Technological Forecasting & Social Change 75 (2008) 131 – 141
Technical note
A fuzzy multiple criteria comparison of technology forecasting methods for predicting the new materials development An-Chin Cheng a,⁎, Chung-Jen Chen b,1, Chia-Yon Chen a,2 a b
Graduate Institute of Resources Engineering, National Cheng Kung University, 1, Ta-Hsueh Road, Tainan, Taiwan, R.O.C. Graduate Institute of Business Administration, National Cheng Kung University, 1, Ta-Hsueh Road, Tainan, Taiwan, R.O.C. Received 4 May 2006; received in revised form 2 August 2006; accepted 7 August 2006
Abstract New materials have been recognized as key drivers for corporate profitability and growth in today's fast changing environments. To predict correctly the development of the new materials becomes a critical issue. However, little has been done in discussing the selection of technology forecasting methods for the new materials development. Accordingly, this study adopted the fuzzy AHP method to obtain professional's opinions on this issue. The efforts result in seven evaluation criteria with one, the “data validity” having the highest weight, followed by “method adaptability” and “technology predictability”. Delphi method and case study method are the two most applicable technology forecasting methods for predicting the new materials development. © 2006 Elsevier Inc. All rights reserved. Keywords: Fuzzy AHP; Technology forecasting; New materials development
1. Introduction New materials have been recognized as key drivers for corporate profitability and growth in today's fast changing environments. Usually, these come about through the replacement of natural materials by synthetic ones that are cheaper or better. The replacement of silk by nylon and the alternative of cotton by ⁎ Corresponding author. Tel.: +886 6 2757575x62826. E-mail addresses:
[email protected] (A.-C. Cheng),
[email protected] (C.-J. Chen),
[email protected] (C.-Y. Chen). 1 Current address: Graduate Institute of Business Administration, College of Management, National Taiwan University, 1, Sec. 4, Roosevelt Road, Taipei, Taiwan, R.O.C. Tel.: +886 6 33661058. 2 Tel.: +886 6 2757575x62826. 0040-1625/$ - see front matter © 2006 Elsevier Inc. All rights reserved. doi:10.1016/j.techfore.2006.08.002
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a whole host of synthetic fibers are examples [1]. Besides, nowadays, there is an emerging and important technological change in new materials, which is nanotechnology. According to the estimation by National Science Foundation, the nanotechnology market will experience pretty steep annual growths capable of bringing it to more than US$1 billion after the year 2010 [2]. However, there are full of uncertainties in those new materials development. Through selecting an appropriate technology forecasting method to gain the useful forecasting information about the new materials development, such as the alternative rates, the breakthrough points, or the degrees of market penetration, becomes more and more significant. A good choice of a technology forecasting method in a particular situation could affect the usefulness and accuracy of the forecast. However, little has been done in discussing the selection of technology forecasting methods on this topic. Accordingly, the main purposes of this study are to identify the critical evaluation criteria and to evaluate the technology forecasting methods for development of new materials. During the past two decades, there has been growth in the number of multiple criteria decision-making methods for assisting decision-making. These allow decision-makers to evaluate various alternatives for achieving their goal. Among these, the fuzzy analytic hierarchy process (FAHP) is one of the most popular [3–6]. People often use knowledge that is imprecise rather than precise. The fuzzy set theory could resemble human reasoning in use of approximate information and uncertainty to generate decisions. It was specifically designed to mathematically represent uncertainty and vagueness and provide formalized tools for dealing with the imprecision intrinsic to many problems [3,7,8]. Consequently, to make this study more sensible and gain a more representative description of the decision-making process, this paper would apply the FAHP to evaluate the technology forecasting methods for the new materials development. 2. Review and classification of technology forecasting method Methods for technology forecasting are broadly classified into two main categories: exploratory forecasting and normative forecasting [1,9]. Exploratory forecasting means forecasting the future based on past data and present conditions, which includes Delphi method, growth curves and the case study method. The Delphi method is an approach used in forecasting the likelihood and timing of future events [10,11]. The method could be more adoptive in the situations, which are few historical data or more external factors [12,13]. The most important prerequisite for using the method is that all participants should be experts in a given aspect of the proposed technology [14]. Forecasting by growth curves method involves fitting a growth curve to a set of data on technological performance, then extrapolating the growth curve beyond the range of the data to obtain an estimate of future performance. The method is based on the parameter estimation of a technology's life cycle curve [15,16]. It is helpful in estimating the upper limit of the level of technology growth or decline at each stage of the life cycle and in predicting when the technology will reach a particular stage [17–20]. However, when using the growth curve method, the technology life cycle of the object must be known and if historical data are not sufficient for a long period of time, only limited information can be obtained from the data [14]. The case study method relies on the study of technological developments that have already occurred in actual firms or organizations. The predictions regarding the development of future technologies are made based upon the analysis of past developments [21,22]. The method is more suitable in the complex technology with only a small number of organizations involved [23,24]. The other category of technology forecasting is normative forecasting. The normative forecasting means predicting the technological performance depended on future needs. In essence, it forecasts the
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capabilities that will be available on the assumption that needs will be met. Normative forecasting methods include the relevance trees and scenario writing method. The relevance tree method essentially involves the drawing of one or more tree diagrams which structure the sequence of technological problems that must be solved in order to reach the objectives [25]. Therefore, prerequisites for using the method are that the hierarchical structure and related factors of technology development must be known [1,14]. The scenario writing method proposes different conceptions of future technology and each alternative scenario is based on certain assumptions and conditions [26,27]. The forecaster evaluates the validity of the assumptions. The results of the evaluation are used to determine the scenario most likely to occur by scenario developers. It is very crucial that scenario developers must be experts in all aspects of the proposed technology [14,28]. To sum up, Table 1 presents the illustrations and prerequisites of the technology forecasting methods. 3. The fuzzy analytic hierarchy process There has been growth in the number of multiple criteria decision-making methods for assisting decision-making during the past two decades. These allow decision-makers to evaluate various alternatives for achieving their goal. Among these, the fuzzy analytic hierarchy process (FAHP) is one of the most popular [3–6]. People often use knowledge that is imprecise rather than precise. The fuzzy set theory could resemble human reasoning in use of approximate information and uncertainty to generate decisions. Table 1 Interpretations of technology forecasting methods Method
Illustration
Prerequisite
Delphi method
The method combines expert opinions concerning the likelihood of realizing the proposed technology as well as expert opinions concerning the expected development time into a single position. (1) The method was based on the parameter estimation of a technology's life cycle curve; (2) it is helpful in estimating the upper limit of the level of technology growth or decline at each stage of the life cycle. The predictions regarding the development of future technologies are then made based upon the analysis of past developments. (1) The method is an normative approach; (2) the goals and objectives of a proposed technology are broken down into lower level in a tree-like format. (1) The method proposed different conceptions of future technology; (2) the each alternative scenario being based on certain assumptions and conditions.
All participants should be experts in a given aspect of the proposed technology.
Growth curve
The case study method
Relevance trees
Scenario writing
(1) Available historical data that covers extended period of time. If historical data are not available from a long enough period, only limited information can be obtained from the data; (2) technology's life cycle must be known. Complex technology with only a small number of organizations involved can be studied. The hierarchical structure of technology development must be known.
Scenario developers must be experts in all aspects of the proposed technology.
Data Source: Adapted from Levary and Han, “Choosing a technological forecasting method”, Industrial Management 37 (1995) 14–18.
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In the fuzzy set terminology, the ratio supplied by the decision-maker is a fuzzy number described by a membership function. The membership function describes the degree with which elements in the judgment interval belong to the preference set [6]. Fuzzy AHP consists of deriving the local priorities from these fuzzy preference ratios, which are subsequently aggregated to form the whole priorities. Because the preferences in AHP are essentially judgments of human beings based on perception (especially for intangibles), the fuzzy approach allows a more representative description of the decisionmaking process [4]. There are several possible ways to represent fuzzy numbers. It is common to use triangular fuzzy number, which is relatively easy to model and works well with most applications. According to the fuzzy set theory, we define the membership function μA(x) of a triangular fuzzy number which is shown in Eq. (1) and Fig. 1: 8 < ð x LÞ=ð M LÞ; L≦x≦M ð1Þ lA ð xÞ ¼ ðU xÞ=ðU M Þ; M ≦x≦U : 0; otherwise where L ≦ M ≦ U, L and U stand for lower and upper value of the support of A, and μA(x)→[0,1]. In this study, we adopt the triangular fuzzy number to represent the measurement of experts' view toward the preference of assessment by forming the pairwise comparison matrix. The matrix is called the ˜ = [m˜ ij]n *n. Here M ˜ represents the fuzzy reciprocal matrix and m˜ ij is fuzzy positive reciprocal matrix, M the fuzzy number of experts' preference. The whole fuzzy AHP processes in our study are as follows: Step 1. Creating the hierarchy layers: Based on the characteristics decomposed by each attribute, the hierarchical structure of our model is constructed as shown in Fig. 2. The top level is the main objective, which is to evaluate what technology forecasting methods would be best in predicting the development of the new materials. The second level is the key evaluation criteria for assessing the objective. Here, there are seven key criteria, identified by interviewing the experts. They are: data availability, data validity, technology development predictability, technology similarity, method adaptability, ease of operation and implementation cost. Finally, five forecasting method candidates are placed at the bottom of the model. These include Delphi method, growth curve, the case study method, relevance trees and scenario writing.
Fig. 1. Membership function of the triangular fuzzy number.
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Fig. 2. The fuzzy AHP model.
Step 2. Group integration: After the polling process, we could convert the experts' opinion into fuzzy numbers by the following formulas: e ij ¼ ð1=N Þ ðe M m1ij Pe m2ij PdddPm e Nij Þ
˜ ij is the integrated triangle fuzzy number; m˜ ijk is the i-th to the j-th factor pair comparison by where M expert k; N is the total number of experts. Step 3. Building the fuzzy positive reciprocal matrix: from the step 2, we could obtain the final calculated fuzzy numbers for each layer. Step 4.: Calculating the factors' fuzzy weights: A modified formula for the fuzzy weights is shown below. ei ¼ ðaei1 aei2 ddde ain Þ1=n ; ji Z where ãij = the i-th to the j-th triangular fuzzy number of the fuzzy positive reciprocal matrix; n = factor numbers in each year; Z˜ = the geometric mean of the triangle fuzzy number e2 dddPZ en Þ−1 e i ¼ Zi ðZe1 PZ W where W˜ i = the fuzzy weight of the i-th factor.
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Step 5. Hierarchy layer sequencing: Calculate the fuzzy weight values for the alternatives as follow: Tei ¼
n X
e j •E e ij W
j¼1
where T˜i is the alternative policy fuzzy weights; W˜ j is the fuzzy weight for the key factor; E˜ij is the score for the selective alternatives Ei to the key factor Ej. Step 6. De-fuzzification: It is necessary to transform a fuzzy number into a non-fuzzy number in order to rank the projects. In many research projects the procedure for de-fuzzification is to locate the Best Nonfuzzy Performance (BNP) value. Methods of such de-fuzzified fuzzy ranking include mean of maximal (MON), centre of area (COA) and a-Cut [5,6]. To utilize the COA method to determine the BNP is simple and practical. The BNP value of the fuzzy number can be calculated as follows: BPNj ¼ ½ðUi −Li Þ þ ðMi −Li Þ=3 þ Li ; ji Step 7. Ranking the projects: At last we could normalize the BPNj. The formula is Pi = BPNj / ∑BPNj. The projects can be ranked according to the Pi value. 4. Research design 4.1. Date collection and analysis The participants include industry practitioners, research analysts and academic researchers experienced in the development of the new materials industries. The research analysts are in the renowned research institutes such as Industrial Technology Research Institute, Metal Industries Research and Development Center, and while the academic researchers are in the prestigious universities including the National Cheng Kung University, National Chiao Tung University. The survey was conducted in two stages; first, eight experts were interviewed for formulating the hierarchy with the seven evaluation criteria and the alternative technology forecasting methods. Then the questionnaires were sent to the targeted experts and 18 responses were received. A script was included to ensure consistency and eliminate any biases that could be caused by the phrasing of the questions. In this study, we followed the fuzzy AHP formulas which were mentioned above, and finally produced a set of global weight or priorities for the alternatives. 4.2. Survey design The survey was conducted to determine how the experts perceived the relative importance of the evaluation criteria and the technology forecasting methods [29]. A questionnaire was developed based on the fuzzy analytic hierarchy. The seven evaluation criteria are: data availability, data validity, technology development predictability, technology similarity, method adaptability, ease of operation and implementation cost. Data availability refers to the extent of availability of data used for the specific technology forecasting method. Data validity reflects the degree of validity of the required data for the specific technology forecasting method. Technology development predictability describes the extent to which the technology forecasting method is able to predict the development of the new technology.
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Technology similarity refers to the level of capability to distinguish the differences between new and existing technologies [14]. Method adaptability describes how match between experts' opinions and technology forecasting methods [30]. Ease of operation reflects the degree of difficulty to use the technology forecasting method. Implementation cost describes the amount of money used for implementation of the technology forecasting method. In addition, five technology forecasting methods, including Delphi method, growth curve model, case study, relevance tree method and scenario analysis, were included in the fuzzy analytic hierarchy as the decision alternatives. Each question in the questionnaire consists of a pairwise comparison of two elements at the same level of the hierarchy. Therefore, the seven criteria in the analytic hierarchy result in a total of 21 questions. For each question, the respondents were asked to indicate the relative importance of the two criteria with respect to the objective. Next, the respondents were asked to pairwise compare the technology forecasting methods with respect to each criterion. There are five technology forecasting methods for the seven criteria, resulting in a total of 70 questions. 5. Results and discussion 5.1. Comparison of the evaluation criteria In this study, we first examined the relative importance of the criteria with respect to the primary objective, the choice of technology forecasting methods. Following the fuzzy AHP methodology, priorities of evaluation criteria were performed to get the relative importance of the factors. Table 2 shows priorities of the evaluation criteria for the goal. The normalized weights and the rank for the criteria are given in the last two columns. According to Table 2, the results indicate that the criterion data validity has the highest weight of 0.222, followed by the criterion method adaptability which has the weight of 0.213. The results show that the most important factor that should be concerned in selecting the forecasting methods of new materials development is the validity of data. Besides, how to match between the experts' opinions and the techonology forecasting method is also very important for decision makers to make a choice. Technology development predictability and data availability have weights of 0.165 and 0.137, respectively. These indicate that they are comparatively perceived as secondary level of factors and are tactically important. Before a new material boomed up, there were a lot of uncertainties and different Table 2 Priorities of evaluation criteria with respect to the goal Relative importance
Fuzzy weight (triangular fuzzy numbers)
Priority
Ranking
Data availability Data validity Technology development predictability Technology similarity Method adaptivity Ease of operation Implementation cost
(0.094, 0.137, 0.200) (0.157, 0.223, 0.316) (0.116, 0.165, 0.236) (0.055, 0.082, 0.122) (0.150, 0.214, 0.303) (0.049, 0.073, 0.108) (0.072, 0.106, 0.156)
0.137 0.222 0.165 0.083 0.213 0.074 0.107
4 1 3 6 2 7 5
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Method
Delphi method Growth curve The Case study method Relevance trees Scenario writing
Criteria Data availability
Data validity
Technology development predictability
Technology similarity
Method adaptability,
Ease of operation
Implementation cost
priority
Rank
0.327 (0.223, 0.315, 0.442) 0.156 (0.103, 0.149, 0.217) 0.213 (0.140, 0.204, 0.294) 0.188 (0.124, 0.179, 0.259) 0.160 (0.108, 0.152, 0.219)
0.307 (0.205, 0.296, 0.421) 0.180 (0.117, 0.171, 0.251) 0.214 (0.138, 0.205, 0.300) 0.176 (0.117, 0.167, 0.244) 0.168 (0.116, 0.161, 0.228)
0.264 (0.172, 0.254, 0.367) 0.125 (0.078, 0.118, 0.178) 0.216 (0.147, 0.207, 0.295) 0.154 (0.103, 0.147, 0.213) 0.285 (0.197, 0.274, 0.383)
0.288 (0.189, 0.276, 0.398) 0.178 (0.118, 0.170, 0.247) 0.249 (0.167, 0.239, 0.340) 0.179 (0.119, 0.171, 0.247) 0.151 (0.100, 0.144, 0.209)
0.309 (0.207, 0.297, 0.422) 0.193 (0.131, 0.185, 0.263) 0.169 (0.111, 0.161, 0.236) 0.175 (0.116, 0.168, 0.242) 0.197 (0.133, 0.189, 0.269)
0.245 (0.155, 0.228, 0.353) 0.288 (0.189, 0.277, 0.398) 0.231 (0.149, 0.221, 0.322) 0.153 (0.098, 0.146, 0.215) 0.135 (0.084, 0.128, 0.194)
0.138 (0.098, 0.132, 0.184) 0.279 (0.192, 0.270, 0.377) 0.281 (0.196, 0.272, 0.376) 0.190 (0.130, 0.183, 0.258) 0.149 (0.100, 0.143, 0.204)
0.267
1
0.181
3
0.207
2
0.167
5
0.178
4
Figures in parentheses represent the triangular fuzzy numbers.
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Table 3 The overall results of the comparative study
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variables affecting the development. A good technology forecasting method should be able to consider the uncertainties and thus predict the development path well. Furthermore, the uncertainties could be reduced as more research data are available for reference. Implementation cost, technology similarity and ease of operation have the weights of 0.107, 0.083 and 0.074, respectively, and are comparatively unimportant. These results indicate that when decision makers choose the technology forecasting methods for new materials development, these three criteria are least concerned. 5.2. Comparisons of the technology forecasting methods with respect to the criteria In this paragraph, we performed comparisons on the alternative technology forecasting methods with respect to each level-two criterion. Table 3 provides a summary of the overall results of the comparative study. The last two columns present the overall weights for the five technology forecasting methods and their ranks, respectively. The Delphi method has the highest weight of 0.267, followed by the case study method with a weight of 0.207. From Table 3, we could see that Delphi method attends the highest local weights in four criteria, while the case study method wins the highest local weight in one criterion and the second highest local weights in four criteria. The rest of the new materials technology forecasting methods in decreasing importance are growth curve, scenario writing and relevance trees method. These findings reflect which technology forecasting method is more suitable for adopting in new materials field. In general, Delphi is the most favorable method for forecasting the development for new materials. However, if the decison makers concern most seriously in implementation cost and technology development predictability, case study and scenario writing may become more favorable for doing the forecasting work. 6. Conclusions New materials have been recognized as key drivers for corporate profitability and growth in today's fast changing environments. However, there are full of uncertainties in those new materials development. Through an appropriate technology forecasting method to gain the useful forecasting information about the alternative rates of the new materials, the breakthrough points of those materials, or the degrees of market penetration and diffusion of the new materials becomes more and more significant. A good choice of a technology forecasting method in a particular situation could affect the usefulness and accuracy of the forecast. The main purpose of this study is to pick up the most suitable technology forecasting method in the field of new materials. Furthermore, little has been done in discussing the selection of technology forecasting methods on the topic of new materials. It increases the importance and exigency in this kind of research. This study applied the fuzzy analytic hierarchy process (FAHP) method to evaluate and select the technology forecasting methods in the field of new materials. People often use knowledge that is imprecise rather than precise. The fuzzy set theory could resemble human reasoning in use of approximate information and uncertainty to generate decisions. It was specifically designed to mathematically represent uncertainty and vagueness and provide formalized tools for dealing with the imprecision intrinsic to many problems. Consequently, to make this study more sensible and gain a more representative description of the decision-making process, this paper adopted the FAHP method to evaluate the technology forecasting methods for the new materials development.
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Two major findings can be made as follows: Firstly, the results of the evaluation criteria indicate that among the seven evaluation criteria, the criterion data validity has the highest weight, followed by the criteria method adaptability and technology development predictability in the second and third place, respectively. Secondly, based on the subjective judgments made by experts, this comparative study shows that Delphi method and the case study method are the two most favorable technology forecasting methods in the field of new materials. The application of FAHP method provided an avenue for corporation policy makers and researchers to evaluate the technology forecasting methods for new materials. References [1] J.P. Martino, Technological Forecasting for Decision Making, 3rd ed., McGraw-Hill, New York, 1993. [2] Business Communications Co., Inc., Advanced Ceramic Powders and Nano Ceramic Powders, 2003. [3] C. Kahraman, U. Cebeci, D. Ruan, Multi-attribute comparison of catering service companies using fuzzy AHP: the case of Turkey, Int. J. Prod. Econ. 87 (2004) 171–184. [4] F. Ghotb, L. Warren, A case study comparison of the analytic hierarchy process and a fuzzy decision methodology, Eng. Econ. 40 (1995) 233–247. [5] J.Y. Teng, G.H. Tzeng, Fuzzy multicriteria ranking of urban transportation investment alternative, Transp. Plan. Technol. 20 (1996) 15–31. [6] R. Zhau, R. Goving, Algebraic characteristics of extended fuzzy numbers, Inf. Sci. 54 (1991) 103–130. [7] E. Williams, Forecasting material and economic flows in the global production chain for silicon, Technol. Forecast. Soc. Change 70 (2003) 341–357. [8] L.A. Zadeh, Fuzzy sets, Inf. Control 8 (1965) 338–353. [9] D. Gabor, Inventing the Future, Alfred A. Knopf, New York, 1964. [10] T. Shin, Using Delphi for long-range technology forecasting, and assessing direction of future R&D activities, Technol. Forecast. Soc. Change 58 (1998) 125–154. [11] W.E. Halal, M.D. Kull, A. Leffmann, The George Washington University forecast of emerging technologies, Technol. Forecast. Soc. Change 59 (1998) 89–110. [12] P.C. Chang, C.P. Wang, B.J.C. Yuan, F.T. Chuang, Forecast of development trends in Taiwan's machinery industry, Technol. Forecast. Soc. Change 69 (2002) 781–802. [13] P. Ronde, Delphi analysis of national specificities in selected innovation areas in Germany and France, Technol. Forecast. Soc. Change 70 (2003) 419–448. [14] R.R. Levary, D. Han, Choosing a technological forecasting method, Ind. Manage. 37 (1995) 14–18. [15] P. Young, Technological growth verves—a comparison of forecasting models, Technol. Forecast. Soc. Change 44 (1993) 375–389. [16] H. Ernst, The use of patent for technological forecasting: the diffusion of CNC-Technology in the machine tool industry, Small Bus. Econ. 9 (1997) 361–381. [17] S.C. Bhargava, A generalized form of the Fisher-Pry model of technological substitution, Technol. Forecast. Soc. Change 49 (1995) 27–33. [18] R.J. Watts, A.L. Porter, Innovation forecasting, Technol. Forecast. Soc. Change 56 (1997) 25–47. [19] N. Meade, T. Islam, Technological forecasting model selection—model stability, and combining models, Manage. Sci. 44 (1998) 1115–1130. [20] L.D. Frank, An analysis of the effect of the economic situation on modeling and forecasting the diffusion of wireless communications in Finland, Technol. Forecast. Soc. Change 71 (2004) 391–403. [21] R.L. Hirsch, Reorienting an industrial research laboratory, Res. Manage. 29 (1986) 26–31. [22] D.N. Fuller, W.T. Scherer, T.A. Pomroy, An exploration and case study of population classification for managed healthcare within a state-based modeling framework, Int. J. Healthc. Technol. Manag. 5 (2003) 123–132. [23] C. Hjelkrem, Forecasting with limited information: a study of the Norwegian ISDN access market, J. Bus. Forecast. Methods Syst. 20 (2001) 18–24. [24] M. Kohlbeck, Reporting earnings at summer technology—a capstone case involving intermediate accounting topics, Issues Account. Educ. 20 (2005) 195–213.
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[25] G. Barbiroli, Towards a definition and a dynamic measure of strategic technology, Technovation 12 (1992) 285–297. [26] L. Hirschhorn, Scenario writing: a developmental approach, American Planning Association, J. Am. Plan. Assoc. 46 (1980) 172–180. [27] S. Steven, P. Ziamou, The essentials of scenario writing, Bus. Horiz. 44 (2001) 25–31. [28] P. Schwartz, The art of the long view, Director 45 (1992) 78–82. [29] C.J. Chen, C.C. Huang, A multiple criteria evaluation of high-tech industries for the science-based industrial park in Taiwan, Inf. Manage. 41 (2004) 839–851. [30] K.L. Poh, B.W. Ang, F. Bai, A comparative analysis of R&D project evaluation methods, R&D Manage. 31 (2001) 63–75. An-Chin Cheng is a candidate for doctor's degree at the Graduate Institute of Resources Engineering, National Cheng Kung University, Taiwan. His current research interests include technology management, intellectual property and new product management.
Chung-Jen Chen is an associate professor at the Graduate Institute of Business Administration, College of Management, National Taiwan University. He received his doctorate in Strategy and Technology Management from Rensselaer Polytechnic Institute, Troy, New York. His current research interests include innovation management, knowledge management, interfirm collaboration and entrepreneurship. He has published papers in IEEE Transactions on Engineering Management, Information and Management, International Journal of Technology Management, R&D Management, Technological Forecasting and Social Change, and other journals.
Chia-Yon Chen is a professor at the Graduate Institute of Resources Engineering, National Cheng Kung University, Taiwan. He received his doctorate in Mining and Energy Economy from West Virginia University. His current research interests include projects evaluation and resources management. He has published papers in Material and Society, The Energy Journal, Journal of Policy Modeling, Energy Economics, International Journal of Electrical Power and Energy Systems, Water Resources Management, The Journal of American Academy of Business, Energy Conversion and Management, International Journal of Management, and other journals.
