1,720,962 research outputs found
Interplay between technologies and development of metropolitan areas and firms
Get PDFThis thesis aims to employ principles and methodologies from physics to survey, comprehend, and model the patent-related activities of companies and cities to uncover and quantify their economic role and impact.
Technological innovation and patenting play pivotal roles in enhancing the economic prosperity of nations, regions, cities and firms. Extensive research has consistently demonstrated a positive correlation between technological innovation and economic growth. Simultaneously, increased patenting activity drives competitiveness in product exports and fosters scientific progress. Thus, technological advancement and patenting are powerful catalysts in pursuing global progress and economic growth. The focus of my research is on cities due to their significant role in various contexts, including production and dissemination, scientific knowledge, and cultural exchange.
Moreover, firms are the main drivers of innovation. Just think of the field of Artificial Intelligence (AI), for which in recent years there has been a dramatic increase in corporate spending on related research projects, or the role of companies during the COVID-19 pandemic in developing vaccines at a rate unsustainable for academic actors. Given companies’ prominent role in scientific and technological progress, understanding the predictability of corporate patent output is critical for several actors. Indeed, it can help managers identify effective innovation strategies and high-potential investment opportunities, and policymakers design effective policies that promote entrepreneurship and accelerate human progress.
This PhD thesis contributes to developing the field of Economic Complexity (EC), i.e. the study of economics through the means used by the physics of Complex Systems, by introducing new methods of analysis and models that can shed light on issues arising from the economic and development impact of companies and cities from their patent activity.
This thesis is structured as follows. First, I present a general discussion on EC to set the framework for this thesis and present general considerations on both macro and micro aspects of EC. The first consists of presenting and discussing the Economic Fitness and Complexity (EFC) algorithm, a tool that quantifies how competitive, for example, a country is in exporting a product or, in the case of this thesis, how competitive a city or company is in technological development. The second concerns Similarity and Relatedness calculations, since they are fundamental tools in my research. These measures quantify respectively the similarity between different technology activities, and the relationship between a city or firm and a specific technology activity. After this general introduction, in chapter 2, I present the database and data preparation, a process essential to the results obtained. Finally, to discuss the contribution of my research, I present the results, which can be found also in the following publications:
[1] Matteo Straccamore, Luciano Pietronero, and Andrea Zaccaria. Which will be your firm’s next technology? Comparison between machine learning and network-based algorithms. Journal of Physics: Complexity, 3(3):035002, 2022;
[2] Lorenzo Arsini, Matteo Straccamore, and Andrea Zaccaria. Prediction and visualization of mergers and acquisitions using economic complexity. Plos one, 18(4):e0283217, 2023;
[3] Matteo Straccamore, Matteo Bruno, Bernardo Monechi, and Vittorio Loreto. Urban economic fitness and complexity from patent data. Scientific Reports, 13(1):3655, 2023;
[4] Matteo Straccamore, Vittorio Loreto, and Pietro Gravino. The geography of technological innovation dynamics. Scientific Reports, 13(1):21043, 2023.
In the last two chapters, I present the main results of this work. Chapter 3 presents the findings related to firms, as published in [1] and [2]. In [1], we show how Relatedness and Similarity measures can predict the future technological output of companies by offering the superiority of Machine Learning (ML). Moreover, we introduce the Continuos Technology Space, a tool able to solve the interpretability problems of the ML by projecting the forecast results on a 2D plane. In [2], we exploit firms’ technology activity to find how Similarity and Relatedness measures can be used to forecast Mergers & Acquisitions between companies. All this is done by only using information about the patent activity of firms. Chapter 4 is devoted to cities. In [3], we highlight the importance of patent activity in determining economic welfare in cities. Also, we show how it is more important for cities to know the degree of coherence of their technology production (i.e., how close and similar the technologies produced by a city) than how many technologies are made. A city with a more coherent technology basket will have a better chance of economic growth.
Finally, in [4], we study state institutions’ importance in inter-city technology diffusion. To do this, we define a new measure of Relatedness that considers the belonging of two cities to the same country. In addition to giving better prediction results, the new measure is fully interpretable. Our evidence suggests that political geography has been highly important for the diffusion of innovation till around two decades ago, slowly declining afterwards in favour of a more global innovation ecosystem.
In conclusion, this thesis presents different findings in the field of technological development and innovation diffusion:
1. Technological Output Prediction in Companies: Demonstrated that Machine Learning, enhanced by the Continuous Technology Space for interpretability, can effectively predict companies' future technological output using Relatedness and Similarity measures.
2. Forecasting Mergers & Acquisitions: Showed that firms' patent activities can predict Mergers \& Acquisitions, emphasizing the importance of technological Similarity and Relatedness in corporate consolidations.
3. Economic Welfare in Cities: Highlighted the crucial role of patent activity and the coherence of technology production in driving a city's economic growth, rather than the quantity of technologies developed.
4. Inter-City Technology Diffusion: Introduced a new Relatedness measure considering cities' political geography, revealing a shift from a politically influenced innovation diffusion to a more globalized approach in recent decades.
These findings collectively enhance our understanding of how technological innovation and strategic alignment influence economic and corporate dynamics
Prediction and visualization of mergers and acquisitions using economic complexity
Get PDFMergers and Acquisitions represent important forms of business deals, both because of the volumes involved in the transactions and because of the role of the innovation activity of companies. Nevertheless, Economic Complexity methods have not been applied to the study of this field. By considering the patent activity of about one thousand companies, we develop a method to predict future acquisitions by assuming that companies deal more frequently with technologically related ones. We address both the problem of predicting a pair of companies for a future deal and that of finding a target company given an acquirer. We compare different forecasting methodologies, including machine learning and network-based algorithms, showing that a simple angular distance with the addition of the industry sector information outperforms the other approaches. Finally, we present the Continuous Company Space, a two-dimensional representation of firms to visualize their technological proximity and possible deals. Companies and policymakers can use this approach to identify companies most likely to pursue deals or explore possible innovation strategies
Which will be your firm’s next technology? Comparison between machine learning and network-based algorithms
Get PDFWe reconstruct the innovation dynamics of about two hundred thousand companies by following their patenting activity for about ten years. We define the technological portfo- lios of these companies as the set of the technological sectors present in the patents they submit. By assuming that companies move more frequently towards related sectors, we leverage on their past activity to build network-based and machine learning algorithms to forecast the future submissions of patents in new sectors. We compare different prediction methodologies using suitable evaluation metrics, showing that tree-based machine learning algorithms outperform the standard methods based on networks of co-occurrences. This methodology can be applied by firms and policymakers to disentangle, given the present innovation activity, the feasible technological sectors from those that are out of reach
Urban economic fitness and complexity from patent data
Get PDFOver the years, the growing availability of extensive datasets about registered patents allowed researchers to better understand technological innovation drivers. In this work, we investigate how the technological contents of patents characterise the development of metropolitan areas and how innovation is related to GDP per capita. Exploiting worldwide data from 1980 to 2014, and through network-based techniques that only use information about patents, we identify coherent distinguished groups of metropolitan areas, either clustered in the same geographical area or similar from an economic point of view. We also extend the concept of coherent diversification to patent production by showing how it represents a decisive factor in the economic growth of metropolitan areas. These results confirm a picture in which technological innovation can lead and steer the economic development of cities, opening, in this way, the possibility of adopting the tools introduced here to investigate the interplay between urban development and technological innovation
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe 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
Get PDF“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
Get PDFWe 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
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
- …
