Financial technology-based firms (FinTech) are crucial to promoting new technologies and advances in innovations related to the financial field, sustainable development, and financial inclusion. This paper aims to assess the Sustainable Development Goals (SDGs) and open innovation integrated within the public discourse in the social media of FinTech firms, using machine-learning-based social media analysis (SMA). Accordingly, we tracked the behavior of 21 firms based on the empirical material of 32,716 posts on Twitter. The outcomes showed dissimilar discourses based on FinTech firms’ activities. However, it was found that only financial infrastructure, lending, and personal finance fields have a discourse related to innovation and, to a lesser extent, related to the SDGs thematic. Thus, awareness of sustainable objectives is still far from being a relevant issue for FinTech, which, in general, has neglected to significantly mention SDGs; nonetheless, innovation and related terms are a consistent topic in this area. Furthermore, hints of the implementation of the open innovation paradigm and interest in novel technologies are demonstrated, in addition to the promotion of different actors and events on social media that serve as a showcase for firms that have a presence on Twitter.

Research on electoral events in conjunction with social media provides opportunities to describe an interesting phenomenon that can be analyzed using sentiment analysis techniques. The goal of the study is to analyze the support of political parties during electoral periods from Twitter comments, including 250,000 tweets regarding the Spanish general elections of 2015 and 2016. Text mining and natural language processing techniques enable information analysis, and the methodology emphasizes good practices for large-scale data collection retrieved from Twitter through a quantitative analysis of text collection written in the Spanish language. After information extraction obtained in three Spanish regions defined by geolocation, as well as feature selection based on keywords of the main four political parties, we conducted an in-depth examination of Twitter users’ support during the course of the election. By weighting the tendency of tweets, we were able to obtain a proposed indicator of support: the positiveness ratio (PR). The results suggest that the PR is a feasible barometer to demonstrate measurable patterns of support tendency regarding political parties and users’ behavioral activity to track their affinity on Twitter. Findings indicate consistent support behavior by users towards traditional parties and optimistic users’ behavior regarding emerging political parties.

The present article analyzes the rationale, types, experience, and potentiality of applying open innovation models in organizations to conduct their research and development (R&D) activities. In order to carry out R&D activities and to demonstrate the utility and applicability of innovation models, a new methodology is developed for the creation of profiles of open innovation models. The comparison of thirty different companies analyzed and later classified in competitiveness matrices, allows a given organization to know what its margins of improvement are. Therefore, along eight dimensions of identified innovation activities, the outcomes demonstrate the high value of the use of this methodology by organizations to increase their levels of R&D and innovation.

The implementation of open innovation models for R&D activities has been extensively tested by many public and private institutions by using several models; however, both lessons learned and rationale for internal decision-making have not been systematically analyzed to serve as a basis for future institutional process management improvements, which increase the success rate. The large diversity of cases, firms, sectors and open activities, and the multiple factors affecting success has made it difficult to elaborate a general framework where to extract from some policy design rules adapted to specific contexts; thus, involved firms do not have a consolidated framework to measure the effectiveness of the open innovation practices in use. Open innovation for R&D activities conducted by multinational firms is usually carried out at the international level looking for specific knowledge and complementarity of partners. One of the critical factors for success in open innovation activities is the “right selection of adequate partners”. This article postulates that the multi-variable selection based on the previous characterization of the relationship between the firm and one potential international partner constitutes a key factor for success. In addition, the article presents a semi-quantitative approach to measure the “differences” (a metrics based on “proximity” is defined) to analyze and compare between different partners from several dimensions. The key goal is to select external partners with the “minimum distance” to the firm. The proposed selection metrics were applied to various open research projects launched with university partners in three Latin American countries (Mexico, Chile, and Peru).

Innovation ecosystems use R&D inputs to generate innovation outputs first and innovation impacts later. But some countries show a relatively low transmission, such as in the case of Kazakhstan, the largest economy in Central Asia. This article analyzes the transmission from R&D into innovation outputs and impacts through a framework for which different factors matter, such as the company size, education and skills, competition, exports, and foreign ownership. Transmission is conceptually understood in two steps: from R&D into innovation outputs, and from innovation output into innovation impacts. The main hypothesis is that the high endowments of these company factors should lead to the better transmission of results and improved performance in terms of outputs and impacts. We test this using new evidence from Kazakhstan and the ECA region (Europe and Central as defined by the World Bank) as benchmarking, and data are from the Global Innovation Index (descriptive section) and the World Bank Enterprise Surveys (analytical section). The econometrics are a Crépon–Duguet–Mairesse (CDM) model in three steps: factors for propensity to invest in R&D, then to innovate, and, finally, innovation impacts on productivity. Results confirm the positive roles of factors, such as exports and education, in positive transmissions and uneven or insignificant results on productivity impacts from characteristics, such as age, size, and foreign ownership. The specifics for Kazakhstan suggest a potential for business innovation growth in the country. The paper concludes by suggesting key policy measures to unlock the potential for business innovation at a country level.