The Knowledge Managment Capability of High-Technology

Enterprises

Evgeniya Gorlacheva

, Alexander Gudkov

, Dmitrij Koznov

and Irina Omelchenko

Industrial Logistics Department, Bauman Moscow State Technical University,

2-nd Baumanskaya,5, 105005, Moscow, Russia

Instrument Technologies Department, Bauman Moscow State Technical University,

2-nd Baumanskaya,5, 105005, Moscow, Russia

Software Engineering Department, Saint Petersburg State University,

Bibliotechnaya sq., 2, 199034, Saint Petersburg, Russia

Keywords: Knowledge Management Capability, High-Technology Engineering Enterprises, Competitive Advantage.

Abstract: Improving the knowledge management (KM) capability in order to gain sustainable competitive advantages

has emerged as an important strategy for addressing recurring problems in a new product development such

a long time-to-market, riskiness and high development costs. Synthesizing prior theoretical research in

innovation management, competitive advantages, KM and practical activities of high-technology

engineering enterprises it is posited that innovation, engineering processes and organizational culture are

important antecedents of KM capability. However, it is vague whether the KM capability as a mediator

affects competitive advantages. The aim of the paper is to explore the impact of the KM capability in high-

technology engineering enterprises. To achieve this aim we have contributed an empiric research in which

50 high-technology engineering enterprises of Russia were involved. The regression analysis is applied to

analyze the obtained empiric data. Among the three selected antecedents the engineering processes have the

most impact on the KM capability. The research hasn’t proved the mediating role between the KM

capability and competitive advantages because of relative novelty of this phenomenon to Russian high-

technology enterprises.

1 INTRODUCTION

In order to survive in a business environment high-

technology engineering enterprises have to gain and

support sustainable competitive advantages. There is

a large variety of ways to be competitive: by means

of product innovation (Pisano, 1997), by various

organizational technologies (Crossan, et al., 1999),

by sophisticated IT-infrastructure (Sabherwal,

2005), etc.

It should be noted that the relatively essentiality

of the high-technology engineering enterprises

competitive factors have remarkably changed.

Knowledge has become the core element that takes

an important place and considers as the main mode

of competition (Eisenhardt, Santos, 2002). If in prior

literature the problem of competitive advantages is

considered in framework of resource-based view,

nowadays there has been a paradigm shift to

knowledge-based perspective (Alavi, Leidner,

2001).

According to knowledge-based view the high-

technology engineering enterprise can be seen as a

knowledge-integration institution which integrates

individual or group. The core of knowledge-based

view lies in its attempt “to understand the

organizational capabilities through which the

enterprises access and utilized the knowledge

possessed by their employees” (Grant, 1996).

Academics and practitioners have recognized the

importance of the Knowledge Management (KM)

capability for an enterprise’s competitive advantages

(Daneels, 2000). Empirically the KM capability has

been found to improve competitive advantages.

Reviewed empiric research (Frishamar et al., 2012)

on the KM capability has largely established the

relationship between various enterprise domains

(intellectual capital, organizational culture,

innovation) and enterprise’s competitive advantages.

Gorlacheva E., Gudkov A., Koznov D. and Omelchenko I.

The Knowledge Managment Capability of High-Technology Enterprises.

DOI: 10.5220/0006497101310138

In Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (KMIS 2017), pages 131-138

ISBN: 978-989-758-273-8

However this stream of research has not covered

such core domains as innovation, engineering

processes and organizational culture, and hasn’t

considered a mediating role of the KM capability.

There is also a lack of empiric data that confirm the

relationship between innovation, engineering

processes, organizational culture and the KM

capability namely in high-technology engineering

enterprises.

This paper aims to examine and empirically test

the relationship between the aforementioned

antecedents’ impact on the KM capability and a

mediating role of the KM capability. For that

purpose the survey of 50 high-technology

enterprises has been conducted. The investigated

enterprises were mainly from machine building

complex, IT-sphere and biotechnology. The

procedure of the research has based on the following

parameters: innovation, engineering processes and

organizational culture, the KM capability,

competitive advantages, their evaluation and

statistical analysis. The regression analysis has been

used while analysing given results. The

interpretation of results has discovered the major

impact of engineering processes to the KM

capability. Our expectations about KM capability as

a mediator haven’t been statistically proved.

2 BACKGROUND

2.1 The KM Capability in KM

Framework

From knowledge management framework high-

technology engineering enterprises should possess a

set of organizational capabilities that allows

achieving the desired outcomes (Lager and Hoerte,

2002). The KM capability is among them as the

most crucial one.

The research conducted by (Gold et al., 2001) the

link between organizational effectiveness and the

KM capability has been found. Our aim is to test

empirically whether KM capability impacts directly

on competitive advantages in high-technology

engineering enterprises.

2.2 Key Antecedents of the KM

Capability

2.2.1 Innovation

The main activity of high-technology engineering

enterprises is a new product development. This

activity is associated with risks and uncertainty. The

literature review (Gudkov, 2016) shows that there

are four types of a new product:

 Totally new product (innovation);

 Modified existing product (modification);

 Enlargement of a product (differentiation);

 Spread of a trade mark (diversification).

It is also highlighted that a new product

development process involves the acquisition,

dissemination and use of new and existing

knowledge. Thus we could recapitulate that

innovation contributes to the KM capability. A new

product development often requires new or modified

engineering processes.

2.2.2 Engineering Processes

The sophistication of engineering processes leads to

elaborate them well-tuned and regulated (Gudkov,

2016). Engineering processes should be supported

both corresponding management, e.g. product

management in software companies (Maglyas et al.,

2012), and design-oriented methods such as

ontology engineering, system analysis and enterprise

architecture management (Gavrilova et al., 2010).

Engineering processes can’t exist without

appropriate communication and information

systems. Taking into consideration that engineering

processes generate a lot of data on its every phase,

we could suppose that engineering processes

enhance the KM capability.

2.2.3 Organizational Culture

Organizational culture is another antecedent that

enhances the KM capability. In (Pearce C. and

Ensley M., 2004) it is highlighted that employee

empowerment, team creativity and a shared vision

are necessary while elaborating a new product

(Popov et al., 2016).

In order to increase the KM capability employees

must be given the opportunity to develop and create

ideas together. Thus we consider organizational

culture as an antecedent factor of the KM capability.

2.3 Mediating Role of the KM

Capability

The KM capability depends on three core

antecedents’ relatedness. These arguments suggest

that the KM capability mediates the relationship

between core antecedents and competitive

advantages.

Thus we expect that the KM capability will

function as a mediator of the relationship between

the core antecedents and competitive advantages.

2.4 Regression Analysis

Undoubtedly there is a huge variety of measurement

models that can be used by a researcher. Some of

them are more reliable, some ones are easier in use.

The regression analysis allows including or

excluding predictors (independent variables) until

the model would be adequate for research purpose.

The embedded method of the partial least square

constructs a regression equation in terms of small

sample data.

Regression analysis (Drayper, Smith, 2003)

reveals the interrelations between dependent and

independent variables in statistical research. An

independent variable means a variable that is chosen

initially to test its impact on a dependent variable. In

turn a dependent variable is a variable that is under

measurement.

Table 1 and 2 presents the description of

evaluation parameters that are commonly used in the

regression analysis.

Table 1: Regression analysis’ parameters: statistical

reliability.

Parameter Possible value range Meaning

Cronbach α 0,5>α>1 Internal

consistency of

parameters

β-coefficient 5<β<7% Error of

approximation

p-value 0,5<p>0,5 Statistical

reliability

value of calculations

depends on sample size

The correctness

of null

hypothesis

Table 2: Regression analysis’ parameters: statistical

significance.

Parameter Possible value range Meaning

(Pearson

coefficient)

0<R

>1 Interaction

detection of

model variables

Deviance

residuals

Depend on calculations Confirmation of

model

assumptions

Darbin-Watson

criterion

0≤d≤4 Confirmation of

model

significance

The choice of the best regression model can be

conducted by four different methods: 1) method of

all possible regression equations; 2) method of the

best regression equations; 3) method of exclusion; 4)

stepwise regression (Drayper, Smith, 2003).

The latter is more optimal as it allows using

resources thrifty.

3 RELATED WORKS

The KM capability is surveyed in different works

and from different aspects. In (Pfeffer and Souton,

1999) it is pointed out that enterprises tend to pay

more attention to knowledge creation and less

attention toward utilizing available knowledge.

The other paper (Hsu and Sabherwal, 2011) is

considered the KM capability in terms of intellectual

capital issues. The main aim of this paper is to

provide insights into the mediating roles of

knowledge enhancement and knowledge utilization

in the relationship between intellectual capital and

innovation.

One more paper (Freeze and Kulkarki, 2013) has

suggested a KM capability assessment instrument to

measure knowledge assets.

In (Dawson, 2000) the KM capability is defined

as the effectiveness of an organization to perform

knowledge processes using resources of intellectual

capital and key information inputs.

In (Gold, Malhotra and Segars, 2001) the KM

capability is considered in two key aspects: a

knowledge process capability and a knowledge

infrastructure capability.

Organizational culture and the KM capability are

depicted in various works related to organizational

climate (Song, Wang, 2016) or organizational

citizenship behaviour (Podsakoff, 2016).

However there is a lack of empiric research that

investigates the mediating role of the KM capability

between innovation, engineering processes,

organizational culture and competitive advantages in

high-technology engineering enterprises.

4 METHODOLOGY

The study of the KM capability bumps into a set of

difficulties due to its multidimensionality (Hsu and

Sabherwal, 2011) and qualitative nature (Malhotra et

al., 2006).

The papers that study the KM capability one

could divide into two large groups: these that

propose the elaboration of conceptual frameworks

(Frishamar et al., 2012) and those that propose

empiric research (Zheng et al., 2010). The latter

group is more numerous.

One more difficulty is the choice of robust

measurement instruments.

The methodology of this particular research is

coincided with the standard procedure, described in

(Tokarev, 2013). It comprises the following stages:

determination of initial conditions; purpose

statement of the research; choice of research type

(an empiric or a desk one); choice of research

method (a sample size determination, a

questionnaire elaboration, a measurement model and

its reliability test); analysis of results and findings.

The whole scheme is presented on fig.1. The brief

description of each stage is presented below.

Figure 1: Scheme of the present research.

The analysis of initial conditions allows

determining the environment where an enterprise

functions and performs its business activities.

Purpose statement is the most important stage of the

whole research. It determines the configuration of

the further work.

The choice of research type strongly depends on

time and budget. Despite the fact that desk research

is more budgetary, only empiric research gives

original information about the real state of art.

However empiric research requires taking in

mind many important factors: the determination of

sample size; time to answer the questionnaire; the

respondents’ willingness to answer; the readiness of

interviewers to explain “bottle necks”, etc.

The elaboration of the questionnaire requires the

critical examination of the selected field and the

deliberate preparation of all questions. The typical

structure of the questionnaire comprises four main

parts: introduction, respondent profile, main part and

detector questions (Tokarev, 2013; Golubkov,

2008).

The most appropriate statistical approach in

determining sample size is the calculation on the

confidence interval (Golubkov, 2008).

Findings of the research present the

interpretation of the given results and its impact on

the purpose statement.

This analysis has allowed elaborating the

questionnaire. The whole questionnaire comprises

20 questions that have been adopted from interviews

with senior executives and added from existing

studies (Frishamar, 2012; Casselman, 2011).

There are four parts included in the

questionnaire. Part A mentions about the attitude to

innovation and engineering processes organization.

Part B deals with the knowledge management and

IT-infrastructure elements. Part C presents questions

concerning organization culture and procedures of

KM whether they are settled in enterprises or not.

Part D concerns the profiles of enterprises, its

geographic characteristics and respondents’ position

in an engineering enterprise.

In this particular paper only four questions are

presented. We have used a five-point Likert-type

scale which ranges from “strongly disagree” (1) to

“strongly agree” (5).

Our regression model is based on the five

independent and two dependent variables. Each of

the independent variable contains at least four items.

The items were codified in order to use constructs in

SPSS Statistics 22.0 toolset (SPSS Statistics, 2017).

Thus, a variable “Innovation” (V1) comprises four

items:

 innovation (IN1…5);

 modification (MOD1…5);

 differentiation (DIF1…5);

 diversification (DIV1…5).

A variable “Engineering processes” (V2)

includes:

 totally regulated (TReg1…5);

 possible changes to employee’s initiative

(EmpIn1…5);

 regulated procedures in “control points”

(ConPnt1…5);

 dependence on project specification

(ProSpf1…5).

A variable “Organizational culture” (V3)

comprises:

 knowledge diffusion among employees

(KnDif1…5);

 improvement of rationalization (ImpRat1…5);

 increase of trust among employees

(IncTr1…5);

 cultivation of “learning organization” values

(LrnOrgVl1…5).

All these variables are considered as predictors.

Let’s consider dependent variables, which are

used in our regression model. The KM capability has

been viewed as a dependent variable (V4) and

described by following items:

 formalization of management processes

(FrMPr1…5);

 diffusion of best practices among employees

(BestPr1…5);

 creation of experts’ list (ExpLs1…5);

 time decrease of business processes

(TiDec1…5).

Due to a mediating role of the KM capability, its

variable has been also viewed as a predictor of the

dependent variable – variable “Competitive

advantages” (V5). The last one was described by the

following items:

 custom retention (CusR1…5);

 sales growth (SalGr1...5);

 financial performance (FinPr1…5);

 reputation (Rep1…5).

In (Frishamar, 2012) these items are described

more detailed.

The next procedure is the determination of the

sample size. The sample size was calculated and

should be about 50 enterprises. Confidence figure is

evaluated at 85 %, as the KM capability is quite new

within the high-technology engineering enterprises

in Russia. 70 % of returned questionnaires is quite

appropriate according to (Golubkov, 2008).

We used multi regression model to evaluate the

variables and find out whether our suppositions

concerning the mediating role of the KM capability

could be proved by reality.

5 RESULTS

5.1 Informant and Company Profiles

The present research uses empiric data. According to

described methodology, we have studied the initial

conditions to clarify whether the notion of the KM

capability is familiar for high-technology

enterprises.

We selected 50 high-technology engineering

enterprises dealing with RandD as a main filed. In

December 2016 we received 35 usable

questionnaires (70 % of response) from senior

executives (31 %) and senior engineering staff

(69 %). The respondents had an average 5 years of

work experience in these current enterprises. The

majority of the respondents are from machine

building complex (see fig. 3).

The enterprises of machine building complex

contribute substantially to gross domestic product.

The age and the size (number of employees) of these

enterprises can be considered as established ones.

More than half (57,1 %) have been founded more

than 50 years ago and 62,8 % have more than 1000

employees (see fig. 4 and fig. 5).

Figure 2: Industrial affiliation of the responded

enterprises.

Figure 3: The age of the responded enterprises.

Figure 4: The number of employees (enterprise size).

Concerning the ownership of the informants the

majority of the enterprises belong to public joint

stock company (28,7 %), 20 % are joint stock

company and 20 % are considered as federal state

unitary enterprise (see fig. 6).

In coincidence with Russian Civil Code (Garant,

2017) these enterprises possess features of state

budgetary supported enterprises from the one side

and from the other they have possibility to raise

capital through mechanism of stock exchanges.

Figure 5: Ownership characteristics of informants.

5.2 Analysis Stage

The first part of the regression analysis dealt with

the antecedents’ impact on the KM capability, where

KM capability was the dependent variable (V4) and

innovation (V1), engineering processes (V2),

organizational culture (V3) were independent ones.

Due to limitation of the paper size there are

presented only final results of the regression model

(Table 3). Firstly, the variable 1 has been included

and tested its impact on the KM capability. Then, the

variable 2 has been added. The predictors

(independent variables) have been included in the

analysis successively.

Table 3: Results of the regression analysis.

Variable

(Ind. and Dep.)

Parameters

F-

value

β t DW coeff.

V1 (In.) 0,5 21,2 0,42 2,59

V2 (Eng.Pr.) 0,7 6,59 0,51 3,1 2,1

V3 (Org.Cult.) was excluded during the machine calculations

V4 (KM Cap.)

The second part of the regression analysis

V4 (KM Cap.) 0,2 4,7 0,4 2,1 2,07

V5 (Com.adv.)

According to the given results shown in Table 3

the variable 1 – innovation – has the impact to the

KM capability. It is proved by the meaning of R

and F-value. R

= 0,5 and F = 21,2. The variable 2 –

engineering process – has more impact to the KM

capability. R

= 0,7 and F = 6,59. Therefore, we

could postulate that innovation and engineering

processes impact positively the KM capability.

As to organizational culture it has been excluded

during the machine calculations. Therefore this

antecedent hasn’t been statistically significant. β

coefficient, as a parameter providing statistical

reliability, equals 5,1% for engineering processes

and 4,2% for innovation respectively. It confirms the

greater impact of engineering processes to the KM

capability.

The lack of correlation (multicollinearity)

between two predictors – innovation and

engineering processes is proved by DW coefficient.

The meaning of which is 2,1; it is proved that

deviations are occasional and the regression model is

statistically significant.

The second part of the model deals with the

mediating effect of the KM capability on

competitive advantages.

Thus as the value of R

is quite low (0,208), we

can’t say that the KM capability influences

competitive advantages in high-technology

engineering enterprises. DW coefficient equals 2,07,

proving the lack of multicollinearity. β coefficient

equals 4,5%. This meaning coincides with the β

coefficient for innovation and explains the

insignificant effect of the KM capability to

competitive advantages.

5.3 Findings of the Research

The paper has searched the relationship between

innovation, engineering processes, organizational

culture and KM capability. The conducted research

included the 35 questionnaires with high-technology

enterprises. The given results are following: among

three chosen antecedents the engineering processes

has mostly impacted the KM capability (R

= 0,7).

We could explain it that for high-technology

enterprises engineering processes are key activities.

Organizational culture hasn’t had a great impact

in high-technology engineering enterprises as the

new product development very often is a chaotic

process. The survey has found out that innovation

has had less impact to the KM capability as high-

technology enterprises deal mostly with the

modification of a new product. Innovation is not

popular among the answers.

The KM capability is paid less attention in high

technology engineering enterprises – that’s why the

mediating role of KM and competitive advantages

hasn’t been proved and hasn’t coincided with the

results of literature review. The more evident

explanation is that in Russia knowledge

management as an organization technology is

relatively new.

5.4 Limitations of the Research

It is important to acknowledge this study’s

limitations.

First, due to relative novelty of KM as an

organization technology, it was quite difficult to

gather valuable data as informants sometimes

needed to have explanations about this or that

question. It is also worth to mention that the research

sample is relatively small. Hence, the research

initially contains occasional statistical errors.

Second, due to our focus on a parsimonious

model, several potentially important factors may

have been excluded in our research as is common in

organization science models.

Third, although the regression analysis is widely

accepted as a robust instrument of organizational

factors’ evaluation, perhaps a system of

simultaneous equations would give other results.

Thus, this issue should be tested in future research.

Nevertheless, the statistical quality of the

investigated model is proved by DW coefficients

and the lack of multicollinearity. Therefore we could

postulate that the model has right to exist.

As for economic interpretation of the given

results it is obvious the engineering processes are the

key antecedent and should be properly supported.

The lack of the KM capability mediating role can be

explained by the relative novelty of this

phenomenon and shortcomings of the questionnaire.

6 CONCLUSIONS

The present paper examines these relations taking

into consideration knowledge-based view.

A common theme running through KM literature

is that the KM capability is an important ability of

an enterprises’ competitiveness. Although much

theorizing about this has taken place in subsequent

literature there is a lack of empiric research how

innovation, engineering processes and the KM

capability affect competitive advantages. This paper

tried to close this gap.

First, the empirical research provides initial

support that this comprehensive theoretical platform

incorporating both antecedents and the KM

capability might provide a valuable alternative to

prior separate focus on innovation, engineering

processes and the KM capability.

Second, this study provides insight into the KM

literature by including innovation, engineering

processes in the research.

Third, the failure to find positive effect of the

organizational culture on the KM capability may

imply that for high-technology engineering

enterprises it plays not significant role in comparison

with innovation and engineering processes.

This study implicitly assumes that the

investigated relationships are stable across various

organizations, industrial and county contexts.

Further research can build on this study by

developing an extension that sees the relationship as

depending upon specific context.

This paper also has several implications for

business practice. Enterprises should enhance the

KM capability for developing inimitable competitive

advantages.

The insignificance of the organizational culture

on the KM capability may cause the following

explanation: the enterprises need information

support and tangible benefits while increasing level

of organizational culture.

The statistically non-proved mediating role of the

KM capability may show that knowledge

management as organization technology is not

spread and well-accepted among high-technology

engineering enterprises. Thus, the information

support about KM is needed.

Finally, economic interpretation of the given

results has revealed the necessity of supporting

engineering processes; the expansion of knowledge

management ideas and further research of the KM

capability impact.

Thus, this study provides a few directions for

future research. Firstly, impact of the KM capability

on organizational performance (mainly financial

results) may be explored. Secondly, moderating

effects of external factors and the KM capability can

be examined. Thirdly, other statistical measurement

instruments such as simultaneous equations should

be used in order to compare given results.

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