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The primary objective of this study is to explore consumers' non-adoption intentions towards meta-commerce (or metaverse retailing). Utilizing the Innovation Resistance Theory (IRT) as the theoretical foundation, this study investigates the impact of diverse barriers on non-adoption intentions within the meta-commerce context.

A total of 356 responses were gathered to test the proposed hypotheses. Structural Equation Modelling (SEM) with SmartPLS 4 software was used to examine these hypotheses.

The findings of this study show that perceived cyber risk, perceived regulatory uncertainty, perceived switching cost and perceived technical uncertainty are significantly linked to non-adoption intention towards meta-commerce. Furthermore, the study suggests that the moderating influence of technostress on these connections is more pronounced for consumers with high technostress compared to those with low technostress.

This study makes a significant contribution to the current body of literature by providing valuable insights into the fundamental barriers that consumers encounter when contemplating the adoption of meta-commerce. This contribution is particularly noteworthy as it fills a gap in the existing literature, as no prior study has comprehensively examined the primary obstacles that shape consumer intentions towards meta-commerce adoption. This novel perspective offers scholars, businesses and policymakers a foundation for developing strategies to address these barriers effectively.

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.

Meta-regression is widely used and misused today in meta-analyses in psychology, organizational behavior, marketing, management, and other social sciences, as an approach to the identification and calibration of moderators, with most users being unaware of serious problems in its use. The purpose of this paper is to describe nine serious methodological problems that plague applications of meta-regression.

This paper is methodological in nature and is based on well-established principles of measurement and statistics. These principles are used to illuminate the potential pitfalls in typical applications of meta-regression.

The analysis in this paper demonstrates that many of the nine statistical and measurement pitfalls in the use of meta-regression are nearly universal in applications in the literature, leading to the conclusion that few meta-regressions in the literature today are trustworthy. A second conclusion is that in almost all cases, hierarchical subgrouping of studies is superior to meta-regression as a method of identifying and calibrating moderators. Finally, a third conclusion is that, contrary to popular belief among researchers, the process of accurately identifying and calibrating moderators, even with the best available methods, is complex, difficult, and data demanding.

This paper provides useful guidance to meta-analytic researchers that will improve the practice of moderator identification and calibration in social science research literatures.

Today, many important decisions are made on the basis of the results of meta-analyses. These include decisions in medicine, pharmacology, applied psychology, management, marketing, social policy, and other social sciences. The guidance provided in this paper will improve the quality of such decisions by improving the accuracy and trustworthiness of meta-analytic results.

This paper is original and valuable in that there is no similar listing and discussion of the pitfalls in the use of meta-regression in the literature, and there is currently a widespread lack of knowledge of these problems among meta-analytic researchers in all disciplines.

Points out how the philosophy of cybernetics may be helpful for the future of artificial intelligence (AI). Starts with the notion of continuous AI and discusses its relationship with meta‐knowledge (namely, knowledge about knowledge). The conjecture is that meta‐knowledge can capture a kind of continuity. Also summarizes the use of meta‐knowledge in knowledge‐based systems.

Digital information retrieval has been as a problem, and at the same time a research interest for information scientists in recent years. They have planned some solutions to solve problems manifested during the 1990s. Designing meta‐tags and applying them to HTML documents was a remedy in this direction. Meta‐tags can help authors, publishers and indexers of Web pages to analyze intended content more precisely and efficiently. The aim of the present survey is to measure meta‐tags of the Iranian Web sites in accordance with an international criterion. To carry out the research, 346 Iranian Web sites were selected among 3,342, which represented a sample of all Web sites existed in Iranhoo, an Iranian Web directory. The source codes of the sample home pages were reviewed in terms of the presence of keywords and description meta‐tags. The findings of the survey showed that 31.5 percent and 24.6 percent of the Iranian Web sites have keywords and description meta‐tags respectively. The paper concludes that the Iranian Web sites are lower than non‐Iranian Web sites in terms of the use of meta‐tags.

The purpose of this paper is to offer a reflection on the role that meta-analysis can play in theory building for service phenomena.

The paper illustrates the benefits of conducting meta-analysis, presents its basic steps, and then uses an example to illustrate how meta-analytic findings can be used to enrich and develop theory.

Meta-analytic findings allow identifying gaps in current theories, thereby offering the opportunity to develop new research. Theoretical advancement through meta-analysis can imply theory borrowing, mutual theoretical development with other disciplines, intradisciplinary and multidisciplinary theorizing.

Experienced researchers might use this essay to better understand how meta-analysis can be used to generate new relevant research. Young researchers and PhD students may benefit from a greater use of meta-analysis to gain a thorough knowledge of their research topic and about the areas that require further exploration.

Rather than thinking of meta-analysis mainly as the conclusion of a well-researched domain and a good summary of cites, this essay underlines to the service scholar community how meta-analysis can boost theoretical advancement in services.

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.

Purpose – This chapter gives an overview of meta-analytic methods and illustrates the use of these methods for synthesising research findings. The advantages of performing a meta-analysis are described. Pitfalls in meta-analyses are also discussed. The chapter is intended to present the main elements of a meta-analysis and guide readers to literature presenting meta-analytic methods in greater detail.

Methodology – Key references in the meta-analysis literature are quoted and examples of meta-analyses are presented.

Findings – A meta-analysis is a useful tool for summarising knowledge in fields where a large number of studies have been reported. In addition to providing summary estimates of results, a meta-analysis can be applied to identify factors that produce systematic variation in study findings.

Research implications – Methods of meta-analyses keep developing to deal with complex data structures, thus extending the type of research findings that are amenable to meta-analyses.

Practical implications – Performing a meta-analysis saves labour by eliminating the need to read and digest a large number of studies in order to get an overview of the current state-of-knowledge in a field. Moreover, a meta-analysis establishes a system for easily and quickly updating knowledge as new studies become available.

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.

A meta-evaluation is an assessment of evaluation practices. Meta-evaluations include assessments of validity and usefulness of two or more studies that focus on the same issues. Every performance audit is grounded explicitly or implicitly in one or more theories of program evaluation. A deep understanding of alternative theories of program evaluation is helpful to gain clarity about sound auditing practices. We present a review of several theories of program evaluation.

This study includes a meta-evaluation of seven government audits on the efficiency and effectiveness of tourism departments and programs. The seven tourism-marketing performance audits are program evaluations for: Missouri, North Carolina, Tennessee, Minnesota, Australia, and two for Hawaii. The majority of these audits are negative performance assessments. Similarly, although these audits are more useful than none at all, the central conclusion of the meta-evaluation is that most of these audit reports are inadequate assessments. These audits are too limited in the issues examined; not sufficiently grounded in relevant evaluation theory and practice; and fail to include recommendations, that if implemented, would result in substantial increases in performance.