On the economic consequences of automation and robotics

Gomes, Orlando

doi:10.1108/jeas-04-2018-0049

Cited by 6 publications

(3 citation statements)

References 44 publications

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“…In the latter context, the fast development of artificial intelligence technologies–e.g. used for patterns recognition or automatised communication–integrated in robotics applications strongly contribute to increasing decision-making capabilities [ 20 , 21 ], and accordingly the attractiveness of robots, mainly in the manufacturing sector but also in service industries [ 22 ].…”

Section: Conceptual Background and Literature Reviewmentioning

confidence: 99%

The geographical dynamics of global R&D collaboration networks in robotics: Evidence from co-patenting activities across urban areas worldwide

2023

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The focus of this study is on the geography of robotics Research and Development (R&D) activities. The objectives are, first, to identify hotspots in robotics R&D worldwide, and second, to characterise structures and dynamics of global robotics R&D collaboration networks through detailed geographical lenses of global urban areas. We use patents as marker for R&D activities, and accordingly focus on technologically oriented R&D, drawing on information from patents applied for between 2002 and 2016. We employ an appropriate search strategy to identify relevant robotics patents based on detailed levels of the Cooperative Patent Classification (CPC) and assign patents to more than 900 global urban areas based on the inventor addresses. The co-patent networks are examined from a Social Network Analysis (SNA) perspective by means of robotics co-patents, contributing to a global network where urban areas are the nodes inter-linked by joint inventive activities recorded in robotics patents. Global SNA measures illustrate structures and dynamics of the network as a whole, while local measures indicate the specific positioning and roles of urban areas in the network. The results are original in characterising the global spatial emergence of this generic new industry, highlighting prominent urban hotspots in terms of specialisation in robotics R&D, pointing to a global shift reflected by the increasing role of emerging economies, in particular China. The global robotics R&D has grown significantly both in total patenting and also in terms of R&D collaboration activities between urban areas. Also, for the networks, growth is not equally distributed, but is rather characterised by significant spatial shifts, both in terms of cities declining or climbing up the specialisation ranking, but even more in terms of the spatial network structure.

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Section: Conceptual Background and Literature Reviewmentioning

confidence: 99%

The geographical dynamics of global R&D collaboration networks in robotics: Evidence from co-patenting activities across urban areas worldwide

2023

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“…Now is a time of astonishing progress with digital technologies. While the transformations brought about by digital technologies are profoundly beneficial, digitization brings some thorny challenges, namely, in terms of employment and income distribution (Gomes and Pereira 2020). In the 21st century, it is becoming increasingly clear that human activities and the activities of enterprises affect the environment.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Robotification on the Financial Situation of Microenterprises: Evidence from the Financial Services Sector in Poland

Cieślukowski

Garsztka

Zyznarska-Dworczak

2022

Risks

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The automation of manufacturing processes as a result of the Fourth Industrial Revolution, rendered faster under the influence of the COVID-19 pandemic, leads to a question as to whether small enterprises, and in particular microenterprises, will still be financially self-sufficient. The literature on the subject so far has not provided an answer to this question, and limited access to financial data concerning microenterprises, as well as data on the robotification of labour in these companies, leads to this problem becoming a research niche. Therefore, this paper aims to assess the attractiveness of the operation of microenterprises that provide financial advisory services in the situation of replacing human labour with robots. For that purpose, we performed a simulation of the state of the microenterprise before and after introducing the robotification of services, taking into account the extra tax on robotification. The findings indicate that anticipating the improvement of financial results following the automation of financial services requires taking into account the replaceability of employees in a particular unit, the form of acquiring a robot and a potential increase in the tax burden (with a tax compensating for the lost state income).

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“…The possibility of improving the management of assets, from the monitoring of the constructions through images to preventing the erroneous installation facilities during the execution of the construction. The need to increase the number of constructions in substations can be a bottleneck, since the number of people available and trained to accompaniment does not grow in the same proportion, hence it is necessary to create solutions for the accompaniment of constructions facilitating management [8]- [10].…”

Section: Introductionmentioning

confidence: 99%

Automated Monitoring of Construction Sites of Electric Power Substations Using Deep Learning

Oliveira

Neto²,

Fernandino³

et al. 2021

IEEE Access

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With each passing year, the consumption of electric energy in Brazil and the world increases, making it necessary to adopt measures such as the construction of new plants and the installation of power distribution structures. Monitoring for construction management in companies is still done in person and manually, resulting in expenses that could be avoided. That said, there are opportunities to automate such processes using artificial intelligence and, therefore, the main objective of this work is the development of an automated constructions management system, whose goal is to increase the management and monitoring of substation constructions with the remote monitoring. The system incorporates resources of deep learning to classify the components in bays, comparing the data generated in this recognition with the engineering projects to verify the progress of the installation of these components and generating indicators of conformity and evolution of the construction. To achieve the main objective, a comparison was made among four convolutional neural network architectures: DenseNet, Inception, ResNet, and SqueezeNet, in the classification task. The models were trained with thousands of images extracted from photos of different bays captured in the field and, additionally, data augmentation techniques were applied. The models were trained using transfer learning and fine tuning starting from pre-trained weights in the ImageNet data set. All models obtained results close to 100% in the images of the test set, hence it is possible to conclude that, for the proposed problem, there was no significant difference between the assertiveness of the architectures. The chosen model was part of the final application that monitors the construction management of the bays in the electricity substations.

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On the economic consequences of automation and robotics

Cited by 6 publications

References 44 publications

The geographical dynamics of global R&D collaboration networks in robotics: Evidence from co-patenting activities across urban areas worldwide

The geographical dynamics of global R&D collaboration networks in robotics: Evidence from co-patenting activities across urban areas worldwide

The Impact of Robotification on the Financial Situation of Microenterprises: Evidence from the Financial Services Sector in Poland

Automated Monitoring of Construction Sites of Electric Power Substations Using Deep Learning

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