Talented Worker
A Talented Worker is a worker who can perform some job better than most other workers.
- Context:
- They can typically be a Gifted Agent.
- They can range from (typically) being a Talented Human Worker to being a Talented Robot Worker.
- They can range from being a Lazy to being a High-Effort Talented Worker.
- Example(s):
- Counter-Example(s):
- See: Skilled Worker, Hard-Working Worker, Dunning–Kruger Effect.
References
2016
- Tomas Chamorro-Premuzic. (2016). “Talent Matters Even More than People Think." HBR, October 04, 2016
- QUOTE: Why are some people more successful than others? Leaving aside luck, which equates to confessing that we don’t really know, there are really just two explanations: talent and effort. Talent concerns the abilities, skills, and expertise that determine what a person can do. Effort concerns the degree to which the person deploys their talents. ...
... For example, stable personality characteristics, such as neuroticism, extraversion, and conscientiousness, account for almost 50% of the measurable variability in motivation, ...
... Besides, even though the remaining 50% of variability is due to environmental factors, many of these occur very early in life ...
... That is not to say that you cannot coach or develop people to improve their performance. But the most effective interventions focus on helping people go against their nature, replacing toxic habits with more effective ones. ...
.... The only aspect of talent that is overrated concerns people’s evaluations of their own talents — most people are not as talented as they think, especially when they have none.
- QUOTE: Why are some people more successful than others? Leaving aside luck, which equates to confessing that we don’t really know, there are really just two explanations: talent and effort. Talent concerns the abilities, skills, and expertise that determine what a person can do. Effort concerns the degree to which the person deploys their talents. ...
- John Boudreau. (2016). “Is Meritocracy Overrated? Use with Caution." eArticle Version Issue 10, 2016
- ABSTRACT: Differentiation isn’t the secret sauce for organizational success, says well-known author and professor John Boudreau, who urges us to identify the (often) hidden few whose job performance truly drives organization outcomes. He says we should only differentiate if:
- 1. Differentiation makes a pivotal impact on valu¬able outcomes
- 2. Differentiation can be achieved with sufficient precision, reliability and accuracy
- 3. The cost of measurement does not exceed its value
- ABSTRACT: Differentiation isn’t the secret sauce for organizational success, says well-known author and professor John Boudreau, who urges us to identify the (often) hidden few whose job performance truly drives organization outcomes. He says we should only differentiate if:
2013
- (Schlösseraet al., 2013) ⇒ Thomas Schlössera, David Dunning, Kerri L. Johnson, and Justin Kruger. (2013). “How Unaware Are the Unskilled? Empirical Tests of the “Signal Extraction” Counterexplanation for the Dunning–Kruger Effect in Self-evaluation of Performance. “ Journal of Economic Psychology, 39
- ABSTRACT: Previous work on the Dunning–Kruger effect has shown that poor performers often show little insight into the shortcomings in their performance, presumably because they suffer a double curse. Deficits in their knowledge prevent them from both producing correct responses and recognizing that the responses they produce are inferior to those produced by others. Krajč and Ortmann (2008) offered a different account, suggesting instead that poor performers make performance estimates with no more error than top performers. Floor effects, coupled with the assumption of a backwards-J performance distribution, force their self-evaluations errors to be frequently positive in nature. Krajč and Ortmann, however, offered no empirical data to test their “signal extraction” account. In three studies, we assessed their theoretical model by examining whether (1) the data producing the Dunning–Kruger effect fit the statistical assumptions considered by Krajč and Ortmann necessary to produce it, and (2) to see if their framework reproduced Dunning–Kruger errors in a data set that fit their statistical assumptions. We found that the Krajč–Ortmann framework failed to anticipate self-evaluative misperceptions on the part of poor performers, but that it does much better at accounting for misperceptions among top performers. Paradoxically, the model suggests that Kruger and Dunning (1999) may have underestimated the accuracy of top performers, even though their account asserts such accuracy.