What matters for knowledge work productivity?

Date07 January 2019
Pages209-227
Published date07 January 2019
DOIhttps://doi.org/10.1108/ER-04-2017-0091
AuthorMiikka Palvalin
Subject MatterHR & organizational behaviour,Industrial/labour relations,Employment law
What matters for knowledge
work productivity?
Miikka Palvalin
Laboratory of Industrial and Information Management,
Tampereen Teknillinen yliopisto, Tampere, Finland
Abstract
Purpose Knowledge work product ivity is a well-studied t opic in the existing litera ture, but it has
focussed mainly on two thi ngs. First, there are man y theoretical models lacki ng empirical research, and
second, there is a very sp ecific research regard ing how something impacts productivity. The purpose of
this paper is to collect emp irical data and test the c onceptual model of knowl edge work productivity in
practice. The paper als o provides information on how different drive rs of knowledge work produ ctivity
have an impact on produc tivity.
Design/methodology/approach Through the survey method, data were collected from 998 knowledge
workers from Finland. Then, confirmatory factor analysis was conducted to confirm the knowledge work
productivity dimensions of the conceptual model. Later, regression analysis was used to analyse the impacts
of knowledge factors on productivity.
Findings This paper increases the understanding of what matters for knowledge work productivity, with
statistical analysis. The conceptual model of knowledge work productivity consists of two major elements:
the knowledge worker and the work environment. The study results showed that the knowledge worker has
the biggest impact on productivity through his or her well-being and work practices. The social environment
was also found to be a significant driver. The results could not confirm or refute the role of the physical or
virtual environment in knowledge work productivity.
Practical implications The practical value of the study lies in the analysis results. The information
generated about the factors impacting productivity can be used to improve knowledge work productivity.
In addition, the limited resources available for organisational development will have the greatest return if
they are used to increase intangible assets, i.e., management and work practices.
Originality/value While it is well known that many factors are essential for knowledge work productivity,
relatively few studies have examined it from as many dimensions at the same time as this study. This
study adds value to the literature by providing information on which factors have the greatest influence
on productivity.
Keywords Measurement, Performance management, Work environment, Productivity, Knowledge work
Paper type Research paper
1. Introduction
Since the days of Frederick Taylor, organisations have tried to increase their workers
productivity by identifying work tasks and optimising work processes. After the majority of
the work has moved towards knowledge work, the productivity of knowledge work has also
raised interest. While knowledge work productivity is a young topic, it has been researched
both directly and indirectly for several decades (Pyöriä, 2005). It has been studied in
conjunction with the topics of white-collar work and office work, with the term knowledge
workbeing established only recently (Dahooie et al., 2011). Drucker (1999) highlighted
the importance of knowledge work productivity by announcing that it could be one of the
biggest challenges of the twenty-first century. Whether he was right or wrong remains to be
seen, but at least it has been of interest to many researchers (see, e.g. Thomas and Baron,
1994; Pyöriä, 2005; Koopmans et al., 2013). In addition to the research topic of knowledge
work productivity, productivityis a common dependent variable in many research areas,
for example, in facility management (e.g. Van der Voordt, 2004), work psychology (e.g. Judge
et al., 2001) and knowledge management (e.g. McCampbell et al., 1999).
The current discussion on knowledge work productivity is twofold. First, several
theoretical models on the phenomenon (see, e.g. Syed, 1998; Davenport et al., 2002;
Bosch-Sijtsema et al., 2009) have little to no empirical evidence, and second, a countless
Employee Relations
Vol. 41 No. 1, 2019
pp. 209-227
© Emerald PublishingLimited
0142-5455
DOI 10.1108/ER-04-2017-0091
Received 20 April 2017
Revised 13 March 2018
29 May 2018
Accepted 31 May 2018
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0142-5455.htm
209
Knowledge
work
productivity
number of empirical studies have very focussed drivers (see e.g. Kearns and Gardiner, 2007;
ONeill, 2010; Palvalin et al., 2013). The literature lacks an empirical examination on how
knowledge work productivity drivers affect productivity. Testing the theoretical model in
practice would take the discussion one step forward. It would also provide evidence for the
discussion on which knowledge work productivity drivers are the most important.
For example, Davenport et al. (2002) requested this kind of research, as they recognised that
three work environmental drivers for knowledge work productivitythe workplace,
technology and managementare closely related and should thus be measured and
managed together. Drucker (1999) was not as specific but emphasised the importance of
understanding knowledge work productivity as a unit.
Understanding knowledge work productivity and its drivers in a more comprehensive
way has become a fairly topical issue due to the concept of new ways of working
(NewWoW). The concept of NewWoW was created in the field of facility management as the
opposite of traditional work practices (Van der Voordt, 2004). Since then, it has evolved to
consist of work in information technology, work in management and personal work
practices as well (Van Meel, 2011; Ruostela et al., 2015). The idea behind NewWoW is to
increase productivity without decreasing job satisfaction (Van der Voordt, 2004). This can
be achieved by increasing the autonomy and flexibility of knowledge workers so that they
are able to find the best ways of working for themselves (Van der Voordt, 2004; Aaltonen
et al., 2012). In western cultures, such as Finland and the Netherlands, an increasing number
of organisations are starting NewWoW changes by implementing activity-based offices,
acquiring portable ICT tools for all employees and improving organisation policies to
support the NewWoW (Appel-Meulenbroek et al., 2011; Ruostela et al., 2015).
The purpose of this paper is to answer the following research question:
RQ1. What matters for knowledge work productivity?
The study approached the problem by building a conceptual model of knowledge work
productivity drivers and testing it in practice. The empirical examination included
surveying knowledge workers in nine organisations, with a total of 998 respondents.
The results were then obtained using regression analysis (RA). The contribution of this
study is the conceptual model and the results of the analysis, which show how the
dimensions highlighted in the conceptual model impact knowledge work productivity.
The results are valuable for managers looking for a competitive advantage, as they can see
how the different drivers impact knowledge work productivity and thus focus their time on
the right things.
The paper is organised according to the following structure. Previous literature is
reviewed and the theoretical background is presented in Section 2. This is followed by the
conceptual model and hypotheses, which are built in Section 3. Section 4 describes the
methods used, including a more detailed description of the sample. In Section 5, the results
of the study are presented, and they are discussed in Section 6. At the end of the paper, there
is a short conclusion on the studys contribution.
2. Theoretical background
2.1 Knowledge work
The term knowledgeworkwas introduced by Drucker (1959). It was created to describe the
work of workers who use intangible resources as their primary assets. It was also created to
distinguish knowledge workers from manualworkers. The line between knowledge workers
and manual workers is still quite unclear, and some jobs include elements of both
(Drucker, 1999). After Drucker, many scholars have created their own definitions of
knowledge work, without a good consensus on what it actually is (Dahooie et al., 2011;
Kelloway and Barling, 2000). Davenport and Prusak (2000), for example, defined knowledge
210
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