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Using a meta-regression analysis, we quantitatively review the empirical literature on the relation between effective tax rate (ETR) and firm size. Accounting literature offers two competing theories on this relation: The political cost theory, suggesting a positive size-ETR relation, and the political power theory, suggesting a negative size-ETR relation. Using a unique data set of 56 studies that do not show a clear tendency towards either of the two theories, we contribute to the discussion on the size-ETR relation in three ways: First, applying meta-regression analysis on a US meta-data set, we provide evidence supporting the political cost theory. Second, our analysis reveals factors that are possible sources of variation and bias in previous empirical studies; these findings can improve future empirical and analytical models. Third, we extend our analysis to a cross-country meta-data set; this extension enables us to investigate explanations for the two competing theories in more detail. We find that Hofstede’s cultural dimensions theory, a transparency index and a corruption index explain variation in the size-ETR relation. Independent of the two theories, we also find that tax planning aspects potentially affect the size-ETR relation. To our knowledge, these explanations have not yet been investigated in our research context.

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.

This paper aims to describe the use of a meta-learning framework for recommending cost-sensitive classification methods with the aim of answering an important question that arises in machine learning, namely, “Among all the available classification algorithms, and in considering a specific type of data and cost, which is the best algorithm for my problem?”

This paper describes the use of a meta-learning framework for recommending cost-sensitive classification methods for the aim of answering an important question that arises in machine learning, namely, “Among all the available classification algorithms, and in considering a specific type of data and cost, which is the best algorithm for my problem?” The framework is based on the idea of applying machine learning techniques to discover knowledge about the performance of different machine learning algorithms. It includes components that repeatedly apply different classification methods on data sets and measures their performance. The characteristics of the data sets, combined with the algorithms and the performance provide the training examples. A decision tree algorithm is applied to the training examples to induce the knowledge, which can then be used to recommend algorithms for new data sets. The paper makes a contribution to both meta-learning and cost-sensitive machine learning approaches. Those both fields are not new, however, building a recommender that recommends the optimal case-sensitive approach for a given data problem is the contribution. The proposed solution is implemented in WEKA and evaluated by applying it on different data sets and comparing the results with existing studies available in the literature. The results show that a developed meta-learning solution produces better results than METAL, a well-known meta-learning system. The developed solution takes the misclassification cost into consideration during the learning process, which is not available in the compared project.

The proposed solution is implemented in WEKA and evaluated by applying it to different data sets and comparing the results with existing studies available in the literature. The results show that a developed meta-learning solution produces better results than METAL, a well-known meta-learning system.

The paper presents a major piece of new information in writing for the first time. Meta-learning work has been done before but this paper presents a new meta-learning framework that is costs sensitive.

Meta-analysis has been relied on relatively infrequently in strategic management studies, certainly as compared to other fields such as the medical sciences, psychology, and education. This may be unfortunate, as there are several aspects of the manner in which strategic management studies are typically conducted that make them especially appropriate for this approach. To this end, we provide a brief foundation for meta-analysis, an example of meta-analysis, and a discussion of those elements that strongly recommend the efficacy of meta-analysis for the synthesis of strategic management studies.

The purpose of the paper is to improve traditional knowledge management models in light of complexity theory, emphasizing the importance of moving away from hierarchical relationships among data, information, knowledge, and wisdom.

Traditional definitions and models are critically reviewed and their weaknesses highlighted. A transformational perspective of the traditional hierarchies is proposed to highlight the need to develop better perspectives. The paper demonstrates the holistic nature of data, information, knowledge, and wisdom, and how they are all based on an interpretation of existence.

Existing models are logically extended, by adopting a complexity‐based perspective, to propose a new model – the E2E model – which highlights the non‐linear relationships among existence, data, information, knowledge, wisdom, and enlightenment, as well as the nature of understanding as the process that defines the differences among these constructs. The meaning of metas (such as meta‐data, meta‐information, and meta‐knowledge) is discussed, and a reconstitution of knowledge management is proposed.

The importance of understanding as a concept to create useful metaphors for knowledge management practitioners is emphasized, and the crucial importance of the metas for knowledge management is shown.

A new model of the cognitive system of knowledge is proposed, based on application of complexity theory to knowledge management. Understanding is identified as the basis of the conversion process among an extended range of knowledge constructs, and the scope of knowledge management is redefined.

Over the past few years, assemblage theory or assemblage thinking has garnered increasing attention in educational research, but has been used only tangentially in explications of the nature of comparative and international education (CIE) as a field. This conceptual examination applies an assemblage theory lens to explore the contours of CIE as a scholarly field marked by its rich and interweaved architecture. It does so by first reviewing Deleuze and Guattari’s (1987) principles of rhizomatic structures to define the emergence of assemblages. Secondly, it transposes these principles in conceiving the field of CIE as a meta-assemblage of associated and subordinated sub-assemblages of actors driven by varied disciplinary, interdisciplinary or multidisciplinary interests. Finally, it interrogates the role of Big Data technologies in exerting (re)territorializing and deterritorializing tendencies on the (re)configuration of CIE. The chapter concludes with reiterating the variable character of CIE as a meta-assemblage and proposes ways to move this conversation forward.

Over the past decade, international business and international management researchers have utilized meta-analytic approaches to synthesizing findings in the extant literature. This chapter reviews the studies published in the top five international business and management journals from 2004 to 2012. The review investigates major problems in the published meta-analyses by evaluating their overall analyses as well as the approaches utilized. The findings of this review reveal differences among the journals and improvements in the approaches applied in recent years. The chapter ends by discussing why and how international business and management researchers need to focus more on methodological fundamentals in their applications of meta-analysis.

Meta-analysis is one of a number of scientific approaches for accumulating knowledge in a research domain. It provides a quantitative synthesis of a literature using various statistical instruments. This chapter introduces the main points underlying meta-analytic methodology by discussing its merits when compared to a conventional literature review and covers the fundamental approaches used when conducting a meta-analysis. Criticism of meta-analysis is briefly discussed in the context of the major issues facing meta-analysis in international business.

The aim of continuous learning is to obtain and fine-tune information gradually without removing the already existing information. Many conventional approaches in streaming data classification assume that all arrived new data is completely labeled. To regularize Neural Networks (NNs) by merging side information like user-provided labels or pair-wise constraints, incremental semi-supervised learning models need to be introduced. However, they are hard to implement, specifically in non-stationary environments because of the efficiency and sensitivity of such algorithms to parameters. The periodic update and maintenance of the decision method is the significant challenge in incremental algorithms whenever the new data arrives.

Hence, this paper plans to develop the meta-learning model for handling continuous or streaming data. Initially, the data pertain to continuous behavior is gathered from diverse benchmark source. Further, the classification of the data is performed by the Recurrent Neural Network (RNN), in which testing weight is adjusted or optimized by the new meta-heuristic algorithm. Here, the weight is updated for reducing the error difference between the target and the measured data when new data is given for testing. The optimized weight updated testing is performed by evaluating the concept-drift and classification accuracy. The new continuous learning by RNN is accomplished by the improved Opposition-based Novel Updating Spotted Hyena Optimization (ONU-SHO). Finally, the experiments with different datasets show that the proposed learning is improved over the conventional models.

From the analysis, the accuracy of the ONU-SHO based RNN (ONU-SHO-RNN) was 10.1% advanced than Decision Tree (DT), 7.6% advanced than Naive Bayes (NB), 7.4% advanced than k-nearest neighbors (KNN), 2.5% advanced than Support Vector Machine (SVM) 9.3% advanced than NN, and 10.6% advanced than RNN. Hence, it is confirmed that the ONU-SHO algorithm is performing well for acquiring the best data stream classification.

This paper introduces a novel meta-learning model using Opposition-based Novel Updating Spotted Hyena Optimization (ONU-SHO)-based Recurrent Neural Network (RNN) for handling continuous or streaming data. This is the first work utilizes a novel meta-learning model using Opposition-based Novel Updating Spotted Hyena Optimization (ONU-SHO)-based Recurrent Neural Network (RNN) for handling continuous or streaming data.

The purpose of the paper is to explore coherence across key disciplines of knowledge management (KM) for a general model as a way to address performance dissatisfaction in the field.

Research employed an evidence‐based meta‐analysis (287 aspects of literature), triangulated through an exploratory survey (91 global respondents), to gather data on the drivers for KM. The paper attempts to demonstrate self‐similarity across six key KM disciplines using fractal theory as a data analysis tool.

Appear to demonstrate self‐affinity between key disciplines in the field of KM. This provides a strong signpost for future research in the field when attempting to address practitioner dissatisfaction in performance.

The paper cannot determine importance, or value of the factors discussed. The meta‐analysis allows us to determine the existence of the identified functions and enablers. Limited representation of literature from outside the Northern Hemisphere will not allow for an assertion as to validity outside of this area. Findings could not determine whether factors were stable through time. While outliers in the data provide signposts for further research, it could be attributed to situated variance.

This paper could influence future research and practice through support for the development of general models for the field. It signposts affinity between disciplines, which could direct theorists and practitioners to explore solutions outside of their situated discipline through a shared understanding.

The fractal theory data analysis approach appears to be unusual if not unique in the field of KM. The evidence‐based meta‐analysis provides depth and rigour with the results triangulated against an exploratory survey, which offers a richness of findings that speaks directly to the needs of the field.