An Analytic Network Process Modeling to Assess Technological Innovation Capabilities: Case Study for...

*Corresponding author (P. Anuntavoranich). Tel/Fax: +66-2-6576334. E-mail addresses: [email protected], [email protected]. 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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American Transactions on Engineering & Applied Sciences

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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. A R T I C L E I N F O

A B S T R A C T

Article history: Received January 08, 2013 Received in revised form March 20, 2013 Accepted March 29, 2013 Available online April 05, 2013 Keywords: Technological Innovation Capability; Analytic network process ; Thai automotive parts firms TICs evaluation criteria.

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.

2013 Am. Trans. Eng. Appl. Sci.

2013 American Transactions on Engineering & Applied Sciences.

190 Detcharat Sumrit, and Pongpun Anuntavoranich

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


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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.


Table 1: Summary of the perspectives and criteria from literatures Evaluation Criteria Description Author

Innovation Management Capability Perspective (P1) Leadership commitment (C1) Firm’s high level manager actively

participates in decision-making related to technological issues.

O’Regan et al., (2006), Grinstein and Goldman (2006), Prajogo and Sohal, (2006), Kyrgidou and Spyropoulou (2012)

Strategic fit (C2) Firm’s technological innovation strategy supports business strategy.

Prajogo and Sohal, (2006), Koc and Ceylan (2007), Yam et al., (2011),

Strategic deployment (C3) Firm’s technological innovation strategy were shared and applied to each department/unit.

Prajogo and Sohal, (2006), Koc and Ceylan (2007), Dobni (2008)

Resource allocation (C4) Firm’s ability to appropriately acquire and allocate capital & technology.

Koc and Ceylan (2007), Wang et al., (2008), Yam et al., (2011)

Investment Capability Perspective (P2) Investment in the existing product/process improvement (C5)

Firm’s ability to continuously invest in existing technological product & process improvement.

Koc and Ceylan (2007), Dobni (2008), Zhou and Wu (2010)

Investment in proprietary technology development (C6)

Firm’s capability to invest in developing proprietary technology.

Yam et al., (2011), Lin et al.,(2012).

Investment in external technology acquisition (C7)

Firm’s ability to invest in external technology acquisition.

Flor and Oltra (2005), Lee et al., (2009)

Organization Capability Perspective (P3) Innovation culture (C8) Firm’s ability to cultivate innovation culture. Dobni (2008), Kyrgidou and Spyropoulou

(2012), Türker (2012) Network linkage (C9) Firm’s ability to transmit information, skills

and technology, and to acquire them from departments, clients, suppliers, consultants, technological institutions, etc.

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)

Response to change (C10)

Firm’s capability in risk assessment , risk taking and response to technological innovation change and adopting

Jansen et al., (2005), Zhou and Wu (2010), Grinstein and Goldman (2006), Mu and Benedetto (2011), Forsman (2011)

Learning Capability Perspective (P4) Internalized external

knowledge (C11)

Firm’s ability to recognize and internalize relevant external knowledge

Camisón and Forés (2010), Forsman (2011), Biedenbach and Müller (2012)

Exploit new knowledge (C12) Firm’s ability to bring in new knowledge or technologies to develop innovative product

Camisón and Forés (2010), Forsman (2011)

Embed new knowledge (C13) Firm’s ability to transplant new knowledge into new operation by creating a shared understanding and collective sense-making.

Camisón and Forés (2010), Forsman (2011)

Technology Development Capability Perspective (P5) Proprietary technology development (C14)

Firm’s ability to develop proprietary technologies from in-house R&D

Grinstein and Goldman (2006), Prajogo and Sohal, (2006), Wang et al., (2008), Forsman (2011), Kim et al., (2011).

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.

Lin (2004), Camisón and Forés (2010), Kim et al., (2011), Mu and Benedetto (2011)

Technology Transformation Capability Perspective (P6) Product structural design and engineering (C16)

Ability to design product structure & modularization & compatible with process.

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

Process design and

engineering (C17)

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

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


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Table 1: Summary of the perspectives and criteria from literatures (Continue) Evaluation Criteria Description Author Technology Commercialization Capability Perspective (P7) Manufacturing Capability (C18)

Firms’ ability in transform R&D output into production and acquire the innovative advanced manufacturing technologies/ methods.

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)

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.

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


(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


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

Definition Explanation

1 Equal importance Two perspective/criterion contribute equally to the objective

3 Moderate i t

Experience and judgment slightly favor one over another

5 Strong importance Experience and judgment strongly favor one over another

7 Very strong importance

Perspective/criterion is strongly favored and its dominance is demonstrated in practice

9 Absolute i t

Importance of one over another affirmed on the highest possible order

2, 4, 6, 8 Intermediate values Used to represent compromise between the priorities listed above

Reciprocal of above non-zero numbers

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.


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.


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 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

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) R&D Project Interfacing (C15) Product Structure Design (C16) Process Design (C17) Manufacturing Capability (C18) Marketing Capability (C19)

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 Table 5: Pairwise comparison with respect to Leadership (C1) with respect to Strategic Fit (C2)

C2 C3 C4 Eigen- vector


Strategic Fit (C2) 1 3 8 0.646 Leadership (C1) 1 6 7 0.739

Strategic Deployment (C3) 1/3 1 6 0.289 Strategic Deployment (C3) 1/6 1 3 0.178

Resource Allocation (C4) 1/8 1/6 1 0.064 Resource Allocation (C4) 1/7 1/3 1 0.082

Note: Consistency Ratio (C.R.) = 0.07 Note: Consistency Ratio (C.R.) = 0.096

Table 6: Pairwise comparison Table 7: Pairwise comparison with respect to Strategic Deployment (C3) with respect to Resource Allocation (C4)



Leadership (C1) 1 4 9 0.709 Leadership (C1) 1 6 5 0.679

Strategic Fit (C2) 1/4 1 5 0.260 Strategic Fit (C2) 1/6 1 1/3 0.098

Resource Allocation (C4) 1/9 1/5 1 0.068 Strategic Deployment (C3) 1/5 3 1 0.218

Note: Consistency Ratio (C.R.) = 0.068 Note: Consistency Ratio (C.R.) = 0.09

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 C2 C3 C4

C1 0 0.739 0.709 0.679

W11 = C2 0.646 0 0.260 0.098

C3 0.289 0.178 0 0.218

C4 0.064 0.082 0.068 0


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

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 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

P1

C1

W11 W12

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 C2 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 C3 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 C4 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

P2 C5

W21 W22 W23 0.000 0.000 0.000

W25 0.000 0.000 0.000 0.000

C6 0.000 0.000 0.000 0.000 0.000 0.000 0.000 C7 0.000 0.000 0.000 0.000 0.000 0.000 0.000

P3 C8

W31 W32 W33 0.000 0.000 0.000 0.000 0.000

W36 0.000 0.000

C9 0.000 0.000 0.000 0.000 0.000 0.000 0.000 C10 0.000 0.000 0.000 0.000 0.000 0.000 0.000

P4 C11

W41 W42 W43 W44 W45 0.000 0.000 0.000 0.000

C12 0.000 0.000 0.000 0.000 C13 0.000 0.000 0.000 0.000

P5 C14 W51 W52 W53 W54 W55

0.000 0.000 0.000 0.000 C15 0.000 0.000 0.000 0.000

P6 C16 W61 W62

0.000 0.000 0.000 W64 W65 W66 W67 C17 0.000 0.000 0.000

P7 C18 W71 W72

0.000 0.000 0.000 W74 W75 W76 W77 C19 0.000 0.000 0.000

P1 P2 P3 P4 P5 P6 P7 P1 0.246 0.393 0 0 0 0 0

P2 0.037 0.063 0.045 0 0.063 0 0

P3 0.144 0.097 0.101 0 0 0.728 0

P4 0.397 0.207 0.572 0.526 0.291 0 0

P5 0.101 0.180 0.280 0.342 0.546 0 0

P6 0.025 0.032 0 0.083 0.039 0.108 0.833

P7 0.045 0.024 0 0.047 0.057 0.162 0.167


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P1 P2 P3 P4 P5 P6 P7 P1 W11 W12 0 0 0 0 0 P2 W21 W22 W23 0 W25 0 0 W = 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


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.

C1 C2 C3 C4 C1 0*0.246 0.739*0.246 0.709*0.246 0.679*0.246 Ŵ11 = C2 0.646*0.246 0*0.246 0.260*0.246 0.098*0.246 C3 0.289*0.246 0.178*0.246 0*0.246 0.218*0.246 C4 0.064*0.246 0.082*0.246 0.068*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

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

00.050.1

0.150.2

0.250.3

0.35

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

ANP final weight


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.

Perspectives Assessment criteria

Final Weights

Rank Company X Company Y Company Z Score Net

ScoreScore Net

Score Score Net

Score Innovation Management Capability (P1)

Leadership (C1) 0.007 14 5 0.035 3 0.021 1 0.007

Strategic Fit (C2) 0.003 17 5 0.015 5 0.015 2 0.006

Strategic Deployment (C3) 0.001 18 4 0.004 4 0.004 2 0.002

Resource Allocation (C4) 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

External Technology Acquisition (C7) 0.007 14 4 0.028 3 0.021 2 0.014 Organization

Capability (P3)

Innovation Culture (C8) 0.065 5 3 0.195 3 0.195 2 0.13

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 Learning Capability (P4)

Internalized External Knowledge (C11) 0.143 3 4 0.572 4 0.572 1 0.143

Exploit New Knowledge (C12) 0.172 2 3 0.516 4 0.688 2 0.344 Embed New Knowledge (C13) 0.032 8 3 0.096 3 0.096 2 0.064

Technology

Development

Capability (P5)

Development Proprietary

Technology (C14)

0.301 1 4 1.204 3 0.903 2 0.602

R&D Project Interfacing (C15) 0.037 7 4 0.148 3 0.111 2 0.074 Technology Transformation Capability (P6)

Product Structure Design (C16) 0.096 4 4 0.384 2 0.192 1 0.096

Process Design (C17) 0.015 10 3 0.045 4 0.06 3 0.045

Technology Commercialization Capability(P7)

Manufacturing Capability (C18) 0.057 6 5 0.285 2 0.114 1 0.057

Marketing Capability (C19) 0.009 12 4 0.036 3 0.027 2 0.018

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

Table 14: Final weights of evaluation criteria.


207

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


(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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D. Sumrit is a Ph.D. Candidate of Technopreneurship and Innovation Management Program, Graduate School, Chulalongkorn University, Bangkok, Thailand. He received his B.Eng in Industrial Engineering from Kasetsart University, an M.Eng from Chulalongkorn University and 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

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