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Machine learning (ML) technologies are increasingly being applied in building cost estimation as an advanced method to overcome the challenge of insufficient data and subjective effects of experts. To address the gap of lacking a review of ML applications in building cost estimation, this research aimed to conduct a systematic literature review to provide a robust reference and suggest development pathways for creating novel ML-based building cost prediction models, ultimately enhancing construction project management capabilities.

A systematic literature review according to preferred reporting items for systematic reviews and meta-analyses (PRISMA) was adopted using quantitative bibliographic analysis and qualitative narrative synthesis based on the 70 screened publications from Web of Science (WOS) and Scopus databases. The VOSviewer software was used to prepare the thematic focus from the bibliographic data garnered.

Based on the results of a bibliographic analysis, current research hotspots and future trends in the application of ML to building cost estimation have been identified. Additionally, the mechanisms behind existing ML models and other key points were analyzed using narrative synthesis. Importantly, the weaknesses of current applications were highlighted and recommendations for future development were made. These recommendations included defining the availability of building attributes, increasing the application of emerging ML algorithms and models to various aspects of building cost estimation and addressing the lack of public databases.

The findings are instrumental in aiding project management professionals in grasping current trends in ML for cost estimation and in promoting its adoption in real-world industries. The insights and recommendations can be utilized by researchers to refine ML-based cost estimation models, thereby enhancing construction project management. Additionally, policymakers can leverage the findings to advocate for industry standards, which will elevate technical proficiency and ensure consistency.

Compared to previous research, the findings revealed research hotspots and future trends in the application of ML cost estimation models in only building projects. Additionally, the analysis of the establishment mechanisms of existing ML models and other key points, along with the developed recommendations, were more beneficial for developing improved ML-based cost estimation models, thereby enhancing project management capabilities.

This study clarifies the history and significance of cryptocurrencies. It explores the underlying decentralisation and trustlessness concepts that set these digital assets apart from conventional fiat currencies. It clarifies how blockchain technology functions as the core component of decentralised money. The mechanics of mining, its function in creating and validating Bitcoin transactions, and the emergence of substitute consensus mechanisms to solve environmental issues are all covered in this study. An in-depth analysis of blockchain technology covers its advantages, such as immutability and transparency, as well as its architecture and consensus processes. This study continues with a focus on the future by examining the development of decentralised finance (DeFi) and showcasing numerous DeFi applications, including yield farming, lending protocols, and decentralised exchanges (DEXs). As a result of the development of cryptocurrencies and blockchain technology, DeFi has become possible, ushering in a new era of financial independence and inclusivity. This study emphasises the significance of striking a balance between innovation and suitable regulatory measures as the globe embraces this revolution in order to enable the proper integration of DeFi into the global financial environment. The revolutionary potential of DeFi, particularly in increasing financial inclusion and empowerment for marginalised groups globally, is one of the major themes discussed. To negotiate legal frameworks while maintaining DeFi's decentralised nature, this study looks at the regulatory problems that come with this potential.

Female smallholder farmers in low-income countries face barriers to accessing capital and commodity markets. While agricultural cooperatives provide services that contribute to the income and productivity of small-scale producers, evidence of cooperatives' social and economic empowerment of female smallholders remains limited. We apply Sen's capability approach to female entrepreneurs' socioeconomic empowerment to examine whether women's participation in a coffee and microfinance cooperative from rural western Uganda benefits their social and economic position within their household. First, we study the relationship between women's cooperative participation and their household coffee sales and savings. Second, we investigate the link between women's cooperative participation and their intra-household decision-making and whether the inclusion of the husband in his wife's cooperative strengthens or lowers women's decision-making power.

We carry out a case study of a hybrid coffee and microfinance cooperative that promotes social innovation through the integration and empowerment of female smallholders in rural Uganda. Using a cross-sectional survey of 411 married female cooperative members from 26 randomly selected self-help groups of Bukonzo Joint Cooperative and 196 female non-members from the identical area, employing propensity score matching, this paper investigates the benefits of women's participation in a coffee and microfinance cooperative in the Rwenzori Mountains of western Uganda. We present and discuss the results of our case study within an extensive literature on the role of institutions in collective action for women's empowerment.

Our findings provide new empirical evidence on female smallholders' participation in mixed cooperatives. Our results indicate that women's participation in microfinance-producer cooperatives appears to be a conditional blessing: even though membership is linked to increased women's intra-household decision-making and raised household savings and income from coffee sales, a wife with a husband in the same cooperative self-help group is associated with diminished women's household decision-making power.

The focus of this study is on female coffee smallholders in an agricultural cooperative in rural western Uganda. In particular, we focus on a case study of one major coffee cooperative. Our cross-sectional survey does not allow us to infer causal interpretations. Also, the survey does not include variables that allow us to measure other dimensions of women's empowerment beyond decision-making over household expenditures and women's financial performance related to savings and income from coffee cultivation.

Our empirical results indicate that female smallholders' cooperative membership is associated with higher incomes and coffee sales. However, husband co-participation in their wives' cooperative group diminishes wives' decision-making, which suggests that including husbands and other family members in the same cooperative group may not be perceived as an attractive route to empowerment for female smallholders. For these reasons, an intervention that encourages the cooperation of both spouses and that is sensitive to context-specific gender inequalities, may be more successful at stimulating social change toward household gender equality than interventions that focus on women's autonomous spheres only.

While the literature thus far has focused on microfinance's potential for women's empowerment, evidence on agricultural cooperatives' affecting women's social and economic position is limited. First, our findings provide novel empirical evidence on the empowering effects of women's participation in a self-help group-based coffee cooperative in rural Uganda. Second, our data allows us to explore the role of husbands' participation in their wives' cooperative and SGH. We embed our hypotheses and empirical results in a rich discussion of female entrepreneurship, microfinance and cooperative literature.

In this paper, we empirically investigate how crypto investments in times of economic downturn would affect credit card usage, a widely used payment method that has a significant impact on individual financial well-being.

We carry out an ordinary least squares regression analysis and an instrumental variable design on data from the most recent National Financial Capability Study 2021 (NFCS2021). The NFCS2021 collects information about various demographic and financial backgrounds of US adults.

We find that crypto investments are associated with a significantly higher likelihood of credit card misuse, as indicated by making only the minim um payments, late payments and using credit cards for cash advances. Meanwhile, social media use is a strong predictor of crypto investments. Results from our analysis are robust after accounting for endogeneity concerns using an instrumental variable design.

Our findings provide new insights into the influence of emerging financial instruments on delinquent credit card behaviors, which can further intensify individual and household financial instability during periods of market stress. Furthermore, our findings underscore the necessity of improving individual awareness of the high-risk characteristics of cryptocurrencies, despite their increasing popularity in the financial marketplace in the current financial marketplace.

Early childhood teachers play a significant role in building children’s success in their first years of school. Therefore, a healthy early childhood workforce in a healthy working environment is an essential aspect of effective early childhood services. This paper aims to explore the extent to which psychological hardiness can be considered as a mediator variable between exposure to workplace bullying and job anxiety among early childhood teachers.

A homogeneous sample comprised of 200 early childhood teachers. For data collection, the researcher used the workplace bullying scale, the psychological hardiness scale and the job anxiety scale among early childhood teachers (prepared by the researcher).

The findings indicated that psychological hardiness mediates the relationship between exposure to workplace bullying and job anxiety among early childhood teachers.

The research result highlighted the necessity of providing counseling programs for early childhood teachers helping them eliminate work stress that affects their job performance. In addition, the kindergarten administration must concentrate on how to effectively communicate and cooperate with early childhood teachers in light of regulations, policies and laws to defeat the spread of workplace bullying. The results of this research contributed to the existing literature by examining the relationship between the research variables, particularly in the early childhood education context.

The purpose of this meta-analysis was to examine the effect of pomegranate juice (PJ) intake on glycemic control in adults.

Materials and methods: PubMed (Medline), ISI Web of Science, Cochrane Library and Scopus databases, measuring glucose and/or insulin and Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) in adults, were searched from inception to December 11, 2021. Moreover, to examine whether grouping factors influenced heterogeneity between research results, subgroup analysis was used.

This meta-analysis showed that PJ intake reduced HOMA-IR significantly, especially if =250 mL was used. This reducing effect remained significant in females, nondiabetic patients and unhealthy subjects.

The authors believe the presented data would be highly motivating and of a wide readership for the readers of your journal, and this paper stimulating a surge of research on the impact of PJ consumption on glycemic indices.

This study aims to investigate the effects of financial aid via zakat and the perceived learning–teaching process on the educational outcomes of undergraduate students in Malaysia.

Approximately 350 zakat recipients (mustahiq) were selected to evaluate their educational outcomes, measured by the learning process, student readiness and academic performance. Partial least squares (PLS) regression was used to test the selected samples and approve the hypothesis.

In accordance with the underlying theories, the results of the PLS regression highlighted several major findings: financial aid, through zakat indeed increases positive effects in the learning process; zakat aid is found to increase student readiness in the education process and academic performance of undergraduate students; and student readiness in the education process plays a significant role in mediating the effect between zakat aid and the learning process and academic performance. This study also demonstrates that the learning process is equally important for student readiness and academic performance.

Importantly, this study contributes novelty by exploring the impact of zakat in education, specifically the higher education sector, because previous studies have enormously discussed zakat as a poverty-mitigating topic. The findings of this study are essential for zakat stakeholders to understand the contribution of zakat to developing human capital, particularly post-COVID-19 in Malaysia.

An AI concierge is a technologically advanced, intelligent and personalized assistant that is designated to an individual customer, proactively taking care of that customer’s needs throughout the service journey. This article envisions the idea of AI concierges and discusses how to leverage AI concierges in the customer journey.

This article takes a conceptual approach and draws insights from literature in service management, marketing, psychology, human-computer interaction and ethics.

This article delineates the fundamental forms of AI concierges: dialog interface (no embodiment), virtual avatar (embodiment in the virtual world), holographic projection (projection in the physical world) and tangible service robot (embodiment in the physical world). Key attributes of AI concierges are the ability to exhibit semantic understanding of auditory and visual inputs, maintain an emotional connection with the customer, demonstrate proactivity in refining the customer’s experience and ensure omnipresence through continuous availability in various forms to attend to service throughout the customer journey. Furthermore, the article explores the multifaceted roles that AI concierges can play across the pre-encounter, encounter and post-encounter stages of the customer journey and explores the opportunities and challenges associated with AI concierges.

This paper provides insights for professionals in hospitality, retail, travel, and healthcare on leveraging AI concierges to enhance the customer experience. By broadening AI concierge services, organizations can deliver personalized assistance and refined services across the entire customer journey.

This article is the first to introduce the concept of the AI concierge. It offers a novel perspective by defining AI concierges’ fundamental forms, key attributes and exploring their diverse roles in the customer journey. Additionally, it lays out a research agenda aimed at further advancing this domain.

This study aims to investigate the use of 20 commonly applied regression methods to predict concrete corrosion. These models are assessed for accuracy and interpretability using SHapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) analysis to provide structural health monitoring prognostic tools.

This study evaluated model performance using standard measures including root mean square error (RMSE), mean square error (MSE), R-squared (R²) and mean absolute error (MAE). Interpretability was evaluated using SHAP and LIME. The X and Y distances, concrete age, relative humidity and temperature were input parameters, whereas half-cell potential (HCP) values were considered output. The experimental data set consisted of observations taken for 270 days.

Gaussian process regression (GPR) models with rational quadratic, square exponential and matern 5/2 kernels outperformed others, with RMSE values around 16.35, MSE of roughly 267.50 and R² values near 0.964. Bagged and boosted ensemble models performed well, with RMSE around 17.20 and R² values over 0.95. Linear approaches, such as efficient linear least squares and linear SVM, resulted in much higher RMSE values (approximately 40.17 and 40.02) and lower R² values (approximately 0.79), indicating decreased prediction accuracy.

The findings highlight the effectiveness of GPR models in forecasting corrosion in concrete buildings. The use of both SHAP and LIME for model interpretability improves the transparency of predictive maintenance models, making them more reliable for practical applications.

Safe infrastructure is crucial to public health. Predicting corrosion and other structural problems improves the safety of buildings, bridges and other community-dependent structures. Public safety, infrastructure durability and transportation and utility interruptions are improved by reducing catastrophic breakdowns.

This study reduces the gap between model accuracy and interpretability in predicting concrete corrosion by proposing a data-driven method for structural health monitoring. The combination of GPR models and ensemble approaches provides a solid foundation for future research and practical applications in predictive maintenance. This comprehensive approach underscores the potential of data-driven methods for predictive maintenance in concrete structures, with implications for broader applications in various industries.

This article focusses on the hybrid strategy, a simultaneous combination of cost leadership and differentiation strategy. The study aims to examine the impact of hybrid strategy on firm performance through its anticipated positive effects on process and product innovation. In addition, we study the moderating role of adaptive capacity in the direct relationships of hybrid strategy with process and product innovation.

Structural equation modelling was used to analyse 1,842 Spanish firms with fewer than 250 employees. We randomly selected small and medium-sized enterprises (SMEs) operating in Spain from the Spanish Central Business Directory (2021) database. The overall sample design was based on stratified sampling.

We found that hybrid strategy is positively related to firm performance and to process and product innovation. Additionally, in firms implementing hybrid strategies, process innovation fostered firm performance. Finally, adaptive capacity strengthened the relationships of hybrid strategy with process and product innovation. This sheds light on how and when hybrid strategy is most effective in fostering SME performance.

We highlight that SMEs need to establish strategies that use diverse resources and capabilities and not just generate competitive advantage using one strategy (cost leadership or differentiation strategy). This requires an agile and flexible systems and structures.

Our research provides novel results by proposing the adoption of hybrid strategies instead of pure strategies (cost leadership and differentiation strategy) as a way for SMEs to survive during crises. Unlike “stuck in the middle” strategies, our study demonstrates the importance of hybrid strategies in a comprehensive model that links them to innovation and firm performance, with adaptive capacity being a determining factor.