Banana macropropagation in a thermal chamber is an economical technology, effective as a phytosanitary cleaning method, and efficient to enhance seedling production. The objective of this work was to evaluate the effects of corm size (CS) and benzylaminopurine (BAP) on plantain cv. Barraganete seedling proliferation in two propagation environments (PE). The treatments consisted of two levels of BAP (with and without BAP), three CS (2 ± 0.5, 4 ± 0.5 and 6 ± 0.5 kg) and two PE (thermal chamber and raised bed). The variables evaluated were sprouting time (days), multiplication rate (MT) per unit (seedlings per corm) and area (seedlings per m²). Sprouting time was significantly influenced (p < 0.05) by the PE, where the thermal chamber advanced shoot emergence by 12 days, with respect to the raised bed. MT of seedlings per corm and m², were significantly influenced (p < 0.05) by BAP × AP and TC × AP interactions, where the highest seedling production per corm occurred inside thermal chamber with BAP and 6 ± 0.5 kg corms, while seedling production per m² was higher with 2 ± 0.5 kg corms under the same thermal chamber conditions and with BAP. The main effects results reported that with BAP there were 30 and 31% increases in MT per corm and per m², respectively, relative to the treatment without BAP. Within the thermal chamber the MT per corm and per m² increased by 44% relative to the raised bed. Regarding the effect of CS, larger corms achieved higher individual MT, while smaller corms achieved higher MT per area. The use of a thermal chamber and BAP is recommended for mass production of banana seedlings through macropropagation.

Purpose: This study investigates the mediating effect of Environmental Attachment (EA) among consumers in an emerging market, concentrating on the impact of two key factors: Green Environmental Awareness (GEA) and Sense of Responsibility (SOR) on Sustainable Product Consumption (SPC). Design/methodology/approach: A thorough online survey was carried out with Google Docs and distributed to 304 Pakistani consumers who now use or are considering purchasing sustainable or green products. Structural Equation Modeling (SEM) was used to rigorously test the suggested model utilizing a non-probability sampling technique, specifically the stratified purposive sampling approach. Findings: Green environmental awareness (GEA) and a sense of responsibility (SOR) have been shown to have a substantial impact on creating environmental attachment (EA) in both existing and potential customers of sustainable products. The findings of this study also revealed that environmental attachment (EA) plays an important role as a mediator in the links between green environmental awareness (GEA) and the consumption of sustainable goods (SPC), as well as between a sense of responsibility (SOR) and SPC. Despite this, it is crucial to note that the projected direct effect of GEA on SPC was shown to be statistically insignificant. This conclusion implies that additional factors outside the scope of this study may influence the relationship between GEA and SPC. Research limitations/implications: It is vital to highlight that the focus of this study is on an online sample of consumers near Punjab, Pakistan. Future studies should look at other parts of Pakistan to acquire a more complete picture of sustainable consumption trends. Furthermore, our findings suggest that characteristics impacting sustainable consumption, such as Green Environmental Awareness (GEA) and Sense of Responsibility (SOR), may differ among countries. As a result, performing a comparison analysis involving two or more countries could provide valuable insights into projecting sustainable product consumption among current and potential sustainable product customers. Originality/Value: This study contributes to the literature by investigating the factors of sustainable consumption using the lens of the Norm Activation Model theory (NAM), notably Green Environmental Awareness (GEA) and Sense of Responsibility (SOR), to predict sustainable product consumption. The findings are important for promoting long-term goals in Pakistan and provide a framework that can be applied in other emerging markets.

Interest in the impact of environmental innovations on firms’ financial performance has surged over the past two decades, but studies show inconsistent results. This paper addresses these divergences by analyzing 74 studies from 1996 to 2022, encompassing 4,390,754 firm-year observations. We developed a probability-based meta-analysis approach to synthesize existing knowledge and found a generally positive impact of environmental innovations on financial performance, with a probability range of 0.85 to 0.97. Manufacturing firms benefit more from environmental innovations than firms in other industries, and survey-based studies report a more favorable relationship than those using secondary data. This study contributes to existing knowledge by providing a comprehensive aggregation of data, supporting the resource-based view (RBV) and the Porter hypothesis. The findings suggest significant policy implications, highlighting the need for tailored incentives and information-sharing mechanisms, and underscore the importance of diverse data sources in research to ensure robust results.

Increasing populations in cities have created challenges for the urban environment and also public health. Today, lacking sport participation opportunities in urban settings is a global concern. This study conceptualizes and develops a theoretical framework that identifies factors associated with effective urban built environments that help shape and reshape residents’ attitude toward sport activities and enhances their participation. Based on a comprehensive review of literature and by following the Stimulus-Organism-Response (SOR) theory and attitude change theory, a four-factor measurement model is proposed for studying urban built environment, including Availability, Accessibility, Design, and Safety. Further examinations are made on how these factors are channeled to transform residents’ attitudes and behavior associated with participating in sport activities, with Affordability as a moderator. Discussions are centered around the viability of the developed framework and its application for future research investigations.

This study evaluated the performance of several machine learning classifiers—Decision Tree, Random Forest, Logistic Regression, Gradient Boosting, SVM, KNN, and Naive Bayes—for adaptability classification in online and onsite learning environments. Decision Tree and Random Forest models achieved the highest accuracy of 0.833, with balanced precision, recall, and F1-scores, indicating strong, overall performance. In contrast, Naive Bayes, while having the lowest accuracy (0.625), exhibited high recall, making it potentially useful for identifying adaptable students despite lower precision. SHAP (SHapley Additive exPlanations) analysis further identified the most influential features on adaptability classification. IT Resources at the University emerged as the primary factor affecting adaptability, followed by Digital Tools Exposure and Class Scheduling Flexibility. Additionally, Psychological Readiness for Change and Technical Support Availability were impactful, underscoring their importance in engaging students in online learning. These findings illustrate the significance of IT infrastructure and flexible scheduling in fostering adaptability, with implications for enhancing online learning experiences.