Knowledge, robots and productivity in SMEs: Explaining the second digital wave

Ballestar, María Teresa; Díaz-Chao, Ángel; Sáinz, Jorge; Torrent‐Sellens, Joan

doi:10.1016/j.jbusres.2019.11.017

Cited by 129 publications

(72 citation statements)

References 102 publications

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“…In addition, foreign direct investment (FDI) (Huang et al, 2018), research and development (R&D) (Chen et al, 2019), ownership type (Luan et al, 2020), enterprise size (Zhang et al, 2010;Lin et al, 2018), factor endowment (Lan et al, 2012;Bu et al, 2019), and other factors affect how AI changes energy intensity. The impact of AI has been heatedly discussed in recent years from different perspectives, including the points of view of economic growth (productivity) (Bard, 1986;Dirican, 2015;Purdy and Daugherty, 2017;Aghion et al, 2017;Brynjolfsson et al, 2017;Graetz and Michaels, 2018;Kromann et al, 2020;Jung and Lim, 2020;Camiña et al, 2020;Ballestar et al, 2020), innovation (Cockburn et al, 2018;Liu et al, 2020;Li et al, 2020;Yang et al, 2020), employment (Howell, 1985;Edler and Ribakova, 1994;Acemoglu and Restrepo, 2018, 2020a, 2020bChiacchio et al, 2018;Dauth et al, 2018;Barbieri et al, 2019;Carbonero et al, 2020;Dekle, 2020;Ballestar et al, 2020;Jung and Lim, 2020), and sustainable economic development (Vinuesa et al, 2020;Machado et al, 2020;Liu et al, 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

“…In contrast to the conclusions about the effect of technological progress and ICT on energy intensity, there is a general consensus that AI penetration positively affects technological progress (Bard, 1986;Dirican, 2015;Purdy and Daugherty, 2017;Aghion et al, 2017;Brynjolfsson et al, 2017;Graetz and Michaels, 2018;Kromann et al, 2020;Jung and Lim, 2020;Camiña et al, 2020;Ballestar et al, 2020). Theoretically, the impact of AI on technological progress is mainly reflected in the following three aspects (Purdy and Daugherty, 2017).…”

Section: Ai and Technological Progressmentioning

confidence: 99%

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Artificial intelligence and energy intensity in China’s industrial sector: Effect and transmission channel

Liu

Yang

Fujii

et al. 2021

Economic Analysis and Policy

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The continued development of artificial intelligence (AI) has changed production methods but may also pose challenges related to energy consumption; in addition, the effectiveness of AI differs across industries. Thus, to develop efficient policies, it is necessary to discuss the effect of AI adoption on energy intensity and to identify industries that are more significantly affected.Using data on industrial robots installed in 16 Chinese industrial subsectors from 2006 to 2016, this paper investigates both the effect of AI on energy intensity and the channel through which this effect is transmitted. The empirical results show, first, that boosting applications of AI can significantly reduce energy intensity by both increasing output value and reducing energy consumption, especially for energy intensities at high quantiles. Second, compared with the impacts in capital-intensive sectors (e.g., basic metals, pharmaceuticals, and cosmetics), the negative impacts of AI on energy intensity in labor-intensive sectors (e.g., textiles and paper) and technology-intensive sectors (e.g., industrial machinery and transportation equipment) are more pronounced. Finally, the impact of AI on energy intensity is primarily achieved through its facilitation of technological progress; this accounts for 78.3% of the total effect. To reduce energy intensity, the Chinese government should effectively promote the development of AI and its use in industry, especially in labor-intensive and technology-intensive sectors.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Ai and Technological Progressmentioning

confidence: 99%

Artificial intelligence and energy intensity in China’s industrial sector: Effect and transmission channel

Liu

Yang

Fujii

et al. 2021

Economic Analysis and Policy

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“…Regarding productivity, robotized enterprises are more efficient than non-robotized enterprises. What is more, employees are more intensively trained and rewarded at robotic enterprises compared to non robotized enterprises [ 55 ].…”

Section: Discussionmentioning

confidence: 99%

Analysis of the Impact of Human–Cobot Collaborative Manufacturing Implementation on the Occupational Health and Safety and the Quality Requirements

Paulíková

Babeľová

Ubárová

2021

IJERPH

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Implementing Industry 4.0 and interconnected robotization in industrial enterprises drifts towards occupational changes. Nowadays, the task is to create cooperation and collaboration between a robot and a human in a common robotized workplace so that it is safe and effective. The type of robot, the robotic device that works in collaboration with a human operator, is called a cobot. In the case of a closer interaction of the robot or cobot with humans, it is necessary to consider where it is possible to replace human work entirely or where it is possible to merely supplement it. The most socially acceptable option is the implementation of robots only for the performance of supplementary tasks, since the traditional work positions of people in manufacturing processes would remain largely preserved. On the other hand, workplace robotization is particularly suitable for work environments with hazardous chemical substances that are carcinogenic and toxic to humans. Similarly, robotization helps to improve workplace ergonomics and also to avoid, for humans, very laborious and often repetitive work. The SWOT analysis (analysis of Strengths, Weaknesses, Opportunities, and Threats) was used as a relevant tool to assess various aspects of the impact of increasing robotization on working positions in industrial enterprises. SWOT analysis is an indicative assessment of the suitability of implementation of robots in a given workplace, which helps to create an optimal solution and indicate new areas of needed analysis and research directions.

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“…With the accumulation of significant intellectual wealth and the development of modern technologies, achievements in labor productivity are minimal (Yanovska et al, 2019). There are various reasons for this contradiction: the imperfection of approaches to statistical collection and processing of data on labor productivity in the digital economy (Ballestar et al, 2020); the presence of a time interval for obtaining the final result and the introduction of innovations (Hu, 2021;Megits, Neskorodieva & Schuster, 2020); and informatization affects labor productivity only in certain high-tech industries (Jeske, Würfels & Lennings, 2021). It seems that the effectiveness of the information economy cannot be assessed by the traditional characteristics of labor productivity as a qualitative parameter of human capital.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The main destructive factors are not only the insufficient level and quality of mobile internet access and the weakness of the institutional environment, but also, above all, the low level of efficiency of human resources. Labor productivity is an important economic indicator of human capital quality, which largely determines economic growth and business digitalization (Ballestar et al, 2020). Today, human capital in Ukraine is characterized by a deficient level of productivity; at the beginning of 2020, labor productivity was $20,496, while the average for a sample of 189 countries was $42,751 in output per worker (International Labor Organization, 2021), which put it 115 th out of 189.…”

Section: Introductionmentioning

confidence: 99%

The effectiveness of human capital in the context of the digital transformation of the economy: The case of Ukraine

Rodchenko¹,

Rekun²,

Fedoryshyna³

et al. 2021

JEECAR

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The purpose of this article is to empirically assess the qualitative parameters of the effectiveness of human capital for business digitalization in Ukraine. Using a survey of 500 business representatives in Ukraine and the Fibonacci rule, and taking into account the digital business transformation level, the qualitative levels of human capital efficiency have been classified. Also, using the method of principal components, the system of factors of human capital's effectiveness for the intensification of digitalization has been empirically substantiated. An empirical model for assessing the quantitative levels of human capital effectiveness in the context of digital business transformation has been developed using integral assessment. The potential for the development of business digitalization based on the factors' actual values has been determined. The results obtained can become the basis to improve the quality of human capital for the successful introduction of modern digital technologies into the country's business processes.

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Knowledge, robots and productivity in SMEs: Explaining the second digital wave

Cited by 129 publications

References 102 publications

Artificial intelligence and energy intensity in China’s industrial sector: Effect and transmission channel

Artificial intelligence and energy intensity in China’s industrial sector: Effect and transmission channel

Analysis of the Impact of Human–Cobot Collaborative Manufacturing Implementation on the Occupational Health and Safety and the Quality Requirements

The effectiveness of human capital in the context of the digital transformation of the economy: The case of Ukraine

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