This research aims at providing a meta-analysis of empirical findings of the literature on the characteristics of social media influencers on customer engagement and purchase intention. For this purpose, researchers derived eight social media influencers’ characteristics, i.e., homophily, expertise, trustworthiness, credibility, congruence with the product, entertainment value, informative value, and attractiveness. The current study synthesizes 176 effect sizes derived from 62 individual studies, and 22,554 individuals act as an aggregate sample. Results revealed that these characteristics have a moderate to high correlation with customer engagement and purchase intention. The entertainment value of social media influencers has the strongest association with customer engagement among all the attributes studied in this analysis. It also concluded that the credibility of influencers impacts purchase intention more than any other attribute. This work provides a novel approach to reducing the heterogeneity in influencer marketing research by empirically specifying the directions of relationships and the extent of the effect of these relationships.

This paper examines the influence of adopting resource efficiency actions, saving water, saving energy, using renewable energy, saving materials, minimizing waste, selling scrap, recycling, using durable products, promoting environmental responsibility, and offering green marketing products and services on the performance of small and medium-sized enterprises (SMEs). More specifically, we investigate specific resource efficiency actions and their impact on production costs, investment, the available support for product expansion, and the effect of encountered barriers on SME performance. We develop a theoretical framework based on stakeholder- and resource-based theories to serve as the foundation for this analysis. We use these theories to explain the link between eco-efficiency actions, firm performance, and ecological behavior, along with public policy and innovation. This study uses Flash Eurobarometer survey datasets FL342, FL381, FL426, and FL456, which cover SMEs across time and sectors in 28 EU countries. The data are analyzed through descriptive and ordered logit regression analysis, using the Statistical Package for the Social Sciences (SPSS) to test the relationship between the above variables and the parameters. In terms of practical implications, these findings are crucial in helping SMEs pursue sustainable development. According to the findings, SMEs lack information on how implementing eco-efficiency action affects their financial health and sustainable innovation. This study can provide valuable insights into how implementing eco-efficiency practices can positively impact a company’s bottom line, good health, and employees’ well-being and how SMEs can use this information to make more informed decisions. Additionally, the findings can help inform policy makers about how to better support SMEs in pursuing sustainable development.

Abiotic stresses, including drought, salinity, extreme temperatures and nutrient deficiencies, pose significant challenges to crop production and global food security. To combat these challenges, the integration of bioinformatics educational tools and AI applications provide a synergistic approach to identify and analyze stress-responsive genes, regulatory networks and molecular markers associated with stress tolerance. Bioinformatics educational tools offer a robust framework for data collection, storage and initial analysis, while AI applications enhance pattern recognition, predictive modeling and real-time data processing capabilities. This review uniquely integrates bioinformatics educational tools and AI applications, highlighting their combined role in managing abiotic stress in plants and crops. The novelty is demonstrated by the integration of multiomics data with AI algorithms, providing deeper insights into stress response pathways, biomarker discovery and pattern recognition. Key AI applications include predictive modeling of stress resistance genes, gene regulatory network inference, omics data integration and real-time plant monitoring through the fusion of remote sensing and AI-assisted phenomics. Challenges such as handling big omics data, model interpretability, overfitting and experimental validation remain there, but future prospects involve developing user-friendly bioinformatics educational platforms, establishing common data standards, interdisciplinary collaboration and harnessing AI for real-time stress mitigation strategies in plants and crops. Educational initiatives, interdisciplinary collaborations and trainings are essential to equip the next generation of researchers with the required skills to utilize these advanced tools effectively. The convergence of bioinformatics and AI holds vast prospects for accelerating the development of stress-resilient plants and crops, optimizing agricultural practices and ensuring global food security under increasing environmental pressures. Moreover, this integrated approach is crucial for advancing sustainable agriculture and ensuring global food security amidst growing environmental challenges.

This research is about the China–Pakistan Economic Corridor (CPEC), which is an important and first project of the “Belt and Road” initiative (BRI). BRI is the framework and manifesto for the wide-ranging, fundamental collaboration signed by China and Pakistan in 2013. The CPEC vision and mission were initiated to develop economic growth and facilitate free trade, the people’s living standards of Pakistan and China through bilateral investments, trade, cultural exchanges, and economic activities between both countries. The initial investment for the project was $46 billion, with a tentative duration of fifteen years. This research aimed to inquire into the effects of legal risks (LR), social security (SS), and employee environmental awareness (EEA) on the project performance (PP) of the CPEC. It further investigates the significance of gender empowerment perspectives (GEP). A research framework consisting of this quantitative analysis and the bilateral impacts of the study were explored through several policies scenarios into 2025. The results of the risk analysis were rated on a Likert scale. A questionnaire survey was used in order to collect data and test the research framework and hypotheses. An empirical test was conducted using a dataset with partial least square structural equation modeling (PLS-SEM) to validate the study.

Legal technologies (digital solutions to providing legal services) have turned into the essential element of competition among legal-market stakeholders. This study highlights the competitive superiority of legal services based on the concept of innovative disruptive technologies in comparison to traditional firms. For this purpose, the present study identified several trends in the evolution of the legal tech market (i.e., changing landscapes of legal business, delivery models, legal-market segments, and disruptive technologies); moreover, qualitative content analysis has been conducted to provide sound grounds for cost-benefit analysis. Furthermore, cost-benefit analysis makes a comparison of the monetary, social and psychological costs, benefits of legal technologies and traditional firms from clients’ and lawyers’ viewpoints. This has been, in fact, neglected by extant research. Based on these analyses, a new model for legal business has developed to highlight how potential advancements can take place in legal practices. Our findings reveal that legal technologies make legal services more affordable and easily accessible, while helping lawyers to develop innovative solutions and a relaxed working environment. In addition, this study contributes to the literature on service innovation with a focus on how new services and business models have been developed in the legal market as a result of digital technologies. Eventually, this study adds theoretical and practical implications to the research discussing the advent of legal technologies.