Swetlana Franken's Lab
About the lab
Seit 2016 befasst sich die Forschergruppe Denkfabrik Digitalisierte Arbeitswelt der FH Bielefeld im Rahmen von mehreren Forschungsprojekten mit den Auswirkungen der Digitalisierung und Industrie 4.0 auf die Arbeitswelt, insbesondere auf die Beschäftigung, Kompetenzanforderungen, Unternehmensorganisation und Führung.
Featured research (5)
Im Kontext der Digitalisierung findet eine neue Arbeitsverteilung zwischen Menschen und Technik statt: Roboter, Algorithmen und Künstliche Intelligenz sind zunehmend in der Lage, standardisierte und Routineaufgaben schnell und kostengünstig zu erledigen, und die Menschen können sich auf ihre Stärken, wie strategische, kreative, Forschung-und-Entwicklungs- sowie sozial-kommunikative Tätigkeiten, konzentrieren. Allerdings erfordern die neuen Aufgaben von den Beschäftigten neuartige Kompetenzen, die im Rahmen der digitalen Transformation vermittelt oder gefördert werden sollen. Wie es gelingen kann, wird in diesem Kapitel anhand theoretischer Ansätze und Praxisbeispiele aufgezeigt.
Projektbericht des BMBF geförderten Projekts: "Women Ressource 4.0 – Potenziale von qualifizierten Frauen, darunter auch mit Zuwanderungsgeschichte, für die Industrie 4.0" an der Fachhochschule Bielefeld
Relevance & Research Question: Many companies see the lack of skilled workers as a central obstacle to the digital transformation. It is well-known that diverse workforces lead to more balanced decisions and more innovation. Nevertheless, women, for example, are still underrepresented in STEM-professions. The following research question arises: Are there any differences in the perception of relevant competencies for the digitalized working world according to gender, age, employment status and migration background? Methods & Data: Following preliminary literature research and qualitative expert interviews [n=6], a quantitative study was conducted from Nov. – Dec. 2018. Participants [n=515] were recruited among students and companies using faculty email lists, paper form and social media. Participants were asked to assess a total of 14 competencies, knowledge resources and behaviours in their relevance for the digitalized working world on a 6-tier scale. Correlations were determined by calculating Chi-square according to Person and Cramer’s V. Means were compared using T-Test and Levene. Results: Respondents consider openness to change (5.50), IT and media skills (5.40) and learning ability (5.36) to be the most relevant. Analytical skills (4.79) and empirical knowledge (4.56) are less in demand. Men rate innovation competence (χ²=10.895, p=.028, V=0.146), decision-making ability (χ²=13.801, p=.017, V=0.164) and ability to think in context (χ²=14.228, p=.014, V=0.167) slightly higher than women. No correlation can be found regarding respondents’ migration background. Among company representatives, eight competencies are rated significantly higher than by students, especially communicative competence (+0.91) and interdisciplinary thinking and acting (+0.74). Moreover, it is noticeable that older participants (generation X, born 1964-1979) consider all competencies to be more important than younger ones (generation Z, 1996-2009), apart from IT and media competence. The items openness to change (T-Test p=.004, Levene p=.004), self-organisation (T-Test p<.001, Levene p=.020) and problem-solving competence (T-Test p=.011, Levene p=.019) show significant correlation between age and assessment. Added Value: First, results reveal a ranking of needed competencies for the digital transition, which companies and educational institutions should address. Second, differences between the employee groups could be discovered which have to be considered in the further approach, be it education or research.
Relevance & Research Question: Technical progress through digitalisation is constantly increasing. Currently, the most relevant and technically sophisticated technology is artificial intelligence (AI). Women are less frequently involved in research and development on AI, clearly in the minority in STEM-professions and study programmes, and less frequently in management positions. Previous AI applications have often been based on data that under-represents women and thus map our society with existing disadvantages and injustices. So do men and women have different ideas about the role and significance of AI in the future? Do women have different requirements or wishes for AI? Methods & Data: Following the previously conducted in-depth literature research, a combination of qualitative interview study [n=6] and quantitative online survey [n=200] is planned. The target group will consist of company representatives and students whereby the evaluation of differences and correlations will be based in particular on gender. Results: A literature review of existing studies reveals that while more people are in favour of AI development than against it, it is mainly men with a high level of education and income. According to their self-awareness, women have a lower understanding of AI than men. Moreover, AI research and development is predominantly in the hands of men. Just under 25% of those employed in the AI sector are women, in Germany even only 16%. Old stereotypes are thus not only the basis for decisions regarding the development of AI but also incorporated into the data basis for AI: Voice and speech recognition systems are less reliable for female voices, as is face recognition for female faces. Search engines more often present male-connoted image and text results for gender-neutral search terms. The expected results of the questionnaire will be gender-relevant aspects in the perception, evaluation, development and use of AI. Added Value: The identification of gender-relevant differences in the perception and attitude towards AI will enable developers and researchers to be sensitised to the possible risks of AI applications in terms of prejudice and discrimination. In addition, opportunities for using AI to strengthen gender equality will be recognized.
AI applications such as robotics, automation or intelligent assistance are becoming drivers of a wide-ranging change process in manufacturing companies, which not only affects the use of algorithms but also affects people and organisation. Automation and algorithmisation will change the working world in a lasting way, whereby all value-adding activities – from operative production work to skilled work and management – will be influenced. It is expected that, due to its learning ability, AI will be able to act autonomously, support people through assistance systems, use resources more effectively, make processes more environmentally friendly and enable new working models with direct participation and greater transparency. It should increase efficiency, enhance customer satisfaction and facilitate and enrich work. Current research confirms that it is less about technology and investment than about the openness of employees and executives combined with a supportive organisational structure and culture that is decisive for the success of digitalisation. The influence of AI on employment is controversial. It should lead to secure and demanding jobs, physical and cognitive relief and an improvement in work-life balance. Yet, there are concerns about job losses, disqualification, growing autonomy of digital systems and increased control potential for employees. However, research demonstrates that in the past one robot has replaced on average two workers in the industry, while two jobs have been created outside. AI will probably demonstrate a similar behaviour. The implementation of AI requires reorganisation of management, cooperation, co-determination, qualification and a high level of knowledge exchange. Digital change requires flexible and agile organisational structures and flatter hierarchies to be able to react to new complexity and dynamics. The participative leadership of the future conducts flexibly within the framework of self-organising networks and interdisciplinary, democratically formed teams. Executives see themselves as coaches and moderators. This paper examines the effects of the introduction of AI in industrial enterprises based on a comprehensive literature review. Particular attention will be paid to effects on employment and organisational structure and culture. Best practice examples for AI applications in industrial companies will also be examined. Finally, a critical discussion examines possibilities and instruments for shaping transformation within companies through AI with the involvement of all relevant actors.