Learning is critical for individuals to increase their performance. However, this benefit of learning is not always realized. Previous studies have distinguished different classifications of learning approaches and reached inconsistent results. Therefore, this study further refines the classification of learning approaches in an open innovation community and explore the individual’s learning curve from a dynamic perspective. Specifically, we focus on whether and under what conditions learning can increase individual’s performance, and how individual's learning curve changes over the tenure. To examine our hypotheses, we collect a dataset includes 48,820 game mods developed by 6,141 creators spanning 7-years from an open game innovation community. The results not only show the significant curve relationship between the four learning approaches and performance, but also demonstrate individual’s learning curve evolves over the tenure. This paper provides valuable suggestions and implications for individuals to choose appropriate learning approaches and obtain better performance under different tenures.
Wang, Mengyuan; Ma, Jifeng; and Lu, Yaobin, "The Impact and Evolution of Individual’s Learning: An Empirical Study in Open Innovation Community" (2023). PACIS 2023 Proceedings. 48.
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