11–20 of 341 posts
This is a well constructed empty argument because it glosses over the central concern, ‘employee performance’. Without defining that we have no idea what the graph represents.
This is a well constructed empty argument because it glosses over the central concern, ‘employee performance’. Without defining that we have no idea what the graph represents.
In a previous job I modelled this and concluded that due to measurement error and year-over-yead enrichment, Welchian rank-and-yank results in firing people at random.
This is a well constructed empty argument because it glosses over the central concern, ‘employee performance’. Without defining that we have no idea what the graph represents.
I'm not sure this is the argument the author is making, but you could claim that the rest of the argument is true for any (or most) reasonable measure of employee performance that a company actually cares about.
This is a well constructed empty argument because it glosses over the central concern, ‘employee performance’. Without defining that we have no idea what the graph represents.
Performance is "visibly doing the things that the company rewards during the performance review process".
Theoretically, each role at a company should have a set of articulated accomplishments that are expected. (This is sadly often not the case.)
But you're right that the subjective nature of "performance", and the lack of a clear numerical scale, are a difficulty of the entire process!
This is a well constructed empty argument because it glosses over the central concern, ‘employee performance’. Without defining that we have no idea what the graph represents.
There are certainly times that you would want them included, but those can be classified under "budgeting," not gaining insight on a workforce.
In a properly functioning team, people perform different, discrete roles which are probably not entirely understood by other team members or management.
To me the biggest insight here is that no matter what data science you're trying to do on a group of employees, the people you already have decided should be fired or promoted from that group are outliers and should be removed from the sample. There are certainly times that you would want them included, but those can be classified under "budgeting," not gaining insight on a workforce.
But in my experiance employee perf evals are more political than data based.
At the end of the day a lot of mgmt at BigCo, esp these days, wants that 10% quota for firing as a weapon/soft layoff and the "data" is a fig leaf to make that happen. More generously it's considered a forcing function for managers to actually find underperformers in their orgs, even if they don't exist. Either way it's not really based on anything other than their own confirmation bias.
IME the scrutiny of perf evaluation is basically tied to the trajectory of the company and labor market conditions. Even companies with harder perf expectations during the good times of ~2021 relaxed their requirements.