An Exploration of Personal Factors Influencing Disposition towards Knowledge Sharing in a South African Context.

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Nov 6, 2013 (4 years and 1 day ago)

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1

An Exploration of Personal Factors Influencing
Disposition towards Knowledge Sharing in a South
African Context.


Valentine Brink & Jean
-
Paul Van Belle

Department of Information Systems,
University of Cape Town

<jvbelle@commerce.uct.ac.za>


Summary


The ai
m of this re
search

wa
s to develop an understanding of the personal factors
influencing an individual’s attitude towards knowledge sharing behaviour in the context
of South African
larger
organisations. The following
personal factor
s were considered
:
g
ender
,
a
ge,
e
xperience,
e
ducation and
l
evel within
organization.
This research objective
was explored using an empirical survey.
From our survey, we
found
that, although
knowledge sharing is perceived to be the most essential process for knowledge
management, o
nly one factor, i.e. the age of respondent, was shown to

be
statistically
significant
in
affect
ing

an individual’s attitude towards sharing knowledge
. Moreover, the
association was not
a linear
one
.

However, the lack of the influence of various specific
pe
rsonal factor
s

on knowledge sharing attitude can be
construed

as positive. It suggests
that there is no discernable deterministic individual barrier against knowledge sharing
attitudes based on gender, experience or education and gives hope to organisation
s
willing to pursue a knowledge management adoption process.

Introduction

As the 21st century unfolds, knowledge has become the most important product in most
organisations
. Employers are hiring minds, not hands, and knowledge is seen as a
company asset.
G
lobalisation, information technology, communications systems and the
exponential growth of knowledge all contribute to an increasingly complex environment
in which information is abundant and volatile. To keep pace with the environment,
business managers m
ust learn as quickly as it changes. To be more competitive within a
given industry, they need to access the necessary knowledge in more efficient ways than
others do (Bourdreau & Couillard, 1999). According to Tiwana (2000), survival depends
on the best po
ssible response to a multitude of challenges. Managing knowledge means
adding or creating value by actively leveraging know
-
how, judgment, intuition and
experience within and outside the company.

Consequently, organisations are seeking to create systemati
c ways to identify and convert
individual expertise, skills and experience into organisational knowledge. The strategic
management of knowledge resources is regarded as one of the key factors for sustainable
competitive advantage.
A survey of 423 organisat
ions in the UK, mainland Europe and
the USA conducted by KPMG Consulting (1999)
, showed

that
81% of respondents had
undertaken, or were considering implementing a knowledge management programme.


2

What is knowledge management?

Kanter (1999) states that
know
ledge management

can be viewed as turning data (raw
material) into information (finished goods) and from there into knowledge (actionable
finished goods). The implication is that managing knowledge gives one the power to act,
to make decisions that are val
ue producing to the individual and to the company as a
whole.

According to Wiig (1999) the “the goal of Knowledge Management is to build
and exploit intellectual capital effectively and gainfully.” Bock & Kim (2002, 14) and
Tiwana (2000, 4) agree that the
management of organisational knowledge can be
extended to creating business value and sustainable competitive advantage.

Leveraging knowledge is possible only when people value building on each other’s ideas
and sharing their own insights.

Bock & Kim (200
2, 14) and Mayo (2001, 36) emphasise
that, in particular,
knowledge sharing

is perceived to be the most essential process for
knowledge management.

I
n trying to answer the question whether
the organisation
is
ready to become a knowled
ge
organisation (Steve
ns, 2000), it should be acknowledged that this knowledge
management readiness is shaped by the culture of the
organisation (
McDermott, 1999).

However, knowledge sharing is perceived to be the most essential process for knowledge
management. In a survey o
f 260 CEOs and directors in European multinational
organisations conducted by the Financial Times in 1999, 94% of respondents answered
that people should share what they know with others in the organisation (Bock & Kim,
2002, 14). However, Davenport (1998)

argues that sharing of knowledge is often an
unnatural act. People will not share their knowledge as they think their knowledge is
important and valuable to them. A 1997 survey by the Ernst & Young Centre for
Business Innovation, cited by Ruggles (1999, 8
6), showed that the biggest difficulty in
managing knowledge was changing people’s behaviour.

Therefore, organisations need to examine people’s attitudes towards and habits
concerning knowledge sharing in the company and learn with whom the employees
col
laborate, how they get the information they need, whether and when they document
their own knowledge and how they store and distribute knowledge.
Hence, the aim of this
research is to develop an understanding of the personal factors influencing an individu
al’s
attitude towards knowledge sharing behaviour in an organisational context.

Factors Influencing Knowledge Sharing

Personal factors influencing knowledge sharing

Sharing knowledge is an unnatural act. People question why they have to share
knowledge as
this is a valuable resource, and sharing it may put their jobs at risk if other
people use their knowledge. Therefore the natural tendency is to hoard knowledge
(Davenport, 1998; Probst et al., 2000, 193; Tiwana, 2000, 37). According to Harris
(1998), empl
oyees perform their work and advance their careers by keeping their
knowledge to themselves for their own benefit, rather than sharing it with others.
Knowledge management demands a shift to a culture where collaboration, knowledge
sharing and team achieve
ment are valued equally with competition and individual
achievement. Frick (1998) further states that the key to a successful knowledge
management implementation is the shift to a belief that knowledge sharing is power.

Education and training


3

According to
Mayo (2001, 27) visionary companies, with an average lifespan of nearly
100 years, demonstrate a continuing focus on intellectual capital by focusing on
employee development. Sveiby (1997, 169)
argues

that in knowledge companies, which
depend heavily on th
e knowledge and competence of their employees, competence
development ought to be an item in which the company invests heavily, and it usually
does. Sveiby (1997, 168) also
found

that the educational level of the professionals
employed affects the assessme
nt of their competence and thus the company’s ability to
achieve future success.

Probst et al. (2000, 175) emphasise that it is important to keep employees’ competencies
at a constant high level, especially in dynamic, knowledge
-
intensive industries.

Expe
rience and skills

According to Davenport & Prusak (1998, 7)
, Mayo

(2001, 36) and Tiwana (2000, 68),
knowledge is largely derived from experience, which leads to sound judgement and
wisdom


the knowledge that is used in making future decisions. Being able
to transfer
knowledge implies that experiential knowledge also gets transferred to the recipient. The
benefit of experience lies in the fact that it provides a historical perspective that helps
people better understand present situations. Experienced peopl
e are usually valued in a
company (and are often paid more) because they possess this historical perspective from
which they can view current situations


something that a typical newcomer will almost
never have.

Attitudes, values and trust

Attitudes are c
losely related to values, and are about how people view their world. They
are shaped by education, by the environment and its demands, and by the culture to
which people belong. Values and attitudes shape many of the ways a person behaves.
Values reflect a

person’s ethos about their work and their interaction with the people
connected with it. They rarely change. Attitudes can change where people see that it is
necessary (Mayo, 2001, 104).

Probst et al. (2000, vii) suggest that the easiest way to approach t
he subject of knowledge
management is for individuals to make themselves aware of how they deal with their own
knowledge and emphasise that an atmosphere of trust is essential for the sharing of
knowledge, but add that it is difficult to create.

Organisati
onal factors influencing knowledge sharing

Size and structure

Organisations need to know what they know and use that knowledge effectively. Size and
geographic dispersion can make it difficult to locate existing knowledge and get it to
where it is needed.
In small
-
localised organisations, a manager probably knows who has
experience in a particular aspect of the business, and can walk across the passage and talk
to him or her. Alternatively, managers in large organisations know how common it is to
reinvent t
he wheel, solving the same problem from scratch, duplicating effort because
knowledge already developed has not been shared within the company (Davenport &
Prusak, 1998, 17).



4

According to Bourdreau & Couillard (1999), traditional top
-
down hierarchies are
not
conducive to the development of knowledge organisations. They typically discourage
individual thinking, creativity and initiative. New organisational structures designed
around teamwork, self
-
managed teams, and overlapping responsibilities facilitate
k
nowledge sharing and development. However, Stewart (1997, 130) warns against these
new organisational structures creating their own difficulties, where the non
-
management
of intellectual capital could lead to over
-
investing in knowledge. Passion for the va
lue of
intellectual capital should not come at the expense of a basic principle of management,
i.e. using assets more efficiently.

Organisational Culture

Organisational culture refers to the shared values and attitudes of the members of an
organisation (J
arvenpaa & Staples, 2001, 156). Every organisation and subgroup within it
has developed it own culture, which is not easily changed. This can be a significant
barrier to any aspirations, blocking vital change (Gold et al., 2001, 189; Mayo, 2001, 159;
Schei
n, 1992, 5; Shields, 1999).

Harris (1998) reports that cultural changes require 50 % to 70 % of the overall knowledge
management implementation effort, and failure to change culture accounts for at least
50% of knowledge management failures. Similar argume
nts were given by Kluge et al.
(2001, 25) and Tiwana (2000, 94).

Leadership

Schein (1992,

15) points out that culture and leadership are two sides of the same coin in
that leaders create initial cultures when they first create groups and organisations. If
an
organisation has strong values, leaders will be expected to be role models for those
values. In many organisations the chief executive has a pivotal role as the final decision
-
maker and as a model of behaviour. Davenport et al. (1998, 55) found that exe
cutives
who championed knowledge initiatives were usually themselves well read and well
educated; they set the tone for a knowledge
-
oriented culture. However, in organisations
and cultures where hierarchy has less influence, collective leadership and decis
ion
-
making are common (Mayo, 2001, 161).

Theoretical Framework.

As the underlying theoretical model to determine personal influences affecting
knowledge sharing atti
tudes, it is proposed that the T
heory of Reasoned Action
(TRA) be
used.
Davis, Bagozzi and
Warshaw, cited by Bock & Kim (2002, 15), state that a
particularly useful aspect of the Theory of Reasoned Action is that it assumes all other
factors influence behaviour only indirectly, by influencing attitude. Because it has this
explanatory power, the
T
heory
of Reasoned

Action can be a useful model for explaining
knowledge sharing behaviour in organisations.

The Theory of Reasoned Action is a widely accepted model in social psychology, used to
explain virtually any human behaviour

(see Figure 1
). Accor
ding to this theory, a
person’s performance of a specific behaviour is determined by his or her behavioural
intention (I) to perform the behaviour (B = f (I)). Next, the intention is jointly determined
by the person’s attitude (A) and subjective norm (SN)
concerning the behaviour in
question, with relative weights typically estimated by the regression coefficients (I = Aw
1

+ SNw
2
). And then, a person’s attitude toward a behaviour is determined by his or her

5

salient beliefs (b
i
) about the consequences of per
forming the behaviour, multiplied by the
evaluation (e
i
) of those consequences (A=

b
i
e
i
).

Figure
1
: Theory of Reasoned Action

Source: Adapted from Bock & Kim (2002, 15)

Finally, an individual’s subjective norm (SN) is determined by a multiplicative func
tion
of his or her normative beliefs (nb
i
) and motivation to comply (mc
i
) (SN =

nb
i
mc
i
)
(Bock & Kim, 2002, 15).

Research Hypothesis and Methodology.

The objective of this research is to explore the personal factors influencing attitudes
towards knowledge
sharing. The purpose of this study is explanatory
.

The following main
hypothesis can be formulated:

H
1
:

There is
a

relationship between personal factors and an
individual’s attitude towards knowledge sharing.

Various sub
-
hypothesis can be stated by substi
tuting the generic “personal factors” with
specific factors under consideration, namely: Gender, Age, Education, Experience and
Level within organisation.

The research strategy will adopt a quantitative survey
-
based approach

to test the
corresponding null
-
hypotheses
. The survey was administered to participants via e
-
mail
and hand delivery. The companies chosen had implemented, or were in the process of
implementing, knowledge management programmes.
The purpose of this study is
explanatory as it seeks to es
tablish whether attitude towards knowledge sharing is
determined by
such

variables
as g
ender,
a
ge,
e
ducation,
e
xperience and
l
evel within
organisation.

Survey Instrument

The questionnaire administered was developed by Bock & Kim (2002). Items to measure
a
ttitude towards knowledge sharing were modified by Bock and Kim from Fishbein and
Ajzen’s works, to make them relevant to the knowledge sharing context. As described in
Bock & Kim (2002), before conducting the main survey, a pre
-
test was performed to test
internal consistency and discriminant validity of the measurement instrument. The main
survey was conducted in four large public organisations in Korea.

The instrument measures respondents’ attitude towards knowledge sharing. Attitude
towards knowledge sh
aring was defined as the degree of one’s positive feelings about
sharing one’s knowledge. Variables were measured on a Likert
-
scale ranging from

The person’s beliefs that the
behaviour leads to certain
outcomes and his or her
evaluations of these outcomes.
The person’s beliefs that
specific individuals or groups
think that he or she should or
should not perform the
behaviour,
and his or her motivation to
comply with the specific referents
referents
Relative importance of
attitudinal and normative
consideration.
Attitude toward
the
behaviour
(A =

b
i
e
i
).
Subjective norm
(SN =

nb
i
mc
i
).
Behavioural intention
(I = A
w1 +
SN
w2).
Behaviour
(B = f (I)).
W1
W
2


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1 (very rarely) through 5 (very frequently). The questionnaire captured a maximum of 65
data
items from each re
spondent.

Pilot

Due to the questionnaire having been previously administered in Korea, the direct
translation by Bock and Kim caused problems with the semantics of some questions.
We
modified some questions so as not to lose their original intent. The ques
tionnaire was
piloted on 12 employees in the insurance sector
.

Respondents were given the opportunity
to make comments about specific questions and about the questionnaire in general.

Sampling design

A random sample of six large South African companies acr
oss different market sectors
was chosen, all of which had implemented or, were in the process of implementing,
knowledge management programmes. The various sectors included Insurance,
Professional Consulting, Banking and Information Technology (IT) Infrast
ructure (see
Figure
2
).


Data collection




Each of the selected companies was initially contacted telephonically, and subsequent
communication was via e
-
mail. The contact person was usually an individual responsible
for a particular company’s knowledge ma
nagement programme. In all cases approval was
required from the company’s executive management before the distribution of
questionnaires throughout the organisation. All participants’ executive managements
requested confidentiality and anonymity as require
ments for participating in the survey
and in the reporting of findings.


For the hand
-
delivered questionnaires correspondence was personalised, with 120
delivered to the insurance sector and 40 to the Information Technology infrastructure
sector. From the
insurance sector 110 (91.7%) questionnaires were returned, of which 94
(85.5%) were useable, and 28 (70.0%) was returned from the Information Technology
infrastructure sector, of which 26 (92.9%) were useable.


For questionnaires administered via e
-
mail r
epeated rounds of follow
-
ups were used to
maintain participants’ interest. From the professional consulting sector 102 e
-
mail
questionnaires were returned, of which 90 (88.2%) were useable, and 27 e
-
mail replies
came from the banking sector, of which 21 (7
7.8%) were useable.

In summary, from the initial six companies approached, four companies responded, one
from each sector, with 267 questionnaires returned, of which 231 (86.5%) were useable.
In order to preserve anonymity, the four companies have been la
belled per sector and no
inferences should be drawn about the sector assigned to a particular company (see Figure
2
).


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Responses by Sector
Insurance
94
Consulting
90
Banking
21
IT
26

Figure 2: Usable Responses by Sector

Data Analysis

To test the empirical stated hypothesis, the numeric variables ‘age’ and ‘experience
’,
captured in years, were transformed into categorical variables. The class intervals chosen
allowed for even distribution across the categories

(see appendix)
. This permitted an
analysis to be performed, namely using the Pearson chi
-
square test for the s
ignificance of
association, which is more relevant to the objectives of the research
.

It must be acknowledged that there is significant correlation between age on the one hand
and experience and education on the other. These were investigated in detail bu
t can not
be reported on due to space constraints. The full findings are available on simple request
from the authors.

The
main
statistical test performed
was the
Pearson chi
-
square test.
The main motivation
for this is that it can deal with the type of ca
tegorical data which constitutes most of the
sample. Furthermore, it is a robust, non
-
parametric test which does not assume anything
about the distribution of any of the variables. In addition, it is able to detect non
-
linear
dependencies
.

This is quite cr
itical to this research,
as will be demonstrated with the
variable

“Age”
. In addition, linear
regression analysis and ANOVA

was performed,
though this
is
only
reported on in the age analysis sub
-
section.

Table
1
: Frequency table fo
r the construct "attitude towards knowledge sharing".

Frequency table: Attitude

Description

Frequency

Percentage

Attitude < 3

Low

31

13.4

Attitude >= 3 and Attitude < 3.5

Medium

65

28.1

Attitude >= 3.5 and Attitude < 4

Medium


High

93

40.3

Attitude >=

4

High

42

18.2

In order to perform chi
-
square analysis, a number of continuous variables had to be
converted into categorical data.
Although t
his process may introduce bias
,

it was found
that the results were not particularly sensitive to the exact cut
-
o
ff values used to group
data. Table 1 above shows the 4 classes which were created for the “attitude” construct.

8

Note that the “description” labels such as “low” and “high” should note be interpreted
strictly: they are fairly arbitrary and very relative to

the responses obtained
. The use of
these labels is purely intended to facilitate the reading of the statistical results and they
should not be interpreted strictly.

Attitude x Gender

The first

personal factor to be investigated was the correlation between

gender on attitude
towards knowledge sharing.

Table
2
: Impact of Gender on Attitude.

Attitude

Gender: Male

Gender: Female

Row totals

Low

17

14

31

Medium

34

31

65

Medium


High

60

33

93

High

24

18

42

All groups

135

96

231


Th
e chi
-
square value was
found to be
2.61, which has a significance of 0.45 (df=3), i.e. p
-
value > 0.05. This implies that there is no association between attitude and gender.

The
lack of association


or even any suggestion of association


is certainly an
interesting
finding and demonstrates clearly the danger of holding gender
-
based prejudices.

Attitude x Age


The second personal factor to be investigated was the correlation between age and
attitude towards knowledge sharing.

Table
3
: Influence of age on attitude.

Attitude

Age: Very young

Age: Young

Age: Middle age

Age: Old

Row totals

Low

11

9

3

8

31

Medium

12

26

9

18

65

Med


e楧h

16

24

21

32

93

High

11

6

14

11

42

All groups

50

65

47

69

231

The chi
-
square value was 18.5, whi
ch has a significance of 0.02, i.e. p
-
value < 0.05. This

implies that there is a

definite

association between attitude and age. Therefore the null
(sub
-
)h
ypothesis of
no association between age and an individual’s attitude towards
knowledge sharing
must

b
e rejected, and the alternate hypothesis of an association
between age and attitude can be accepted.

However, if the influence of age on attitude towards knowledge sharing is tested by
means of linear regression analysis, The regression test only indicates

a weak
ly

positive
(
r²= .014)
influence on attitude by the respondents’ age with a p
-
value of only 0.069 i.e.
not statistically significant. An ANOVA analysis reveals that some of the problems may

9

be due to a more dispersed spread of attitudes for this gro
up, with a heavy low
-
end tail,
indicating that there is more probability of very young respondents having a low attitude
than the other age categories.

Hence, it must be concluded that there is a definite
correlation between age and attitude sharing, but t
his is not of a linear nature.

Attitude x Experience

The
next personal factor to be investigated was experience. Note that there is some
autocorrelation with the age factor (young staff cannot have as much experience as
significantly older staff). This cor
relation effect was investigated in more detail and
found to be relatively small.

Table
4
: Influence of experience on attitude.

Attitude

Experience:
Trainee

Experience:

Junior

Experience:
Experienced

Experience:

Senior

Row totals

L
ow

16

10

4

1

31

Medium

39

14

5

7

65

Med


e楧h

42

28

6

17

93

High

23

14

1

4

42

All groups

120

66

16

29

231

The chi
-
square value was 11.4, which has a significance of 0.25, i.e. p
-
value > 0.05. This
suggest
s that there is no
statistical basis to assume

an
association between attitude and
experience.

Attitude x Education

The correlation of education on one’s attitude towards knowledge sharing was
investigated next. Note that this is one of the influences about which it is possible to
postulate quite a fe
w plausible a priori theories. One defensible position would be that
more educated people have relied more on other people sharing knowledge throughout
their education and would therefore appreciate its benefit more. A contrary position
would hold that mor
e educated people have more knowledge capital to protect and might
therefore be more protective of “their” intellectual assets.

Table
5
: Influence of education on attitude.

Attitude

Education:
High School

Education:

Bachelor’s

Educa
tion:

Tech / College

Education:

Master’s

Education:

Other

Row
totals

Low

9

10

12

0

0

31

Medium

18

29

13

4

1

65

Med


e楧h

25

38

19

8

3

93

High

10

21

9

0

2

42

All groups

62

98

53

12

6

231

It turns out that t
he chi
-
square value
i
s 13.5, which has a sig
nificance of
only
0.33, i.e.
a
p
-
value
well above

0.05. This implies that there is no association between attitude and
education.


10


Attitude x Level within organisation

The next factor under consideration was a person’s level in the organisation. A careful
analysis of job descriptions was done to allocated respondents to one of seven distinct
categories.

Table
6
: Influence of level within the organisation and attitude.

Attitude

Level:

Executive

Level:

Supervisor

Level:

Technical

Level
: M
-

Manag.

Level:

Profess
l.

Level:
Clerical

Level:
Other

Row
totals

Low

0

3

12

1

6

9

0

31

Medium

2

5

17

18

16

6

1

65

Med
-
High

6

6

29

21

19

10

2

93

High

2

4

9

13

7

4

3

42

All groups

10

18

67

53

48

29

6

231

The chi
-
square value was 25.5, which has a s
ignificance of 0.11, i.e. p
-
value > 0.05. This
implies that there is no
statistically significant
association between attitude and level
within organisation.

Note that a number of cells have expected frequencies of less than 5,
so the chi
-
square was recalc
ulated by combining certain levels and leaving out others, but
this resulted in an even lower significance level. However, if a larger sample had been
used, it may well have produced a more significant result.

Organisational
/ Sector
Influence

Although no
t part of this research, a test was done to see if the organisation for which the
respondents worked had an influence. Interestingly, it appears that the
organisation

or
,
since the variables coincide in our sample,

industry sector

in which the respondents
were
working
,

did
also
not affect an individual’s attitude towards knowledge sharing. The chi
-
square analysis on organisation was an extremely small 2.86 i.e. a p
-
value of 0.97,
indicative of almost pure random distribution across the different
organisatio
ns
.

Summary of findings

Table
7

below presents a summary of the test results

for each of the personal factors
which were hypothesised to have a potential influence on attitude towards knowledge
sharing.
It must be noted that the actual chi
-
square analysis

tests (two
-
way) association
rather than unidirectional influence.

Table
7
: Summary of findings.

Personal Factor

Pearson chi
-
square
test
statistic

P< 5%
significance level

Association
/
influence
?

Gender

2.61

0.45

No

Age

18.5

0.02

Yes

Experience

11.4

0.25

No

Education

13.5

0.33

No

Level in org.

25.5

0.11

No



11

From the Pearson chi
-
square test for significance of association
,

the only significant
finding was the association between attitude and age. Although there was a strong
corr
elation between age and experience, it was interesting to note that no association
between attitude and experience emerged from the hypothesis test results.

The categories that contribute the most to this differentiation were very young
respondents (age >=

21 and age <= 25), with a ‘low’ attitude
-

ranging from 1 (least
positive) through 5 (very positive)
-

and the young (a
ge > 25 and age <= 30), with a
‘medium’ attitude. The number of very young respondents (observed) for low attitude
response (11) was hig
her than expected (6.7). The number of young respondents
(observed) for medium attitude response (26) was also higher than expected (18.3).
This
suggests
that very young respondents are significantly more negative towards
sharing
their

knowledge and young
respondents more positive
.

Interestingly, it appears that the organisation or industry sector in which the respondents
were working also did not affect an individual’s attitude towards knowledge sharing. The
chi
-
square analysis on organisation was an extre
mely small 2.86 i.e. a p
-
value of 0.97,
indicative of almost pure random
distribution across the different categories.

Tentative Interpretation

Although the aim of the research is not to explain why personal factors influence attitudes
towards knowledge sh
aring, the author
s wish

to speculate on possible
reasons, based on
the findings of

Jarvenpaa and Staples (2001).

The authors hypothesise that the very young respondents may not have social networks as
large as those of older respondents. This could result
in feelings of inadequacy in sharing
their knowledge, or in lack of opportunities to collaborate with more experienced
m
embers of staff. This could then lead to knowledge hoarding or distrust and result in
low attitudes
.
As people’s social networks increas
e over time, their confidence grows.
The respondents that were previously in the low attitude category (11) move into the
medium attitude category of the observed frequency (26) which is more than the expected
frequency (18.3) (see Table
3
).

Conclusion

The

re
search

focused on the personal factors gender, age, experience, education and level
within organisation and on how these factors influence an individual’s attitude towards
knowledge sharing.

The findings of the report suggest that the attitude towards k
nowledge sharing is
influenced by age. It was interesting to note that, although there was a strong correlation
between age and experience, there was no association between attitude and experience.

Although interesting, the findings must be treated with c
aution given the limitations of
the study. The sample design of randomly selected companies across certain sectors may
have been biased. The effect of this sampling design on the ability to generalise results to
other companies in a South African context i
nvolved in knowledge management is not
clear. Also, the sample was of
large

companies, which therefore limits the ability to
generalise to smaller companies.

However, the lack of support for the influence
on knowledge sharing attitude
of various
personal f
actor
s

such as experience (in years), job level within the organisation, education
or gender
can be seen as a positive and hopeful indicator. It suggests that there is no

12

discernable deterministic individual barrier against knowledge sharing attitudes base
d on
gender, experience or education
. This may well be a unique strength of South Africans
who, in view of their tumultuous and dynamic history, could be hypothesized to be more
open and amenable to change. Thus it

gives hope to organisations willing to pu
rsue a
knowledge management adoption process.


Areas for future research could include possible theoretical explanations for why age
influences this attitude.
Also, a larger sample may reveal additional factors


in particular
the level within the organisa
tion (for large, relatively structured organisations) may yet
prove to be an influence after all.

Additionally, i
t would be useful to research the organisational factors that influence
knowledge management implementations in a South African context, as th
is encompasses
trust and willingness to collaborate. Finally, the effect of rewards or incentives on
knowledge sharing, could be investigated, to determine whether they are a significant
factor in the South African context.

Finally, it is hereby suggested
that a more
qualitative

research methodology be more appropriate to investigate in more detail what (personal or
other) factors that affect knowledge sharing attitude.

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