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Career Anchors: Comparison
Please note this is a comparison between Version 1 by Stefano Toderi and Version 2 by Pearl Wu.

The career anchor (CA) is a metaphor created by Edgar Schein to illustrate the role of patterns of self-perceived talents, motives, and values in guiding, stabilizing (i.e., anchoring), and integrating a person’s work career. With the early years of work experience, this pattern tends to stabilize into one of the possible CAs and plays two main roles: guiding the selection of specific occupations and work environments; shaping individual reactions to the actual occupation and work environment. Since Schein’s initial conceptualization, theoretical refinements have been proposed, suggesting that CAs can change over time and that multiple CAs can coexist. Although substantial evidence supports the theory’s key predictions, the available literature appears fragmented, with a primary focus on descriptive concerns. Actual measurement issues also limit the development of theoretical knowledge. This entry provides an updated overview of the central predictions related to CAs, aiming at promoting greater integration and coherence in research and practice.

  • career anchors
  • career orientation inventory
  • career preferences
  • career choices
  • career congruence
Around 50 years ago, Edgar Schein [1] studied 44 MBA students and their career progressions longitudinally, discovering that their work histories showed significant similarities in the motivations underlying their career decisions. Schein organized the qualitative content collected through interviews and analyzed how personal values and motives influenced career-related experiences. Despite the pressure exerted by organizations, the author recognized the power individuals have in shaping their work histories and identified five distinct patterns of talents, needs, and values, self-defined by the person, that he suggested serve to guide, integrate, and stabilize the individual’s career [1][2][1,2]. Focusing on the career-stabilizing role of the emerging pattern, the author used the metaphor of the Career Anchor (CA hereinafter) to describe and name the personal emerging pattern. The most innovative aspect of Schein’s conceptualization lies in the simplification of the reality and complexity of individual work histories, obtained through the development of the taxonomy of anchors. This classification clarifies what anchors individual career decisions and suggests predictions about expected outcomes.
In the more recent decades, career research has placed increasing emphasis on internal or subjective career characteristics, and the term career orientations is widely used to refer to a variety of personal (e.g., career capitals, career competencies, career resources) and environmental factors that support people’s proactivity and self-direction in constructing their work history [3][4][3,4]. In this scenario, the concept of CA remains widely prevalent in the scientific literature, particularly in studies examining how an individual’s values, beliefs, attitudes, and motivations shape their career [5]. However, the available literature appears fragmented and driven by somewhat different objectives. Furthermore, to our knowledge, there are no recent reviews on the topic, with the sole exceptions of Cabot and Gagnon [6] as well as Woldeamanuel [5]. However, the former is limited to studies focused on information technology professionals, and the latter is a “scientometric” analysis primarily oriented towards mapping the characteristics of published research (e.g., main topics, keywords, geographic areas, trends in the number of publications).
With this entry, we do not aim to provide an exhaustive review of the existing literature but rather to highlight and systematize the main knowledge available on the key predictions of the CA concept and methodological issues, promoting greater integration and coherence in research and practice. The entry is divided into three parts: the conceptual development of CAs, the methodological issue, and the knowledge available regarding the model’s key predictions.
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