Evaluating firm technological innovation capability under uncertainty

Evaluating firm technological innovation capability under uncertainty

ARTICLE IN PRESS Technovation 28 (2008) 349–363 www.elsevier.com/locate/technovation Evaluating firm technological innovation capability under uncert...

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ARTICLE IN PRESS

Technovation 28 (2008) 349–363 www.elsevier.com/locate/technovation

Evaluating firm technological innovation capability under uncertainty Chun-hsien Wanga,, Iuan-yuan Lub, Chie-bein Chenc,d a

Department of International Business, Asia University, Taichung, Taiwan Department of Business Administration, National Sun Yat-sen University, Taiwan c Department of International Business, National Dong Hwa University, Taiwan d College of Management, Takming College, Taipei, Taiwan

b

Abstract Technology innovation capability (TIC) is a complex, elusive, and uncertainty concept that is difficult to determine. Measuring TICs requires simultaneous consideration of multiple quantitative and qualitative criteria. By adopting a fuzzy measure and non-additive fuzzy integral method, this study evaluates the performance of synthetic TICs in hi-tech firms. The analytical results indicated that the non-additive fuzzy integral is an effective, simple and suitable method for identifying the primary criteria influencing TICs at hi-tech firms, especially when evaluation criteria are interactive and interdependent. The proposed approach is an effective method for assessing the TICs of a firm and obtains useful information regarding hierarchical TIC frameworks. r 2007 Elsevier Ltd. All rights reserved. Keywords: Technology innovation capabilities; Fuzzy measure; Non-additive fuzzy integral

1. Introduction Facing constantly fluctuating environments, firms require continual technological innovation and managerial response to changing environments to maintain competitive edge. Such a response requires a firm to reorganize its organizational assets to harmonize with the external environment. Consequently, firms must integrate organizational resources and technological innovation to ensure corporate survival. Technological innovation capabilities (TICs) engender multi-dimensional difficulties that involve numerous organizational functions and resource integration among various departments. The TICs of a firm have seldom been determined. As the technological innovationrelated activities of a firm have inherent and highly uncertainty and imprecision, innovation processes are uncertain and unpredictable, and difficult to assess accurately. Afuah (1998) proposed that technological innovation involves three uncertainties, namely technoloCorresponding author. Tel.: +886 4 2332 3456x1818; fax: +886 4 232 1190. E-mail addresses: [email protected], [email protected] (C.-H. Wang).

0166-4972/$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.technovation.2007.10.007

gical and market- and enterprise-based uncertainties. From this perspective, numerous sources of uncertainty and ambiguity are embedded within each phase of the technological innovation process. Garcı´ a-Muin˜a and Navas-Lo´pez (2007) also defines a similar concept with the term ‘‘degree of uncertainty’’ which refers to each of phase of technological growth trajectory. The extent to which technological innovation will be successful requires increased amounts of information regarding a firm’s organizational management, organizational innovation decisions, and R&D capability to fully represent firm TIC. Conventional statistical decision making can be characterized as a set of decision alternatives, state spaces and a relationship assigned to each decision pair, resulting in a decision and, finally, a utility function that orders results based on desirability (Zimmermann, 1991). However, under uncertain the difficulty in decision making increases as decision makers lack accurate information. Consequently, Bellman and Zadeh (1970) examined this conventionally adopted decision-making model and proposed a novel model for decision making in a fuzzy environment, comprising a point of departure from conventional fuzzy decision theories fuzzy decision theory. Since the TICs of a firm is typically subjective and imprecise, such

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subjectiveness and imprecision increase the complexity of the TICs evaluation process. Therefore, those evaluating a TIC will likely perceive objectives and criteria differently. Evaluators generally depend on subjective judgment, previous experience, and professional knowledge and information, all of which are difficult to define and interpret accurately. There are five primary interactive aspects that engender difficulty in evaluating TICs: R&D capability; innovation decision capability; marketing capability; manufacturing capability; and capital capability. For example, a firm that has outstanding R&D capability to perform advanced research and create unique designs often possesses good manufacturing capabilities such as advanced manufacturing technology for producing and improving high-quality products. Such an R&D capability is also related to capital capabilities which are required to fund R&D activities. Guan et al. (2006) argued that TIC depends not only on technological capability, but also on critical capability in the area of manufacturing, marketing, organization, strategy planning, learning and resources allocation. Further, they also recommended that TIC is complexity and interaction process of many different resources, and therefore multi-dimensional and corresponding indicators reflect the TIC of a firm. Additionally, numerous constructs are interdependent when evaluating TICs. Since a traditional multi-criteria approach is not suited to evaluate TICs. The traditional multi-criteria approach assumes dependence of attributes. That is, criteria are considered additive. As a typical aggregation, the weight average method is based on assumed dependence of criteria and widely applied in information fusion. However, in realword systems, this assumption is not suitable for many applications. Particularly, when information sources are non-interactive, their weighted effects would still be viewed as additive (Wang et al., 1999). To overcome these shortcomings, fuzzy measures (Wang and Klir, 1992; Murofushi et al., 1994)—or more generally, non-additive set functions—can be used. Therefore, a nonlinear integral, such as the Choquet integral—or so-called non-addition fuzzy integral (Wang et al., 1999; Wang and Klir, 1992; Murofushi and Sugeno, 1989) with respect to non-additive set functions—should be employed to replace the weighted average (Wang et al., 1999). By using the fuzzy measure and non-addition fuzzy integral, subjectivity, uncertainty and interactivity can be combined with triangular fuzzy numbers to eliminate expert subjective judgment problems involving complex TICs. This study attempts to develop a hierarchical analytical system for evaluating TICs that is sufficiently general that it can be applied under various research settings. To date, few studies have adopted a strict methodology when assessing TICs and, consequently, assessment is difficult. This work presents a novel hierarchical analytical system for assessing TICs that is sufficiently general that it can be applied under various research settings. This innovative capability assessment

method can help firms to measure their strengths and weaknesses in terms of technology-based core capabilities and, furthermore, identify breakthroughs to improve technological capability. Consequently, resolving problems in evaluating TICs is fundamentally important to both researchers and practitioners. This study quantitatively and qualitatively evaluates TIC performance of a high-tech firm by applying a novel multicriteria analytical framework. The remainder of this paper is organized as follows. Section 2 clarifies the background of the TICs of a firm and discusses relevant literature. Section 3 presents a hierarchical model of hi-tech TICs of a firm to clarify the aspects and criteria involved in innovation performance evaluation. Next, Section 4 presents the proposed fuzzy multi-criteria analytical approach for evaluating TICs of high-tech firms. Section 5 subsequently applies a rigorous methodology and algorithm in evaluating a hi-tech firm’s TICs. Section 6 then presents empirical results associated with constructing importance weightings and of evaluating the performance of TICs of a firm. This is followed by a discussion and managerial implications in Section 7. Finally, conclusions are finally drawn in Section 8. 2. Background of firm TICs Due to increasing global competitive pressure, shortened product life cycles and ease of imitation, firms must continue to innovate to maintain competitiveness. Thus, innovation has become the primary basis of productivity improvements, sales volume growth, and a firm’s competitiveness. Increased global competition pressures are also forcing firms to continuously adopt, develop and innovate to enhance product competitiveness such as product design and quality, technological service and reliability. For these reasons, a firm must upgrade its innovation capability for developing and commercializing new technologies more rapidly than other firms, and must facilitate creation and dissemination of technological innovations within its organization to strengthen its competitive advantage. Guan et al. (2006) developed innovation measure framework to provide a benchmark for auditing the quantitative relationship between competitiveness and TIC based on traditional DEA approach. Their research found that improving TIC could enhance competitiveness of a firm. Afuah and Bahram (1995) argue that various innovation approaches, including radical, incremental and architectural, are applied to the same innovation at different stages in the innovation value-added chain. They also proposed that for many high-tech products, any technology strategy ignoring these aspects of innovation can have disastrous strategy results. Numprasertchai and Igel (2005) explored the problem of managing knowledge through collaboration by using multiple case studies from different university research unit. In the research, they discovered through research collaboration can improve and create new knowledge and innovation. From quality point of view, the

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empirical study of Perdomo-Ortiz et al. (2006) found that a positive relationship between total quality management and business innovation capability. Their study result shows that firm had focusing more on quality activities, for example, promote team work, empowerment of the workers, the training of personnel in matter of quality, the design of a system of incentives for work well done are lead to a better innovation capability. In addition, Kim et al. (2005) examined the role of new product development proficiencies according to cross-industries sample of Korean manufacturers, which identify marketing capabilities, relative to process-planning and technical proficiencies were significant improving product family technical performance. Garcı´ a-Muin˜a and Navas-Lo´pez (2007) defined technological capabilities can be divided into two classifications. Technological exploitation capabilities are initially responsible for the obtaining of radical innovation that becomes the dominant technological design over a long period of time and, secondly, for successive incremental innovation that improve certain features, until overtaken by a change towards a new system. The technological exploration capabilities are technology-intensive systems that are responsible for obtaining innovation and become the dominant technological design. With regard to technological and organizational capabilities, Brown and Fai (2006) studied transition of production, organizational capabilities, and innovation processes within automobiles and computing industries. Their studies gives a good account of technological and organizational capabilities need to align with firm strategic implementation in order to reach innovation processes development success and further result in strategic resonance. However, at firm level, many empirical case studies demonstrated that innovation capabilities are a key factor for improving strategic implementation and further building up form competitiveness. According to Fan (2006) presented evidence demonstrating that the late-industrialized countries’ firms, for example, Chinese telecomequipment firms, need to develop profoundly their innovation capability and self-developed technologies in order to catch up with the advanced countries’ MNCs. Moreover, innovation capability also determined who the leading domestic firms in the industry are. Her studies further found that there are two major channels that can improve their innovation capability, namely, in-house R&D development and external alliance. The qualitative studies of Jonker et al. (2006) analyze the relationship among the TICs and economic performance by using a case study of the paper manufacturing sector at machine level. Their empirical studies also found that TIC and economic performance have a significant positive correlation. Various definitions of TICs prioritize different technological capabilities, including innovation origin, innovation features and dimensions, innovation processes, etc. (OECD, 1992, 1996; Panda and Ramanathan, 1996). All TICs can be defined as comprehensive characteristics in an organization facilitating and supporting an innovation strategy

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(Burgelman, 1996; Burgelman et al., 2004). According to Guan and Ma (2003), innovation capability should be defined by employing various scopes and levels to meet the requirements of firm strategy and accommodate special firm conditions and competitive environments. Similarly, Lall (1992) adopted the same position defining innovation capability as the skills and knowledge needed to effectively absorb, master, and enhance existing technologies and create new technologies. Christensen (1995) differentiated between innovation assets classifying them as scientific research assets, process innovation assets, product innovation assets and aesthetic design assets. He further argued that while most firms typically focus on one of these assets, successful innovation requires combining multiple assets. Based on differing perspectives, studies have proposed various components of TICs of a firm. Adler and Shenbar (1990) defined four innovation capabilities: (1) ability to satisfy market requirements by developing new products; (2) ability to manufacture new products using appropriate technological processes; (3) ability to satisfy future market needs by developing and marketing new products and technological processes; and (4) ability to effectively respond to unanticipated technological activities by competitors and unforeseen market forces. Based on Adler and Shenbar’s (1990) study, Yam et al. (2004) applied statistical regression analysis to determine the TICs at Chinese firms in Beijing based on seven capabilities: learning; R&D; resource allocation; manufacturing; marketing; organizing; and, strategic planning. Guan et al. (2006) have defined TIC as consisting of seven capability dimensions, namely learning capability, R&D capability, manufacturing capability, marketing capability, resource exploiting capability, organization capability, and strategic capability. In conclusion, the TICs of a firm are based on multiple criteria, comprising both quantitative and qualitative criteria. Based on literature findings, activities, processes and characteristics associated with innovation success and failure are adopted as TIC dimensions. Barton (1984) proposes that TICs result from interaction among technical engineers, innovation management, technological systems and scientific theories. Successful technological innovation depends on both technological capability and other critical capabilities, such as organizational, marketing, capital funds, manufacturing, strategic planning, and resource allocation (Yam et al., 2004). Such manufacturing capabilities determine a firm’s ability to transform R&D into products and processes. Cooperating R&D, manufacturing, and capital capabilities provide effects of complement to accelerate successfully technological innovation activities. Thus, the TICs of a firm are multi-dimensional, complex, and interactive innovation activities. Several interactive capabilities should be considered and evaluated in terms of numerous criteria, resulting substantial amounts of data that are commonly inaccurate or uncertain. The TICs of a firm are difficult to quantify and outcome is highly uncertain when data and information is lacking. To address

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uncertainty and incomplete data, natural language in fuzzy set theory can be employed (Zadeh, 1965). Natural language effectively expresses human thought and subjective preferences. Fuzzy theory utilizes linguistic variables to accommodate natural language. The TIC evaluation models permitting intuitive judgment have garnered acceptance by various experts, such as scholars and R&D industry (RDI) representatives. For uncertain and fuzzy environments, Zadeh (1965) proposed fuzzy theory and a fuzzy decision making method (Bellman and Zadeh, 1970). Many other studies have employed fuzzy set theory to fuzzy problems with substantial uncertainty (Chiou et al., 2005; Chen and Chiou, 1999; Chen and Hwang, 1992). Hence, this study attempts to apply a novel fuzzy hierarchical analytical approach by explicitly describing the decision structure of TICs by utilizing subjective judgments of evaluators based on this decision structure. 3. Fuzzy hierarchical analysis of TICs of a firm Numerous technological innovation criteria must be considered when evaluating the TICs of firms. However, many technological innovations are fuzzy and conflict with each another. For example, if a firm only focuses on R&D capability, it will have fewer resources to support marketing capability. Therefore, selecting a technological innovation criterion that achieves a compromising solution is critical to successful innovation activities. In a multicriteria problem, numerous indicators of evaluation can be considered. By considering specific problem requirements, these criteria can be identified. Information of TICs of a Objective

Aspect

Firm TICs Evaluation

~ R&D capabilities (X1)

~ Innovation decision capabilities (X2)

~ Marketing capabilities (X3)

~ Manufacturing capabilities (X4)

~ Capital capabilities (X5)

firm in the context of firm history must be collected by a literature review, expert R&D staff and interviews with senior managers. Particularly, interviews should obtain data regarding changes in TICs of a firm, and what capabilities have enabled the firm to sustain innovation ability. Evaluating TICs of firms creates typical multiplecriteria problems based on varying capabilities and effects of technological innovation activities. Keeney and Raiffa (1976) suggested that five principals must be followed when criteria are formulated: completeness, criteria must cover all primary aspects of the decision-making problems; operational, criteria must be meaningful for decisionmaking analysis; decomposable, criteria can be decomposed from a hierarchies to a low hierarchy to simplify the evaluation process; non-redundant, criteria must not be counted twice; and, minimum size, the number of criteria should be kept as small as possible. Based on the literature review findings and expert opinion, various criteria must be considered when evaluating overall performance of TICs of a firm. A comprehensive analysis of the factor influencing the performance of TIC of a firm must be performed by consulting experienced R&D staff and scholars focusing on technology management. Fig. 1 presents the hierarchical structure of the problems encountered when evaluating the TICs of a firm. Past research has offered valuable structures for TICs based R&D capabilities (Guan and Ma, 2003; Burgelman et al., 2004; Yam et al., 2004), innovation decision capabilities (Barton, 1984), marketing capabilities (Achilladelis, and Antonajis, 2001; Guan and Ma, 2003; Yam et al., 2004; Kim et al., 2005), manufacturing capabilities Criteria Percentage of researchers to overall employees Success rate of R&D products Self-generated innovative products Number of patents R&D intensity The degree of Innovativeness of R&D ideas Intensity of collaboration with other firms or R&D centers R&D knowledge sharing ability Forecasting and evaluating technological innovation Entrepreneurial innovation initiatives Marketing share Degree of new product competitiveness Monitoring the market forces Specialized marketing unit Export percentage Advanced manufacturing technology Productquality level Commercialization success rate Production staff quality level Product cycletime Fundraising ability Optimalcapital allocation Intensityof capitalinput Return on investment

Fig. 1. Hierarchical structural of TICs of a firm evaluation.

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(Guan and Ma, 2003; Yam et al., 2004), and capital capabilities (Yam et al., 2004), but not for the generation and adoption of entire technological innovations perspective. Our TICs model integrate and find the relevant literature mentioned above, activities, or components, or characteristics that are found to be associated with technological innovation are put forward as TICs aspects. This study discusses these factors and their associated TIC criteria below. The R&D capabilities (X~ 1 ) helps a firm expand its existing technologies and establish novel technologies or improve R&D function. The R&D capabilities primarily comprise the percentage of researchers to overall employees (Lefebvre et al., 1998), success rate of R&D products (proposed by this study), self-generated innovative products (proposed by this study), number of patents (OEDC, 1992/1996; Achilladelis and Antonajis, 2001; Damanpour and Wischnevsky, 2006), and R&D intensity (Sterlacchini, 1996; Manu and Sriram, 1996; OEDC, 1992/1996; Achilladelis and Antonajis, 2001; Yam et al., 2004; Damanpour and Wischnevsky, 2006); these criteria are measured quantitatively and qualitatively. Innovation decision capabilities (X~ 2 ) denotes a firm’s ability to execute technological innovation decisions for improving a firm’s technological innovation ability. These abilities comprise the degree of innovativeness of R&D ideas (proposed by this study), intensity of collaboration with other firms or R&D centers (Lefebvre et al., 1998; Achilladelis and Antonajis, 2001), R&D knowledge sharing ability (Guan and Ma, 2003), forecasting and evaluating of technological innovation (Yam et al., 2004; Burgelman et al., 2004), and entrepreneurial innovation initiatives (Guan and Ma, 2003). These abilities are assessed subjectively. Marketing capabilities (X~ 3 ) signify a firm’s ability to promote and sell products on the basis of understanding customer demand, which is primarily influenced by market share (Manu and Sriram, 1996), degree of new product competitiveness (proposed by this study), monitoring market forces (Guan and Ma, 2003), specialized marketing unit, (Achilladelis and Antonajis, 2001), and export percentages (Sterlacchini, 1999; OEDC, 1992/1996; Guan and Ma, 2003), all of which must be assessed subjectively. Manufacturing capabilities (X~ 4 ) indicates a firm’s ability to transform R&D results into product techniques and improvements in product quality. Manufacturing capabilities, such as advanced manufacturing technology (Guan and Ma, 2003), product quality level (proposed by this study), commercialization success rate (Yam et al., 2004), production staff quality level (Yam et al., 2004), and product cycle time (Guan and Ma, 2003), are assessed subjectively. Capital capabilities (X~ 5 ) comprise the necessary conditions to guarantee that firms advance their technological capabilities, and are primarily instigated by fundraising ability (proposed by this study), optimal capital allocation (Burgelman et al., 2004), intensity of capital input (Sterlacchini, 1999; Guan and Ma, 2003), and return on investment (Manu and Sriram, 1996). These criteria are determined quantitatively and qualitatively.

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Overall TICs performance of a firm (Alternatives A, B, C, D) can be obtained by (1) assigning weights to five aspects (X~ 1 , X~ 2 , X~ 3 , X~ 4 , X~ 5 ) and their associated pj criteria (X~ ij ; i ¼ 1; 2; . . . 5; j ¼ 1, 2,y, pj) and (2) assessing the performance ratings of each aspect and its associated criteria. Model creation requires defining a reliable procedure for assessing the TICs of a firm (Table 1). Table 1 shows that these aspects and criteria have both quantitative and qualitative variables. For example, determining values, such as the number of patents or the percentage of researchers, is effortless. Conversely, numerous variables are difficult to assess; for example, a firm’s capability to obtain information or the intensity of collaboration with other firms or R&D centers are not easily determined. Furthermore, based on incomplete information, and imprecise, vague, and uncertain data for technological innovation, the fuzzy theory can address situations that lack well-defined boundaries of activity or observation sets (Chen and Hwang, 1992). Furthermore, owing to the increasingly complex and diversified decisionmaking environment, considerable correlated information requires analysis. Additionally, conventional analysis is inappropriate for such problem solving (Tang et al., 1999; Tzeng and Tsaur, 1993). Therefore, this study adopts fuzzy theory to assess TICs of various firms. Multi-criteria analysis is performed to assist decision makers in prioritizing or selecting one or more alternatives from a finite set of alternatives with reference to multiple, usually conflicting, criteria (Chen and Hwang, 1992; Yeh et al., 1999). Multi-criteria analysis can be performed of TIC characteristics when evaluating overall performance of hi-tech TICs of a firm. Fuzzy theory, as developed by Zadeh (1965), was first utilized for multi-criteria analysis, providing an effective method for developing decision problems in vague and uncertainty environments, in which information available is subjective and imprecise. Restated, imprecise and subjective assessment of a decision problem can be effectively handled by fuzzy data (Bellman and Zadeh, 1970; Danielson and Ekenberg, 1998). 4. Method and algorithm of hi-tech TICs of firms evaluation To determine the overall performance of TICs of firms, evaluation criteria are multiple and frequently structured into multi-level hierarchies (see Fig. 1), with simultaneous quantitative (e.g., percentage of researchers to overall employees and number of patents) and qualitative (e.g., innovativeness of R&D idea) assessment (see Table 1). A fuzzy hierarchical system can accommodate quantitative and qualitative data while the evaluators process TICs evaluation. This proposed fuzzy hierarchy allows experts to identify options using fuzzy expressions. Thus, to effectively solve the problems encountered when evaluating the TICs of a firm with a fuzzy hierarchical methodology, this study presents a novel algorithm that uses crisp (quantitative) and fuzzy (qualitative) numbers in a straightforward method.

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Table 1 Firm technological innovation capability evaluation criteria Aspect

Criteria

Description

R&D capabilities (X1)

Percentage of researchers to overall employees (X11) Success rate of R&D products (X12) Self-generated innovative products (X13) Number of patents (X14)

R&D staff as a percentage of total firm employment (last 3 years average)

The degree of Innovativeness of R&D ideas (X~ 21 )

Proportion of successful R&D to entire (last 3 years average) Products developed through research and development Number of approved patent applications are used to measure innovation capabilities (last 3 years average). R&D intensity is measured as a ratio of R&D expenditure to total number of employees. This ratio avoids artificial relationship with firm size (last 3 years average). How innovative is the research ideal, i.e., is it incremental improvement or a radical innovation?

Intensity of collaboration with other firms or R&D centers (X~ 22 ) R&D knowledge sharing ability (X~ 23 ) Forecasting and evaluation of technological innovation (X~ 24 ) Entrepreneurial innovation initiatives (X~ 25 ) Market share (X31)

Ability to build and develop contacts and collaborate with other firms, universities and R&D centers. Mechanisms for sharing technological knowledge across business boundaries Capability to identify and anticipate facilitating/impeding external forces impacting technological innovation, thereby reducing uncertainty and risk Degree of entrepreneurship with respect to technological innovation. Percentage of total sales/market total sales in a particular year

Degree of new product competitiveness (X~ 32 ) Monitoring market forces (X~ 33 ) Specialized marketing unit (X~ 34 )

Intensity of market competition for new products Firm awareness of customer requirements and preferences Specialized departments responsible for marketing new products and market research Percentage of total export volume/market total sales during a particular year Level of advancement of targeted technology compared with existing technology

R&D intensity (X15)

Innovation decision capabilities (X~ 2 )

Marketing capabilities (X~ 3 )

Manufacturing capabilities (X~ 4 )

Export percentage (X35) Advanced manufacturing technology (X~ 41 )

Product quality level (X~ 42 ) Commercialization success rate (X43)

Capital capabilities (X~ 5 )

Production staff quality level (X~ 44 ) Production cycle time (X45) Fundraising ability (X~ 51 )

Optimal capital allocation (X~ 52 ) Intensity of capital input (X~ 53 ) Return on investment (X54)

Overall quality and technological innovation matching market requirements Probability of successful technology transfer, product development and commercialization Capability of manufacturing staff Time from design to final product Fundraising ability of a firm when pursuing technology innovation

Corporate R&D expense allocation among basic research, application research, and development research Average R&D spending on each innovative project Return on technology output

4.1. Determining the qualitative criteria For qualitative and incomplete information fuzzy linguistic variables are expressed as an important rating for evaluation criteria and innovation performance. A linguistic variable is a variable whose value is not a number but a phrase or sentences expressed in a natural or artificial language. For example, ‘‘very high’’ is a linguistic variable if its value is linguistic rather than numerical. Furthermore, by using approximate reasoning of fuzzy set theory, the linguistic variable can be represented with a fuzzy number. This study employed triangular fuzzy numbers to represent linguistic variables to assess innovative capability and rate the importance of evaluation

criteria. The triangular membership functions are overlaps which represent the different linguistic models depend on the evaluator who is more professional than others. Each qualitative criterion can be assessed as {Very Poor (VP), Poor (P), Fair (F), Good (G), Very Good (VG)} when evaluating innovativeness capability and its degree of importance can be expressed as {Very Low (VL), Low (L), Medium (M), High (H), Very High (VH)}. Combining these two sets of linguistic variables, two types of linguistic models with triangular fuzzy numbers can be constructed. One type of three linguistic models, LMI1–LMI3 represent the degree of innovation capabilities (see Figs. 2–4 and Table 2) and the other type of three linguistic models LMG1–LMG3 represent the degree of importance for

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VP

P

F

G

VG

X~ (x)

1.0

0

0.2

0.4

0.6

0.8

1.0

x

expert R&D staff, scholars, and entrepreneurs), the assessed values (or ratings) of qualitative criteria metrics for performance of innovative capability, X~ ij ¼ ðL xij ; M xij ; R xij Þ, i ¼ 1; 2; . . . ; 5and j ¼ 1; 2; . . . ; 4or 5 in this study and the degree of importance of criteria metrics, G~ ij ¼ ðL gij ; M gij ; R gij Þ, i ¼ 1; 2; . . . ; 5; and j ¼ 1; 2; . . . ; 4 or 5 for the TICs of a firm are based on Dubois and Prade (1980) fuzzy arithmetic to three vertices of triangular fuzzy number and calculated aggregation determined by m evaluators using X¯~ ij ¼ ðL x¯ ij ; M x¯ ij ; R x¯ ij Þ ! ! ! ! m m m X X X p p p xij =m; xij =m; xij =m , ¼

Fig. 2. Linguistic models LMI1 for innovation capability.

p¼1

VP

1.0

P

G

F

355

p¼1

VG

X~ (x)

¯~ ¼ ð g¯ ; g¯ ; g¯ Þ G ij L ij M ij R ij ! ! ! ! m m m X X X p p p ¼ gij =m; gij =m; gij =m , p¼1

0

0.2

0.4

0.6

0.8

1.0

x Fig. 3. Linguistic models LMI2 for innovation capability.

VP

P

F

G

VG

X~ (x)

1.0

0

0.2

0.4

0.6

ð1Þ

p¼1

0.8

1.0

x Fig. 4. Linguistic models LMI3 for innovation capability.

criteria (see Figs. 5–7 and Table 2). For example, LMI3 for explaining the degree of innovation capability and LMG3 for explaining the degree of importance criteria are the fuzziest among other and are used by the evaluators. The use of the varied linguistic models depends on the evaluator who has more professional ability to clearly distinguish the varying of technological innovation criteria than others. In addition, Table 2, and Figs. 2–4 and 5–7 present the linguistic variables for innovation capability, their degree of importance and their corresponding membership functions. During overall fuzzy evaluation, every evaluator perceptions of criteria vary and all apply varying levels of importance to linguistic variables. Given m evaluators (e.g.,

p¼1

ð2Þ

p¼1

¯~ ¼ ð g¯ ; g¯ ; g¯ Þ are where X¯~ ij ¼ ðL x¯ ij ;M x¯ ij ;R x¯ ij Þ and G ij L ij M ij R ij triangular fuzzy numbers, and their points at the left, middle and right positions, L x¯ ij , M x¯ ij andR x¯ ij , represent overall average ratings of aspect i, criteria j over m p p evaluators, and both X~ ij and G~ ij , p ¼ 1; 2; . . . ; m, are fuzzy numbers for each evaluator. For the following calculation, these fuzzy numbers must be transformed into crisp numbers. Many methods can achieve this transformation (e.g., means of maxima, center of sum, center of gravity, and the a-cut method). The defuzzying method developed by Chen and Klein (1997) is a very sensitive and effective approach that discriminates between two fuzzy numbers during fuzzy ranking by performing numerous simulated experiments in which various linear or nonlinear fuzzy numbers and various degrees of overlap of fuzzy numbers are applied. They employed a method utilizing fuzzy subtraction of a ~ from a fuzzy number,X~ ; the referential rectangle, R, rectangle is obtained by multiplying the height of the membership function of X~ by the distance between the two crisp maximizing and minimizing barriers. Here, R~ can be considered as a fuzzy number. Fuzzy subtraction of the ~ from the fuzzy number, X~ , can be referential rectangle, R, performed at level ai as follows. X~ ai hiR~ ¼ ½l i ; ri ½½c; d ¼ ½l i  d; ri  c, i ¼ 0; 1; 2; . . . ; 1,

ð3Þ

where /S and [] denote fuzzy subtraction and interval subtraction operators, respectively; li and ri are the left and ~ and c and d are the left and right barriers, right loci of R, respectively. The defuzzification rating of a fuzzy number, then, is Pn c i¼0 ri P ~ and n ! 1, (4) DðX Þ ¼ Pn n i¼1 ðri  cÞ  i¼0 ðl i  dÞ

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Table 2 Linguistic values of innovation capability and degree of importance Innovation capability (LMI1)

Degree of importance (LMG1)

Assessment

Membership function

Assessment

Membership function

Very Poor (VP) Poor (P) Fair (F) Good (G) Very Good (VG)

(0.00, (0.20, (0.35, (0.60, (0.75,

Very Low (VL) Low (L) Medium (M) High (H) Very High (VH)

(0.00, (0.20, (0.40, (0.60, (0.80,

0.00, 0.30, 0.50, 0.70, 1.00,

0.30) 0.40) 0.65) 0.80) 1.00)

Innovation capability (LMI2)

0.00, 0.30, 0.50, 0.70, 1.00,

0.20) 0.40) 0.60) 0.80) 1.00)

Degree of importance (LMG2)

Assessment

Membership function

Assessment

Membership function

Very Poor (VP) Poor (P) Fair (F) Good (G) Very Good (VG)

(0.00, (0.15, (0.30, (0.55, (0.70,

Very Low (VL) Low (L) Medium (M) High (H) Very High (VH)

(0.00, (0.20, (0.35, (0.60, (0.75,

0.00, 0.30, 0.50, 0.70, 1.00,

0.30) 0.45) 0.70) 0.85) 1.00)

Innovation capability (LMI3)

0.00, 0.30, 0.50, 0.70, 1.00,

0.30) 0.40) 0.65) 0.80) 1.00)

Degree of importance (LMG3)

Assessment

Membership function

Assessment

Membership function

Very Poor (VP) Poor (P) Fair (F) Good (G) Very Good (VG)

(0.00, (0.15, (0.25, (0.55, (0.65,

Very Low (VL) Low (L) Medium (M) High (H) Very High (VH)

(0.00, (0.15, (0.30, (0.55, (0.70,

VL

L

M

0.35) 0.45) 0.75) 0.85) 1.00)

H

VH

VL

1.0

L

M

0.00, 0.30, 0.50, 0.70, 1.00,

0.30) 0.45) 0.70) 0.85) 1.00)

H

VH

G~ (g)

G~ (g)

1.0

0.00, 0.30, 0.50, 0.70, 1.00,

0

0.2

0.4

0.6

0.8

1.0

Fig. 5. Linguistic models LMG1 for importance weight.

VL

L

0.2

0.4

0.6

0.8

1.0

M

H

Fig. 7. Linguistic models LMG3 for importance weight.

VH

G~ (g)

1.0

0

g

g

where n denotes the number of a-cuts; as n approaches N, the sum is the measured area. Pn Pn In Eq. (6), ðr  cÞ is positive, i i¼1 i¼1 ðl i  dÞ is negative value and 0pDðX~ Þp1; if 0pxp1. 4.2. Determining the quantitative number

0

0.2

0.4

0.6

0.8

g Fig. 6. Linguistic models LMG2 for importance weight.

1.0

The quantitative (or crisp) numbers of criteria have varying values that cannot be compared; thus, the crisp number must be normalized. The crisp number is normalized to achieve criteria values that are unit-free and comparable among all criteria. For crisp numbers, normalized values of Xij are calculated as expressed in the

ARTICLE IN PRESS C.-H. Wang et al. / Technovation 28 (2008) 349–363

following equation (Karsak, 2002). X nij ¼

X pij



min X pij

max X pij  min X pij

X nij 2 ½0; 1; p ¼ 1; 2; . . . ; m,

;

(5) X pij

where max ¼ maxfX 1ij ; X 2ij ; . . . ; X m ij g and min 1 2 minfX ij ; X ij ; . . . ; X m ij g.

X pij

¼

To address the assumption that all criteria are not completely independent, Sugeno (1974) developed monotonic and non-additive fuzzy integrals that can be employed to determine the degrees of importance for fuzzy criteria. This study also applied ‘‘importance’’ in modeling the preference structure. Thus, a fuzzy measure in this study can be explicated as the subjective importance of an evaluator’s criterion during the evaluation process. Sugeno and Terano (1977) incorporated the l-additive axiom to reduce the difficulty in accumulating information. In the fuzzy measure space ðX; b; gÞ, let l 2 ð1; 1Þ. If A 2 b, B 2 bA \ B ¼ f, and gl ðA [ BÞ ¼ gl ðAÞ þ gl ðBÞ þ lgl ðAÞgl ðBÞ,

(6)

hold, then the fuzzy measure g is l-additive. This particular fuzzy measure is presented as l-fuzzy measure because it has to satisfy l-additive and is also called the Sugeno measure (1974). To differentiate it from other fuzzy measures, l-fuzzy measure is denoted by gl . When l ¼ 0, the measure is additive. Currently, the l-fuzzy measure has been widely employed as a fuzzy measure. Additionally, the l-fuzzy measure of the finite set is derived from fuzzy densities, as indicated in the following equation. Based on Eq. (4), the fuzzy measure gðX Þ ¼ gl ðfDðX~ 1 Þ; DðX~ 2 Þ; . . . ; DðX~ n ÞgÞ ¼ gl ðfx1 ; x2 ; . . . ; xn gÞ can be formulated as follows (Keeney and Raiffa, 1976; Leszczyn´ski et al., 1985): gl ðfx1 ; x2 ; . . . ; xn gÞ ¼

n X

gi þ l

i¼1

n1 X n X

gi1  gi2 þ    þ ln1 g1  g2    gn

i ¼1 i ¼i þ1

1 2 1     n Y 1  ¼  ð1 þ l  gi Þ  1  l  i¼1

n Y ð1 þ l  gi Þ.

a2½0;1

where F a ¼ fxjhðxÞXag Wang and Klir (1992) and A is the domain of a fuzzy integral.R When A ¼ X, the fuzzy integral may also be denoted by h dg. For simplicity, assume a fuzzy measure g of (X, P(X)) and X is a finite set. Let h : X ! ½0; 1 and assume, without loss of generality, that the function hðxi Þ is monotonically decreasing in i, such that hðx1 ÞXhðx2 ÞX    Xhðxn Þ. Elements in X can be renumbered to ensure that Eq. (9) has the following equilibrium: Z n hðxÞ  g ¼ _ ½hðxi Þ ^ gðH i Þ, (10) i¼1

where H i ¼ fx1 ; x2 ; . . . xi g, i ¼ 1; 2; . . . ; n. In this study, h(  ) can be considered as the performance of a particular criterion of a particular aspect and g(  ) represents the degree of subjective importance of each criterion. The fuzzy integral of h(  ) with respect to g(  ) generates the overall assessment of the criterion. For simplicity, the same fuzzy measure of a Choquet integral, rather than the fuzzy integral in Eq. (10), is applied as follows: Z ðcÞ h dg ¼ hðxn ÞgðH n Þ þ ½hðxn1 Þ  hðxn ÞgðH n1 Þ þ    þ ½hðx1 Þ  hðx2 ÞgðH 1 Þ ¼ hðxn Þ½gðH n Þ  gðH n1 Þ þ hðxn1 Þ½gðH n1 Þ  gðH n2 Þ þ    þ hðxn ÞgðH 1 Þ, ð11Þ where H 1 ¼ fx1 g; Hfx1 ; x2 g; . . . ; H n ¼ fx1 ; x2 ; . . . ; Rxn g ¼ X . In literature, the fuzzy integral defined by ðcÞ h dg is considered a non-additive fuzzy integral. The proposed model uses the non-additive fuzzy integral does not require assumed mutual independence of criteria. It can therefore be applied to nonlinear cases. Even when any two criteria are objectively and mutually independent, they are not considered independent of subjective evaluators. 6. Empirical performance evaluation of hi-tech firms TICs

for  1plo1.

ð7Þ

Based on the boundary conditions in Eq. (7), gl ðXÞ ¼ 1, the parameter l can be uniquely determined with the following equation: lþ1¼

mined, acquiring consistent measure values satisfying fuzzy measure properties from human experts is difficult. In a fuzzy measure space (X,b, g), let h be a measurable function from X to [0, 1]. Then, the definition of the fuzzy integral of h over A with respect to g is Z hðxÞ dg ¼ sup ½a ^ gðA \ F a Þ, (9) A

5. Fuzzy measures and non-additive fuzzy integral for measuring firm simulated performance

357

(8)

These hi-tech firms are cited for two reasons. First of all, hi-tech firms continue to improve manufacturing processes and face challenge to how they manage the technological innovation activities in the competitive environment. Second, hi-tech firms had to sustain reform technological capabilities in order to deal with modern competitive market and customer requirement.

i¼1

In the case of l-fuzzy measure identification, fuzzy density gi, i ¼ 1; 2; . . . ; k and parameter l must be determined. Since l-fuzzy measures value, gl(A), and A 2 bðXÞ for a set X ¼ {x1,x2,yxk} are subjectively deter-

6.1. Sample and data collection The data used in this study come from multi-source database in order to increase data source accurate and

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decrease artificial error. Due to many high-tech firms without complete coverage of standard financial statistics and treated the data as secret, thus, all the above lists quantitative data from each high-tech firm are difficult to collect, completely. Therefore, the secondary data from multi-source database were collected validity data, namely, each high-tech firm annual operation reports published by Securities and Futures Institute (SFI) of Taiwan, Taiwan stock market (TEJ, 2005) database published by the Taiwan economic journal company were collected. The data of quantitative are taken from the multi-source which provides information on each firm’s published as secondary data. These data include percentage of researchers to overall employees (X11), success rate of R&D products (X12), self-generated innovative products (X13), number of patents (X14), R&D intensity (X15), market share (X31), export percentage (X35), commercialization success rate (X43), production cycle time (X45), and return on investment (X54). A complete list of the quantitative criteria and initial data in the TICs is given in Table 1 and original information is showed in Appendix. For the qualitative criteria include the degree of innovativeness of R&D ideas (X~ 21 ), intensity of collaboration with other firms or R&D centers (X~ 22 ), R&D knowledge sharing ability (X~ 23 ), forecasting and evaluation of technological innovation (X~ 24 ), entrepreneurial innovation initiatives (X~ 25 ), degree of new product competitiveness (X~ 32 ), monitoring market forces (X~ 33 ), specialized marketing unit (X~ 34 ), advanced manufacturing technology (X~ 41 ), product quality level (X~ 42 ), production staff quality level (X~ 44 ), fundraising ability (X~ 51 ), optimal capital allocation (X~ 52 ), intensity of capital input (X~ 53 ). According to 14 qualitative criteria, we develop and design the checklist of technological innovation performance level, and then the senior managers and senior R&D managers of each chosen high-tech firm are asked response to evaluate current technological innovation performance level by using the checklist. The initial data of qualitative criteria are also shown in Appendix. 6.2. Actual application of TICs at hi-tech firms This study invited two expert groups—six scholars (SC) and six RDI representatives—to weight objectives and criteria using fuzzy triangular fuzzy numbers (see Table 1). The first group comprises SC from the Department of Technology Management at a University. The second group comprises six RDI representatives with, on average, over seven years of experience in R&D management and technological innovation management. These participants were requested to complete a questionnaire using subjective judgment for the importance of each criterion based on the evaluation criteria for the hierarchical structure of hi-tech TICs of a firm. Quantitative and qualitative performance evaluation of TIC aspects and their associated criteria were assigned crisp numbers and linguistic variables, respectively. Table A in Appendix lists these crisp numbers and linguistic variables. The quantitative criteria of innovative

capability of TICs of a firm utilized a linear scale transformation for normalization based on Eq. (5), allowing for slight variation of measurements in the [0, 1] interval of each quantitative criteria. Assessment of performance of qualitative criteria was also performed by both groups using the proposed method. The resulting data (see Table A in Appendix) are processed using Eqs. (1)–(5), the values of innovative capability are normalized and the defuzzying values of four firms are thus obtained. Table B in the Appendix list these values. Fuzzy linguistic variables were employed to represent importance weighting for TICs of a firm’s hierarchical evaluation strategies. According to the formulated structure used to evaluate hi-tech TICs of a firm, each group generated weighted the aspects/criteria, and defuzzying average weights were then obtained. Fuzzy weight assessment and defuzzying procedures utilized Eqs. (2)–(4) combined with the hierarchical structure of TICs of a firm (see Fig. 1). Obtaining analytical results involved analyzing each group’s data and deriving the average weights gi of each aspect and criterion (see Tables 3 and 4). Applying the same measure procedure derives each qualitative aspect and criterion has an average innovation performance, hðxi Þ (see Tables 3 and 4). The lfuzzy measure values are derived using Mathematical 5.0 and Eq. (9) with corresponding measure density, gi. Tables 3 and 4 also list computational results of each aspect l-value. All l values are approximately 1, in which there is a high degree of interdependent influence, that is, l ¼ 0:999, for criteria under R&D capability (or X1). Similarly, for innovation decision capability (or X~ 2 ) its l ¼ 0:998; for marketing capability (orX~ 3 ) its l ¼ 0:936; manufacturing capability (or X~ 4 ) was l ¼ 0:939; and, capital capability (or X~ 5 ) was l ¼ 0:914. That the l-values were close to 1 indicates completely dependent and mutual influence among these criteria. The dependent and mutually influential relationship among these aspects shows the importance of the criteria. The proposed method also showed that all criteria must be considered simultaneously during the TICs of a firm evaluation. That is, such correlation and dependence must be accounted for while analyzing a firm’s TICs to obtain correct innovation decision inferences. Moreover, the non-additive fuzzy R integral ðcÞ h dg in Eq. (11) is used to determine the aggregated value of each aspect based on criteria (see Table 4). Table 3 presents the measured ratings h(xi) and their degrees of importance gi as evaluated by the experts, the l value ofR gl , obtained with Mathematical 5.0 and Eq. (8) and ðcÞ h dg represents the overall performance by the experts in the five aspects of R&D capability (or X~ 1 ), which is 0.790, innovation decision capability (or X~ 2 ) is 0.649, marketing capability (or X~ 3 ) equals 0.723, manufacturing capability (or X~ 4 ) equals 0.831, and capital capability (or X~ 5 ) is 0.914 (see Fig. 1). Table 4 presents h(xRi) and gi as evaluated by experts; the l value of gl and ðcÞ h dg for the five aspects of innovation decision capability, marketing capability, manufacturing capability and capital capability are for Firm A (see Fig. 1).

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359

Table 3 Summary of measured aggregated values of the five aspects—performance of Firm A R ðcÞ h dg (l value)

Aspect

Criteria

H(xi)

gi

gl

x1

x11 x12 x13 x14 x15

0.827 0.597 0.194 0.364 0.526

0.857 0.718 0.722 0.790 0.874

gl ðx11 Þ gl ðx11 ; x12 Þ gl ðx11 ; x12 ; x15 Þ gl ðx11 ; x12 ; x15 ; x14 Þ gl ðx11 ; x12 ; x15 ; x14 ; x13 Þ

0.857 0.960 0.995 0.999 1.000

0.790 (0.999)

x2

x21 x22 x23 x24 x25

0.500 0.674 0.500 0.326 0.500

0.578 0.658 0.822 0.609 0.857

gl ðx22 Þ gl ðx22 ; x21 Þ gl ðx22 ; x21 ; x23 Þ gl ðx22 ; x21 ; x23 ; x25 Þ gl ðx22 ; x21 ; x23 ; x25 ; x24 Þ

0.857 0.945 0.991 0.998 1.000

0.649 (0.998)

x3

x31 x32 x33 x34 x35

0.278 0.500 0.326 0.326 0.880

0.561 0.621 0.545 0.485 0.608

gl ðx35 Þ gl ðx35 ; x32 Þ gl ðx35 ; x32 ; x33 Þ gl ðx35 ; x32 ; x33 ; x34 Þ gl ðx35 ; x32 ; x33 ; x34 ; x35 Þ

0.608 0.875 0.963 1.000 1.000

0.723 (0.936)

x4

x41 x42 x43 x44 x45

0.500 0.500 0.471 0.807 1.000

0.561 0.621 0.545 0.485 0.608

gl ðx45 Þ gl ðx45 ; x44 Þ gl ðx45 ; x44 ; x41 Þ gl ðx45 ; x44 ; x41 ; x42 Þ gl ðx45 ; x44 ; x41 ; x42 ; x43 Þ

0.437 0.740 0.916 1.000 1.000

0.831 (0.939)

x5

x51 x52 x53 x54

0.674 0.500 0.807 1.000

0.669 0.755 0.823 0.613

gl ðx54 Þ gl ðx54 ; x53 Þ gl ðx54 ; x53 ; x51 Þ gl ðx54 ; x53 ; x51 ; x52 Þ

0.613 0.934 0.988 1.000

0.914 (0.994)

Table 4 Firm overall TIC performance values—firm A of overall performance

Frim A

R ðcÞ h dg (l value)

Aspects

H(xi)

gi

gl

x1 x2 x3 x4 x5

0.790 0.649 0.723 0.831 0.914

0.872 0.803 0.563 0.594 0.836

gl ðx5 Þ gl ðx5 ; x4 Þ gl ðx5 ; x4 ; x1 Þ gl ðx5 ; x4 ; x1 ; x3 Þ gl ðx5 ; x4 ; x1 ; x3 ; x2 Þ

0.836 0.933 0.992 0.997 1.000

0.897 (0.999)

Table 5 The overall TIC performances of Firms A–D Firm

Frim Frim Frim Frim

Non-additive fuzzy integral value of each aspect

A B C D

x1

x2

x3

x4

x5

0.790 0.941 0.868 0.836

0.649 0.760 0.595 0.806

0.726 0.804 0.625 0.857

0.831 0.786 0.513 0.990

0.914 0.666 0.764 0.772

R Table 5 presents the ðcÞ h dg value of overall performance of TICs for the four hi-tech firms based on the five aspects criteria level. The ranking order of TICs performance is Firm D  Firm B  Firm A  Firm C, where P  Q, indicating that P is preferred to Q. Firm D has the optimum TICs overall performance (0.932% or 93.2%) as a result of the fuzzy measure with non-additive fuzzy integral procedure.

Overall performance

Ranking order

0.897 0.922 0.851 0.932

3 2 4 1

7. Managerial implications The TICs framework can be used to evaluate the impact at various technological innovation activities and thus provide a mechanism in order to monitor and establish measurement platform of technology-based innovation performance for the high-tech firms. Although previous studies there were a great deal of varieties in technological

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innovation measurement, however, these varieties did not generally appear to have a clear link to these organizational and innovation decision context. Indeed, as regards the prior study that only stress on single variable, and furthermore the challenge for business environment has changed, single model or variable was not good enough to explain the impact of technological innovation. Namely, technological innovation is the nature of multi-dimensional concept and single model cannot be good enough to explain the technological innovation performance and innovation behavior of high-tech firms. Especially, when evaluating the impact of introduction of novel technology and developed new technological innovation activities’ need from overall perspective and further consider its effect on the organization contextual. TICs evaluation framework can provide managers and researchers to better understand the differences in technological innovation activities needs and specific management interventions that would improve the likelihood of excellent and useful research by examining the 24 criteria of the TICs. These criteria serve as bridging mechanisms, helpful in enhancing technological innovation performance in high-tech firms. TICs framework also provides the function of management control and track, further, help to describe the technological innovation dilemmas. For example, In Table R 3, ðcÞ h dg represents the overall perceive performance value of the evaluators’ perception to the five aspects of R&D capabilities, innovation decision capabilities, marketing capabilities, manufacturing capabilities, and capital capabilities, which are all in Table 1. Here, the values of R ðcÞ h dg for R&D capabilities equal to 0.790, for innovation decision capabilities equal to 0.649, and for marketing capabilities equal to 0.723, respectively. However, the R overall perceive performance (or the value of ðcÞ h dg) of aspect of ‘‘R&D capabilities’’ is set by the high-tech firm, since there are no relevant performance standard. It must be grater than 0.8, otherwise, it do not achieve the requirement of the aspect. For the Firm A, there are three aspects that do not meet the requirement, namely, R&D capabilities, innovation decision capabilities, and marketing capabilities in Table 3. Furthermore, in the case of R&D capabilities aspect, the R&D manager must be tracked back and improve regarding the criteria percentage of researchers to overall employees, success rate of R&D products, self-generated innovative products, number of patents, and R&D intensity in order to correspond with the requirement or performance level. Similarly, the innovation decision capabilities aspect are also tracked back and inspected their criteria and the decision department are asked for improving them. Throughout analyzing all set of TICs aspects and criteria by different department managers the performance level is then determined by them. In the broader sense, the TICs framework can be used as an analytical and monitor tool to develop and construct an overall technological innovation development strategic and capabilities of the high-tech firms. For the practice of management, TICs are sufficient for organizational man-

agers to greatly understand technology innovation as an interrelated and interacting combination of resource components. It proved the manager to aware that technology innovation is not just a black box. Through the TICs evaluation framework, the managers are able to capture a fairly complete picture of technological innovation contextual. In other words, managers may find that application of the TICs framework for assessing the relative performance of the components of TICs developed, validated, and operationalized in their technological innovation activities is a useful decision-making framework for reviewing and improving technological innovation investment strategies, which may lead to enhancing productivity and sustaining a competitive advantage. In addition, the TICs framework may also advantage other high-tech firm’s senior managers and R&D managers can take the framework and customize it for use in their own technological innovation activities. In this manner, evaluators need to take the TICs and delete their relevant criteria from it and to add what is missing. Consequently, the TICs can be used in different technological development phases and can be further modified and refined if required. 8. Conclusion This study focus on developing quantitative evaluates of technological innovation uncertainty using fuzzy set theory. A consequence of express representation of uncertainty in evaluation model formulation is that results are fuzzy set theory, which reflects these uncertainties. Particularly, TICs and fuzzy multi-criteria decision-making (MCDM) problems that are conflict in the real world. Consequently, five aspects must be considered and evaluated simultaneously in terms of numerous criteria. These criteria comprise qualitative and quantitative and are typically inaccurate or uncertain. The proposed technique was used to evaluate TICs that accommodate both qualitative and quantitative data. The fuzzy decision model enables an evaluator to utilize quantitative with inherent imprecision in weighting criteria and performance in relation to qualitative criteria by transforming linguistic expressions into numerical values. This study employed triangular fuzzy numbers to represent linguistic variables in dealing with fuzzy subjective judgments by evaluators and, thus, reduces the evaluator cognitive burden during TIC evaluations. The fuzzy averaging technique and defuzzying method developed by Chen and Klein (1997) are effective in the final weighting of each criterion by various experts. However, in real TCIs and fuzzy MCDM evaluation problems in which criteria are not always mutually independent, a vast amount of criteria are typically interactive and dependent. The proposed model incorporates a hierarchical TIC structure, fuzzy measure, nonadditive fuzzy integral, comprise an effective fuzzy method for weighting of candidate criteria from subjective judgment and non-additive integrals. This method is also useful for evaluating final aggregate performance of hi-tech firms.

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Furthermore, this proposed model can also easily and effectively accommodate criteria that are not independent. The TICs evaluation problem addressed by the proposed model is appropriately very interactive. This proposed model with a hierarchical TIC structure, fuzzy measure, and non-additive fuzzy integral establishes a foundation for future research and is appropriate for predicting uncertain innovation capabilities in real hi-tech firms. Furthermore, technology and R&D managers can apply this model to evaluate and determine a firm’s innovation capabilities to improve the firm’s TIC performance and hereby bring the information for manager that will have the great effect in reducing overall technological innovation uncertainty.

361

Table B Summary of normalized and defuzzed values of Firms A—D Aspect

X~ 2

X~ 3

The authors would like to acknowledge the contribution of the two anonymous referees and the Editor-in-Chief, Dr Linton J., his valuable comments and suggestions have contributed significantly to this paper.

Firms

X11 X12 X13 X14 X15 X~ 21 X~ 22 X~ 23 X~ 24 X~ 25 X31 X~ 32 X~ 33 X~ 34 X35 X~ 41 X~ 42 X 43 X~ 44 X45 X51 X~ 52 X~ 53 X54

X1

Acknowledgment

Criteria

X~ 4

Appendix X~ 5

The initial input data and summary of normalized and defuzzed values of Firms A–D are shown in Tables A and B.

A

B

C

D

0.710 0.230 0.000 0.000 0.370 0.500 0.670 0.500 0.330 0.500 0.080 0.500 0.330 0.330 0.870 0.500 0.500 0.270 0.870 1.000 0.674 0.500 0.870 0.350

1.000 0.000 0.160 1.000 1.000 0.889 0.682 0.682 0.500 0.318 0.015 0.500 0.318 0.500 0.920 0.682 0.682 0.400 0.500 0.714 0.682 0.500 0.682 0.115

0.000 1.000 1.000 0.505 0.000 0.326 0.326 0.500 0.674 0.500 0.270 0.500 0.500 0.500 0.890 0.326 0.326 0.320 0.326 0.000 0.500 0.674 0.500 0.300

0.242 0.886 0.480 0.290 0.112 0.807 0.807 0.807 0.674 0.674 0.104 0.807 0.674 0.500 0.459 0.807 0.807 0.573 0.674 1.000 0.807 0.674 0.674 0.168

Table A Initial input data for Firms A—D Aspect

X1

X~ 2

X~ 3

X~ 4

X~ 5

Criteria

X11 X12 X13 X14 X15 X~ 21 X~ 22 X~ 23 X~ 24 X~ 25 X 31 X~ 32 X~ 33 X~ 34 X 35 X~ 41 X~ 42 X43 X~ 44 X45 X~ 51 X~ 52 X~ 53 X54

Firm A

B

C

D

0.100 0.400 6.000 120.000 443.320 (0.30, 0.50, (0.55, 0.70, (0.30, 0.50, (0.15, 0.30, (0.30, 0.50, 0.075 (0.30, 0.50, (0.15, 0.30, (0.15, 0.30, 0.810 (0.30, 0.50, (0.30, 0.50, 0.270 (0.70, 1.00, 14.000 (0.55, 0.70, (0.30, 0.50, (0.70, 1.00, 0.350

0.120 0.320 10.000 330.000 843.040 (0.75, 1.00, (0.60, 0.70, (0.60, 0.70, (0.35, 0.50, (0.20, 0.30, 0.015 (0.35, 0.50, (0.15, 0.30, (0.35, 0.50, 0.920 (0.60, 0.70, (0.60, 0.70, 0.400 (0.35, 0.50, 12.000 (0.60, 0.70, (0.35, 0.50, (0.60, 0.70, 0.115

0.050 0.670 31.000 226.000 209.000 (0.15, 0.30, (0.15, 0.30, (0.25, 0.50, (0.55, 0.70, (0.25, 0.50, 0.270 (0.25, 0.50, (0.25, 0.50, (0.25, 0.50, 0.890 (0.15, 0.30, (0.15, 0.30, 0.320 (0.15, 0.30, 7.000 (0.25, 0.50, (0.55, 0.70, (0.25, 0.50, 0.300

0.0659 0.630 18.000 181.000 280.330 (0.70, 1.00, (0.70, 1.00, (0.70, 1.00, (0.55, 0.70, (0.55, 0.70, 0.104 (0.70, 1.00, (0.55, 0.70, (0.30, 0.50, 0.459 (0.70, 1.00, (0.70, 1.00, 0.573 (0.55, 0.70, 14.000 (0.70, 1.00, (0.55, 0.70, (0.55, 0.70, 0.168

0.70) 0.85) 0.70) 0.45) 0.70) 0.70) 0.45) 0.45) 0.70) 0.70) 1.00) 0.85) 0.70) 1.00)

Note: Content in ‘‘( )’’ denotes the triangular fuzzy number of qualitative criteria.

1.00) 0.80) 0.80) 0.65) 0.40) 0.65) 0.45) 0.65) 0.80) 0.80) 0.65) 0.80) 0.65) 0.80)

0.45) 0.45) 0.75) 0.85) 0.75) 0.75) 0.75) 0.75) 0.45) 0.45) 0.45) 0.75) 0.85) 0.75)

1.00) 1.00) 1.00) 0.85) 0.85) 1.00) 0.85) 0.70) 1.00) 1.00) 0.85) 1.00) 0.85) 0.85)

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Chun-Hsien Wang is an assistant professor in the Department of International Business, Asia University, Taiwan, ROC. He received his Ph.D. in Business Administration in 2006 from National Sun Yat-sen University, Taiwan and M.S. in Department of International Business from National Dong Hwa University, Hualien, the Taiwan, ROC, in 2002 and M.S. in the Department of Marketing from National Chung Hsing University, Taichung, Taiwan, ROC, in 1999 and received his B.S. in the Department of Industrial Engineering and Management from I-Shou University, Taiwan, ROC, in 1996. Mr. Wang research interests include quality management, information management, technological innovation and knowledge management, high technology management, and fuzzy decision-making theory and its applications. His papers appeared in Information and Software Technology, International Journal of Technology Management, and international conference proceedings.

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Iuan-Yuan Lu is a professor in the Department of Business Administration, National Sun Yat-sen University, Taiwan. Dr. Lu received a Ph.D in Administration Engineering in 1978 from Keio University, Japan. Dr. Lu is the President of the Chinese Society for Quality, the Board of Directors of China Steel Crop, the Board of Committee of the Fortune Institute of Technology, the Board of Committee of the Electronics Testing Center (ETC), the Board of Directors Chinese Institute of Industrial Engineering, the Board of Directors Chinese Management of Technology Society, the Member of Board of the Asian Network for Quality, the Publisher of the Journal of Quality, and Quality Magazine. His current research interests include Industrial Engineering and Management, Technology Management, Technology Innovation, Technology Policy, R&D Management, TQM, Product Development and Production management, SCM, Fuzzy Evaluation and Decision Making. His papers appeared in Total Quality Management & Business Excellence, Journal of Organization Change Management, International Journal of Technology Management, Information Sciences, International Journal of Production Economics, The Service Industries Journal, The Asian journal on Quality, The Journal of Technology Transfer, and others.

Chie-Bein Chen is currently a Professor and Chairman in the Department of International Business, National Dong Hwa University, Hualien, Taiwan, ROC. He holds his B.S. in Industrial Engineering from Tunghai University, Taiwan, ROC, and both M.S. and Ph.D. in Industrial Engineering from University of Missouri, Columbia, in 1994. His research interests include fuzzy set theory and its applications, quality engineering and multiple criteria decision making, investment engineering. His papers appeared in IEEE Transactions on Systems, Man and Cybernetics, IEEE Transactions on Components, Packing and Manufacturing Technology, Part C: Manufacturing, Fuzzy Sets and Systems, International Journal of Uncertainty, Fuzziness Knowledge-based Systems, International Journal of Computer Integrated Manufacturing, Computer Integrated Manufacturing Systems, Assembly Automation Journal, International Journal of Computer Applications in Technology, Journal of the Operational Research Society, Quality and Reliability Engineering International, International Journal of Manufacturing Technology and Management, International Journal of Advanced Manufacturing Technology, Journal of Interdisciplinary Mathematics, Statistics and Computing, International Journal of Operations Research, Information Sciences, Information and Software Technology, Journal of Information & Optimization Sciences and others.

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