Wednesday, September 17, 2025
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Performance Appraisal Models: Which One is Better?

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Time immemorial, evaluating one’s capabilities and working on areas to improve has been a major area of individual development and grooming. All historical writings are full of examples of mentoring, experiential learning and coaching by wise men. Therefore there will always be reference to and discussion about performance and its evaluation.

Over last many years since Industrial revolution, the face and methods of performance appraisal have changed and evolved.

“There are multiple models in use to assess one’s performance output where productivity, efficiency and value addition are key metrics in one or other form.  They are well accepted in today’s work culture as well. However there have been debates on individual and collective effectiveness of these models and practitioners have critical views on how these models have been used”

And before we further elaborate on which model is better, let’s take a look at what these models are:

I would like to split the performance assessment methods in two groups

  1. Forced Ranking Evaluation
  2. Absolute Performance Evaluation

For a general understanding the forced ranking is derived from a well-known practice of “Normal distribution (or Bell Curve)”. Classical Bell curve is derived through measured deviation from average performance of the group and largely focus remains around top and bottom of the curve. It brings outliers (on better and worst) on performance grid. However since the bell curve gives you output basis available distribution of data points (in this case performance achievement scores) and tells you who are on higher (better) and lower (Poor) side of average. This does not signifies if the data points meets or exceeds the goal and therefore if the average performance of the group members is below the goal, you may end up calling someone with sub optimum performance a “top performer”.  Therefore many organizations have lately modified this concept to bring in goal and achievement with ref to group average.

Second widely used model is evaluation of performance on basis of absolute performance to goal where the assessment is based on target achieved v/ s goal and therefore the ref point would be higher (or lower) achievement to target. In this case if your average group performance is better to goal, you may have many overachieving the goal and vis a versa.

If we take a macro view of these appraisal models, they work fine as long organisations are able to use the output information effectively and meaningfully. Purpose of a performance appraisal should be to provide constructive feedback to team members and help them build competencies to do better and as long each manager do this religiously, the organizations can achieve the objective.

Most of these debates come across as a reflection of linkage of output of the performance evaluation to either individual compensation or career decisions and when we do that it becomes more tricky. While the individual pay is a combination of outside (market dynamics) and inside (internal parity, skill etc) factors, pay progression is always linked in addition to performance of individual to bring a pay for performance concept.

The question comes is which model is better?

In my opinion both types of performance assessments philosophies are effective as long we use them appropriately. Example, if we have to assess a huge population of associates doing repetitive jobs with same performance goals then Bell curve based forced ranking may be a good option. It also helps you give a sense of fairness (similar rewards to all with same performance output) and keep a check on your budget. On the other side where the groups are diverse with diff skill, small in numbers and completely distinct (and sometime individual skill based outcome parameters), an evaluation of absolute performance against goal may be a good method. In my view at times when managers want to avoid a difficult conversation, they tend to choose a method that gives them comfortable landing. I believe managerial courage lies in delivering right performance evaluation and not a feel good evaluation.

No individual model is competent enough in isolation unless a concept of walking management is infused where an associate gets feedback instantly and gets necessary support. As they say, yearend / term end evaluation is best when the outcome observation is not a surprise to associates and that’s only possible if we follow frequent and timely discussions highlighting what went well and what could be better. We believe managers can do better evaluations when they are Story tellers (describing best examples and narrative of better performance), not PPt dictators (focus more on discussion, inputs, views) and are grounded (provide feedback based on facts and data) and be a Truth teller (be honest to the facts and remain candid)

We are moving faster and closer to digital workplace providing us tools and instruments to make performance evaluation more accurate, fact and data based and this brings a great opportunity for managers to use them in the best fashion for their teams. At the same time we need to remembers and ensure that this does not create distance between a manager and team because a conversation is a matter of emotional connect.

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Kalpak Huddar
Kalpak Huddar
Kalpak Huddar, Head of HR, Sitel Group. He is a seasoned HR professional with over 2 decades of core HR domain expertise in various Industry verticals, including Manufacturing, Automobile, Aviation and Business Process Management. Before he joined Sitel in 2012, he worked with HAL, Dell, IBM & Metlife. Kalpak has a keen eye on process management, having worked on process automation in nearly all his professional experiences. His understanding of end-to-end process mapping and result outlook helped him and his technology teams create strong process driven workflows and platforms. He holds a post-graduate degree in Human Resources Management from Devi Ahilya University and completed his Accelerated Management Programme from Harvard Business.