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Artificial Intelligence and Industrial Innovation: Evidence From Firm-Level Data
... Additionally, firm size maybe an important determinant of AI adoption. Empirical evidence from Rammer et al. (2021) shows that in Germany, large firms (with at least 1,000 employees) are nearly ten times more likely to adopt AI compared to small businesses (5 to 9 employees). Furthermore, AI adoption is also uneven across sectors, with the latest Eurostat ICT business survey indicating higher AI adoption rates in ICT services and professional business activities. ...
This study explores exposure to artificial intelligence (AI) technologies and employment patterns in Europe. First, we provide a thorough mapping of European regions focusing on
the structural factors—such as sectoral specialisation, R&D capacity, productivity and workforce skills—that may shape diffusion as well as economic and employment effects of AI. To capture these differences, we conduct a cluster analysis which group EU regions in
four distinct clusters: high-tech service and capital centres, advanced manufacturing core, southern and eastern periphery. We then discuss potential employment implications of AI in
these regions, arguing that while regions with strong innovation systems may experience employment gains as AI complements existing capabilities and production systems, others are likely to face structural barriers that could eventually exacerbate regional disparities in the EU, with peripheral areas losing further ground.
... The advantages of the digitalization process have expanded and deepened with the advanced digital technologies. Increasing productivity (Ballestar et al., 2020), competitiveness, speed (De Caria, 2017, quality of production (DeStefano & Timmis, 2024), product diversity, international trade (Denicolai et al., 2021), employment (Kromann et al., 2020), revenue (Xu et al., 2021), performance (Ferraris et al., 2019), and innovation (Rammer et al., 2021), coupled with the minimization of costs (Nicholas-Donald et al., 2018), time, waste (Pellegrini et al., 2021) information asymmetries, etc., constitute the core of these advantages. The importance of transitioning to advanced digital technologies becomes evident upon considering these benefits. ...
Advancements in digital technologies, especially computer and information systems, force firms to adopt them in their production processes. Artificial intelligence, cloud computing, big data analytics, robotics, smart devices, and blockchain are the leading advanced technologies. This study explores the drivers of firms’ adoption of these technologies by estimating a multivariate probit model utilizing a Eurobarometer dataset. A statistically significant and positive correlation between the error terms of all models indicates that investigating the adoption of all advanced digital technologies together is more appropriate than independent analyses. Drivers of advanced digital technologies appear similar with decisive factors in using new technologies, and implementation of any type of innovation significantly increases the probability of adoption. The other determinants are the firm size, interaction with international markets, and the network structure of the market in which firms operate. Furthermore, location positively impacts the adoption of cloud computing and big data analytics, while it exerts no significant influence on the adoption of other types of advanced digital technologies.
... Following the resource-based theory of the firm, not only eco-innovations profit from digitalisation measures but also other non-environmentally related innovations because digitalisation measures may generally improve the overall resources of the firm (Rammer et al. 2021). Nevertheless, from an empirical side of view, it is important to know whether eco-innovations profit more or less from digitalisation measures compared to other innovations and which type of ecoinnovation is particularly connected with digitalisation leading to Hypothesis 3: ...
The paper analyses the “twin transition” of digitalisation and sustainability at the firm level. Operational definitions of digitalisation and sustainability allowing the development of fitting empirical indicators are discussed. The possible technical and social transmission channels of the effects of digitalisation on a sustainable firm development are analysed. Less energy consumption induced by intelligent sensoring systems, the reduction of meetings in presence by video conferences or the promotion of home office work leading to less travel activities may lead to a more sustainable production. Digitalisation might also act as pre-condition of eco-process innovations (e. g. the introduction of intelligent control systems leading to material and energy savings). The empirical analysis is based on firm data of the recent Eurobarometer 486/2020 of the European Commission. The econometric results show that “digitally active” firms seem to be more sustainable for all available indicators, but the marginal effects considerably differ between measures such as artificial intelligence, machine learning, or the use of smart devices and intelligent sensors for the various sustainability-related activities of the firms.
... Performance is a crucial reflection of corporate factor productivity. Relevant studies have shown that utilizing digital technology in the design of products, processes, organizations, and business models can enhance operational efficiency (Porter & Heppelmann, 2014;Tortorella et al., 2020), thereby improving corporate performance (Chen et al., 2016;Gupta et al., 2019;Luo et al., 2018;Rammer et al., 2022;Sousa-Zomer et al., 2020;Yunis et al., 2018). The digital economy and digitization are crucial drivers for increasing regional labor productivity (Aly, 2020;Gaglio et al., 2022;Pan et al., 2022). ...
Recent research provides limited knowledge about how and whether digital transformation related to environmental, social, and governance (ESG) affects the total factor productivity (TFP). To fill the gap, this study explores the impact of digital transformation on TFP based on a fixed‐effect model and the staggered difference‐in‐differences method and reveals that digital transformation can boost corporate TFP. Mechanism analysis reveals that digital transformation promotes ESG performance in companies, thereby affecting TFP. Social performance in ESG plays a mediating effect, while environmental performance plays a suppressing effect. Besides, the primary driver of TFP improvement through digital transformation lies in its direct effect, whereas the indirect effect of ESG factors is relatively limited. Our findings provide new insight into how digital transformation promotes corporate ESG and productivity.
... Furthermore, recent empirical findings highlight the role of firm size in AI adoption. Rammer et al. (2021), for instance, found that in Germany large firms with at least 1000 employees are nearly ten times more likely to adopt AI compared to the small business (5 to 9 employees). Similarly, AI adoption is unevenly distributed across sectors, with the latest Eurostat ICT business survey suggesting higher AI adoption rates in ICT services and professional business activities. ...
This study provides evidence of the employment impact of AI exposure in European regions, addressing one of the many gaps in the emerging literature on AI’s effects on employment in Europe. Building upon the occupation-based AI-exposure indicators proposed by Felten et al. (2018, 2019, 2021), which are mapped to the European occupational classification (ISCO), following Albanesi et al. (2023), we analyse the regional employment dynamics between 2011 and 2018. After controlling for a wide range of supply and demand factors, our findings indicate that, on average, AI exposure has a positive impact on regional employment. Put differently, European regions characterised by a relatively larger share of AI-exposed occupations display, all else being equal and once potential endogeneity concerns are mitigated, a more favourable employment tendency over the period 2011-2018. We also find evidence of a moderating effect of robot density on the AI-employment nexus, which however lacks a causal underpinning.
... The prerequisites that companies believe are necessary for companies to enter active AI use are only partly the reasons for abandonment. The most frequently cited factor that would increase the likelihood of actively using AI is government financial support (Rammer et al., 2021). ...
The various applications of AI have not yet led to widespread acceptance in marketing. Nevertheless, AI has enormous potential to fundamentally change the field of marketing with its applications, making the topic highly relevant for companies. By analyzing current applications, potential use cases in the near future, implementation opportunities and optimization areas, the study can present a comprehensive understanding of the long-term impact of AI in marketing and on marketing as a discipline. In particular, it looks at potential applications and challenges for companies that provide insight into the future of marketing. These are relevant to remain competitive in the future. In particular, the sub-area of Robotic Process Automation (RPA) will be addressed. For this purpose, two research questions were posed. On the one hand, the use cases in marketing that are given by the automation of processes by means of AI are examined. Secondly, the question is posed as to how the potential of AI in marketing can be further expanded and what trends can be identified or what significance RPA forms in this context. The analysis is based on a qualitative survey in the form of interviews with experts from the field.
This study explores the role of perceived utility, social influence, and ethical concerns in the adoption of AI-based data analysis tools among academic researchers in China, focusing on differences between public and private universities. The research aims to identify key drivers and barriers influencing the integration of AI technology in academic settings. A quantitative approach was employed, using a multi-group structural equation model (SEM) analysis to assess data collected from 750 academic researchers across various disciplines (Npvt = 402; Npub = 348). The findings reveal that both perceived utility and social influence significantly influence the adoption of AI tools. Higher perceived utility and stronger social influence lead to greater adoption. However, ethical concerns were found to moderate these relationships, particularly in public universities, where researchers with high ethical concerns perceived greater risks, thereby reducing their likelihood of adoption. In contrast, private university researchers showed a higher tolerance for perceived risks when utility and social influence were evident. The study’s implications suggest that to promote AI adoption, institutions must address ethical concerns and perceived risks, particularly in public universities, by enhancing transparency, providing ethical guidelines, and offering comprehensive training. These efforts can lead to more effective integration of AI technologies, ultimately enhancing research productivity and innovation across diverse academic environments.
The digital transformation of our world and the inevitable interplay between people, digital technologies, and physical assets are creating a rapidly changing and complex environment that requires organizations to be more agile and ready to embrace new ways of working. Businesses are realizing the need for change to succeed in the digital age. In the period of global digitalization, information and communication technologies are one of the most important aspects of existence for a business, which makes it more efficient and effective and allows you to quickly respond to a rapidly changing external environment, as well as customer needs. At the moment, there is a high interest in the possibilities of artificial intelligence for use in business tasks in the world, as there are already examples of successful implementation when artificial intelligence and machine learning are fundamentally changing the way people work and increasing the profits of organizations in different countries. The purpose of this case study is to consider how artificial intelligence affects the value proposition and how elements of the business model change when using this technology. The paper presents the existing examples of the use of technology, the consequences of its application and the prospects for using artificial intelligence as one of the advanced digital transformation technologies. With a literature review and case studies analysis, the article aims to provide a comprehensive understanding of the impact of AI on business models, drawing from both theoretical insights and practical experiences documented in case studies. This approach allows for a nuanced exploration of the topic and contributes to advancing knowledge in the field.
AI is an intangible capital asset that corporations may invest into and use to make output through a production function. Present AI systems are software programs interacting with physical or digital environments. Corporations have gradually embraced automated systems to set prices and track business transactions. Effective competition is a vital driver of the growth. AI shifts the sources of competitive advantage and generates new persistent sources of competitive advantage. Antitrust laws stimulate fair competition. Indeed, competition laws make certain that fair competition occurs in an open-market economy. It is supposed that antitrust law guarantees substantively unrestricted economic outcomes, eliminating the immense influence of the economically powerful.
Artificial Intelligence (AI) is likely to be the next general purpose technology. The U.S. and China are important players in the development of AI. Germany has a vibrant AI startup scene and is among the first third of EU countries in applying AI technologies. In order not to lose touch with international developments, Germany should work toward creating research- and innovation-friendly framework conditions. Appropriate measures include improving data availability, building AI expertise and enabling flexible regulation.
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