2013 American Transactions on Engineering & Applied Sciences.

American Transactions on
Engineering & Applied Sciences
htt...
1. Introduction 
The Thai automotive parts industry is one of the most important manufacturing sectors of the
country. The...
alternatives to serve such requirements. This research proposed the TICs assessment which
applied systematic MCDM method t...
Table 1: Summary of the perspectives and criteria from literatures
Evaluation Criteria
Description
Innovation Management C...
Table 1: Summary of the perspectives and criteria from literatures (Continue)
Evaluation Criteria
Description
Technology C...
(Saaty, 1996):
(1) Conducting pairwise comparisons on the elements.
(2) Placing the resulting relative importance weights ...
Figure 1: The proposed ANP model for TICs assessment

3.1 Step 1: Define problems of TICs assessment 
To clearly define th...
classification of TICs assessment criteria. The information was used to revise the appropriated
TICs evaluation perspectiv...
based on ANP Saaty’s scale ranging between 1 (the equal importance) to 9 (the extreme
importance) (Saaty, 1996; Huang et a...
3.6 Step 6: Construct supermatrix 
This step was to establish three table supermatrices i.e. the unweighted, the weighted,...
4. Results 
4.1 Result of Step 1: Define problems of TICs assessment 
The first step of the ANP algorithm was to analysis ...
4.3 Result of Step 3: Select a group of qualified experts 
In this study, six experts’ panel was chosen from three differe...
Examples for results of pairwise comparison of criteria under Innovation Management
Capability (P1) were showed in Table 4...
Likewise, the pairwise comparisons on perspectives were also conducted in the same
calculation of such criteria. Based on ...
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...
Commercialization Capability Perspective).
In this study, the Super Decision Software Version 16.0 was processed to calcul...
For example, all of the elements of Ŵ11were multiplied by the corresponding weight of
perspective P1 = 0.246, as displayed...
an audit tool to measure TICs on three selected Thai automotive parts firms. Each firm had
different TICs’ roles in the Th...
company X, an innovative leader, appeared to be the strongest firm in aspects of Development
Proprietary Technology (C14),...
(C12 = 0.172), Internalized External Knowledge (C11 = 0.143), Product Structure Design (C16 =
0.096), and Innovation Cultu...
Antony, J., Leung, K., Knowless, G., and Gosh, S. (2002). Critical success factors of TQM
implementation in Hong Kong indu...
Research Policy, 35, 121-143.
Guan, J.C., Yam, R.C.M., Mok, C.K., and Ma, N. (2006). A study of the relationship between
c...
Research, 37(2), 241-261.
Mu, J., and Benedetto, C.A.D. (2011). Strategic orientations and new product commercialization:
...
Tseng, M.L. (2011). Using a hybrid MCDM model to evaluate firm environmental knowledge
management in uncertainty. Applied ...
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An Analytic Network Process Modeling to Assess Technological Innovation Capabilities: Case Study for Thai Automotive Parts Firms

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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.

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  1. 1. 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 189
  2. 2. 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
  3. 3. 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. *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 191
  4. 4. 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) 192 Firm’s ability to design process to support design for manufacturing and design for assembly activities. Detcharat Sumrit, and Pongpun Anuntavoranich 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)
  5. 5. 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 *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 193
  6. 6. (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. 194 Detcharat Sumrit, and Pongpun Anuntavoranich
  7. 7. 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 *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 195
  8. 8. 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 196 Detcharat Sumrit, and Pongpun Anuntavoranich
  9. 9. 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. *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 197
  10. 10. 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. 198 Detcharat Sumrit, and Pongpun Anuntavoranich
  11. 11. 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. *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 199
  12. 12. 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) 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. 200 Detcharat Sumrit, and Pongpun Anuntavoranich
  13. 13. 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 Strategic Deployment (C3) 1/3 Resource Allocation (C4) 1/8 Table 5: Pairwise comparison 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 Strategic Deployment (C3) 1/6 1 0.064 1/6 1 3 0.178 Resource Allocation (C4) Note: Consistency Ratio (C.R.) = 0.07 C3 1/7 1/3 1 0.082 Note: Consistency Ratio (C.R.) = 0.096 Table 6: Pairwise comparison with respect to Strategic Deployment (C3) C1 C2 C4 Leadership (C1) 1 4 9 Strategic Fit (C2) 1/4 Eigenvector 0.709 1 5 Resource Allocation (C4) 1/9 1/5 1 Table 7: Pairwise comparison 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 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 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 *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 201
  14. 14. 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 Detcharat Sumrit, and Pongpun Anuntavoranich
  15. 15. 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 *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 203
  16. 16. 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 204 Detcharat Sumrit, and Pongpun Anuntavoranich
  17. 17. 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 *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 205
  18. 18. 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 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 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) R&D Project Interfacing (C15) 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 Marketing Capability (C19) 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 Detcharat Sumrit, and Pongpun Anuntavoranich
  19. 19. 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 *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 207
  20. 20. (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. 8. References  Agarwal, A., Shankar, R., and Tiwari, M.K. (2007). Modeling agility of supply chain. Industrial Marketing Management, 36, 443-457. 208 Detcharat Sumrit, and Pongpun Anuntavoranich
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