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OR_on1345253095 |
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OCoLC |
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20231017213018.0 |
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220920s2022 mau ob 000 0 eng d |
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|a ORMDA
|b eng
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|d OCLCQ
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024 |
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|a 53863MIT64131
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|a AU@
|b 000072790653
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|a (OCoLC)1345253095
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|a 53863MIT64131
|b O'Reilly Media
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|a HF5549.5.P35
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|a 658.3/125
|2 23/eng/20220920
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|a UAMI
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100 |
1 |
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|a Leschke-Kahle, Amy,
|e author.
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|a Dump traditional reviews to better measure performance /
|c Amy Leschke-Kahle.
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250 |
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|a [First edition].
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264 |
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1 |
|a [Cambridge, Massachusetts] :
|b MIT Sloan Management Review,
|c 2022.
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300 |
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|a 1 online resource (5 pages)
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336 |
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|a text
|b txt
|2 rdacontent
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337 |
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
|2 rdacarrier
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500 |
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|a "Reprint 64131."
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|a Includes bibliographical references.
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520 |
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|a The traditional performance management process does not provide good data and inspire employees to perform better. We need to move on to a new way of measuring — and accelerating — performance. The first step to a better system is to remove the common practices that cause so much strife: no mandated goals, complicated rating approaches, or strictly annual performance conversations. We also need to stop collecting and using unreliable, stale data to inform downstream talent decisions.
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590 |
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|a O'Reilly
|b O'Reilly Online Learning: Academic/Public Library Edition
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650 |
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0 |
|a Performance
|x Evaluation.
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650 |
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|a Employees
|x Rating of.
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650 |
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|a Performance
|x Management.
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650 |
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7 |
|a Employees
|x Rating of
|2 fast
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650 |
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7 |
|a Performance
|x Evaluation
|2 fast
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650 |
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7 |
|a Performance
|x Management
|2 fast
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856 |
4 |
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|u https://learning.oreilly.com/library/view/~/53863MIT64131/?ar
|z Texto completo (Requiere registro previo con correo institucional)
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994 |
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|a 92
|b IZTAP
|