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1. Introduction
As we have move rapidly
into the 21st century organisations face the challenge of being effective in a
global knowledge environment. In his book PowerShift, Toffler (1990) made it
clear that knowledge has become the global competitive driver. The real
challenge for organisations is “capturing the tacit knowledge which is in
people’s heads – the experience, knowledge and judgement you get from doing
something for a long time,” says Stephanie Pursley [1]. But actively managing
(acquiring) knowledge relies on individual’s effort and co-operation, so the
new model of knowledge management is about personal relevance (Bailey &
Clarke, 2001), it is about people and actions and their behaviour in aligning
knowledge processes with organisational objectives (Politis, 2003). It is
about how we move from the old way of doing things where knowledge was power,
to sharing knowledge and achieving a competitive advantage. Sharing the
individual and collective brain power of people (knowledge) however, cannot be
harnessed in the absence of trust and cooperation, help and care, shared
values and vision, sincerity and goodwill (Rastogi, 2000). Professor John
Kotter told the Australian Institute of Management that “if people don’t trust
the information they are getting from you they won’t necessarily act on it;
they won’t pass it on as if it is credible, and that’s a killer” (Kotter,
2003:1). In line with Kotter’s comment, it has even been argued that “trust
is, after all, the single most important precondition for knowledge exchange”
(Rolland & Chauvel, 2000: 239).
The importance of trust
has been supported in a study by Politis (2002). In this study, respondents
indicated that most of the interpersonal trust dimensions are positively
related to the skills and traits of knowledge acquisition. It is also
acknowledged that power is often employed by management to influence the
behaviour of employees (Fairholm, 1993). Although in a recent study Politis
(2003) reported that most of the dimensions of power associated with French
and Ravens’ (1959) power-based taxonomy enable followers’ knowledge
acquisition, current research lacks the empirical evidence supporting the
prediction of the skills and traits of knowledge acquisition from the combine
effect of the relational (interpersonal) trust and managerial power factors.
To this end, this research started by asking the following questions. Is the
influence of managerial power more important than the influence of relational
(interpersonal) trust in the process of knowledge acquisition? Are the
correlations derived from the factors of interpersonal trust and knowledge
acquisition stronger, and more positive, than those with the managerial power
factors? Will the statistical prediction of the knowledge acquisition
attributes be increased with the addition of managerial power factors in the
set of the predictor variables? Answers to these questions are some of the
objectives of this paper.
2. Managerial power and the
determinants of knowledge acquisition
According to Sir
Francis Bacon “knowledge is power” [2], and where power resides, resides
success. Within the managerial power literature, power refers to the “capacity
that A has to influence the behaviour of B so that B acts in accordance with
A’s wishes” (Robbins, 2003: 366). In line with this definition, Kanter (1979:
66) argues that power is fundamentally “the ability to mobilise resources
(human and machine) to get things done”. It is thus, implied that leaders use
power as a means of attaining organisational goals. According to Kipnis and
Schmidt (1988), favourable performance gain ratings are largely affected by
the manager’s effective use of influence behaviour (power). In this context,
power is defined as the ability of management to influence the behaviour,
intentions, attitudes, beliefs, emotions, and the values of subordinates
(French & Raven 1959). But where does power come from? What is it that gives
an individual (i.e. leader) influence over others?
Over the years a number
of power sources have been presented by Stephenson (1985), Hunt (1986), and
Morgan (1986), with French and Raven (1959) being the authors most heavily
utilised. Frence and Raven’s power-based taxonomy consists of five important
bases of managerial power: coercive, expert, legitimate, referent, and reward.
Coercive power is based on the target’s belief that the manager has the
ability to punish employees; expert power is based on the target’s belief that
the manager can provide him or her with special knowledge; legitimate power is
based on the target’s perception that the manager has the legitimate right to
influence the target and that he or she is obligated to comply; referent power
is based on the target’s identification with or desire to be associated with
the manager; and reward power is based on the target’s belief that the manager
has the ability to provide him or her with desired tangible or intangible
objectives.
On the other hand,
knowledge management (acquisition) is jointly a goal and a process. As an
organisational outcome or goal, knowledge management is entirely focused on
sharing information for the benefit of the organisation (Bollinger & Smith,
2001). Of central importance to organisations however, is to define the term
knowledge and identify the type of knowledge that they are forced to manage.
Although it seems obvious to define the seemingly self-evidence term –
knowledge, the reality is that knowledge and knowledge management are quite
complex (Clark & Rollo, 2001); that is because knowledge is usually classified
as either explicit or tacit (Nonaka, 1998). Explicit knowledge is described as
formal, systematic knowledge that can be expressed or communicated without
vagueness or ambiguity. It can be stored in books, manuals, and databases.
Tacit knowledge, on the other hand, is considered as highly personal know-how
that is derived from experience and beliefs and usually hard to articulate and
communicate. Moreover, Bollinger and Smith (2001) explained that “tacit
knowledge is unarticulated knowledge that is in a person’s head that is often
difficult to describe and transfer” (p. 9).
Given that 42 percent
of corporate knowledge is held within employee’s minds (Clark & Rollo, 2001),
it is important for organisations to set up processes whereby tacit knowledge
is more accessible and people are easily connected enabling them to think
together and to take time to articulate and share information (Lang, 2001).
Although setting up such processes could be a complex exercise, authors (Galagan,
1997; Bath, 2003) and organisations concur that a common business practice
that is connected to knowledge acquisition is that of “acquiring information
directly from domain experts” (Mykytyn, Mykytyn & Raja, 1994: 98). Mykytyn and
colleagues revealed 26 behavioural skills and traits (attributes) that are
essential for knowledge acquisition. These attributes are presumed to produce
seven factors:
§ Communication/problem
understanding;
§ Personal traits
§ Control
§ Organisation
§ Negotiation
§ Liberal arts and
§ Non-verbal communication.
Communication/problem
understanding refers to interviewing; listening; sensitivity; open-minded;
probing; conceptualising; rational thinking; and hindsight. Personal traits
refer to empathy; sense of humour; tolerance; and amiable. Control refers to
politics; organisational knowledge; assertiveness; and salesmanship.
Organisation refers to leadership; speaking; writing; management; and domain
knowledge. Negotiation refers to diplomacy; patience; and co-operation.
Liberal arts and non-verbal communication refer to being broadly educated,
well informed, having knowledge on subjects dealing with humanities,
philosophy and literature and having a broad view of company’s goals and
operations.
However, these
behavioural skills and traits do not emerge spontaneously or in a vacuum. They
evolve out of the context and the history of the organisation and their impact
is conditioned by the subjective perceptions of knowledge workers whose
experience is ruled by that history. This draws attention among other things
(i.e. organisational process and mechanisms of knowledge creation) to the
influence, and hence the power, exercised by management in developing and
linking these attributes to successful knowledge acquisition. But like
influence, power involves human relationships among leaders and employees (Ivancevich
& Matterson 1993).
In relation to human
relations it is being argued that relationships within an organisation are
crucial for knowledge creation, sharing, and utilisation (Lang, 2001).
Moreover, recently Politis (2003) found that a number of managerial power
dimensions are positively related to knowledge acquisition attributes of
knowledge workers. It is thus reasonable to hypothesise that the factors
representing managerial power will be predictive variables of the traits and
skills of knowledge acquisition. This prediction is further reinforced by the
findings of the empirical work in which ‘knowledge leaders’ were found to be
positively related to the skills and traits (attributes) that are essential
for knowledge acquisition (Politis, 2001). The assumed connectedness between
managerial power and knowledge acquisition attributes is expressed in the
following hypotheses:
Hypothesis 1a: Coercive
power will be positively related to the skills and traits of knowledge
acquisition.
Hypothesis 1b: Expert
power will be positively related to the skills and traits of knowledge
acquisition.
Hypothesis 1c:
Legitimate power will be positively related to the skills and traits of
knowledge acquisition.
Hypothesis 1d: Referent
power will be positively related to the skills and traits of knowledge
acquisition.
Hypothesis 1e: Reward
power will be positively related to the skills and traits of knowledge
acquisition.
3. Relational trust and
determinants of knowledge acquisition
It is being argued that
knowledge management (KM) is the combination of human resource management and
information management, and thus relates to all processes that are combined
with the identification, acquisition, creation, distribution and use of both
information and knowledge (Iivonen & Huotari, 2000). Therefore, human factors
are essential components for effective knowledge acquisition and must be taken
into account. But, trust belongs to the area of human factors in KM. While it
has not been extensively discussed, it has been suggested that trust is
required for knowledge generation and knowledge sharing (Probst, Raub &
Romhardt, 2000; Rolland & Chauvel, 2000; Kotter, 2003). The employees must
trust each other to share their information and knowledge (Connelly & Kelloway,
2000), to generate knowledge. One reason that individuals might be willing to
share information is due to the individual’s identification with the
organisations’ goals and the simple action of sharing information within a
relationship creates relational trust (Ford, 2001).
The promotion of
relational trust is illustrated through the recommendation to create
communities of practice for knowledge generation and sharing (von Krogh,
Ichijo & Nonaka, 2000). Communities of practice are groups in which the social
cohesiveness has been promoted, and the groups assist on the generation of new
knowledge (Davenport & Prusak, 1998). The promotion of social ties within
these groups is related to the development of knowledge-based,
identification-based and relational trust. With respect to relational trust,
Cook and Wall (1980) have distinguished two components of dyadic or
interpersonal trust: faith and confidence. Interpersonal trust is been viewed
as faith and confidence in peers (that is, co-worker trust), as well as, as
faith and confidence in management (that is, trust in both the supervisor and
top management). The definitions of faith and confidence have been adopted
from Cook and Wall (1980: 40).
§ Trust refers to the “faith in the
trustworthy intentions of others”.
§ Trust refers to the “confidence
in the ability of others, yielding ascriptions of capability and
reliability”.
Research reported in
the literature suggests that high levels of trust between managers and
employees are correlated with more open communication (Ruppel & Harrington,
2000) fostering generative learning. Moreover, evidence has shown that
collaborative problem solving in organisations presupposes interpersonal trust
(Davenport & Prusak, 1998; Politis, 2002), and specifically co-worker trust.
Furthermore, Ford (2001) argued that acquisition of knowledge from an
individual outside the organisation couldn’t benefit from organisational
trust, as the individual is not part of the organisation. Yet, impersonal
trust would not be effective as the trust is directed to the position within
the organisation; therefore, “interpersonal trust is the best type of trust
for knowledge acquisition” (Ford, 2001: 14). Therefore, it is reasonable to
assume that the factors of interpersonal trust will be the predictive
variables of the determinants of knowledge acquisition. The assumed
connectedness between interpersonal (relational) trust and knowledge
acquisition is expressed in the following hypotheses.
Hypothesis 2a: Faith in
peers will be positively related to the skills and traits of knowledge
acquisition.
Hypothesis 2b: Faith in
management will be positively related to the skills and traits of knowledge
acquisition.
Hypothesis 2c:
Confidence in peers will be positively related to the skills and traits of
knowledge acquisition.
Hypothesis 2d:
Confidence in management will be positively related to the skills and traits
of knowledge acquisition.
The nine hypotheses are
summarised in the research model shown in Figure 1
. 
Figure 1: Summary of
variables used in the paper
Moreover, in a recent
study Politis (2001) found strong positive relationships between various
leadership style dimensions and knowledge acquisition attributes. Yet,
performance is largely affected by leadership’s effective use of power (Kipnis
& Schmidt, 1988). It is thus, reasonable to hypothesise that the dimensions of
managerial power would provide an increase in the level of prediction of
knowledge acquisition, after being statistically controlled for the predictive
effects of interpersonal trust.
Hypothesis 3: The
statistical prediction of the knowledge acquisition factors from the
relational (interpersonal) trust variables will be increased with the addition
of power factors in the set of interpersonal trust predictor factors.
4. Sample and procedures
4.1 Sample
The sample was selected
from service (telecommunications and banking) and manufacturing organisations
operating in the United Arab Emirates. Discussions with both management and
employees suggested that the selected organisations were relatively flat with
maximum six levels of hierarchy. First line managers/supervisors, namely
knowledge workers, who were engaged in selling services, servicing customers
and manufacturing operations, participated in the study. One hundred and
nineteen first line managers (82.5 percent response rate) provided the data.
Twenty-one first-line-managers returned incomplete questionnaires, which were
excluded, from the final sample of 119. The sample consisted of 100% males.
Approximately two-quarters of participants had attained a college diploma or
degree qualifications and almost one-half had received technical college
qualifications.
4.2 Procedures
Survey questionnaires
were pre-tested, using small number of respondents (about one dozen; the
pre-test participants did not participate in the final data collection). As a
consequence of the pre-testing, relatively minor modifications were made in
the written instructions and in several of the demographic items. The revised
survey, written in English, was then administered to the organisational
respondents in a class room environment. Written instructions, along with
brief oral presentations, were given to assure the respondents of anonymity
protection and to explain (in broad terms) the purpose of the research. The
participants were all given the opportunity to ask questions and were
encouraged to answer the survey honestly; anonymity was guaranteed and no
names or other identifying information was asked.
4.3 Analytical
procedure
Confirmatory factor
analysis (CFA) is a widely acknowledged technique for testing the psychometric
properties of measurement instruments. Bagozzi, Yi and Phillips (1991)
emphasised the superiority of CFA to other methods such as the traditional
factor analysis and Campbell and Fiske’s (1959) multi-trait/multi-methods
approaches for examining the construct validity of survey instruments. Thus, a
CFA was used for the factor analysis (measurement model) and for the
regression analysis (structural model). Following the recommendations of
Sommer, Bae and Luthans (1995), a measurement model was developed and then,
with this held, a structural (path) model. The factorial validity of the
measurement model was assessed using CFA. Given adequate validity coefficient
of the measurement model, the number of indicator variables in the model was
reduced by creating a composite scale for each latent variable (Politis,
2001). The parameters of regression coefficient li and measurement error qi,
of each composite latent variable, were used as fix parameters in the
structural model. The analytical procedure, to calculate the li and qi, is
detailed in Politis’s (2001) study. All of the CFAs were run using the
Analysis of Moment Structures (AMOS, version 4) software (Arbuckle, 1997).
As a test of the
measurement and structural models, a mixture of fit-indices was employed to
assess model fit. The ratio of chi-square to degrees of freedom (c2/df) has
been computed, with ratios of less than 2.0 indicating a good fit. However,
since absolute indices can be adversely affected by sample size (Loehlin,
1992), three other relative indices; the goodness-of-fit index (GFI), the
adjusted goodness-of-fit index (AGFI) and the Tucker and Lewis index (TLI)
were computed to provide a more robust evaluation of model fit (Tucker &
Lewis, 1973; Tanaka, 1987). For GFI, AGFI and TLI, coefficients closer to
unity indicate a good fit, with acceptable levels of fit being above 0.90
(Marsh, Balla & McDonald, 1988). For root mean square residual (RMR) and root
mean square error approximation (RMSEA), evidence of good fit is considered to
be values less than 0.05; values from 0.05 to 0.10 are indicative of moderate
fit and values greater than 0.10 are taken to be evidence of a poorly fitting
model (Browne and Cudeck, 1993).
To improve the
psychometric properties of either the measurement or structural model, without
altering the base models, the Modification Indices (MI) provided by AMOS were
utilised to trim individual items contained in each factor. The author chose
to trim items from the survey to eliminate items that cross-loaded on
different factors. Refinements to survey instruments using ‘item trimming’
without altering the underlying model can help further organisational research
on survey measures (Podsakoff & Organ, 1986), without necessarily modifying
the conceptual model it was designed to assess.
5. Measurement models
5.1 Managerial
power variables
For this research,
managerial power was assessed by using French and Raven’s (1959) power-based
taxonomy. We measured French and Raven’s (1959) bases of power using a
modified version of Hinkin and Schriesheim’s (1989) 20-item power scale, as
adapted by Nesler, Aguinis, Quigley and Tedeschi (1993). The scale employs a
nine-point response scale (1 = disagree; 9 = agree), and consists of five
subscales: coercive power, expert power, legitimate power, referent power, and
reward power. Based on the results of a CFA supporting five power factors,
these items were used to create five composite scales: coercive power (3
items, a = 0.71); expert power (4 items, a = 0.76); legitimate power (4 items,
a = 0.81); referent power (4 items, a = 0.89); and reward power (3 items, a =
0.77). Two items were dropped due to cross loading; these being of the order
of, or less than, 0.16.
5.2 Relational
(interpersonal) trust variables
Relational
(interpersonal) trust measures were assessed by using Cook and Wall’s (1980)
12-item scale. The scale employs a seven-point response scale (1 = strongly
disagree; 7 = strongly agree), and consists of four subscales: faith in peers,
faith in management, confidence in peers, and confidence in management. Based
on the results of a CFA supporting three factors, these items were used to
create three scales: faith in peers (3 items, a = 0.82), confidence in peers
(4 items, a = 0.79), and confidence in management (4 items, a = 0.69). One
item was dropped due to cross loading; this being of the order of, 0.15.
5.3
Determinants of knowledge acquisition
The skills and traits
of knowledge acquisition were assessed by using Mykytyn, et al.’s (1994)
26-item scale. The scale employs a seven-point response scale (1 = very
unqualified; 7 = very qualified), and consists of six subscales:
communication/problem understanding, personal traits, control, organization,
negotiation, liberal arts and non-verbal communication. Based on the results
of the CFA four factors were supported: communication (6 items, a = 0.74),
personal traits/control (6 items, a = 0.77), problem understanding (5 items, a
= 0.82), and organisation (6 items, a = 0.70). Three items were dropped due to
cross loading; these being of the order of, or less than, 0.11.
6. Path modelling
As discussed earlier in
the analytical procedure section, the parameters in the path model (i.e. li
and qi) we calculated. Table 1 reports the means, standard deviations,
reliability estimates, and li and qi, estimates for the analysis. Once these
parameters—regression coefficients (li), and the measurement error variances (qi)
— were calculated, this information was fed into the path model to examine the
relationships among the latent variables. The model of Figure 2 contains the
five dimensions of managerial power, the three relational (interpersonal)
trust dimensions and the four knowledge acquisition variables.
Table 1: Descriptive
statistics, reliabilities, and li and qi estimates
N = 119
The analysis revealed
that the structural model of Figure 2 fit the data fairly well, with c2 =
69.6; df = 24; (c2/df = 2.90); GFI = 0.90; AGFI = 0.88; TLI = 0.86; CFI =
0.89; RMR = 0.106; and RMSEA = 0.083. Alternative models were examined with
either paths added, reversed or removed, but none improved the model fit.
6.1 Hypotheses
testing
Figure 2 displays
results of the best fit structural equations model. As predicted by hypothesis
1a (H1a), there were significant positive relationships between coercive power
and knowledge acquisition attributes. Coercive power was strongly and
positively related to communication (g1 = 0.32, p < 0.001), personal
traits/control (g2 = 0.21, p < 0.01), problem understanding (g3 = 0.30, p <
0.001), and organisation (g4 = 0.27, p < 0.01), supporting H1a.
Hypothesis 1b (H1b)
predicted that expert power will be positively related to knowledge
acquisition attributes. The standardised path coefficient between expert power
and problem understanding was strong and significant (g5 = 0.57, p < 0.001),
marginally supporting H1b. The expected relationship between expert power and
the other dimensions of knowledge acquisition, viz. communication, personal
traits/control, and organisations, was not supported. Contrary to Hypothesis
1c (H1c), legitimate power was negatively related to problem understanding (g6
= - 0.11, p < 0.10), and organisation (g7 = -0.15, p < 0.05), while the
results showed no other relationship between legitimate power and
communication or personal traits/control.
Figure 2: Structural
estimates of the hypothesised model a
Note: a Standardised
path coefficients,
N = 119
+ p < 0.10
*p < 0.05
** p < 0.01
*** p < 0.001
All corelations of
predictor variables were statistical significant at 0.01 level.
As predicted by
Hypothesis 1d (H1d), there were significant positive relationships between
referent power and two dimensions of knowledge acquisition. Specifically,
referent power was strongly and positively related to problem understanding
(g8 = 0.55, p < 0.001) and organisation (g9 = 0.35, p < 0.001). The expected
relationship between referent power, communication and personal traits/control
was not supported. Finally, the relationship between reward power and
organisation was in the wrong direction (g10 = -0.20, p < 0.05), not
supporting predictions. No paths were significant between reward power and the
other knowledge acquisition attributes, hence, not supporting Hypothesis 1e
(H1e).
In relation to
relational (interpersonal) trust-knowledge acquisition relationship, the
findings are not consistent with the hypotheses. Specifically, the results
showed that faith in peers was negatively related to communication (g11 = -
0.12, p < 0.10) and organisation (g12 = -0.12, p < 0.10), not supporting
Hypothesis 2a (H2a). Hypothesis 2b was not tested, because the variable faith
in management was not supported by the CFA. Moreover, Hypothesis 2c (H2c)
predicted that confidence in peers will be positively related to knowledge
acquisition attributes. This prediction was not supported (see Figure 2), in
that no paths were significant between confidence in peers and the factors of
knowledge acquisition. Finally, Hypothesis 2d (H2d) predicted a positive and
significant relationship between confidence in management and knowledge
acquisition. Contrary to prediction, the relationships between confidence in
management and both personal traits/control and problem understanding, were in
the wrong direction (g13 = -0.19, p < 0.05 and g14 = -0.22, p < 0.05,
respectively), not supporting H2d. No other paths were significant between
confidence in management and the dimensions of knowledge acquisition.
The structural
equations results supported Hypothesis 3 (H3) for all dimensions of knowledge
acquisition attributes (see Table 2). As expected, the dimensions of power
measured by Nesler et al. (1993) scale provided small but statistical
significant incremental validity for the knowledge acquisition attributes. For
example, it was found that the coefficient of determination for the structural
equations for communication was 0.39 (R2 = 0.39). In other words, the combined
effect of the five managerial power dimensions and the dimension of
interpersonal trust (predictor variables) explains 39 per cent of the
variation in communication. The remaining 61 percent are not explained. As
shown in Table 2, the results revealed that the measures of managerial power
provided a small but statistically incremental validity for the dependent
variables of communication (9 percent), personal traits/control (2 percent),
problem understanding (4 percent), and organisation (10 percent), supporting
H3.
Table 2 Coefficient of
determination (R2) of knowledge acquisition attributes
7. Discussion
The aim of this study
was to extend the field of research by investigating the combine effect of
managerial power and relational (interpersonal) trust on the skills and traits
of knowledge acquisition. Furthermore, the predictive power of the factors of
managerial power in the set of the predictor variables was examined.
To a large extent the
results are consistent with the realm of power and organisational performance
literature, in that managerial power is necessary to produce effective results
(Fairholm, 1993), and to increase performance output (Kipnis & Schmidt, 1988).
The findings are also consistent to those of previous studies in which Politis
(2003) found that some power dimensions are positively related to knowledge
acquisition attributes. The results showed that coercive power, referent
power, and expert power are important determinants of communication, personal
traits/control, problem understanding, and organisation (i.e. dimensions of
knowledge acquisition). Specifically, the results suggest that those leaders
who provide employees with special knowledge, i.e. expert power, can encourage
and facilitate specific behavioural skills and traits of knowledge workers
(i.e. problem understanding) that are essential for knowledge acquisition. In
that regard, Politis (2001) chose to refer to those leaders as
‘knowledge-enabled leaders’, while Brenneman, Keys and Fulmer (2000) describe
then as ‘servant leaders’. Such leaders encourage personal traits,
negotiation, and other learning activities and act as servants to others in
order to stimulate and inspire organisational learning.
Furthermore, referent
power (personality power) does facilitate negotiation between knowledge
workers. In other words, the ability of leaders to develop followers from the
strength of their own personalities does encourage followers’ problem
understanding, viz. open-minded; probing; conceptualising; rational thinking;
and hindsight, and organisation, viz. leadership; speaking; writing;
management; and domain knowledge, all of which being essential ingredients for
knowledge acquisition and knowledge sharing.
Moreover, the findings
are not consistent with the literature of relational trust and knowledge
management. The study failed to identify strong relationships between the
dimensions of interpersonal trust and knowledge acquisition attributes, not
supporting previous empirical findings. It is implied in these results that
organisations may acquire and share knowledge via technology and through
individuals who never develop strong interpersonal relationships, thus
interpersonal trust (Ford, 2001). These organisations may run into a risk of
developing a culture whereby employees through words, actions, or decisions,
act ‘opportunistically’ (Robbins, 2003), in a way that individuals are steeped
as being strongly antagonistic to knowledge sharing. This type of culture
raises the concern of emebeddedness, that is, the type of behaviour embedded
in structures of social relations (Granovetter, 1985). This should be examined
through a series of field studies or experimental studies.
Finally, it was found
that the dimensions of managerial power provided statistically significant
additional predictive power, after having statistically controlled for the
predictive effects of interpersonal trust dimensions. This implies that
managers in countries with high power distance (i.e. approximately 82 out of
110 points in Hofstede’s (1991) Power Distance Index) are more likely to be
paternalistic towards employees, thereby, facilitating their skills and traits
for knowledge acquisition. An issue that has been raised by this paper is that
it may be possible for cultures with high power distance (i.e. Arab, Far
Eastern and Latin countries) to do some, if not all the knowledge processes
without interpersonal trust (i.e. solely through organisational trust and
managerial power); an argument supported by Ford (2001).
In conclusion, managers
can exercise power through their position and rewards, but cannot force
relational (interpersonal) trust to occur. They can actively encourage and
facilitate however, a knowledge-sharing environment, and discourage industrial
age thinking and opportunistic behaviours.
7.1
Limitations and future work
The present study
limited its focus to a key set of managerial power, relational trust and
determinants (skills and traits) of knowledge acquisition. Although the
variables of relational (interpersonal) trust and managerial power used in
this study were considered important in facilitating a knowledge-sharing
culture, future research models should examine the relationship of knowledge
acquisition to other factors, such as task complexity, organisational trust
(Ford, 2001), culture and leadership (Davenport, DeLong & Breers, 1998), and
organisational and social networks (Lincoln & Miller, 1979; Granovetter,
1985).
Although from the
analytical perspective structural equations modelling has a number of
advantages in testing statistical causal relationships, actual causality
cannot be tested directly. So ideally future research must test causality
using experimental or longitudinal data for more define results. Finally, the
cross-sectional nature of the study renders it vulnerable to problems
typically associated with survey research (common method variance). To account
for the common method variance problems, it would have been advantageous for
future researchers to gather data from multiple sources.
7.2 Notes
[1] Stephanie
Pursley, Knowledge Management Partner at Freehills, Sydney Office, Australia (www.freehills.com).
[2] Sir Francis
Bacon, (www.brainyquote.com/quotes/quotes/s/q100764.html).
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