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Marketing communication practitioners in higher education have not yet identified specific subgroups with similar characteristics within the prospective student (target) market, and do not always know which preferred sources learners consult when deciding on a tertiary institution for further or higher education. In addition, little is known about prospective students' information needs when deciding which institution to attend. As a result many tertiary institutions still use one message in one medium for all target markets. The aims of this paper are: to determine if sub‐groups exist within the student recruitment market that need tailor made persuasive messages; to determine the preferred sources of information gathering for prospective students that influences their choice of tertiary institution; and to determine the information needs of prospective students that influence their decisions to select an institution of higher education.

A stratified random sample of 19 schools was drawn, from which a total of 716 successfully completed questionnaires were used to conduct factor and cluster analyses to determine information needs and source preferences of these respondents.

Ten factors under the VARCLUS procedure with high eigen values were grouped together with the following labels: university, college, employability aspects, course content, student experience, sporting aspects, financial aspects, direct sources, media sources and social sources. Five sub‐groups clustered together with the K‐means analysis, labelled: have lots, aspiring have lots, little direction and new lifers.

A limited amount of literature is available on the current use of media and sources by the target market. Decisions on the choice of a tertiary institution for higher education are most probably influenced by socio‐economic circumstances in a developing context, like South Africa.

An integrated and well co‐ordinated media and message strategy for each of the various sub‐groups should be specified for an IMC plan to be successful.

This is one of the few research articles on media and source preferences of potential students in the Cape Metropolitan. The cluster analysis revealed five clusters of sub‐groups that have specific media and source needs to make the decision on furthering studies at tertiary level.

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.

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.

In higher education, blended learning is already strongly established. The e-courses vary in their structure, assignments, prompt examinations, interaction between students and teachers, etc. Such aspects may influence the students’ perception of usefulness of blended learning. The purpose of this paper is to identify the factors which influence that feeling and to look for possible differences in perception by different subgroups of students.

Students in the survey evaluated 13 aspects of e-courses in which they were enrolled. From enrolment documents, additional demographic data were collected (gender, high-school grade, study programme, etc.). A multiple linear regression was used with perceived usefulness as the response variable and the 12 other e-course aspects as predictors. Further, the same regression analysis was performed on different subgroups of students based on demographical data.

The empirical results showed that the general impression regarding the e-courses, their consistency with the face-to-face teaching and the teachers’ responsiveness had a significant influence on the students’ perception of the usefulness of e-courses. Further analysis based on demographic data revealed several subgroups of students where the perception of usefulness was influenced by different aspects. The teachers’ feedback and supplementing the tutorial play an important role in higher years of study, while the general impression loses its influence.

The paper is the first to explore the importance of demographic determinants of perceived usefulness of e-learning tools in EAPAA (European Association of Public Administration Accreditation)-accredited undergraduate public administration programmes.

This study aims to prove the complexity of the relationship between CSR and financial performance (FP) and to decompose the complexity of the relationship using neo-institutional theory.

This research employs a meta-analysis that integrates 55 various contexts studied between 1998 and 2017 using correlation coefficient as the effect size.

This study proves that the nature of the relationship between CSR and FP is complex and suggests that the analysis of the relationship between the two variables includes institutional factors to produce generalizable conclusions. Country characteristics, forms and dimensions of CSR, CSR measurements and FP measurements explain the complexity of the relationship between CSR and FP.

Future research is expected to include industry characteristics and the corporate governance model in the analysis of the relationship between CSR and FP. Differences in industry characteristics affect the selection of CSR forms and dimensions, bringing it the potential to influence the relationship between CSR and FP. The corporate governance model adopted by developing countries and developed countries also has the potential to be an institutional factor to influence the relationship between CSR and FP.

This research proves that the complexity of the relationship between CSR and FP is nature given. This research explores the factors causing the complexity of the relationship using neo-institutional theory, which, to the author's knowledge, has not been done by other researchers.

The purpose of this paper is to evaluate the impact of interventions focused on reducing screen time in children.

Studies that aim to investigate the effects of interventions aimed at reducing the time spent in front of the screen (i.e. screen time). A Random-effects model was used to calculate the pooled standard mean differences. The outcome was to evaluate the screen time in children in the 0–18 age range. A subgroup analysis was performed to reveal the extent to which the overall effect size varied by subgroups (participant age, duration of intervention and follow).

For the outcome, the meta-analysis included 21 studies, and the standard difference in mean change in screen time in the intervention group compared with the control group was −0.16 (95% confidence interval [CI], −0.21 to −0.12) (p < 0.001). The effect size was found to be higher in long-term (=7 months) interventions and follow-ups (p < 0.05).

Subgroup analysis showed that a significant effect of screen time reduction was observed in studies in which the duration of intervention and follow-up was =7 months. As the evidence base grows, future researchers can contribute to these findings by conducting a more comprehensive analysis of effect modifiers and optimizing interventions to reduce screen time.

The purpose of this paper is to examine the evolution of collaboration among researchers in Library Hi Tech based on the co-authorship network analysis.

The Library Hi Tech publications were retrieved from Web of Science database between 2006 and 2017. Social network analysis based on co-authorship was analyzed by using BibExcel software and a visual knowledge map was generated by Pajek. Three important social capital indicators: degree centrality, closeness centrality and betweenness centrality were calculated to indicate the co-authorship. Cohesive subgroup analysis which includes components and k-core was then applied to show the connectivity of co-authorship network of Library Hi Tech.

The results indicated that around 42 percent of the articles were written by single author, while an increasing trend of multi-authored articles suggesting the collaboration among researchers in librarian research field becomes popular. Furthermore, the social network analysis identified authorship network with three core authors – Markey, K., Fourie, I. and Li, X. Finally, six core subgroups each included six or seven tightly connected researchers were also identified.

This study contributed to the existing literature by revealing the co-authorship network in librarian research field. Key researchers in the major subgroup were identified. This is one of the limited studies that describe the collaboration network among authors from different perspectives showing a more comprehensive co-authorship network.

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.

The purpose of this paper is to target on individual perceptions of BIM practice in terms of BIM benefits, critical success factors (CSFs) and challenges in Chongqing which represented the less BIM-developed metropolitan cities in China.

Adopting a questionnaire-survey approach followed by statistical analysis, the study further divided the survey population from Chongqing into subgroups according to their employer types and organization sizes. A further subgroup analysis adopting statistical approach was conducted to investigate the effects of employer type and organization size on individual perceptions.

Subgroup analysis revealed that governmental employees held more conservative and neutral perceptions toward several items in BIM benefit, CSFs and challenges. It was inferred that smaller organizations with fewer than 100 full-time employees perceived more benefits of BIM in recruiting and retaining employees, and considered more critical of involving companies with BIM knowledge in their projects.

This study contributed to the body of knowledge in managerial BIM in terms that: it extended the research of individual perceptions toward BIM implementation by focusing on less BIM-mature regions; it contributed to previous studies of influencing factors to BIM practice-based perceptions by introducing factors related to organization type and sizes; and it would lead to future research in establishing BIM climate and culture which address perceptions and behaviors in BIM adoption at both individual and organizational levels.

The study aims to examine the relationship between the corporate disclosure on intellectual capital and five firm characteristics, namely, size, leverage, profitability, age and industry type.

The research uses a meta-analysis technique by taking 19 articles published between 2003 and 2013. Thus, this study integrates and accumulates the findings of prior studies.

The research finds a significant relationship between intellectual capital disclosure (ICD) and the independent variables: size, profitability and industry.

This study provides a systematic overview of the determinants of ICD by using a meta-analysis approach. A systematic analysis is currently lacking in the ICD literature; hence, this study attempts to resolve the mixed findings of prior studies.