As a consequence of an aging workforce, electric utilities are at risk of losing their most experienced and knowledgeable electrical engineers. In this research, the problem was a lack of understanding of what electric utilities were doing to capture the tacit knowledge or know-how of these engineers. The purpose of this qualitative research study was to explore the tacit knowledge capture strategies currently used in the industry by conducting a case study of 7 U.S. electrical utilities that have demonstrated an industry commitment to improving operational standards. The research question addressed the implemented strategies to capture the tacit knowledge of retiring electrical engineers and technical personnel. The research methodology involved a qualitative embedded case study. The theories used in this study included knowledge creation theory, resource-based theory, and organizational learning theory. Data were collected through one time interviews of a senior electrical engineer or technician within each utility and a workforce planning or training professional within 2 of the 7 utilities. The analysis included the use of triangulation and content analysis strategies. Ten tacit knowledge capture strategies were identified: (a) formal and informal on-boarding mentorship and apprenticeship programs, (b) formal and informal off-boarding mentorship programs, (c) formal and informal training programs, (d) using lessons learned during training sessions, (e) communities of practice, (f) technology enabled tools, (g) storytelling, (h) exit interviews, (i) rehiring of retirees as consultants, and (j) knowledge risk assessments. This research contributes to social change by offering strategies to capture the know-how needed to ensure operational continuity in the delivery of safe, reliable, and sustainable power.
| Identifer | oai:union.ndltd.org:waldenu.edu/oai:scholarworks.waldenu.edu:dissertations-3524 |
| Date | 01 January 2016 |
| Creators | Perjanik, Nicholas Steven |
| Publisher | ScholarWorks |
| Source Sets | Walden University |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Walden Dissertations and Doctoral Studies |
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