This article analyzes the influence and foreseeable evolution of three main leadership styles in sustainable development for social reconstruction in two leading companies, the European Airbus and the Indian Tata Advanced Systems Ltd. (TASL) (Hyderabad, India). Inductive research was employed, using two cross-national case studies and a comparative analysis applying statistical testing. Data were collected through interviews and a survey among principal company leaders with several functions. The study focused on three styles: transactional, transformational, and servant leadership. The strength of this paper is that it sheds light on an understudied industry by providing findings on both the present (as-is) state and the advisable reorientation (i.e., future state suggested by respondents). This could contribute to filling the current gap in understanding the influence of leadership styles on the aerospace and defense industries’ economic, social, and environmental outcomes. By analyzing the results obtained in both companies, we concluded that even though all three styles are present in both companies, servant and transformational leadership provide better results for sustainable development for social reconstruction. Regarding the advisable reorientation, according to the respondent’s suggestions, the style best suited to influence sustainable development for social reconstruction in the long term in both companies is the servant style. As for the transitional phase, respondents suggest a hybrid leadership model, encompassing features of both the transformational and the servant styles.

This article undertakes a comparative analysis of the corporate social responsibility (CSR) drivers and barriers in the European multinational Airbus and the Indian Tata Advanced Systems Ltd. (TASL), both in their current, as-is state, and in the desirable future, should-be state. These in-depth case studies are based on structured interviews, complemented with a survey. The research findings provide qualified, bounded support to the currently preponderant view among CSR scholars, regarding both the preeminent role of the internal (vs. external) drivers and the key importance of moral-driven (vs. profit-driven) drivers such as the values of the top management. The caveat here reflects that, contingent on cultural issues, individual companies might not have reached this state yet. Furthermore, these findings suggest that the current CSR situation could be characterized as contingent, transitional and convergent; the practical implications for the various CSR stakeholders of this characterization are discussed. The most salient trait, convergence, suggests the existence of a certain commonality in CSR’s foreseeable evolutionary path, even among disparate companies, converging on such aspects as the ever-increasing role of the value system and of the shared, internalized company culture, and the shift of CSR’s role from obligation to potential source of competitive advantage.

The field of natural language processing has experienced significant advances in recent years, but these advances have not yet resulted in improved analytics for instructors on MOOC platforms. Valuable information, such as suggestions, is generated in the comment forums of these courses, but due to their volume, manual processing is often impractical. This study examines the feasibility of fine-tuning and effectively utilizing state-of-the-art deep learning models to identify comments that contain suggestions in MOOC forums. The main challenges encountered are the lack of labeled datasets from the MOOC context for fine-tuning classification models and the soaring computational cost of this training. For this study, we manually collected and labeled 2228 comments in Spanish and English from 5 MOOCs and scraped 1.4 million MOOC reviews from 3 platforms. We fine-tuned and evaluated 4 pretrained models based on the transformer architecture and 3 traditional machine learning models to compare their effectiveness in the suggestion mining task in this domain. Transformer-based models proved to be highly effective in this task/domain combination, achieving performance levels that matched or exceeded those deemed appropriate in other contexts and were significantly greater than those achieved by traditional models. Domain adaptation led to improved linguistic understanding of the target domain; however, in this project, this approach did not translate into an observable improvement in suggestion mining. The automated identification of comments that can be labeled as suggestions can result in considerable time savings for instructors, especially considering that less than a quarter of the analyzed comments contain suggestions.

Con los últimos avances en el campo del Big Data, el uso, análisis e interpretación de los datos cobra una especial relevancia en la definición y ajuste de la estrategia de muchas empresas. En este contexto, se identifica la necesidad de introducir en el perfil de los ingenieros y titulados en empresariales, el uso de herramientas de análisis y visualización de datos. Este artículo presenta un caso de diseño de una actividad práctica de sistemas de información basado en aprendizaje experiencial, que combina el uso de un ERP con herramientas de gestión y análisis de datos. La práctica persigue fomentar habilidades ligadas al uso de estas herramientas además de transmitir la importancia de su integración para generar valor en las empresas.