts and BPM efforts by presenting a five‐steps analysis method and testing it in a real case. This method considers both KM and business process management points of view.

The general applicability of the method due to the weak generalization of the single case study is a limitation.

This paper combines the advantages of the business process improvement and knowledge management approaches and suggests a practical method for modeling and analyzing the knowledge management status in knowledge‐intensive business processes. After analysis, managers should put emphasis on improving the arrangement of KM processes for critical knowledge objects which led to improve the performance of knowledge‐intensive business process trough removing KM problems. The paper concludes by suggesting some topics for future research.

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.

Reviewing the existing literature in the field of e-learning success reveals a considerable number of studies that primarily investigate the causal relationships proposed by the DeLone and McLean (D&M) information system (IS) success model. However, the various relationships in the D&M model have found different levels of support or even contradictory results within the empirical literature. To synthesize the existing knowledge in the field of e-learning success, the authors have conducted a meta-analysis of e-learning success studies using D&M to combine the quantitative results and validate the model in this field. Furthermore, a moderator analysis involving user types was performed to examine the situation under which they may have different effects.

For this purpose, through a systematic review of the studies, 44 independent studies were selected from 29 qualified related journals. In order to analyze the quantitative results of the studies, the meta-analysis of the effect sizes of the casual relationships in the D&M model has been used.

The findings indicated that all relationships of the model were supported. It was also revealed that the extent of effect sizes of the examined relationships depends on the type of user. Except for one relationship (user satisfaction and net benefit), all effect sizes of employees were more than those of students and teachers.

This meta-analysis reviewed the relationships found in the literature on D&M constructs in e-learning contexts. This study better explains the e-learning success factors by consolidating contradictory findings in the past researches and contributes to the existing e-learning success literature. The findings can assist educational institutions and organizations in decision-making because the findings resulting from the meta-analysis are more consistent than previous primary researches.

Despite the widespread use of the D&M model in the field of e-learning success, no study has yet consolidated the quantitative findings of these studies and the current field abounds in some controversies and inconsistent findings. This paper integrates the results of empirical studies that examined the relationships within the D&M model. The main contribution of this paper, which is the first of its kind, is to apply meta-analysis to reconcile the conflicting findings, investigate the strengths of the relationships in the D&M model and provide a consolidated view.

A novel approach to modeling exchange rates is presented based on a set of models distinguished by the drivers of the rate and regime duration. The models are combined into a “meta model” using model averaging and non-nested hypothesis-testing techniques. The meta model accommodates periods of stability and slowly evolving or abruptly changing regimes involving multiple drivers. Estimated meta models for five exchange rates provide a compelling characterization of their determination over the last 40 years or so, identifying “phases” during which the influences from policy and financial market responses to news succumb to equilibrating macroeconomic pressures and vice versa.

This paper aims to deal with the optimal choice of a novel extreme learning machine (ELM) architecture based on an ensemble of classic ELM called Meta-ELM structural parameters by using a forecasting process.

The modelling performance of the Meta-ELM architecture varies depending on the network parameters it contains. The choice of Meta-ELM parameters is important for the accuracy of the models. For this reason, the optimal choice of Meta-ELM parameters is investigated on the problem of wind speed forecasting in this paper. The hourly wind-speed data obtained from Bilecik and Bozcaada stations in Turkey are used. The different number of ELM groups (M) and nodes (N_h) are analysed for determining the best modelling performance of Meta-ELM. Also, the optimal Meta-ELM architecture forecasting results are compared with four different learning algorithms and a hybrid meta-heuristic approach. Finally, the linear model based on correlation between the parameters was given as three dimensions (3D) and calculated.

It is observed that the analysis has better performance for parameters of Meta-ELM, M = 15 − 20 and N_h = 5 − 10. Also considering the performance metric, the Meta-ELM model provides the best results in all regions and the Levenberg–Marquardt algorithm -feed forward neural network and adaptive neuro fuzzy inference system -particle swarm optimization show competitive results for forecasting process. In addition, the Meta-ELM provides much better results in terms of elapsed time.

The original contribution of the study is to investigate of determination Meta-ELM parameters based on forecasting process.

The purpose of this paper is to formally verify the composition of web services to reduce inconsistencies in software architectures.

In order to check the web services composition, the authors use a model‐driven engineering (MDE)‐based approach and to achieve the formalization of web service composition in ACME and check the consistency of this composition, the authors introduce the pattern mediation to formalize web services composition with the ADL ACME, using the concept of architectural style of ACME. Subsequently, a scenario shows how this style can be used in ACMEStudio to detect inconsistencies. The example shows a web travel organization application.

The authors ensure reliability defined through non‐functional properties. To do so, use ACME was used to check assembling consistency of web service composition. In a second part, a SWC2ACME tool was designed and implemented to check if the web services meta‐model conforms to ACME model.

The paper describes a framework which has proven to be useful to ensure a safe design and execution of software architectures, specifically web services composition.