Journal of Knowledge Management
Emerald Article: Disentangling value-enhancing and cost-increasing effects
of knowledge management
Petra Andries, Annelies Wastyn
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To cite this document: Petra Andries, Annelies Wastyn, (2012),"Disentangling value-enhancing and cost-increasing effects of knowledge management", Journal of Knowledge Management, Vol. 16 Iss: 3 pp. 387 - 399
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Disentangling value-enhancing and cost-increasing effects of knowledge management
Petra Andries and Annelies Wastyn
Abstract
Purpose –The main purpose of this paper is to provide large-scale empirical evidence on the
value-enhancing and cost-increasing effects of knowledge management (KM)techniques.
Design/methodology/approach –The authors conduct structural equation analyses,using data from the Community Innovation Survey 2007and from annual accounts of 705innovative Belgian ?rms.Findings –Results con?rm that the use of KM techniques has an indirect positive impact on ?nancial performance via increased innovation performance.In addition,a direct cost-increasing effect of KM practices on ?nancial performance is observed.In the short term,this direct cost-increasing effect exceeds the indirect value-generating effect of KM techniques.Research limitations/implications –This study investigates the short-term effects of KM techniques.Future research should study the long-term costs and bene?ts.Data were collected in Belgium and may not re?ect the impact of KM practices in other geographic,economic or cultural settings.Practical implications –The ?ndings clearly indicate that the implementation of KM techniques entails signi?cant costs.Within a two-year time frame,the ?nancial costs of KM techniques are more visible than their potential bene?ts.An exclusive focus on the short-term implications of the use of KM techniques is hence likely to give a too pessimistic view on their potential ?nancial contribution.
Originality/value –This article is the ?rst large-scale study that disentangles both the value-enhancing and cost-increasing effects of KM techniques on ?nancial performance and that uses time lags and accounting data (as opposed to self-reported performance measures)to do so.
Keywords Knowledge management,Innovation performance,Financial performance,Belgium,
Value analysis,Cost effectiveness
Paper type Research paper
1.Introduction
A sustainable competitive advantage may not only result from tangible assets and resources,but also from knowledge that can be transferred,aggregated and appropriated and that is dif?cult to imitate (Grant,1996;Teece et al.,1997;Wernerfelt,1984;Earl,2001).It is therefore no surprise that ?rms have developed several techniques to manage the generation and sharing of knowledge within the ?rm and that their investments in these techniques have been increasing dramatically (Mills and Smith,2011).Also the scienti?c research on knowledge management (KM)has developed greatly in the past decade,resulting in important conceptual frameworks and theoretical models (Zack et al.,2009).Empirical evidence on how KM techniques in?uence ?rm performance is however mixed.Some studies demonstrate a positive impact of KM techniques on product innovation performance (e.g.Darroch,2005;Czarnitkzi and Wastyn,2009)and organizational performance in general (Zack et al.,2009;Mills and Smith,2011).It is assumed that by affecting innovation performance and organizational performance in general,KM techniques will have a positive effect on the ?rm’s ?nancial performance.However,with the exception of DOI 10.1108/13673271211238724VOL.16NO.32012,pp.387-399,Q Emerald Group Publishing Limited,ISSN 1367-3270j JOURNAL OF KNOWLEDGE MANAGEMENT j PAGE 387
Petra Andries is Senior
Researcher at the Centre
for R&D Monitoring,KU
Leuven,Leuven,Belgium.
Annelies Wastyn is a PhD
Student at the Department
of Managerial Economics,
Strategy and Innovation,
KU Leuven,Leuven,
Belgium.Received September 2011Revised December 2011December 2011
Accepted December 2011
‘‘Empirical evidence on how KM techniques in?uence?rm
performance is mixed.’’
Marque′s and Simon(2006)and Zack et al.(2009),extant empirical work(e.g.Darroch,2005;
Kalling,2003)does not con?rm this.Several causes for this lack of empirical clarity exist.
Firstly,there have been few large-scale studies on the relationship between KM and
organizational performance.The studies that have been published(for an excellent overview
see Zack et al.,2009)use relatively small samples.Secondly,with the exception of the work by
Zack et al.(2009),they aggregate a broad set of?nancial and non-?nancial performance
indicators,making it impossible to study the?nancial performance effects in particular.In
addition,the existing studies all rely on self-reported performance measures which differ
between studies and can therefore lead to divergent?ndings.Finally,these studies measure
the use of KM techniques and?rm performance at the same point in time,making it impossible
to establish whether KM techniques drive?rm performance or vice versa.
In this article,the authors do not intend to question the positive indirect relationship between
KM and?nancial performance.However,they argue that the simultaneous adoption of a
wide variety of KM techniques also triggers costs that might negatively in?uence the
?nancial performance of the?rm.For instance,some scholars(e.g.Choi and Lee,2003;
Williamson,1979)have suggested that techniques aimed at internal knowledge creation
and sourcing of external knowledge are costly and time-consuming.The purpose of this
study is therefore to:
B empirically disentangle the value-enhancing and cost-increasing effects of KM
techniques on?nancial performance;
B for a large sample of companies in a variety of industries;
B using accounting data as opposed to internally re?ective performance measures;and
B while controlling for the possibility of reverse causality.
The authors rely on a sample of705innovative Flemish manufacturing and service?rms.The
data on the use of KM techniques and innovation performance is collected via the
Community Innovation Survey2007which covers the years2004-2006.This is
complemented with information on?nancial performance from the annual accounts.The
structural equation analysis generates two important?ndings.The authors?nd empirical
con?rmation that the use of KM techniques has an indirect positive impact on?nancial
performance via product innovation performance.However,they also observe a direct
cost-increasing effect of the range of implemented KM techniques on?nancial performance.
Moreover,the analyses suggest that,within the observed time frame,the direct
cost-increasing effect of KM techniques is larger than the indirect value-generating effect
of these techniques,resulting in an aggregated negative effect of KM techniques on
?nancial performance.
This article consists of four sections.First the conceptual framework is introduced,
hypothesizing on the value-enhancing and cost-increasing effects of the range of
implemented KM techniques on?nancial performance.Subsequently,the methodology is
discussed.Next,the results of the analyses are presented.Finally,the authors point to the
main theoretical and managerial implications of their?ndings,they discuss the study’s main
limitations,and suggest interesting avenues for future research.
2.Conceptual framework and hypotheses
The purpose of this article is to disentangle the value-enhancing and cost-increasing effects
of KM techniques on?nancial performance.In order to do so,the authors developed a PAGE388j JOURNAL OF KNOWLEDGE MANAGEMENT j VOL.16NO.32012
conceptual framework that is presented in Figure1.This framework provides particular hypotheses on the connections between the range of KM techniques used by the?rm,its other innovation efforts,product innovation performance,and?nancial performance.
2.1The direct impact of knowledge management techniques on product innovation performance
It is widely accepted that an organization’s capability to innovate is closely tied to its intellectual capital,i.e.to its ability to utilize its individual knowledge resources.Several studies have underscored how new products and processes embody knowledge (e.g.Stewart,1997),described innovation as a KM process(e.g.Madhavan and Grover, 1998),and characterized innovative companies as knowledge creating(e.g.Nonaka and Takeuchi,1995).A key principle in the literature on new product innovation is that the rate of new product introduction is a function of a?rm’s ability to manage,maintain,and create knowledge(Nonaka,1994;Nonaka and Takeuchi,1995;Cohen and Levinthal,1990; Cusumano and Elenkov,1994;Clark and Fujimoto,1991;Wheelwright and Clark,1992; Kogut and Zander,1992;Henderson and Clark,1990).
As a result,KM techniques are considered important stimuli for product innovation in?rms (for an overview see Zack et al.,2009).McAdam(2000)developed and tested a theoretical model showing that KM allows?rms to innovate.Darroch’s(2005)analysis of443?rms across several sectors indicates that?rms effectively managing knowledge are likely to be more innovative.Czarnitkzi and Wastyn(2009),in their analysis of1,282manufacturing and service?rms,?nd that different KM techniques have a positive effect on product or process innovation performance.Also Gloet and Terziovski(2004)?nd a positive effect of knowledge management on innovation,when combined with human resources management and IT practices.In line with these previous?ndings,it is hypothesized that:
H1.The?rm’s use of knowledge management techniques has a positive direct effect on its product innovation performance.
2.2The direct impact of other innovation efforts on product innovation performance
Not only the use of KM techniques,but also other innovation efforts–such as investments in internal and external R&D,in new machines and software,in the acquisition of licenses,the training of personnel,or the marketing of new products–will affect the innovation performance of?rms(e.g.Ahuja et al.,2008;Schumpeter,1939).Therefore,the?rm’s other innovation efforts are also included in the conceptual model.In line with existing product innovation research,it is expected that,as?rms increase their investment in these innovation efforts,the probability of successfully launching innovative products increases signi?cantly.
H2.The?rm’s other innovation efforts have a positive direct effect on its product innovation performance.
Figure1Conceptual Framework
VOL.16NO.32012j JOURNAL OF KNOWLEDGE MANAGEMENT j PAGE389
2.3The indirect impact of knowledge management techniques and other innovation efforts on
?nancial performance
Most scholars assume that KM techniques will lead to increased?nancial performance via
increased product innovation performance.In the conceptual framework,the authors
explicitly test this assumption(see also Darroch,2005).Additionally,they test for a similar
indirect relationship between other innovation efforts and?nancial performance.
H1and H2state that both KM techniques and other innovation efforts positively affect the
product innovation performance.It is well accepted in the existing literature that this
performance in terms of(product)innovation will also affect a?rm’s performance(e.g.Han
et al.,1998;Manu and Sriram,1996;Mavondo,1999),and its?nancial performance in
speci?c.When a?rm is able to be the?rst one to launch a new or strongly improved product
on the market,it is expected to gain market share in that particular market.Moreover,?rms
can realize larger margins on such highly innovative products.It is therefore hypothesized
that:
H3a.The?rm’s use of knowledge management techniques–through its positive
impact on product innovation performance–has an indirect positive effect on its
?nancial performance.
H3b.The?rm’s other innovation efforts–through their positive impact on product
innovation performance–have an indirect positive effect on its?nancial
performance.
2.4The direct impact of knowledge management techniques and other innovation efforts on
?nancial performance
It has been suggested that the proper maintenance and implementation of KM techniques
entails signi?cant costs.For example,the successful implementation of a codi?cation
strategy is resource-consuming.The identi?cation,codi?cation,storage and–possibly
most important–update of the knowledge requires time and effort in addition to investment
in hardware and software(see,e.g.Choi and Lee,2003).When it comes to tacit knowledge,
the creation of a culture that encourages communication and cooperation often goes hand in
hand with expensive personal incentives such as gainsharing(Arthur and Aiman-Smith,
2001).Also the acquisition of external knowledge can be costly.The involvement of external
consultants not only induces monetary cost,but also transaction costs involved with the
acquisition of external knowledge which may be substantial.Cooperation with external
partners involves uncertainty,for example frequency of transaction recurrence and how
parties deal with the idiosyncratic aspect of the investment in collaborative effort,and
contracts that can never be designed optimally for both parties(see,e.g.Williamson,1979;
Faems et al.,2010).In line with these suggestions,it is hypothesized that:
H4.The?rm’s use of knowledge management techniques has a direct negative effect
on its?nancial performance.
Similarly the authors hypothesize a signi?cant negative direct relationship between
innovation efforts and the?rm’s?nancial performance.Investments in internal and external
R&D,in new machines and software,in the acquisition of licenses,the training of personnel,
or the marketing of new products,naturally have a cost-increasing effect.In addition,
companies need portfolio managers in order to coordinate and manage potential synergies
and con?icts between the different internal innovation projects as the intensity of innovation ‘‘Results con?rm that the use of KM techniques has a positive
indirect effect on?nancial performance via product innovation
performance.’’
PAGE390j JOURNAL OF KNOWLEDGE MANAGEMENT j VOL.16NO.32012
efforts increases(e.g.Cooper et al.,2004;Grif?n,1997).Recruiting such managers implies additional personnel costs.Therefore it is hypothesized that:
H5.The?rm’s other innovation efforts have a direct negative effect on its?nancial performance.
3.Methodology
3.1Sample
In order to test the conceptual framework,data on the?rms’use of KM techniques,their other innovation efforts,product innovation performance and?nancial performance is needed.Therefore the authors merged two different databases:the Flemish Community Innovation Survey(CIS)2007database and the BELFIRST database.
The CIS2007was conducted in several Member States of the European Union.The survey sought to develop insights into the innovative behavior of private organizations.For the Flemish part of the CIS2007survey,a representative sample of4,871–mostly private–Belgian manufacturing and service?rms was selected and a20-page questionnaire was sent out to them,inquiring about different innovation-related issues in the period2004-2006. The response rate was43.48percent(2118?rms).
Each year,the majority of Belgian?rms are legally bound to?le their annual accounts at the Central Balance Sheet Of?ce in order to provide third parties with reliable information on their ?nancial health,employment and development(Sels et al.,2006).Subsequently,these data are added to the BELFIRST database,an electronic database containing?nancial information on Belgian companies and businesses.As a result,this database provides detailed information on the?nancial performance of Belgian?rms.
After merging the Flemish CIS2007database and the BELFIRST database,the authors restricted their analyses to1,227companies that had at least tried(successfully or unsuccessfully)to develop a new product or service in the period2004-2006.Because of missing values on the variables that were constructed,the size of the sample was further restricted.In total,the sample of this study consists of705?rms.
3.2Measures
3.2.1Range of knowledge management techniques.In the CIS2007survey,organizations indicated whether they used a range of KM techniques in the period2004-2006. Respondents speci?ed whether or not they used:
B a written policy regarding KM;
B incentives for employees to share information within the company;
B speci?c resources to detect and acquire knowledge outside the company;
B a policy to involve external experts from universities,research institutes or other
companies in projects if necessary;and
B regular updating of internal databases or manuals regarding common practices,lessons
learned,or expert advice.
More speci?cally,they had to specify for each of these techniques whether they introduced them before2004and/or in the period2004-2006.These dummies were summed up to construct a variable,ranging from1until10,as a proxy for the range of implemented KM techniques.For example,if a company only used written policies regarding KM,and introduced such policies both before2004and in the period2004-2006,it gets a score of2. If a company introduced written policies regarding KM only in the period2004-2006,and also introduced policies to involve external experts,both before2004and in the period 2004-2006,it would receive a score of3.In order to account for the skewed distribution of the variable,the natural logarithm of1plus the range of implemented KM techniques was taken, which got labeled as ln(KnowledgeManagement).
VOL.16NO.32012j JOURNAL OF KNOWLEDGE MANAGEMENT j PAGE391
3.2.2Other innovation efforts.In the conceptual framework,the authors also incorporated
other innovation efforts as a factor that in?uences product innovation and?nancial
performance.In the CIS2007survey,each respondent had to report whether it had invested
in a range of innovation activities.More speci?cally,respondents speci?ed whether,in the
period2004-2006,they had:
B performed internal R&D;
B outsourced R&D to other parties;
B bought machines or software;
B bought external knowledge s.a.licenses;
B invested in training of personnel;
B developed activities for the market introduction of new products or services;or
B performed other activities for the implementation of new products or services.These
dummies were summed up to construct the variable InnovationEfforts,ranging from1
until7.
3.2.3Product innovation performance.In line with previous research(Belderbos et al.,2004;
Faems et al.,2005;Nieto and Santamaria,2007),product innovation performance is
measured as the proportion of turnover in2006attributed to new or strongly improved
products or services that the company introduced between2004and2006and that were
new to the market.In the CIS2007survey,‘‘new to the market’’means that the company was
the?rst one to introduce this new or strongly improved product in its markets.
The analyses do not incorporate the proportion of turnover attributed to new
products/services itself but instead the natural logarithm of1plus the proportion of
turnover attributed to new products in order to obtain a normal distribution,labeled
ln(InnovationPerformance).
3.2.4Financial performance.In order to assess the?nancial performance implications of KM
techniques,the variable Pro?tMargin06,representing the?rm’s pro?t margin in2006,is
used.
3.2.5Control variables.Several variables have been introduced in order to control for
possible confounding effects.Because of potential industry differences in terms of turnover
from new products/services and in terms of?nancial performance,the authors tried to
control for them by introducing a dummy variable called ServiceIndustry which is one if the
?rm is a service?rm,and zero if it is a manufacturing?rm.The authors also tried to include
dummies for more detailed industry classi?cations,but this caused problems for the
estimation of the model.Our?nal sample contained237service and468manufacturing
?rms.
Since the seminal writings of Schumpeter(1939),the relation between size and?rm
performance has been much debated(Ahuja et al.,2008;Cohen,1995).Within the
framework of our analyses,the variable ln(Size),measured by natural logarithm of1plus the
number of full time equivalents employed in2004,has therefore been included within the
different models as a control variable.
With R&D remaining a centralized function within numerous?rms,the product innovation
performance of companies may be affected by whether or not they are part of a group.
Deeds and Hill(1996),for example,found evidence that?rms listed as a subsidiary of
another?rm,performed signi?cantly better in terms of bringing new products to the market
than non-subsidiary?rms.To test the differential behavior of subsidiaries,a dummy,labeled
GroupMember,has been included in the analyses.
Finally,past?nancial performance Pro?tMargin04is measured using the pro?t margin in
2004.This will control for any residual unobserved heterogeneity across?rms leading to
systematic differences in?nancial performance.In addition,it allows assessing whether
past?nancial performance has an impact on the use of KM techniques.
PAGE392j JOURNAL OF KNOWLEDGE MANAGEMENT j VOL.16NO.32012
4.Results
4.1Descriptive statistics
Table I gives an overview of the most important descriptive statistics.The average?rm in the
sample employed275full time equivalents in2004.It had a pro?t margin of3.91and4.63for
2004and2006respectively.From Table I it becomes apparent that the number of
implemented KM techniques was rather limited.On average,the?rms in the sample scored
only1.92out of10(and had an average value of0.85for Ln(KnowledgeManagement)).
When looking at the variable InnovationEfforts,we see that the average?rm conducted3.85
types of innovation activities(out of7).On average,the respondents attributed8.59percent
of their turnover to new or strongly improved products that the company introduced between
2004and2006and that were new to the market(and had an average value of0.07for
ln(InnovationPerformance).About73percent of the?rms in our sample belonged to a group,
while about34percent were service?rms.
4.2Model
To test the hypotheses,the authors used structural equation modeling(SEM)with manifest
variables.SEM is used to test models which contain causal relationships between variables.
It allows for testing direct and indirect relationships of one or more independent variables on
one or more dependent variables.Manifest variables are variables which are actually
observed and measured;they are present in the dataset.SEM models which only examine
manifest variables are also called path analysis models(Hatcher,1994).Compared to
ordinary linear regression models,this technique has two advantages(Sels et al.,2006;
Hatcher,1994).First,the method enables to de?ne and test hypothesized relationships
between variables.The output indicates whether the model is supported by the data as a
whole and gives a signi?cance test for the various individual relationships.Second,a
variable in a SEM model can either be dependent or independent.This allows testing the
indirect in?uence,if any,of certain variables.
The goodness-of-?t overview(see Table II)indicates that the theoretical model is adequately
supported by the data(Hatcher,1994).The results are presented in Figure2.The control
variables have been omitted in this graphical representation in order not to overload the Table I Descriptive statistics and correlations
Variable Mean S
Pro?t
Margin04
Pro?t
Margin06
Ln(Knowledge
Management)
Ln(Innovation
Performance)
Innovation
Efforts Ln(Size)
Group
member
Service
industry
Pro?tMargin04 3.9099.213 1.000
Pro?tMargin06 4.63411.0120.508 1.000
Ln(Knowledge
Management)0.8490.6930.02420.029 1.000
Ln(Innovation
Performance)0.0750.1190.0500.1170.134 1.000
Innovation
efforts 3.851 2.0540.0730.0710.3560.156 1.000
Ln(Size) 4.674 1.2930.0830.0430.12820.0110.271 1.000
GroupMember0.7350.4420.0460.0370.1520.0110.1050.303 1.000 ServiceIndustry0.3360.47320.11720.10520.05520.00420.12420.2540.067 1.000
Table II Goodness-of-?t measures
Industry Model
Bentler’s comparative?t index0.999
Bentler and Bonett’s non-normed index0.979
Bentler and Bonett’s normed?t index0.998
Chi-square test(p-value)0.236
VOL.16NO.32012j JOURNAL OF KNOWLEDGE MANAGEMENT j PAGE393
Figure2Results of estimated model
?gure.Below,the effects are interpreted and explained.First,the direct effect of KM
techniques and other innovation efforts on product innovation performance is investigated.
Second,the indirect impact of KM techniques and other innovation efforts on the pro?t
margin of the?rm is assessed.Third,the direct effect of KM techniques and innovation
efforts on the pro?t margin of the?rm is evaluated.Fourth,it is discussed how the
combination of these relationships leads up to overall?nancial performance.The
standardized path coef?cients are listed in Table III.
4.3The direct impact of knowledge management techniques and other innovation efforts on
product innovation performance
Based on previous studies(Darroch,2005;Czarnitkzi and Wastyn,2009),the authors
hypothesized a positive impact of the use of KM techniques on product innovation
performance.Tables III and IV indicate that Ln(KnowledgeManagement)indeed has a
signi?cant positive impact on Ln(InnovationPerformance).H1is thus con?rmed.
As stated in H2,the authors expected?rms which increase their innovation efforts to
generate signi?cantly more turnover from innovative products.This is supported by our data
Table III Standardized path coef?cients
Path from/to(1)(2)
(1)Ln(InnovationPerformance)0.0945**
(2)Pro?tMargin06
(3)Ln(KnowledgeManagement)0.0921*20.0725**
(4)InnovationEfforts0.1357***0.0432
Control variables
(6)Ln(Size)20.0708?20.0144
(7)GroupMember0.00370.0259
(8)Pro?t Margin040.04060.4987***
(9)ServiceIndustry20.026920.0269
Notes:?p,0.10;*p,0.05;**p,0.01;***p,0.001
Table IV Direct,indirect and total effects of Ln(KnowledgeManagement)on Pro?tMargin06
Pro?tMargin06Direct effects Indirect effects Total effects
Ln(KnowledgeManagement)20.07250.0087120.06381 PAGE394j JOURNAL OF KNOWLEDGE MANAGEMENT j VOL.16NO.32012
as the variable InnovationEfforts has a positive signi?cant(p,0.001)direct effect on Ln(InnovationPerformance).H2is thus also con?rmed.
4.4The indirect impact of knowledge management techniques and other innovation efforts on ?nancial performance
The authors expected that increased product innovation performance signi?cantly contributes to the?nancial performance of the?rm.The data indeed show a positive and signi?cant(p,0.01)impact of Ln(InnovationPerformance)on Pro?tMargin06.Taking into account the signi?cant direct impact of Ln(KnowledgeManagement)and InnovationEfforts on Ln(InnovationPerformance),H3a and H3b can therefore be con?rmed.The indirect effect of Ln(KnowledgeManagement)on Pro?tMargin06is0.0087(0.0921*0.0945),while the indirect effect of InnovationEfforts on Pro?tMargin06is0.0128(0.1357*0.0945).
4.5The direct impact of knowledge management techniques and other innovation efforts on ?nancial performance
In line with H4,Ln(KnowledgeManagement)has a signi?cant(p,0.01)direct negative effect on Pro?tMargin06.These?ndings seem to con?rm that the implementation and use of KM techniques is likely to trigger signi?cant costs,which in-turn have a negative impact on ?nancial performance.
The authors also hypothesized a direct negative effect of innovation efforts on the?rm’s ?nancial performance.However,in contrast to the expectations,results show no signi?cant relationship between InnovationEfforts and Pro?tMargin06.Hence,no support for hypothesis5is found.This suggests that the costs of innovation efforts are balanced by positive innovation outcomes other than product innovations.Examples are process innovations(Tidd et al.,2005,p.10)which may improve ef?ciency and therefore?nancial performance(Davenport,1993).
4.6The overall impact of knowledge management techniques and other innovation efforts on ?nancial performance
Table IV summarizes the direct,indirect and total effects of KM techniques on the?rm’s pro?t margin.When the indirect and direct effects of Ln(KnowledgeManagement)on Pro?tMargin06are summed,a slightly negative total effect is found.These?ndings suggest that,in the particular time frame that we observed,the cost-increasing effects of the implementation and use of KM techniques are higher than their value-enhancing effects. Given that a signi?cantly positive indirect effect of InnovationEfforts on Pro?tMargin06is found and no signi?cant direct effect of InnovationEfforts on Pro?tMargin06,the authors can conclude that the overall effect of InnovationEfforts on Pro?tMargin06is positive.In the next section,the theoretical and managerial implications of these?ndings are discussed.
4.7Impact of control variables
As Table III indicates,a signi?cant negative relationship between the size of the?rm and its product innovation performance is found.As expected,also a signi?cant relationship between Pro?tMargin04and Pro?tMargin06is observed.All other control variables turn out to be insigni?cant.
5.Discussion and conclusion
5.1Implications
Firms increasingly adopt KM techniques to spur the generation and acquisition of new knowledge.Whereas the use of KM techniques is assumed to have an indirect positive impact on?nancial performance via increased product innovation performance,extant empirical evidence is inconclusive.In this study the authors have empirically tested a conceptual model that connects the use of KM techniques,product innovation performance, and?nancial performance.
VOL.16NO.32012j JOURNAL OF KNOWLEDGE MANAGEMENT j PAGE395
‘‘An exclusive focus on the short-term effect of KM techniques
will give a pessimistic view on their potential?nancial
contribution which will keep managers from investing in KM.’’
The results contribute to the literature in several ways.First,the work meets the need for
large-scale empirical evidence on the contribution of KM to?rm performance(e.g.Zack
et al.,2009).Secondly,it is one of the few studies that looks at?nancial performance in
particular(for other exceptions see Tanriverdi,2005;Zack et al.,2009).In addition,it is one
of the?rst studies that relies on objective measures for innovation performance and?nancial
performance,the latter gathered from annual accounts.The authors believe that this is
preferable over the use of self-reported,internally re?ective performance measures,such as
used by,e.g.Darroch(2005)and Zack et al.(2009).By measuring the use of KM techniques
for a period that proceeds the point in time at which product innovation performance as well
as?nancial performance are measured,and by controlling for past?nancial performance,
problems of reverse causality are avoided.Results con?rm that the use of KM techniques
has a positive indirect effect on?nancial performance via product innovation performance.
The?ndings also provide more insight into the direct effect of KM on?nancial performance
by explicitly measuring the cost-increasing effects of such techniques.More precisely,the
authors?nd that–in the short term–the costs of implementing and maintaining KM
techniques outweighs the bene?ts.The total effect of the use of KM techniques on the
?nancial performance of the?rm turns out to be negative.
This latter?nding has important managerial implications.It indicates that an exclusive focus
on the short-term effect of KM techniques will give a pessimistic view on their potential
?nancial contribution which will keep managers from investing in KM.However,in reality the
value-enhancing effect of new KM techniques is likely to manifest itself over a longer period
of time than the cost effect of implementing them.Managers should hence be careful not to
ignore the long-term bene?ts that such techniques might bring along.
5.2Limitations and future research
The use of similar time-frames to measure the value-enhancing and cost-increasing effects
of the use of KM techniques on?nancial performance is clearly a?rst important limitation of
this study.The authors assessed how the use of KM techniques between2004and2006
affected?nancial performance in2006.Although they think that this time-frame is adequate
to assess the cost implications of KM techniques,they realize that it might be too short to fully
grasp their value-enhancing effects.Turning knowledge into innovation takes time.It is
highly likely that knowledge acquired and/or distributed in the period2004-2006will only
result in new products some years later.This implies that the current study is probably
underestimating the positive effects of KM techniques on innovation performance,and
hence also on?nancial performance.Therefore the authors stress the importance of future
research that systematically assesses the performance implications of KM techniques
across different time-frames.Such work should verify whether the net effect of KM
techniques on?nancial performance becomes positive over time and,if yes,at which point
in time this happens.
Although they were able to control for past?nancial performance in the analyses,they could
not control for past product innovation performance and past innovation efforts in our model.
As a result,it could not be assessed how previous innovation efforts and innovation
performance might in?uence the?rm’s use of KM techniques.In addition,the model
incorporates direct effects on product innovation performance of both KM and other
innovation efforts.Previous research suggests a complementary relationship between KM
and innovation efforts on product innovation performance,in the sense that innovation
efforts only contribute to product innovation performance when the knowledge generated in PAGE396j JOURNAL OF KNOWLEDGE MANAGEMENT j VOL.16NO.32012
these efforts is managed appropriately.In the SEM model,however,it was not possible to control for the potential complementarities of internal and external innovation efforts.Testing for complementarity between the range of knowledge management techniques and innovation efforts would have required adding an interaction effect in our SEM model. However,adding such interaction effects would trigger high multicollinearity and distributional problems(Rigdon et al.,1998;Williams et al.,2009).
Finally,it should be noted that KM techniques positively affect the?nancial performance of ?rms not only through their impact on innovation,but also through increased customer intimacy and operational excellence(Zack et al.,2009).These effects are not explicitly estimated in the empirical study,but are instead captured in the coef?cient of the direct relationship between the use of KM techniques and?nancial performance.This implies that the cost effect is probably even stronger than estimated in the empirical study.
Despite these limitations,the authors believe that this study has provided valuable insights in both the value-enhancing and cost-increasing implications of the use of KM techniques. They hope that their?ndings may help managers in optimizing their KM processes and that the suggestions for future research might stimulate academic research.
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About the authors
Petra Andries is a Senior Researcher at the Centre for R&D Monitoring(KU Leuven).Her research interests are innovation,entrepreneurship and technology-based ventures. Annelies Wastyn has been a PhD Student at KU Leuven since2008.Her research interests are innovation,R&D and management.Annelies Wastyn is the corresponding author and can be contacted at:annelies.wastyn@econ.kuleuven.be
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