As organizations evaluate increasing retirement rates coupled with the rate of entering engineers, there is a need to address the presence of engineering generational knowledge transfer boundaries. These undefined boundaries impede the strategic transfer of engineering and architectural knowledge (STEAK) required for continued engineering growth. Borrowing from established knowledge transfer boundaries outside of engineering and existing engineering knowledge transfer boundaries, this research identifies the engineering generational knowledge transfer boundary. This research also expands the relationship of this boundary to generational characteristics while concurrently investigating the impact of the engineering generational knowledge transfer boundaries on knowledge managers and knowledge workers.During this dissertation research, the inaccessible generational knowledge from engineering communities is evaluated and the relationship between engineering generational knowledge transfer and engineering generational characteristics is explored to garner improvements to engineering knowledge transfer methods. It was discovered that the characteristics of each generation help bound and classify knowledge but also restricts the transfer of that knowledge to other generations. Exploring the domain of engineering knowledge transfer highlights commonalities among generations, associating similar characteristics that allow generations to transfer and absorb pertinent generational knowledge at a more suitable rate. This dissertation research provides a level of insight not previously explored and presents techniques that can be used to evaluate how organizations will react to and leverages knowledge across generations.Using an interdisciplinary approach to define the engineering generational knowledge transfer boundaries provides useful knowledge transfer processes and procedures which all engineering generations and engineering disciplines can utilize. This identification will allow management to propose strategies to attract and retain the next generation of knowledge workers. This will enact performance improvements, using the existing generational knowledge within the workforce to accomplish the mission requirements. This dissertation research expands these findings in detail and summarizes the results in a proposed STEAK model, framework and graphical algorithm.
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