فرآیند تصمیم گیری خبره تحلیلی فازی سلسله مراتب جدایی ناپذیر در ارزیابی ورودی مدل انتخاب سرمایه گذاری خارجی برای شرکت های زیست فناوری تایوان

The purpose of this study is to help bio-tech firms solve the foreign investment (FI) entry mode selection problem. This study combines the concepts of factor analysis, analytic hierarchy process (AHP), genetic algorithm (GA), and fuzzy integral to construct an entry mode selection approach. This study produces several interesting findings. (1) In the different investment entry modes, there are large differences in evaluation focus when investors select their entry modes. (2) For example, Taiwanese bio-tech firms entering mainland China consider merger and acquisitions to be the first priority, and followed by strategic alliances. This research shows that if the stock share holding is unlimited, Taiwanese bio-tech firms prefer to select a high stock share holding investing mode. (3) In various investment modes, the aspects of “Capital and Risk” and “Technology Ability” have the most consistent effect on entry mode selection.

Zadeh (1965) introduced fuzzy set theory to illustrate the fuzzy phenomena occurring in human activities. Human behaviors and conceptual languages can be converted into fuzzy numbers using the uncertain elements of fuzzy set membership. Van Laarhoven and Pedrycz (1983) showed that these fuzzy numbers can be calculated and ranked. In addition, Mikhailov and Singh (1999) performed a comparative study on traditional crisp values and fuzzy intervals, and found that fuzzy measures perform better than crisp values. In complex multi-criteria scenarios, an expert decision-maker often has too much information to analyze and evaluate, and thus cannot easily make consistent decisions. Chen, Lin, Wang, and Chang (2006) used four different types of membership functions to represent the weighted linguistic variables of the different professional abilities of expert decision-makers. They also measured these linguistic variables using three distinct types of membership functions, and quantified linguistic variables. Chen and Klein (1997) introduced the defuzzying method to calculate crisp values by the relationship between the referential rectangle and triangle fuzzy numbers. Fuzzy measures view the performance of criteria as candidate fuzzy sets, and can be used to determine the degree to which are involved in the performance of criteria in fuzzy set membership. The value of the fuzzy measure includes the connotative weights of criteria performance. In other words, the fuzzy measure has an dependent interaction effect on the criteria under consideration. Eliminating the assumption that the probability of all sets is 1, the fuzzy measure transfers the additive probability into non-additive fuzzy measure. The λ-fuzzy measure, called the Sugeno measure ( Sugeno, 1974), can fulfill the λ additive axiom, making it easier to define the fuzzy measure. The constrained parameter, λ, of the λ-fuzzy measure indicates additivity among its elements. Compared with other fuzzy measures, the λ-fuzzy measure is easily and extensively applied to determine the value of fuzzy measure ( Chen and Wang, 2001 and Lee and Leekwang, 1995). When an expert decision-maker evaluates the alternatives, more criteria create more sophisticated calculations of the λ-fuzzy measure. Lee and Leekwang (1995) employed a genetic algorithm (GA) to calculate the value of the λ-fuzzy measure incomplete information. Chou (2007) provided a GA computer program to obtain the optimal value of λ using Matlab R2007a software. Takahagi (2000) normalized the λ-fuzzy measure to easily explain the value of the fuzzy measure. In 1970, Thomas L. Saaty developed an analytic hierarchy process (AHP) decision model that is suitable to exercise the multi-criteria group decision of subjective judgment (Lai, Wong, & Cheung, 2002). Even through Saaty’s AHP has many defects in reality, it can decomposes complex problems using hierarchical structures, and ultimately benefit the construction of the decision model. Chen (2001) employed the fuzzy integral to amend the disadvantages of traditional AHP. The fuzzy integral successfully accounts for the process of human subjective judgment and more accurately reflects real situations. Moreover, the revised fuzzy integral considers the relationships between criteria at any given time. By calculating the weights of AHP through manipulated or transited effects among criteria, the revised fuzzy integral can accurately reflect real situations. Comment integrals include the Sugeno Integral, Weber Integral, and Choquet Integral. Among these integrals, the Choquet Integral is a non-additive utility function that is suitable to exercise multi-criteria decision problems. Therefore, this study adopts Choquet’s fuzzy integral to calculate the overall performance of each alternative. Furthermore, Takahagi (2005) designed a Choquet Integral program of λ fuzzy measure to calculate the value of the Choquet Integral more easily. From the viewpoints of organizational management and operation, Root (1994) separated the foreign investment (FI) entry modes into: (1) exports, including indirect exports, direct exports and others; (2) contract cooperation, including licensing, franchising, technical agreements, service contracts, management contracts, turnkey, contract/manufacture, counter trade arrangements, and others; and (3) local investment, including unique investment – investing in a new establishment, unique investment – acquisition, joint venture – creating a new establishment, joint venture – purchasing the stocks of existing company, and others. Pan and Tse (2000) differentiated the level of entry modes between equity and non-equity relationships. Furthermore, Chen and Lou (2004) illustrated the modes of strategic alliance using the exchange types of the relationship between the degree of integration and control. Yoshino and Rangan (1995) divided strategic alliances into contract agreements and equity agreements based on the types of equity. Finally, Narula and Hagedoorn (1999) differentiated between the modes of technology transfer for equity and non-equity agreements. To summarize the categories of entry modes in the literature described above, this study simplifies these research processes and considers current bio-tech developing situations. The FI entry modes of the bio-tech (or pharmacy) industry indicated by Chen and Lou (2004) not only effectively measure different assessment criteria, but can also reduce the complexity of assessment factors in various entry modes and their alternatives. (1) Considering the entry mode survey questionnaires release and the respondent willingness to reply, Chen and Lou (2004) developed the following entry modes: (1) joint venture, (2) minority holding strategic alliance, (3) joint R&D, (4) joint production, (5) joint marketing and promotion, (6) enhancing the partner relationship with a provider, (7) R&D contract, and (8) licensing agreement. This study simplifies and rearranges theses entry modes into the following four categories: “Joint Venture,” “Strategic Alliance,” “Merger and Acquisition,” and “Cooperation Contract.” (2) The research subjects in this study are Taiwanese bio-tech firm experts who are willing to invest in, or are currently investing in, Mainland China. Facing growing international competition, many Taiwanese bio-tech firms have begun to invest heavily in R&D to develop innovative products or processes. Bio-tech firms in particular face the challenge of high barriers to entry, long-development time, and a high failure rate. Most of bio-tech firms are small and medium enterprises whose main revenues come from manufacturing and selling products, and they often lack investment capital. The models in Table A1 in Appendix A show that cooperation among universities, research institutes, bio-tech firms, and other related industrial companies is becoming one of the major strategies in bio-tech business operations. According to previous studies (Dunning, 1988 and Kim and Hwang, 1992), the aspects of strategic motivation, knowledge, and techniques (Agarwal and Ramaswami, 1992, Cho and Yu, 2000, Pearce and Papanastassiou, 1996 and Shan and Song, 1997), location-specific advantage (Agarwal and Ramaswami, 1992, Allansdottir et al., 2002, Brouthers, 2002, Cho and Yu, 2000, Dalton and Serapio, 1999, Deeds et al., 2000, Isbasoiu, 2006, Pearce and Papanastassiou, 1996, Richards and DeCarolis, 2003, Robertson and Gatignon, 1998, Shan and Song, 1997, Shih, 2006 and Yiu and Makino, 2002), ownership-specific advantages (Agarwal and Ramaswami, 1992, Coombs et al., 2006, Deeds and Hill, 1996, Ekeledo and Sivakumar, 2004 and Shih, 2006), internalization advantage (Woiceshyn & Hartel, 1996), and their influence on FI are the primary factors affecting FI for bio-tech firms. Other research on the bio-tech industry is directed at internationalized joint ventures. Richards and DeCarolis (2003) found that similar and complementary product lines from the cooperative enterprises, culture distances, country risks, and prior cooperative experiences all result in different forms of joint-ventures in R&D activities. Vanderbyl and Kobelak (2007) conducted a study on the key success factors of 247 Canadian bio-tech firms. Their study demonstrates that bio-tech firms rely more on external resources in the early stages, and modern bio-tech firms usually acquire more venture capital. No matter what stage bio-tech firms are in, the key success factors is the accumulation of intellectual capital. As a result, the number of patents a bio-tech firm possesses can measure its technical capital (Deeds et al., 1997 and Greetham, 1998). Shan and Song (1997) assumed that the key success factors for bio-tech firms lied in the acquisition of venture capital (VC), business partners, success of initial public offering (IPO), accomplishment of clinical trials, electable products, or technology commercialization. Hence, sufficient long-term capital is an important factor in the survival of bio-tech firms. Table A2 in Appendix B lists the influential factors of foreign investment and their related studies.

The results of this study show that different assessment criteria affect the decisions of Taiwanese bio-tech firms which plan to invest, or have already invested, in Mainland China. The following section summarizes some key points. For “View the MathML sourceX∼¯1: Environment of Host Country,” the “View the MathML sourceX∼¯13: Tax Preference” is a very important factor for choosing the models “A1: Joint Venture,” “A2: Strategy Alliance,” and “A4: Acquisition and Merger.” This shows that if there a country has a tax preference, it is a significant attraction of investment for bio-tech firms. The “View the MathML sourceX∼¯17: Public Acceptance and Attitude for Bio-tech Products” factor is important in considering the models “A3: Acquisition and Merger,” and “A4: Cooperative Contract.” This result implies that if bio-tech products are accepted by local customers, they fill a niche market that in turn improves the cooperation with local manufacturers. The factors of “View the MathML sourceX∼¯12: Country Risk” and “View the MathML sourceX∼¯14: Consistency of Industry Policy” are of medium and equal importance. For this reason, we can presume that some countries that support the improvement of the bio-tech industry are almost developed or developing countries with a basic foundation in politics, economy, society, regulation, and conducting policy. Thus, its importance is in the medium while the bio-tech enterprise decides to invest. Other assessment criteria like “View the MathML sourceX∼¯11: Governmental Rules and Regulation,” “View the MathML sourceX∼¯15: Bio-tech Park and Cluster Benefit” and “View the MathML sourceX∼¯16: Support of Basic Research” create different effects depending on the kind of entry mode selected, and have inconsistent importance for decision-making. Based on seven assessment criteria, the “View the MathML sourceX∼¯2: Enterprise Competence” factor exhibits the significant differences depending on the four kind of entry mode selected. For “A1: Joint Venture,” the following factors are ranked in terms of importance: “View the MathML sourceX∼¯23: Enterprise Scale,” “View the MathML sourceX∼¯21: Characteristics of Executive Manger,” “View the MathML sourceX∼¯26: Number of Patent,” “View the MathML sourceX∼¯24: R&D Competence,” “View the MathML sourceX∼¯22: R&D Management Groups with Rich Experience,” and “View the MathML sourceX∼¯25: Strategic Motivation.” This shows that a bio-tech firm have competence in the input resource if they want to obtain more holdings; in general, large-scale firms are usually competent enough to support R&D activities with long-term and high cost investment. The factors “View the MathML sourceX∼¯21: Characteristics of Executive” and “View the MathML sourceX∼¯22: R&D Management Groups with Rich Experience” can help a company cooperate with partners in “A1: Joint Venture.” It is very important and good for product if bio-tech enterprise has patents. If the bio-tech firm develops or obtains patents, it increases their willingness to cooperate with partners. For “A2: Strategic Alliance,” the factors “View the MathML sourceX∼¯26: Number of Patents” and “View the MathML sourceX∼¯24: R&D Competence” are most important for decision-making. Currently, bio-tech firms have difficulty acquiring patents quickly due to the strict regulations established by many countries. Therefore, if a bio-tech firm can form a strategic alliance with manufacturers in the host country, it will help that company acquire patent and R&D competence quickly. For “A3: Acquisition and Merger,” the factors “View the MathML sourceX∼¯24: R&D Competence,” “View the MathML sourceX∼¯21: Characteristics of Executive Manger” and “View the MathML sourceX∼¯22: R&D Management Group with Rich Experience” are for the most important to policy-making. As a rule, a business will experience some reorganization after conducting “A3: Acquisition and Merger.” This highlights the necessity of competence in leadership, team management, and executives. For “A4: Cooperative Contract,” policy consideration is primarily based on the factors “View the MathML sourceX∼¯23: Enterprise-Scale,” “View the MathML sourceX∼¯26: Number of Patent” and “View the MathML sourceX∼¯25: Strategic Motivation.” For medium and small-scale bio-tech firms, the main reason to conduct foreign investment is based on a consideration of overall strategies, including acquiring the foreign market, developing the R&D technology, building a base for future development, or attacking global competitors to obtain an overall competitive advantage. Consequently, a cooperative contract is a good way for bio-tech firms to reduce the risk of entering a host market. For “View the MathML sourceX∼¯3: Industrial Development,” each assessment criterion has a significantly different effect on decision-making; however under different modes. In general, however, the factor “View the MathML sourceX∼¯32: Development Priorities Fits Original Industrial” is a vital consideration in the modes of “A2: Strategic Alliance and “A3: Acquisition and Merger.” The factor “View the MathML sourceX∼¯34: Using the Local Environment for R&D” is also very important in all four entry modes when a company makes consideration to perform its policy. Therefore, each country should follow a clear direction in developing their biotechnology industry, and must plan a complete infrastructure including water and electricity, medical treatment, education, and transportation to achieve better quality of life and advanced R&D facilities. In addition, the “View the MathML sourceX∼¯31: Government Supporting for R&D Cooperation” and “View the MathML sourceX∼¯33: Government Supporting for Industrial Development” factors do not have a significant influence on decision-making processes since many country governments have highly respect to this industry in present, thus, many enterprises will not do more thinking on them when they decide to choose the entry modes. For “View the MathML sourceX∼¯4: Capital and Risk,” the factor “View the MathML sourceX∼¯41: Diversity Channel for Capital” has the most significant influence while choosing the models of “A1: Joint Venture,” “A2: Strategic Alliance” and “A4: Cooperative Contract.” These results indicate that a company that selects the entry mode with no or minimal holdings is more likely to consider using capital. Hence, a country can attract the investment of many bio-tech enterprises by establishing a complete financial system and capital market that makes it easy for bio-tech firms to obtain long-term capital from diversity number of different channels. The results of this research also show that “View the MathML sourceX∼¯41: Diversity Channel of Capital” has the lowest influence on the mode “A3: Acquisition and Merger” but the “View the MathML sourceX∼¯45: Flexibility of Exit Mechanism” was not. These results show that an investment model with high holdings must input much more capital than usual, and a strong capital foundation is necessary. Therefore, “View the MathML sourceX∼¯45: Flexibility of Exit Mechanism” and quick returns on capital are vital in this situation. For “View the MathML sourceX∼¯5: Technical Competence,” the factors “View the MathML sourceX∼¯54: Technical Uncertainty,” “View the MathML sourceX∼¯51: Competence of Technical and Commodity Reality” and “View the MathML sourceX∼¯52: Competence of Product Differentiation” have high and consistent importance in the four kinds of entry modes. For this reason, regardless of which model is selected, a bio-tech firm can earn high profits by acquiring stable technology and competence in of commodity. For “View the MathML sourceX∼¯6: Latest Industry News,” there has consistent idea between the criteria of “View the MathML sourceX∼¯62: Establishment of Overseas Technical Information Center” and “View the MathML sourceX∼¯61: The Advanced Knowledge and Technology Acquiring” when a company selects its preferred entry mode. Although knowledge acquisition has become easier in the information era, each country has their priority development for bio-tech industry and have more protection and management for the technology and resource that they have. Therefore, it is crucial that the host country collect information to encourage a company to conduct foreign investment. For the assessment aspects in Table 6, different entry modes create different priorities. For “A1: Joint Venture,” the factors “View the MathML sourceX∼¯6: Latest Industry News,” “View the MathML sourceX∼¯5: Technical Competence” and “View the MathML sourceX∼¯2: Enterprise Competence” are the most important. These results show that the industrial knowledge, technology, and operation ability are vital to bio-tech firms conducting foreign investment with the host country partners. For “A2: Strategic Alliance,” of the most important factors are “View the MathML sourceX∼¯3: Industrial Development,” “View the MathML sourceX∼¯1: Environment of Host Country,” and “View the MathML sourceX∼¯2: Enterprise Competence.” Currently, many developed countries have already developed their bio-tech industries, environment, and infrastructure. Therefore, the “A1: Joint Venture” is a good way to rapidly develop a technology or product. The consideration of “A3: Acquisition and Merger” focuses on the aspects of “View the MathML sourceX∼¯5: Technical Competence,” “View the MathML sourceX∼¯4: Capital and Risk” and “View the MathML sourceX∼¯1: Environment of Host Country.”. This shows that although technical competence can be an assurance of making high profits in the future, the risk of politics, society, regulation, and operation are higher than previously thought. Consequently, when a bio-tech firm conducting foreign investment must face many risks, thus only making the well risk prevention, solution and avoidance can declined the influence of investment risk. This study shows that the “A4: Cooperative Contract” focuses on the aspects of “View the MathML sourceX∼¯2: Enterprise Competence,” “View the MathML sourceX∼¯5: Technical Competence” and “View the MathML sourceX∼¯6: Latest Industry News.” These results show that a bio-tech firm must carefully consider its partner’s abilities and competency, as well as of its level of contract control. Bio-tech firms should cooperate with local partners to enhance their overall competitive abilities with complementing resources or cooperating with each other. Finally, this study conducts an order ranking of all assessment values for the four kinds of entry modes (see Table 7) The results indicate that the appropriate order of entry modes for Taiwanese bio-tech firms who want to invest in the Chinese market is “A3: Acquisition and Merger,” “A2: Strategic Alliance,” “A1: Joint Venture” and “A4: Cooperative Contract.” This also indicates that Taiwanese bio-tech firms prefer the entry mode with high holdings. This prediction is consistent with actual the development modes of some foreign bio-tech companies, who usually adopt “A3: Acquisition and Merger” or “A2: Strategic Alliance” (see Table A1 in Appendix A). For “A3: Acquisition and Merger,” there are still some problems that must be solved in the Chinese market despite the fact that the Chinese bio-tech industry is gradually catching up with more advanced countries. For example, the vertical stream can not be effectively integrated, fakes flooding, serious repeat of R&D and many small-scale companies has mushroomed all over the market but with less competition. Therefore, if a company wants to get ahead in the market and effectively integrate its production, selling, and R&D and expand its influence in the market, acquiring and merging with local bio-tech firms is actually viable option. However, the entry mode of “A3: Acquisition and Merger” is not a commonly-used method in the Chinese market. This is because regulations are not complete, the market system is not mature enough, and a comprehensive capital market is not yet fully formed. For these reasons, the optimal approach for Taiwanese bio-tech firms is to select the entry mode with high holdings. The reason why the mode “A1: Joint Venture” and “A4: Cooperative Contract” is not very important is easy to determine. Despite the fact that Chinese investment regulations the “A1: Joint Venture” must be the Sino-foreign type, endless financial disputes still arise from many Taiwanese investment cases. In addition, Chinese partners often intentionally deceive the firm, mis-appropriate funds or escape with cash, quarrel with the local government, and share profit unequally. These and other industrial disputes are problems which are difficult to solve immediately. Therefore, only carefully choosing the cooperative partner or joint objective can bio-tech firms reduce their risk of investment.