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The purpose of this paper is to highlight the relevance of intellectual capital for IT companies in general and, specifically, to analyze the importance of adequately managing intellectual capital in small IT companies.

Two small successful IT companies (one in Spain, another in Finland) are studied, in order to reach a better understanding of how intellectual capital is managed in small companies.

The companies analyzed show the dynamic relationships among the three sides of intellectual capital, and the challenges that managers have to face both to take the advantage of all these connections and mitigate its possible negative effects.

All three areas of intellectual capital (human, social and organizational) require special attention, especially in small knowledge‐intensive companies. Furthermore, the structural embeddedness of social capital seems to develop by following an emergent pattern, especially in small companies where organizational borders do not hinder it. The relational embeddedness of social capital – especially maximizing the spread of knowledge in a company and minimizing the risk of key competence walking out through the door – requires a conscious action from the management and use of work‐rotation techniques.

The paper demonstrates that small companies, even without the organizational resources of the large companies, can however be successful in managing the intellectual capital if their managers are aware of its potential, and understand the idea of the intellectual capital. The paper can help the managers of these kind of companies to know this potential as well as the way to manage it.

This paper analyzes the influence of intellectual capital on firms’ technological innovation, and the intermediary effect of supply chain learning in the relationship between different dimensions of intellectual capital and technological innovation. Using a questionnaire to survey 167 Chinese high and medium-high technological manufacturing firms, our research provides a new insight with interesting results. (1) Among the four dimensions of intellectual capital, only two dimensions, internal social capital and external social capital, exert positive effect on technological innovation; (2) Among the two dimensions of supply chain learning, learning from both suppliers and customers exerts a significant effect on technological innovation, and learning from the customer has a more significant effect; (3) A complete intermediary effect occurs from supply chain learning in the relationship among human capital, structural capital and technological innovation, while an incomplete intermediary effect occurs from supply chain learning in the relationship among external social capital, internal social capital, and technological innovation.

In this chapter, we analyse the role of women in armed Basque nationalist and separatist terrorist group Euskadi Ta Askatasuna (ETA) from a gender perspective. ETA women were essential agents in the armed conflict, but their image in the mass media and society has been very different from that of their male colleagues (Rodríguez Lara, 2017). Also, their role in the terrorist gang has described a sexually based functional specialisation. In addition, this chapter seeks to contribute to the area of feminist studies on women and political violence. Women's role in one of the most important armed nationalist groups in Europe, ETA, stands as a suitable case study to understand how the mass media (press, journalism), audiovisual content, and social representations of ETA have portrayed these women. The final conclusion of this chapter is that women were not portrayed in the same way as their male colleagues. Women of ETA were doubly penalised because they were women and because they were terrorists.

Recent years have seen increased participation by women in Science and Technology activities, but parity with men remains a distant goal. This paper provides a framework for improving knowledge on the dynamics of women’s contributions to innovation, as measured through their contributions as inventors.

Analysis was conducted using data from the United States Patent and Trademark Office (USPTO) database, covering over 40 years from 1976 to 2019. The study provides a breakdown of women’s inventiveness/innovation by technology area, which permits analysis of whether female inventors tend to work individually or as part of a team, as well as analysis of the effects of international networks.

Specific data have been analyzed around innovative activity by women in Spain, providing new insights on gender gaps. Key findings include those recent increases in inventions by women (as measured through patents) have occurred mostly via participation in internationally connected mixed-gender teams. Furthermore, the inventive creativity of women is found to be sector-biased and framed mainly in the chemical sector.

There are no similar contributions using USPTO data for Spanish inventions. This is an original paper that emphasizes the potential of collaborative networks as key driving mechanisms for the reduction of gender gaps and for the larger integration of women into innovation activities, with potential implications for stakeholders at both firm and governmental levels.

In the wine industry, maintaining superior quality standards is crucial to meet the expectations of both producers and consumers. Traditional approaches to assessing wine quality involve labor-intensive processes and rely on the expertise of connoisseurs proficient in identifying taste profiles and key quality factors. In this research, we introduce an innovative and efficient approach centered on the analysis of volatile organic compounds (VOCs) signals using an electronic nose, thereby empowering nonexperts to accurately assess wine quality.

To devise an optimal algorithm for this purpose, we conducted four computational experiments, culminating in the development of a specialized deep learning network. This network seamlessly integrates 1D-convolutional and long-short-term memory layers, tailor-made for the intricate task at hand. Rigorous validation ensued, employing a leave-one-out cross-validation methodology to scrutinize the efficacy of our design.

The outcomes of these e-demonstrates were subjected to meticulous evaluation and analysis, which unequivocally demonstrate that our proposed architecture consistently attains promising recognition accuracies, ranging impressively from 87.8% to an astonishing 99.41%. All this is achieved within a remarkably brief timeframe of a mere 4 seconds. These compelling findings have far-reaching implications, promising to revolutionize the assessment and tracking of wine quality, ultimately affording substantial benefits to the wine industry and all its stakeholders, with a particular focus on the critical aspect of VOCs signal analysis.

This research has not been published anywhere else.