Industrial companies are currently facing major challenges such as volatility and uncertainty (Würzburger, 2019). Digitalization is offering various opportunities, especially for the manufacturing industry. By using digital technologies, it is not only possible to increase productivity and efficiency in the value chain, but also to meet the challenges in a volatile business environment (Schuh et al., 2017). Several studies show that small and medium-sized enterprises (SMEs) in particular are still clearly behind large companies in terms of digital transformation and the use of digital technologies (Hölzl et al. 2019). Especially SMEs in Austria are struggling to take advantage of the opportunities presented by digitization in the value chain (Arthur D. Little, 2017; Sonntag and Gangl, 2020). It was found in research studies (Lindner, 2019; Hölzl et al. 2019) that this might be due to the fact that SMEs have limited time, financial and personnel resources and the management as well as employees´ lack in competencies regarding the digital transformation and digitalization. As a result, it is of particular importance to investigate the competencies needed in order to support the digital transformation and the use of digital technologies along the value chain (Buer et al. 2020).

(Digital) Competencies can be subsumed in competency models. Lucia and Lepsinger (2003) define a competency model as “a descriptive tool that identifies the competences needed to perform a role effectively in the organization and help the business meet its strategic objectives”. 35 studies regarding competency models and relevant digital competencies in SMEs were analyzed (e.g. Buer et al. 2020; Eller et al. 2020). It is concluded that there is a lack of focus on current challenges in SMEs, such as volatility and uncertainty. Moreover, these models do not regard the value adding process but they primarily focus on leadership. Furthermore, it is stated that competency requirements of workers of SMEs differ from the demands of larger companies. While the focus of requirements of competencies in larger companies majorly lays on the usage of technologies, the workforce in SMEs needs to know more about process and data analytics (Acatech et al., 2016). Nevertheless, most trainings are either independent of company size or focus on larger companies.

Currently, there are very few practical training courses that concentrate on the practical implementation of digitalization (Block et al. 2018). Moreover, it was found that traditional teaching methods show limited effectiveness in terms of developing competencies of students as well as of the workforce for the current and future value creation processes. However, as one of the most promising approaches for effective vocational training courses for topics such as digitalization and digital transformation, learning factories offer hands-on learning environments. (Abele et al. 2015; Cachay et al. 2012)

The goal of this paper is to compare existing competency models in the field of digital transformation and digitalization, derive a competency model based on 12 expert interviews with Austrian SMEs. This will then be compared to existing competency models of different European countries, and implications for vocational trainings in learning factories and makerspaces will be derived.

Therefore, the following research questions arise:

1. What can a competency model for digitalization and digital transformation in the value chain of small and medium-sized manufacturing enterprises (in Austria) look like?

2. What are the differences between this competency model compared to competency models in the same field in other European countries?

3. What are implications for the vocational training in the field of digital transformation and digitalization in learning factories and makerspaces?

First of all, a literature review is performed on the competencies and the competency models regarding digitalization and the digital transformation in the value creation process in the manufacturing industry. In order to select suitable interview partners, the procedures of theoretical and purposive sampling are performed. Theoretical sampling is an iterative process in which data gathering and data analysis alternate while the sample size is not determined in advance. (Birks and Mills, 2012) It is decided to interview general managers from SMEs in Austria. Concerning the business sector following industries are included in the study: metal production and processing, manufacturing of fabricated metal products, manufacturing of computers, electronic and optical products, manufacturing of electrical equipment, manufacturing of motor vehicles, trailers and semi-trailers and other transport equipment. The interviews were conducted between June and September 2020 using videoconference software. The length of the interviews ranged from 0.5 up to 1 hour. The interview guideline was created according to the SPSS procedure of Helfferich (2009). The interviews are analyzed qualitatively via MAXQDA according to the qualitative content analysis of Mayring (2010). The coding system based on a combined deductive and inductive system. Categories include amongst others general competencies, competencies in production and competencies in product development. As a result, the competency model for digitalization and the digital transformation can be derived. Via a literature study competency models of different European countries were analyzed and differences between them and the Austrian are extracted. As a last step, the implications for vocational trainings in learning factories and makerspaces are derived. The term "learning factory" is composed "learning" which stands for the overall objective, the development competencies, and "factory" for the replica of a realistic production site. A learning factory is a special learning environment in which (value creating) processes and technologies are modelled based on a real industrial company. The didactical concept of learning factories grounds on experimental and problem-based learning. Participants are able to improve processes and experience the improvement in the learning environment. (Abele et al. 2015) Makerspaces are places where makers can come to use tools alone or together or to carry out projects. Moreover, they are suitable learning environments in the field of product development and innovation. (Peppler et al. 2016) The findings are based on a study, and on experiences in makerspaces designed and operated by the Graz University of Technology.

In the literature, competencies are described in connection with digitization that the workforce of the future should have, whereby creativity, flexibility, agility, the ability to innovate, the exchange in networks, working in a team and the implementation of ideas are mentioned above all. The most relevant technical competencies include interaction with digital technologies, data and information processing and analysis, and ICT competencies. In order to cluster these competencies according to Erpenbeck and von Rosenstiel (2007) proving to be suitable. Main characteristics of the competency models including their limitations, potentials and relevance for training programs will be defined. From the results of the qualitative interviews and the findings of the analysis of the competency models, criteria for the effectiveness of learning factories and makerspaces will be developed. Regarding the implications for vocational trainings in learning factories and makerspaces, it can be predicted that there are positive effects on the mindset of the trainees, creative solution finding for potential improvements, the implementation process of digitalization, and the whole value creation process.

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