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Engineering graduates are expected to have certain attributes in addition to technical expertise that includes development in personal and interpersonal skills with societal concern. Pedagogical strategies have been continuously evolving to improve the graduate attributes. An efficient framework for blended learning that improves the graduate attributes is the need of the hour now.

A blended course model based on TPACK is proposed and the same is evaluated with Kirkpatrick evaluation method to assess the attainment of the attributes. A mapping strategy is developed for the relation between course outcomes and graduate attributes. The proposed model is tested with “Microcontroller” course in undergraduate program with students of three consecutive years in three different learning environments: offline, online and blended. The performance of the students in assessments, students’ feedback and their interest towards additional learning, project skills and job recruitment are the different elements taken for analysis.

The results obtained show that the impact of the proposed blended learning framework in improving the graduate attributes is greater than the offline environments. The analysis is done based on Kirkpatrick evaluation, which demonstrates the improvement in graduate attributes in blended learning by 18% compared to offline mode.

It is seen that blended learning shall be implemented using TPACK model effectively and the proposed model results in improvement of graduate attributes. Though the findings are good enough, the case study is limited to a particular organization and so, the various underlying parameters may vary for different institutions.

The methodology proposed is viable in any institution and may be tested for any program. The effectiveness of the blended learning is known and in this case study, the analysis from the course to the level of program is done.

The research work highlights the integration of technology, pedagogy and content knowledge to enhance engineering students' skills. Hence, it explores a new required norms of education, potentially shaping future teaching learning methodologies. By employing the Kirkpatrick evaluation, it offers insights into the model's effectiveness and influences educational practices in the need of the hour.

The proposed method and results signifies an innovative endeavor that combines technological expertise, pedagogical methods and subject matter knowledge to enhance the attributes of engineering graduates. Kirkpatrick evaluation adds a distinct dimension by objectively assessing the model's impact. The results are analyzed from the original data obtained from a particular institution.

This study aims to investigate the impact of geopolitical risk (GPR) on supply chain concentration (SCC) and the roles of operational capabilities and resources in this relationship.

Secondary longitudinal data from multiple sources is collected and combined to test for a direct impact of GPR on SCC. We further examine the moderating effects of firms’ operational capabilities and resources (i.e. firm resilience, operational slack and cash holding). Fixed-effect regression models are applied to test the hypotheses, followed by a series of robustness tests to check the consistency of the results.

Consistent with the tenets of resource dependence theory, our analysis reveals a significant negative impact of GPR on SCC. Moreover, we find that this adverse effect is attenuated for firms with higher levels of resilience, more operational slack and greater cash holdings. Further analysis suggests that maintaining a diversified supply chain base during heightened GPR is associated with a firm’s improved financial performance.

This study contributes to the supply chain management (SCM) literature by integrating GPR into the supply chain risk management framework. Additionally, it demonstrates the roles of diversification and operational resources in addressing GPR-induced challenges.

This study aims to analyze the relationship between environmental, social and governance (ESG) ratings and corporate bond credit spreads within the oil and gas (O&G) industry. Given the sector’s significant environmental impact and the current energy transition, it is crucial to understand how ESG disclosure affects financial performance, particularly in terms of debt market dynamics. This research aims to provide empirical evidence on whether ESG efforts by O&G companies influence their cost of borrowing.

This study employs a quantitative approach using secondary data from Refinitiv for the period 2018–2022. To address potential endogeneity issues, we utilize two-stage-least-squares regressions. The analysis focuses on corporate bond spreads as the dependent variable and ESG as the key independent variable.

Our findings indicate a negative association between ESG disclosure and corporate bond spreads. Specifically, companies with higher ESG ratings tend to experience lower credit spreads, suggesting that improved ESG practices may lead to reduced borrowing costs. Additionally, the results show that non-state-owned companies (SOC) benefit more from ESG in terms of financial performance compared to state-owned counterparts.

The study is limited by its reliance on secondary data from Refinitiv, which may not capture all nuances of ESG practices and financial performance. Additionally, the analysis is confined to the O&G industry, potentially limiting the generalizability of the findings to other sectors. Future research could expand the scope to include other industries and incorporate primary data to provide a more comprehensive understanding of the ESG–financial performance relationship.

The study’s findings suggest that O&G companies can potentially reduce their borrowing costs by improving their ESG ratings. This insight is valuable for corporate managers and investors, as it highlights the financial benefits of sustainable practices. Additionally, policymakers could use these findings to encourage better ESG disclosure and practices within the industry, ultimately promoting a more sustainable energy sector.

By demonstrating the financial advantages of ESG disclosure, this study underscores the broader social benefits of sustainable business practices. Improved ESG ratings not only contribute to environmental and social well-being but also enhance a company’s financial performance. This dual benefit can motivate more companies to adopt sustainable practices, leading to positive societal impacts such as reduced environmental damage and improved community relations.

This study contributes to the existing literature by providing empirical evidence on the relationship between ESG ratings and corporate bond credit spreads specifically within the O&G industry. By highlighting the differential impact of ESG disclosure on state-owned versus non-SOC, the research offers unique insights that can inform corporate strategies in the context of sustainability and financial performance.

Analizar la relación entre las calificaciones-ESG y los diferenciales de bonos corporativos en la industria del petróleo y gas (PyG). Dada la significativa huella ambiental del sector y la transición energética, es crucial comprender cómo la divulgación-ESG afecta el desempeño financiero en términos de dinámicas del mercado de deuda.

Se emplea un enfoque cuantitativo utilizando datos secundarios de Refinitiv para 2018–2022. Para abordar problemas de endogeneidad, utilizamos regresiones en dos-etapas. El análisis se centra en los diferenciales de los bonos corporativos (variable dependiente) y las calificaciones ESG (variable independiente).

Nuestros resultados indican una asociación negativa entre ESG y los diferenciales de de bonos corporativos en la industria PyG. Las empresas con mejores calificaciones ESG tienden a experimentar diferenciales de crédito más bajos, sugiriendo que las prácticas ESG pueden llevar a una reducción en los costos de endeudamiento. Además, los resultados muestran que las empresas no estatales se benefician más de la divulgación ESG en términos de desempeño financiero en comparación con sus contrapartes estatales.

Se proporciona evidencia empírica sobre la relación entre las calificaciones-ESG y los diferenciales de bonos corporativos. El uso de regresiones en dos etapas para abordar los problemas de endogeneidad añade robustez a los hallazgos. Al resaltar el impacto diferencial de la divulgación-ESG en las empresas estatales versus no estatales, la investigación ofrece perspectivas únicas que pueden informar estrategias corporativas de sostenibilidad y desempeño financiero.

This paper aims to analyze the performance of artificial neural networks with filling methods in predicting the minimum fluidization velocity of different biomass types for bioenergy applications.

An extensive literature review was performed to create an efficient database for training purposes. The database consisted of experimental values of the minimum fluidization velocity, physical properties of the biomass particles (density, size and sphericity) and characteristics of the fluidization (monocomponent experiments or binary mixture). The neural models developed were divided into eight different cases, in which the main difference between them was the filling method type (K-nearest neighbors [KNN] or linear interpolation) and the number of input neurons. The results of the neural models were compared to the classical correlations proposed by the literature and empirical equations derived from multiple regression analysis.

The performance of a given filling method depended on the characteristics and size of the database. The KNN method was superior for lower available data for training and specific fluidization experiments, like monocomponent or binary mixture. The linear interpolation method was superior for a wider and larger database, including monocomponent and binary mixture. The performance of the neural model was comparable with the predictions of the most well-known correlations from the literature.

Techniques of machine learning, such as filling methods, were used to improve the performance of the neural models. Besides the typical comparisons with conventional correlations, comparisons with three main equations derived from multiple regression analysis were reported and discussed.

Brewer's spent grain (BSG) is the main by-product of the brewing industry, holding significant potential for biomass applications. The purpose of this paper was to determine the effective thermal conductivity (k_eff) of BSG and to develop an Artificial Neural Network (ANN) to predict k_eff, since this property is fundamental in the design and optimization of the thermochemical conversion processes toward the feasibility of bioenergy production.

The experimental determination of k_eff as a function of BSG particle diameter and heating rate was performed using the line heat source method. The resulting values were used as a database for training the ANN and testing five multiple linear regression models to predict k_eff under different conditions.

Experimental values of k_eff were in the range of 0.090–0.127 W m⁻¹ K⁻¹, typical for biomasses. The results showed that the reduction of the BSG particle diameter increases k_eff, and that the increase in the heating rate does not statistically affect this property. The developed neural model presented superior performance to the multiple linear regression models, accurately predicting the experimental values and new patterns not addressed in the training procedure.

The empirical correlations and the developed ANN can be utilized in future work. This research conducted a discussion on the practical implications of the results for biomass valorization. This subject is very scarce in the literature, and no studies related to k_eff of BSG were found.

This study aims to critically evaluate the competitiveness of Transformer-based models in financial forecasting, specifically in the context of stock realized volatility forecasting. It seeks to challenge and extend upon the assertions of Zeng et al. (2023) regarding the purported limitations of these models in handling temporal information in financial time series.

Employing a robust methodological framework, the study systematically compares a range of Transformer models, including first-generation and advanced iterations like Informer, Autoformer, and PatchTST, against benchmark models (HAR, NBEATSx, NHITS, and TimesNet). The evaluation encompasses 80 different stocks, four error metrics, four statistical tests, and three robustness tests designed to reflect diverse market conditions and data availability scenarios.

The research uncovers that while first-generation Transformer models, like TFT, underperform in financial forecasting, second-generation models like Informer, Autoformer, and PatchTST demonstrate remarkable efficacy, especially in scenarios characterized by limited historical data and market volatility. The study also highlights the nuanced performance of these models across different forecasting horizons and error metrics, showcasing their potential as robust tools in financial forecasting, which contradicts the findings of Zeng et al. (2023)

This paper contributes to the financial forecasting literature by providing a comprehensive analysis of the applicability of Transformer-based models in this domain. It offers new insights into the capabilities of these models, especially their adaptability to different market conditions and forecasting requirements, challenging the existing skepticism created by Zeng et al. (2023) about their utility in financial forecasting.

This study aims to test the effects of enterprise risk management (ERM) on firms’ outcomes and the moderating role of knowledge management (KM) on ERM–firms’ outcomes relationship.

Data were collected via a questionnaire survey among public listed companies on the principal stock exchange market in Malaysia. A total of 124 questionnaires were received by mail questionnaire. The results were examined through structural equation modelling and partial least squares.

The outcomes specified that ERM has a positive and noteworthy influence on firms’ outcomes, and KM has a moderating influence on the correlation among ERM and firms’ outcomes.

The qualities, procedures and laws of the Malaysian corporations chosen as the sample firms, as well as their regulations, may not be representative of all other countries. Moreover, this study considered only one variable as a moderator, while there are many variables that different studies can consider as moderator or mediators.

The results of this research imply that employees’ awareness and knowledge of events, opportunities and risk, along with their engagement in the institute’s strategy, are critical for risk management and controlling. For the managers, the results of this research can be helpful to their businesses by identifying the effective KM capability that may enhance their positive outcomes. Managers and organizations can use KM as an instrument to increase ERM effect on firms’ outcomes.

KM and ERM are both significant intangible resources that are hard to imitate and are uniquely specified programs, which are important contributors to firm success in the long run. Moreover, the contingency theory of ERM was proved through the results of this study as it was identified in the public companies, that implementation of ERM as a strategic management practice, by organizations along with an effective KM may enhance the achievement of objectives and outcomes.

This study helps to measure ERM comprehensively and how intangible assets such as KM can affect the comprehensive risk management process and its effectiveness.

This article examines strategic management research across Latin America from 1990 to 2023, addressing four critical inquiries: the themes prevalent in strategic discussions, the leading countries in strategic management (SM) publications, the defining characteristics of strategic research in major Latin American economies and the reflection on whether Latin America is a region that generates or follows the knowledge of the Global North.

Utilizing co-occurrence analysis, this study maps the terrain of SM research in the region, analyzing 4,963 articles indexed in the Scopus database. The authors employed a co-occurrence analysis to map SM research in Latin America, analyzing 4,963 articles from the Scopus database.

Predominant themes include the theoretical underpinnings of strategy, sustainable development, innovation, tourism and international trade. Brazil, Mexico, Colombia and Chile have emerged as leaders in research volume and thematic diversity, particularly in sustainable development and innovation.

By identifying patterns, behaviors and trends in SM research, the authors uncover methods and tools that, once contextualized for the region, can significantly enhance organizational performance.

This investigation is a pioneering effort, providing a focused analysis on SM research within Latin America. It highlights significant contributions since 1990 across the region's main economies. This study represents one of the first comprehensive mappings of this academic field within Latin America. This is the first article, to the authors’ knowledge, developed to map the intellectual structure of the SM field in Latin America through an analysis of co-occurrences, with emphasis on the region's main economies.