Knowledge Sharing Model for Integrated Development of Products in
Machine-building (Results of the Exploratory Study)
Diana Antonova
a
, Svilen Kunev
b
and Irina Kostadinova
c
Department of Management and Social Activities, University of Ruse “Angel Kanchev”, 8 Studentska str., Ruse, Bulgaria
Keywords: Knowledge Creation, Exchange and Transfer, Shared Supplier Knowledge, Process Performance,
Core Competencies, NPD, Value for Consumers.
Abstract: Every industrial enterprise should apply effective knowledge creation and transfer as an instrument for
increasing its competitiveness in strategic, long-term horizon. It makes the research of different techniques
adopted by companies for turning knowledge into a competitive advantage extremely important both for
academics and practitioners. Research works that analyse the key characteristics of creation of new knowledge,
exchange and knowledge transfer put focus mainly on activities for research and development (R&D) in
knowledge intensive industrial sectors, such as biotechnologies and information and communication
technologies. In this paper, we explore the knowledge creation, exchange and transfer in the traditional
mechanical engineering in Bulgaria, Germany, Japan and the USA.
1 INTRODUCTION
The capabilities of the companies to develop products
stem from their skills to create, disseminate and
implement knowledge in the various phases of the
innovation process (Kunev et al., 2012).
The majority of studies is directed to the issues of
integration of activities (Griffin, 1997; Shah at al.,
2009; Kostadinova et al., 2019; Kunev, 2010;
Ruskova et al., 2018) and the phases in the
development of new products (Vitliemov et al., 2001;
Iliev et al., 2018; Zlatarov et al., 2018) as opposed to
the issues of integration of knowledge (sharing).
This paper is based on the integration (sharing) of
knowledge in the new product development –
IKNPD, (Orstavik, 2004). Empirical research of
IKNPD (Todorova et al., 2011; Stoycheva et al.,
2016; Antonova, Stoycheva, 2018) prove the
importance of organisational integration for the
competitive advantage of the industrial enterprise
through a correlation between the interaction patterns
and the opportunities for success. Such collaborative
efforts contribute to marked improvements in the
innovation activities of industrial enterprises and lead
to good market results (Stoycheva et al., 2018).
a
https://orcid.org/0000-0002-6060-6974
b
https://orcid.org/0000-0001-8726-935X
c
https://orcid.org/0000-0001-8845-7598
Although there is no doubt about the importance of
shared knowledge, concerning NPD, it is hard to
create such useful knowledge sharing spontaneously,
due to the different cognitive worlds of departments
and individuals and the „basic information", which
consists essentially of what must be separated from
the specific content, particularly in the thematic
setting of IKNPD. Among the seven phases of the
IKNPD process – idea, concept elaboration, design of
the system, testing and improvement, production,
commercialization – the first and the last phase
include concept development and precede the design
of the system (Antonova, et al, 2018). This is the
level, at which knowledge sharing should be executed
among the development teams (Ruskova, 2012). An
important aspect of knowledge access and exchange
are also the user requirements, supplier capacity and
core competences (Orstavik, 2004).
Multifunctional coordination is enhanced through
increasing the in-depth knowledge with every
function. In reality, there is no practical evidence that
knowledge integration improves IKNPD results. In
order to summarize the preliminary study on the
topic, this study analyses the content of knowledge
integration and the eventual reasons for correlation of
126
Antonova, D., Kunev, S. and Kostadinova, I.
Knowledge Sharing Model for Integrated Development of Products in Machine-building (Results of the Exploratory Study).
DOI: 10.5220/0010657900003064
In Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2021) - Volume 3: KMIS, pages 126-132
ISBN: 978-989-758-533-3; ISSN: 2184-3228
Copyright
c
2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
performance in NPD. It proves the interdependence
between the three types of knowledge sharing: of the
consumers (users); the core (basic) competencies of
the organization; and the suppliers’ capacities.
2 RESEARCH METHOD
The goal of this research is to develop and test a
knowledge sharing model for integrated industrial
product development, using indexes for the level of
teamwork, which try to clarify the three categories of
shared knowledge (i.е. of the users, suppliers and core
company competences) and the product presentation
(i.e. time to commercialization and value to users).
The main task is to elaborate a valid and reliable scale
for measuring IKNPD by presenting the process and
the product, i.e. cause-and-effect links of the impact
of shared knowledge on the IKNPD process
execution (mostly evaluated by the degree of
teamworking and R&D productiveness), as well as
the links between the impact of process presentation
on the main strategic imperatives such as time to
commercialization and value to users. The research
includes hypotheses testing, empirically derived from
the model:
H1. The greater the extent of shared knowledge of
users, the higher the degree of teamwork and R&D
productiveness.
Н2. The greater the extent of shared knowledge of
suppliers, the higher the degree of teamwork and
R&D productiveness.
Н3. The greater the extent of shared knowledge of
core competences, the higher the degree of teamwork
and R&D productivity.
Н4. The higher the degree of teamwork and R&D
productivity, the shorter the time to commerciali-
zation is.
Н5. The higher the degree of teamwork and R&D
productiveness, the higher the value to the user is.
2.1 Pilot Study
The design of the research process is grounded on
generally accepted methods for amplification of
standardized tools. The pilot study was conducted by
on-line monitoring of machine-building enterprises
with similar profile in May 2018. The survey
questionnaire was revised to adopt some changes,
suggested by academic experts and industry
specialists (Frascati Manual by OECD, 2018). After
it was completed, it was sent to 500 managers of large
machine-building enterprises in Bulgaria, Germany,
Japan and the USA.
Festo Vertrieb GmbH&Co. KG – Germany; The
American Investment Fund Anchorage Capital
Partners, Sydney, Australia and Advanced
Technological R&D and Product sales, Yazaki
Corporation, Japan provided the contacts of 500
managers, selected randomly according to their
participation in machine-building enterprises. Their
parameters were as product managers, with positions
and geographical locations in four sectors (production
of automobile components and parts; refrigeration
equipment; hydraulic systems; medical and
physiotherapy equipment) with codes according to
the standardized industrial classification (SIC) 34, 35,
36, and 37. The answers in the initial pilot study are
excluded from the full survey and the enterprises
included in the pilot study are not present in the list
for the large study. These steps have been undertaken
in order to ensure the above-mentioned desired
characteristics. The instruments used in the large-
scale survey are shown in Appendix 1. The answers
have been measured against five-stage Likert scales.
The period of conducting the main survey was June-
September 2018.
2.2 Sample
Of the total of 500 companies, 30 respondents were
used for the pilot study and 205 – for the large one.
205 credible responses came from refrigeration
manufacturers (22.93%); manufacture of gaming
equipment (7.32%); medical and physiotherapy
equipment (17.56%); production of automobile com-
ponents and parts (30.12%) and hydraulic systems
(16.32%). The positions of the respondents are as
follows: executive directors/presidents (2.44%),
senior managers (36.10%), project managers
(32.68%), and others (28.29%). More than 70% of the
interviewed persons have a real experience in
managing multi-functional international project
teams. The number of employees in the respondents’
companies is: less than 500 (40%); 500-599
(15.12%); 1000-4999 (22.44%), 5000-9999 (8.78%)
and over 10000 (12.20%). The companies with over
1000 employees are 43.42 % of the total.
Knowledge Sharing Model for Integrated Development of Products in Machine-building (Results of the Exploratory Study)
127
3 METHODS FOR DATA
ANALYSIS
Linear modelling of structural equations (LISREL) is
applied to describe the strength of the correlations
among: Shared knowledge of core/basic organization
competencies; Shared knowledge of consumers
(users) and supplier; Process performance; Time to
commercialization; and Value for consumers (users).
LISREL provides an accurate method for testing
conditional models, as it can implement simulative
evaluation of both conditional components and
indicators in complex models. Standardized
coefficients and t-values of conditional links between
the elements are applied to test the hypotheses set in
the study. The software package LISREL, applied for
the calculations in the study is described as: Software
for modelling structural equations, generated by the
path of diagrams in an easy-to-use interface and
syntax that is generated directly from the scheme. The
calculations were performed with SSI's LISREL 8.8
licensed software for Microsoft Windows Vista.
4 RESULTS AND DISCUSSION
For the first time, an analysis of the state of product
design was conducted by the Product Development
and Marketing Association (PDMA), USA, through a
survey among 189 American companies in 1989,
followed by a second survey in 1995 with 383
respondents. The third application of the methodology
was in 2003 (Barczak et al., 2009). The questionnaire
developed by PDMA, as the main tool of the survey in
2003, contains 7 modules: (1) Shared knowledge of
users (AD); (2) Shared knowledge of core (basic)
competencies (AF, AE, AH, AN); (3) Process
performance (DEVPRO); (4) Value for consumers
(users) (CA, CC,CK, CM); (5) Shared Supplier
Knowledge (AG,AK, AC, AJ, AA); (6) Teamwork
and (7) Time to commercialisation (CIE, CIL, CIK).
The encoding is done by PDMA.
Later, the PDMA tool was partially used to survey
industrial companies in Sweden in 2004 (Rundquist,
Chibba, 2004). A follow-up survey focusing solely on
outsourced NPD was conducted in 2008 by Rundquist
and Halila (Rundquist, Halila, 2010).
A parallel study on the NPD process took place in
Malaysia in 2006, based on the methodological
foundations of PDMA (1995) and Sweden (2004).
The project coordinators are Shalabi, Omar and
Rundquist (Shalabi et al., 2008). The study covers:
documented process and strategies in NPD,
outsourcing and organization of the process in NPD.
Another independent parallel study of integrated
product development that surveyed 205 US
automotive engineers also reveals some
interconnections with knowledge sharing with
customers and suppliers (Hong et al., 2004).
In this research to test hypothetical links, a
Confirmatory Factor Analysis (CFA) is done. A lot of
the literature on CFAs is based on LISREL modelling.
The co-variant structure of the model includes two
components: a measurement model and a structural
model. The measurement model establishes how
hypothetical (latent) concepts are evaluated against
observed variables. One of the dimensions is defined
as the presence of a latent distinctive characteristic of
the concept that gives grounds for a set of indicators.
Table 1: Investigated model: assessment of the parameters
of the measured variables (n=205). Source: authors’
elaboration, adapted from PDMA model, 2019.
Indicators
Factor
loading
t-
value
Total
standard
factor load
Uniqueness/
term of the
error
R
2
-
reliability
Shared knowledge of users
AID 1.00 0.61 0.62 0.58
A2D 1.36 8.01 0.75 0.44 0.56
А2К 1.56 9.42 0.86 0.25 0.75
A3D 1.54 9.10 0.88 0.20 0.77
Shared Supplier Knowledge
A1G 1 0.77 0.30 0.62
А1К 1.06 11.17 0.77 0.40 0.60
А2С 0.91 9.90 0.69 0.52 0.48
A2J 1.05 10.81 0.85 0.44 0.56
АЗА 1.04 10.70 0.84 0.45 0.55
Shared knowledge of core (basic) competencies
A1F 1.00 0.81 0.35 0.65
АП 0.99 10.66 0.78 0.40 0.60
АЗЕ 0.76 8.52 0.62 0.62 0.58
АЗН 0.77 8.05 0.59 0.65 0.35
Process performance
Тeam-
wor
k
0.90 0.67 0.24 0.67
DEVPR
O
1.00 15.78 0.66 0.23 0.67
Time to comme
r
cialisation
С1Е 1.00 0.95 0.09 0.91
C1L 0.68 10.25 0.63 0.60 0.40
C1
K
0.90 15.34 0.84 0.30 0.70
Value for consumers (users)
C2A 1.00 0.74 0.46 0.54
C2C 1.30 11.86 0.85 0.20 0.72
C2
K
1.14 11.64 0.83 0.31 0.69
C2M 1.30 11.53 0.82 0.32 0.68
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Table 2: Levels of reliability, correlation, and discriminant validity of the components. Source: authors’ elaboration, adapted
from PDMA model, 2019.
1 2 3 4 5 6
1. Shared knowledge of users 0.86
2. Shared Supplier Knowledge 0.21
3. Shared knowledge of basic competencies 347.47
0.52
0.82
0.44 0.85
4. Process performance 163.28
0.68
171.15
0.41 0.64
0.90
5. Time to commercialisation 192.78
0.47
371.88
0.28
135.00
0.44
0.69
0.88
6. Value for consumers (users) 252.74
0.54
282.51
0.33
157.79
0.51
171.61
0.80
0.55
0.91
7. Mean 3.92 3.10 3.79 3.52 3.57 3.77
SD 0.08 0.80 0.64 0.73 0.97 0.92
Based on the assessment of the compliance of a
one-dimensional model for each variable, iterative
modifications were undertaken. Modifications are
made to improve the model's compliance, as well as
to deliver parameters that are of real importance and
significance.
The greater the loading of the factor or the
coefficient, compared to its standard error and
displayed by the corresponding t-values, the stronger
the proof that the measured variables or factors
confirm the basic ideas. Generally, if these t-values
are greater than 2 or 2.576, they are considered
significant at a level of 0.05 to 0.01. In Table 1 above,
it can be seen that all t-values exceed 2.576.
Consequently, all indicators are significantly
related to their defined concepts. Factor loads are over
0.5, which means that all indicators have good values
compared to their thresholds. The R2 values refer to
the reliability of the indicators. The values of R2 over
0.5 mean that less than 50% variation will be a
variation error, which provides evidence of
acceptable reliability.
Most of the R2 values are above 0.5. The values
of R2 and t-values provide evidence of convergence
validity. Table 2 shows a correlation matrix, as well
as the internal consistency coefficients Cronbach
Alpha. The reliability of all metrics is over 0.80.
According to similar calculations (Nunnally, 1978),
reliability over 0.70 is considered satisfactory.
Discriminant validity is reached when the difference
between a restricted and an unlimited model is
significant (x2 of df, x2=1). As shown in Table 2
above Chi-square values are all at a significant level.
With these results, the testing of the proposed
models was done using LISREL. Analysis of
structural equations was used to test these models.
The results are shown in Table 1. For a complete
assessment of the conformity data-model x2, the
Number of Degrees of Freedom, Compliance Index
(CFI), and Bonnett Non-Shared Compliance Index
(NNFI) were used. With respect to NNFI and CFI,
values between 0.80 and 0.89 represent a good match
data-model, while values of 0.90 or higher represent
a very good match. This shows a range of indices
from 0.0 (no match) to 1 (full match). The RMSEA
(square estimate value error) of less than 0.05 is a
close match data-model. As shown in Table 2, the
structural model outputs the covariate matrix very
well (x2=298.71; df=201; NNFI=0.96; CFI=0.96,
RMSEA=0.049). Because the structural model has a
reasonably matching model-data pattern, a study over
the path of the coefficients might be done. Figure 1
bellow shows the test results of the proposed
hypothesises.
H1 – Н3 provide that the shared knowledge of
users, suppliers and core competences will be directly
connected to the results from the presentation of the
process. As seen in Fig. 1, the maximum probability
estimations for the path from shared knowledge of
users, suppliers and core competences are significant
and positive (standard coefficients of 0.48, 0.17 and
0.35, with t-values of 5.71, 2.58 and 3.79,
respectively). This shows that teams, operating with
high levels of shared knowledge of users, suppliers
and core competences demonstrate much better
results in presenting the process than those with low
levels of shared knowledge.
Knowledge Sharing Model for Integrated Development of Products in Machine-building (Results of the Exploratory Study)
129
Figure 1: Evaluation of the elaborated model by analysis of structural equations.
Н4 and Н5 provide that the degree of presentation
of IKNPD process (i.e. team work and R&D
productivity) will be connected to the results from the
presentation of the product (i.e. the time to
commercialisation and value for the user). In Fig. 1 it
is clearly seen that the maximum probability
estimations for the path from the presentation of the
process to the time to commercialisation and value for
the user are significant and positive (standard
coefficients of 0.69 and 0.80, with t-values of 10.45
and 9.81, respectively). This shows that the higher
levels of team work and R&D productivity lead to
shorter time to commercialisation and provide higher
value for the users.
5 CONCLUSIONS
First of all, one contribution of this study is the
amplification of a reliable tool for measuring the level
of knowledge sharing in the field of IKNPD, which
can be used to assist future studies. The results
obtained from the survey of managers in machine-
building prove that knowledge generation is a pre-
requisite for creating successful innovation projects
not only in the Hi Тech Industries, as it is commonly
believed, but also in traditional ones. Identifying
streams of shared knowledge allows researchers to
implement the approach of knowledge management
in applied fields such as NPD, e-commerce or
marketing of industrial products.
Secondly, as it was assumed, the three
components of shared knowledge (users, suppliers
and core competences) are positively related to the
presentation of NPD process. The influence of shared
knowledge is reviewed in another context as well – as
characterisation of research teams, defining success
in outsourcing and building capacity for NPD. This
study shows how the specific components of
knowledge sharing support the IKNPD process (i.e.
team work and R&D productivity) and what the
strategic results (i.e. time to commercialisation and
value for the user) are. The study confirms the fact
that when teams act in an external environment that
fosters knowledge sharing between users, suppliers
and core company competences, the presentation of
the process (team work and R&D productiveness)
connects the effect of knowledge sharing with
strategic directions - time to commercialisation and
value for the users. The results from the study propose
that knowledge must be shared reasonably within the
members of the teams in their efforts to design
products or processes. Managers should concentrate
on methods for improving team work and R&D
productiveness through intensifying the knowledge
sharing among the team members.
Thirdly, if time to commercialisation and value
for the users represent strategic directions, knowledge
sharing is an important driving force. It can also be a
drive for other strategic imperatives like production
opportunity and thus enhance the general
organisational competitiveness.
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Last but not least, in order to introduce IKNPD
efficiently, integration must take place at conceptual
level first because product development is a job,
related to intensive use of knowledge. The study
conducted provides support for the five hypotheses
and better understanding of the elements in the
foundation of shared knowledge in IKNPD, as well as
proofs of claims not tested before with regard to the
elements of integrated knowledge.
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Knowledge Sharing Model for Integrated Development of Products in Machine-building (Results of the Exploratory Study)
131
APPENDIX
Indexes included in the basic study (end indexes)
Indexes Measured indexes Mean SD
This IKNPD team shares knowledge of
Shared knowledge of users A1D Customer’s requirements 4.18 0.84
A2D Which characteristics are most valued by the target customers 3.93 0.94
A2K Current needs of the customer 3.93 0.94
A3D What does our customer want 4.02 0.91
Shared Supplier Knowledge A1G What are the capacities of our suppliers for implementing the
process
3.11 0.92
А1К
Capacities of our supplier to meet the requirements for target
expenses
3.03 0.99
А2С Supplier capacities for design 2.98 0.95
А2J Capacities of our supplier to meet the requirements about the time
factor
3.29 1.01
Shared knowledge of basic
competencies
А3А Capacities of our supplier to meet the requirements about quality 3.17 1.02
А1F Capacities of our engineering staff 3.95 0.80
А1I Strengths of the capacities of engineering staff 3.91 0.82
А3Е Strengths of our production facilities 3.73 0.84
А3Н Capacity of the technologies used in the process 3.61 0.84
This R&D team
Team work С1С Good teamwork 3.82 0.92
С1Н Activities are well-coordinated 3.51 0.98
С1М Solutions are successfully implemented 3.57 0.95
С1N Communication is carried out smoothly 3.53 0.97
NPD activity С1D Productive 3.97 0.85
C1G Uses financial resources rationally 3.58 0.90
C1J Uses all resources for R&D rationally 3.33 0.98
С1L Uses time for engineering work efficiently 3.32 0.95
Market launch time С1В Keeps the deadline for launching on the market 3.69 1.18
С1Е Develops the product on time 3.60 1.11
С1I Reduces the product development time 3.29 1.13
Value for the customer С2А The product is of high quality 4.00 0.94
С2С The product surpasses the customer’s expectations 3.57 1.06
С2К This product is of high value for the customer 3.91 0.95
A3D What does our customer want 4.02 0.91
С2М This product is successful in the market 3.79 1.10
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