Exploring the relationship between knowledge management practices and innovation performance

Journal of Manufacturing Technology ManagementEmerald Article: Exploring the relationship between knowledge management practices and innovation performanceMarianne Gloet, Milé Terziovski

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Exploring therelationship betweenknowledge managementpractices and innovationperformance

Marianne Gloet and

Mile Terziovski

The authors

Marianne Gloet is Senior Lecturer, School of Management,RMIT University, Melbourne, Australia.Mile Terziovski is Associate Professor, Euro-AustralianCooperation Centre for Continuous Improvement and GlobalInnovation Management, The University of Melbourne, Parkville,Australia.

Keywords

Knowledge management, Innovation, Performance appraisal

Abstract

The process of innovation depends heavily on knowledge, andthe management of knowledge and human capital should be anessential element of running any type of business. Recentresearch indicates that organisations are not consistent in theirapproach to knowledge management (KM), with KM approachesbeing driven predominantly within an information technology(IT) or humanist framework, with little if any overlap. This paperexplores the relationship between KM approaches andinnovation performance through a preliminary study focusing onthe manufacturing industry. The most significant implication thathas emerged from the study is that managers in manufacturingfirms should place more emphasis on human resourcemanagement (HRM) practices when developing innovationstrategies for product and process innovations. The study showsthat KM contributes to innovation performance when asimultaneous approach of “soft HRM practices” and “hard ITpractices” are implemented.

Electronic access

The Emerald Research Register for this journal isavailable atwww.emeraldinsight.com/researchregister

The current issue and full text archive of this journal isavailable atwww.emeraldinsight.com/1741-038X.htm

1. Introduction

In its ideal form, innovation has the capacity to

improve performance, solve problems, add value

and create competitive advantage for

organisations. Innovation can be broadly

described as the implementation of both

discoveries and inventions and the process by

which new outcomes, whether products, systems

or processes, come into being (Williams, 1999).

The process of innovation depends heavily on

knowledge, particularly since knowledge

represents a realm far deeper than simply that of

data, information and conventional logic; indeed,

the power of knowledge lies in its subjectivity,

underlying values and assumptions that underpin

the learning process (Nonaka and Takeuchi,

1995). As old distinctions between manufactured

objects, services and ideas are breaking down;

knowledge assumes a more pivotal role within

organisations (Davenport and Prusak, 1998).

According to Stewart (1997), the management of

knowledge and human capital should be an

essential element of running any type of business,

yet few individuals understand this challenging

area; and, given the potential of knowledge

management (KM) and intellectual capital as

sources of innovation and renewal, business

strategy should be focusing more on these issues.

This paper explores the relationship between KM

and innovation through measuring the effects of

knowledge management approaches and

innovation performance through a preliminary

study focusing on the manufacturing industry.

2. Literature review

2.1 Defining KM

The focus on issues of power and intellectual

capital in the general business and management

literature has implications for the study of KM.

Where information management was viewed as a

somewhat neutral and normative servicing system

in the organisational literature in the 1970s

(Handy, 1976; McRae, 1971), today KM has

emerged as a discrete area in the study of

organisations to the extent that it has become

recognised as a significant source of competitive

advantage (Nonaka, 1991; Nonaka and Takeuchi,

1995; Davis, 1998; Matusik and Hill, 1998;

Miller, 1999; Moore and Birkinshaw, 1998;

Stewart, 1997). Although having emerged as a field

of study in its own right, KM has been criticised

for being a misnomer and an oxymoron

Journal of Manufacturing Technology Management

Volume 15 · Number 5 · 2004 · pp. 402–409

q Emerald Group Publishing Limited · ISSN 1741-038X

DOI 10.1108/17410380410540390

The authors wish to thank Christopher J. Miller for

making available his database and allowing them to

utilise certain aspects of the data for this paper.

402

(Coleman, 1999), or for being “fuzzy” and

imprecise (McCune, 1999). While KM has

a concrete and tangible side characterised by

people, physical systems and processes, there is

a great deal of scope for interpretation, as KM

practices are highly subjective in nature and

subject to various interpretations. There is no

shortage of definitions of KM (Liebowitz, 1999);

however, for the purposes of this paper we will

highlight two broad definitions. For Beckman

(1999), KM concerns the formalisation of and

access to experience, knowledge, and expertise

that create new capabilities, enable superior

performance, encourage innovation, and enhance

customer value. Coleman (1999) defines KM as

an umbrella term for a wide variety of

interdependent and interlocking functions

consisting of: knowledge creation; knowledge

valuation and metrics; knowledge mapping and

indexing; knowledge transport, storage and

distribution; and knowledge sharing.

2.2 Approaches to KM

Definitions of the term “knowledge” vary

considerably, and often such definitions are not

clearly explicated in either the research literature or

in the operational context. For the purposes of this

paper, information can be characterised as “data

endowed with relevance and purpose” (Drucker,

1998), while knowledge can be defined as

“information combined with experience, context,

interpretation, and reflection” (Davenport et al.,

1998). Accordingly, all organisations deal in

knowledge. However, organisations can choose

between competing systems and processes to

acquire, manage, and disseminate knowledge.

These systems and processes are explicit as well as

implicit and can be influenced by personal and

organisational values and ideologies. In terms of an

organisation’s internal systems, organisations

actually filter acquired knowledge. For example,

one organisational culture may support a devolved

structure in KM while another’s culture may

choose more centralised systems. In another

organisation, information technology (IT) will

drive KM while another organisation will favour

a more human approach. At various points as

knowledge moves through an organisation, choices

are made about the most appropriate way to

manage its flow.

Research by Hansen et al. (1999) has indicated

that organisations do not adopt a uniform

approach to knowledge management. They outline

two distinct strategies utilised when selecting

a KM approach: a codification strategy, centred

around IT resources; and a personalization

strategy, centred around human resources (HR).

Their research also suggests that in the rare cases

when organisations attempt to adopt elements of

both approaches, this leads to problems of a

serious enough nature to undermine a business.

Sveiby (1997) has also referred to two distinct

approaches to knowledge management, one

focusing more on people, the other more on

technology.

Indeed, contemporary knowledge management

approaches appear to represent extensions of

either organisational learning or business

information systems, and these KM approaches

tend to be driven predominantly within an IT or

humanist framework or paradigm, with little if any

overlap (Gloet, 2000). This divide between KM

approaches has ramifications for both

organisational learning and innovation processes.

One body of literature on KM has its origins in

approaches to IT, information systems and related

issues. This canon supports an IT paradigm.

In contrast, a competing body of literature

supports a humanist paradigm in which the social

relations of organisational knowledge are

paramount. While this latter paradigm recognises

the technical side of KM, it also highlights the

significant influence of people in the process of

managing and interpreting knowledge. Whereas

literature in the IT paradigm focuses more on

tangible aspects of KM, such as collection and

manipulation of information, the humanist

paradigm concerns itself more with the nature of

learning and the harnessing knowledge as an

organisational resource. Compared to the “hard”

IT paradigm, the “soft” humanist paradigm

accords more attention to organisational slogans,

metaphors, and symbols (Nonaka, 1991).

Consequently, the analysis of KM in a humanist

paradigm is open to more interpretive

explanations.

To confound the study of KM in general, the

two paradigms necessitate two very different

approaches. In the IT paradigm, researchers have

accepted various extensions of information

processing/business information systems

management as springboards into KM. As a

consequence, their research focuses on the

collection, storage, and manipulation of essentially

objective or explicit data, employing

methodologies that implicitly construct an

organisation as an information processing system.

This diverts attention to how data are processed,

collected, and stored (Lado and Zhang, 1998).

Given this implicit focus in the IT paradigm, most

KM tools revolve around information systems and

software (Fusaro, 1998).

Within the humanist paradigm, recent literature

highlights the role of individuals and groups in the

processes of knowledge sharing and manipulation,

particularly with regard to highly interpretative

The relationship between KM practices and innovation performance

Marianne Gloet and Mile Terziovski


Volume 15 · Number 5 · 2004 · 402–409

403

forms of knowledge. Other themes in the paradigm

include the distinctions between tangible and

intangible knowledge, or explicit versus tacit

knowledge (Nonaka and Takeuchi, 1995; Nonaka,

1991). In addition, other studies explore the role of

knowledge and learning at the systems,

organisational, and cultural level of an

organisation (Nevis et al., 1995).

Other literature in the area of KM suggest that

a number of organisational or infrastructural

elements have the power to influence the success

or otherwise of KM within an organisation. These

include: a healthy organisational culture and

support infrastructure (Beckman, 1999; Zand,

1997; Quinn et al., 1997); management support

and proactive leadership (Davenport, 1996;

Beckman, 1999), empowerment of employees

(Davenport and Prusak, 1998; Liebowitz and

Beckman, 1998); understanding KM as a business

strategy (Ruggles and Holtshouse, 1999); strong

communication channels (Koulopoulos and

Frappaolo, 1999); and a commitment to

developing and sustaining a climate for learning

within the organisation (Starbuck, 1997;

Liebowitz and Beckman, 1998).

2.3 Innovation

There are numerous definitions of innovation in the

literature; however,most definitions share common

themes relating to knowledge, whichmay be turned

into new products, processes and services to

improve competitive advantage and meet

customers’ changing needs (Nystrom, 1990).

Carnegie and Butlin (1993) define innovation as

“something that is new or improved done by an

enterprise to create significantly added value either

directly for the enterprise or directly for its

customer.” Livingstone et al. (1998) refer to

innovation as “new products or processes that

increase value, including anything frompatents and

newly developed products to creative uses of

information and effective human resource

management systems”. Regarding the sources of

innovation in management, De Toni et al. (1998)

identify six sources of innovation, Drucker (1985)

identifies seven sources and Edquist (1997) refers

to nine. More recently, the Continuous

Improvement and InnovationManagement Project

(CIMA) has identified four enabling mechanisms

that contribute to continuous innovation and

improvement, these being capabilities, behaviours,

contingencies and levers (Gieski, 1999).

2.4 KM and innovation

From the literature, a number of elements of

successful KM have been identified. HR can be

seen as a strategic lever in creating competitive

advantage through the value of the knowledge,

skills and training (Becker and Gerhart, 1996).

There is also reference to the need for a strong IT

infrastructure within the organisation (Beckman,

1999; Libowitz and Beckman, 1999; Zand, 1997;

Davenport and Prusak, 1998). In addition, in

order to understand better the nature of

innovation, management must ensure that

innovation is woven into an organisational culture

(Cottrill, 1998). Several researchers have

emphasised the pivotal role of the management of

knowledge, particularly in creating an internal

working environment that supports creativity and

fosters innovation (Amabile et al., 1996; Carnegie

and Butlin, 1993; Soderquist et al., 1997).

The literature indicates the need to formulate a

method within a framework, to confront empirical

data in the interest of pursuing further insights into

the complex relationship between knowledge and

innovation. The following research questions are

articulated for analysis in this paper:

RQ1. Is a KM model based on IT and human

resource managment (HRM) a reliable

and valid instrument for measuring and

predicting the relationship between KM

practice and innovation performance?

RQ2. Is there a significant and positive

relationship between KM practices based

on IT and HRM and innovation

performance?

3. Theory and framework

Rigorous research involving the management of

innovation is scarce (AECD, 1998). While a

growing body of literature has attempted to

understand innovation, the literature shows

definite gaps in the investigation of KM processes

and innovation. Therefore, this study will pose

specific, relevant hypotheses in an attempt to gain

a greater understanding of the relationship

between innovation and KM practices relating to

both human resources and IT resources. The

following hypotheses are tested in this study:

H1. A KM model based on humanist/IT

criteria is a reliable and valid instrument

for measuring and predicting the

relationship between KM practice and

innovation performance.

H2. There is a significant and positive

relationship between elements of HR/

humanist approaches to KM and



relationship between elements of IT

focus on technological advancement

(e-commerce) to KM and innovation

performance.




Volume 15 · Number 5 · 2004 · 402–409

404

4. Methodology

This study utilised the results of a questionnaire

which had been previously developed by one of the

authors’ research students. This original

questionnaire had been developed to investigate

much broader perspectives on innovation in the

manufacturing industry. The instrument was

completed by operational or strategic managers

within the selected organisations. The objectives of

the original questionnaire were to establish the

extent to which companies were innovating in their

operations and to gain greater insight into the

drivers of continuous improvement and innovation

in manufacturing. Specific variables were isolated

from the original questionnaire and utilized for the

purposes of this research. In order to guarantee

content validity, the literature on KM and

innovation was canvassed in order to ensure

a match.

The target population included Australian and

New Zealand manufacturing companies across

a large range of industries in both the private and

public sector. The sample size totalled 70.

The data were analysed using SPSS for

Windows version 10.0. In order to reduce the

number of independent and dependent

variables, factor analysis was performed.

Cronbach alpha coefficients were calculated to

test the instrument’s reliability.

Multiple regression was used to test the

relationships between the variables as stated in

the hypotheses.

5. Quantitative analysis

5.1 Factor analysis results

Hair et al. (1987, p. 225) describe the

application of factor analysis as a means by

which to analyse the interrelationships among

a large number of variables (questionnaire items/

responses) and explaining these research variables

in terms of their common underlying dimensions

(factors/constructs). Factor analysis resulted in the

15 independent variables being reduced to three

factors/constructs, and the ten dependent variables

being reduced to one construct. A cut-off loading

of 0.300 was used to screen out variables that were

weak indicators of the constructs. According to

Hair et al. (1987, p. 239), factor loadings greater

than +0.30 are considered significant, while

loadings over +0.50 are considered very

significant. Most factor loadings were well above

the +0. 50 level, and as such, can be considered as

demonstrating a high level of significance

(see Tables I-III).

6. Discussion

The regression analysis produced equations that

represent the best prediction of the dependent

variable from several of the independent variables.

Therefore, the objective of the multiple regression

analysis was to determine which independent

variables (constructs) were important in predicting


6.1 Bi-variate correlation analysis

Table IV shows the bi-variate correlation

coefficients of factors of KM model and their

relationship with the innovation performance

construct developed earlier. Although the

correlation coefficients in Table IV were generally

above 0.2 and were highly significant it is

interesting to note that three out of the four

constructs have a significant and positive

relationship with innovation performance.

6.2 Regression model

Table III shows the multiple regression of the four

factors of the KM model regressed on the

dependent variable F5: innovation performance.

From these analyses, our intent was to testH1,H2

and H3 and hence contribute to knowledge about

the relationship of KM and innovation

performance.

6.3 Testing of H1

H1. A KM model based on humanist/IT

criteria is a reliable and valid instrument

for measuring and predicting the

relationship between KM practice and


Content validity. A category is considered to have

content validity if there is general agreement from

the literature that the model being tested has

measurement items that cover all aspects of

the variable being measured. Since selection of the

initial measurement for the KM items was based

on the extensive review of international literature

we claim that we have captured sufficient

independent variables to develop constructs of

sufficiently high explanatory power.

Construct validity. A measure has construct

validity if it measures the theoretical construct that

it was designed to measure. The construct validity

of each of the four constructs was evaluated by

using Principal Components Factor Analysis

(Nunnally, 1978). The measurement items for

each of the categories were factor analysed.

The results in Tables I and II show items with

factor loadings greater than 0.30.




Volume 15 · Number 5 · 2004 · 402–409

405

Criterion validity. This is also known as predictive

validity or external validity. In this instance, it is

concerned with the extent to which the model

is related to independent measures of innovation

performance. The criterion related validity of the

KM model was determined by examining the

multiple correlation coefficient computed for

the four categories and a measure of innovation

performance (R ¼ 0:923 as shown in Table III).

This indicates that the four categories of the KM

have a high degree of criterion-related validity

when taken together. The Adjusted R Squared for

this model was found to be 0.842. This means that

84.2 per cent of the variance in innovation

performance is explainable by the four constructs

developed in this study.

Table II Factor analysis results: dependent variables

Variables Factor loading Cronbach’s alpha

Factor 1 – innovation performance

Productivity/process improvement 0.560

Product innovation 0.766

Quality (product/process/service) 0.644

Lead-time (customer response/time to market) 0.841

Production cycle time 0.768 0.77

Dependent variable entered: innovation performanceMultiple R 5 0.923

R squared 5 0.851

Adjusted R squared 5 0.842Standard error 5 0.39744

ANOVA DF Sum of squares Mean square

Regression 4 58.733 14.683

Residual 65 10.267 0.158

F ¼ 92:955 Signf F ¼ 0:000

Table I Factor analysis results: independent variables

Variables

Factor

loading

Cronbach’s

alpha

Factor 1– IT focus on technological advancementsIT innovations are often aligned with technological advancements 0.353

IT systems are directly related to innovations in “product” development 0.444

IT is the key to diffusing knowledge throughout the enterprise 0.429

E-commerce is an innovation that has the potential to “revolutionise” this company 0.449 0.63

Factor 2 – IT focus on quality and productivityImpact of IT on productivity/process improvements 0.695

Impact of IT on product innovation 0.457

Impact of IT on quality (product/process/service) 0.395

Impact of IT on lead time (customer response/time to market) 0.482 0.67

Factor 3 – HRM focus on product and process innovationHRM’s impact on product innovation 0.430

HRM’s impact on lead time (customer response/time to market) 0.651

HRM’s impact on production cycle time 0.692

IT systems are directly related to innovations in “process” development 0.295 0.71

Factor 4 – HR and it focus on organizational learning and knowledge managementHRM’s impact on productivity/process improvement 0.396

HRM’s impact on quality(products/processes/services) 0.416

IT functions are a key element in the operation of the enterprise 0.325

Organisational learning is assisted with a constantly updated knowledge database 0.603 0.70




Volume 15 · Number 5 · 2004 · 402–409

406

Reliability. Internal consistency for the four

categories of the KM was estimated using

Cronbach’s alpha, which ranges between the

values 0.00 and 1.00 (Hair et al., 1992). Using the

SPSS for Windows reliability test program, an

internal consistency analysis was performed

separately for each of the categories of the KM

model. The analysis revealed that maximisation of

the Cronbach alpha coefficient would require

eliminating some items from each category of KM.

The reliability values shown in Tables II and III

generally meet or exceed prevailing standards of

reliability for survey instruments (Hair et al.,

1992). The first hypothesis, which stated that the

KM model is a reliable and valid instrument for

measuring and predicting the relationship between

KMpractice and innovation performance, is found

to be supported. Each of the four categories did

form a “solid” construct, and based on the above

we support H1.

6.4 Testing of H2


relationship between elements of HR/

humanist approaches to KM and


With reference to Tables III we make the

observation that the NR construct has a t ¼ 8:577,Sig:t ¼ 0:000. Furthermore, from Table IV we

make the observation that the Pearson Correlation

Coefficient for the relationship between the HR

construct and innovation performance r ¼ 0:741significant at the 0.01 level of significance. Based

on these data we support H2.

6.5 Testing of H3


relationship between elements of IT

focus on technological advancement

(e-Commerce) to KM and innovation

performance.

With reference to Tables III we make the

observation that the IT focus on technological

advancement construct has a t ¼ 0:363,Sig :t ¼ 0:718. Furthermore, from Table IV we

make the observation that the Pearson Correlation

Coefficient for the relationship between the IT

focus on technological advancement construct and

innovation performance r ¼ 20:418�� significant

at the 0.01 level of significance. Based on this data

we reject H3.

7. Discussion of results

It is important to note from these results, that we

cannot suggest that for a single company, IT/

technological advancements should not be

improved because they are not related to

organisational performance. Nor can we directly

say that this construct leads to worse performance,

and that the KM is “wrong” because some of the

factors do not contribute positively to explain

performance variance. The study was cross-

sectional and descriptive of a sample at a given

point in time. It appears that this explanatory

variable had a significantly negative regression

coefficient because of the way the least squares fit

treated the common variance of the explanatory

Table IV Correlation matrix of independent and dependent constructs

F1: e-commerce

and technology

F2: quality and

productivity

F3: human resource

management

F4: organisational

learning/KM

F5: innovation

performance

F1 1.00

F2 20.640** 1.00

F3 20.093 0.208* 1.00

F4 20.279* 0.232* 0.555** 1.00

F5 20.418** 0.657** 0.741** 0.632** 1.00

Notes: * Significant to 0.05, one-tailed; ** Significant to 0.01, one-tailed

Table III .Summary of regression analysis

Independent variables

Standardised

coefficients beta t Sig. of t

Factor 1 – IT focus on technological advancements 0.023 0.363 0.718

Factor 2 – IT focus on quality and productivity 0.511 8.047 0.000

Factor 3 – HRM focus on product and process

innovation 0.504 8.577 0.000

Factor 4 – HR and IT focus on organisational learning

and knowledge management 0.240 4.008 0.000




Volume 15 · Number 5 · 2004 · 402–409

407

variables. In terms of managerial insights, the

correlations and regression show that Constructs

2, 3, and 4 in Table III are highly significant

predictors of innovation performance. However,

the analysis shows that, if the objective of a model

such as the KM model described above was to

maximise the variance explanation of innovation

performance, then HRM focus on product and

process innovation is the strongest predictor,

followed by IT focus on quality and productivity

and a combination of HRM and IT focus on

organisational learning and KM.

8. Conclusions and implications formanagers

Based on the results of this exploratory study we

conclude by answering the two questions

articulated at the beginning of this paper. Our first

conclusion is that a KM model based on IT and

HRM focus is a reliable and valid instrument for

measuring and predicting the relationship between

KM practices and innovation performance. Our

second conclusion is that there is a significant and

positive relationship between KM practices based

on a combination of IT/HRM and innovation

performance. From this point it can be argued that

organisations should strive for an integrated

approach to KM in order to maximise innovation

performance leading to competitive advantage.

However, we found a significant and negative

relationship between elements of IT focus on

technological advancement (e-commerce) and

innovation performance. This may be explained to

a certain extent by the fact that e-commerce is still

in its early stages, and therefore a sense of

confidence in e-commerce as a major force in

improving and sustaining innovation performance

maynot be shared by themanagers surveyed.As the

study was limited to the manufacturing sector, it

may be argued that a multiple sector survey could

yield different results. It may, for instance, be

speculated that managers in the service sector may

view e-commerce as having greater potential to

influence innovation performance. Given the

exploratory nature of the study, and the small

sample size, it is clear that further investigation into

the relationship between e-commerce and

innovation performance is needed on a larger scale.

The most significant implication that has

emerged from the study is the conclusion that

managers in manufacturing firms should place

more emphasis on HRM practices when

developing innovation strategies for product and

process innovations. The study shows that KM

contributes to innovation performance when a

simultaneous approach of “soft HRM practices”

and “hard IT practices” are implemented.

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Documents

Exploring the relationship between knowledge management practices and innovation performance