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The authors’ conclusions are based on mathematical derivations that are supported by computer simulations and three worked examples in applications of economics and finance. Finally, the authors provide a link to a computer program so that researchers can perform the analyses easily.

Based on a parameter estimation goal, the present work is concerned with determining the minimum sample size researchers should collect so their sample medians can be trusted as good estimates of corresponding population medians. The authors derive two solutions, using a normal approximation and an exact method.

The exact method provides more accurate answers than the normal approximation method. The authors show that the minimum sample size necessary for estimating the median using the exact method is substantially smaller than that using the normal approximation method. Therefore, researchers can use the exact method to enjoy a sample size savings.

In this paper, the a priori procedure is extended for estimating the population median under the skew normal settings. The mathematical derivation and with computer simulations of the exact method by using sample median to estimate the population median is new and a link to a free and user-friendly computer program is provided so researchers can make their own calculations.

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.

This study aims to address the growing importance of online health information (OHI) and the associated uncertainty. Although previous research has explored factors influencing the credibility of OHI, results have been inconsistent. Therefore, this study aims to identify the essential factors that influence the perceived credibility of OHI by conducting a meta-analysis of articles published from 2010 to 2022. The study also aims to examine the moderating effects of demographic characteristics, study design and the platforms where health information is located.

Based on the Prominence-Interpretation Theory (PIT), a meta-analysis of 25 empirical studies was conducted to explore 12 factors related to information content and source, social interaction, individual and media affordance. Moderators such as age, education level, gender of participants, sample size, platforms and research design were also examined.

Results suggest that all factors, except social support, have significant effects on the credibility of OHI. Among them, argument quality had the strongest correlation with credibility and individual factors were also found to be relevant. Moderating effects indicate that social support was significantly moderated by age and education level. Different sample sizes may lead to variations in the role of social endorsement, while personal involvement was moderated by sample size, platform and study design.

This study enriches the application of PIT in the health domain and provides guidance for scholars to expand the scope of research on factors influencing OHI credibility.

In mobile banking (m-banking), understanding the factors contributing to customer satisfaction is crucial for bank managers to design effective strategies for enhancing the uptake of mobile banking services. This study assesses the relationships between quality, technology acceptance and credibility factors and behavioural outcomes (actual use, continuance intention and loyalty) and satisfaction with m-banking. It further investigates the moderating influence of economy type, innovation level, connectivity level and sample size on all these relationships.

The study employs a meta-analysis technique and reviews 54 published studies to investigate the antecedents and consequences of satisfaction with m-banking.

The study finds a significant relationship between satisfaction with m-banking and quality, technology acceptance and credibility factors and behavioural outcomes. It concludes that the moderating effect of economy type, innovation level, connectivity level and sample size partially moderate the majority of the hypothesized relationships.

Drawing on a comprehensive literature review, this study presents a novel framework elucidating the antecedents and behavioural outcomes of satisfaction with mobile banking. It contributes to the literature by exploring the moderating effects of sample size and country context on the relationships between these factors, presenting important implications for future mobile banking research.

This study has practical implications for m-banking service providers, offering insights into the factors that drive user satisfaction with mobile banking and highlighting the need for tailored strategies in different country contexts.

This study examines the effects of factors leading to satisfaction and the subsequent outcomes within the context of m-banking. The findings offer fresh perspectives that can be valuable for managers and policymakers, enabling them to enhance customer satisfaction in the realm of m-banking.

Despite the dramatic increase in construction toward additive manufacturing, several challenges are faced using natural materials such as Earth and salt compared to the most market-useable materials in 3D printing as concrete which consumes high carbon emission.

Characterization and mechanical tests were conducted on 19 samples for three natural binders in dry and wet tests to mimic the additive manufacturing process in order to reach an efficient extrudable and printable mixture that fits the 3D printer.

Upon testing compressive strength against grain size, compaction, cohesion, shape, heat and water content, X-Salt was shown to record high compressive strength of 9.5 MPa. This is equivalent to old Karshif and fire bricks and surpasses both rammed Earth and new Karshif. Material flow analysis for X-Salt assessing energy usage showed that only 10% recycled waste was produced by the end of the life cycle compared to salt.

Findings are expected to upscale the use of 3D salt printing in on-site and off-site architectural applications.

Findings contribute to attempts to resolve challenges related to vernacular architecture using 3D salt printing with sufficient stability.

Benefits include recyclability and minimum environmental impact. Social aspects related to technology integration remain however for further research.

This paper expands the use of Karshif, a salt-based traditional building material in Egypt's desert by using X-Salt, a salt-base and natural adhesive, and investigating its printability by testing its mechanical properties to reach a cleaner and low-cost sustainable 3D printed mixture.

Following the adoption of International Financial Reporting Standards (IFRS), firms are required to recognize gains or losses from investment property revaluation in the income statement, instead of equity in the balance sheet. This results in both a “materiality effect” (as auditors set a higher materiality level and require lower audit efforts) and a “cushion effect” (as revaluation gains serve as a cushion and reduce earnings manipulation incentives). Utilizing this unique setting, this study investigates whether the use of fair value measurement for investment property affects audit pricing before and after IFRS convergence in the Hong Kong real estate industry.

Using a sample of 78 real estate companies listed on the Hong Kong Stock Exchange in the pre-IFRS period (2001–2004) and the post-IFRS period (2005–2008), this study employs multivariate regression analyses to test the research hypotheses with respect to the association between investment property revaluation and audit fees and the role of corporate governance structures in the context of family control.

The empirical results suggest that audit fees decrease with revaluation gains or losses from investment property revaluation after IFRS convergence, but not before. Furthermore, the negative association is stronger in companies controlled by founders, with proportionally more independent directors on the board and with a smaller board size. This is consistent with the moderating effect of corporate governance.

The findings shed more light on the consequences of fair value accounting for non-financial assets and are of interest to regulators for assessing the benefits of the wide use of fair value measurement under IFRS in emerging markets, especially where the corporate ownership structure is typically controlled by founding families. This study also provides recommendations for the audit community to fully consider the impact of asset revaluation on audit procedures and audit pricing.

The main goal of this paper is to study the specific characteristics of the performance of companies in the metallurgical sector, in the northern region of Portugal.

To achieve this aim, the authors have used data from 325 companies manufacturing metal products, except machinery and equipment (CAE Rev.3 25) and 27 companies that manufacture machinery and equipment (CAE Rev. 3 28). The models were estimated by using the panel data methodology for the period between 2011 and 2019. Specifically, the estimation method of the generalized method of moments system (GMM system) proposed by Arellano and Bover (1995) and Blundell and Bond (1998) was used.

The results show that the main decisions on the performance of metallurgical companies in Northern Portugal depend on the dimensions of sales in the domestic market (SDM), sales in the community market (SCM), and sales in the foreign market (SFM) and also highlight that the signal and significance of the specific variables depends on how the different stakeholders understand performance.

As far as the authors know, this is the first study to comparatively analyze the two metallurgical databases in Portugal. Despite the huge difference in the size of the sample, this study’s results show that in an era of paradigm shift about what business objectives should be, stakeholders are still not environmentally aware and the social dimension is only considered by shareholders, but not yet by the manager and the general community.

By introducing Stacking algorithm to solve the underfitting problem caused by insufficient data in traditional machine learning, this paper provides a new solution to the cold start problem of entrepreneurial borrowing risk control.

The authors introduce semi-supervised learning and integrated learning into the field of migration learning, and innovatively propose the Stacking model migration learning, which can independently train models on entrepreneurial borrowing credit data, and then use the migration strategy itself as the learning object, and use the Stacking algorithm to combine the prediction results of the source domain model and the target domain model.

The effectiveness of the two migration learning models is evaluated with real data from an entrepreneurial borrowing. The algorithmic performance of the Stacking-based model migration learning is further improved compared to the benchmark model without migration learning techniques, with the model area under curve value rising to 0.8. Comparing the two migration learning models reveals that the model-based migration learning approach performs better. The reason for this is that the sample-based migration learning approach only eliminates the noisy samples that are relatively less similar to the entrepreneurial borrowing data. However, the calculation of similarity and the weighing of similarity are subjective, and there is no unified judgment standard and operation method, so there is no guarantee that the retained traditional credit samples have the same sample distribution and feature structure as the entrepreneurial borrowing data.

From a practical standpoint, on the one hand, it provides a new solution to the cold start problem of entrepreneurial borrowing risk control. The small number of labeled high-quality samples cannot support the learning and deployment of big data risk control models, which is the cold start problem of the entrepreneurial borrowing risk control system. By extending the training sample set with auxiliary domain data through suitable migration learning methods, the prediction performance of the model can be improved to a certain extent and more generalized laws can be learned.

This paper introduces the thought method of migration learning to the entrepreneurial borrowing scenario, provides a new solution to the cold start problem of the entrepreneurial borrowing risk control system and verifies the feasibility and effectiveness of the migration learning method applied in the risk control field through empirical data.

Homeownership for younger generations is exacerbated by the deterioration in affordability worldwide. As a result, the role of parental support in facilitating homeownership requires attention. This study aims to assess the influence of parental wealth and housing tenure as support mechanisms to facilitate homeownership for their children.

This study uses data from a representative survey of the New Zealand population.

Parents who are homeowners tend to offer more financial support to their children than those who rent. Additionally, the financial support increases when parents have investment housing as well. The results further reveal differences in financial support when considering one-child and multi-child families. The intergenerational transmission of wealth inequality appears to be more noticeable in multi-child families, where parental housing tenure plays a dominant role in determining the level of financial support provided to offspring.

The insights gained serve as a basis for refining housing policies to better account for these family transfers and promote equitable access to homeownership.

Informed by the resource-based view of the firm, dynamic capabilities theory and contingency theory, this study examines the impact of Industry 4.0 (IR4.0) technologies adoption on firm performance (FP) while accounting for the mediating role of innovation ambidexterity (IA) and moderating roles of contextual and methodological factors that drive the performance gains of the phenomenon.

A random-effect model in comprehensive meta-analysis (CMA) is used to synthesize 113 studies in 115 independent samples with 192,188 observations.

This analysis demonstrates that IR4.0 digital technologies are directly related to financial and non-financial performance, disclosing that the performance effect on non-financial is the largest. Moreover, there is a complementary partial mediation role of the impacts of IR4.0 on FP by IA. Furthermore, this focal relationship is moderated by boundary-spanning conditions: contextual factors – firm size, business type, economic development, industry sector and methodological factors – proxy of FP, sample size and study type.

The results imply that IR4.0 produces financial and non-financial benefits by enabling firms to develop dynamic capabilities like innovation ambidexterity, which informs managers and practitioners that unless IR4.0 technologies and IA strategies are combined together to generate superior FP, IR4.0, in and of itself, would produce a less positive impact on FP than the combined impact of IR4.0 and IA. Therefore, managers should focus on converting IR4.0 resources to dynamic capabilities like IA by leveraging open innovation strategies or building IR4.0-based coordination mechanisms by creating cross-unit business synergies.

To the best of the authors' knowledge, per the literature review, this is the first meta-analysis structural equation modeling study on the interplay between IR4.0, innovation ambidexterity and firm performance.