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Article

Effect of Usage of Industrial Robots on Quality, Labor Productivity, Exports and Environment

by
Iztok Palčič
1 and
Jasna Prester
2,*
1
Faculty of Mechanical Engineering, University of Maribor, 2000 Maribor, Slovenia
2
Faculty of Business and Economics Zagreb, University of Zagreb, 10000 Zagreb, Croatia
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(18), 8098; https://doi.org/10.3390/su16188098
Submission received: 2 July 2024 / Revised: 16 August 2024 / Accepted: 13 September 2024 / Published: 16 September 2024
(This article belongs to the Section Sustainable Management)
Review Reports Versions Notes

Abstract

:
Industrial robots are slowly finding their way into manufacturing companies. This paper examines the impact of robots on productivity, exports, quality, sustainability and labor in European manufacturing companies. There is little research on the use of industrial robots and their impact in developed countries. Most research relates to Chinese companies, and often, the data are outdated. The data in this paper come from the European Manufacturing Survey project, which was conducted in 2022 and includes 476 manufacturing companies. The results of the impact of industrial robots on quality, labor productivity, exports and green technologies are determined using a T-test between companies that use industrial robots and those that do not. However, the impact of higher investment in environmental technologies by industrial robot users was examined by a two-stage OLS regression analysis with control variables representing the contextual characteristics of the companies. The results show positive effects on all of the variables. The results show that the greater use of robots occurs in industries with low-to-medium technology intensity, that robots contribute to labor productivity and exports and that companies that use robots also tend to use environmentally friendly technologies.

1. Introduction

Industrial robots are becoming safer and more affordable, so many companies are using them in their production as the cost of robots decreases. According to the 2024 report of the International Federation of Robotics (IFR) [1], the number of installed robots is around 4 million units, with an annual growth trend of 7%. The main reasons for the introduction of robots are quality (less waste), increasing productivity through faster cycle times, improving work safety, reducing costs, reducing work in progress and greater flexibility in production [2,3]. A total of 79% of the world’s robot installations are in the following five countries: China, Japan, the United States, Korea and Germany [1]. By 2020, 3 million robots (32%) were installed in China [4]. There is extensive literature on how robotics can increase growth and potentially replace humans, but nothing has been written about the current status. Even the IFR report only analyzes a few key countries. Duan et al. [4] conducted a comprehensive literature review on this topic. Most of the research performed in developed countries is at the country level and not at the company level. This is, therefore, a gap that we want to close with this study. Research by [5] theoretically predicts that robots will have a negative impact on jobs and wages in the US. We investigate the impact of robots with a survey and verify the claims of [5] that robots have a negative impact on employment with a recent large-scale survey of manufacturing companies in Europe. However, Aggogeri et al. [6] show in a recent survey of 660 Italian companies that, not only has productivity increased through the use of industrial robots, but there have also been no layoffs. The same is shown [7] for Europe using IFR data. One aim is, therefore, to review these contradictory results in the literature: Do robots really reduce the number of employees? Acemoglu et al. [8] show an increase in production output and productivity growth for the French manufacturing sector due to the use of robots. Similarly, Kromann et al. [9] show that industrial robots have increased productivity. Graetz and Michaels [10] show an increase in labor productivity and total factor productivity using industrial robot data from 17 countries. Cheng and Yuan [11] show that the use of robots leads to an increase in productivity and quality through product and process innovation. They have shown that robots improve productivity, but this is based on data from 2019 and earlier [11]. Our aim is, therefore, to test whether robots increase productivity and decrease scrap rates in our sample of more recent data. In this paper, we will analyze the impact of robots on productivity, quality, exports and sustainability. Exports are particularly important for GDP growth and job creation, especially for countries with a small local market [12]. In addition, companies invest heavily in sustainability to have a competitive advantage and meet customer demand for green technology. Through these investments, they also reduce costs and waste, which, in turn, increases operational efficiency [13,14].
We will analyze four slightly less developed countries (Spain, Slovakia, Slovenia and Croatia) than the five countries mentioned in the IFR report [1] (China, Japan, the United States, the Republic of Korea and Germany). According to this report, Spain and France have the lowest density of robots, while Slovakia, Slovenia and Croatia are not even mentioned. According to [15], richer countries are investing more in robots, and their productivity is increasing, while poorer countries, which are not investing as much in robots, are lagging behind and widening the productivity gap. In addition, we will ask questions about contingency factors such as company size, product complexity and technology intensity.
Productivity is important because it brings prosperity to companies by producing more with less labor [16]. If robots also produce more with higher quality, companies become more competitive, as customers want better quality for their money [17]. This, in turn, also increases exports, because companies are more competitive. However, being competitive in the global market also requires sustainability in production, as customers have become more environmentally conscious. In the US, labor productivity in the manufacturing sector increased by 1.8% in the second quarter of 2024, or 0.3% on an annualized basis. This increase is due to the reduction in working hours, i.e., more output with less work [18]. Zhu et al. [19] show that the introduction of smart manufacturing has significantly increased Chinese labor productivity. However, they come to curious results that require further investigation. For example, they find that richer companies invest more and achieve better productivity, but they find that larger companies have lower labor productivity in terms of the number of employees. On the one hand, there is a complete lack of research on less-developed countries such as Spain, Slovakia, Slovenia and Croatia regarding the use of robots to increase productivity, quality, exports and sustainability. On the other hand, given the recent [19] findings on wealth and company size, there is still disagreement on whether the mere installation of robots is sufficient to increase productivity and, thus, all other variables such as quality, exports and sustainability. Therefore, we would like to contribute to solving the labor productivity conundrum puzzle by using a rather large sample of 476 manufacturing companies, using recent data and taking into account the size of the company, the complexity of the product and industry specifics.
Our contribution is mainly in the area of robot use based on data from the European Manufacturing Survey (EMS), which is more recent than the usual studies conducted on IFR data. We did find some recent research (e.g., [7,19,20,21,22,23,24]), but the most recent data in these papers are from 2019. This is problematic because the installed robot base is growing annually. The most recent IFR report shows that the number of installed robots increased by 7% annually between 2022 and 2023 [1]. The other contribution is the fact that our research is based on factory-level (manufacturing) data, where most of these robots are actually deployed, unlike the IRF data, which varies across the sectors studied [1].
The paper is organized as follows: In Section 2, we present a recent literature review of all of our research topics/company characteristics. Section 3 describes the research methodology. Section 4 presents the results of our study, followed by a discussion and conclusion with practical and theoretical contributions, research limitations and suggestions for further research.

2. Literature Review

2.1. Industrial Robots

Industrial robots are divided into the following four groups in our work: robots for manufacturing processes, robots for handling, autonomous mobile robots and collaborative robots (cobots). Robots for manufacturing processes are usually multi-purpose machines that have at least one reprogrammable robot arm. Robots in manufacturing processes are generally used for repetitive tasks and can perform them with greater accuracy than humans. When performing these repetitive tasks, robots make fewer errors and produce less waste [25]. They are used for various tasks, including grinding, drilling, cutting, polishing and the like. The introduction of a technology is usually performed after a careful cost–benefit analysis, taking into account the expected return on investment. Mechanical cutting robots, for example, not only contribute to less waste due to their precision but also to higher quality [26]. Soori et al. [27] show that today’s robots also make a significant contribution to energy savings. Soori et al. categorize the energy savings into the following three groups:
(1)
Type of robot: A heavy-duty robot requires more energy to operate than a small, lightweight robot; robots consume more energy depending on their mass.
(2)
Task performance: The robot’s energy consumption depends on the task in question. A lot of movement and heavy lifting consume more energy than static robots.
(3)
Operating conditions: Operating conditions in the form of temperature, humidity, dust or other contaminants increase energy consumption [24].
Cheng and Yuan [11] prove with their simulation that energy consumption can be reduced by 22%. In addition, the use of industrial robots reduces total carbon emissions [28].
The second group of robots are material-handling industrial robots. Their purpose is to load and unload heavy materials and pick and pack products, replacing many humans in these heavy-lifting tasks. Some robots perform hazardous tasks, reducing the risk of injury to humans [29].
The third group of robots are autonomous mobile robots. These robots move materials and products from one place to another. In contrast to the robots described above, which are fixed in their position, these robots can move. To enable them to move without crashing, they are equipped with sensors, which makes them more expensive and energy-intensive. Since they are powered by batteries, the problem is that they do not last long at one time [30]. According to [31,32], these types of robots are mainly used in warehouses. These robots have advanced vision systems to detect objects accurately [33].
The fourth group of robots are collaborative robots, also known as cobots. Cobots allow humans to work with them and communicate directly. Cobots help automate tasks, such as assisting people to carry parts or on assembly lines. They are designed to prevent injuries. Cobots are not designed for heavy lifting. They are not fully automated to perform complex tasks; therefore, a human must be present [34].

2.2. Labor Productivity

One of the postulates of capitalism is economic growth. The growth of labor productivity is closely linked to economic growth. Labor productivity increases the prosperity and development of nations and indirectly improves the standard of living [15]. The use of industrial robots affects the production and operation activities of companies by expanding the scale of production, improving production efficiency and increasing production capabilities. According to [35], robots even increase the demand for labor. The reason why we still do not see a large increase in labor productivity is that robots are expensive to purchase, maintain and operate, and they can be more inflexible when adapting to new tasks or unforeseen situations [36]. Automation has increased labor productivity but has also created new tasks. For example, a newer model of welding robot that can weld faster and more accurately than an earlier model increases productivity without displacing the worker. Finally, new robots can create new tasks that need to be performed by workers [24]. Yuan and Lu [37] even go so far as to claim that the installation of robots not only increases productivity but can also make the whole country more competitive. In a very detailed analysis, Acemoglu et al. [38] show that companies that introduce robots perform better than their competitors that do not invest in robots. They also have a much lower proportion of replaceable employees than companies that do not introduce robots, and their employees are more competent.

2.3. Quality

At the company level, companies that produce higher-quality goods have higher revenues and more jobs because customers are willing to pay a higher price for quality. Such companies that compete for differentiation usually pay their employees higher wages, are more productive and sell their products to a wider range of customers [27]. Yang and Liu [39] studied quality in Chinese manufacturing companies and the introduction of robots. They find that China is now quality-oriented instead of quantity-oriented, and that this issue is not studied at all. Robots are gradually penetrating all aspects of manufacturing production in China, changing the way the manufacturing industry works, in the sense that quality is increasing alongside mass production and quality products have a higher market potential. Industrial robots improve the production quality of the manufacturing industry by improving production methods and optimizing the management mode. However, Azamfirei et al. [40] warn that the mere investment in defect detection by robots will not solve the quality problems unless humans do not respond and change the process that produced the defective part.

2.4. Exports

Export quality has attracted attention because of its importance to a country’s development and growth [26]. Zhang et al. [41] studied industrial robots and exports in China and conclude that the exports of those who use industrial robots are much higher than those who do not. However, they also say that most SMEs belong to the group of non-users. Li et al. [42] find that industrial robots increase exports through higher quality and innovation. This effect is more pronounced in economically advanced countries. Technological innovations such as the introduction of robots and investments in education increase the impact of industrial robots on exports [43]. According to [12], it is not necessary for exports to exceed imports; rather, productivity gains contribute to the increase in GDP. Nevertheless, Yang [44] shows that exporters are also more innovative companies and therefore more competitive. However, the analysis was carried out for the manufacturing industry in China in the period 2004–2007. In our study, we therefore use current data to examine whether exports in our four less-developed European countries have increased as a result of the installation of robots.

2.5. Sustainability

It has already been mentioned that the introduction of new robots can save energy [27]. Zhu et al. [45] note that it is not obvious how the installation of robots is related to a reduction in pollution, but their empirical results support this. Yang and Liu [39] also show that companies with more robots also have more environmentally friendly technologies, but they do not investigate why. They only find that industrial robots improve quality through improved and optimized production methods, which also induces the manufacturing company to adopt energy-saving and emission-reducing technologies to achieve green development. Companies are not investing in robots because of environmental sustainability. By using industrial robots, companies increase productivity and reduce waste, and sustainability is just a by-product. Thus, the effect of robot adoption mediates the sustainability of production, as proposed by Cheng et al. [11].

2.6. Research Hypothesis Formulation

So far, we have seen that there are no studies on the use of robots in less-developed European countries such as Spain, Slovakia, Slovenia and Croatia. According to the IFR report, these countries have a low robot density. Robot density is defined as the number of industrial robots used in relation to the number of employees [1]. However, the IFR report only looks at regional and country-wide robot density and not at individual companies. As we described in our theoretical overview, most research is conducted at the country level and not at the level of individual companies, usually using old IFR data. Some valuable exceptions are studies in France [8], the Netherlands [38] and Italy [6]. If a company becomes more competitive by using robots, we should see the same pattern in less-developed countries. The first hypothesis, therefore, relates to productivity. We put forward the first hypothesis that an increase in labor productivity can be observed in companies that use robots, even though they are less-developed countries.
H1: 
In companies that have introduced robots, we will see higher productivity than in companies that have not introduced robots.
We will also investigate whether the number of employees has decreased due to the installation of robots. Chung et al. [24] and Acemoglu et al. [5] show that the number of employees has not decreased due to the installation of robots. However, they find that there may be a decrease in jobs in the long term but not in the short term. Therefore, we will investigate whether the number of workers in manufacturing companies that have installed industrial robots has decreased. Since we have data on the number of workers in a company in 2022 and 2019, this is easy to assess. Therefore, the second hypothesis is as follows:
H2: 
There is no statistically significant difference in the number of employees over a three-year period in companies that have industrial robots.
The third hypothesis is about quality. The impact of industrial robots on quality has only been researched in China [27,39]. We will measure the quality based on the scrap rate between users and non-users. Therefore, the third hypothesis is as follows:
H3: 
Adopters of industrial robots will have statistically significantly lower scrap rates than non-adopters.
Exports are almost exclusively researched in China [26], per Zhang et al. [41] and Li et al. [42]. There are no studies on exports in European countries. The countries considered in our study, including Spain, Slovakia, Slovenia and Croatia, are heavily dependent on exports. The exports of goods and services as a % of GDP in 2023 [46] for the countries studied are as follows: Spain (39%), Slovakia (91.4%), Slovenia (84%) and Croatia (54%). Apart from Spain, all of the other countries analyzed export more than 50% of their goods and services. Exports are particularly important for small countries with small markets that are heavily dependent on exports. Our fourth hypothesis is therefore as follows:
H4: 
Adopters of industrial robots will have statistically significantly higher exports than non-adopters.
Yang and Liu [39] show that companies that use robots also use much more energy-friendly technologies. They do not know why. This is an interesting conundrum that has not yet been explained in the current literature. We will analyze the use of energy recovery technologies and water reuse technologies. As with all current research, we hypothesize that the users of industrial robots will make greater use of these ecological technologies. The fifth hypothesis is as follows:
H5: 
Adopters of industrial robots will have statistically significantly higher usage of energy-friendly technology than non-adopters.
But the question of why remains. Cheng et al. [11] state that sustainability is mediated by changing productivity or by intervening in production. We will analyze industrial robots for environmentally friendly technologies and the conceptual factors of the manufacturing company, such as the company size, product complexity, industry and technological intensity, which could play a role in explaining why robots are usually accompanied by waste energy recovery and water recycling technologies.

3. Materials and Methods

The European Manufacturing Survey (EMS) was used for this analysis. The full description of the EMS coordinated by the Fraunhofer Institute for Systems and Innovation Research (ISI) [47], the largest European survey on manufacturing activities developed by the Survey Research Center guides , can be found in [48]. Most of the research on robots is conducted using data from the International Federation of Robotics and usually uses simple correlation or econometric models (i.e., some form of regression analysis). However, these studies have shortcomings, as comparison is difficult due to the time periods of the data and the countries they cover. The problem is nicely described in [23]. And although the articles are more recent (published in 2024), the most recent data are from 2016 [7,21,22].
Our EMS survey was conducted among manufacturing companies with more than 20 employees (NACE Revision 2 codes from 10 to 31). The survey was conducted in 2022, so the data are relatively recent.
Only a subset of four countries was used for this study. The main reason for this is that highly developed countries such as Germany are already well-researched and already have a high robot density. For this study, we selected less-developed European countries that could provide insight into robot use in these less-developed countries. We therefore selected 476 responses for Spain (86), Slovakia (102), Slovenia (146) and Croatia (142). This is a relatively large sample that includes countries that are not considered to be as industry-intensive, such as Spain and Croatia, which generate a large part of their gross value added in tourism rather than manufacturing, and Slovenia and Slovakia, which are somewhere in the middle. This can be seen in Table 4 of the World Bank (2023) [49] report, which shows the share of agriculture, industry and services in GDP as a percentage. If Germany serves as a benchmark, then Slovenia and Slovakia have a similar share of industry to Germany, while Spain and Croatia have a higher share of GDP in services.
Each country that participated in the European Manufacturing Survey had to check for non-respondent bias and common method bias. The check for non-respondent bias and common method bias is performed by each country itself. All precautions are performed at the design phase of the survey, as recommended by Podsakoff et al., 2003 [50].
Since we used four countries that differ in their industry intensity, we had to check for variances. We checked our sample for bias using Levene’s test for equality of variances and a t-test for equality of means. There is no evidence of a significant difference in the populations [51].
Finally, we computed Harman’s one-factor test with exploratory factor analysis to account for common method bias [50] in the common data from four countries. This test, with all independent and dependent variables, yielded a first factor that explained only 19% of the observed variance. Since there was no single factor that explained most of the variance in the model, this test suggests that common method bias is not a problem in this sample.
Due to the nature of our hypotheses 1 to 5, simple statistics suffice. That is, we tested whether there is a difference between those who have robots and those who do not. To test hypotheses 1 to 5, we performed a simple t-test for equality of means. However, we had to calculate some variables. For example, labor productivity had to be calculated from the revenue of the year in question and divided by the number of employees in that year. Other variables, such as the scrap rate or exports, were direct objective responses that we did not need to calculate.
We also used control variables. Control variables included the company size (with the following three groups: 1: less than 50 employees, 2: from 50 to 249 employees and 3: over 250 employees) and product complexity (1: for simple products, 2: for products of medium complexity and 3: for products of high complexity). The sample was also subdivided according to technology intensity as defined by the European Commission [52]: high technology (NACE 21 and 26), medium–high technology (NACE 20, 27 to 30), medium–low technology (NACE 19, 22 to 25) and low technology (NACE 10 to 18, 31 to 32).
We tested all of the hypotheses for the following four types of robots: robots for manufacturing processes, robots for handling processes, mobile industrial robots and collaborative robots. However, for the 5th hypothesis, we had to use an OLS regression model to test the impact of environmental technologies on the use of robots. This was not possible with simple statistics, as we had more variables in the model. All of the calculations were performed with SPSS. 29.

4. Results

4.1. Descriptive Results

We begin with the descriptive results of our sample. Although we did not analyze the entire sample, but rather only the individual countries, Table 1 shows the distribution of the four robot types by country.
Table 1 shows that industrial robots for manufacturing processes and industrial robots for handling processes are present in slightly less than a third of manufacturing companies. The proportion of the other two types of robots is, as expected, much lower. The total figures in the sixth column are the sum of the robots. The seventh column shows the proportion of companies that have a particular type of robot as a percentage of companies that have a particular type of robot divided by the number of companies in the dataset. The sixth row, on the other hand, shows the number of companies that have at least one type of robot in each country and in total (195). We conclude that 41.3% of companies have at least one type of robot installed in their production facility.

4.2. Hypotheses Testing

The hypothesis tests were performed for all four robot types using the standard Student t-test. We present the results in Table 2.
Productivity: From the second and third rows, we can see a statistically significant lower productivity among adopters compared to non-adopters, but only for manufacturing robots for manufacturing processes. For the other robots (handling robots, mobile industrial robots and cobots), we see that productivity is higher for adopters than for non-adopters, but the difference is not statistically significant. If we compare the mean values for productivity from 2019 to 2022, we see a significant increase in labor productivity in all cases.
Exports: From the fourth row, we can see that exports are statistically significantly higher for adopters than for non-adopters, except for mobile robots. However, this deviating result for mobile robots could be due to the small number of mobile robots implemented. As we can see, there are only 11 mobile robots installed in the entire sample of 476 manufacturing companies.
Scrap rate: The scrap rate (fifth row) is only statistically significantly lower for production robots. This is to be expected, as the scrap rate only measures the number of errors in the manufacturing process. Handling errors are normally recorded but not in the scrap rate. Handling errors are usually measured in cooperation with the robot manufacturer to adjust the robot to reduce the occurrence of these errors.
Number of employees: If we look at rows 6 and 7, we can see that the number of employees is statistically significantly higher for all robot types compared to non-adopters. Also, when we look at the average number of employees between the years 2022 and 2019, we see an increase in employees in companies that use robots, as opposed to non-adopters, where the average number of employees has remained almost the same. The question of how company size affects the use of robots is examined later using a regression analysis.
Technologies for recovering kinetic energy and process energy, as well as technologies for recycling and reusing water, are seen as a means of improving the company’s environmental impact. From rows 7 and 8, we can see that all users of industrial robots use these two technologies to a greater extent and that they are statistically significant, with the exception of industrial mobile robots, which, in turn, could be due to the small number of mobile robots installed in the sample. As we have seen in the theoretical part, there is still no explanation as to why companies that invest in robots also invest in these green technologies. They are not forced to do so by any legislation, but simply invest more in sustainability as well.

4.3. Regression Analysis

We conducted a two-stage OLS regression with two models. Model 1 has the dependent variable of the number of different robots installed and only control variables (company size, industry intensity and product complexity). Model 2 has additional independent variables, technologies for recovering kinetic and process energy and technologies for recycling and reusing water. In the first step, we only included control variables, and, in the second step, we included our independent variables, as shown in Table 3.
As shown in Table 3, contextual factors influence the decision to use robots in model 1. If we consider company size as a contextual factor, we see that the larger the company, the higher the probability of introducing robots. Therefore, we show the distribution of robots by company size in Table 4.
According to our model 1, where the number of robots is the dependent variable, which can take a value between one and four different types of robots, the size of the company plays a role. A look at Table 4 shows that, as the company size increases, so does the percentage of robots. Yes, smaller companies have fewer robots, but contrary to the literature [53], medium-sized companies also have a lot of robots. The main obstacle to the introduction of robots in SMEs, according to [53], is the lack of processes to be automated. Other literature only deals with the readiness of SMS for Industry 4.0 or only with cobots.
From our two-step OLS regression in Table 3, we can also see that industry intensity also plays a role in the adoption of robots. In step two, where we added energy recovery and water reuse technologies, only industry intensity mattered. This means that the number of robots depends on the industry intensity. Since the standardized beta coefficient is negative, this would indicate that companies with lower intensity have installed more robots. Therefore, we also created Table 5 to show the descriptive statistics between robot deployment and industry intensity. So far, we can only find that the highest implementation of robots is found in the medium-to-low and medium-to-high intensity. We could even hypothesize that the relationship between industry intensity and the number of robots is inversely U-shaped or, according to the normal distribution, probably due to a small number of high-intensity companies in the sample.
Product complexity and technological intensity play no role. However, it is very interesting that some technologies correlate with each other. Kinetic and process energy recovery technologies tend to be purchased together with robots for manufacturing and industrial mobile robots, while recycling and water reuse technologies tend to be purchased together with industrial robots for handling processes. This is an interesting result because, as we have shown in the theoretical part, there is still no explanation as to why industrial robots go hand-in-hand with environmentally friendly technologies. Several authors show that the use of robots reduces CO2 emissions, but this still does not explain why companies invest in recovery and cleaning technologies. The two authors [54] and [55] argue that countries should establish environmental guidelines. However, both researchers are from China. Our finding could mean that managers who invest in industrial robots are more proactive, and because of their proactivity, these technologies are adopted in tandem. However, we cannot prove this using production data. Zhang et al. [56] mention the phenomenon of robotization increasing green productivity. They proved it but also did not elaborate on why this happens.
Therefore, we performed an additional regression analysis to find out which environmental technology is related to which type of robot. The results can be found in Table 6.
From Table 6, we can see that company size plays a role in the installation of energy recovery technologies and that these are related to robots for manufacturing processes and mobile robots (significances are in bold). In contrast, company size does not play a role in the implementation of water recovery technologies, but they are associated with robots for handling processes.

4.4. Summary of Findings

Table 7 summarizes the results in one place. However, when testing H5, we found that different robots incorporate different environmental technologies. This is already a step forward in solving the puzzle of why robot entrants usually invest in environmental technology.

5. Discussion

SMEs account for 91% of all jobs in the European Union, and 68.2% of all jobs are in manufacturing. However, according to [53], SMEs have not yet embraced robotic manufacturing. Our results show that company size is important for the adoption of robots, but small companies also have robots. As expected, medium-sized and large companies have more robots than smaller companies.
When it comes to productivity, there is a large gap in the literature regarding the current state of robot adoption. Most analyses have been conducted on Chinese manufacturing companies based on old IFR data. The reason for this large number of studies conducted in China is that, according to the IFR report, China is one of the countries with the highest density of robot use (number of robots divided by the number of employees). In Europe, Germany is the only leader in robot density. There are very few studies on the European manufacturing sector [6,8,38] (France, Italy and Norway). There is much less data available for less-developed European countries such as Spain, Slovenia, Slovakia and Croatia. These countries are very export-oriented, as their domestic market is small, so they rely on exports for their growth. We have shown with a series of t-tests that robots indeed increase exports and decrease the scrap rate. Productivity increased from 2019 to 2022, but the productivity of those who use industrial robots for manufacturing is statistically significantly lower than in the group of those who do not use them. This could mean that these robots have only just been introduced and still need humans to do their work. For all other types of robots, we see that productivity is higher in the adoption group, but this is not statistically significant. This also means that the relationship between installed robots and productivity growth is not so simple. We argue that the mere implementation of robots will not solve the company’s problems.
We also addressed the question raised by [6] that robots will reduce the number of workers. Therefore, we used data from 2019 and 2022 to compare whether the number of workers will decrease. In rows 6 and 7 of Table 2, we see that the number of workers is statistically significantly higher for all robot types than for non-robot users and that the number of workers has actually increased for robot users. However, we cannot predict whether this is only a short-term effect or whether the number of workers will decrease in the long term, as also predicted by [6].
The question still remains as to why robots are increasing the use of energy recovery and water recycling technologies. We have succeeded in finding combinations of environmental technologies and robots. For example, kinetic energy and process energy recovery technologies are usually used in conjunction with robots for manufacturing and mobile industrial robots, while recycling and water reuse technologies are usually implemented in conjunction with industrial robots for handling processes. In this way, we contribute to Zhang et al. [56], who predicted that sustainability is related to the adoption of robots but could not explain why. In this paper, however, we find a direct relationship between energy recovery technologies and manufacturing robots. The other OLS regression showed that water recycling and recovery are related to handling robots.
Zhang et al. [41] find that SMEs rarely use robots, and our regression analysis confirms this. However, in Table 4, we show that small companies also use robots and that the highest number of robots is found in larger companies. Industry intensity also played an important role in our analysis. Therefore, we show in Table 5 that most robots are found in the medium technology intensity group. The authors are not aware of any other examples of the distribution of robots by technology intensity. We obtained the same mixed results for technology intensity as [2] for Industry 4.0 Readiness.
Our research has some practical implications. We argue that the introduction of robots is beneficial for manufacturing companies in terms of productivity, the scrap rate, exports and sustainability, but the impact study needs constant revision, as we still do not know whether the installation of robots has a negative long-term effect in terms of fewer jobs. In this study, we have shown that the installation of robots also increases the number of workers, but we cannot predict the future.
As [7] notes, there are problems in analyzing IFR data due to the size of the data, their age and the representation of industries in these data. Therefore, according to [7], a generalization of these data is not possible. Our analysis is based on fresh data of manufacturing companies, and our results can be generalized to less-developed countries in the European Union. The theoretical conclusion of our research, therefore, shows the benefits of installing robots in this particular manufacturing environment. However, further research is needed, including in more developed countries, to verify the existence of a productivity paradox, i.e., that more developed countries achieve higher productivity through the introduction of robots.

6. Conclusions

With this work, we fill the gap in the literature on the introduction of robots in European countries. Most studies focus on China because it has the highest robot adoption rate and is transforming from a volume producer to a quality producer. Although the papers dealing with robots have been published recently, they relied on old data and looked at whole countries or regions rather than at the company level. We fill this gap by analyzing productivity, exports, quality and environmental technologies at the company level. For this purpose, we used a subset of the European Manufacturing Survey that includes the following four countries: Spain, Slovakia, Slovenia and Croatia.
We have proven four out of five hypotheses. We found statistically significantly higher productivity among those who use robots, except for robots for manufacturing processes. We have shown that the number of employees has not decreased as a result of the introduction of robots but has actually increased. We have shown that adopters of industrial robots have a lower scrap rate than non-adopters. We have shown that the introduction of industrial robots leads to higher exports.
We have shown that industrial robot users use more energy-efficient technologies than non-users. We have also shown that the use of robots promotes the application of environmental technologies, and we have shown which environmental technologies fit which type of robots. Kinetic and process energy recovery technologies tend to be purchased together with robots for manufacturing and industrial mobile robots while recycling and water reuse technologies tend to be used together with industrial robots for handling processes. This is an interesting result because, as we have shown in the theoretical part, there is still no explanation as to why industrial robots go hand-in-hand with environmentally friendly technologies. Several authors show that the use of robots reduces CO2 emissions, but this still does not explain why companies invest in recovery and cleaning technologies. Both authors of [54,55] argue that countries should establish environmental policies. Our finding could imply that managers who invest in industrial robots are more proactive, and because of their proactivity, these technologies are adopted in tandem.
Our paper mainly refers to the use of robots based on the European Manufacturing Survey (EMS) data, which is more recent than the usual studies conducted on the basis of IFR data. We did find some recent research, but the most recent data in these papers are from 2019, which is problematic because the installed robot base is growing annually. The latest IFR report predicts that robot installation will increase by 7% annually to 2030. The other contribution is the fact that our research is based on plant-level (manufacturing) data, where most of these robots are deployed, as opposed to the IRF data, which vary across the sectors studied.
One limitation of our study is that it only covers four countries, but this was sufficient for the questions analyzed (not for the country comparison). However, the notion of productivity paradox [57] needs to be explored further. It has to do with both the level of technological progress of a country and the way technology is used. This should definitely be explored further by including more advanced countries in the sample.
Further research should include more countries, but the analysis should be conducted with robot density as a control variable. We will also investigate whether the technology paradox only occurs in poorer countries compared to more developed countries. This would be a similar test to the one conducted by Chang et al. [58], but only in China. That is, they divided China into more and less developed regions. In addition, the study will be repeated with new data from the EMS (2025) to see whether the effect of robot installation continues to maintain the level of labor force or whether the number of labor force decreases after a certain threshold of robot installation. Therefore, a longitudinal analysis is needed to examine the long-term impact of robots on the labor force.

Author Contributions

Conceptualization, J.P. and I.P.; methodology, J.P. and I.P.; validation, J.P. and I.P.; formal analysis, J.P.; investigation, J.P. and I.P.; writing—original draft preparation, J.P. and I.P.; writing—review and editing, J.P. and I.P.; visualization, I.P.; supervision, J.P.; project administration, I.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Slovenian Research Agency (Research Core Funding No. P2-0190).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The datasets presented in this article are not readily available because of the commitments to the research consortium. Requests to access the datasets should be directed to Fraunhofer Institute for Systems and Innovation Research ISI.

Conflicts of Interest

The authors declare no conflicts of interest.

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Table 1. Distribution of robots by country.
Table 1. Distribution of robots by country.
Type of RobotsSpainCroatiaSlovakiaSloveniaTotalShare
Industrial robots for manufacturing processes1721287714330.3%
Industrial robots for handling processes2126285713228.0%
Mobile industrial robots0218112.3%
Collaborating industrial robots73615316.6%
Number of companies having at least one type of robot2539419019541.3%
Table 2. T-test for differences in the means between robot adopters and non-adopters.
Table 2. T-test for differences in the means between robot adopters and non-adopters.
T Test for Equality of Means Industrial Robots for Manufacturing ProcessesIndustrial Robots for Handling ProcessesMobile Industrial RobotsCollaborating Industrial Robots
NMeanF (Sig.)NMeanF (Sig.)NMeanF (Sig.)NMeanF (Sig.)
LaborProductivity2022yes12617.484.884 (0.028)12124.161.616 (0.204)1026.950 (0.998)2634.063.028 (0.083)
no28120.65 28817.63 38318.94 36718.41
LaborProductivity2019yes12113.887.098 (0.008)11421.991.707 (0.192)1025.360.01 (0.919)2528.231.027 (0.312)
no27619.35 28515.86 37417.72 35917.19
Exportsyes12571.564.075 (0.044)11771.5112.299 (0.000)961.670.005 (0.943)2584.0813.355 (0.000)
no26058.88 26959.71 36263.82 34662.33
scrap rate [%]yes1331.89712.328 (0.000)1252.8090.002 (0.961)102.030.393 (0.531)282.0060.495 (0.482)
no2903.267 3002.833 3982.885 3802.927
No. of emp.2022yes138343.2219.568 (0.000)127402.446.718 (0.000)11977.6422.129 (0.000)29837.21105.968 (0.000)
no290173.8 302151.42 400211.24 382185.85
No. of emp.2019yes125306.8511.569 (0.000)117372.2238.435 (0.000)10956.328.424 (0.000)25800.3291.149 (0.000)
no282173.39 292146.09 383191.87 368176.95
Tech to recuperate kinetic and process energyyes1420.457.768 (0.000)1310.598.98 (0.000)110.821.355 (0.245)310.5510.057 (0.002)
no2980.2 3090.17 4080.25 3880.24
Tech for recycling and reuse of wateryes1390.3214.754 (0.000)1250.459.201 (0.000)110.453.338 (0.068)300.4710.568 (0.001)
no2980.22 3120.19 4090.25 3900.24
Table 3. Two-step OLS regression for contextual factors.
Table 3. Two-step OLS regression for contextual factors.
ModelDependent Variable: Number of Different Kinds of Robots Collinearity Statistics
Stand. BetatSig.ToleranceVIF
1(Constant) 1.4120.16
No. of employees in 3 groups0.1952.3390.0210.9951.005
Industry intensity−0.186−2.0830.0390.8691.151
Product complexity−0.022−0.2420.8090.8711.148
2(Constant) 2.0860.039
No. of employees in 3 groups0.0630.7630.4470.8771.14
Industry intensity−0.198−2.3860.0180.8671.153
Product complexity−0.041−0.4870.6270.8471.181
Technologies to recuperate kinetic and process energy0.3113.7070.0010.8441.184
Technologies for recycling and reuse of water0.2032.550.0120.9441.059
Model Summary Change Statistics
ModelRR SquareΔ R SquareΔ Sig. F
10.2660.0710.050.02
20.4630.2140.1840.001
Table 4. Breakdown of the implementation of robots by company size.
Table 4. Breakdown of the implementation of robots by company size.
SmallMediumLarge
Industrial robots for manufacturing processes21%32%43%
Industrial robots for handling processes20%27%45%
Mobile industrial robots1%1%8%
Collaborating industrial robots4%5%17%
Table 5. Number of robots by industry intensity.
Table 5. Number of robots by industry intensity.
Type of RobotLow TechMedium–Low TechMedium–High TechHigh Tech
Industrial robots for manufacturing processes17.42%36.73%39.05%25.00%
Industrial robots for handling processes29.55%29.08%26.67%28.57%
Mobile industrial robots1.52%1.02%5.71%3.57%
Collaborating industrial robots1.52%6.12%14.29%7.14%
Share of companies having at least one robot type32.6%46.9%46.7%39.3%
Table 6. OLS regression only of the second step, with environmental technologies as dependent variables.
Table 6. OLS regression only of the second step, with environmental technologies as dependent variables.
Dependent VariableTechnologies to Recuperate Kinetic and Process EnergyTechnologies For Recycling And Reuse Of Water
Collinearity Statistics Collinearity Statistics
Standard BetaSig.ToleranceVIFStandard BetaSig.ToleranceVIF
(Constant) 0.001 0.475
No. of employess in 3 groups0.2870.0010.9261.0790.0880.2970.9271.079
Industry intensity0.0750.3730.4452.2480.0750.4060.8161.225
complexity of the product0.1310.110.8641.158−0.1050.2330.8661.155
Industrial robots for manufacturing processes0.1980.0130.8861.1280.0960.2590.9241.082
Industrial robots for handling processes0.1040.2220.7891.2680.2730.0030.7891.268
Industrial robots: mobile industrial robots0.2360.0030.9171.09−0.1360.1080.9281.078
Industrial robots: collaborating industrial robots0.0080.9150.971.0310.1350.1050.971.031
R = 0.514Adjusted R Square = 0.264Sig of the model (<0.001) R = 0.385Adjusted R Square = 0.102 Sig of the model (0.003)
Table 7. Summary of findings.
Table 7. Summary of findings.
HypothesesConclusion
H1: In companies that have introduced robots, we will see higher productivity than in companies that have not introduced robots.Partially confirmed
H2: There is no statistically significant difference in the number of employees over a three-year period in companies that have industrial robots. Confirmed
H3: Adopters of industrial robots will have statistically significantly lower scrap rates than non-adopters.Confirmed
H4: Adopters of industrial robots will have statistically significantly higher exports than non-adopters.Confirmed
H5: Adopters of industrial robots will have statistically significantly higher usage of ecological technology than non-adopters.Confirmed
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Palčič, I.; Prester, J. Effect of Usage of Industrial Robots on Quality, Labor Productivity, Exports and Environment. Sustainability 2024, 16, 8098. https://doi.org/10.3390/su16188098

AMA Style

Palčič I, Prester J. Effect of Usage of Industrial Robots on Quality, Labor Productivity, Exports and Environment. Sustainability. 2024; 16(18):8098. https://doi.org/10.3390/su16188098

Chicago/Turabian Style

Palčič, Iztok, and Jasna Prester. 2024. "Effect of Usage of Industrial Robots on Quality, Labor Productivity, Exports and Environment" Sustainability 16, no. 18: 8098. https://doi.org/10.3390/su16188098

APA Style

Palčič, I., & Prester, J. (2024). Effect of Usage of Industrial Robots on Quality, Labor Productivity, Exports and Environment. Sustainability, 16(18), 8098. https://doi.org/10.3390/su16188098

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