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Abstract
Transferring knowledge from experts to apprentices is critical for sustaining a company’s competitive advantage. Further, existing literature indicates that employees in the Millennial (Y) generation and Generation Z prefer collaboration in the work environment. To address such needs, this research study aims to analyze the factors that enable knowledge collaboration among apprentices and experts in the practice of engineering. Survey data collected from 138 apprentices in the practice of engineering revealed that apprentices are more likely to collaborate with experts within their companies when they can trust the experts and perceive support for collaboration from senior management. This research adds to the body of knowledge by explicitly addressing apprentices’ trust, senior management support, intrinsic motivation, and knowledge collaboration with experts. Engineering managers can use this article to utilize trust, support from senior management, and employee’s intrinsic motivation to increase knowledge collaboration success among engineering experts and apprentices.
Notes
1 As indicated, this paper focuses on three knowledge collaboration enablers (trust, senior management support, and intrinsic motivation) and one knowledge collaboration behavior (knowledge collaboration with experts). The larger study included one additional knowledge collaboration enabler, two additional knowledge collaboration behaviors, two knowledge collaboration outcomes, and one moderating variable. The interested reader can find more information on the larger study in Wong (Citation2018).
2 The potential critical knowledge management success factors identified by the literature review in Wong (Citation2018) are: trust, enjoyment in helping others, knowledge self-efficacy or intrinsic motivation, reciprocal benefits, top (senior) management support, organizational rewards, organizational culture, Knowledge Management System (KMS) infrastructure, KMS quality, openness in communication, and face-to-face interactive communication.
3 Survey questionnaires were received from 286 respondents with 49 unusable responses, which ultimately resulted in overall usable data of 237 participants.
4 In this research, an expert in the practice of engineering must have at least fifteen years of industrial experience. This criterion is equivalent to Engineering Grade VII or above (American Society of Civil Engineers, Citation2007) and ensures that a practicing engineer had spent enough time on deliberate practice to attain exceptional performance.
5 As compared to the national distribution of engineering professions (Yoder, Citation2015), participants from Civil/environmental/structural/transportation engineering might be overrepresented relative to others in the population. The discrepancy might be caused by the background of the researcher who has a civil/structural engineering education and professional background; that is, potential participants who had similar background to the researcher would more likely take the survey upon reading the researcher’s LinkedIn posting.
6 IBM® SPSS® Statistics, version 24.0.0.0.
7 IBM® SPSS® AMOS, version 24.0.0.
8 The research presented in this article is part of a larger study which included five additional variables (Wong, Citation2018).
9 These 27 items comprise 14 items included in this research (as described in the next sentence) and 13 items that were part of the full-scale study but not related to the research of this article.
10 According to Cheung and Lau (Citation2008), at least 500 bootstrap samples should be generated to ensure that the differences in bias-corrected bootstrap confidence intervals for the same dataset are negligible.
11 The research presented in this article is part of a larger study (Wong, Citation2018), which included nine constructs in total.
12 Variables that are unrelated to the research presented in this article are also omitted. See Wong (Citation2018) for the results of the full-scale study.
Additional information
Notes on contributors
Silky S. K. Wong
Silky S. K. Wong, PhD, SE, PE, CEng MICE, M. ASCE, is a Civil and Structural Engineer Manager at Dow Inc. She also serves as a voting member of American Society of Civil Engineers (ASCE) Energy Division Executive Committee. Her research interests lie at the intersection of expertise development, leadership, and mentorship in the domain of engineering practice. She holds a BSCE from UT–Austin, a MSCE from UC–Berkeley, and a PhD in In Systems and Engineering Management from Texas Tech University.
Jennifer A. Cross
Jennifer A. Cross, PhD, is an Associate Professor in the Department of Industrial, Manufacturing & Systems Engineering at Texas Tech University. She received her BS in Industrial Engineering from the University of Arkansas and her MS and PhD in Industrial and Systems Engineering from Virginia Tech, where she also served as a Postdoctoral Associate in the Enterprise Engineering Research Lab. Her research interests are organizational assessment/performance measurement, teams, and performance improvement methodologies.
Cherise M. Burton
Cherise M. Burton, PhD, PMP, has earned a BS in Mathematics from the University of the Virgin Islands, MS in Industrial Engineering and MS in Engineering Management from the University of South Florida, and Doctor of Philosophy in Systems and Engineering Management from Texas Tech University. Her career path has allowed her exposure as a manufacturing/industrial engineer at Ford Motor Company for 4.5 years and more recent, Sr. Lead Project Manager at Delta Airlines.