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The purpose of this paper is to examine the likely impact of AI robotics technology on the labor market through the lens of comparative advantage.

The first section reviews the recent success of AI in outperforming humans in cognitive intense activities such as Go, poker and other strategic games, which portends a shift in comparative advantage in human brain power work to machines. It notes the potential for a portfolio of specialized computer algorithms to compete with human general intelligence in work. The analysis contributes to the debate between economists dubious about claims that AI robotics will disrupt work and futurists who expect many jobs to be fully automated in coming years. It advances three “laws of robo-economics” to guide thinking about the new technologies and presents evidence that growing robot intensity has begun to impact the job market.

The paper finds that the case for AI robotics substantially changing the world of work and the distribution of income is more compelling than the case that it will have similar impacts on wages and employment as past technological changes. It advances an ownership solution to spread the benefits of AI robot-driven automation widely.

To the extent that who owns the robots rules the world, it argues for a concerted social effort to widen the “who” in ownership from the few to the many. It reviews policies to expand employee ownership of their own firm and of the stream of revenue via profit-sharing and gain-sharing bonuses. But the paper notes that ensuring that growth of AI robotics benefits all through ownership will require expansion of workers’ and citizens’ stake in business broadly, through collective investment via pension funds, individual investment in mutual funds and development of sovereign wealth funds.

This paper aims to offer a long-term systematic picture of the evolution of manufacturing offshoring (in terms of intensity, geography and drivers) highlighting the changes in the surrounding context and the resulting transitions points (“points in time”) that have shaped its development path.

Three statistical tools were adopted on a dataset of 644 cases. First, the authors resorted to multiple structural change tests to identify the transition points. Second, the authors explored offshoring geography by conducting a network analysis. Finally, the authors adopted gravity models to shed light on offshoring drivers.

Results highlight three offshoring phases: expansion (2002–2006), reconsideration (2007–2009) and rationalization (2010 onwards). During the first phase, characterized by economic growth, firms were mainly interested in economic savings; offshoring to low-cost countries was the prevailing location strategy. Subsequently, during the economic crisis, the number of cases declined and the main drivers became market-based factors together with the research for cost savings. Finally, in the third phase, when the economy was still stagnating and new manufacturing technologies appeared, the number of offshoring cases has further decreased, and technological- and market-based factors have become the main location drivers.

The study is the first to adopt a systematic, empirical and quantitative approach to analyze the evolution of the manufacturing offshoring considering both the phenomenon itself and the triggering changes in the surrounding context. In doing this, the authors also tested the importance of considering the point in time in offshoring strategies.

This paper aims to develop a quality evaluation model for artificial intelligence (AI)-based products/services that is applicable to startups utilizing AI technology. Although AI-based service has risen dramatically and replaced many service offerings, in reality, startups are rarely to develop and evaluate AI services. The features of AI service are fundamentally different from the properties of existing services and have a great influence on the customer's service selection.

This paper reviews startups' development process, existing quality evaluation models and characteristics of services utilizing AI technology, and develops a quality evaluation model for AI-based services. A detailed analysis of a survey (application of the model) on customer satisfaction for AI speakers is provided.

This paper provides seven key features and 24 evaluation items for evaluating AI-based services.

This paper contributes to the growing need for methodologies that reflect the new era of AI-based products/services in quality evaluation research.

The purpose of this paper is to review the literature and offer a more generalizable empirical investigation on the performance impact of implementing Industry 4.0, and the way important contingency factors (plant size, multinational status, country context) affect implementation efforts.

Following a systematic literature review, the empirical research is based on a large-scale survey of 705 manufacturing plants from 22 countries. Structural equation modeling is employed to discover the relationships between the main constructs of interest, complemented with subgroup analyses to offer a more detailed understanding of the main effects.

We provide evidence that technologies enabling Industry 4.0 have a positive impact on operational performance, including cost, quality, delivery and flexibility performance. Results of the analyses further indicate that (1) larger firms invest more in implementing Industry 4.0 technologies, (2) manufacturing firms in less competitive countries, especially in the South-East Asian region invest significantly more effort than competitive countries, while (3) multinational companies have no advantage over local firms.

The survey data employed in this study refers to the early years of companies embracing Industry 4.0 solutions, and thus does not contain the most recent advances in manufacturing technologies.

The paper represents one of the first studies in the literature to assess on a large-scale survey the performance impact of Industry 4.0 technologies, as well as the main contingency factors affecting the implementation of these technologies.

Aims to provide a summary of global robot statistics as presented in World Robotics 2004, a new publication prepared jointly by the United Nations Economic Commission for Europe (UNECE) and the International Federation of Robotics (IFR). Also aims to highlight the trends displayed at the small exhibition of service robots that accompanied the publication launch.

Summarises the UNECE/IFR global robot statistics for 2003, separating the market for industrial robots from that of service robots, and providing a detailed analysis of both. Explanations from Jan Karlsson, who was responsible for the publication of the statistics, are included.

2003 was a bumper year for robots overall. The market for service robots is accelerating rapidly, in particular. Sales of robotic vacuum cleaners are rising fast.

Provides an overview of in‐depth statistics on robotics, and highlights the trends for those interested in the robotics market.

This study aims to investigate whether the increasing robot adoption will affect employment rate and wages to contribute to the economic cycle and sustainable development in the world.

The authors introduce a two-way fixed effect model and ordinary least-squares (OLS) model to evaluate the influence based on relevant data of the eighteen countries with the largest robot stocks and robot densities in the world from 2006 to 2019 to test the influences and do the robustness test and endogeneity test by using empirical models.

The authors’ research findings suggest that increasing robot adoption can cause strong negative impacts on employment for both males and females in these economies. Second, the effect of robots on reducing job opportunities has penetrated different industries. It means that this negative impact of robots is comprehensive for the industry. Third, robot adoption can have a strong positive influence on wages and increase workers' incomes.

The limitations of the study are that the influence of industrial intelligence technologies on the circular economy is diversities in different countries. Thus, this study should consider the development levels of different economies to do additional confirmatory studies.

This study makes out the correlations between industrial robots and the employment market from the circular economy perspective. The result proves the existence of this influence relationship, and the authors propose some suggestions to promote sustainable economic development.

This paper addresses the activity of industrial intelligence technologies in the labor market. The employment market is an important part of the circular economy, and it will benefit social development if the government provides appropriate guidance for social investment and industrial layout.

This study is one of the few studies which considered the impact of industrial robots on employment and wages from the perspective of different industries, and this is very important for the circular economy in the world. The results of this paper provide an instructive reference for government policymakers and other countries to stabilize the labor market and optimize human resources for sustainable economic development.

As a new research interest, robots have surpassed human performance across several aspects. In this research, the authors wish to investigate whether robot adopters perform better than non-adopters in terms of export behavior, especially when distinguishing between different types of firms.

The authors try a new strategy to identify the extent of robot adoption by import data and compare the export trajectories of robot adopters and non-adopters by employing the propensity score matching-difference in difference (PSM-DID) method.

The authors find that robot adopters are more likely to enter export markets and improve subsequent export performance, as the gains from doing so can spread the reduction in variable production costs to a larger customer base abroad. But this rule does not always seem to work; for large-scale firms, robot adoption makes it easier to win export competition and increase market share, while small and medium-sized enterprises (SMEs) do not seem to enjoy any benefits from adoption. More importantly, robot adoption also leads to the fiercer market competition when improving the productivity of firms, which will threaten smaller non-adopters.

The findings provide new evidence for the scale bias of robotics and offer new insights into whether exporters or future exporters ought to adopt robots in production.

1. First, distinguishing from existing research, we explain the controversial results of previous work on robotics by providing evidence from export markets and using the concept of size bias, which helps to update the theoretical interpretation of robotics and provides new insights for current and future exporters to evaluate their robot adoption decisions.

2. Second, we extend previous research by further considering the potential robotics threats faced by non-adopters, especially we record that export gains of robot adopters are partially at the expense of smaller non-adopters, which provides new evidence for the rationale of SME protection policies and supplements robotics theory with new knowledge, such as the competitive game of firms related to robot adoption.

3. Third, to our knowledge, prior research tended to examine the economic effects of robotics through industry data provided by the IFR, this may lead to systematic bias due to the inability to distinguish the robot adoption intentions of different firms. In this respect, we try a new strategy through robot import data and further distinguish between robot adopters and non-adopters in the sample, which helps to mitigate the potential bias in the findings and provide a complement to the recently developed literature related to robotics.

4. Finally, as we pointed out earlier, robot adoption could be an interesting research work for the Chinese export market, which helps us to obtain some special findings, such as in assessing whether the benefits of robots are equally appropriate for economies that previously had an advantage in terms of labor.

First, distinguishing from existing research, we explain the controversial results of previous work on robotics by providing evidence from export markets and using the concept of size bias, which helps to update the theoretical interpretation of robotics and provides new insights for current and future exporters to evaluate their robot adoption decisions.

Second, we extend previous research by further considering the potential robotics threats faced by non-adopters, especially we record that export gains of robot adopters are partially at the expense of smaller non-adopters, which provides new evidence for the rationale of SME protection policies and supplements robotics theory with new knowledge, such as the competitive game of firms related to robot adoption.

Third, to our knowledge, prior research tended to examine the economic effects of robotics through industry data provided by the IFR, this may lead to systematic bias due to the inability to distinguish the robot adoption intentions of different firms. In this respect, we try a new strategy through robot import data and further distinguish between robot adopters and non-adopters in the sample, which helps to mitigate the potential bias in the findings and provide a complement to the recently developed literature related to robotics.

Finally, as we pointed out earlier, robot adoption could be an interesting research work for the Chinese export market, which helps us to obtain some special findings, such as in assessing whether the benefits of robots are equally appropriate for economies that previously had an advantage in terms of labor.

A new round of technological revolution is impacting various aspects of society. However, the importance of technology adoption in fostering firm innovation is underexplored. Therefore, this study aims to investigate whether robot adoption affects technological innovation and how human capital plays a role in this relationship in the era of circular economy.

Based on the robot adoption data from the International Federation of Robotics (IFR) and panel data of China's listed manufacturing firms from 2011 to 2020, this study uses regression models to test the impact of industrial robots on firm innovation and the mediating role of human capital.

The results demonstrate that the adoption of industrial robots can significantly promote high-quality innovation. Specifically, a one-unit increase in the number of robots per 100 employees is associated with a 13.52% increase in the number of invention patent applications in the following year. The mechanism tests show that industrial robots drive firm innovation by accumulating more highly educated workers and allocating more workers to R&D jobs. The findings are more significant for firms in industries with low market concentration, in labor-intensive industries and in regions with a shortage of high-end talent.

Due to data limitations, the sample of this study is limited to listed manufacturing firms, so the impact of industrial robots on promoting innovation may be underestimated. In addition, this study cannot observe the dynamic process of human capital management by firms after adopting robots.

The Chinese government should continue to promote the intelligent upgrading of the manufacturing industry and facilitate the promotion of robots in innovation. This implication can also be applied to developing countries that hope to learn from China's experience. In addition, this study emphasizes the role of human capital in the innovation-promoting process of robots. This highlights the importance of firms to strengthen employee education and training.

The adoption of industrial robots has profoundly influenced the production and lifestyle of human society. This study finds that the adoption of robots contributes to firm innovation, which helps people gain a deeper understanding of the positive impacts brought about by industrial intelligence.

By exploring the impact of industrial robots on firm innovation, this study offers crucial evidence at the firm level to comprehend the economic implications of robot adoption based on circular economy and human perspectives. Moreover, this study reveals that human capital is an important factor in how industrial robots affect firm innovation, providing an important complement to previous studies.