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Capoprogetto: Strozzi Fernanda

Durata : 2018 – 2019


In this project we propose to apply a method developed at Cattaneo University which we refer to as Systematic Literature Network Analysis (SLNA) and which has already been applied to detect trends in many research areas, to shed light on the digital transformation of the construction sector  i.e. a critical sector which in a near future will require radical changes due to the fast-increasing world population living in urban areas.


Societal good governance of both public and private enterprise requires decision-making based on accountable policy arrived at through a rational analysis of credible sources  this is particularly so at the interface of science and society. Yet, the fast pace of technological development and scientific research poses a significant challenge on all aspects of contemporary society to keep abreast of potentially disruptive changes.

The emergence of radical transformations is not limited to the advent of new technologies but is also linked to the availability of resources or societal changes. The capacity to anticipate such transformations and identify incoming threats is central in order to adapt and respond adequately in our highly interconnected world. This scenario is further complicated by the many news-feeds and information sources, often of dubious quality, or purposely generated to confuse or confabulate, that may overload and incapacitate the process of good social governance.

For these reasons, so-called, horizon scanning has rapidly increased its importance in the definition of governments´ strategies and policy development, as well as R&D investments in the private sector to secure leading market positions

Traditional horizon scanning methodologies were based on the central role of qualified experts whose insight and expertise provided the main elements for forecasting techno-economic processes with the potential to generate impactful changes. The success in anticipating these future scenarios in this manner relies on subjective analyses grounded in specialized expertise, thus the development of insights and perspectives can differ in the approach and methodology rely heavily on the work of individuals organized around a chosen approach or topic. This methodology may leave out latent interconnections between fields that may not be picked up by highly specialised experts in a single field.

There appears therefore to be a need for a more structured approach based on mathematical analysis of text and data that could develop a generic approach in order to condense the huge amount of information available, that cannot reasonably be manageable by single-field experts. The development of new methodologies can possibly overcome the limitations of the actual approaches and boost the experts´ role.

In the scientific literature, it is difficult to find approaches to horizon scanning that are both systematic and quantitative, but a systematic approach to extract the relevant papers of the scientific literature was first developed by Denyer and Transfield, (2009). To map scientific developments some authors applied quantitative tools such as citation, co-occurrence networks (Zhao and Strotmann 2015, Lucio-Arias and Leydesdorff 2008, Ding and al.2001). Colicchia and Strozzi (2012) proposed the analysis of scientific publications, selected with SLR, using networks analysis and developed the Systematic Literature Network Analysis (SLNA) approach. This methodology was subsequently applied to different areas, i.e. education (Colicchia et al, 2017 and Strozzi et al., 2017), smart factory concept and industry 4.0 (Strozzi et al, 2017), environmental indicators (Colicchia et al. 2012), wireless sensor networks (Strozzi et al. 2014) and enriched with more quantitative tools, both from network analysis and bibliometric data in general. The methodology is constructed in such a way as to compensate for the limitations of the individual tools involved.

SLNA makes it possible to identify the most important works for the development of a research area, to detect trends and to make predictions about future developments.

The aim of this proposal is to investigate the feasibility of the SLNA to identify trends from data on scientific publications, patents and funded research with a practical application to the case of digital transformation in the construction sector. This sector contributes significantly to the European GDP and is crucial to tackling the forthcoming demands of increased population and limited resources.

According to the UN, today 54 percent of the world´s population lives in urban areas, a proportion that is expected to increase to 66 percent by 2050 (https://esa.un.org/unpd/wup/publications/files/wup2014-highlights.pdf).  The management of urban areas will be a major challenge and modern technology will play a key role in supporting a secure, sustainable and livable modern built environment. The capability of anticipating these issues along with the technological prospects can provide a strong decisional support both for policymakers and the industry.

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