At the 2019 R&D Management Conference in Paris, ESTT member P. Belingheri hosted a track on Data Science for Innovation Challenges together with F. Chiarello from the University of Pisa.
The information field has changed dramatically over the past years, affecting the economy, technology, culture and society. However, these changes have left an even stronger mark on business systems. Considering the mass of digital information produced in the past 10 years, companies have found themselves in a chaotic and constantly expanding digital universe. To innovate and stay competitive, companies must master methods and tools to prevent information overload, while gaining useful knowledge from the available data. The discipline of Data Science has emerged as a clear (although broad) field of research to solve data-related problems. Data science is an interdisciplinary field that uses scientific methods to extract knowledge and insights from structured and unstructured data. It attracts researchers and encompasses methodologies from wide-ranging fields such as statistics, mathematics, information science, computer science , data analysis, machine learning and communication and is therefore an ideal tool to bridge the gap between research, industry and society. The track showcased works that use state-of-the-art Data Science tools and techniques to gather, transform, model and visualize data to gain valuable information relevant for firm innovation. The main contributions highlighted in particular:
– data science for product innovation,
– data science for technology intelligence,
– data science for open innovation & co-creation,
– data science for new skill identification & mapping.
Moroever, ESTT member P. Belingheri presented two papers:
Value Creation in Emerging Technologies Through Sentiment Analysis of Scientific Papers: The Case of Blockchain
Scientific papers contain a large quantity of valuable R&D information which is hard to automatically mine due to its unstructured form. Through an innovative application of text mining we are able to identify advantages and disadvantages of specific technological solutions from scientific literature highlighting opportunities for value creation. Using the perspective of value creation this paper will develop a new methodology to list the problems of a technological field and the solutions currently being pursued, correlate problems between them and map the research agenda. In doing so we will identify key attributes of value creation within emerging technologies.
This research seeks to bridge the gap between research and industry, by developing and demonstrating a tool that will systematically analyze scientific publications in emerging technology to identify the most promising avenues for value creation.
Attracting Talent Through the Elimination of Gender Bias in Job Vacancies: A Preliminary Lexical Approach
Language can often be considered gender specific. In the workplace, this can lead to biases in recruitment processes, creating barriers especially for women to access male-dominated industries. Using Text Mining and standard gender-biased lexicons we identify bias in vacancy notices and present an instance where standard lexicons don’t uncover bias as the literature would predict. Based on this, we
propose an alternative methodology to uncover bias in vacancy notices and apply it to a sample of job vacancies in the space sector.