Determinants of Technological Innovation Capabilities:

Using Fuzzy-DEMATEL and ANP

Chien-Ke Huang

, Chi-Hui Wu

2,3,*

, Jing Li

1,4

and Chih-Ling Hsieh

Ph.D. Program in Management, Da-Yeh University, University Rd., Changhua, Taiwan

Department of Management and Information, National Open University, Zhong Zheng Rd., New Taipei, Taiwan

Department of Information Management, Da-Yeh University, University Rd., Changhua, Taiwan

Music and Film College, Tainjin Normal University, Binshui West Rd., Tianjin, China

Professional Development in Education, Da-Yeh University, University Rd., Changhua, Taiwan

Keywords: Technological Innovation Capabilities, Fuzzy DEMATEL, ANP.

Abstract: This paper applies Fuzzy-DEMATEL and ANP to analyse the decisive determinants of technological

innovation capabilities (TICs) and the weight relation in high-tech and traditional manufacturing industries.

Technological innovation capabilities comprise various features that are complex and interrelated. Thus, the

purpose of this paper is to clarify the causal relationship of the various features to precisely provide the

enterprises with some determinants to maximize the effectiveness of resource utilization. In terms of

dimension, the research shows that the innovation management capability is the decisive factor of

technological innovation capabilities for high-tech industry and traditional manufacturing industry, which

affects the other five dimensions. However, in terms of criteria, learning capabilities are decisive determinants.

Therefore, in the face of competitive environment with continuous shortening of the product life cycle and

rapidly changing customer demands, overall, in order to enhance innovation capabilities, enterprises must

focus on the improvement of innovation management capabilities. In regard to the detail factors, the

enterprises must enhance the learning capabilities to recognize, absorb and make use of knowledge from the

internal organization in order to strengthen the knowledge management capability, the innovative decision-

making capability and technology of the enterprise itself.

1 INTRODUCTION

Innovation is the key factor and source of the

competitiveness of an enterprise (Hult, Hurley, &

Knight, 2004). Also it is the motive power for

economic growth (Schumpeter, 1942). Especially, the

application of technological innovation is the major

source of economic growth (Kuznets, 1973) and the

innovation capability also affects the performances of

an enterprise (Jayani Rajapathirana & Yan, 2018).

Therefore, in the face of changing circumstances, the

enterprise needs constant technological innovations

in order to maintain competitive advantages (Wang,

Lu, & Chen, 2008).

Because the traditional technology indexes are no

longer sufficient to reflect the environmental and

development conditions of the knowledge economy,

the relevant technology measurement indexes and

Corresponding author. Tel.: +886-938-567-834

research methods have been constantly adjusted and

developed since the 1990s. Technological innovation

capabilities are defined as the ability of enterprises to

create new value for their customers by introducing

new products and services, developing new

technologies, and exploring new skills and

capabilities (Huang, 2011). Because the

technological innovation capability is a complex,

multidimensional and uncertain concept (Ince,

Imamoglu, & Turkcan, 2016), many researchers have

developed different methods of measurement to

assess the technological innovation capabilities of an

enterprise (Shafia, Shavvalpour, Hosseini, &

Hosseini, 2016). However, there is no consistent

conclusion due to various indexes for measuring

technological innovation capabilities, so it is difficult

to evaluate from a single point of view (Burgelman,

Christensen, & Wheelwright, 2004). Therefore, the

106

Huang, C., Wu, C., Li, J. and Hsieh, C.

Determinants of Technological Innovation Capabilities: Using Fuzzy-DEMATEL and ANP.

DOI: 10.5220/0009412101060114

In Proceedings of the 2nd International Conference on Finance, Economics, Management and IT Business (FEMIB 2020), pages 106-114

ISBN: 978-989-758-422-0

measurement of technology innovation requires a

variety of quantitative and qualitative criteria (Wang

et al, 2008). Besides, the assessment of the

enterprise's technological innovation capabilities is

considered subjective and inaccurate, this subjectivity

and inaccuracy increase the complexity of the

assessment. In general, evaluators judge it

subjectively by means of past experience,

professional knowledge and information.

However, many decision-making issues, such as

technological innovation capabilities, the

characteristics of their internal complexities cannot

be expressed in a hierarchical manner (Sumrit &

Anuntavoranich, 2013a). This is because it interacts

with each other at the upper and lower levels, and the

elements at the lower level also have interdependence

between the elements at the top. (Saaty, 1996).

According to the research results about the

literatures of the capabilities of technological

innovation, studying the technological innovation

capabilities of different industries, the factors that

affect technological innovation capabilities, the

degree of correlation between each factor, and the

results of causality are different. For example, some

scholars’ research objects are the traditional

industries and the research results show that the

technology development capability and the

innovation management capability are crucial factors

to an enterprise’s capabilities of the technological

innovation (Kumar, Kaviani, Hafezalkotob, &

Zavadskas, 2017). Other scholars’ research objects

are the high-tech industries and the research results

show that collective learning capability and

innovation management capability are crucial factors

to an enterprise’s capabilities of the technological

innovation (Ravari, Mehrabanfar, Banaitis, &

Banaitiene, 2016).

The purpose of the research is to find out and

compare the decisive factors of the capabilities of

technological innovation between the traditional and

high-tech industries in order to upgrade the

capabilities of the technological innovation of

industries and to maintain the reference of the

competitive advantage. Therefore, the research

objects of this research are the traditional and high-

tech industries and to obtain the indexes of

technology innovations by fuzzy Delphi method’s

questionnaire and build the structures on the

capabilities of the technological innovation.

Additionally, it analyses the relations of the cause and

effect that affect the structures and criteria of the

capabilities of the technological innovation and to

ascertain the decisive factors. Finally it applies ANP

to analyse the weight of the priority on the criteria of

the capabilities of the technological innovation in

order to provide enterprise operators with the

reference of the strategy planning.

2 LITERATURE REVIEW

Facing the incessant change around the global

environment, technological innovation capabilities

are the important and unique strategies to upgrade the

enterprise’s competition advantages (Shafia et al.,

2016). Technological innovation capabilities and

information technology capabilities would affect the

business performance (Yuan, Shin, He, & Kim, 2016;

Bergeron, Croteau, Raymond & Uwizeyemungu,

2017) and innovation performance of an enterprise

(Mir, Casadesus, & Petnji, 2016).

Adler and Shenhar (1990) first bring up the

concepts about Technological Innovation

Capabilities (TICs), TICs are an enterprise’s special

assets including the crucial fields of the technology,

manufacture, process, knowledge, experience and

organization (Türker, 2012). Burgelman et al.(2004)

defines technological innovation capabilities as a set

of complete strategies that the organization promotes

the technological innovation. According to Adler and

Shenhar’s (1990) researches, technological

innovation capabilities are classified to 4 kinds

including (1) the capability to satisfy the needs of

market through developing new products (2) the

capability to manufacture these products with suitable

technologies (3) the capability to satisfy future’s

needs by developing and introducing new products

and technologies (4) the capability to respond to

abrupt development of technologies and the

unpredictability conditions that the rivals bring about.

Therefore, technological innovation capabilities are

defined as: an organization upgrades technological

innovation capabilities by providing new products

and services, adopting new technologies, innovating

new skills and capabilities or abilities of creating new

clients’ claims on value (Huang, 2011).

The scholars researching technological

innovation capabilities have different viewpoints to

the indexes of measuring technological innovation

capabilities. Christensen (1995) brings up the asset

approach to measure the indexes of technological

innovation capabilities. Chiesa, Coughlan, and Voss

(1996), Burgelman et al. (2004), Turker (2012) and

Ravari et al. (2016) measure the technological

innovation capabilities by the process approach.

Romijn and Albaladejo (2002) bring up the output-

based approach to measure the indexes of

technological innovation capabilities. Yam, Guan,

Determinants of Technological Innovation Capabilities: Using Fuzzy-DEMATEL and ANP

107

Pun, and Tang (2004), Guan, Yam, Mok, and Ma

(2006) and Wang et al. (2008) bring up the functional

approach to measure the indexes of technological

innovation capabilities Besides, Sumrit,

Anuntavoranich (2013b) and Kumar et al. (2017)

measure the technological innovation capabilities by

the comprehensive perspective approach.

As for Resource Based View(Wernerfelt, 1984;

Grant, 1991), the technological innovation

capabilities are enterprises’ unique assets(Guan & Ma,

2003; Guan et al., 2006), it comprises of many

different fields including resource allocation

capability, resource exploiting capability,

organizational capability, innovation planning

capability and project cross functional team

integration capability. As for the perspective from

core competence (Prahalad & Hamel, 1990), the

technological innovation capabilities are meant to

integrate the management procedures for every

department of an enterprise into unique competence

including market capability, manufacturing

capability and R&D capability. As for the viewpoint

from Dynamic Capability (Teece, Pisano, & Shuen,

1997) and Knowledge Based View (Kogut & Zander,

1992; Grant, 1996), the technological innovation

capabilities are viewed as the learning process (Cohen

& Levinthal, 1989; Hitt, Ireland, & Lee, 2000) in

order to enhance the enterprises’ necessities of the

knowledge and skills including learning capability,

absorptive capability, knowledge management,

innovative organization culture, network linkage

capability and technology acquisition capability; And

there is no way to measure the technological

innovation capabilities by a single perspective

(Chiesa et al., 1996; Burgelman et al., 2004; Guan &

Ma, 2003; Guan et al., 2006). Additionally, different

scholars have different viewpoints on the levels and

frameworks of the technological innovation

capabilities. Some scholars adopt the single-level

index (Yam et al., 2004; Guan et al., 2006; Kumar et

al., 2017), others adopt two-level dimension and

index (Christensen, 1995; Chiesa et al., 1996; Romijn

& Albaladejo, 2002; Sumrit & Anuntavoranich,

2013b). Because the technological innovation

capabilities are complex and multi-dimension

concepts, this research adopts two-level dimension

and index to integrate what mentioned above by

scholars and to coordinate and conclude six

dimensions and twenty criteria.

3 RESEARCH METHODOLOGY

AND MODEL DEVELOPMENT

3.1 Research Framework

The research is divided into three phases to analyse

and collect information in order to construct

dimensions and criteria; First of all, the first phase is

to retrospect relevant literatures as a foundation,

archiving the past scholars’ perspectives, inducing

technological innovation capabilities of affecting

enterprises. It has six dimensions and twenty criteria

and adopts Fuzzy Delphi method to make the group

decisions to scholars and experts of the technological

innovation in order to solve ambiguous problems and

obtain consensuses. So it depends heavily upon the

knowledge and experiences from scholars and experts

and converge the dimensions and criteria to

consistency and reliable structures for sieving out

comparatively important and higher criterion items in

order to compile questionnaires through the feedback

of experts and scholars’ opinions.

The second phase is the DEMATEL questionnaire.

The main researching objects are Taiwan’s high-tech

and traditional manufacturing industries. The

practical experts of technological innovation in high-

tech and traditional manufacturing industries use

Fuzzy DEMATEL to construct relation matrixes

between dimensions and criteria, illustrating the

graphs of the causal relationship, making path

analysis about the cause- and -effect relevant

affection . Furthermore, it finds out the decisive

factors of technological innovation capabilities and to

construct the causal relationship of the decisive

factors in technological innovation capabilities.

The third phase is the ANP questionnaire. The

main researching objects are Taiwan’s technology

and traditional manufacturing industries. The

practical experts of the technological innovation in

technology and traditional manufacturing industries

analyse ANP to find out the comparative weight and

relevancy of every dimension for the technological

innovation capabilities in order to bring out

conclusions and concrete suggestions on the

important factors that affect technological innovation

capabilities.

3.2 Calculating Steps on Fuzzy Delphi

Method

This research uses fuzzy Delphi method to sieve out

the comparatively important items of the dimension

on the technological innovation capabilities. The

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steps of fuzzy Delphi method are as follows (Liang,

Lee, & Huang, 2010):

Step 1: Collect the decision-making group’s

opinions; Step 2: Set up triangle fuzzy numbers; Step

3: Defuzzification; Step 4: Sieve out the assessment

criterion.

3.3 Contents and Objects of

Questionnaire Design

It compiles six dimensions including innovation

management capability (A), collective learning

capability (B), innovation sourcing capability (C),

technology development capability (D), robustness

product & process design capability (E), technology

commercialization capability (F) and to sieve out six

dimensions and twenty criteria by fuzzy Delphi

method. We issue questionnaires to twenty scholars

and practical experts of the innovation research, high-

tech and traditional industry. They judge whether to

maintain dimensions according to their knowledge

and experiences. The threshold value in this research

is 60% which means that if over 60% of the scholars

and experts agree to maintain on dimensions of the

certain items, they are kept. This research sorts out 6

dimensions and twenty criteria. The criterion of the

minimum threshold value is 60.3% of the product

structure design and engineering capability. The

criterion of the maximum threshold value is 87.9% of

the learning capability. Every item has over 60%

experts and scholars’ agreements which mean that all

criteria are kept. Finally, twenty criteria and operating

definitions in this research are as follows: Strategic

management capability (A

), Organization capability

), Resource allocation capability (A

), Risk

management capability (A

), Innovation decision

capability (A

), Resource exploiting capability (A

Learning capability (B

), Absorptive capability (B

Knowledge management capability (B

), Innovative

organization culture (B

), Network linkage capability

), Technology acquisition capability (C

Investment capability (C

), R&D capability (D

Project cross functional team integration capability

), Technology change management capability

), Product structural design and engineering

capability (E

), Process design and engineering

capability (E

), Manufacturing capabilities (F

Market capability (F

3.4 Fuzzy DEMATEL Computing and

Steps

Li (1999), Lin and Wu‘s (2008) classifications are

references of the linguistic scale and triangular fuzzy

numbers in the fuzzy DEMATEL part. The linguistic

scale is divided into 5 kinds. In order to facilitate the

subjects to consider and fill up, it provides concretely

4 to 0 points in the questionnaire.

Fuzzy DEMATEL is the method that combines

the fuzzy linguistic variations with DEMATEL and

the steps of formulas and computing are as follows

(Wu, Liao, Tseng, & Chiu, 2015):

Step 1: Define the evaluation criteria and

determine the fuzzy linguistic scale; Step 2: Establish

the directed-relation matrix; Step 3: Establish and

analyse the structural model; Step 4: Establish the

total-relation matrix; Step 5: Conduct defuzzification;

Step 6: Centrality and relation; Step 7: The result

analysis.

3.5 ANP Computing and Steps

ANP is the method used in Multi-Criteria Decision-

Making, MCDM. It deduces the comparative priority

of the criterion through experts’ judgement or

practical measurement. The judgement shows the

comparative affection relations (Saaty, 2006). The

applications of ANP are divided into five steps as

follows:

Step 1: Establish the pairwise comparisons

matrix; Step 2: Compute the eigenvalue of the pairwise

comparisons matrix and the eigenvector; Step 3:

Consistency test; Step 4: Calculate limit supermatrix

formation; Step 5: Choose the optimum criterion.

4 RESEARCH THE RESULT

ANALYSES AND DISCUSS

4.1 The Results of Fuzzy DEMATEL

4.1.1 The Analysis Results of Every

Dimension

Step 1: Define the Evaluation Criteria and Determine

the Fuzzy Linguistic Scale

The assessment dimensions are as follows: A, B,

C, D, E, and F.

Step 2: Establish the Direct Relative Matrix

It is able to obtain experts’ opinions after experts

compare dimensions in pair. And it integrates every

expert’s opinions by numbers in order to decrease

affections from extreme values; afterward, it is able

to obtain the fuzzy, direct and relevant matrixes.

Step 3: Establish and Analyze the Structure Model

It converts linear scales into normalization

formulas by transforming the criterion scale into the

Determinants of Technological Innovation Capabilities: Using Fuzzy-DEMATEL and ANP

109

comparable scale. It calculates the maximum value r,

4.612. It obtains the fuzzy, direct and relevant

matrixes of the normalization by transforming all

values in the fuzzy, direct and relevant matrixes.

Step 4: Establish the Fuzzy, Total, Direct and

Relevant Matrixes

After it obtains the fuzzy, direct and relevant

matrixes of the normalization, it obtains the fuzzy,

total and relevant matrixes.

Step 5: Defuzzification

It proceeds to have defuzzification on the fuzzy,

direct and relevant matrixes in order to obtain the total

and relevant matrixes.

Step 6: Centrality and Relation

It is able to calculate the row values (d), column

values (r), the sum of the rows and columns (d+r) and

the difference of the rows and columns (d-r). The

compilings show in Table 1. As for centrality (d+r),

the affection importance of 2 dimensions about D and

E is the biggest; and as for relation (d-r), the pluses

on 2 dimensions about A and B represent causal

dimensions; meanwhile, A is the strongest.

Otherwise, the minuses on 4 dimensions about C, D,

E and F represent effect dimensions; meanwhile, F is

the strongest. It obtains the causal relationship after

combining analyses of centrality and relation;

meanwhile, B is the strongest factor; D is the most

affected factor. And F is classified as the independent

dimension because centrality and relation are low; it

shows in Table 1. B is the main decisive dimension

that if enterprises consider only and upgrade

technological innovation capabilities.

Table 1: Row and column values among dimensions.

d(row

value)

r(column

value)

d+r

(centrality)

d-r

(relation)

Quadrant

Causal

relationship

A 41.661 39.543 81.204 2.118 2nd quad

affecting criteria

B 41.573 41.225 82.798 0.348 1st

Core criteria

C 41.343 41.440 82.783 -0.097 4th quad

Criteria affected

D 41.548 41.656 83.204 -0.108 4th quad

Criteria affected

E 41.328 41.578 82.906 -0.250 4th

Criteria affected

F 39.661 41.671 81.332 -2.010 3rd quad

Independent

Average

82.371 0

Step 7: The Result Analysis

After obtaining d+r (centrality) and d-r (relation),

it illustrates the causal relationship diagram as Figure

1 according to values. The arithmetic mean of

centrality (d+r) is 82.371 which is settled as the

threshold value; dimensions of technological

innovation capabilities are located on different

quadrants. The quadrant locations of the causal

relationship diagram in Figure 1 show as follows: one

dimension located on the first quadrant of

technological innovation capabilities represents B; it

belongs to the dimension of high centrality and high

relation. This dimension not only affects the

dimension on quadrant 4 but also is the core

dimension of technological innovation capabilities;

meanwhile it is classified as the core dimension of

technological innovation capabilities. One dimension

located on the second quadrant of technological

innovation capabilities represents the A; it belongs to

the dimension of low centrality but high relation. This

dimension can affect the dimension on quadrant 4;

meanwhile it is classified as the important dimension

of technological innovation capabilities. One

dimension located on the third quadrant of

technological innovation capabilities represents F; it

belongs to the dimension of low centrality and low

relation. Because the dimension on the quadrant does

not affect any dimension of technological innovation

capabilities; meanwhile it is classified the less

important dimension of technological innovation

capabilities. Three dimensions located on the fourth

quadrant of technological innovation capabilities

represent C, D and E; they belong to the dimension of

high centrality but low relation. These 3 dimensions

can upgrade and enhance their capabilities by

dimensions on quadrant 1 and 2.

Figure 1: Cause and effect diagram among dimensions.

4.1.2 The Results of Every Criterion

Analysis

The research will analyses the criterion of every

dimension in order to understand the complex

relevance of technological innovation capabilities in

enterprises.

Step 1: Define the assessment criteria and design

the fuzzy linguistic scale.

The assessment criteria: A

, A

, B

, C

, D

, E

, F

and

Step 2 to 7:

The analysis step 2 to 7 of every criterion

calculates according to analysis step 2 to 7 of every

above-mentioned dimension 4.1.1. The row values

(d), the column values(r), the sum of the row and

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column (d+r) and the difference of the row and

column (d-r) are compiled in Table 2.

Table 2: Row and column values among criteria.

d (row

value)

r(column

value)

d+r

(centrality)

d-r

(relation)

Quadrant Causal relationship

57.629 57.526 115.155 0.103

1st quad Core criteria

57.727 55.948 113.675 1.779

2nd quad Affecting criteria

57.505 56.736 114.241 0.769

2nd quad Affecting criteria

57.401 56.682 114.083 0.719

2nd quad Affecting criteria

57.835 57.436 115.271 0.399

1st quad Core criteria

56.734 56.823 113.557 -0.089

3rd quad Independent

57.547 56.668 114.215 0.879

2nd quad Affecting criteria

57.491 56.597 114.088 0.894

2nd quad Affecting criteria

57.495 57.385 114.880 0.110

1st quad Core criteria

56.830 56.650 113.480 0.180

2nd quad Affecting criteria

55.369 57.375 112.744 -2.006

3rd quad Independent

57.637 57.626 115.263 0.011

1st quad Core criteria

57.417 56.680 114.097 0.737

2nd quad Affecting criteria

57.548 57.755 115.303 -0.207

4th quad Criteria affected

56.031 57.647 113.678 -1.616

3rd quad Independent

57.606 57.571 115.177 0.035

1st quad Core criteria

56.711 57.706 114.417 -0.995

4th quad Criteria affected

57.513 57.678 115.191 -0.165

4th quad Criteria affected

56.561 57.654 114.215 -1.093

3rd quad Independent

57.261 57.703 114.964 -0.441

4th quad Criteria affected

Average

114.385 0

It shows on the quadrant locations in Table 2 that

the 5 criteria located on the first quadrant of

technological innovation capabilities represent

individually A

, A

, B

, C

, and D

; they belong to the

criterion of high centrality and high relation. The

criteria on this quadrant of technological innovation

capabilities occupy comparatively important

positions compared to the criteria on other quadrants

of technological innovation capabilities. 7 criteria

located on the second quadrant of technological

innovation capabilities represents individually A

, A

, B

and C

; they belong to the criterion of low

centrality but high relation. These 7 criteria can affect

the criteria on the fourth quadrant; meanwhile they

are classified as important criteria of technological

innovation capabilities. 4 criteria located on the third

quadrant of technological innovation capabilities

represents individually A

, C

, D

and F

; they belong

to the criterion of low centrality and low relation.

Because the criteria on this quadrant do not affect any

criterion of technological innovation capabilities;

meanwhile they are classified as the less important

criteria of technological innovation capabilities. 4

criteria located on the fourth quadrant of

technological innovation capabilities represents

individually D

, E

and F

; they belong to the

criterion of high centrality but low relation. These 4

criteria can upgrade and strengthen their abilities by

the criteria on quadrant 1 and 2. If the criteria of

technological innovation capabilities on quadrant 1

upgrade, they will improve the criteria of

technological innovation capabilities on quadrant 4.

The enterprises should engage in the criteria about A

, B

, C

and D

under the conditions of limited

resources and to classify them as the first priority of

upgrading technological innovation capabilities.

Additionally, the criteria of technological innovation

capabilities on quadrant 2 upgrade, they will also

improve the criteria of technological innovation

capabilities on quadrant 4; so they are classified as the

second priority of upgrading technological

innovation capabilities.

4.2 The Results of the ANP

4.2.1 Develop the Network Structure Model

This part will probe priorities of the factors for

technological innovation capabilities. So it constructs

the weights of every dimension and criterion by the

network procedure analyzing method. It analyzes the

structure model of ANP by means of 6 main

dimensions and 20 criteria of technological

innovation capabilities and inputs Super Decisions

software to establish the structure, calculating

analyses.

4.2.2 Establishment of the Pairwise

Comparison Matrix in the ANP Model

and the Consistency Test

It obtains the pairwise comparison matrix values

through calculating of geometric mean by the results

of 30 filling-up effective questionnaires and proceeds

to have consistency tests. The CI values of every

pairwise comparison matrix among 0.00545 to

0.02298 are all less than 0.1 in this research;

meanwhile, the CI values also less than 0.1 represent

passing the consistency test.

4.2.3 Pairwise Comparison of ANP

Dependency

Finally, the data from the ANP questionnaire, obtain

from the practitioners, were analysed and are

presented in Table 3.

4.2.4 The Results of the Analysis

As for the parts of the analysis results of dimensions,

innovation management capability has the highest

among the weights of dimensions. Innovation

management capability can affect the factors about

learning capability of the whole, innovation sourcing

capability, technology development capability,

robustness product & process design capability and

technology commercialization capability. And

Determinants of Technological Innovation Capabilities: Using Fuzzy-DEMATEL and ANP

111

innovation management capability is the most

important factor that enterprises develop

technological innovation capabilities. Consequently,

enterprises should engage in the innovation

management capability including the factors about

strategic management capability, organization

capability, resource allocation capability, risk

management capability, innovation decision

capability and resource exploiting capability in order

to upgrade their technological innovation capabilities.

Table 3: The weights of evaluation items for ANP method.

Dimensions Criteria Weights Total weights Rank

0.207 0.037 15

0.151 0.027 19

0.156 0.028 18

0.162 0.029 17

0.173 0.031 16

0.151 0.027 19

0.242 0.039 13

0.236 0.038 14

0.273 0.044 10

0.248 0.040 12

0.338 0.051 9

0.384 0.058 6

0.278 0.042 11

0.394 0.069 5

0.303 0.053 7

0.500 0.086 2

0.549 0.089 1

0.451 0.073 4

It also means that for the sake of improving

innovation management capability, enterprises

should be able to have abilities on discerning their

advantages and disadvantages from the interiority and

opportunities and threats from the external

environment, being able to make plans according to

enterprises’ visions and missions, ensuring the

organization operation mechanism and the

coordinating capability, cultivating technological

innovation culture of the organization, obtaining the

capital, technology and allocating the capital suitably

during technological innovation process, assessing

and enduring the risk of technological innovation,

organizing creative ideas of the high research and

development from the interiority, cooperating and

sharing the knowledge of the research and

development with other strategy alliance enterprises

or researching centers, being able to forecast and

assess the future trend of technological innovation,

having abilities to modulate and expand the

technology, human and the capital resource for

upgrading enterprises’ innovation management

capabilities.

As for the parts of the analysis results of

dimensions, the top 3 important capabilities at the last

priorities of the criterion assessment are

manufacturing capability, product structural design

and engineering capability and process design and

engineering capability individually. It means that

enterprises more emphasize the products that are

transformed from the results of the research and

development, designing the product structure,

establishing the modularization of the product,

supporting the manufacture design and devising the

design process of the assembling activity.

5 CONCLUSIONS

The purpose of this research is to try finding out

relations between dimensions and criteria that affect

the technical innovation capabilities and the

reciprocal affections; ranking the following important

weight of every dimension. As for the results from

Fuzzy-DEMATEL, there exist the relevant affections

indeed among dimensions and criteria. It also obtains

more precise weight measurement base through the

results of ANP analysis. In the future, it will be a good

reference according to the conclusion of the research

and provides the following researchers who intend to

research the issues on the technical innovations with

new thinking directions. The results in this research

show that there exist the relevant affections between

dimensions and criteria. The degrees of the reciprocal

affections of the technical innovation capabilities at

every dimension or criterion are different.

So far as the dimensions are concerned, the

capabilities on the technology commercialization

respondent to other 5 dimensions have less manifest

affections at the critical dimensions of the

technological innovation capabilities. The reason is

that the capabilities on the technological

commercialization are affected easily by other

factors; learning capabilities of the whole have most

affections to other factors. Furthermore, innovation

management capabilities are the key and important

factor for success. It shows according to the results

and relations of criteria that the enterprises will

upgrade collective learning and innovation

management capabilities of the whole if they can

enhance the innovation decision, strategy

management, knowledge management capabilities.

Consequently, the interiority of the enterprise’s

organization should have ideas on high innovations of

research and development and to cooperate with other

strategy alliance enterprises or researching centers,

being able to share the knowledge of research and

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development, forecasting and assessing the future

trends of the technology innovations, discerning the

advantages and disadvantages in the interiority,

making plans according to the enterprise’s prospects

and missions ,having capabilities to accumulate and

manage crucial knowledge resources and then

absorbing to use in order to upgrade learning and

innovation management capabilities of the whole

enterprise. However, the six dimensions affect

undoubtedly the enterprise’s decisive factors on

technological innovation capabilities according to

their strength. This research probes the reciprocal

affections and degrees under the criteria of every

dimension thoroughly. In practice, the enterprises can

choose dimensions they want to enhance and then

choose the suitable and manifest criteria that upgrade

this dimension according to results of the research in

order to increase and enhance enterprises’ technology

innovation capabilities, competition advantage and

core competences.

So far as the criteria are concerned, the criteria

about the organization, absorptive and learning

capabilities are important factors that affect other

criteria at the decisive criteria of technological

innovation capabilities. It means that the enterprises

should upgrade their collective learning capabilities

of the whole incessantly in complex environments

that change strongly in order to deal with the severe

competitions and enhance to affect other dimensions

of technological innovation capabilities for

maintaining their competition advantages. Therefore,

the enterprises should have abilities to discern, absorb

and apply new values of the exterior information to

enterprises’ products and services. And they have

abilities to discern, absorb and apply knowledge from

the interiority of the system to accumulate and

manage crucial knowledge resources. Finally, the

enterprises can ensure operation mechanisms and

coordinate capabilities in the interiority of the system,

cultivating to innovative organization culture and

heading to upgrade the criteria of technological

innovation capabilities. From viewpoints of the most

strong and bigger affections, the enterprises should

improve the criteria of technological innovation

capabilities by means of the core criteria of

innovation decision, technology acquisition and

technology change management capabilities under

the conditions of limited sources. It means that the

enterprises should have abilities to accumulate and

manage crucial knowledge resources, then absorbing,

using them. The enterprises can ensure operation

mechanisms and coordinate capabilities, having

abilities to discern, absorb and apply new values of

the exterior information to enterprises’ products and

services, cultivating the technological innovation

culture of the organization, organizing ideas on high

innovations of research and development in the

interiority of the organization, cooperating with other

strategy alliance enterprises or researching centers,

sharing the knowledge of research and development,

forecasting and assessing the future trends of the

technological innovations to upgrade their

technological innovation capabilities.

REFERENCES

Adler, P.S., & Shenhar, A., 1990. Adapting Your

Technological Base: The Organizational Challenge,

Sloan Management Review, 32(1), 25-37.

Bergeron, F., Croteau, A.M., Raymond, L., &

Uwizeyemungu, S., 2017. Path to IT Performance: A

Configurational Analysis of IT Capabilities. In

Proceedings of the 19

International Conference on

Enterprise Information Systems (ICEIS 2017), 3, 294-

305.

Burgelman, R.A, Christensen, C.M., & Wheelwright, S.C.,

2004. Strategic Management of Technology and

Innovation. New York: McGraw-Hill.

Chiesa, V., Coughlan, P., & Voss, C.A., 1996.

Development of a technical innovation audit. Journal of

Product Innovation Management, 13(2), 105-136.

Christensen, J.F., 1995. Asset profiles for technological

innovation. Research Policy, 24(5), 727-745.

Cohen, W.M., & Levinthal, D.A., 1989. Innovation and

learning: The two faces of R&D. The Economic Journal,

99(397), 569-596.

Grant, R.M., 1991. The resource-based theory of

competitive advantage: Implications for Strategy

Formulation. California Management Review, 33(3),

114-135.

Grant, R.M., 1996. Toward a knowledge-based theory of

the firm. Strategic Management Journal, 17(S2), 109-

122.

Guan, J.C, & Ma, N., 2003. Innovation capability and

export performance of Chinese firms. Technovation,

23(9), 737-747.

Guan, J.C., Yam, R.C.M., Mok, C.K., & Ma, N., 2006. A

study of the relationship between competitiveness and

technological innovation capability based on DEA

models. European Journal of Operational Research,

170(3), 971-986.

Hitt, M.A., Ireland, R.D., & Lee, H.U., 2000. Technological

learning, knowledge management, firm growth and

performance: An introductory essay. Journal of

Engineering and Technology Management, 17(3-4),

231-246.

Huang, H. C., 2011. Technological innovation capability

creation potential of open innovation: A cross-level

analysis in the biotechnology industry. Technology

Analysis and Strategic Management, 23(1), 49-63.

Determinants of Technological Innovation Capabilities: Using Fuzzy-DEMATEL and ANP

113

Hult, G.T.M., Hurley, R.F., & Knight, G.A., 2004.

Innovativeness: Its antecedents and impact on business

performance. Industrial Marketing Management, 33(5),

429-438.

Ince, H., Imamoglu, S.Z., & Turkcan, H., 2016. The Effect

of Technological Innovation Capabilities and

Absorptive Capacity on Firm Innovativeness: A

Conceptual Framework. Procedia - Social and

Behavioral Sciences, 235, 764-770.

Jayani Rajapathirana, P.R., & Yan, H., 2018. Relationship

between innovation capability, innovation type, and

firm performance. Journal of Innovation & Knowledge,

3(1), 44-55.

Kogut, B. and Zander, U., 1992. Knowledge of the firm,

Combinative capabilities, and the replication of

technology. Organization Science, 3(3), 383-397.

Kumar, A., Kaviani, M.A., Hafezalkotob, A., & Zavadskas,

E.K., 2017. Evaluating innovation capabilities of real

estate firms: a combined fuzzy Delphi and DEMATEL

approach. International Journal of Strategic Property

Management, 21(4), 401-416.

Kuznets, S., 1973. Modern Economic Growth: Findings

and Reflections. American Economic Review, 63(3),

247-258.

Li, R., 1999. Fuzzy method in group decision making.

Computers & Mathematics with Applications, 38(1),

91-101.

Liang, L.W., Lee, T.T., & Huang B.Y., 2010. Investigation

of merging and acquisition of Taiwanese banks-

application of the fuzzy delphi method. Taiwan Banking

& Finance Quarterly, 11(4), 31-65.

Lin, C.J., & Wu, W.W., 2008. A causal analytical method

for group decision-making under fuzzy environment.

Expert Systems with Applications, 34(1), 205-213.

Mir, M., Casadesus, M., & Petnji, L.H., 2016. The impact

of standardized innovation management systems on

innovation capability and business performance: An

empirical study. Journal of Engineering and

Technology Management, 41, 26-44.

Prahalad, C.K., & Hamel, G., 1990. The core competence

of the corporation. Harvard Business Review, 68(3), 79-

91.

Ravari, S.S.M, Mehrabanfar, E., Banaitis, A. and

Banaitienė, N., 2016. Framework for assessing

technological innovation capability in research and

technology organizations. Journal of Business

Economics and Management, 17(6), 825-847.

Romijn, H., & Albaladejo, M., 2002. Determinants of

innovation capability in small electronics and software

firms in Southern England. Research Policy, 31(7),

1053-1067.

Saaty, T.L. (1996). Decision Making with Dependence and

Feedback: The Analytic Network Process. Pittsburgh,

PA: RWS Publication.

Saaty, T.L., 2006. Rank from comparisions and from

ratings in the analytic network hierarchy/processes.

European Journal of Operational Research, 168(2),

557-570.

Schumpeter, J., 1942. Capitalism, Socialism and

Democracy

. New York: Harper and Brothers.

Shafia, M. A., Shavvalpour, S., Hosseini, M., & Hosseini,

R., 2016. Mediating effect of technological innovation

capabilities between dynamic capabilities and

competitiveness of research and technology

organizations. Technology Analysis and Strategic

Management, 28(7), 811-826.

Sumrit, D., & Anuntavoranich, P., 2013a. An Analytic

Network Process Modeling to Assess Technological

Innovation Capabilities: Case Study for Thai

Automotive Parts Firms. American Transactions on

Engineering & Applied Sciences, 2(3), 189-212.

Sumrit, D., & Anuntavoranich, P., 2013b. Using

DEMATEL method to analyze the causal relations on

technological innovation capability evaluation factors

in Thai technology-based firms. International

Transaction Journal of Engineering, Management, &

Applied Sciences & Technologies, 4(2), 81-103.

Teece, D.J., Pisano, G. and Shuen, A., 1997. Dynamic

Capability and Strategic Management. Strategic

Management Journal, 18(7), 509-533.

Turker, M.V., 2012. A model proposal oriented to measure

technological innovation capabilities of business firms–

a research on automotive industry. Procedia - Social

and Behavioral Sciences, 41, 147-159.

Wang, C.H., Lu, I.Y., & Chen, C.B., 2008. Evaluating firm

technological innovation capability under uncertainty.

Technovation, 28(6), 349-363.

Wernerfelt, B., 1984. A resource-based view of the firm.

Strategic Management Journal, 5(2), 171-180.

Wu, K.J., Liao, C.J., Tseng, M.L., & Chiu, A.S.F., 2015.

Exploring decisive factors in green supply chain

practices under uncertainty. International Journal

Production Economics, 159, 147-157.

Yam, R.C.M., Guan, J.C., Pun, K.F., & Tang, E.P.Y., 2004.

An audit of technological innovation capabilities in

Chinese firms: Some empirical findings in Beijing,

China. Research Policy, 33(8), 1123-1140.

Yuan, X., Shin, S., He, X., & Kim, S.Y., 2016. Innovation

capability, marketing capability and firm performance:

A two-nation study of China and Korea. Asia Business

& Management, 15(1), 32-56.

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