An Analytic Network Process Modeling to Assess Technological Innovation Capabilities: Case Study for Thai Automotive Parts Firms

2013 American Transactions on Engineering & Applied Sciences.

American Transactions on
Engineering & Applied Sciences
http://TuEngr.com/ATEAS

An Analytic Network Process Modeling to Assess
Technological Innovation Capabilities: Case
Study for Thai Automotive Parts Firms
Detcharat Sumrit a*, and Pongpun Anuntavoranich a*
a

Technopreneurship and Innovation Management Program, Graduate School, Chulalongkorn University,
Bangkok, Thailand.

ARTICLEINFO

ABSTRACT

Article history:
Received January 08, 2013
Received in revised form March 20,
2013
Accepted March 29, 2013
Available online April 05, 2013

To handle swift changes in global environment,
Technological Innovation Capabilities (TICs) is one crucial and
unique strategy to increase firms’ competitiveness. This research
proposed a systematic framework of TICs assessment by employing
Analytic Network Process (ANP) method for solving the complicate
decision-making and assessing the interrelationship among various
evaluation factors, whereas the relative important weight data were
provided by industrial experts based on pair-wise comparison.
With the novel TIC assessment model, high-level managers could
easily gain management information to rationalizes the
decision-making process based on the most important criteria which
affect the firms’ competitive advantages and the highest priority
factors which were needed to be handled. The last section also
displayed the application of TICs assessment on three Thai
automotive parts firms, as case study.

Keywords:
Technological Innovation
Capability;
Analytic network process ;
Thai automotive parts firms
TICs evaluation criteria.

2013 Am. Trans. Eng. Appl. Sci.

*Corresponding author (P. Anuntavoranich). Tel/Fax: +66-2-6576334. E-mail
addresses: dettoy999@gmail.com, p.idchula@gmail.com.
2013. American
Transactions on Engineering & Applied Sciences. Volume 2 No. 3 ISSN 2229-1652
eISSN 2229-1660 Online Available at http://TuEngr.com/ATEAS/V02/189-212.pdf

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1. Introduction
The Thai automotive parts industry is one of the most important manufacturing sectors of the
country. The industry plays an essential role in exporting with positive growth and involvement
in technological R&D. Based on the national’s plan in research and cluster development to be
implemented in 2011-2016, government agencies have been promoting the automotive parts
industry since it promises high potential to shift to a higher level of technological and innovative
capability.

To compete in volatile condition in the world’s economic competition, the

development of the Technological Innovation Capabilities (TICs) and the measurement of TICs in
the Automotive parts firms are therefore considered to be some of the measures in the
enhancement of the industry’s competitive advantages.
OECD and European Committee (2005) conceded that the impact of innovations on firms’
performance was not limited to sales & market shares but also to the changes in productivity and
efficiency which have impact at both the industry and the local level. Prajogo and Ahmed (2006)
explained that innovation is a vital source of competitive advantages in the midst of the present
knowledge economy.

Firms become inevitably involved with the rapid changes of global

circumstances, they significantly need to implement and exploit strategies that improve their
internal strengths and create external opportunities and at the same time eradicate their internal
weaknesses and external threats in order to retain and improve their competitive advantage (Porter,
1985; Barney, 1991).

Also firms’ performances were highly impacted by technology,

globalization, knowledge and changes of competitive approaches (Scott, 2000; Hitt et al., 2001).
Therefore, to assure the firm’s sustainability, the integration of internal organizational resources
and technological innovation are required. TICs are essential solutions for firm’s development and
at the same time the response in multi-criteria decision making (MCDM). The MCDM involves
multi-organizational functions and resources composition among different criteria (Betz, 1998,
Agarwal et al., 2007, Wang et al., 2008, Tseng, 2011). Tan (2011) explained that the differences
of firms’ innovation capabilities are regarded as the key compositions of innovation system. Study
by Tan (2011) revealed that firms’ innovation capabilities were largely affected by the external
information availability. In this regard, TICs have been described as the important instruments to
enhance the competitive advantage and many firms are seeking for the better technological
innovation that fits their organizational culture. TICs, therefore, are considered to be the excellent
190

Detcharat Sumrit, and Pongpun Anuntavoranich

alternatives to serve such requirements. This research proposed the TICs assessment which
applied systematic MCDM method to solve some of the complex decision making problems. It
is, therefore, the main objective of this study to develop the TICs.

2. Literature Review
2.1 Technological Innovation Capabilities
Burgelman et al., (2004) defined innovation capabilities as a comprehensive set of firm’s
characteristics, which facilitates the firm’s strategies. Under high pressure of global competition,
firms was forced to constantly pay attention on innovation development in aspect of new product
launching and product design and quality, technological service, reliability and the product
uniqueness. The integration of innovation capabilities for developments and new technology
commercialization are highly important as well as the construction and the dissemination of
technological innovations in such organizations. Guan et al., (2006) discussed that TICs depend
on both critical technological and capabilities in the fields of manufacturing, organization,
marketing, strategic planning, learning and resource allocation. The approach is considered as a
complicated interactive process as it involves various different resources. Gamal (2011) described
that innovation has many dimensions and is extensive in concepts. The innovation measurement
is also complicated.
Panda and Ramanathan (1996) defined that technological capability assessment provided
useful information that contained the indication of inputs that firms needed to improve in relation
to its competitiveness and to sustain its strategic decision making. Yam et al. (2004) proposed
seven characteristics of TICs framework, which reflect and sustain the Chinese firms’
competitiveness. As stated the two most important TICs were i.e. (i) R&D capability to protect
the innovation rate and product competitiveness in medium & large sized firms, and (ii) resource
allocation capabilities to increase sales growth in small enterprises. However, they viewed that the
capability of the individual department of such firms could generate the innovation and then
developed an audit model.
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Table 1: Summary of the perspectives and criteria from literatures
Evaluation Criteria
Description
Innovation Management Capability Perspective (P1)
Leadership commitment (C1)
Firm’s high level manager actively
participates in decision-making related to
technological issues.
Strategic fit (C2)
Firm’s technological innovation strategy
supports business strategy.
Strategic deployment (C3)
Firm’s technological innovation strategy were
shared and applied to each department/unit.
Resource allocation (C4)
Firm’s ability to appropriately acquire and
allocate capital & technology.
Investment Capability Perspective (P2)
Investment in the existing
Firm’s ability to continuously invest in
product/process improvement
existing technological product & process
(C5)
improvement.
Firm’s capability to invest in developing
Investment in proprietary
proprietary technology.
technology development (C6)
Investment in external
Firm’s ability to invest in external technology
technology acquisition (C7)
acquisition.
Organization Capability Perspective (P3)
Innovation culture (C8)
Firm’s ability to cultivate innovation culture.
Network linkage (C9)

Firm’s ability to transmit information, skills
and technology, and to acquire them from
departments, clients, suppliers, consultants,
technological institutions, etc.
Response to change (C10)
Firm’s capability in risk assessment , risk
taking and response to technological
innovation change and adopting
Learning Capability Perspective (P4)
Internalized external
Firm’s ability to recognize and internalize
relevant external knowledge
knowledge (C11)
Exploit new knowledge (C12)

Firm’s ability to bring in new knowledge or
technologies to develop innovative product
Embed new knowledge (C13) Firm’s ability to transplant new knowledge
into new operation by creating a shared
understanding and collective sense-making.
Technology Development Capability Perspective (P5)
Firm’s ability to develop proprietary
Proprietary technology
technologies from in-house R&D
development (C14)
R&D Project Interfacing (C15)

Firm’s ability to coordinate and integrate all
phases
of
R&D
processes
and
interrelationship of engineering, production
and marketing.
Technology Transformation Capability Perspective (P6)
Ability to design product structure &
Product structural design and
modularization & compatible with process.
engineering (C16)
Process design and
engineering (C17)

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Firm’s ability to design process to support
design for manufacturing and design for
assembly activities.


Author
O’Regan et al., (2006), Grinstein and
Goldman (2006), Prajogo and Sohal,
(2006), Kyrgidou and Spyropoulou (2012)
Prajogo and Sohal, (2006), Koc and Ceylan
(2007), Yam et al., (2011),
Prajogo and Sohal, (2006), Koc and Ceylan
(2007), Dobni (2008)
Koc and Ceylan (2007), Wang et al.,
(2008), Yam et al., (2011)
Koc and Ceylan (2007), Dobni (2008),
Zhou and Wu (2010)
Yam et al., (2011), Lin et al.,(2012).
Flor and Oltra (2005), Lee et al., (2009)
Dobni (2008), Kyrgidou and Spyropoulou
(2012), Türker (2012)
Wang et al., (2008), Spithoven et al.,
(2010), Huang (2011), Zeng et al., (2010),
Forsman (2011), Mu and Benedetto (2011),
Kim et al., (2011), Voudouris et al., (2012)
Jansen et al., (2005), Zhou and Wu (2010),
Grinstein and Goldman (2006), Mu and
Benedetto (2011), Forsman (2011)
Camisón and Forés (2010), Forsman
(2011), Biedenbach and Müller (2012)
Camisón and Forés (2010), Forsman (2011)
Camisón and Forés (2010), Forsman (2011)

Grinstein and Goldman (2006), Prajogo and
Sohal, (2006), Wang et al., (2008), Forsman
(2011), Kim et al., (2011).
Lin (2004), Camisón and Forés (2010), Kim
et al., (2011), Mu and Benedetto (2011)

De Toni & Nassimbeni, (2001), Nassimbeni
& Battain, (2003), Lin (2004), Ho et al.,
(2011)
De Toni & Nassimbeni (2001), Antony et
al., (2002), Nassimbeni & Battain (2003),
Ho et al., (2011)

Table 1: Summary of the perspectives and criteria from literatures (Continue)
Evaluation Criteria
Description
Technology Commercialization Capability Perspective (P7)
Firms’ ability in transform R&D output into
Manufacturing Capability
production and acquire the innovative
(C18)
advanced
manufacturing
technologies/
methods.
Marketing Capability (C19)
Firm’s ability to deliver and market products
on the basis of understanding customers’
needs competitive environment, costs and
benefits, and the innovation acceptance.

Author
Lin (2004), Yam et al.,(2004), Guan et al.,
(2006), Prajogo and Sohal, (2006),Wang et
al.,(2008), Yam et al., (2011), Kim et al.,
(2011), Yang (2012)
Lin (2004), Yam et al., (2004), Guan et al.,
(2006), Dobni (2008), Wang et al., (2008),
Yam et al., (2011), Forsman (2011), Mu
and Benedetto (2011), Kim et al., (2011)

Yam et al. (2011) reviewed the evaluation of innovation performance, and found that the
utilization of information sourcing could create the development of performance, and displayed
high impact on firms’ TICs enhancement. Forsman and Annala (2011) suggested that the
diversity in innovation development directly related to degree of enterprises’ innovation
capabilities . The higher the level of capabilities, the more diversity of innovations is developed.
Also, Sumrit and Anuntavoranich (2013) analyzed the cause and effect relationship of TICs
evaluation factors. This study conducted extensive theoretical literatures review and empirical
studies to explore the TICs criteria assessment, as summarized in Table 1.

2.2 ANP Theoretical Framework
Analytic Network Process (ANP) is a multi criteria method of measurement (Saaty, 1996),
applied to handle complicated decision-making which carriers interrelationship among various
decision levels and attributes. The importance of the criteria defines the importance of the
alternatives based on a hierarchy, at the same time; the importance of the alternatives may impact
criteria. Therefore, the complicated issues are better solved by applying ANP method which is
more suitable than the hierarchical framework with a linear top to bottom structure.

The

unidirectional hierarchies’ relationship framework can be substituted with a network by ANP
feedback approach in order to solve more complex problems where relationships between levels
were not simply displayed in hierarchy or in non-hierarchy, direct or indirect (Meade, L.M. and
Sarkis, J., 1999). According to Saaty (1980), a network represents a system which included
feedback where nodes corresponded to levels or components. Node elements can also affect some
or all the elements of any other node. ANP model process comprises five major steps as follow
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(Saaty, 1996):
(1) Conducting pairwise comparisons on the elements.
(2) Placing the resulting relative importance weights in pairwise comparison matrices within
the supermatrix (unweighted supermatrix).
(3) Conducting pair wise comparisons on the clusters.
(4) Weighting the partitions of the unweighted supermatrix by the corresponding priorities of
the clusters.
(5) Raising the weighted supermatrix to limiting powers until the weights convergence
remain stable (limit supermatrix).
During the recent years, many researchers have utilized ANP methods in various
environmental areas.

For examples, prioritizing energy policies in Turkey (Ulutas, 2005);

selecting optimal fuel for residential hearing in Turkey (Erdoğmuş et al., 2006); evaluating fuels
for electricity generation (Köne and Büke, 2007); selecting technology in a textile industry
(Yüksel and Dağdeviren, 2007); finding the location of the municipal solid waste treatment plants
(Aragonés-Beltrán et al., 2010a). However, there have been no ANP applications found in
literature reviews on the contexts of evaluating TICs.

The reasons using ANP method in this study were (i) TICs assessment involved multi-criteria
decision problems, (ii) this model taken into considerations of dependencies among perspectives
and criteria as well as opinions of a multidisciplinary expert team, (iii) the model provided the
systematic analysis of the interrelationships among perspectives and criteria, which could
carefully assist decision makers for gaining understanding the problems, and reliably making the
final priority decision.

3. Proposed TICs Assessment based ANP Algorithm
To identify TICs assessment criteria of the Thai Automotive Parts firms by utilizing ANP
model, this study constructed a TICs assessment model to enumerate the interrelationship weights
of criteria. The development of TICs assessment model is laid out into seven steps as shown in
Figure 1.

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Figure 1: The proposed ANP model for TICs assessment

3.1 Step 1: Define problems of TICs assessment
To clearly define the problem of perspectives and criteria in decision-making, the
identification of the relevant perspective and criteria is developed by means of literature reviews.
A group of experts in decision-making provided opinions in order to construct the
decision-making structured model into a rational network system, which can be obtained by means
of various methods such as in-depth interview, Delphi method, focus group. The model
appropriately consolidated the set of evaluation perspectives and criteria, which were categorized
to relevant clusters (Meade, L.M. and Sarkis, J., 1999; Saaty, 1996).

3.2 Step 2: Identify TICs assessment perspective and criteria
After the problems were clearly stated, this step was to find the components of TICs
assessment. The literature related to this research was empirically reviewed and extracted based on
the outlined classification of TIC evaluation perspectives or criteria.

3.3 Step 3: Select a group of qualified experts
This step is to ensure the independent opinions from experts towards the outlined
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classification of TICs assessment criteria. The information was used to revise the appropriated
TICs evaluation perspective/ criteria and their interrelationship. These experts would provide their
independent opinions on reviewing TICs assessment criteria, including reviewing TICs model, in
next following step.

3.4 Step 4: Construct and validate ANP model
In this step, the ANP algorithm was taken into account in order to identify the influences
between the components of the problems (perspectives and criteria). The procedures needed for
the establishment of the network were i) determination of criteria, ii) determination of the
perspectives, and iii) determination of the influence network. In this study, these first two
procedures of determination and categorizing of criteria were explained in the step 2. The result
shown the nineteen criteria grouped under seven perspectives were transformed into an ANP
network model. For the determination of the influences ANP network model of TICs assessment,
the interdependencies among perspectives were presented by arcs with each direction.
Table 2: Saaty’ fundamental scale.
Intensity of
importance
1

Definition

Explanation

Equal importance
Moderate
i importance
Strong t

3
5

Two perspective/criterion contribute equally to the objective
Experience and judgment slightly favor one over another

7

Very strong
importance

9

Absolute
i
t
Intermediate values

2, 4, 6, 8
Reciprocal of above
non-zero numbers

Experience and judgment strongly favor one over another
Perspective/criterion is strongly favored and its dominance is
demonstrated in practice
Importance of one over another affirmed on the highest possible order
Used to represent compromise between the priorities listed above

If activities i has one of the above non-zero numbers assigned to it when compared with
activity j, the j has the reciprocal value when compared with i

3.5 Step 5: Formulate pairwise comparisons among perspectives/ criteria
and calculate priority eigenvectors
3.5.1 Formulate pairwise comparisons
After obtaining the network structure compounding with the connections among perspectives
and criteria, a group of expert was asked to provide sets of pair wise comparisons of two criteria or
two perspectives to be evaluated in views of their contributions. These experts’ preferences were
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based on ANP Saaty’s scale ranging between 1 (the equal importance) to 9 (the extreme
importance) (Saaty, 1996; Huang et al., 2005), as shown in Table 2.
The comparisons between perspectives and criteria could be separately explained as below;
(i) Criteria comparisons: Operate pairwise comparisons on criteria within the perspectives
based on their influences on a criterion in another perspective where they were linked. Then,
pairs of criteria at each perspective were compared with respect to their importance towards their
control criteria.
(ii) Perspective comparisons: Operate pair wise comparisons on perspectives that influence or
be influenced by a given perspectives with respect to the TICs assessment for that network. The
perspective themselves were also compared pair wise with respect to their contribution to the goal.
3.5.2 Test consistency
In the pairwise comparisons process of ANP method, the judgments or preferences obtained
from experts would be conducted the consistency test based on consistency ration (C.R.). C.R. of a
pairwise comparison matrix is the ratio of its consistency index to the corresponding random value
and when C.R. < 0.1 meant that the consistency of pair-wise of comparison matrix was acceptable
(Saaty, 2005).
3.5.3 Calculate priority eigenvectors
According to Saaty (1980); Meade and Presley (2002), three steps for synthesizing the
priorities eigenvectors were shown below:
(i) Aggregate the values in each column of the pairwise comparisons matrix.
(ii) Divide each criterion in a column by the sum of its respective column in order to obtain
the normalized pairwise comparisons matrix.
(iii) Aggregate the criteria in each row of the normalized pairwise comparisons matrix. Then
divide the summation by the n criteria in the row. These final numbers (eigenvectors) provided an
estimate of the relative priorities for the elements being compared with respect to its control
criterion.

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3.6 Step 6: Construct supermatrix
This step was to establish three table supermatrices i.e. the unweighted, the weighted, and the
limit supermatrix, which were following explained as below.
3.6.1 Unweighted supermatrix
The unweighted supermatrix was derived by placing the resulting relative important weights
(eigenvectors) in pairwise comparisons of criteria within supermatrix.
3.6.2 Weighted supermatrix
With respect to the control criterion, the influence of the perspectives on each perspective was
indicated. The weighted supermatrix was obtained by multiplying all criteria in a component of the
unweighted supermatrix by the corresponding perspective relative important weight (Saaty, 2008).
3.6.3 Limit supermatrix
The limit supermatrix was gained by raising the weighted supermatrix to a significantly large
power in order to obtain the stable values (Saaty, 2008). The values of this limit supermatrix were
the desired priorities of the criteria with respect to firm’s TICs. Then the global priority vector or
weight is obtained to raise the weighted super-matrix to limiting power as depicted in Eq. (3).
∞

(3)

where Ŵ denotes as the weighted supermatrix and n is determined as number of limiting
power. This equation means multiplying the weighted supermatrix by itself until all elements in
each row/column are convergence.

3.7 Step 7: Implement ANP model for firm’s TICs assessment as case study
From limit supermatrix, once the global relative important weights of each TICs assessment
criteria were received, a group of experts provided their rating scores ranging from 1 (poor) to 5
(excellent). The final scores were calculated by multiplying the global weights in conjunction with
their rating scores.

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4. Results
4.1 Result of Step 1: Define problems of TICs assessment
The first step of the ANP algorithm was to analysis the firm’s TICs assessment problem. Two
main objectives of the firm’s TICs assessment problems were (i) to indicate the crucial TICs
assessment perspectives and criteria and (ii) to construct the firm’s TICs assessment model by
using multi-criteria decision making (MCDM) approach.

Figure 2: ANP assessment model of TICs

4.2 Result of Step 2: Identify TICs assessment perspective and criteria
Based on the extensive literature reviews, the nineteen evaluation criteria, and grouped into
seven perspectives were extracted and categorized, as depicted in Table 1.

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4.3 Result of Step 3: Select a group of qualified experts
In this study, six experts’ panel was chosen from three different fields i.e., 2 academic, 3
technological innovative industrial and 1 audit-consulting firms. These specific six experts had
highly knowledge and experienced in areas of R&D management, and innovation technology
management. Their opinions were for revising the appropriated TICs evaluation perspective/
criteria and their interrelationship

4.4 Result of Step 4: Construct and validate ANP model
In this step, the proposed TICs assessment model was confirmed and validated by consensus
of the 6 experts’ panels, as displayed in Figure 2. Also, the interaction between each evaluation
criteria was illustrated in Table 3.
Table 3: The interaction between evaluation criteria for ANP assessment model.
P1
C1

C2

P2
C3

C4

C5

C6

P3
C7

C8

C9

P4

P5

P6

C10 C11 C12 C13 C14 C15 C16 C17

P7
C18

C19

Leadership (C1)
Strategic Fit (C2)
Strategic Deployment (C3)
Resource Allocation (C4)
Improve Existing Product/Process (C5)
Invest in Proprietary Technology (C6)
External Technology Acquisition (C7)
Innovation Culture (C8)
Network Linkage (C9)
Response to Change (C10)
Internalized External Knowledge (C11)
Exploit New Knowledge (C12)
Embed New Knowledge (C13)
Development Proprietary Technology(C14)
Product Structure Design (C16)
Process Design (C17)
Manufacturing Capability (C18)

Remark: The symbol

represents the interaction among evaluation criteria

4.5 Result of Step 5: Formulate pairwise comparisons among criteria
/perspectives and calculate priority eigenvectors
According to proposed TICs assessment model, the pairwise comparisons of criteria and
perspectives were following performed in order to obtain the eigenvectors.
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Examples for results of pairwise comparison of criteria under Innovation Management
Capability (P1) were showed in Table 4 to Table 7. From Table 4, under Leadership (C1), the
relative weight values for Strategic Fit (C2), Strategic Deployment (C3), and Resource Allocation
(C4) were 0.646, 0.289, 0.064, respectively. It was found that Strategic Fit (C2) had the greatest
impact to Leadership (C1), based on Innovation Management Capability (P1). Also C.R. value was
0.07 and was less than 0.1, meaning the experts’ appraisal were consistent.
For other pairwise comparisons under other perspectives, the calculations of relative
important weight of criteria under their corresponding perspectives were similarly performed.
Table 4: Pairwise comparison
with respect to Leadership (C1)
C2

C3

C4

1

3


1/3


1/8

with respect to Strategic Fit (C2)

Strategic Fit (C2)

8

Eigenvector
0.646

C1
Leadership (C1)

1

6

0.289

C4

1

6

7

Eigenvector
0.739


1/6

1

0.064

1/6

1

3

0.178


Note: Consistency Ratio (C.R.) = 0.07

C3

1/7

1/3

1

0.082


with respect to Strategic Deployment (C3)
C1

C2

C4

Leadership (C1)

1

4

9

Strategic Fit (C2)

1/4

Eigenvector
0.709

1

5


1/9

1/5

1

with respect to Resource Allocation (C4)
C1

C2

C3

Leadership (C1)

1

6

5

Eigenvector
0.679

0.260

Strategic Fit (C2)

1/6

1

1/3

0.098

0.068


1/5

3

1

0.218



According to above pairwise comparisons, the example of relative important weight
among TICs assessment criteria under perspective (P1), represented by W11, was shown below.
C1

C3

C4

C1

0.739

0.709

0.679

C2

0.646

0

0.260

0.098

C3

0.289

0.178

0

0.218

C4

W11 =

C2

0

0.064

0.082

0.068

0

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Likewise, the pairwise comparisons on perspectives were also conducted in the same
calculation of such criteria. Based on TICs assessment goal, the final relative important weights of
perspectives was shown in Table 8.
Table 8: Relative important weights of perspectives
P1

P2

P3

P4

P5

P6

0.246

0.393

0

0

0

0

0

0.037

0.063

0.045

0

0.063

0

0

0.144

0.097

0.101

0

0

0.728

0

0.397

0.207

0.572

0.526

0.291

0

0

0.101

0.180

0.280

0.342

0.546

0

0

0.025

0.032

0

0.083

0.039

0.108

0.833

0.045

P1
P2
P3
P4
P5
P6
P7

P7

0.024

0

0.047

0.057

0.162

0.167

4.6 Result of Step 6: Construct supermatrix
4.6.1 Result of unweighted supermatrix
Since the unweighted supermatrix was derived by placing the resulting relative important
weights (eigenvectors) in pairwise comparisons of criteria within supermatrix. Based on TICs
assessment model in Figure 2, the partition matrix of the unweighted supermatrix was structured,
as magnificently illustrated in Table 9. Also the unweighted supermatrix could be then
transformed as shown in matrix below.
Table 9: The structure of unweighted supermatrix of TICs assessment by using ANP method
P1
C1

P1

P2

P3

P4

P5
P6
P7

C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19

202

C2

C3

C4

C5

P2
C6

C7

C8
0.000
0.000
0.000
0.000

P3
C9
0.000
0.000
0.000
0.000

W11

W12

W21

W22

W23

W31

W32

W33

W41

W42

W43

W51

W52

C10
0.000
0.000
0.000
0.000

W61

W62

W71

W72

0.000
0.000
0.000
0.000

P4
C12
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000

P5
C13
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000

C14
0.000
0.000
0.000
0.000

P6
C15
0.000
0.000
0.000
0.000

W25
0.000
0.000
0.000

0.000
0.000
0.000

C16
0.000
0.000
0.000
0.000
0.000
0.000
0.000

P7
C17
0.000
0.000
0.000
0.000
0.000
0.000
0.000

W36
0.000
0.000
0.000
0.000
0.000

0.000
0.000
0.000
0.000
0.000

C18
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000

C19
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000

W44

0.000
0.000
0.000
0.000

W45

W54

W53
0.000
0.000
0.000
0.000

C11
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000

W55

W64

W65

W66

W67

W74

W75

W76

W77


P1

P3

P4

P5

P6

P7

W11

W12

0

0

0

0

0

P2
W =

P2

P1

W21

W22

W23

0

W25

0

0

P3

W31

W32

W33

0

0

W36

0

P4

W41

W42

W43

W44

W45

0

0

P5

W51

W52

W53

W54

W55

0

0

P6

W61

W62

0

W64

W65

W66

W67

P7

W71

W72

0

W74

W75

W76

W77

As above matrix, P1, P2, …, P7, represented the TICs perspectives which were Innovation
Management Capability Perspective (P1), Investment

Capability Perspective (P2), …, and

Technology Commercialization Capability Perspective (P7), respectively.
In this unweighted supermatrix, Wij exhibited the relative important weight of sub-matrices.
W21 meant that P2 (Investment Capability Perspective) depended on P1 (Innovation Management
Capability Perspective). W33 represented that P3 (Organization Capability Perspective) also had
interaction and influenced within itself or inner feedback loop.
Table 10: Unweighted super-matrix

The perspectives having no interaction were shown in the supermatrix with zero (0) such as P3
(Organization Capability Perspective) had no influence on P1 (Innovation Management Capability
Perspective), P6 (Technology Transformation Capability Perspective), and P7 (Technology
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Commercialization Capability Perspective).
In this study, the Super Decision Software Version 16.0 was processed to calculate the
unweighted supermatrix, which the result of the unweighted supermatrix was shown in Table 10.
4.6.2 Result of weighted supermatrix
The weighted supermatrix was calculated by multiplying all criteria in a component of the
unweighted supermatrix with the corresponding perspective relative important weight (Saaty,
2008). The structure of weighted supermatrix was exhibited in Table 11. The result of weighted
supermatrix was exhibited in Table 12.
Table 11: The structure of weighted supermatrix of TICs assessment by using ANP method.

Ŵ11 =

C1
C2
C3
C4

C1
0*0.246
0.646*0.246
0.289*0.246
0.064*0.246

C2
0.739*0.246
0*0.246
0.178*0.246
0.082*0.246

C3
0.709*0.246
0.260*0.246
0*0.246
0.068*0.246

C4
0.679*0.246
0.098*0.246
0.218*0.246
0*0.246

Table 12: Weighted super-matrix

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For example, all of the elements of Ŵ11were multiplied by the corresponding weight of
perspective P1 = 0.246, as displayed in Ŵ11 matrix above. For next elements in W12 would be then
multiplied by 0.393, W21 was multiplied by 0.037, and so on. Based on the Super Decision
Software Version 16.0, once all elements in each corresponding perspective were completely
multiplied, the result of weighted supermatrix was shown in Table 12.
4.6.3 Result of limit supermatrix
Finally, the limit supermatrix was resulted by raising the weighted supermatrix to a power
until all columns were convergence by certain value. The results of final weights were as shown in
Table 13. Also each ANP weight of criteria was plotted as depicted in Figure 3.
Table 13: Limit super-matrix

ANP final weight
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19

Figure 3: The ANP final prioritize weight for each TICs assessment criteria.

4.7 Result of Step 7: Implement ANP model for firm’s TICs assessment as case
study
As a case study, the completed TICs assessment based ANP model was to be implemented as
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an audit tool to measure TICs on three selected Thai automotive parts firms. Each firm had
different TICs’ roles in the Thai automotive parts industry i.e. company X (leader), Y (follower)
and Z (laggard), respectively. The 13 special experts from the Thai automotive parts firms
provided the rating scores from 1 (poor) to 5 (excellent). These experts were from famous firms
which had been awarded Thailand’s Outstanding Innovative Company recognition for year 2010.
They acknowledged the importance of R&D. They are high-level managers with direct
responsibilities in innovative areas at the minimum of 5 years i.e. engineering director, R&D
director, and Chief Project Manager. Finally, the final scores were derived by multiplying the
global weights (from limit supermatrix, as shown in Table 14) and the experts’ rating scores. The
results of overall scores for these three companies were shown in Table 15.
Table 14: Final weights of evaluation criteria.
Perspectives

Assessment criteria

Rank
Final
Weights

Company X
Score
Net
Score
0.035
5

Company Y
Score
Net
Score
0.021
3

Company Z
Score
Net
Score
0.007
1

Innovation
Management
Capability (P1)

Leadership (C1)

0.007

14

Strategic Fit (C2)

0.003

17

5

0.015

5

0.015

2

0.006


0.001

18

4

0.004

4

0.004

2

0.002


0.001

18

5

0.005

3

0.003

3

0.003

Investment
Capability (P2)

Improve Existing Product/Process (C5)

0.008

13

4

0.032

4

0.032

1

0.008

Invest in Proprietary Technology (C6)

0.010

11

4

0.04

5

0.05

1

0.01

14

External Technology Acquisition (C7)

0.007

4

0.028

3

0.021

2

0.014

Organization

Innovation Culture (C8)

0.065

5

3

0.195

3

0.195

2

0.13

Capability (P3)

Network Linkage (C9)

0.007

14

4

0.028

4

0.028

1

0.007

Response to Change (C10)

0.023

9

5

0.115

3

0.069

2

0.046

Internalized External Knowledge (C11)

0.143

3

4

0.572

4

0.572

1

0.143

Exploit New Knowledge (C12)
Embed New Knowledge (C13)

0.172

2

3

0.516

4

0.688

2

0.344

0.032

8

3

0.096

3

0.096

2

0.064

Technology

Development Proprietary

0.301

1

Technology (C14)

4

1.204

3

0.903

2

0.602

Development

0.037

7

4

0.148

3

0.111

2

0.074

Product Structure Design (C16)

0.096

4

4

0.384

2

0.192

1

0.096

Process Design (C17)

0.015

3

0.045

4

0.06

3

0.045

Manufacturing Capability (C18)

0.057

6

5

0.285

2

0.114

1

0.057


0.009

12

4

0.036

3

0.027

2

0.018

Learning Capability
(P4)

Capability (P5)
Technology
Transformation
Capability (P6)
Technology
Commercialization
Capability(P7)

10

The score values of the assessment criteria from the three companies were also multi-plotted
separately in the same evaluation criteria. The multivariate observations were displayed in chart
Figure 4. In the chart, the plots identified firms’ characteristics under the same evaluation criteria
as well as the comparison among them. Thereafter, this TICs assessment model was applied and
206


company X, an innovative leader, appeared to be the strongest firm in aspects of Development
Proprietary Technology (C14), R&D Project Interfacing (C15), Product Structure Design (C16),
Manufacturing Capability (C18), Response to Change (C10), Marketing Capability (C19),
Leadership (C1), External Technology Acquisition (C7), and Resource Allocation (C4). For a
follower, company Y, had slightly better scores in terms of Invest in proprietary technology (C6),
Process design (C17), and Exploit new knowledge (C12). For company Z or a weak company
obviously had the lowest score and needed to develop in most aspects of the assessment criteria.

Figure 4: Comparison of each TICs assessment criteria among three companies

5. Conclusion
The improvement of the TICs is described as one of the most important business strategies
for top managements in the strengthening of the firms’ competitive advantages. It is necessary for
decision makers to acknowledge the effectiveness of TICs assessment criteria prior to
implementation. This study proposed an effective MCDM method by utilizing ANP technique in
order to handle the complexity of multiple TICs assessment criteria for the Thai automotive parts
firms. With ANP approach, it enables for taking into consideration both tangible and intangible
criteria and it can systematically deal with all kinds of dependencies. The results showed that Thai
automotive parts firms should give high consideration to the top five criteria based on the scores
prioritization i.e. Development Proprietary Technology (C14 = 0.301), Exploit New Knowledge
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(C12 = 0.172), Internalized External Knowledge (C11 = 0.143), Product Structure Design (C16 =
0.096), and Innovation Culture (C8 = 0.065), respectively. And from the three selected Thai
automotive parts firms in the case study, the leader portrayed the characteristics which should be
followed by other companies on certain criteria. Meanwhile, the follower and the laggard were
obviously scored lower and revealed weaknesses in many criteria and needed to improve. As for
other industries, in order to assess their own TICs, managements could generally apply this TICs
assessment model with some adjustment especially in Step 5 by obtaining experts’ opinions on
factors which are specific to such industry and apply ANP method. Thereafter, new relative weight
of criteria would be developed. This model by comparison would provide useful information as a
benchmarked approach and to simultaneously measure each TICs’ criteria for further
improvement.

6. Recommendation for Further Study
In this study, main drawbacks are the complexity in model construction among various
criteria and their relationship influences involved in the assessment process.

The TICs

assessment model proposed in this research still lacks the systematic method to select TICs
evaluation perspectives or criteria.

Future research may consider the extraction of the

appropriated TICs assessment factors by means of Delphi or Fuzzy Delphi methods. Also the
model construction is suggested for future work to use more systematic approach for finding the
interaction among TICs factors such as Interpretive Structural Modeling (ISM) or Decision
Making Trial and Evaluation Laboratory (DEMATEL). Moreover, in order to improve the
decision making process, the ranking on the selected companies is recommended for future study
by using Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) or
Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) methods.

7. Acknowledgements
The authors would like to thank the anonymous reviewers for their very helpful and
constructive comments on the earlier version of this paper.

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MBA from Thammasat University.
Dr. P. Anuntavoranich is an Assistant Professor of Department of Industrial Design at Faculty of
Architecture, Chulalongkorn University, and he is now Director of Technopreneurship and
Innovation Management, Chulalongkorn University. He received his Ph.D. (Art Education) from
the Ohio State University, Columbus, OH, USA. His specialty is creative design and innovation
management.

Peer Review: This article has been internationally peer-reviewed and accepted for
publication according to the guidelines given at the journal’s website.
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