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Speed is Significant in Short‐Loop Experimental Learning: Iterating and Debugging in High‐Tech Product Innovation
Author(s) -
Alblas Alex,
Notten Miel
Publication year - 2021
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/deci.12477
Subject(s) - debugging , computer science , product (mathematics) , loop (graph theory) , high tech , industrial engineering , knowledge management , mathematics , engineering , operating system , geometry , combinatorics , political science , law
Digital technology is fundamental to experimentation, learning, and the rate of innovation. Digital technology facilitates the rapid distribution of experimental design and debug information. However, we should consider how this fundamentally changes organizational learning and experimentation when managing the rate of product innovation. We address this issue by investigating what drives experimentation‐based learning in high‐tech product innovation and production. The longitudinal dataset in our study consists of 216 projects over a period of almost 5 years, involving thousands of digitally recorded design iterations and design debugs. Based on a time series linear regression analysis, we demonstrate that learning from an accumulation of completed projects drives learning in experimentation more than failure experience in successfully completed design debugs. Furthermore, we show that processing iterations and debugs rapidly enhances the speed of product innovation learning as this allows for short‐loop experimentation that restricts superstitious learning when conditions change over time. The results also show this can be achieved using digital tools as a source of agility.