1,720,974 research outputs found
Sustainability and Digital Transformation as key drivers in pharma and healthcare M&A deals during covid-19 pandemic
No full textEnhancing decision-making with data-driven insights in critical situations: impact and implications of AI-powered predictive solutions
Full text linkIdentifying early signals of crisis or insolvency is essential, for firms, to enable timely
interventions, preserve financial health, and sustain competitive advantage. Traditional financial
models, while foundational in this domain, often fall short in complex environments and there is a
widening gap between the theoretical advancements in predictive modeling and their practical
application in real-world decision-making, particularly for small and medium-sized enterprises, that
may lack the resources to implement sophisticated risk management tools. Considering the Italian
business landscape, this research examines the integration of advanced AI-driven techniques,
showcasing their ability to handle vast and diverse data sources while improving predictive accuracy.
By employing an inductive, multi-layered approach to model implementation and refinement, the
study demonstrates how AI-based models can identify early indicators of distress up to five years before
insolvency, thus providing a robust framework for preventive strategies. A key added value is the focus
on enhancing explainability and understandability within AI models, delivering critical insights for
both academia and managers; by improving transparency, these models not only strengthen predictive
capacity but also enable stakeholders to better understand and interpret the drivers of financial and
economic distress.
Furthermore, the study highlights the challenges of adopting new technologies in
organizational contexts, addressing issues such as data ethics, managerial literacy, over-reliance risks
and the alignment of AI-based decision tools with regulatory standards. The findings contribute both
to academic discourse and practical applications by advocating for a cultural shift toward data-driven
decision-making in critical phases of business life; by balancing the precision of Machine Learning with
the interpretability required for managerial planning, it is underscored that, while AI just complements
human expertise, it is, today, instrumental in surviving in complex economic landscapes with greater
foresight and resilience
Corporate social responsibility and financial performance: An empirical analysis of the Italian case
No full textExploring the dynamics of external knowledge acquisition in family businesses: factors, constraints, and success indicators
No full textThis research delves into the dynamics influencing family businesses' engagement with external knowledge, aiming to shed light on the motivations, challenges, and benefits associated with seeking knowledge beyond organizational boundaries. Drawing on a single case study methodology, we provide a detailed framework that offers insights into the factors driving family entrepreneurs to explore external knowledge sources. Additionally, we uncover the primary constraints and challenges faced by family businesses in accessing and leveraging external knowledge, offering actionable insights for overcoming these hurdles. Our findings suggest that addressing these challenges head-on can enhance family firms' capacity for innovation, adaptation, and success in dynamic market environments. Furthermore, we identify specific indicators that highlight the tangible benefits derived from family businesses' interactions with external knowledge sources, emphasizing the significance of cultivating collaboration networks beyond the confines of the family firm. Overall, this study contributes to the existing literature by offering a nuanced understanding of the intricate relationship between family businesses and external knowledge, with implications for both scholarly research and managerial practice
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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