The role of sustainable innovation and factors influencing AI implementation : SMEs in the machine manufacturing and agricultural sector

Abstract: Sustainable innovation is becoming increasingly important for today’s society due to the existing ecological threats to the earth. Thus, companies have a special responsibility with regard to new technologies and products. One important emerging technology is artificial intelligence (AI), which can help to make processes more efficient and save resources. However, managers are usually not supported by scientists when implementing AI in their businesses, which leads to companies failing in this endeavor or achieving unintended results, especially in small to medium-sized enterprises (SMEs). Research on the combination of AI with sustainable innovation has received fairly little attention to date. Therefore, the purpose of this study is to find out the role of sustainable innovation for SMEs in different sectors and how they perceive a possible implementation of AI. This includes factors such as motivation, barriers, solutions, and advantages. The selected sectors are machine manufacturing and agriculture as they are highly relevant to the topic. The study also looks at possible differences and similarities between the sectors. For this purpose, an exploratory and qualitative research approach was adopted by performing a multiple case study. Four semi-structured interviews with SMEs, two from each sector, were conducted to provide insights into the research field. The results reveal that sustainable innovation plays an important role within these two sectors, as all interviewed companies have a common motivation and responsibility to save the earth’s ecosystem. However, in most aspects the sectors differ here, especially regarding barriers. The companies also reveal a lot of interest in the implementation of artificial intelligence, but barely any differences were found between the sectors, in particular concerning, barriers, solutions, and possible AI types. The study has limited generalizability, as studies with larger companies or different turnovers, in other countries and sectors, and/or with different existing technologies might generate alternative results.