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The purpose of this manuscript is to provide a step-by-step primer on systematic and meta-analytic reviews across the service field, to systematically analyze the quality of meta-analytic reporting in the service domain, to provide detailed protocols authors may follow when conducting and reporting these analyses and to offer recommendations for future service meta-analyses.

Eligible frontline service-related meta-analyses published through May 2021 were identified for inclusion (k = 33) through a systematic search of Academic Search Complete, PsycINFO, Business Source Complete, Web of Science, Google Scholar and specific service journals using search terms related to service and meta-analyses.

An analysis of the existing meta-analyses within the service field, while often providing high-quality results, revealed that the quality of the reporting can be improved in several ways to enhance the replicability of published meta-analyses in the service domain.

This research employs a question-and-answer approach to provide a substantive guide for both properly conducting and properly reporting high-quality meta-analytic research in the service field for scholars at various levels of experience.

This work aggregates best practices from diverse disciplines to create a comprehensive checklist of protocols for conducting and reporting high-quality service meta-analyses while providing additional resources for further exploration.

No study has investigated the effects of different parameters on publication bias in meta-analyses using a machine learning approach. Therefore, this study aims to evaluate the impact of various factors on publication bias in meta-analyses.

An electronic questionnaire was created according to some factors extracted from the Cochrane Handbook and AMSTAR-2 tool to identify factors affecting publication bias. Twelve experts were consulted to determine their opinion on the importance of each factor. Each component was evaluated based on its content validity ratio (CVR). In total, 616 meta-analyses comprising 1893 outcomes from PubMed that assessed the presence of publication bias in their reported outcomes were randomly selected to extract their data. The multilayer perceptron (MLP) technique was used in IBM SPSS Modeler 18.0 to construct a prediction model. 70, 15 and 15% of the data were used for the model's training, testing and validation partitions.

There was a publication bias in 968 (51.14%) outcomes. The established model had an accuracy rate of 86.1%, and all pre-selected nine variables were included in the model. The results showed that the number of databases searched was the most important predictive variable (0.26), followed by the number of searches in the grey literature (0.24), search in Medline (0.17) and advanced search with numerous operators (0.13).

The results of this study can help clinical researchers minimize publication bias in their studies, leading to improved evidence-based medicine.

To the best of the author’s knowledge, this is the first study to model publication bias using machine learning.

The purpose of this paper is to measure the relationship of technology acceptance model (TAM) variables (PEOU and PU) with behavioral intention (BI) and attitude in mobile and digital libraries context. This study also examines the relationship of external variables (information quality and system quality) with TAM variables (PEOU and PU) in mobile and digital libraries context.

This meta-analysis was performed through PRISMA-P guidelines. Four databases (Google Scholar, Web of Science, Scopus and LISTA) were used for searching, and the search was conducted according to defined criteria.

Findings of this study revealed a large effect size of PU and PEOU with BI. There was also a large effect size of PU and PEOU with attitude. A medium effect size was found between SysQ → PU, InfoQ → PU and SysQ → PEOU. However, there was a small effect size between InfoQ and PEOU.

To the best of the authors’ knowledge, there was no study published till the time of conducting this meta-analysis. Hence, this study fills the literature gap. This study also confirms that TAM is a valid model in the acceptance and use of technology in mobile and digital libraries context. Thus, the findings of the present study are helpful for developers and designers in designing and developing mobile library apps. It will also be beneficial for library authorities and system librarians in designing and developing digital libraries in academic settings.

This study conducts a meta-analysis to investigate the impact of board diversity, independence and size on carbon emission disclosure.

The results of 22 empirical investigations on the association between board qualities and carbon emission disclosure are synthesised using a meta-analysis approach. Inclusion and exclusion criteria are established, and search strategies are devised to locate relevant material. Data extraction entails gathering important information such as the names of the authors, variables and correlation coefficients. Fisher's z-transformation is used to compute and synthesise effect sizes and assumptions, sensitivity testing and subgroup analysis are performed to assess the robustness of the findings.

A substantial association was discovered between board characteristics and carbon emission disclosure. Board independence and gender diversity revealed small to medium-strength positive relationships, whilst board size had a medium-strength positive correlation. The study periods varied from 2011 to 2022, with 2018 having the most studies. However, highly heterogeneous groups were discovered; further subgroup analyses were then carried out to sort out this issue.

Several limitations were recognised due to the limited number of studies and heterogeneity, although subgroup analysis was used to reduce the influence of heterogeneity. To investigate alternate outcomes, more analysis of the heterogeneity level and potential modifications to the model assumptions may be required.

Companies should consider board size, independence and gender diversity when formulating long-term competitive strategies in the climate change movement. These characteristics can aid in bridging information gaps and garnering stakeholder support for carbon-reduction initiatives.

This meta-analysis addresses a gap in the literature by addressing prior studies' conflicting and inconsistent findings on the association between board characteristics and carbon emission disclosure. It employs a rigorous approach and synthesis strategy to provide a thorough and robust understanding of the crucial role of board characteristics in carbon emission disclosure.

With the aim to provide a global view of factors affecting mark-up size on construction projects, this study performs a meta-analytical review of the relevant studies over the past 20 years.

The analytical process involved the identification and evaluation of the importance of critical factors affecting mark-up size on construction projects, and the assessment of the generalisability of findings of the meta-analysis. A random-effects model was adopted in the statistical meta-analysis.

The results show that there are 23 critical factors, and the top five factors are: (1) competitiveness of other bidders; (2) number of bidders; (3) relationship and past experience with client; (4) experience on similar project; and (5) project size. A heterogeneity test further shows that there is no statistically significant heterogeneity across the studies, reinforcing the generalisability of the findings to a global context.

The list of critical factors from a global perspective should form a good basis for future efforts in bidding model development.

The research findings have practical implications to both construction clients and contractors in formulating their contracting practices and strategies.

This is the first meta-analysis of a sizeable collection of replicated studies on factors affecting mark-up size on construction projects in the literature.

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.

This study aims to meta-analytically investigate the impact of educational technology interventions on the development of creative thinking in educational settings. In recent years, the debate among researchers has persisted regarding the impact of various educational technologies, including interactive learning environments, digital instruction and platforms, and educational games and robotics, on students' creative thinking in diverse educational settings due to inconsistent findings.

This study, conducting a meta-analysis by synthesizing 35 relevant empirical studies with 2,776 participants, aims to investigate the association between educational technology interventions and the Torrance Tests of Creative Thinking (TTCT) and its subscales (fluency, flexibility, originality and elaboration).

No evident publication bias was found. From a general perspective, the results demonstrate a moderate level of influence of educational technology on the overall TTCT scale, with high heterogeneity attributed to the adopted instruments, mixed methods and target outcomes. Additionally, the results indicate that only three of the TTCT subscales (fluency, flexibility and originality) are influenced by educational technologies. Among the interventions, interactive learning environments yielded medium to the largest mean effect size. Furthermore, moderator analyses suggest that the effects of interventions on two subscales of TTCT (flexibility and originality) are moderated by school types, research design and the duration of intervention. The conclusion drawn is that interventions promoting students' creative thinking in different educational settings are efficacious.

Despite the low homogeneity of the results, which might have influenced the findings, the large fail-safe N suggests that these findings are robust. The study examined potential causes of heterogeneity and emphasized the importance of further research in this area.

The present study uses a meta-analysis technique to explore the association between corporate governance and dividend policy. The extant literature delivers inconclusive findings on the relationship between corporate governance and dividend policy. Therefore, this study aims to resolve the issues and deliver comprehensive results.

The study involves a meta-analysis of 53 research studies using preferred reporting items for systematic reviews and meta-analyses and population, intervention, comparison, outcome and study design approaches. The paper examines the impact of moderators: corporate governance structure (Anglo-American, communitarian or emerging system) and dividend distribution metrics (dividend over net income, dividend over total assets and absolute amount of dividend/dividend per share). The study involves subgroup analysis and meta-regression analysis to examine the impact of moderators.

The study’s results specify that board size and percentage of female directors significantly impact the dividend decisions of the company. In addition, subgroup analysis and meta-regression results demonstrate that dividend measurement proxy moderates the association between corporate governance and dividend policy.

Based on the existing literature surveyed, to the best of the authors’ knowledge, the current study is the first to conduct a meta-analysis on the relationship between corporate governance and dividend policy. This paper is unique and the first one of its kind (to the best of the authors’ knowledge) to cover all these moderating variables under an umbrella and consolidate the results to understand the existing knowledge and direct future research in the area of corporate governance and dividend decisions.

Companies around the world have included ecolabels as a marketing strategy to convince consumers to choose products with lower environmental impact. However, the literature lacks a consensus on the effectiveness of ecolabels in persuading consumers to choose green products. The present meta-analysis addresses this gap by first evaluating the net persuasion effect of using ecolabels and second by investigating the role of cultural orientations, operationalized at the country level, in this effect.

This cross-cultural meta-analysis analyzed data from 26,116 consumers across 18 countries, encompassing 75 papers published between 1995 and 2023. Univariate and meta-regression analyses were utilized.

The results demonstrate that the presence (vs absence) of ecolabels has a medium positive persuasion effect. Findings show cultural orientations moderate the persuasion effect of ecolabels in that the effect is stronger in countries with high power distance, individualism, masculinity (motivation towards achievement and success) and uncertainty avoidance orientation, which create contingent conditions to ecolabels’ persuasion. In addition, results show other methodological factors that affect ecolabel persuasion. Contributions and implications of the findings are discussed.

This meta-analysis is distinctive for its global scope, including diverse countries and cultures. It addresses a crucial gap in ecolabel persuasion research, providing insights that reconcile discrepancies in existing studies. It offers practical implications for businesses and policymakers while laying the groundwork for future cross-cultural research in this field.

The successful implementation of hospital projects (HPs) tends to confront sundry challenges in the planning and construction (P&C) phases due to their complexity and particularity. Employing key performance indicators (KPIs) facilitates the monitoring of HPs to advance their successful delivery. This study aims to comprehensively investigate the KPIs for hospital planning and construction (HPC).

The KPIs for HPC were identified through a systematic review. Then a comprehensive assessment of these KPIs was performed utilizing a meta-analysis method. In this process, basic statistical analysis, subgroup analysis, sensitive analysis and publication bias analysis were performed.

Results indicate that all 27 KPIs identified from the literature are significant for executing HPs in P&C phases. Also, some unconventional performance indicators are crucial for implementing HPs, such as “Project monitoring effectiveness” and “Industry innovation and synergy,” as their high significance is reflected in this study. Despite the fact that the findings of meta-analysis are more trustworthy than those of individual studies, a high heterogeneity still exists in the findings. It highlights the inherent uncertainty in the construction industry. Hence, this study applied subgroup analysis to explore the underlying factors causing the high level of heterogeneity and used sensitive analysis to assess the robustness of the findings.

There is no consensus among the prior studies on KPIs for HPC specifically and their degree of significance. Additionally, few reviews in this field have focused on the reliability of the results. This study comprehensively assesses the KPIs for HPC and explores the variability and robustness of the results, which provides a multi-dimensional perspective for practitioners and the research community to investigate the performance of HPs during the P&C stages.