Hochschulbibliographie

What predicts entry into entrepreneurship? Using a longitudinal sample of 873 adolescents from Finland, we set up a path model investigating the effects of an early variety of interests and activities on the development of skill variety. This developmental approach takes into account an entrepreneurial personality profile (Big Five), a "taste for variety" (proxied by openness), entrepreneurial parents and the impact of skill variety on entrepreneurial intentions. As expected entrepreneurial intentions were predicted by skill variety. Further findings of structural equation modelling pointed to the importance of an early variety of interests and activities as well as an entrepreneurial personality for the formation and development of skill variety. A "taste for variety" affects skill variety via an early variety of interests and activities. The results confirm the relevance of a developmental life span approach in entrepreneurship, highlighting the possibility to promote entrepreneurship via arousing broad interests early in life. Further implications arise for entrepreneurship education programs. The programs might be more effective for a participant with high innate abilities and personality traits that are conducive for entrepreneurial action.

Product information sharing, i.e., inter-organizational transfer of master data relating to products, is a problematic, error-prone, labor-intensive, and costly process in many companies. This paper presents findings of a focus group interview and case studies at three wholesale trading companies that share product information with hundreds of suppliers. We identify and assess coordination mechanisms and tools used to facilitate product information sharing. Spreadsheet files, e-mail messages, telephone calls, and personal meetings are predominant coordination tools. EDI connections, product identification and classification standards, online product catalogs, and data pools are not widely adopted in the trading organizations covered by our study. Reasons for the low adoption rate are that employees responsible for master data quality are either unaware of these resources or that they are convinced that the tools are too cost-intensive or not flexible enough.