Knowledge sharing and individual work performance: an empirical study of a public sector organisation

Abstract

Purpose
The purpose of this study is to examine whether individual-level knowledge sharing (in terms of attitudes, benefit estimations, self-efficacy and actualised behaviours) affects individual work performance.

Design/methodology/approach
Hypotheses are tested through structural equation modelling of survey data collected from 595 members of a public organisation.

Findings
The findings confirm the hypothesis that knowledge-sharing propensity impacts positively on knowledge-sharing behaviour. Additionally, knowledge-sharing behaviour mediates the relationship between knowledge-sharing propensity and individual performance. The latter effect is also significant amongst the most highly educated members of the organisation but not among those with the lowest educational levels.

Originality/value
This paper provides insights into the knowledge-sharing–attitude–behaviour–work performance linkage. It thus addresses a relatively neglected area in knowledge management (KM) research, namely, that of individual knowledge behaviours and their performance impact, with an aim to better understand the micro-foundations of KM. It also contributes to knowledge on KM in the public sector.

Full text

Journal of Knowledge Management
Knowledge sharing and individual work performance: an empirical study of a public sector organisation
Kaisa Henttonen Aino Kianto Paavo Ritala
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To cite this document:
Kaisa Henttonen Aino Kianto Paavo Ritala , (2016),"Knowledge sharing and individual work performance: an empirical study
of a public sector organisation", Journal of Knowledge Management, Vol. 20 Iss 4 pp. 749 - 768
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Knowledge sharing and individual work
performance: an empirical study of a
public sector organisation
Kaisa Henttonen, Aino Kianto and Paavo Ritala
Kaisa Henttonen is based
at the School of Business
and Management,
Lappeenranta University of
Technology, Lappeenranta,
Finland. Aino Kianto is a
Professor at the School of
Business and Management,
Lappeenranta University of
Technology, Lappeenranta,
Finland. Paavo Ritala is
based at the School of
Business and Management,
Lappeenranta University of
Technology, Lappeenranta,
Finland.
Abstract
Purpose –The purpose of this study is to examine whether individual-level knowledge sharing (in terms
of attitudes, benefit estimations, self-efficacy and actualised behaviours) affects individual work
performance.
Design/methodology/approach –Hypotheses are tested through structural equation modelling of
survey data collected from 595 members of a public organisation.
Findings –The findings confirm the hypothesis that knowledge-sharing propensity impacts positively
on knowledge-sharing behaviour. Additionally, knowledge-sharing behaviour mediates the relationship
between knowledge-sharing propensity and individual performance. The latter effect is also significant
amongst the most highly educated members of the organisation but not among those with the lowest
educational levels.
Originality/value –This paper provides insights into the knowledge-sharing–attitude– behaviour–work
performance linkage. It thus addresses a relatively neglected area in knowledge management (KM)
research, namely, that of individual knowledge behaviours and their performance impact, with an aim to
better understand the micro-foundations of KM. It also contributes to knowledge on KM in the public
sector.
Keywords Knowledge sharing, Individual work performance, Knowledge-Sharing behaviour,
Micro-Foundations, Public organisation
Paper type Research paper
Introduction
Nowadays, public sector organisations are widely regarded as knowledge-based
organisations that focus on developing and providing knowledge services for stakeholders
(Luen and Al-Hawamdeh, 2001;Huang, 2014). This means that knowledge is considered
their key resource (Willem and Buelens, 2007;Sandhu et al., 2011;Siong et al., 2011);
therefore, facilitating knowledge sharing and improving the management of knowledge are
seen as critical challenges in the public sector (Silvi and Cuganesan, 2006;Kim and Lee,
2006). In the knowledge-based economy, the ability of organisations to create, transfer and
adopt knowledge, rather than allocate efficiency, determines their long-run performance
(Prahalad and Hamel, 1990). An increasing number of public sector organisations are
therefore making an effort to set up knowledge management (KM) systems and practices
to more effectively share and use the knowledge they possess. Moreover, an expanding
body of research has highlighted the importance of knowledge in organisations.
KM has traditionally focused on information technology and information-driven
perspectives (Davenport et al. 1998). Today, there is increasing recognition of the role of
individuals in KM processes, as well as a greater level of interest in the people perspective
of knowledge in organisations (Stenmark, 2001). This perspective acknowledges that the
individuals in organisations are those who possess knowledge (Grant, 1996;Spender and
Grant, 1996), and thus, the key to successfully managing knowledge is now seen as
dependent on the connections between individuals in the organisation (McDermott, 1999).
Received 30 October 2015
Revised 18 March 2016
31 March 2016
Accepted 4 April 2016
The authors would like to
thank TEKES (Finnish Funding
Agency of Innovation) for
funding this research, and
especially the INWORK
programme, which provided
access to the data and other
support.
DOI 10.1108/JKM-10-2015-0414 VOL. 20 NO. 4 2016, pp. 749-768, © Emerald Group Publishing Limited, ISSN 1367-3270 JOURNAL OF KNOWLEDGE MANAGEMENT PAGE 749
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There is also increasing empirical evidence highlighting the importance of people and
people-related factors as key priorities in knowledge processes within organisations
(Andrews and Delahaye, 2000). Among these knowledge processes, the effective sharing
of relevant specialised knowledge plays a fundamental role in an organisation’s
competitive advantage and sustained performance (Kogut and Zander, 1996;Argote and
Ingram, 2000;Wang and Noe, 2010). Therefore, the effective sharing of knowledge can be
a key productivity driver in public sector organisations (Silvi and Cuganesan, 2006). Along
with several recent studies (Bock and Kim, 2002;Chow and Chan, 2008;Chang and
Chuang, 2011), the authors assume that knowledge-sharing behaviour is motivated and
executed mainly at the individual level. In general, knowledge sharing is the contribution
that individuals make to the collective knowledge of the organisation (Cabrera and
Cabrera, 2002). On a fundamental level, it forms the micro-foundations that explain how
knowledge is utilised to reach organisational-level outcomes (Foss et al., 2010). Thus, an
organisation’s ability to effectively utilise its knowledge relies substantially on its people,
who actually share, create and use knowledge.
However, a coherent understanding of the factors impacting knowledge sharing in terms of
individual-level performance outcomes appears to be lacking (Lin et al., 2006). While there
are many studies discussing the drivers of individual-level knowledge sharing in
organisations (Chow and Chan, 2008;Tohidinia and Mosakhani, 2009;Chang and Chuang,
2011), there is not much evidence of how the actualised knowledge-sharing behaviour
eventually affects the performance of those individuals who share knowledge. This gap is
also highlighted by He and Wei (2009), who suggest that previous studies tend to neglect
the link between the attitude leading to the intention to share knowledge and actual
knowledge-sharing behaviour. These studies either stop at the prediction of behavioural
intention or directly examine a group of factors that impact actual behaviour. There is thus
a need for further studies to provide more accurate explanations of the mediating role of the
knowledge-sharing behaviour of individuals between knowledge-sharing propensities and
performance.
Additionally, most research on knowledge sharing concentrates on private companies, with
relatively few empirical studies on knowledge sharing in public sector organisations
(Amayah, 2013;Sandhu et al., 2011;Willem and Buelens, 2007). However, the
phenomenon of knowledge sharing might have some unique characteristics in the public
sector context (Yao et al., 2007); for example, hierarchical structures, bureaucratic systems
and the perception of knowledge being withheld as a source of power might pose
additional challenges to knowledge sharing (Liebowitz and Chen, 2003). McAdam and
Reid (2000) found that KM in the public sector tends to utilise more people-based
approaches and focuses more on social interaction than KM in private companies. The
uniqueness of the public sector suggests that it might be unwise to directly apply the
results of studies conducted in private sector firms to public sector organisations and that
there is a need to conduct empirical investigations of public organisations.
To address these research gaps, the present study examines whether individual-level
knowledge sharing (i.e. the propensity and actualised behaviours of individuals in sharing
knowledge with other organisational actors) affects individual work performance in public
sector organisations. The argument put forward is that shifting the perspective from an
organisational level to the level of individual employees contributing their knowledge to
‘‘The results demonstrated that in the public sector, the
propensity to share knowledge predicts the likelihood of
engaging in knowledge-sharing behaviours.’’
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others in the organisation, to a greater or lesser extent, offers a more fine-grained analytical
approach that helps untangle the “black box” of knowledge-based value creation in
organisations (cf. Minbaeva et al., 2009). Although the importance of knowledge sharing for
overall organisational performance appears to be well established in the literature (Lee and
Choi, 2003;Du et al., 2007;Hsu, 2008;Saenz et al., 2009), there is a dearth of studies
demonstrating such a relationship at the level of individuals – even though individuals are
precisely those who share (or withhold) knowledge with one another within an organisation.
To examine this issue, the authors use a framework dividing individual knowledge sharing
into two dimensions: the individual propensity towards sharing knowledge and the
executed knowledge-sharing behaviour. In this study, knowledge sharing is referred to in
the context of “sharing” as such and does not take a specific stance on the actualised
transfer, as per Foss et al. (2010) and Chatzoglou and Vraimaki (2009). The authors then
examine the impact of these on individual work performance by statistically analysing
survey data collected from 595 members of a public organisation. The empirical analysis
confirmed the stated hypotheses that positive knowledge-sharing propensity impacts
positively on knowledge-sharing behaviour and that knowledge sharing mediates the
relationship between knowledge sharing and individual performance. Moreover, the latter
effect was found to be significant amongst the most highly educated members of the
organisation but not amongst those with the lowest educational levels. In what follows, the
authors discuss the theoretical background and hypotheses, followed by the research
design and data collection. On the basis of the results and analyses, the paper ends with
an analysis on the implications for research and practice.
Theoretical background and hypotheses
In this section, the authors formulate the theoretical basis for the main argument. The
setting is structured around the knowledge governance perspective, specifically the notion
of micro-foundations of knowledge sharing (Felin and Foss, 2005;Abell et al., 2008;Foss
et al., 2009,2010;Minbaeva, 2013). From this perspective, organisational performance is
oftentimes measured with organisational-level antecedents and outcomes, while, in reality,
these are formed from individual (i.e. micro-level) activities, processes, behaviours and
outcomes (Felin and Foss, 2005;Abell et al., 2008;Foss et al., 2009,2010;Minbaeva,
2013). This line of argumentation is then followed by focusing on knowledge-sharing
propensities and behaviour at the individual level and examining the effects of this process
on individuals’ work performance outcomes.
Knowledge sharing in organisations
Knowledge sharing generally refers to moving knowledge between different organisational
actors, both within and between departments and hierarchical levels (Bhatt, 2001;
Szulanski, 1996). The key goal of knowledge sharing amongst employees in an
organisation is to transfer knowledge into organisational assets and resources (Dawson,
2001). Knowledge sharing is important because it enables the spread of knowledge as
organisational collective knowledge and helps the company use available resources in an
efficient and effective manner (Grant, 1996;Argote and Ingram, 2000). Furthermore,
knowledge sharing leads to better utilisation of existing knowledge and is also a key to
‘‘It can be argued that public organisations are nowadays
rather knowledge-intensive, and focusing knowledge
management research efforts on them would be a feasible
area for research.’’
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knowledge creation (Nonaka, 1994;Kogut and Zander, 1996) and innovation (Cohen and
Levinthal, 1990). The key issue from the organisational perspective is that knowledge
should be transferred to where it is needed and where it can be applied to productive use.
Various channels can be used in knowledge sharing: official (e.g. trainings) or unofficial
(e.g. coffee table discussions), personal (e.g. work rotation) or impersonal (e.g. company
databases) (Alavi and Leidner, 2001). The sharing process can occur either explicitly,
through direct advisory communication, or implicitly, without the recipient being able to
articulate the acquired knowledge (Argote and Ingram, 2000). Knowledge sharing is an
important basis for competitive advantage in firms (Kogut and Zander, 1996;Argote and
Ingram, 2000). It has been argued that encouraging knowledge sharing is the most
important aspect of consciously managing knowledge (Bock and Kim, 2002).
Correspondingly, several reviews in the field of KM have found knowledge sharing to be the
most common concept explored in the related literature (Hislop, 2010;Edwards et al.,
2009).
The importance of knowledge sharing underlines the agentic power of individual
employees, recognising that the individuals engaging in knowledge sharing decide how
they want to utilise their skills and intellect, as well as direct their efforts on the basis of
personal motivation. As knowledge is largely tacit and embedded in individual experiences
(Polanyi, 1966; Nonaka), perspectives and values, it is dispersed and distributed all around
the organisation (Tsoukas, 1996). Each member of the organisation is likely to have some
important knowledge that no one else in the firm possesses. Sometimes, relevant
knowledge is in the customer interface or marketing and sales department; other times, it
is on the shop floor. Furthermore, the individual decision to either share or withhold
knowledge in work-related interactions comes down to individual motivations and cannot
be forced via means of management control (Spender, 1996;Käser and Miles, 2002). As
knowledge sharing is mainly motivated and executed at the individual level (Bock and Kim,
2002), it should be explained by using individual-level constructs. More specifically, the
argument here is that to understand the micro-foundations of organisational knowledge
sharing, one has to distinguish the knowledge-sharing propensities of individuals from their
knowledge-sharing behaviours. The first represents the propensity of organisational actors
to share their knowledge with one another as well as organisational impersonal systems;
the latter is the actualised behaviour taken to enact the intentions. The expectation is that
both of these will impact the resulting performance in terms of the general individual-level
work performance of the knowledge-sharing employee.
Knowledge-sharing propensity, behaviour and work performance
As argued above, knowledge-sharing activities cannot be forced; they ought to be
voluntary (Käser and Miles, 2002). The authors posit that an individual’s propensity to share
knowledge consists of three elements: a general positive attitude towards sharing
knowledge; the perceived benefits of knowledge sharing and knowledge sharing-related
self-efficacy. Taken together, these three factors represent an individual’s likelihood for
sharing knowledge in his/her work organisation and impact the extent to which they engage
in knowledge-sharing behaviours.
Concerning knowledge-sharing attitude, in general, an attitude is “a psychological
tendency that is expressed by evaluating a particular entity with some degree of favour or
‘‘The present study found interesting results regarding how
the model works with employees of different educational
levels.’’
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disfavour” (Eagly and Chaiken, 1993). Attitudes exert a directive influence on behaviour.
Positive attitudes towards an object generally lead to behaviours that support the attitude
towards the object, whereas negative attitudes are predisposed to unfavourable
behaviours towards the object. A knowledge-sharing attitude can be defined as the degree
of one’s positive feelings about sharing one’s knowledge (Bock and Kim, 2002). In terms of
knowledge sharing, this leads to the assertion that positive attitudes towards knowledge
sharing are likely to increase the propensity of knowledge-sharing behaviours.
There are several theoretical arguments supporting the attitude–behaviour linkage.
According to a review of the knowledge-sharing literature (Wang and Noe, 2010), the most
utilised theoretical framework for examining the impact of individual attitudes on knowledge
sharing is the theory of reasoned action (TRA) (Fishbein and Ajzen, 1975). The TRA posits
that individual behaviour is grounded in the rational calculation of alternatives, and a given
behaviour is more likely to the extent that an individual has positive attitudes towards it, i.e.
believes that it is likely to lead to valued outcomes, and that it meets the subjective norms
of the agent. The related theory of planned behaviour (TPB) (Ajzen, 1991) adds to TRA the
criterion that the behaviour in question should also be subject to volitional control by the
individual. Thus, according to these perspectives, an individual is likely to engage in
knowledge-sharing behaviours if he/she has a positive attitude towards knowledge sharing,
sees it as encouraged by the meaningful peer groups to whose opinions he/she is willing
to comply and believes that he/she is competent to deliver the behaviour in question. Both
the TRA and TBP have been found to be useful in analysing a variety of behaviours in social
settings (Shappard et al., 1988), including knowledge sharing. For example, Bock and Kim
(2002);Ruy et al. (2003) and Tohidinia and Mosakhani (2009) found that positive attitudes
were an important factor in explaining behavioural intentions to share knowledge.
Furthermore, by grounding on the TRA and social capital factors in organisations, Chow
and Chan (2008) found that social capital in organisations affects attitudes and norms
relating to knowledge sharing, as well as the related behavioural intentions. However, it
should be noted that these studies focus on the impact of knowledge-sharing attitudes
towards behavioural intentions rather than actualised behaviours (cf. He and Wei, 2009).
Perceived benefits and costs is another explanatory factor in an individual’s positive
propensity towards knowledge-sharing behaviour. It is grounded in social exchange
theory, which argues that actions are based on evaluations of the relative cost/benefit ratios
of a given behaviour (Blau, 1964). Expected rewards might be of a monetary or
non-monetary nature. For example, individuals may fear loss of superiority and knowledge
ownership if they share their own personal knowledge (Bartol and Shrivastava, 2002) and
therefore hoard it rather than share it with others (Wah, 2000). It would appear that the
hoarding of knowledge exists especially when the sole ownership of knowledge is
considered as a power resource. However, lack of knowledge sharing may simply be
attributed to disengagement (Ford and Staples, 2010;Ford et al., 2015), meaning that there
is neither active knowledge sharing taking place nor intentional knowledge withdrawal. This
can be the case especially when the individual does not perceive any benefits from sharing
knowledge, which would have otherwise justified the effort of doing it without being afraid
of the potential dire consequences of doing it in terms of losing ownership of that
‘‘It was found that knowledge sharing only improves the work
performance of more highly educated employees, while
those employees with only an elementary or high school
education did not seem to benefit from knowledge sharing in
terms of outperforming their colleagues.’’
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knowledge. In sum, based on social exchange theory, it can be expected that perceived
benefits will lead to an increased likelihood of knowledge-sharing behaviour, whereas
perceived costs have a negative influence on engaging in knowledge sharing.
Finally, in addition to individual beliefs and attitudes regarding the consequences of a given
behaviour (proposed by the TRA/TPB and social exchange theory), an individual’s
perception of his/her self-efficacy might impact the propensity to share knowledge.
Self-efficacy (Bandura, 1986) means that an individual believes in his/her capacities to
accomplish a given task; it is argued that people will opt to perform tasks in which their
competency beliefs are high and will avoid behaviours in which they anticipate low skill
levels to perform. In the context of knowledge sharing, several studies have found that
knowledge sharing self-efficacy, i.e. one’s confidence in the ability to provide knowledge
that is valuable to others, influences knowledge-sharing behaviours. For example, Cabrera
et al. (2006) and Lin (2007) found that confidence in one’s ability to share useful knowledge
leads to knowledge sharing. Similarly, Kankanhalli et al. (2005);Hsu et al. (2007) and Chen
and Hung (2010) found that employees with high knowledge-sharing self-efficacy were
more likely to engage in knowledge-contributing behaviour than those with low self-efficacy
beliefs.
Taking into account the argumentation thus far, the authors suggest the first hypothesis:
H1. Positive knowledge-sharing propensity is positively associated with knowledge-sharing
behaviour.
The second key concept in the model presented here is a definition of knowledge-sharing
behaviour as action in which employees spread relevant information to other employees
within their organisation (Bartol and Shrivastava, 2002). It concerns the actualised
behaviours taken to share one’s experiences with colleagues or the organisation at large,
either in codified or non-codified forms of knowledge.
There are three main lines of argumentation that suggest that knowledge-sharing behaviour
leads to increased individual-level work performance. First, sharing significant amounts of
relevant knowledge typically serves as a function of a person’s expertise. Maintaining and
developing a specific bundle of skills in an organisational setting requires utilising those
skills repeatedly (Winter, 2003). This is connected to knowledge sharing, in that, the sharing
of knowledge on relevant issues with other organisational members signals the beneficial
utilisation of relevant skills and competences, as other members have deemed receiving
such knowledge useful. Furthermore, sharing relevant knowledge across organisations
signals individuals’ expertise in relation to other actors and could open up additional
possibilities for utilising this knowledge in ways that lead to improved job performance.
Second, sharing considerable amounts of relevant knowledge in an organisation improves
the organisational embeddedness and influence of that particular actor. It has been found
that the higher the amount of useful knowledge shared by a particular individual in an
organisation, this individual becomes a more visible and desirable source of knowledge
(Cross and Gray, 2013). In a modern organisational environment where the key
value-creating resource is knowledge, these central knowledge “hubs” possess great
influence and legitimacy through their networks and, consequently, have greater potential
for higher individual job performance. Third, based on social capital theory, knowledge
sharing often (while not always) leads to reciprocity (Nahapiet and Ghoshal, 1998;Schultz,
2001). In general, the norm of reciprocity is one of the key components of the moral codes
within social systems (C
ˇerne et al., 2014), which means that employees who share
significant amounts of work-related knowledge are also more likely to receive relevant
knowledge in the same process. However, knowledge sharing is not always reciprocated
and might exhibit a level of asymmetry (Cabrera and Cabrera, 2002). At the same time,
knowledge itself is an organisational and collective phenomenon (Spender, 1996), and the
sharing of that knowledge links the sharer and the recipient into an interactive context
where the value of the knowledge is put under collective scrutiny and, often, under
discourse (Tsoukas, 1996). The authors thus argue that individuals who share considerable
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amounts of knowledge are more likely to be connected to the relevant knowledge
generated by other organisational actors and can use such knowledge to perform better in
their daily work assignments. The contention here is that this increased connectivity and
reach of relevant intra-organisational knowledge will very likely lead to enhanced work
performance.
Grounded on these arguments, the second hypothesis is formulated:
H2. Knowledge-sharing behaviour is positively associated with individual performance.
Finally, the authors argue that knowledge-sharing behaviour mediates the relationship
between knowledge-sharing propensity and individual work performance. Here, the contention
is that the propensity towards knowledge sharing does not only explain why some employees
perform better at their jobs than others. According to TRA, the propensity to do something
should lead to actual behaviour and that it is only through realised behaviour that actual
performance can be affected. Similarly, so as to impact organisational knowledge processes,
the propensity for knowledge sharing (including attitudes, utility expectation and
self-effectiveness) needs to lead to behaviour. While the causal relationship between
propensity and behaviour is accepted as given in everyday thinking, the extensive research
conducted in the field of social psychology has demonstrated that people’s attitudinal
propensities tend not to accurately predict their future behaviours, for example, in the fields of
health (e.g. smoking, using condoms) and ecology (e.g. recycling). Rather, they impact
behavioural intentions, which then impact actualised behaviours, as illustrated by the TRA and
TPB (Fishbein and Ajzen, 1975;Ajzen, 1991). Thus, acknowledging that the linkage between
the propensity towards knowledge sharing and actualised behaviours is mediated by
behavioural intentions makes it reasonable that the path from propensity to performance is far
from self-evident. This is illustrated by the fact that in the literature search conducted for this
research, the authors found no previous studies examining the full path from
knowledge-sharing propensity to behaviour and then performance. For instance, among the
existing studies examining the link between knowledge-sharing motivation and some
performance variables, Husted et al. (2006) measured motivation on the individual level but
performance on the organisational level.
In organisational reality, these phenomena work in parallel. Employees with a positive
propensity towards knowledge sharing realise it through their behaviour, and that behaviour
affects their work performance. As work performance in contemporary organisations is linked
directly to the knowledge content of employees, and for the reasons laid out in the earlier
hypotheses, the authors expect this mediation link to be a strong predictor of individual work
performance. Based on the discussions thus far, the mediation hypothesis is posited as follows:
H3. Knowledge-sharing behaviour mediates the relationship between positive
knowledge-sharing propensity and individual work performance.
Methods
Data collection and sampling
Using a Web-based questionnaire, the research data were collected in 2011 from employees
of a city-based organisation located in South-East Finland. There were 5,086 employees
altogether, and 595 answered our survey, yielding a response rate of 12 per cent. Among the
respondents, there were 386 (64.9 per cent) employees, 87 (14.6 per cent) experts, 77 (12.9
per cent) supervisors, 38 (6.3 per cent) unit directors and seven (1.1 per cent) belonged to the
top management group of the city. A total of 489 (82.1 per cent) were female, and 106 (17.8 per
cent) were male. The analysis of the subsamples yielded the following educational categories:
elementary (94), high school (221), vocational (152) and university (128) education. For the
most part, the questionnaire dealt with the respondents’ perceptions about the presence of
innovation as well as renewal-enabling and -hindering characteristics in their working
environment.
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Analysis
First, the authors validated the measurement model, including knowledge-sharing
propensity, behaviour and individual performance by means of confirmatory factor
analysis (CFA). Thereafter, structural equation modelling (SEM) was used to test each
hypothesis. All cases were processed through LISREL. PRELIS was used to compute
the covariance matrix. The maximum likelihood estimation method was also used. The
survey relied on self-report measures. The authors therefore used Harmon’s one-factor
test to check for common method variance (Podsakoff and Organ, 1986) and included
all the independent variables and the dependent variable in an exploratory factor
analysis. The data would have had a common method bias problem if a single factor
emerged that accounted for a large percentage of the variance in the resulting factors.
However, no single factor emerged: the first factor accounted for 37 per cent of the total
variance, and all the items retained in the factor analyses accounted for 69 per cent of
the total variance.
Measures
The multi-item measures for the key constructs were adopted and modified to fit a survey-style
questionnaire based on existing measures in the extant literature. The aim was to reach an
empirically sound model with a high level of construct and discriminant validity, which meant
that some items were dropped based on their low loadings to their respective constructs, and
that the remaining items indicated a good fit with the overall model (see Table I for full list of
measures and eventual empirical constructs). Knowledge-sharing propensity was measured
by asking the respondents to answer items relating to knowledge sharing in terms of their
attitude, benefits and self-efficacy on a scale from 1-7 (1 ⫽totally disagree, 7 ⫽totally agree).
The measure was based on Collins and Smith (2006), and it covers the key facets impacting
an individuals’ propensity towards sharing knowledge: attitudes towards knowledge sharing,
estimation of the benefits of knowledge sharing and related self-efficacy beliefs.
Knowledge-sharing behaviour was measured by asking the respondents how frequently they
shared specific types of knowledge with their organisational members on a scale from 1-7. The
measure covers the sharing of different types of knowledge – tacit, implicit and explicit – and
is based on Chatzoglou and Vraimaki (2009).Individual performance was measured by asking
the respondents to compare their performance, on a scale from 1-7, with that of their colleagues
who were doing similar work. This comprised items covering the key issues of overall work
performance, including the substance, quality, creativity and collaboration in relation to the job
profile and relative to the respondents’ colleagues. The measure was adopted from Walumba
et al. (2008). The control variables included four educational levels: elementary, high school,
vocational and university education. This helped to control for whether one’s educational level
affected relative work performance.
Measurement model, reliability and correlations
CFA was used to build and confirm the overall measurement model. Items with poor loadings
were omitted; for the remaining items, the loadings were high and statistically significant
(Table I). This supports the verification of the relationships between the indicators and the latent
constructs. The construct reliability of all the dimensions by far exceeded the recommended
level of 0.60; therefore, the model provided a reliable measurement of the construct.
Furthermore, all the constructs exceeded the acceptable level of 0.70 when measured with
Cronbach’s alpha. The measures of extracted variance on all dimensions were also on the
cut-off of 0.5 or exceeded the suggested cut-off of 0.50. In summary, the assessment of the
models provided good evidence of the validity and reliability of the measured constructs as well
as of the discriminant validity between the constructs (chi-square ⫽45.77, df ⫽32, p⫽0.5449,
RMSEA ⫽0.027, NFI ⫽0.985, NNFI ⫽0.993, CFI ⫽0.995, AGFI ⫽0.974). Note that there are
statistically significant positive correlations between them all, suggesting an empirical linkage
between these dimensions (Table II).
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SEM was used to test the hypotheses. To test for mediation using SEM, the authors followed the
suggestions of Hair et al. (2006). First, the base model was one of full mediation (i.e. that
knowledge-sharing propensity predicts knowledge-sharing behaviour and knowledge-sharing
behaviour predicts individual performance). Second, the direct effect of knowledge-sharing
propensity was tested on individual performance. Finally, the authors tested the partial
mediation model (whether the relationship between knowledge-sharing propensity and
individual performance remained significant when knowledge-sharing behaviour is added to
the model). Upon introducing knowledge-sharing behaviour in the model, the relationship
Table I Measurement items and results of CFA
Concept Item
Factor
loading AVE CR
␣
Knowledge-sharing
propensity
1. I see benefits from exchanging and
combining ideas with one another
Item
dropped
0.5 0.786 0.761
2. I believe that by exchanging and
combining ideas I can move new
projects or initiatives forward more
quickly than by working alone
Item
dropped
3. At the end of each day, I feel that I
have learned from other members
from my organisation by
exchanging and combining ideas
0.77
a
4. I am proficient at combining and
exchanging ideas to solve
problems or create opportunities
Item
dropped
5. I do not do a good job of sharing
my individual ideas to come up with
new ideas, products or services.
(reverse coded)
Item
dropped
6. I am capable of sharing my
expertise to bring new projects or
initiatives to fruition
0.81***
7. I am willing to exchange and
combine ideas with their co-workers
0.73***
8. It is rare for me to exchange and
combine ideas to find solutions to
problems. (reverse coded)
0.42***
Knowledge-sharing
behaviour
1 Reports, official documents (explicit
knowledge)
Item
dropped
0.596 0.698 0.813
2 Manuals, methodologies, models
(explicit knowledge)
0.70
a
3 Know-where, know-whom (implicit
knowledge)
0.80***
4 Experience, know-how (implicit
knowledge)
item
dropped
5 Expertise from education and
training (implicit knowledge)
0.81***
Individual work
performance
1 How good you are in your work
compared to your colleagues
0.85
a
0.525 0.763 0.746
2 How effective you are in your work
compared to your colleagues
0.72***
3 How would you estimate the quality
of your work compared to your
colleagues
Item
dropped
4 How creative you are in your work
compared to your colleagues
Item
dropped
5 How good collaborative capability
you have compared to your
colleagues
0.56
Notes:
a
Significance level is not available because the coefficient is fixed at 1; ***statistically significant at 0.01 significance level;
** statistically significant at 0.05 significance level
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between knowledge-sharing propensity and individual behaviour turned out to be insignificant.
Thus, full mediation (i.e. the base model), and not partial mediation, was supported.
Path models reflecting the posited relationship between knowledge-sharing propensity,
knowledge-sharing behaviour and individual performance were estimated to test the
hypotheses. The results of the path analysis are presented in Tables III–VII. To control the
effect of education, the authors tested the models among four groups (elementary school,
high school, higher vocational level and university education).
Results
The path coefficients and fit indices are presented in Tables III–VII (one table includes all
educational levels and one for each educational level). The path models reflecting the positive
relationship between knowledge propensity, knowledge sharing and individual performance
were estimated to test the hypotheses.
Table III (all data included) shows significant support for the direct positive path from
knowledge-sharing propensity to behaviour (the mediator) and from behaviour to individual
performance. The indicators show good fit with the model. Thus, overall, the result supports H1
(knowledge-sharing propensity is positively associated with knowledge-sharing behaviour)
and H2 (knowledge-sharing behaviour is positively associated with individual job
performance).
The direct-effect model exhibits a significant association between knowledge-sharing
propensity and individual performance. There is also a good fit. The mediation model
shows that the paths from knowledge-sharing propensity to knowledge-sharing behaviour
Table II Correlation matrix
Variable Mean SD 1 2 3
1. Knowledge-sharing propensity 5.2212 1.042 1
7. Scientific (national)
2. Knowledge-sharing behaviour 5.0720 1.243 0.558*** 1
8. Market (international)
3. Individual work performance 4.7215 0.732 0.102** 0.178*** 1
Notes: ***Statistically significant at 0.01 significance level; **statistically significant at 0.05 significance level
Table III The fit indices and path coefficients of the tested model
Path model
Base model Direct effect Mediation
Standardised
coefficient
Standardised
coefficient
Standardised
coefficient
Knowledge-sharing propensity ¡Knowledge-sharing
behaviour 0.71*** 0.71**
Knowledge-sharing behaviour ¡Individual work
performance 0.23*** 0.28**
Knowledge-sharing propensity ¡Individual work
performance 0.15*** ⫺0.05 n.s.
Overall fit
Chi-square (df)
45.92 (33) 18.75 (13) 45.77 (32)
p⫽0.05575 p⫽0.13110 p⫽0.05449
RMSEA 0.026 0.027 0.027
GFI 0.985 0.991 0.985
CFI 0.996 0.996 0.995
NNFI 0.994 0.993 0.993
IFI 0.996 0.996 0.995
Notes: Base model included; all educational levels, N⫽595; ***statistically significant at 0.01 significance level; **statistically
significant at 0.05 significance level
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and from knowledge-sharing behaviour (mediator) to performance are significant. The
statistics also reveal good fit indices for this mediation model. The path from knowledge
propensity to individual performance is not significant after knowledge-sharing behaviour is
introduced in the model. However, when running the models with subsamples of different
educational levels (Tables IV-VII), the results show that in the case of those with an
elementary or high school education, H1 is supported but that H2 and H3 are rejected.
Conversely, H1,H2 and H3 all find support amongst those with a vocational or university
education. Taken together, this analysis supports H1,H2 and H3: knowledge-sharing
behaviour mediates the relationship between knowledge-sharing propensity and individual
performance amongst those who have either a vocational or a university education.
Discussion
The results obtained from the overall dataset provide support for the three hypotheses.
Knowledge-sharing propensity leads to knowledge-sharing behaviour (H1), and behaviour
leads to improved individual job performance (H2). Further, the results support that
knowledge-sharing behaviour acts as a moderator between propensity and performance (H3).
Table IV The fit indices and path coefficients of the tested model
Path model
Base model Direct effect Mediation
Standardised
coefficient
Standardised
coefficient
Standardised
coefficient
Knowledge-sharing propensity ¡Knowledge-sharing
behaviour 0.821** 0.881***
Knowledge-sharing behaviour ¡Individual work
performance 0.028 n.s. 0.159 n.s.
Knowledge-sharing propensity ¡Individual work
performance ⫺0.034 n.s. ⫺0.171 n.s.
Overall fit
Chi-square (df) 31.34 (33) 8.05 (13) 29.27 (32)
p⫽0.55004 p⫽0.83949 p⫽0.60564
RMSEA 0.00 0.00 0.00
GFI 0.937 0.976 0.941
CFI 0.997 1.00 0.999
NNFI 0.996 1.00 0.998
IFI 0.997 1.00 0.999
Notes: Elementary school, N⫽94; ***statistically significant at 0.01 significance level; **statistically significant at 0.05 significance level
Table V The fit indices and path coefficients of the tested model
Path model
Base model Direct effect Mediation
Standardised
coefficient
Standardised
coefficient
Standardised
coefficient
Knowledge-sharing propensity ¡Knowledge sharing
behaviour 0.669*** 0.669***
Knowledge-sharing behaviour ¡Individual work
performance 0.062 n.s. 0.020 n.s.
Knowledge-sharing propensity ¡Individual work
performance 0.049 n.s. 0.042 n.s.
Overall fit
Chi-square (df) 40.05 (33) 14.77 (13) 39.95 (32)
p⫽0.18572 p⫽0.32189 p⫽0.15765
RMSEA 0.032 0.025 0.034
GFI 0.965 0.981 0.965
CFI 0.994 0.996 0.993
NNFI 0.991 0.994 0.990
IFI 0.994 0.996 0.993
Notes: High school, N⫽221; ***statistically significant at 0.01 significance level; ** statistically significant at 0.05 significance level
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Interestingly, when examining the different models with various educational backgrounds, the
arguments herein seemed to work only for individuals with higher education. Overall, these
results contribute to the literature on organisational knowledge sharing on an individual level
along the entire length of the propensity–behaviour–performance chain, which has until now
remained unexplored.
It is rather intuitive to suggest that knowledge sharing leads to improved performance in the
contemporary organisational context, which is based on knowledge and its utilisation (in
the current case, a public organisation). However, so far, there are not many studies
establishing this linkage at the level of the individual employee. Studies in
inter-organisational settings have shown that knowledge sharing leads to improved firm
performance (Ritala et al., 2015). The present study supports a similar argument at the
individual level. In relation to individuals in organisations, the results of this study suggest
that “knowledge is power” in the sense that sharing expert knowledge signals a legitimate
and desirable organisational position (Cross and Gray, 2013). The results also corroborate
Table VI The fit indices and path coefficients of the tested model
Path model
Base model Direct effect Mediation
Standardised
coefficient
Standardised
coefficient
Standardised
coefficient
Knowledge sharing propensity ¡knowledge sharing
behaviour 0.679*** 0.679***
Knowledge sharing behaviour ¡individual work
performance 0.290*** 0.283**
Knowledge sharing propensity ¡individual work
performance 0.109*** 0.007 n.s.
Overall fit
Chi-Square (df) 38.83 (33) 15.45 (13) 38.84 (32)
p⫽0.22359 p⫽0.28024 p⫽0.18875
RMSEA 0.034 0.035 0.038
GFI 0.951 0.972 0.951
CFI 0.988 0.993 0.987
NNFI 0.984 0.989 0.982
IFI 0.988 0.993 0.987
Notes: Vocational education, N⫽152; ***statistically significant at 0.01 significance level; **statistically significant at 0.05 significance
level
Table VII The fit indices and path coefficients of the tested model
Path model
Base model Direct effect Mediation
Standardised
coefficient
Standardised
coefficient
Standardised
coefficient
Knowledge sharing propensity ¡knowledge sharing
behaviour 0.651*** 0.651***
Knowledge sharing behaviour ¡individual work
performance 0.248*** 0.276***
Knowledge sharing propensity ¡individual work
performance 0.138** ⫺0.044 n.s.
Overall fit
Chi-Square (df) 34.88 (33) 16.48 (13) 34.92 (32)
p⫽0.37889 p⫽0.22424 p⫽0.33054
RMSEA 0.021 0.046 0.027
GFI 0.948 0.964 0.984
CFI 0.992 0.984 0.991
NNFI 0.990 0.974 0.987
IFI 0.993 0.985 0.991
Notes: University education, N⫽128; ***statistically significant at 0.01 significance level; **statistically significant at 0.05 significance
level
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the discussion on reciprocity and social capital (Nahapiet and Ghoshal, 1998;Schultz,
2001) by showing that sharing with others can eventually lead to focal actor benefits.
The findings relating to the educational level of the employees (that the models work only
in groups with higher education) warrant further scrutiny. Considering that the more highly
educated employees were more likely to conduct “knowledge work” types of tasks,
whereby utilising and producing information and knowledge were key elements of the job,
it makes sense that knowledge sharing would be an important issue for such employees,
thereby enabling proficient performance. In the old-school way of thinking, knowledge
hoarding rather than sharing was believed to benefit career advancement. Knowledge
sharing was seen to weaken an employee’s position, power and status in the organisation
(Probst et al., 2000). Moreover, today, there may be fears amongst employees that
knowledge sharing may reduce their job security because employees are uncertain about
the sharing objectives as well as the intentions of senior management (Lelic, 2001).
Alternatively, the lack of knowledge sharing might, in many cases, be characterised as
disengagement rather than hoarding. Disengagement in relation to knowledge sharing is
characterised by low communication and low protection of knowledge: the individual
neither actively shares nor actively withholds his/her understanding and expertise (Ford
and Staples, 2010;Ford et al., 2015). It is likely that this is what was being witnessed,
especially among the lower- and middle-level employees of our sample, which could
explain why the models did not work for these settings. However, understanding this in full
requires further study.
The present findings support those of Constant et al. (1994), who found that employees with
a higher educational level are more likely to be more favourable towards and active in
knowledge sharing. Especially if the knowledge sharing was perceived in terms of sharing
expertise, rather than in terms of passing on more simple information, it was more likely to
take place (i.e. the propensity for information sharing depends on the form of information).
Husted and Michailova (2002) argue that one reason for knowledge hoarding, or the
reluctance of the knowledge transmitter to share knowledge, relates to the potential loss of
value and bargaining power and, therefore, the protection of one’s personal competitive
advantages at work. The present results demonstrate that for employees with a higher level
of education, knowledge sharing constitutes power at work, as knowledge-sharing
behaviour leads to higher work performance. Conversely, for employees with a lower level
of education, it might be a question of simply refraining from knowledge sharing because
of disengagement (Ford et al., 2015) rather than actively and intentionally withholding
knowledge.
Conclusion
In this study, the authors examined the effect of knowledge-sharing propensity on
knowledge-sharing behaviour and on individual work performance among employees in a
public sector organisation. A hypothesis model was proposed such that knowledge-sharing
behaviour mediates the linkage between propensity and performance. This model was
empirically tested using a field survey of 595 members of a Finnish-based public sector
organisation. The model found strong support for the stated hypotheses. The results have
several theoretical and practical implications.
Implications for research
The present study makes several contributions to the literature. First, several studies
have noted the lack of research on knowledge sharing in the public sector (Willem and
Buelens, 2007;Syed-Ikshsan and Rowland, 2004). The present study addressed this
gap by empirically examining employees in a government organisation in Finland. The
results demonstrated that in the public sector, the propensity to share knowledge
predicts the likelihood of engaging in knowledge-sharing behaviours. This means that
extrapolating the findings of studies addressing private sector firms to public sector
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organisations in terms of knowledge sharing might be more valid than previously
argued (Yao et al., 2007;McAdam and Reid, 2000). In fact, it can be argued that public
organisations are nowadays rather knowledge-intensive, and focusing KM research
efforts on them would be a feasible area for research.
Second, while earlier studies have already touched upon the relationship between
knowledge-sharing propensity and behaviour (Bock and Kim, 2002;Ruy et al., 2003;
Chow and Chan, 2008;Tohidinia and Mosakhani, 2009;Reychav and Weisberg, 2010),
to the authors’ knowledge, this is the first empirical study examining the outcomes of
this linkage on individual work performance. The mediating model herein provides
novel results in this regard. In fact, based on the authors’ knowledge, only Reychav and
Weisberg (2009) have examined the impact of knowledge-sharing behaviour by the
knowledge provider (sharer) on individual work performance. According to their results,
tacit knowledge sharing had a positive impact on employee performance, while explicit
knowledge sharing exerted an indirect influence, mediated by the sharing of tacit
knowledge. Their model, however, did not take into account the knowledge-sharing
propensity aspect of the research setting; therefore, the present results provide a more
in-depth understanding by supporting and complementing these findings. Furthermore,
although several studies (Bock and Kim, 2002;Ruy et al., 2003;Tohidinia and
Mosakhani, 2009) have demonstrated that positive attitudes towards knowledge
sharing are an important factor in explaining behavioural intentions to share knowledge,
there is a major gap in terms of the scant attention paid to the actual impact of
knowledge-sharing propensity and knowledge-sharing behaviour on individual
performance. The focus here on actualised behaviour helps to highlight how intentions
are translated into behaviour and eventually impact work performance.
Third, the present study found interesting results regarding how the model works with
employees of different educational levels. As research on the knowledge-based issues
in organisations matures, it also fragments and gains deeper insights into the more
specific aspects of management (Haas and Hansen, 2005). By examining the role of the
educational level in the relationship between knowledge-sharing propensities and
behaviours with work performance, this research deepens the emerging discussion on
the micro-foundations of KM and contributes to the knowledge governance perspective
(Foss et al., 2010). Specifically, it was found that knowledge sharing only improves the
work performance of more highly educated employees, while those employees with
only an elementary or high school education did not seem to benefit from knowledge
sharing in terms of outperforming their colleagues.
Implications for practice
For managers of public organisations, a key lesson in the knowledge-based view is that
the role of human capital and individual employees is highly important. Individuals are
no longer mere elements of a production system but owners and controllers of the most
important factor of production – knowledge. The results herein present practical
implications, especially for managers and professionals in knowledge-intensive
organisations who endeavour to assess the role of knowledge sharing in work
performance outcomes. Knowledge sharing was in general found to be positive for work
performance, which is a well-recognised suggestion in the KM literature. However, as
the results propose the linkage with work performance, it is worthwhile to take this into
account in work design. For instance, barriers to knowledge sharing should be
minimised, while efficient knowledge-sharing areas should be made available. In
addition, as the propensity towards knowledge sharing eventually influences actual
behaviour, it is important to focus on how to affect those propensities among
organisational members, e.g. through management practices, compensation and
culture. Especially interesting is the finding that the knowledge-sharing–propensity–
behaviour–work performance linkage works only among the highly educated workforce.
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Thus, managers should consider providing various kinds of knowledge-sharing support
mechanisms and systems for different employee groups.
Limitations and further research directions
The present study has a specific research setting and survey study design that are
suggestive of several limitations. First, the study did not address the underlying issues
that are likely to impact the individual’s propensity towards knowledge sharing (such as
the individual’s propensity to trust others, or organisational issues such as rewarding
practices). Moreover, the study did not examine other potentially relevant antecedents
such as social capital or involvement (cf. Chang and Chuang, 2011), group-based
norms (Quigley et al., 2007), organisational climate (Bock et al., 2005) or rewards and
different types of motivations (Wang and Hou, 2015). Rather, the model focused on the
effects of individual propensity on actualised behaviours and work performance.
The empirical data utilised in this paper included observations from one public
organisation. It may be that in other contextual settings, the knowledge-sharing–
propensity–behaviour–work performance linkage might be different. The patterns might
differ, especially in organisations with an explicit strategic focus on knowledge sharing,
along with supporting human resources management practices such as rewarding and
recognition for knowledge sharing. Testing these additional contingencies in wider
contextual settings might be a fruitful avenue for further study. Also, a question left open
by the present study is whether the picture might be different in the event the sharing
of various types of knowledge, such as technical information and expert knowledge
(Constant et al., 1994), is examined.
While the research only addressed the assumed causal impact of knowledge-sharing
propensity and behaviour on individual performance, it should be noted that there might also
be a feedback loop from performance to propensity and behaviours. De Vries and van den
Hooff (2006) found that employees who believe in their own performance ability at work are
more likely to be willing and able to share knowledge with their colleagues. This might therefore
be a useful area for future research.
The present study used the survey method to study the phenomenon of knowledge sharing
and its performance implications. While this has certain merits, other types of research methods
could provide a better understanding of the propensity–behaviour–performance linkage. For
instance, laboratory experiments where knowledge-sharing situations can be tested might
provide a better understanding of how knowledge sharing is reciprocated. Further, studies
could utilise observational or other methods that do not rely on self-assessment, especially in
examining knowledge-sharing behaviour. Knowledge-sharing instances can be documented
and measured in a number of ways, including log data, observation and peer assessment.
Finally, it should be noted that the current study only addressed one dimension of the
knowledge-sharing phenomenon, that of the knowledge provider/contributor. The study did not
examine the knowledge-receiving/seeking part of the equation (Cummings, 2004; cf.
Cummings and van Zee, 2005). Further studies should complement the present work by
addressing the impact of the knowledge receiver’s propensity and behaviours towards work
performance. Future studies could also include the reception of knowledge as an explanatory
factor in work performance. While it is intuitive to assume that knowledge sharing also leads to
the reciprocal reception of relevant knowledge, the empirical inclusion of such a variable would
make the research setting more robust. Also, the boundary conditions relating to the benefits
of knowledge sharing could be assessed as too much knowledge sharing may lead to
redundancy and additional costs (Foss et al., 2010). Future research could also include
multi-level models in which the individual propensity–behaviour–performance linkage would be
nested within the larger organisational performance framework. Such a task is difficult to
conduct empirically, but it could potentially lead to significant insights.
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Further reading
Adler, P.C. and Kwon, S.W. (2002), “Social capital: prospects for a new concept”, Academy of
Management Review, Vol. 27 No. 1, pp. 17-40.
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pp. 571-582.
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motivation, opportunity, and ability to process brand information from ads”, Journal of Marketing,
Vol. 55 No. 4, pp. 32-53.
Siemsen, E., Roth, A.V. and Balasubramanian, S. (2008), “How motivation, opportunity, and ability
drive knowledge-sharing: the constraining-factor model”, Journal of Operations Management,
Vol. 26 No. 3, pp. 426-445.
About the authors
Kaisa Henttonen DSc (Economics and Business Administration) is an Associate professor at
LUT School of Business and Management and University Researcher at University of Eastern
Finland. Her research interest focuses on innovation, commercialization of knowledge and
(online) networks and entrepreneurship. Her work has been published, for example, in R&D
Management,Team Performance Management,Journal of Engineering and Technology
Management and International Journal of Innovation Management. She also works as a
consultant and, thus, she is involved in development activities in organisations also in practice.
She has over 10 years of experience on research and development projects conducted in
collaboration with industry. Also, she is a regular speaker in executive and professional
education programmes. Kaisa Henttonen is the corresponding author and can be contacted
at: kaisa.henttonen@lut.fi
Aino Kianto, DSc (Economics and Business Administration) is Professor of Knowledge
Management at the School of Business and Management of Lappeenranta University of
Technology, and the Academic Director of the Master’s Program in Knowledge Management
and Leadership. Her teaching and research focus on Knowledge Management, Intellectual
Capital, Creativity, Innovation and Organizational Renewal. She has authored and co-authored
more than 100 academic articles, papers, books and book chapters on these topics, and
received several awards for research excellence. Her expertise spans outside the academia:
for example, she is the inventor of the ORCI-method, used for assessing and developing
organizational renewal capability in more than 100 organizations across Europe, has worked
with the Future committee of the Finnish parliament and regularly lectures for companies and
practitioners.
Paavo Ritala, DSc (Economics and Business Administration) is Professor of Strategy and
Innovation at the School of Business and Management at Lappeenranta University of
Technology (LUT), Finland. He is interested in questions and themes concerning the
organization of value creation and appropriation in heterogeneous systems and networks,
where different actors and institutions co-evolve, collaborate and compete. In particular, his
research has focused on the topics of value creation and appropriation, innovation, networks
and ecosystems, coopetition and business models. His research has been published in
journals such as the Journal of Product Innovation Management,Industrial Marketing
Management and the British Journal of Management & Technovation. He is also closely
involved with business practice related to these topics through company-funded research
projects, executive and professional education programmes and in speaker and advisory
roles.
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