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Institut / FIR-Bereiche
Forecasting-based skills management, which is oriented to the respective corporate goals, is gaining enormous importance as a central management tool. The aim is to predict future skills requirements and match them with existing interorganizational skills. Companies are required to anticipate changes in markets, industries, and technologies at an early stage as well as to identify changes in job profiles within an occupational profile by tapping into and evaluating various data sources. Based on these findings, they can then make informed decisions regarding skill gaps, for example, to implement targeted further training measures. Forecasting-based skills management offers the opportunity to optimally qualify employees for constantly changing tasks. At the same time, however, the targeted development of such skills requires a high level of time, financial and personnel resources, which small and medium-sized enterprises (SMEs) generally do not have at their disposal. In addition, many SMEs are not yet aware of the importance of this issue. Within the framework of research and industrial projects of the Smart Work department at the FIR (Institute for Industrial Management) at the RWTH Aachen University, an AI-based skills forecasting tool will be developed. The goal of the paper is to conceptualize the future machine learning method, that is able to generate individualized skills forecasts and recommendations for SMEs. This is achieved by linking societal forecasts and sector trends with company-specific conditions and skills. In order to generate a corresponding database, the derivation system is made available to various companies (large companies and SMEs) in order to obtain as many data sets as possible. The data sets obtained via the derivation system are then used as training data sets for the machine learning method, with the help of which an automatic derivation of competencies depending on new trends is to be made possible.
The Learning Organization
(2023)
This Research Full Paper deals with the institutionalization of diversity management in German universities, which only started after the Bologna Reform at the end of the 1990s, the Excellence Initiative starting in 2006 and the passing of the General Equal Treatment Act (AGG) in 2006. The aim is to explore the motives of universities behind the implementation of diversity management and identify isomorphic mechanisms in the process of the implementation.
Further, the paper conducts a first stocktaking of practical anti-
discrimination work at German universities carried out through the organizational practice to identify further connecting factors and problematic situations. To answer the research questions, five expert interviews were conducted with diversity officers from two universities and three engineering universities in five different German states. Among other things, it was found that the interviewees rejected the term diversity management due to its underlying economic logic and preferred the more current term diversity policies. Moreover, from a university perspective, a total of eight different motives for implementing diversity policies were identified: Anti-discrimination, external effectiveness, acquisition of external
funding, legislation, favorable investment compared to other measures, intrinsic motivation, potential approach, and exemplary function. Thus, universities initially try to appear diverse externally to meet the rationality expectations of their environment and only supplement this external effect with the appropriate measures and structures over time, while the motivation of diversity officers is intrinsic. This is also related to the fact that voting rounds slowed down processes, but universities would have to position themselves on current discourses, such as in the summer of 2020 after the racially motivated murder of George Floyd. Accordingly, it could be highlighted that the motives of diversity officers and their universities are not automatically congruent.