Knowledge worker survey

Knowledge Workers and
Knowledge Work

A Knowledge Economy Programme Report

Prepared by Ian Brinkley, Rebecca Fauth, Michelle Mahdon and Sotiria Theodoropoulou

Contents

Acknowledgements 2
List of Figures and Tables 3
Executive summary 4
1. The knowledge economy and knowledge work: A review of the existing
definitions and measures 9
2. Redefining knowledge work and knowledge workers 19
3. Knowledge work across industries and regions 41
4. The changing nature of work roles and the returns to knowledge 49
5. The job characteristics of knowledge workers 54
6. Organisational culture in the knowledge economy: preferences
and reality 61
7. Conclusion and recommendations 68
Appendix A. Work-related tasks and activities by factor 76
Appendix B: Sample demographic and background characteristics 82
Appendix C: Description of organisational variables 83
Appendix D: Composition of workforce in the distribution and repairs and
in the hotels and restaurants sectors 84
References 85

Acknowledgements

This report has drawn on some of the initial research work and discussions from The Work
Foundation’s three year Knowledge Economy Programme, to be completed in April 2009.
However the views set out here are entirely those of The Work Foundation and do not represent
those of the sponsoring organisations.

We would like to thank Alana McVerry and Sezis Okut for their contributions to this paper.

2 Knowledge Workers and Knowledge Work

List of Figures and Tables

Figure 1: The 30-30-40 knowledge economy workforce 5
Figure 1.1: Growth of knowledge based service industries in Europe and UK 1970-2005 10
Figure 1.2: Shares of graduates and workers with only basic schooling in UK workforce, 1970-2006 14
Figure 2.1: What work tasks are most common across the workforce? 23
Figure 2.2: Number of different computer uses and how often computers are used each week 28
Figure 2.3: Share of workers that frequently perform at least one specialist computer task 29
Figure 2.4: Importance of ‘teach others’ task for different clusters 31
Figure 2.5: Perceived complexity of tasks performed by surveyed workers 32
Figure 2.6: The 30-30-40 knowledge workforce 34
Figure 2.7: Share of women in jobs by knowledge content 36
Figure 2.8: Share of jobs in the top three occupational groups by knowledge content 38
Figure 2.9: Share of graduates by knowledge intensity of the job 39
Figure 3.1: Share of jobs in knowledge industries by knowledge intensity 42
Figure 3.2: Composition of the knowledge-intensive services sector 43
Figure 3.3: Workforce composition in the health and welfare industry by worker cluster 44
Figure 3.4: Employment in knowledge intensive and more traditional services compared 45
Figure 3.5: Composition of the manufacturing sector 46
Figure 3.6: Regional composition of the workforce 47
Figure 4.1: Percentage earning more than median wages by worker cluster 53
Figure 5.1: Percentage of workers in the same job for more than 10 years by worker cluster 56
Figure 5.2: Percentage of workers working day shifts by worker cluster 57
Figure 5.3: Percentage of workers doing weekend work at least once/month by worker cluster 58
Figure 5.4: Percentage of workers with flexibility in choosing work schedule by worker cluster 59
Figure 6.1: Percentage prefer innovative firms by worker cluster 67

Table 2.1: Task factors with sample items 22
Table 2.2: Number of methods used to acquire new information and learn new tasks 30
Table 2.3: Prevalence of methods used for sharing and capturing knowledge 32
Table 3.1: Regional concentration of knowledge workers in the UK 47
Table 4.1: Job-skills/experience match by worker cluster 52
Table 4.2: Shares of women and men earning above the median wage 53
Table 6.1: Perceived organisational characteristics by worker cluster 63
Table 6.2: Preferred organisational characteristics 64

Knowledge Workers and Knowledge Work 3

Executive summary

The purpose of this report is to provide a portrait of work and the workforce in the knowledge
economy. We wanted to find out who the knowledge workers are, what they do in their
jobs, where they are employed and what employment structures, job characteristics and
organisational structures look like in the knowledge economy.

Knowledge work and knowledge workers are terms often used but seldom defined. When
knowledge work is defined it is usually by broad measures such by job title or by education
level. At best this gives us a partial and simplistic view of knowledge work in the UK.

This report takes a new approach. In a large and unique survey, we have asked people what
they actually do at work and how often they perform particular tasks. We have used that
information to assess the knowledge content of their jobs. The key test was the cognitive
complexity required for each task – the use of high level ‘tacit’ knowledge that resides in
people’s minds rather than being written down (or codified) in manuals, guides, lists and
procedures.

We then grouped the workforce into seven distinct clusters of jobs ranging from ‘expert thinkers,
innovators and leaders’ (the most knowledge intensive groups) to ‘assistants and clerks’ (the
least knowledge intensive)1. We describe the two highest knowledge groups as our ‘core’
knowledge worker.

With this measure we estimated that we have a 30-30-40 workforce – 30 per cent in jobs with
high knowledge content, 30 per cent in jobs with some knowledge content, and 40 per cent in
jobs with less knowledge content.

Within our 30 per cent ‘core’ knowledge worker group, the highest group of all (‘leaders and
innovators’) constituted just 11 per cent of the workforce. These high intensity knowledge jobs
combined high level cognitive activity with high level management tasks.

These high knowledge intensive jobs are, we suspect, what some of the more excitable
accounts of knowledge work we have in mind. The reality is that even after 40 years
uninterrupted growth in knowledge based industries and occupations, such jobs account for only
one in ten of those in work today.

1
These groupings are described in more detail on page 24


Executive summary

The 30-30-40 knowledge economy workforce

Many knowledge
tasks, 33%
Few knowledge
tasks, 40%

Some knowledge
tasks, 27%

We confirmed that knowledge work cannot be adequately described simply by looking at job
titles or education levels. About 20 per cent of people engaged in jobs with high knowledge
content – our core group of knowledge workers – were not graduates.

We also show that current job titles understate the knowledge content of jobs within some
sectors such as manufacturing. When jobs are classified by knowledge content, high tech
manufacturing has as many knowledge intensive jobs, proportionately, as high tech services.

Although our survey did not look in great detail into the geographical distribution of knowledge
workers, there were nevertheless indications that core knowledge workers tend to cluster in
urban areas, particularly in London, the South East and North of England and Scotland. This
is not a surprising finding given that face-to-face contact and the development of relationships
are important for exchanging information and especially tacit knowledge. Cities across the UK
– including Manchester, Leeds, Bristol and Edinburgh outside the South East – also provide


Executive summary

businesses with access to wider markets and to specialist skills. This result resonates with the
insights of our Ideopolis programme on the growing importance of cities in world economies.

Our results confirm high economic returns to knowledge – the vast majority of those in the most
knowledge intensive jobs enjoyed pay well above the median. But this was not true for those in
jobs with some knowledge content – such as care and welfare work.

The most knowledge intensive jobs were almost equally likely to be held by men and women,
but those jobs with some knowledge content – such as care and welfare workers, information
handlers, and sellers and servers – were overwhelmingly female. Woman have benefitted from
the growth of knowledge work, but the growth of more knowledge intensive work has not, of
itself, overcome the gender pay gap.

Some people have speculated that the growth of knowledge work is weakening the attachment
to permanent and long term employment relations. We find no evidence for this. Those in the
most knowledge intensive jobs are no more likely to be in temporary jobs than those in the least
knowledge intensive jobs and job tenures are also very similar.

Knowledge workers are not spear-heading radical changes in the way we work. As expected,
they do have more flexibility at work than those in less knowledge intensive jobs, but the
differences were not overwhelming. The reality is that less than 50 per cent of all workers
and less than 60 per cent of knowledge workers said they have some flexibility in their work
schedule, and only a very small minority said they can freely determine their own hours.

Perhaps not surprising, attachment to the standard nine to five day is still a central feature of
the labour market for both knowledge workers and non-knowledge workers alike. Knowledge
workers were far more likely to do occasional work at home, although over 60 per cent said they
did no home-working. Weekend working is relatively common across the workforce, but was
much less prevalent among knowledge workers.

We found two big labour market mismatches. The first was between the skills that people
said they had and the demands their current job made of them. The second was between the
organisational culture people perceived they actually worked in and the organisational culture
they would like to work in.


Executive summary

Significant minorities of workers reported their current jobs under-used their skills. The gap
was less marked for knowledge workers, but nonetheless significant. About 36 per cent of
knowledge workers said they were in jobs that under-used their skills compared with over 44 per
cent of those in jobs with some or little knowledge content.

Taken at face value, employers are not making the most of knowledge worker skills despite
such workers representing a substantial investment in human capital within the organisation.
However, these mismatches are even worse for jobs with low knowledge content – suggesting
a more general problem with labour utilisation rather than a particular difficulty with knowledge
work.

Some have expressed concern that the economy is producing too many graduates for the
available jobs that require graduate skills, forcing more graduates to accept lower pay jobs and
worsening the prospects for non-graduates.

We found mixed evidence. About 20 per cent of graduates were in low knowledge content
jobs. This is potentially worrying. However, the average job tenure for graduates in such jobs
was much lower than for non-graduates – suggesting graduates spend less time in these jobs.
Moreover, about 44 per cent of graduates in low knowledge content jobs reported that their job
duties corresponded well with their current skills.

Taken with the evidence on returns from knowledge and our previous work on labour market
polarisation2, the overall picture does not strongly support the idea that the UK is producing too
many graduates. The situation may be worse for those who entered the labour market more
recently, but we found little variation in these responses by age.

The vast majority of people in work think their organisation is characterised by formal rules
and policies, but very few say this is the sort of organisation they really want to work for. The
mismatch is even greater for knowledge workers: 65 per cent said their organisations were rule
and policy bound, but only 5 per cent expressed a preference for such organisations.

There is a much better match when it comes to characteristics such as loyalty and mutual trust
for both knowledge and non-knowledge workers. About 50 per cent of all workers said this
was a predominant characteristic of their organisation, and over 60 per cent said it was their
preferred organisational characteristic.

2
Fauth and Brinkley (2006) Polarisation and labour market efficiency, The Work Foundation


Executive summary

Knowledge workers are more likely to work for organisations that they think are innovative
or achievement orientated – not in itself a surprising result. What is surprising is that neither
feature seems to appeal to them very much. For example, 50 per cent of knowledge workers
said their organisation’s predominant feature was innovation, development and being at the
cutting edge, but only 24 per cent preferred this type of organisation.

Some of the differences in how people characterised their organisation can be partly explained
by whether the organisation was in a public based industry (education, health, public
administration) or in a private market based industry. But such differences between a public
and private based organisational culture did not explain preferences. It seems people reject
rule bound cultures and value loyalty and trust regardless of whether they work in the public or
private based sectors.

The gap between reality and organisational preference was wider in the public sector than in
the private sector. Public service workers were more likely to say they worked in a rules bound
organisation, which is predictable; but they also said they were less likely to be characterised by
mutual rust and loyalty than in the private sector.

These are the first set of findings from our knowledge working survey. We will be publishing
a second set of findings later in 2009 that look more closely at how knowledge work can be
regarded as ‘good work’ and how it relates to health and well-being at work.


1. The knowledge economy and knowledge work:
A review of the existing definitions and measures

Introduction The purpose of this report is to provide a portrait of the work and the workforce in the knowledge
economy. We want to find out who the knowledge workers are, what they do in their jobs, where
they are employed and what employment structures, job characteristics and organisational
structures look like in the knowledge economy.

The term ‘knowledge economy’ is often used but seldom defined. Essentially, it refers to a
transformed economy where investment in ‘knowledge based’ assets such as R&D, design,
software, and human and organisational capital has become the dominant form of investment
compared with investment in physical assets – machines, equipment, buildings and vehicles.
Thus, the term ‘knowledge economy’ captures the subsequently changed industrial structure,
ways of working, and the basis on which organisations compete and excel.

The presence and use of knowledge-based assets in the economy is of course not new –
knowledge based institutions such as universities go back centuries. However, in the late 1970s
and early 1980s three major economic and social forces combined to trigger the radical change
in economic structures that expanded the use of knowledge based assets and brought them to
the centre of economic activity across the OECD:

• The introduction of increasingly powerful and relatively cheap general purpose
information and communication technologies has not only been eliminating the physical
and geographical barriers of sharing information and ideas, but also expanding the
possibilities of generating new knowledge.
• Globalisation has been acting as an accelerator by opening up both markets of global
scale and an endless variety of niche markets as well as speeding up the spread and
adaption of new technologies and ideas.
• The rising standards of living in the advanced industrialised economies have, over the
years, created well-educated and demanding consumers with a voracious appetite
for the high value added services that the knowledge economy can characteristically
supply.

These changes are universal – they affect all industrial sectors, all sizes of firms, the public
sector as much as the private sector. And they are global – we have yet to find an advanced
industrial economy where these changes are not taking place.

The graphs below illustrate the growth of the knowledge economy in Europe by showing the
evolution of the share in value added, in the EU and the UK, of the sectors that the OECD and


The knowledge economy and knowledge work:

Eurostate commonly define as knowledge-based industries. These industries include high- to
medium-technology manufacturing and knowledge intensive services such as financial and
business services, telecommunications and health and education.3 The decline in manufacturing
is somewhat misleading, as we show in the report Manufacturing and the Knowledge Economy
(The Work Foundation, January 2009).

Figure 1.1: Growth of knowledge based service industries in the UK 1970-2005

50%
45%
40%
35%

30%
25%

20%

15%

10%
TOTAL MANUFACTURING
5% KE
Other services
0%
_1970 _1973 _1976 _1979 _1982 _1985 _1988 _1991 _1994 _1997 _2000 _2003

Source: The Work Foundation estimates from EU KLEMS database

Note: OECD definition – knowledge based services includes financial and business services,
communications, health and education services. Other services includes distribution, hospitality, public
administration, other services.

This change in industrial structure has also changed the structure of the workforce. The
interaction of technology with workers’ intellectual and human capital has, some argue, created
a new class of worker in today’s economy – the knowledge worker.

Peter Drucker, the management guru, is credited with popularising the term ‘knowledge worker’
as long ago as 1968 (Drucker 1968). Back then he argued, ‘Today the center is the knowledge
worker, the man or woman who applies to productive work ideas, concepts, and information
rather than manual skill or brawn…Where the farmer was the backbone of any economy a
century or two ago…knowledge is now the main cost, the main investment, and the main

3
It is interesting to note that knowledge-based industries in manufacturing are delineated by their high shares of sales
devoted to R&D, whereas knowledge-based industries in services are distinguished by their high levels of ICT usage
and graduate employment of graduates



product of the advanced economy and the livelihood of the largest group in the population’ (p.
264). Even in its nascent form, the very term ‘knowledge worker’ hints at a shift in nature of
some jobs where knowledge – not physical capital – is increasingly becoming the core currency
on the job market.

Forty years on, and we seem little closer to pinning down the terms ‘knowledge worker’ or
‘knowledge work.’ There are no official agreed definitions and no standardised measures.
As with the term ‘knowledge economy’, the term ‘knowledge worker’ is used frequently and
indiscriminately. It encompasses anybody from a relatively small number of professional and
technical specialists to a sizeable chunk of the workforce.

The following section reviews the diverse, but surprisingly sparse, literature on the definitions
and measurement of knowledge work and knowledge workers, including the definition used by
The Work Foundation thus far. In reviewing this literature, we highlight the important features
that a data-driven account of knowledge work and knowledge workers should reflect and the
shortcomings of previous attempts at providing such an account. Moreover, this review frames
our own method of deriving a better definition of knowledge work within the existing literature.
In later sections of this report we will use our newly developed definition of knowledge work
to explore the consequences of the knowledge economy in the structure of employment, job
characteristics organisational culture and good work.

Defining Definitions of knowledge
knowledge and One of the central problems in defining knowledge work has been the difficulty of defining
knowledge knowledge itself and distinguishing knowledge from information. Indeed, the terms ‘information
workers worker’ and ‘knowledge worker’ can be used interchangeably. There is a vast literature in
which the concept of management of knowledge is hard to distinguish from the management
of information. For example, the general conclusion from one meta-analysis is that much of
what is described as knowledge management is really either management of information or a
description of organisational changes that improved information sharing (Wilson 2002).

We argued in The Work Foundation’s Knowledge Economy Programme interim report (Brinkley
2008) that what distinguishes knowledge from information is the way in which knowledge
empowers actors with the capacity for intellectual or physical activity. Knowledge is a matter of
cognitive capability and enables actors to do and reflect. Information, by contrast, is passive
and meaningless to those without suitable knowledge. Knowledge provides the means by which
information is interpreted and brought to life.



An alternative distinction is between ‘tacit’ and ‘codified’ knowledge (see Lundavall and Johnson
1994 and OECD 1996: 12). The latter can be written down, for example, in manuals, guides,
instructions and statements and is easily reproduced. Tacit knowledge, however, resides
with the individual in the form of expertise and experience that often cannot be written down
and is expensive to transfer to others. In many respects, codified knowledge and information
are indistinguishable. The significant difference is, therefore, between tacit knowledge and
information.

Conceptual definitions of knowledge work
Even with these distinctions in mind, knowledge work remains an elusive concept. Definitions
and descriptions of knowledge work have ranged from the theoretical to the anecdotal and are
very infrequently based on a robust assessment of data on workers and what they actually do.
When data are used, usually proxy measures for highly skilled labour are employed. Depending
what resource we look to for evidence, we might come away thinking that nearly everyone in the
workforce today is a knowledge worker or that almost no one is, with the exception of a select
few.

Several experts have outlined conceptual definitions of knowledge work. For example, Drucker
(1999) focused on the differences between ‘manual worker productivity’ and ‘knowledge worker
productivity.’ The key enablers of the latter include abstractly defined tasks (vs. clearly defined,
delineated tasks), flexible application of knowledge, workers’ autonomy, continuous innovation
and learning into job roles, assessment based on quality (not just quantity) of output and
perceiving workers as organisational assets. While this general outline is useful, Drucker did not
take the additional useful step of specifying the occupations that fit into the knowledge worker
category. One could argue that he simply outlined a more modern conception of a good job
where workers are viewed as more than what they produce.

Robert Reich (1992) was a bit more explicit in outlining what he terms as the ‘symbolic analysts’,
the workers who engage in non-standardised problem solving using a range of analytic tools
often abstract in nature. The keys to these workers’ success include creativity and innovation
and incorporate occupations ranging from lawyers to bankers to researchers to consultants.

Another US-based researcher took a fairly divisive stance on knowledge work by declaring
that, ‘all knowledge work is intellectual work. Thus, a job that is not intellectual enough will not
contribute to knowledge work. Such jobs should not be allowed in a knowledge organisation’
(Amar 2002). The paper argued further that knowledge organisations should only have jobs



that involve at least 50 per cent intellectual content (eg, analysis, decision making, creativity). In
turn, the author suggested that knowledge organisations should do away entirely with traditional
manual jobs that require only physical skills.

It is hard to know whether this should be taken literally or if the argument is that knowledge-
intensive tasks should be shared by all workers. After all, even in knowledge organisations,
knowledge workers need to be supported, offices need to be cleaned and machinery serviced
and so on. This definition would also appear to rule out high-tech manufacturing, including some
of the most R&D intensive companies in the world.

Data-driven definitions of knowledge work
Moving on to more data-driven definitions of knowledge work, some analysts have tried to
describe knowledge workers as all those who work in particular organisations or in particular
sectors or institutions – sometimes under the dubious impression that knowledge workers make
up the overwhelming majority of workers in such industries. However, in practice, organisations
in these industries need to deploy a wide range of complementary jobs with varying degrees of
intellectual content.

Another class of proxies that economists often use for distinguishing knowledge workers
is based on the investment expenditures in activities such as education and research and
development. In line with this approach, one of the definitions of knowledge workers that The
Work Foundation (TWF) has been using so far for their research is university graduates as a
proxy for highly-skilled workers and investment in human capital.

There has been a strong association between the rise of employment in knowledge intensive
industries and the employment of graduates in the workforce. There has also been a major shift
in the share of the workforce with some form of qualification across all sectors of the economy.
As Figure 1.2 below shows, in 1970, for example, less than 10 per cent of the workforce had a
degree and 60 per cent of people in work had had only basic schooling. By 2005 the share of
graduates had increased to around 19 per cent, while the share of people with no qualifications
had fallen to 12 per cent. The latest figures show that graduate employment accounted for just
under 23 per cent of workers in the UK.



Figure 1.2: Shares of graduates and workers with only basic schooling in UK workforce,
1970-2006

70
Degree holder
60 No qualification

50

40

30

20

10

0
70

72

74

76

78

80

82

84

86

88

90

92

94

96

98

00

02

04
19

19

19

19

19

19

19

19

19

19

19

19

19

19

19

20

20

20
Source: EU KLEMS Database

Economists often suggest that knowledge economies need to invest in skills at all levels – from
improving basic numeracy and literacy to expanding the share of young people entering the
university system, strengthening vocational skills, and promoting life-long learning. However, it
has typically only been investment in higher education that has defined knowledge work.

The premise underlying these measures of knowledge work is that in advanced industrialised
economies investment in higher education earns economic returns in the form of higher wages,
and hence knowledge workers are those with at least a graduate-level education.

The World Bank’s Knowledge Economy Index (KEI) uses the distinction between information
and knowledge to separate investment in basic education and higher education (Chen
and Dahlman 2005). Basic education is required to use and process information. Higher
level education is required for what the Bank calls, ‘the production of new knowledge and
its adaptation to a particular economic setting’ (p. 5). The OECD’s composite indicator of
knowledge investment similarly includes includes spending on higher education as a share of
GDP.4

However, it is less clear whether such distinctions can be easily made for vocational skills. The
evidence suggests that while lower level vocational skills may have relatively little impact on

4
OECD Science and Technology indicators. The other components are investment in ICT and R&D



wages, higher level vocational skills undoubtedly offer an economic return even if it is not as
significant as from higher education. And it would be hard to argue that the more sophisticated
vocational skills – for example, in diagnostic work – are not also engaged in the production and
adaptation of new knowledge.

Other proxies for knowledge work and workers have focused more narrowly on the link between
investment in scientific and technical skills and technological innovation. The narrowest
measure is the share of workers in R&D: typically, these more specialist types of knowledge
workers account for between 1 and 1.5 per cent of the workforce across the major OECD
economies even using the wider OECD definition that includes support technicians. A wider
measure is the share of workers with a science, technology, engineering or mathematics degree
(STEM graduates). Both can be used as a proxy for the ability of an economy to generate and
absorb technological innovations.5

Job-content definitions of knowledge work
A final approach to defining knowledge work has been to look at the sort of jobs that people do.
Here we see a very wide variety of examples. Suff and Reilly (2005) provide a useful summary
of some of the approaches adopted. Most studies give examples of managerial professional and
associate professional workers and often concentrate on particular groups. For example, a 2007
report on ‘enterprise knowledge workers’ was based on a sample survey of senior business
executives and managers (Economist Intelligence Unit 2007).

Broader measures of knowledge workers have been based on occupational classifications
within the official statistics. One of the more widely used measures adopted by The Work
Foundation has been to group together the three top occupational groups of managers,
professionals and associate professionals. These are jobs that, at least traditionally, require a
certain level of educational and/or vocational training and are the least likely to be affected by
technological advances and competition from low-wage manufacturing imports. Using this broad
stroke definition, 42.5 per cent of the workforce would be classified as a knowledge worker in
2007.

This broad classification has the virtue of providing readily available statistics on the extent and
growth of knowledge work. But it is also clear that some of the classifications do not work well.
The job title ‘manager’ is applied to a much wider range of jobs in the UK than elsewhere in
Europe, likely including many relatively low paid, basic supervisory roles (European Foundation
for the Improvement of Living and Working Conditions 2007). The category ‘managers,

5
Also referred to as HRST (human resources in science or technology)



legislators and senior officials’ accounts for about 15 per cent of the UK and the US work forces,
but less than 10 per cent in Germany, France, Italy and Spain, according to estimates by the
ILO (all figures 2007 or latest available). Moreover, other job categories are also likely to include
people undertaking similar tasks to those within the top three occupational groups.

More sophisticated approaches by researchers in Australia, the US and the UK have regrouped
the existing statistical occupational codes (Webster 1999; Autor, Levy, and Murnane 2003; Elias
and Purcell 2004).

The Australian research was primarily interested in trying to measure the production of
intangible ‘intellectual’ assets, and so regrouped occupations according to whether they were
associated with the production of such assets (Webster 1999). A further distinction was made
between workers that directly produce intangible assets for others including teachers, sales and
marketing workers, consultants, researchers and financial advisors. These workers also include
those who acquire and use skills, knowledge and talent to make a contribution to the goodwill or
efficiency of their firms including medical staff, scientists, managers and engineers.

The US researchers were interested in the impact of computerisation on the workforce (Autor,
Levy, and Murnane 2003). Notably, they wished to assess whether computers were more
substitutable for routine than non-routine forms of work. To do so, the researchers took the
existing statistical occupational codes and recategorised jobs into five groups based on the
degree of computer substitution and adherence to strict rules – both proxies for more routine
forms of work. The groups included:

1. Expert thinking: includes solving problems outside of rules based solutions, with
computers assisting but not substituting. As well as high level research and creative
work, this might also include the mechanic who is able to identify a solution to a
problem that computer based diagnostics could not.

2. Complex communication: includes interacting with other people to acquire or convey
information and persuading others of their implications, with computers assisting
but unlikely to replace – examples might include some managers, teachers and
salespeople.

3. Routine cognitive: includes mental tasks closely described by rules such as routine form
processing and filling, often vulnerable to computerisation.



4. Routine manual: includes physical tasks closely described by rules, such as assembly
line work and packaging, that may be replaced by machines.

5. Non-routine manual: includes physical tasks hard to define by rules because they
require fine optical or muscle control such as truck-driving and cleaning, and unlikely to
be either assisted or replaced by computers.

This delineation recognises the importance of workers’ inputs and serves as a useful guide
for understanding the types of job roles that are unaffected or even enhanced by mass
computerisation relative to the jobs that have become less relevant to the economy. From this,
we can argue that knowledge work goes beyond basic processing of information and cannot
be based on strict adherence to rules; in other words, it can be assisted and enhanced, but
not replaced, by computers. Thus, expert thinking, complex communication and analytical
reasoning – defined by the authors as making effective oral and written arguments – help define
knowledge work, as opposed to the routine cognitive along with routine and non-routine manual
categories.

Finally, UK research focuses on the links between occupations and graduate qualifications
(Elias and Purcell 2004). Over time, the researchers have assessed the average educational
attainment of workers in each of the minor occupational groups (ie, 371 occupations in total),
accounting for workers’ age given the increase in degree holders over time. Based on this
analysis, five umbrella groups of occupations based on educational qualifications were created:

1. Traditional graduate occupations: includes professions that historically have required an
undergraduate degree (eg, solicitors, scientists, doctors, teachers).

2. Modern graduate occupations: includes newer professions that graduates have been
entering since the 1960s (eg, chief executives, software professionals, writers).

3. New graduate occupations: includes occupations where entry-level has recently shifted
to incorporate degree holders (eg, marketing and sales managers, physiotherapists,
welfare officers, park rangers).

4. Niche graduate occupations: includes jobs where majority of entry-level workers are not
graduates, but there is a growing number of specialists who do come in with degrees
(eg, sports managers, hotel managers, nurses, retail managers).



5. Non-graduate jobs: includes professions where a graduate degree is not required and
most employees do not have degrees.

Similar to the US approach, this methodology directly incorporates the changing nature of the
labour market to analyse how occupations shift over time.

These three categorisations get us closer to what knowledge work might be, but they are still
constrained by the existing occupational codes. In all three studies, there was a strong overlap
between the sort of jobs that were classified as producing intellectual assets or associated with
expert thinking and complex communication skills or affiliated with graduate workers and the top
three occupational codes.

At one level this is reassuring: it suggests the top three occupational codes are capturing many
‘knowledge work’ jobs and so serve as a reasonable proxy. At the same time, it is important to
keep in mind that they are proxy measures nonetheless and hence only give us a partial picture
of knowledge work in today’s economy.

To sum up, what is missing from all of these attempts at defining knowledge work is a thorough
analysis of workers themselves and what they do at work. Moreover, different definitions provide
fairly divergent estimations of the size of the knowledge workforce in the UK. For example,
graduate employment in the UK in 2008 was just over 20 per cent of the workforce, while the
top three occupations (managers, professionals, associate professional and technical) account
for over 40 per cent. As we describe in more detail later in this report, the aim of the present
study is to focus directly on a large sample of UK workers to better understand the key tasks
and activities that make up their daily working life and develop a more robust measure of
knowledge work within the economy.


2. Redefining knowledge work and knowledge workers

This section develops our definition of knowledge work and knowledge workers. We do that in
three stages:

• First, we discuss the technical aspects of our survey and its analysis, and how we
reclassify the workforce into task-based ‘clusters’ on the basis of the distinguishing
features in the jobs they do.

• Secondly, we identify the different sorts of knowledge content within each of our
clusters, allowing us to identify these task-based characteristics that distinguish
knowledge work.

• Thirdly, we use our new definition of knowledge work to provide a cross-sectional
picture of the UK’s workforce today and how the new definition measures up against
previous definitions.
6
Research We performed our analysis in several steps. We started off by conducting a survey of, among
design6
others, the tasks that people employed in the knowledge economy frequently do at work. We
presented our survey respondents with a list of 186 tasks and asked them to rate how frequently
they perform each of them. We then analysed this survey information along two lines. On the
one hand, and to make our data more easily manageable, we identified groups of tasks, (eg
data analysis, administrative tasks, people management, maintenance moving and repairing)
that were frequently performed together by the same survey participants. On the other hand, we
identified groups of workers depending on how frequently they performed particular groups of
tasks.

In addition, our survey provided information on the use of technology, the methods of sharing
and acquiring knowledge and the complexity of the tasks that the participants perform at work.
The survey information allowed us to come up with a fresh taxonomy of both the types of tasks
that characterise work in the knowledge economy and the different groups of workers within the
labour force. In what follows, we present some important details on the methods we used and
then discuss our results regarding the definition of work in the knowledge economy.

6
Readers who are not interested in the specific technical details of our methodology can largely omit reading this sub-
section in full without losing track of our analysis


Redefining knowledge work and knowledge workers

Our survey
Our knowledge workers’ survey was designed in four phases.

First, we conducted an extensive literature review of existing sources on job and task analysis,
job content and job design. From this review, we compiled an initial list of approximately
125 work-related tasks or activities featuring manual tasks, cognitive tasks, social tasks and
technical tasks, to name a few.

Second, we conducted qualitative case studies of workers in two knowledge-based
organisations. For these case studies, we conducted focus groups and interviews with more
than 40 workers employed in a range of jobs within the organisations.

Third, we collated the evidence to finalise our list of tasks and activities for a pilot version of the
survey. The initial survey included 138 work-related tasks and activities as well as additional
items on workers’ background and job characteristics, features of job quality and working
conditions and work-related outcomes. The pilot survey was distributed to 200 workers who
participated in an online panel. Participants were required to work at least 20 hours per week in
one job, although they could have more than one job.

Finally, based on the evidence from the pilot study, we revised our survey further, incorporating
more work-related tasks and activities and deleting the tasks that did not appear to distinguish
workers. Our final survey comprised 186 work-related tasks and activities. The full list of the 186
work-related tasks and activities is provided in Appendix A.

The survey was sent out to 2,011 online panel respondents. All participants had to be working in
at least one job for a minimum of 20 hours per week for at least 3 months. Descriptive statistics
for the sample are found in Appendix B. With a few exceptions, our sample demographics were
comparable to those found in the 2007 Labour Force Survey (LFS) data. Our sample included
slightly more workers in the managers and senior officials along with administrative and
secretarial occupational categories than LFS estimates, and slightly fewer skilled tradespeople
and workers in elementary occupations. We captured a range of demographic and background
information about respondents as well as both general and specific characteristics of their jobs.
Appendix C provides a summary of these variables. The respondents indicated the frequency
with which they engaged in each of the tasks on a 4-point scale ranging from 1=never to
4=often.



Exploring work tasks in the knowledge economy: Factor analysis
To help make our data analysis more manageable, we ran an exploratory factor analysis (EFA).7
Our ultimate goal is to classify the respondents of our survey into groups depending on the
tasks they perform most frequently. Given that the list of tasks on whose frequency we asked
them to report was a long one, exploratory factor analysis helped us to shorten it by grouping
the tasks into 10 groups. For that purpose, this technique used the responses of our survey
participants on how frequently they perform each of the tasks to group these tasks into a few
distinct groups (‘factors’). The factors with sample tasks are detailed below with the figures in
brackets detailing the number of tasks from the original list that were included in the relevant
group (see Table 2.1 on the next page).

Each of the 10 factors was created by computing the mean of the relevant items. Figure 2.1
below displays the average factor scores across the full sample, that is, the average frequency
with which the tasks classified under each of the factors (groups) were performed in our
sample of workers. A score of one means the task is not very common across the sample –
either because it is rarely performed or because it tends to be confined to a specialist group of
workers. A score of four means it is very widely performed across the sample of workers. So for
example, people management tasks, data and analytical tasks, and administrative tasks are the
most frequently performed. In contrast, personal and domestic tasks, creative tasks and caring
tasks are the least frequently performed across the sample as a whole.

The high frequency of people management tasks and of data manipulation and analysis
underlines the emphasis of the knowledge economy in tacit knowledge that resides with
individuals and in information. The high prevalence of these tasks is consistent with the
importance of investment in both human capital and in software and computerised databases
in the UK economy.8 Data processing and analysis tasks are quite wide-ranging, spanning from
specialist analysis to mere data entering.

On the other hand, the relatively low incidence of care and creative tasks might seem surprising
given the large numbers employed in care-based industries and occupations and in the creative

7
In general, factor analysis is a statistical technique used to explain variability among a set of ‘observed’ variables (ie,
the 186 tasks in this case) through the creation of fewer ‘unobserved’ variables called factors or latent variables. By
finding the commonalities between different sets of items, we can effectively collapse our 186 individual items into a
more analysable set of factors. EFA was used in the first instance to get a sense of the number of factors comprised in
the 186 items as well as to identify the items that were poor factor indicators (ie, items that do not load on any factor or
load onto more than one factor). Confirmatory factor analysis (CFA) was subsequently used to validate the hypothesised
factor structure and our model exhibited adequate fit. The analysis suggested that 126 of the 186 tasks in our survey
could be collapsed into 10 distinct factors. The 60 excluded items tended to be very general types of tasks and activities
that most workers engaged in
8
HMT October 2007.Intangibles and Britain’s productivity performance



Table 2.1: Task factors with sample items

Factor Sample items
Data processing Compile data; Statistically analyse data; Identify patterns in data/information;
and analysis (9) Interpret charts/graphs; Enter data

Leadership & Make strategic decisions; Develop organisational vision; Identify issues that will
development (28) affect the long-term future of organisation; Foresee future business/financial
opportunities; Manage strategic relationships
Administrative Manage diaries; Order merchandise; Organise/send out mass mailings; Make and
tasks (10) confirm reservations; Sort post
Perceptual & Judge speed of moving objects; Visually identify objects; Judge which of several
precision tasks objects is closer or farther away; Judge distances; Know you location in relation to
(11) the environment or know where objects are in relation to you
Work with food, Clean/wash; Prepare/cook/bake food; Stock shelves with products/merchandise;
products or Gather and remove refuse; Serve food and beverage
merchandise (5)
People Assign people to tasks; Manage people; Teach others; Motivate others; Mentor
management (16) people in your organisation
Creative tasks Create artistic objects/works; Use devices that you draw with; Take ideas and turn
(10) them into new products; Take photographs; Engage in graphic design
Caring for others Provide care for others; Dispense medication; Diagnose and treat diseases,
(5) illnesses, injuries or mental dysfunctions; Expose self to disease and infections;
Administer first aid
Maintenance, Install objects/equipment; Use tools that perform precise operations; Use hand-
moving & powered saws and drills; Test, monitor or calibrate equipment; Take equipment
repairing (18) apart or assemble it
Personal, animal Excavate; Dig; Plant/maintain trees, shrubs, flowers, etc.; Feed/water/groom/
and home bathe/exercise animals; Sew/knit/weave
maintenance (14)

and cultural industries9. The former reflects the fact that care-related tasks are relatively
specialised, so are not frequently used at work outside the health and social care area. The
low incidence of creative tasks also reflects the fact that these tasks are relatively specialised.
Moreover, a common feature of sectors such as creative and cultural industries is that they
generate large numbers of jobs for people in non-creative roles, so even within these industries
the number of people working in specialised creative tasks may be relatively small.

About 17 per cent of the tasks originally included in the survey were excluded from the final
identification of group (factor) tasks. These excluded tasks are reported at the end of Appendix
A. In most cases, tasks were excluded from factor analysis, because they were too common

9
The Work Foundation, 2007 Staying Ahead: the economic performance of the UK’s creative industries



across these groups to be classified under a specific group or another. Notable examples fall
under various forms of communication, collaboration, advice giving and problem solving. In
other words, these tasks are so common they do not help us differentiate between workers who
can be described as knowledge workers and other groups in the workforce. However, there
were also tasks, most notably falling under ‘creative tasks’, that the survey participants hardly
reported to perform with any frequency.

Figure 2.1: What work tasks are most common across the workforce?
2.5

2.1
2.0 1.9
1.7
Mean frequency 1 to 4

1.5 1.4 1.4 1.4
1.3 1.3
1.2
1.1
1.0

0.5

0.0
t

g
is

s

ts

n

ip

g

e

tic
en

sk

iv
io

in

in
ys

uc

sh

es
at
em

is

ar

ov
ta
al

od

er

re

m
ec

C

m
an

in
ag

ad

do
C
pr

pr
dm

r&
an

Le
&

ith

&
&

ai
ng

A
m

al
n
w

ep
tio
le

si

on
k

R
op

es

or

ep

rs
W
oc
Pe

Pe
rc
pr

Pe
a
at
D

Source: Knowledge Workers Survey, The Work Foundation, 2008

Note: 1 = least common, 4 = most common

Exploring the different types of workers in the knowledge economy: Cluster analysis
Having identified the broad types of tasks that workers in the knowledge economy perform, we
then proceeded to creating a new taxonomy of workers based on what they actually do in their
jobs on a day-to-day basis. Using the 10 task factors, we ran a cluster analysis, a technique
used to identify homogenous subgroups within our sample of UK workers. What the analysis
does is create groups or clusters of workers based on commonalties of task content and



frequency. Thus, our worker clusters are entirely based on workers’ reported tasks and activities
on the job.10

The novelty of our results is that our taxonomy cuts across classifications of workers according
to their educational attainment and occupation, that is, the proxies used in previous research for
identifying knowledge workers.

Based on the task factors, 1,744 of the 2,011 (87 per cent) workers in our sample best fit into
seven worker clusters. The analysis revealed that 267 workers reported very high frequencies
on each of the tasks (ie, 1-2 standard deviations above the mean) and were identified as
outliers. These workers were subsequently omitted from the analytic sample.11 The composition
of the seven clusters is detailed below. Appendix D presents the average factor scores within
each of the seven clusters.

The list below offers a snapshot of each of the seven cluster groups. We provide in parentheses
the share of workers in the sample that is classified under each cluster. We detail the most
common groups of tasks (as identified in our factor analysis) in each of the seven clusters as
well as the five specific tasks that workers engage in most frequently in their jobs. We list five
minor occupations that workers are classified in to give a sense of the occupational variability in
the worker clusters.

• Leaders and innovators (11 per cent)
◦ Frequently performed tasks: Data and analysis, leadership and development, people
management.
◦ Occasionally performed tasks: Administrative tasks, creative tasks.
◦ Specific tasks: Collaborate with people inside organisation on project/programme,
analyse information to address work-related problems, manage people, write reports,
provide consultation/advice to others.
◦ Example occupations: Production and functional managers, financial institution and
office managers, business and finance associate professionals.

10
We first ran a two-step cluster analysis to identify any outliers in the sample as well as to get an estimate of the
optimal number of clusters in the sample. Based on this initial analysis, we subsequently ran a k-means cluster analysis
specifying seven clusters. We also ran a latent class analysis and found that the seven cluster solution best fit the data.
The clusters used in the remainder of the report are based on the k-means analysis
11
We examined the individual background characteristics of this omitted group and found that the omitted group was
more likely to be male and more likely to have been at their current organisations for 20 years or more relative to the
average. No other significant differences were observed



• Experts and Analysts (22.1 per cent)
◦ Frequently performed tasks: Data and analysis, people management.
◦ Occasionally performed tasks: Leadership and development, administrative tasks.
◦ Specific tasks: Collaborate with people inside organisation on project/programme,
enter data, compile data, analyse information to address work-related problems, write
reports.
◦ Example occupations: ICT professionals, teaching professionals, managers and
proprietors in service industries, research professionals, customer service occupations.

• Information handlers (12.8 per cent)
◦ Frequently performed tasks: Administrative tasks.
◦ Occasionally performed tasks: People management, data and analysis.
◦ Specific tasks: File (physically/electronically), sort post, manage diaries, enter data,
handle complaints, settle disputes and resolve grievances.
◦ Example occupations: General administrative occupations, secretarial occupations,
financial institution and office managers, managers and proprietors in service industries,
financial administrative occupations.

• Care and welfare workers (7.5 per cent)
◦ Frequently performed tasks: Caring for others, people management, work with food,
products or merchandise.
◦ Occasionally performed tasks: Data and analysis, administrative tasks, perceptual
and precision tasks.
◦ Specific tasks: Provide care for others, administer first aid, clean/wash, dispense
medications, expose self to disease/infections, write reports.
◦ Example occupations: Care associate professionals, care services, childcare
services, social welfare associate professionals.

• Servers and sellers (7.0 per cent)
◦ Frequently performed tasks: Work with food, products or merchandise, people
management, administrative tasks.
◦ Occasionally performed tasks: Data and analysis, perceptual and precision tasks,
leadership and development.
◦ Specific tasks: Clean/wash, handle complaints, settle disputes and resolve
grievances, manage people, stock shelves with products or merchandise, order
merchandise.



◦ Example occupations: Managers in distribution, storage and retailing, managers
and proprietors in hospitality and leisure services, food preparation trades, elementary
personal services.

• Maintenance and logistics operators (11.3 per cent)
◦ Frequently performed tasks: Perceptual and precision tasks, maintenance, moving
and repairing.
◦ Occasionally performed tasks: People management, work with food, products or
merchandise, data and analysis, administrative tasks.
◦ Specific tasks: Visually identify objects, know location in relation to the environment
or know where objects are in relation to you, judge distances, lift heavy objects, load/
unload equipment/materials/luggage.
◦ Example occupations: Protective services, security occupations, transport drivers,
metal machining, fitting and instrument making trades, science and engineering
technicians, construction trades.

• Assistants and clerks (28.3 per cent)
◦ Occasionally performed tasks: People management, data and analysis, work with
food, products or merchandise, administrative tasks.
◦ Specific tasks: Handle complaints, settle disputes and resolve grievances, collaborate
with people inside organisation on project/programme, teach others, clean/wash, coach
or develop others, provide consultation/advice to others, motivate others.
◦ Example occupations: Customer service occupations, sales assistants and retail
cashiers.

The assistants and clerks cluster was the least well-defined group of workers as its members
tended to report engaging in all but the most general tasks relatively infrequently in their jobs.
We explored the specific occupations of this group to see if we had systematically omitted
relevant tasks and found this not to be the case.

To sum up, the results of our cluster analysis have allowed us to make a first attempt at
classifying workers in the knowledge economy on the basis of what they do. In what follows we
try to refine this classification in order to gain a better understanding of the cognitive complexity
of the tasks that workers belonging to different clusters perform most frequently and the sectors
in which they are employed.



Bright minds The next stage was to gauge the cognitive complexity of the tasks that workers in different
and powerful clusters mostly perform. This helped us distinguish, for example, between basic processing
machines for tasks such as data processing from higher level analytical tasks. We used three of their work
tasks of varying characteristics for which we got information through our survey:
complexity
• First, the extent to and ways in which workers in various clusters use (IT) technology.
• Secondly, the type of and variability in methods of sharing and capturing knowledge and
ideas when performing new tasks.
• Thirdly, the perception of workers about the complexity of the tasks that they have to
perform at work.

The assumptions that underlie the selection of these three criteria are that frequent and
specialist use of computing technology and frequent use of methods of sharing and garnering
new knowledge involving direct human interaction will characterise clusters of workers that
perform more tacit knowledge-intensive tasks. Similarly, the perceived complexity of tasks will
be higher for those clusters of workers that perform more tacit-knowledge-intensive work.

One of the hallmarks of the knowledge economy, and indeed one of its key enablers, is
the ubiquity of computing technology. In addition to facilitating work processing and email
communications, computers have sped up processing times for many work-related tasks,
thereby increasing workers’ efficiency or to engage in more difficult tasks that were not possible
previously.

We captured the importance of computing technology for the tasks that our survey respondents
perform by asking them two questions as part of our survey. First, we enquired how often
they use a computer at work. Across the full sample, workers reported using the computer 3-4
times per week on average. Secondly, we asked respondents to choose from a list of 12 tasks/
activities those that they do on their computer at work.

As seen in Figure 2.2 below, there was significant variation in the reported frequency of usage
and variability of activities performed on computers, suggesting varying degrees of importance
of information technology in workers’ jobs.

Those who used computers most frequently and for the greatest number of tasks were leaders
and innovators, experts and analysts and information handlers. They used computers daily in
their jobs, while performing an above average number of tasks on them. At the other extreme,



Figure 2.2: Number of different computer uses and how often computers are used each
week

8
Index frequency 1 to 5; number of uses 0 to 12

7.2 Number of computer uses Frequency of use each week
7
6.3
6 5.5
5.2
5.0 4.9 4.8
5
4.2 4.2
4 3.7 3.7 3.6 3.6 3.5
3.4
2.9
3

2

1

0
Innovators Experts Info All groups Assistants Servers Care and Operatives
handlers and clerks welfare
workers
Worker clusters

maintenance and logistics operators reported using computers once or twice per week to
perform around three tasks on average.

Tasks such as email, word processing, internet research, spreadsheet calculation, presentations
and managing diaries emerged as the most common work-related uses of computers across
worker clusters. Most of these tasks are relatively basic and likely follow an explicit set of rules.
Possible exceptions are internet research, spreadsheet calculation and presentations, which
can vary substantially on difficulty (eg, depending on whether a worker designs his/her own
presentation or types up someone else’s).

On the other hand, more specialist tasks such as statistics, system maintenance, graphic design
and software design are less common and likely to require expertise that is independent of the
technology itself. A recent study examining computer usage in the UK reported that only about
a quarter of workers used computers for complex or advanced tasks (Green et al. 2007). Our
estimates (shown in Figure 2.3 below) suggested the use of computers for specialist tasks
ranged from just 10 per cent in the case of care and welfare workers to 60 per cent for leaders
and innovators.



Figure 2.3: Share of workers that frequently perform at least one specialist computer task

70.0%
60.4%
60.0%
51.8%
50.0%

40.0% 35.0%
30.4%
30.0%
23.7% 22.5% 22.2%
20.0%
9.9%
10.0%

0.0%
Innovators Experts Total Operatives Info Servers Assistants Care and
handlers and and clerks welfare
sellers workers

Worker clusters

The extent of use of information technology in combination with the extent to which it is used
for performing specialist tasks suggest a distinction between, on the one hand, leaders and
innovators and experts and analysts and, on the other hand, the rest of the worker clusters.
According to this criterion, the workers in the former three clusters seem to perform the more
tacit-knowledge-intensive tasks12 compared to the workers in the rest of the clusters. However,
this criterion is not sufficient for refining the clusters of workers in terms of the required level of
knowledge, as, by nature, the tasks of clusters such as carers focus more on work with humans
rather than information alone (as eg, in the case of information handlers).

Workers were also asked to identify the range of methods they use to share and capture
knowledge in two contexts:
1. When performing a new task at work;13
2. When sharing information with others.

These results, illustrated in Table 2.2 below, suggest that the leaders and innovators cluster
displayed the most versatility and variety in the methods used for that purpose. Experts and
analysts and, to a lesser extent, care and welfare workers also used a wide array of methods.
These findings confirm that the clusters of leaders and innovators and experts and analysts
include the workers that are most likely to frequently perform (tacit) knowledge intensive tasks,
while assistants and clerks and maintenance and logistics operators are the least likely.

12
In Section 1, we distinguished tacit knowledge from codified knowledge or information. The latter is easily reproduced
through eg manuals and guides. The former resides with the individual in the form of expertise and/or experience and
for that, it is more expensive to transfer across workers
13
Only 6 per cent of the sample reported not ever having to do new tasks on the job



Table 2.2: Number of methods used to acquire new information and learn new tasks

Task based groups Acquiring new information Learning new tasks
(0 to 9) (0 to 16)
Leaders and innovators 7.4 4.6
Experts and analysts 6.0 3.4
Information handlers 5.2 2.8
Assistants and clerks 4.9 2.7
Servers and sellers 4.6 2.7
Care and welfare 4.6 2.3
Maintenance and logistics 4.1 2.1
Average all groups 3.3 1.8

Source: Knowledge Worker Survey, The Work Foundation, 2008

Evidence that further supports this picture is provided by the average frequency with which
the task of ‘teach others’ has been reported across clusters (see Figure 2.4 below). The more
abstract and tacit the knowledge that workers use is, the more it has to be developed through
experience and human interaction, for which teaching is an important means. This task is part
of the ‘people management’ group of tasks that workers across all clusters (but assistants and
clerks) report relatively frequently. However, there is some variety in the average frequency with
which workers report ‘teach others’ as part of what they do. The reported frequency of this task
is relatively higher in clusters such as ‘leaders and innovators’, ‘experts and analysts’, ‘care and
welfare workers’.

Moreover, there are differences in the consistency with which this task is reported as a
frequently performed one14 across clusters with similar average frequency, suggesting for
example teaching others is more common within the experts and analysts cluster than it is
within servers and sellers.

More generally, the responses of our survey participants point to a high level of ‘tacit’ knowledge
within workplaces, ie of knowledge that resides with individuals. This finding underlines how
important social relations still are within the workplace for sharing and capturing knowledge, with
informal discussions with colleagues, supervisors and managers and less specific socialising
and conversing with others amongst the most frequent. Rather less frequent but still cited by

14
That is, there is variation in the standard deviation of the reported values



Figure 2.4: Importance of ‘teach others’ task for different clusters

Variability in frequency of ‘teaching others’ Mean reported frequency of ‘teaching others’
3.5
3.3
Index frequency 1 to 5; number of uses 0 to 12

3.0
2.8 2.8
2.6
2.5
2.3

2.0
2.0 1.8

1.5
1.08 1.04
1.0 0.91 0.91 0.88 0.89
0.85

0.5

0.0
Innovators Experts Servers and Care and Operatives Info Assistants
sellers welfare handlers and clerks
workers

Worker clusters

nearly 30 per cent of the sample were more informal debates and discussion through
‘brainstorm’ or ‘white board’ meetings.

That said, large numbers of workers also relied on more codified forms of knowledge such as
the internet/intranet and printed material such as procedural and technical manuals, and trade
magazines and journals.

Finally, we also asked the survey participants to identify how complex they perceive their
work tasks to be. Leaders and innovators and experts and analysts all reported higher than
sample average complexity in their tasks. The complexity of tasks performed by information
handlers and care and welfare workers was of average complexity, closely followed by the tasks
performed by sellers and servers and maintenance and logistics operators. At the other end,
assistants and clerks reported the lowest task complexity scores in the sample.



Table 2.3: Prevalence of methods used for sharing and capturing knowledge

Publish written material 15%
Attend induction meetings 18%
Attend events/trade shows 21%
Contact a chat/information exchange group 23%
Read professional journals/trade magazines 26%
Attend an external training session 26%
Hold ‘brainstorming’ or ‘whiteboard’ meetings 29%
Read technical material 34%
Talk to outside experts 34%
Use the intranet 36%
Attend an internal training session 42%
Read procedure manual 43%
Socialise/converse with others 44%
Ask supervisor/manager 60%
Use the internet 60%
Talk informally to colleagues 90%

Figure 2.5: Perceived complexity of tasks performed by surveyed workers

3.0
2.6
2.4
2.5
Job complexity 1 to 3

2.1
1.9
2.0 1.8 1.7
1.6
1.5 1.3

1.0

0.5

0.0
Innovators Experts Info Care and Servers Operatives Total Assistants
handlers welfare and and clerks
workers sellers

Worker clusters


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