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With a focus on learning, we employ a range of strategies to support innovation, collaboration across centers, and university-wide discussion and decision-making

 

Fifteenth Annual Grant Winners 2014-2015

Title

Career Anchors of Workforce Generations

Dean

Preston Jones, Ph.D. (SBE)

Faculty and Students

Baiyun Gong, Ph.D. (SBE)
Regina Greenwood, DBA (SBE)
Arlene Ramkissoon, MS (SBE)

Abstract

A workforce generation is defined by the age of the workforce. With Baby Boomers phasing into their retirement, Gen Xs taking dominant positions in organizations, and Gen Ys entering the workforce, differences in their competence profiles and values present significant challenges to management (Gibson, Greenwood, & Murphy Jr., 2011; Greenwood, Gibson, Murphy Jr., 2008; Greenwood et al., 2012; Murphy, Mujtaba, Manyak, Sungkhawan, & Greenwood, 2010; Uy, et al., 2008). A career anchor refers to one's self-concept in terms of: “1) self-perceived talents and abilities, 2) basic values, and… 3) the evolved sense of motives and needs as they pertain to the career” (Shein, 1993). Career anchors reflect value systems and are not likely to change. Among the eight career anchors identified by Shein (1993), we propose that Boomers will anchor with security/stability and be motivated by incentives such as recognition and profit sharing, the Xs will anchor with lifestyle and technical competence and prefer incentives such as flexi-hours and advancement, whereas the Ys will anchor with entrepreneurial creativity and be motivated by delegation and suggestion rewards. Understanding the career anchors of workforce generations enables organizations to engage employees and apply effective motivation techniques. The study will examine differences in workforce generations in their career anchors. Further, it will link anchors to motivation practices and organizational rewards. One outcome would be the development of best practices within organizations for dealing with generational differences. A survey questionnaire will be sent to working adults, using Amazon Mechanical Turk and a snowball strategy. Variables such as year of birth (i.e., generation), career anchors, career stages (Super, Thompson, & Lindeman, 1988), preferred motivation practices, and demographic data will be measured. Regression analysis and structural equation modeling will be used to analyze data. A focus group will be conducted in the effort to better interpret the survey findings.