According to the literature on innovation, several vital factors or determinants favor innovation in companies. In the case of R&D, significant advances have been made in the last two decades, which have enriched our understanding of its impact on various innovation outcomes. However, due to a lack of data availability, its study is difficult to address in emerging markets. This is why, using microdata from 5588 firms, we investigate the relationship between R&D investment and the impact on product and process innovations in different Latin American countries. We contribute to the literature by employing a machine learning methodology and comparing its results to a traditional innovation model. Our findings demonstrate the behavior of both product and process innovation methods.

This study delves into the intricate interplay between gender stereotypes and financial reporting through an aspect-level sentiment analysis approach. Leveraging Big Data comprising 129,251 human face images extracted from 2085 financial reports in Chile, and employing Deep Learning techniques, we uncover the underlying factors influencing the representation of women in financial reports. Our findings reveal that gender stereotypes, combined with external economic factors, significantly shape the portrayal of women in financial reports, overshadowing intentional efforts by companies to influence stakeholder perceptions of financial performance. Notably, economic expansion periods correlate with a decline in women's representation, while economic instability amplifies their portrayal. Furthermore, the financial inclusion of women positively correlates with their presence in financial report images. Our results underscore a bias in image selection within financial reports, diverging from the neutrality principles advocated by the International Accounting Standards Board (IASB). This pioneering study, combining Big Data and Deep Learning, contributes to gender stereotype literature, financial report soft information research, and business impression management research.

Crowdfunding has become a central mechanism for entrepreneurial finance, yet the evolution and thematic structure of its intersection with entrepreneurship remain insufficiently understood. This study provides an integrated, longitudinal, and structurally grounded bibliometric analysis of 1518 documents indexed in the Web of Science Core Collection using VOSviewer (version 1.6.20) and SciMAT (version 1.1.04). We examine the intellectual structure, thematic evolution, and performance dynamics of the field across three periods, revealing a progression from financial and relational foundations to socio-technological consolidation and, more recently, thematic diversification toward impact, information asymmetry, behavioral mechanisms, and other explanatory determinants, while innovation remains a central organizing theme. The resulting thematic structure can be interpreted through a multi-level perspective, encompassing micro-level behavioral mechanisms, meso-level platform and firm strategies, and macro-level institutional and technological dimensions. Despite the growth of the field, the bibliometric structure remains thematically differentiated, with behavioral, technological, social, and geographic dimensions displaying uneven levels of prominence and connectivity within the analyzed corpus. Technofinancial themes such as blockchain and ICOs constitute specialized thematic streams with comparatively limited connectivity to the broader bibliometric structure. By identifying dominant trajectories, structural patterns, and comparatively underrepresented areas within the WoS-indexed corpus, this study provides a structured basis for future research and advances understanding of crowdfunding as a financial, social, organizational, and technological phenomenon.