Measuring Productivity of Knowledge Workers

Figure 1: Stopwatch (JoLin, 2023)

In Human Resource Management (HRM) workers are divided into two types: knowledge workers and manual workers. Knowledge workers use their cognitive skills to make a living while manual workers use their physical skills to make a living (CFI Team, 2022). It can also be said that knowledge work is information based while manual work is material based (Nickols, 2012). The beginnings of the term knowledge worker go back to 1950s where it was first introduced by Peter F. Drucker in his book, The Landmarks of Tomorrow (1959) (CFI Team, 2022). In his book, Drucker explained knowledge workers in the following way.

The man who works exclusively or primarily with his hands is the one who is increasingly unproductive. Productive work in today’s society and economy is work that applies vision, knowledge and concepts – work that is based on the mind rather than on the hand. (Nickols, 2017, p. 1)

Knowledge work and manual work can be distinguished by certain characteristics as follows.

Table 1: Characteristics of Knowledge Work and Manual Work (Nickols, 2012)

Knowledge workers are usually present in technology fields. For example, software engineers, systems engineers, web designers, and various other information technology related fields are based on the cognitive skills. However, workers from other industries such as medicine, engineering, law, and research can be considered knowledge workers as well (CFI Team, 2022).

Measuring Productivity

Measuring productivity of manual work is relatively easy as inputs and outputs are tangible. There is a definite input and there is a definite output measured against a definite time duration. It can be typically measured by asking questions like how many parts can one worker assemble per day and how many vehicles can this supply chain deliver in a week.

Measuring the productivity of knowledge work is complex and hard mainly due to the intangibility of its elements (Duffy, 2019). However, ways must be defined to measure its productivity to ensure that the contribution of knowledge workers to the organisation is in fact in par with their compensation.

Perceived productivity

As Duffy (2019) explains, sometimes the productivity is measured via self-assessments where knowledge workers are requested to share their perception of their own productivity. Duffy (2019) points out that this may be the only measurement mechanism that is realistic at times. However, it is often inaccurate as there is a high possibility for a less productive worker to perceive that they are highly productive due to the Dunning-Kruger effect (Skillicorn, 2021). This effect was observed by psychologists David Dunning and Justin Kruger in their study in 1999 (Kruger & Dunning, 1999). Similarly, a highly productive worker might perceive that they are less productive knowing how much they do not know about their field of work.

Time on task

The time spent by a knowledge worker on a certain task can be used as a gauge for productivity (Duffy, 2019). If more than one worker spends time on the same type of task that is repeated over time, it can help to devise a baseline. This measurement mechanism can mature based on the number of data points such as number of workers, number of tasks, and the duration of the dataset.

However, while using this mechanism, it must be kept in mind that in dynamic environments such as Information Technology (IT), every task can be unique and different. One specific scenario is where one task is simply to issue a refund on a purchase while the other is to deeply investigate the loss of money in an investment to find the cause (Duffy, 2019). When the difficulty and knowledge required to complete a task differs from one task to another, the strength of this measurement mechanism starts to weaken.

Time in app

In fields where workers perform their work through computers, applications like RescueTime and Exist can help collect data on the workers' behaviour with regard to the use of different apps (Duffy, 2019). For example, these applications can show how much time a worker spent browsing internet on a given day compared to the time he or she spent on writing software code.

This mechanism is not a great one. A person can spend an entire day browsing the internet finding the information he needs to complete a task. Some information gained this way could be useful to the final outcome while others may not. The time spent on analysing the less useful information will be considered unproductive time under this mechanism which is not entirely accurate. Therefore, this mechanism can be used as a supporting mechanism to complement other measurement mechanisms as it can highlight red flags in productivity effectively (Duffy, 2019).

Tasks completed versus intended completions

Another way to measure productivity would be to look at the number of tasks a knowledge worker intends to complete within a given period of time compared to the number of tasks he or she actually completes within that period of time (Duffy, 2019). 

This mechanism must be used cautiously. One reason is that not every worker can gauge their performance properly. Workers may agree to complete many tasks due to misunderstandings on the real nature of them and miss the deadlines. They may agree on the workload simply to keep their superiors satisfied or out of fear of superiors. Another reason is that not every manager can gauge their subordinates properly. Managers may misunderstand the depth of the tasks and consider them to be simple or misjudge the capacity of the workers. It can finally lead to workers being unable to deliver as promised.

Having looked into different mechanisms that can be used to measure a knowledge worker’s productivity, it is evident that there is no one-size-fits-all solution. There are other mechanisms like social cohesion, perceived supervisory support, information sharing within teams, goal clarity, external outreach, and trust that can help assess productivity (Serraview, 2022) too. It is best to shift the focus from trying to measure specific criteria to measure the contribution of knowledge workers in their line of work (Duffy, 2019). Therefore, a good mechanism to measure the productivity of knowledge workers can be concluded as one that is designed by combining different mechanisms that are appropriate for the workers' industry and the line of work.

References

CFI Team, 2022. Knowledge Workers. [Online]
Available at: https://corporatefinanceinstitute.com/resources/valuation/knowledge-workers/
[Accessed 17 August 2023].

Duffy, J., 2019. 4 Ways to Measure Productivity of Knowledge Workers. [Online]
Available at: https://productivityreport.org/2019/08/26/how-to-measure-productivity/
[Accessed 17 August 2023].

JoLin, 2023. Stopwatch. [Art] (Advameg, Inc.).

Kruger, J. & Dunning, D., 1999. Unskilled and Unaware of It: How Difficulties in Recognizing One's Own Incompetence Lead to Inflated Self-Assessments. Journal of Personality and Social Psychology, 77(6), pp. 1121-1134.

Nickols, F., 2012. The Shift from Manual Work to Knowledge Work. [Online]
Available at: https://www.nickols.us/shift_to_KW.htm
[Accessed 17 August 2023].

Nickols, F., 2017. Knowledge Worker: Drucker's Dictums. [Online]
Available at: https://www.nickols.us/Druckers_Dictums.pdf
[Accessed 17 August 2023].

Serraview, 2022. How Do You Measure Knowledge Worker Productivity?. [Online]
Available at: https://serraview.com/how-do-you-measure-knowledge-worker-productivity/
[Accessed 17 August 2023].

Skillicorn, N., 2021. The Dunning-Kruger effect: Why stupid people think they are smart. [Online]
Available at: https://www.ideatovalue.com/curi/nickskillicorn/2021/08/the-dunning-kruger-effect-why-stupid-people-think-they-are-smart/
[Accessed 17 August 2023].