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The purposes of this study were to calculate the confirmatory factor analysis in the measurement model using robust measures (McDonald’s omega and Cronbach’s alpha) to ensure the reliability of the proposed scale and to explore the measurement invariance of the scale per the participants’ gender.

The methodological sample had 408 students from a private university in northern Mexico, of which 200 (49%) were male and 208 (51%) were female; the sample age range was 18–58 years (M = 22.4 years, SD = 6.0). The data analysis included descriptive and normality, dimensionality, reliability and measurement invariance.

The social entrepreneurship competency measurement model showed acceptable adjustment indexes in evaluating the internal structure, reliability and factorial invariance by gender of the study participants.

Even though the contributions of this study are evident, these findings must be taken with caution due to some limitations. First, the proposed measurement model uses a self-reported scale, so it is essential to include other measurement methods with less implicit social desirability. Second, although the sample was intended to be representative, it only drew from a specific geographical area, making it difficult to generalize these findings to culturally diverse areas. Third, this study did not consider other validity measures; for example, concurrent, divergent and predictive, so future studies should consider examining the relationship of social entrepreneurship with other factors.

From the practical perspective, this study provides a parsimonious instrument regarding the number of items included in the measurement model. From the theoretical perspective, the present study contributes to delimiting the dimensions of social entrepreneurship competency.

This study contributes to the field of social entrepreneurship, particularly in the area of construct measurement, by offering a measurement model with solid evidence of internal structure validity, reliability and factorial invariance for the perceived achievement of social entrepreneurship competency.

Total productive maintenance consists of strategies and procedures that aim to guarantee the entire functioning of machines in a production process so that production is not interrupted and no loss of quality in the final product occurs. Planned maintenance is one of the eight pillars of total productive maintenance, a set of tools considered essential to ensure equipment reliability and availability, reduce unplanned stoppage and increase productivity. This study aims to analyze the influence of statistical reliability on the performance of such a pillar.

In this study, we utilized a multi-method approach to rigorously examine the impact of statistical reliability on the planned maintenance pillar within total productive maintenance. Our methodology combined a detailed statistical analysis of maintenance data with advanced reliability modeling, specifically employing Weibull distribution to analyze failure patterns. Additionally, we integrated qualitative insights gathered through semi-structured interviews with the maintenance team, enhancing the depth of our analysis. The case study, conducted in a fertilizer granulation plant, focused on a critical failure in the granulator pillow block bearing, providing a comprehensive perspective on the practical application of statistical reliability within total productive maintenance; and not presupposing statistical reliability is the solution over more effective methods for the case.

Our findings reveal that the integration of statistical reliability within the planned maintenance pillar significantly enhances predictive maintenance capabilities, leading to more accurate forecasts of equipment failure modes. The Weibull analysis of the granulator pillow block bearing indicated a mean time between failures of 191.3 days, providing support for optimizing maintenance schedules. Moreover, the qualitative insights from the maintenance team highlighted the operational benefits of our approach, such as improved resource allocation and the need for specialized training. These results demonstrate the practical impact of statistical reliability in preventing unplanned downtimes and informing strategic decisions in maintenance planning, thereby emphasizing the importance of your work in the field.

In terms of the originality and practicality of this study, we emphasize the significant findings that underscore the positive influence of using statistical reliability in conjunction with the planned maintenance pillar. This approach can be instrumental in designing and enhancing component preventive maintenance plans. Furthermore, it can effectively manage equipment failure modes and monitor their useful life, providing valuable insights for professionals in total productive maintenance.

This study aims to identify sustainability indicators in the energy sector through a literature review and validate them from the perspective and context of professionals working in the sector in an emerging economy country, Brazil, considering the relationship of these indicators with the achievement of the targets set by the United Nations sustainable development goals (UN SDGs).

To accomplish this, a literature review on sustainability indicators specific to the energy sector was conducted. Subsequently, a research instrument (questionnaire) based on the identified indicators was developed and a survey was administered to professionals in the field. The collected data were analyzed using the Lawshe method.

The results revealed 20 indicators, distributed across environmental, economic and social dimensions. Among these, nine indicators were validated, including global impacts, local impacts, renewable energy production as a percentage of total production, greenhouse gas emissions, access to electricity, investment in the energy sector, installed capacity in the electricity sector, energy prices in the end-use sector and energy distribution and conversion efficiency.

Consequently, it was possible to determine which SDGs are directly impacted and provide a foundation for future actions that can contribute to the sustainable advancement of the energy sector in emerging countries.

Coffee is one of the most consumed beverages in the world and the global coffee industry is worth over $100bn. However, the industry faces significant sustainability challenges. Developing a quality traceability system to select the coffee beans and to ensure their authentication would result in economic advantages, because it allows for fraud to be avoided and increases consumer confidence.

Traceability is one of the key elements of sustainability in the coffee sector. The literature reveals that near-infrared (NIR) approaches have a huge potential for gaining rapid information about the origin and properties of coffee beans, without invasive procedures. This study demonstrates the scalability potential of automated methods of manipulation and image acquisition of coffee beans, from experimental scale to industrial lines.

A solution based on the interaction of a manipulation system, a NIR spectrometer acquisition station integrated with a machine learning infrastructure and a compressed air classifier allows for the automatic separation of coffee beans into different classes of origin.

Apart from traceability, the wide industrialization of this system offers further advantages, including reduced workforce, decreased subjectivity in the evaluation and the acquisition of real-time data for labeling.

Higher education is undergoing a significant transformation, demanding a rethinking of instructional methods, the establishment of novel assessment frameworks and the incorporation of state-of-the-art technologies to advance the development of learning competencies. The use of simulations is an alternative that offers satisfactory results and favors learning in new generations. This study aims to present a methodology for assessing the advancement of self-directed learning (SDL) in participants through simulation techniques and show that the incorporation of simulations can improve participants’ SDL skills.

This study uses a quantitative approach to evaluate the evolution of various competencies related to SDL among participants. Includes the application of scales specifically designed to measure readiness for SDL, the use of factor analysis to identify and validate essential factors and conducting hypothesis testing to verify shifts in students’ perceptions regarding the significance of SDL-related skills.

The findings of this study reveal a positive impact of simulations on the SDL readiness. Factor analysis identified five key factors: self-management skills, openness to learning opportunities, initiative and independence in learning, self-concept as an effective learner and desire for Learning. The use of simulations significantly improved critical thinking, logical reasoning, analysis and decision-making capabilities. These results demonstrate the effectiveness of this technique as a valuable tool for developing SDL skills and highlight the potential of such interventions in complex and uncertain learning environments.

The study provided valuable insights into the impact of simulations in the logistics field, particularly in the development of SDL-related skills. There is a need for studies in other areas of knowledge that would allow for the generalization of these results. Likewise, it is advisable to use scales that measure the development of other types of skills. Additionally, the study’s focus on short-term effects may not fully capture the long-term development of SDL-related skills. Future research should address these limitations, expand the sample size, conduct longitudinal studies and explore possible contextual and external factors that may influence SDL outcomes in various educational settings, leading to broader and more robust conclusions.

The practical implications of this study are significant for both higher education institutions and educators in the logistics domain. The positive impact of the serious game on participants’ SDL skills highlights the potential of incorporating interactive and engaging learning tools in the curriculum. Educators can leverage serious games to foster critical thinking, logical reasoning and decision-making abilities in their students. Moreover, the identified key factors influencing SDL development provide valuable guidance for designing targeted interventions to enhance students’ self-management, initiative and love for learning.

The social implications of this study underscore the importance of equipping individuals with SDL skills in a rapidly changing world. By integrating serious games and fostering SDL competencies, higher education institutions can contribute to a more adaptable and resilient workforce. Empowering students with critical thinking, innovative thinking and independent learning, they can enhance their ability to navigate the challenges of the labor market. This, in turn, may lead to a more informed and proactive society capable of addressing complex issues and making well-informed decisions.

This article arises from empirical exploration with students from two countries in Latin America (Mexico and Colombia), examining the importance of games in developing SDL skills. Furthermore, it promotes the use of a methodology with a low level of complexity, and that can be replicated with different games. This perspective adds valuable insights to the existing literature, offering practical implications for educators and institutions seeking effective strategies to promote SDL skills among students.

Our objective was to evaluate the cost-utility of a health and social care integration programme for frail older adults in Buenos Aires, Argentina.

Based on a study of the programme’s effectiveness, a Markov model was conducted to assess its cost-utility. The active intervention was the health and social care integration programme, and the control was the best standard of care so far. The setting was the patients' home of residence. A third-party payer perspective and a lifelong time horizon were adopted. All transition probabilities, quality-adjusted life years (QALYs) and costs were estimated from the effectiveness study. A discount rate of 3.5% was applied to costs and benefits. Costs are expressed in international dollars (Int$), calculated according to the International Monetary Fund’s purchasing power parity rate. Different sensitivity analyses were performed. The model was built in Excel 365. Construct validity, verification during model construction and internal consistency of the results were assessed.

The programme had an average cost of Int$18,768.22/QALY, and the control Int$42,609.68/QALY. In the incremental analysis, the programme saved Int$26,436.10 and gained 0.81 QALYs over the control. In the sensitivity analyses, in 99.96% of cases, the programme was less costly and more effective.

The cost savings can facilitate the scalability.

The health and social care integration programme for frail older adults was more effective and less costly than the best standard of care to date. This study contributes to the scarce evidence on the efficiency of integrated care strategies for frail older persons.

The objective of the research is to evaluate the carbon footprint of the green asparagus (Asparagus officinalis L.) supply chain, encompassing the agricultural production to the packaging stage in Italy, as it is the sixth largest producer and the second largest in Europe. It provides an assessment in the province of Foggia and highlights the global perspective of the carbon footprint application in agro-food systems.

The carbon footprint (ISO 14067:2018) considers 1 t of packaged fresh asparagus as a functional unit in the agricultural production and packaging stage and is based on primary data collected in one of the leading companies of asparagus production in the province of Foggia, which markets about 0.21 kt of asparagus per year produced in about 31 ha. Data were integrated with face-to-face in-depth interviews and pre-filled checklists.

Findings show that the carbon footprint of 1 t of packaged fresh asparagus is equivalent to 335.31 kgCO2eq, of which 61% in the agricultural stage and 39% in the packaging one. The majority of the emissions are associated with the fertigation and the diesel consumption for the transportation of workers. Farmers should adopt green electricity so as to reduce the emissions associated with the electric pump for the extraction of water from artesian wells. Moreover, it would be desirable to replace mineral urea phosphate with organic fertilizers.

To the best of the authors’ knowledge, scholars have not yet investigated the environmental impacts of the green asparagus supply chain, even if it represents one of the most cultivated vegetables worldwide, with a global production that amounts to 8.5 Mt per year.

This paper presents a new and well-structured framework that aims to assess the current environmental impact from a Greenhouse Gas (GHG) emissions perspective. This tool includes a new set of Lean Key Performance Indicators (KPIs), which translates the well-known logic of Overall Equipment Effectiveness in the field of GHG emissions, that can progressively detect industrial losses that cause GHG emissions and support decision-making for implementing improvements.

The new metrics are presented with reference to two different perspectives: (1) to highlight the deviation of the current value of emissions from the target; (2) to adopt a diagnostic orientation not only to provide an assessment of current performance but also to search for the main causes of inefficiencies and to direct improvement implementations.

The proposed framework was applied to a major company operating in the plywood production sector. It identified emission-related losses at each stage of the production process, providing an overall performance evaluation of 53.1%. The industrial application shows how the indicators work in practice, and the framework as a whole, to assess GHG emissions related to industrial losses and to proper address improvement actions.

This paper scrutinizes a new set of Lean KPIs to assess the industrial losses causing GHG emissions and identifies some significant drawbacks. Then it proposes a new structure of losses and KPIs that not only quantify efficiency but also allow to identify viable countermeasures.

This study aims to compare the influence of origin types (country-of-origin – COO versus geographic indication – GI) and wine worlds (new versus old wine country – represented by Brazil and Italia, respectively) on the attitude of low-involvement Generation Z potential consumers from Brazil toward wine ads.

The authors conducted a two (COO vs GI) by two (New vs Old World) within-subject experimental design with 98 Brazilians from Generation Z. Latin square was used to systematically counterbalances the sequence in which participants were exposed to ads.

The results reveal that potential consumers’ attitudes are more positive when a COO is used in wine advertisements than when a GI is used. Besides, the consumers’ conative response is more positive when an Old World country (Italy) is used in wine ads than when New World country (Brazil) is used.

This study expands existing literature by showing that, in wine ads, COO information elicits more positive responses than GI. It also suggests a preference among potential consumers for Old over New World wines in purchase, recommendation and consumption decisions.

The wine advertisements for the market segment investigated should highlight the COO instead of geographical indication (GI). Besides, the authors point out the importance of promoting the concept of GI among low-involvement Generation Z potential consumers.

To the best of the authors’ knowledge, this is the first study to assess potential consumers’ attitudes toward wine ads, comparing COO and GI and determining which geographical cue elicits more positive consumer attitudes: origin types (COO vs GI) or wine worlds (Old vs New).

This study aims to evaluate in situ oxidative polymerization of aniline (Ani) as a post-processing method to promote extrusion-based 3D printed parts, made from insulating polymers, to components with functional properties, including electrical conductivity and chemical sensitivity.

Extrusion-based 3D printed parts of polyethylene terephthalate modified with glycol (PETG) and polypropylene (PP) were coated in an aqueous acid solution via in situ oxidative polymerization of Ani. First, the feedstocks were characterized. Densely printed samples were then used to assess the adhesion of polyaniline (PAni) and electrical conductivity on printed parts. The best feedstock candidate for PAni coating was selected for further analysis. Last, a Taguchi methodology was used to evaluate the influence of printing parameters on the coating of porous samples. Analysis of variance and Tukey post hoc test were used to identify the best levels for each parameter.

Colorimetry measurements showed significant color shifts in PP samples and no shifts in PETG samples upon pullout testing. The incorporation of PAni content and electrical conductivity were, respectively, 41% and 571% higher for PETG in comparison to PP. Upon coating, the surface energy of both materials decreased. Additionally, the dynamic mechanical analysis test showed minimal influence of PAni over the dynamic mechanical properties of PETG. The parametric study indicated that only layer thickness and infill pattern had a significant influence on PAni incorporation and electrical conductivity of coated porous samples.

Current literature reports difficulties in incorporating PAni without affecting dimensional precision and feedstock stability. In situ, oxidative polymerization of Ani could overcome these limitations. However, its use as a functional post-processing of extrusion-based printed parts is a novelty.