{A Personal View}
Background
Apprisals are an yearly ache that we have imposed on ourselves to make ourselves a better person than we were previously. It is stressful to both the assessor (manager) who is on one side of the spectrum and the assessee (team member). It is at this time of the year the distance between the manager and the team members is at the maximum, even though they are part of the same team.
Irrespective of which role you play you have a solid list of greviences against the other role. So it is nothing to do with the role, it is to do with the situation. While the tension is on during the apprisal time - the folks in human resource bring in their wisdom and introduce the dreaded Gaussian curve\band and introduce a constraint where you as a manager have to necessarily have a certain number of your team members in a certain region under the curve and then the finance folks attach a monetary value to each part of the curve. These two, the constraint linked to money, makes the situation even more tense than it actually is and widens the gap further between the assessor and assessee. Put all this together and you have a self created "आ बैल मुझे मार" situation. Year after year we all go through this annual ache, grudging it but without trying to ease the ache and it looks like we take it for granted that this is something we can not avoid because this is the process that we have been following!
Nobody seems to own this situation. While in private all agree that this is an ache that should be avoided and push the blame on someone up the decision making chain and sincerely hoping that something has to be done. But in public, most of them claim that this is a necessary evil to reward the people who do their job well and penalize the people who were unable to do their job as well so that people work to make them selves better.
Does this reward-penalize actually work? How objective can the assessor be? Does this actually help the person being penalized to do better or for that matter the person rewarded to do better. The statistics are sketchy and probably derived from a set of data points that might be not free of errors scientifically.
Elementary understanding says that the Gaussian curve probably is alright if you had a very huge number of people in a team but often teams are very small and the concept of the curve fails miserably. Somehow we miss this point and try to map a small team to fit into this curve. This probably is the reason for the ache. Even if you had a great talented team, some one among them has to occupy the dreaded part of the curve. Seems unfair! Na. "The 12th man in the Australian team was good enough to be the captain of the (newly formed) Zimbawae team" in a close analogy for those of us who follow the game of cricket.
Force Gaussian
If you come to look at it, as a manager you select the best talent\people to form your team, you spend a lot of time questioning them, evaluating them before you actually recruit them. But during annual apprisal time you find the same very person who you diligently selected, surprisingly not upto the mark.
A case of "we recruit the best" and then at some point of time saying they are "not as good as" we thought they were!
This seems like a paradox. Was it that the "best talent" that you selected using your wisdom suddenly turned out to be not so good under your supervision? Or did you as the manager fail to actually spot the right talent? Either way the failure seems to be of the manager during the recruitment process.
But, I guess, the truth is the "act of trying to fit the curve on a population for which it is not suitable"! The imposed Gaussian curve fitment. The sum effect is the widended gap between the manager and the team member which results in only one thing "dip in the morale of the team".
A (fresh?) Look
While people in management and sociology might not agree and bring in theories to support the reward-penalize approach. Isn't empathy missing? Are we being robots (loosely defined a "human minus emotion"). Is a low morale team better for the organization? Answers depends on the person with whom you have this dialogue.
For an organization which recruits the best people from the very top Engineering colleges in India and then for their R&D unit take the top 1% of the recruited students
The annual appriasal for the R&D unit should be into three bands A+, A, A-. Exceptionally poor performers in band A-, exceptionally excellent performers in A+ and the rest (99.7%) in A.
Details
If we consider all the students in all the engineering colleges in India, we can safely assume that they fit a Gaussian curve (see Figure 1; the 0 indicates a dumb student and 10 indicates a smart student with an average student being represented by 5.) with mean 5 and variance 1.
Assuming that the organization recruits only the best among the best students from, which probably means that they do not recruit anyone who is labeled between 0 and 4, implying that the student that they recruit are atleast better that a shade below average (this is pessimistic, because most R&D organizations demand a minimum of 60% marks during their academic stint).
Under this assumption (recruits students above 4) we see that the associates
in the organization form a skewed Gaussian with a mean 5.3 and a variance of 0.63. And that in the R&D unit (top 1% of the recruits) has a mean of 7.8 and variance of 0.09. Observe that there is so small variation in the talent of the students recruited, just 0.09.
Just to give a better view - Distribution of Engineering Students (top; G(5.00488, 0.99750)) recruited by the organization (middle; G(5.28819, 0.62633)) into R & D unit (bottom; G(6.831203, 0.16680)).
A very rough interpretation is that the recruits in R & D (selecting top 1% from recruits in the organization) would be hovering at 7.8 (on a scale of 1 to 10) with almost all (99.7% [*]) of them being between 6.87 and 8.732. The super small range implies that a 3 band distribution might be best suited. Further, the thin spread suggests that exceptional over performers (A+) are too few and so are exeptional under performers (A-) meaning that a majority (≡ almost all) in R & D are A.
[*] Using the well known P(μ−2σ < X ≤ μ+3σ) ≈ 99.7%
Comments
Avnish