Exploring research trends and network characteristics in construction automation and robotics based on keyword network analysis

Kim, Taehoon; Lee, Dongmin; Lim, Hyunsu; Lee, Ung-Kyun; Cho, Hunhee; Cho, Kyuman

doi:10.1080/13467581.2020.1798774

Cited by 14 publications

(12 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Below, we present trends in salience and sentiment towards behavioural-science keywords over time, followed by reference and sentiment toward public policy application and co-occurrence. As frequently the case in descriptive exploratory studies of linguistic data (e.g., Bian et al, 2016;Kim et al, 2020;Sharma et al, 2020), we contain our results to descriptive findings.…”

Section: Resultsmentioning

confidence: 96%

“…Simply put, two keywords that never co-occur have a coefficient of 0 and two keywords with identical occurrence have a coefficient of 1 1 . We visualised the keyword association network structures, one for each time window, where keywords are displayed in nodes and edge weights reflect the strength of their co-occurrence with others (Katsurai & Ono, 2019;Kim et al, 2020;Liu et al, 2012) using the Python NetworkX 2.5 library (Hagberg et al2008).…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Lessons from lockdown: Media discourse on the role of behavioural science in the UK COVID-19 response

Sanders¹,

Tosi²,

Obradović³

et al. 2021

Preprint

View full text Add to dashboard Cite

In recent years behavioural science has quickly become embedded in national level policy making. As the contributions of behavioural science to the UK’s Covid-19 response policies in early 2020 became apparent, a debate emerged in the British media about its involvement. This served as a unique opportunity to capture public discourse and representation of behavioural science in fast-track, high-stake national policy making. Aimed at identifying elements which foster and detract from trust and credibility in emergent scientific contributions to policy making, in study 1 we use corpus linguistics and thematic analysis to map the narrative around the key behavioural science actors and concepts which were discussed in the 650 news articles extracted from the 15 most read British newspapers over the 12-week period surrounding the first hard UK lockdown from March 2020. We report and discuss 1) the salience of key concepts and actors as the debate unfolded, 2) quantified changes in the polarity of the sentiment expressed toward them and their policy application contexts, and 3) patterns of co-occurrence via network analysis. In Study 2, we investigate how salience and sentiment of key themes observed in traditional media discourse tracked on original Twitter chatter (N = 2,187). In Study 3, we complement these findings with a qualitative analysis of the subset of news articles which contained the most extreme sentiments (N = 111), providing an in-depth perspective of sentiments and discourse developed around keywords, as either promoting or undermining their credibility in, and trust toward behaviourally informed policy. We discuss our findings in light of the integration of behavioural science in national policy making under emergency constraints.

show abstract

Section: Resultsmentioning

confidence: 96%

Section: Methodsmentioning

confidence: 99%

Lessons from lockdown: Media discourse on the role of behavioural science in the UK COVID-19 response

Sanders¹,

Tosi²,

Obradović³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…In the co-occurrence networks, nodes represent terms and an edge between two nodes indicates the terms' co-occurrence, with a weight proportional to the strength of their association (i.e., the Dice coefficient) (Liu et al, 2012;Katsurai and Ono, 2019;Paranyushkin, 2019;Kim et al, 2020;Puerta et al, 2020). By graphically representing patterns of co-occurrence between terms, co-occurrence networks identify the importance of terms and their inter-relatedness (Van Eck et al, 2006;Van Eck and Waltman, 2007).…”

Section: Co-occurrencementioning

confidence: 99%

“…To calculate co-occurrence, the opinion contexts were preprocessed as follows to reduce noise interference (cf. Véronis, 2004;Jurgens, 2011;Kim et al, 2020). (i) Common two-and three-word phrases that did not involve any of our behaviouralscience keywords, were replaced with the corresponding bigrams/trigrams (e.g., "public health" with "public_health") based on collocation statistics across all 647 articles, using Python gensim's Phrases model (Rehurek and Sojka, 2010).…”

Section: Co-occurrencementioning

confidence: 99%

Lessons From the UK's Lockdown: Discourse on Behavioural Science in Times of COVID-19

et al. 2021

View full text Add to dashboard Cite

In recent years behavioural science has quickly become embedded in national level governance. As the contributions of behavioural science to the UK's COVID-19 response policies in early 2020 became apparent, a debate emerged in the British media about its involvement. This served as a unique opportunity to capture public discourse and representation of behavioural science in a fast-track, high-stake context. We aimed at identifying elements which foster and detract from trust and credibility in emergent scientific contributions to policy making. With this in mind, in Study 1 we use corpus linguistics and network analysis to map the narrative around the key behavioural science actors and concepts which were discussed in the 647 news articles extracted from the 15 most read British newspapers over the 12-week period surrounding the first hard UK lockdown of 2020. We report and discuss (1) the salience of key concepts and actors as the debate unfolded, (2) quantified changes in the polarity of the sentiment expressed toward them and their policy application contexts, and (3) patterns of co-occurrence via network analyses. To establish public discourse surrounding identified themes, in Study 2 we investigate how salience and sentiment of key themes and relations to policy were discussed in original Twitter chatter (N = 2,187). In Study 3, we complement these findings with a qualitative analysis of the subset of news articles which contained the most extreme sentiments (N = 111), providing an in-depth perspective of sentiments and discourse developed around keywords, as either promoting or undermining their credibility in, and trust toward behaviourally informed policy. We discuss our findings in light of the integration of behavioural science in national policy making under emergency constraints.

show abstract

“…In parallel to the growing interest in PPP project structures, the use of construction automation and robotics (CAR) is being explored to improve productivity, stakeholder collaboration and high-risk working conditions, especially in mega projects [4]. While a clear definition is missing, CAR sees applications across the whole asset life cycle in both "design (e.g., using Computer Aided Design), construction (e.g., to control the use of machines onsite) and management (e.g., the use of Building Information Models (BIM))" of buildings and public infrastructure [5].…”

Section: Introductionmentioning

confidence: 99%

Toward Life Cycle Sustainability in Infrastructure: The Role of Automation and Robotics in PPP Projects

et al. 2021

View full text Add to dashboard Cite

This article identifies how project life cycle characteristics and automation and robotic technologies influence the sustainability of public-private partnership (PPP) infrastructure projects. The result of the article is a model of how public and private collaborations can leverage technology and project organization to make infrastructure more sustainable. Based on a comprehensive literature review, the model subdivides sustainability into engineering, project management, environmental, social, and economic dimensions. Engineering sustainability concerns the applicability of technologies to infrastructure PPP sustainability. The project management sustainability is decisive for ultimately creating environmental, social and economic sustainability within and beyond infrastructure PPP projects. The model identifies that the procurement phase is of particular importance for sustainable infrastructure PPPs. Successful sustainable infrastructure procurement likely includes such factors as increased transparency, participation, and stable, capable project alliances with a shared vision and clear goals. The model also identifies that, throughout the whole project life cycle, actions in the form of collaboration, experimentation and platformization promote sustainability. The findings in this article add to the understanding of how transformation toward increased sustainability can be achieved by individual organizations, their network, and ecosystems of public, private and civic actors.

show abstract

Exploring research trends and network characteristics in construction automation and robotics based on keyword network analysis

Cited by 14 publications

References 22 publications

Lessons from lockdown: Media discourse on the role of behavioural science in the UK COVID-19 response

Lessons from lockdown: Media discourse on the role of behavioural science in the UK COVID-19 response

Lessons From the UK's Lockdown: Discourse on Behavioural Science in Times of COVID-19

Toward Life Cycle Sustainability in Infrastructure: The Role of Automation and Robotics in PPP Projects

Contact Info

Product

Resources

About