Productivity of knowledge workers. How can we analyze and measure it?

Productivity of knowledge workers.
How can we analyze and measure it?
Costin Ciora Ion Anghel Vasile Robu
Department of Financial Analysis and Valuation (AEEF)
The Bucharest University of Economic Studies (ASE)
Prepared for AMIS 2017. WORK IN PROGRESS

2
1. Motivation
2. Theoretical background
3. Hypothesis
4. The challenge: input, activities and output
5. Indicators to measure productivity: a critical analysis
6. A quantitative view on productivity
Agenda
Ciora | Anghel | Robu

3
1.Motivation
Research questions:
• How can we measure knowledge-worker productivity?
• What are the challenges from the methodological point
of measuring productivity?

4
1.Motivation
The importance of assessing motions, effort and time in
order to increase the efficiency of repetitive tasks.
(Taylor, 1911)
=> So, in today’s knowledge economy, managers often
think in terms of standardizing the activities and derive
them to a more repetitive ones.

5
1.Motivation
knowledge worker = “the most valuable asset of a 21st-
century institution, whether business or non-business, will
be its knowledge workers and their productivity”
(Druker, 1959)
To achieve higher productivity, knowledge workers
should be seen a capital asset, with the purpose of
growing and acquiring new knowledge.
(Druker, 1999)

6
1.Motivation
Industrial
production
Eight-fold
increase in
2015 compared
to 1997.
(The World Bank,
2016).

7
1.Motivation
Figure 1. Evolution of employment worldwide, by sectors
(Source: International Labour Organization, 2014)

8
1.Motivation
(Source: International Labour Organization, 2014)
Services weight more than 46% in the total employment, with more than
74% in developed economies.
The industry employment weights 24% globally and less than 22.5% in
developed economies (International Labour Organization, 2014).
The same organization states that more than 1.56 billion people are
estimated to work in services until 2018.

9
1.Motivation
(Tully & Rapp, 2016)
Fortune 500 financial data> cumulated profits
FinancialHealthcare
Technology 37.9%
FinancialHealthcare
Technology 57.2%

10
2.Theoretical background
Druker, 1995: One of the most important challenges for
managers today is to increase productivity of knowledge and
service workers.
Maruta, 2012:
• in the production activities, there is a skilled work where
front-line workers yield uniform outputs according to given
standard procedures.
• in the non-production activities, the first level are front-line
workers that yield individually unique outputs according to
self judgements. This difference is crucial related to outputs
in the knowledge economy.

11
2.Theoretical background
This difference is known in the literature as
WHITE-COLLAR versus BLUE-COLLAR work.
Davis, 1991:
• the blue-collar work which is more standardized, related to
manufacturing, can be planned and scheduled, and create
the traditional model of productivity for increasing output and
lowering input.
• the white-collar productivity becomes a broader concept
because of the involvement of efficiency, effectiveness and
transformations.
Robu et al., 2014: there are two methods of analyzing
productivity: extensive, through working time and intensive
through work productivity and profit per employee

12
3. Hypothesis
Hypothesis 1 Traditional measures should be understood in a larger context
(revenues workers vs. non revenues workers that provide important information
for revenues workers)
Hypothesis 2 The need of adding other key performance indicators as KPIs for
the effect in the Effect/Effort formula of calculating productivity.
Hypothesis 3 Rethinking the "Effort" not only related to time not only in the time
spent but in the idea of "time well spent" (e.g. a knowledge worker could work on
a project for 6 hours instead of average 4 hours of a team).

13
The data related to manual workers is so vast that there are a lot of
studies on the impact of night shifts on workers’ productivity and
influence on health.
(Folkard & Tucker, 2003)
INPUT
In production, this type of schedule is common as there is a strong
need to continue the production seven days a week.

14
For manual workers, the activities consisted in standardized
procedures and movements set in order to increase the productivity
and reach the target production.
ACTIVITIES
Knowledge workers are expected to produce better results than
before, by innovating simple tasks and being more creative than
ever. In this sense, the actual activities are not a measure of
quantity and uniformity, but rather a challenge of innovation and
discovering unique individuals

15
As there are many attempts to standardize the knowledge worker
output through managerial systems or human resources
procedures, this could only lead to poorer outputs.
For example, if a knowledge worker has to prepare a monthly
report, this will be completely different depending on the experience
in the field, amount of information and time for the analysis. Even if,
the report would have standard fields, we cannot consider the
output the report itself, but the quality of the date in the report.
OUTPUT

16
Effect = Gross Value Added (GVA) and Value of industrial production (Q).
Effor = number of employees (Ne) and the total time expressed by
number of hours worked in that sector (T)
Effect/ Effort or
Effort/Effect.
Annual work productivity based on
• gross value added per employee (GVA/Ne) and
• gross value added per hour (GVA/T) and
• indicators of productivity based on industrial production (Q/Ne and
Q/T).
For the traditional sectors of the economy

17
Effect = revenues (R), profit (Pr), gross value added (GVA)
Effor = number of employees (Ne)
Effect/ Effort or
Effort/Effect.
Efficiency of workforce, through productivity based on
• revenues (R/Ne);
• productivity based on gross value added (GVA/Ne) and
• profit per employee (Pr/Ne).
In the knowledge economy, at the sector level

18
Efficiency of human potential, can be measured through
productivity per employee
• revenues (R/Ne),
• gross value added (GVA/Ne),
• earnings before interest and taxes per employee (EBIT/Ne) and
• economic value added per employee (EVA/Ne).

19
(R/Ne) has the advantage of comparison between companies from the
same sector, and for average of the sector, but has the disadvantage of
lack of comparison between different sectors.
(GVA/Ne) has a higher informational value than Revenues/employee
because it takes into consideration the higher degree of integration of
activities and allows comparison between companies from the same
industries but also between different sectors.
The analysis of work productivity in the knowledge economy must be
correlated with the analysis of the evolution of salary expenses per
employee (Sal.exp/Ne), resulting in the efficiency of such expenses.

20
• For presenting the difference in terms of measuring
productivity, we chose two sectors related to the Romanian
economy.
• Food industry – a traditional sector with mainly blue collar
workforce;
• IT services sector – a recent developed industry, with mainly
white collar workforce.
• We identified two samples based on an initial sample at the
economy level of 1102 companies with good performance, with
a COFACE rating good and very good, revenues higher than 1
million euro.

21
• The final sample included 44 companies in the food sector and
22 companies in IT, analyzed between 2008 and 2015.
• Our analysis included 31 indicators. The result shows
significant difference between the two sectors in terms of
profitability ratios, as presented in the table below.

22
Food industry IT services sector
Median Average Median Average
Difference in
average
EBITDA 7.70% 9.00% 13.10% 14.30% 58.30%
Profit
margin
4.70% 6.10% 11.10% 10.80% 77.60%
Net profit 232,450 1,044,921 494,583 1,919,296 83.70%
Equity 3,025,504 9,093,138 1,116,997 6,869,331 -24.50%
ROE 11.60% 12.30% 38.00% 48.00% 290.80%
ROA 4.00% 5.50% 15.70% 21.00% 278.20%
OROA 7.50% 10.50% 26.60% 40.60% 286.10%
ROIC 14.20% 19.50% 42.70% 49.50% 154.30%
Sal. Exp 1,348 1,792 3,928 4,558 154.40%
Value
added (%)
20.70% 21.10% 40.30% 40.10% 90.30%
Sal.exp/ VA 48.00% 47.40% 54.80% 63.60% 34.20%

23
• One sector, the food industry, is considered to be a blue
collar sector, in which the productivity is measured through
the number of units made by each employee.
• In the IT sector, characterized as a white collar sector,
measuring productivity is correlated with the actual work of
employees involved in different projects.
• This difference is almost 300% for return on equity. By
looking at the salary expenses on value added we
calculated a difference of 34.20% between the two sectors
in favor of the IT sector.

24
Conclusions
• A company working with many sub-contractors will be interested
to measure, as pointed before, the partial productivity of different
stages of the product development or service delivery.
• The partial productivity could be analysis as cost per suppliers
per hours.
• Because of increase in outsourcing activities, this could become
another important indicator.
• Working from home might affect productivity through the time
needed for performing a task.

25
Conclusions
• Measuring productivity becomes an important challenge for
companies in the knowledge economy, also because of frequent
interruption (email, social networks) of employees, which reduce
the focus on the actual tasks and projects.
• As new type of business will be created in the next years, new
jobs will provide the need of measuring productivity
• And because of this complexity of the business world, the
quantitative measures will be correlated with other factors like
behavior, expectations or overall impact of the work on the value
of the business.

Corresponding author:
Costin Ciora: costin.ciora@cig.ase.ro
Thank you!
Costin Ciora Ion Anghel Vasile Robu
Department of Financial Analysis and Valuation (AEEF)
The Bucharest University of Economic Studies (ASE)

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