Defining Pricing Strategies for Silicon Intellectual Properties of Late Coming Providers by a Hybrid MCDM Framework

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1 International Journal of Information Systems for Logistics and Management Vol. 5, No. 2 (2010) Defining Pricing Strategies for Silicon Intellectual Properties of Late Coming Providers by a Hybrid MCDM Framework Chi-Yo Huang 1, *, Gwo-Hshiung Tzeng 2,3, Chao-Yu Lai 1 and Chien-Pen Chuang 1 1 Department of Industrial Education, National Taiwan Normal University No. 162, He-Ping East Road I, Taipei 106, Taiwan 2 Institute of Project Management, Kainan University No. 1, Kainan Road, Luchu, Taoyuan 338, Taiwan 2 Institute of Management of Technology, National Chiao-Tung University 1001, Ta-Hsueh Road, Hsin-Chu, Taiwan Received 17 January 2010; received in revised form 10 May 2010; accepted 10 June 2010 ABSTRACT In the knowledge-based economy era, transactions of intellectual properties (IPs) in general and silicon intellectual properties (SIP) in special have played a daily important role in fostering innovation and achieving economic growth. The emergence of system-on-a-chips (SOCs) and increasing IC design complexity have further accelerating the popularity and transactions of IPs which can be used to assist IC/SOC designers crossing the gap between the growth of numbers of transistors on an IC and the growth of IC design engineering productivity. However, as transactions of SIPs have already become one of the most important business activities being related to IC/SOC designs in the semiconductor value chain, very few researches discussed the transaction related issues. Further, albeit an appropriate pricing strategy is one of the most important elements of a marketing-mix strategy, very limited researchers tried to uncover how SIP pricing strategies can be defined, not to mention the SIP strategy definition for late comers in the SIP industry, e.g. SIP providers in Japan, South Korea, Taiwan or other Asian countries. The late comers SIP prices usually follow those of western (e.g. U.S. and U.K.) industry leaders while these me too pricing strategies usually results in lower than expected revenues and profits. Apparently, how to price SIPs appropriately and achieve higher profitability have already become the most important marketing issues of the SIP providers. To price appropriately, this research aims to propose a VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) based multiple criteria decision making (MCDM) approach. The feasibility of this VIKOR based MCDM framework was verified by Taiwanese SIP experts for defining an SIP pricing strategy for late coming SIP providers in Taiwan which mainly sell low value added peripheral or memory SIPs or purchase high value added embedded processors (e.g. ARM cores) and then resell the cores with design services. Twelve criteria and nine strategies were summarized by literature and recognized by the experts at first. Then influence relationships between the twelve criteria were derived as the structure of the decision problem by using the Decision Making Trial and Evaluation Laboratory (DEMATEL) method for serving as the basis of the weight versus each criterion by using the Analytic Network Process (ANP). Finally, the nine strategies were graded and ranked based the VIKOR method. The optimal strategy, which the SIP providers sell optional extras along with the SIP to maximize its turnover, was recommended by the experts as the most suitable one. The proposed SIP pricing strategy is appropriate for the *Corresponding author: Chi-Yo Huang; georgeh168@gmail.com; Tel:

2 48 International Journal of the Information Systems for Logistics and Management (IJISLM), Vol. 5, No. 2 (2010) current situation of most late coming SIP providers since the value added of their products are usually low. More revenues and profits are expected to be generated by extra services or products like IP integration services or turnkey services. In the future, this already-verified framework can be applied on other real world IP or technology pricing problems. Keywords: Technology pricing; Intellectual Property (IP); Integrated Circuit (IC); Decision Making Trial and Evaluation Laboratory (DEMATEL); Analytic Network Process (ANP); VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR); Multiple Criteria Decision Making (MCDM). 1. INTRODUCTION Intellectual property (IP), a legal term used to describe legally protected intellectual assets of the different forms available (Sullivan et al., 2002), could play a significant role in encouraging innovation, product development, and technical change (Maskus, 2000) by making more investment activities possible, particularly research and development activities. Park and Ginarte (1997) also made the same conclusion that IP rights (IPRs) affect economic growth by stimulating the accumulation of factor inputs like research and development capital and physical capital. IPRs, like general property, give their holders a bundle of rights, most importantly the rights of exclusion, transfer and licensing as well as facilitate bargaining and investment activity (Drahos, 2005). Patents, trademarks, copyrights, trade secrets, and semiconductor mask works are five major forms of Silicon IPs (SIPs) available in the integrated circuit industry (Sullivan et al., 2002). SIP has existed since the advent of the semiconductor industry. In the early years, integrated circuit (IC) suppliers such as Fairchild, Intel, and Motorola developed proprietary SIP for their internal use. During the past decade, IC design productivity has failed to keep pace with Moore s Law, which predicts that the number of electronic devices that can be fabricated on an IC chip doubled every 18 months (Moore, 1979); and a design gap between IC design complexity increase and productivity increase has emerged (Semiconductor Industry Association, 2002). IC suppliers began looking for ways to narrow the gap by designing chips with reusable SIP that tended to contain increasingly complex functionality (Ratford et al., 2003). As IC designs become more complex, a large number of SIP products are being embedded into the designs. The SIP has become a key part of the electronic design process as it can reduce IC development costs, accelerate time-to-market, reduce the time-to-volume and to increase the end-product value. The nature of SIP, which can narrow the design productivity gap, has made SIP critical for the design and implementation of complex systems on chips (SOC) which have become the mainstream solution for realization of electronic system products after year In the new economy, knowledge is the principal economic asset and its management and protection have become the cornerstones of corporate strategy (Hanel, 2006). Albeit important, management of IPRs in general and pricing, one of the most important activities of marketing management, in special, is not easy, not to mention the pricing of SIPs. According to Bidault (1989), technology pricing is difficult for most people including managers involved in negotiations, civil servants concerned with regulation of international technology flows, and economists interested in the understanding of the global technology market. Furthermore, pricing of IP is even more difficult and comparatively fewer scholars tried to discuss related issues. Albeit marketing managers in the industry have started to handle related issues during the past decades, related researches still focus on patents. However, most works were focused on the valuation of patents. But for the IC design sector, except for patents, trade secrets about the circuit design have already became the major SIP which dominated the major portion of the overall SIP market regarding to IC design. Apparently, how to price SIPs appropriately by adopting suitable strategies is one of the most important marketing strategies. However, there are always complicated business issues in this area, including license models and business terms, SIP evaluations and legal issues associated with each SIP transaction (Huang and Shyu, 2006). Even today, to make such a valuation is no easy matter. Rather, it is extremely difficult to be precise. The difficulties arise from the unique nature of intellectual property, the long time horizons (up to 20 years), and the technical, commercial and economic uncertainties involved (Pitkethly, 2001). Further, it is not that easy to price SIPs appropriately due to the over difficult budget estimation during the SIP development processes (Trappey et al., 2002). Apparently, SIP pricing and strategies definitions are not easy. Thus, how to define appropriate SIP pricing strategies, prices and further maximize SIP provider profits and revenues are the most critical problems (Trappey et al., 2002) being faced by IC marketers as well as top management. Therefore, the research objective of this paper is to study how the pricing strategies of SIPs can

3 C. Y. Huang et al.: Defining Pricing Strategies for Silicon Intellectual Properties of Late Coming Providers 49 be defined and thus, the prices can be appropriately determined. Further, how the pricing strategies of a specific SIP provider can be enhanced will be suggested. To achieve the research goal, the authors proposed a hybrid VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) (Opricovic, 1998) based MCDM (Multiple Criteria Decision Making) SIP pricing framework consisting of the DEMATEL (Decision Making Trial and Evaluation Laboratory) and the ANP (Analytic Network Process). The possible SIP pricing strategies as well as criteria for evaluating the strategies were first be selected by literature review. Then suitable strategies were recognized by SIP industry experts by using the Delphi process. The DEMATEL (Gabus and Fontela, 1972) was leveraged to derive the structure of this decision problem while the ANP was leveraged to derive weights versus each criterion. Finally, the VIKOR (Opricovic, 1998) was leveraged to derive a compromise solution of the ranks versus the proposed strategies. Finally, the proposed pricing strategy will be verified by SIP experts as feasible for profit and revenue maximization. The feasibility of this VIKOR based MCDM framework was verified by Taiwanese SIP experts for defining an SIP pricing strategy for late coming SIP providers in Taiwan which mainly sell low value added peripheral or memory SIPs or purchase high value added embedded processors (e.g. ARM cores) and then resell the cores with design services. The most suitable strategies were selected. Further, how the current pricing strategies can further be enhanced based on the evaluation criteria so as to achieve the aspired level of satisfactory will be recommended. More revenues and profits are expected to be generated by extra services or products like IP integration services or turnkey services. In the future, this already-verified framework can be applied on other real world IP or technology pricing problems. The reminder of this paper is organized as follows. The literature regarding to IP pricing will be reviewed in Section 2. In Section 3, the analytic framework and methods consisting of DEMATEL, ANP and VIKOR will be introduced. Section 4 presents an empirical study. Discussion will be presented in Section 5. Section 6 will conclude the whole paper with observations, remarks and recommendations for further study. 2. TRANSACTION AND PRICING OF INTELLECTUAL PROPERTY In this section, the research background including the relationships between IPR and economic growth in the knowledge based economy, management of IPR with an emphasis on marketing management, valuation and pricing of IPR will be introduced. In the marketing mix strategies, pricing strategies is one of the four most important ones other than product, place and promotion. Major factors influencing pricing is how the contracting parties view the costs and benefits of technology and the competitiveness in donor and recipient markets (Baranson, 1970). However, intellectual property grows rapidly in IC design and due to its unique cost structure and characteristics, it is almost impossible for its providers to take the traditional pricing strategy (cost plus). Therefore, how to price SIP is an issue cannot be ignored. 2.1 IPRs and Economic Growth in the Knowledge Based Economy Knowledge is the key economic resource and perhaps the only source of competitive advantage in the new environment of knowledge-based economies, which was defined by OECD as: economies which are directly based on the production, distribution and use of knowledge and information (OECD, 1996). The knowledge economy has a dramatic impact on the way in which firms compete today. It affects every aspect of modern business from a corporation s strategy to its products, from its processes to its organization and, last but not least, its people (Drucker, 1996). Thus, the overall economic performance of major economies is increasingly and more directly based upon their knowledge stock (Neef et al., 1998). As summarized by David and Foray (2002) from the works of Abramovitz and David (1996, 2000), a related characteristic of economic growth, that became increasingly evident from the early twentieth century onwards, is the growing relative importance of intangible capital in total productive wealth, and the rising relative share of GDP attributable to intangible capital. One of the two major categories intangible capital is the investment geared to the production and dissemination of knowledge (i.e. in training, education, R&D, information and coordination) (David and Foray, 2002). Production of new knowledge would be optimized by establishing strong intellectual property (IP) that create incentives to generate knowledge (Adler, 2001). As summarized by Harrison and Sullivan (2000), major elements of intellectual capital include humans (with their embedded tacit knowledge) and codified knowledge. Codified knowledge has come to be called the firm s intellectual assets (IA). When someone s tacit knowledge is committed to article (it could also be canvas, electronic media, or any other medium), it becomes a codified asset of the firm. Some of these codified assets (intellectual assets) are legally protected as patents, copyrights, trademarks, trade secrets, or semiconductor masks. Intellectual assets that are legally protected are referred to by the legal term intellectual property. According to Drahos (2005), like general property, IPRs give their holders a bundle of rights, most importantly the rights of exclusion, transfer and licensing and facilitate bargaining and investment activity. But there are also important differences between IPRs and other

4 50 International Journal of the Information Systems for Logistics and Management (IJISLM), Vol. 5, No. 2 (2010) types of property, differences that flow from the fact that the object of IPRs protection is, at base, information of one kind or another (Drahos, 2005). 2.2 Management and Marketing of IPRs The management of intellectual capital has a history that began in the early 1980s as managers, academics and consultants around the world slowly became aware that a firm s intangible assets, its intellectual capital, were often a major determinant of the corporation s profits (Harrison and Sullivan, 2000). The rapid development of the knowledge economy, in which a company s major resources are its legally protected intellectual assets, has undoubtedly moved the topic of IP into a sharp focus for decades to come (Huang and Shyu, 2006). In the New Economy, the knowledge capital and not bricks or heavy machinery is the principal source of value; the protection of IP acquired a new importance for intellectual capitalism, for firm as knowledge creating entity and for the core competence of a corporation (Hane, 2005). In today s successful high-tech companies, the management of intellectual property has become a core competence because intellectual assets rather than physical assets are the principal source of competitive advantage while unlocking the hidden power of these assets is often a key to success (Mohr et al., 2009). According to Mohr et al. (2009), active management of intellectual property assets is vital because: (1) patents can be tapped as a revenue source (e.g., via licensing); (2) costs can be reduced by cutting maintenance fees on unneeded patents (that could be donated to universities or nonprofit, for a tax write-off); (3) patents can be repackaged to attract new capital and communicate an asset picture in a more attractive way to investors; (4) patents can help a firm establish R&D priorities; and finally; (5) a patent strategy can help companies respond to shifts in the marketplace in an effective manner, by acquiring or partnering with firms that own patent rights to important developments. Apparently, efficient management of IPR related issues would be very critical for a firm s success. Finally, based on Hanel s (2006) summary, the major management issues related to IPRs include: (1) assessing, measuring and auditing IP portfolios; (2) valuation of IP; (3) managing of IP assets; (4) accounting and IP, and (5) IP as financial asset. 2.3 Technology Pricing and IPRs Technology comes in different forms, e.g. IP (e.g., patents) or intangibles (e.g., a software program or a design), being embodied in a product (e.g., a prototype or a device), or comprise technical services, and no general definition will fit all circumstances (Arora et al., 2008). Technology transactions can take different forms from pure licensing of well-defined IP to complicated collaborative agreements that may include further development of the technology (Arora et al., 2008). The most frequently used method of compensating a provider of technology is through royalties based on a percentage of the sales that result from the technology acquired (Miller, 1998). Thus, technology pricing strategies therefore are difficult to determine and require nimble and effective decision making (Frankel, 1990). Apparently, how the IPR can be priced and how the pricing strategies as well as mechanisms can be defined should be the focus of most technology marketer, IPR researchers and decision theory scholars. The U.S. Department of Justice (1995,6 Cite) defined markets for technology as markets for IP that is licensed and its close substitutes that is the technologies or goods that are close enough substitutes significantly to constrain the exercise of market power with respect to the IP that is licensed. Prices are a critical variable in any market but are harder to establish for technologies while mechanisms for pricing technologies can be crucial for the rise and growth of markets in which they transact (Arora et al., 2008). Unfortunately, technology pricing, is, as yet, a vague and non-transparent art and research on the subject is in its infancy (Arora et al., 2008). Patenting (or IPR) is a distinctive feature of the patterns of technological entry and exit across sectors and over time (Etemad and Seguin-Dulude, 1986). As summarized by Dodgson (2000), the difficulties with remuneration for the sale of IPR: (1) IPR is intangible and decisions about its value are often more speculative and can sometimes be assessed by only a few people (mainly scientists and engineers); (2) the IPR one wants to purchase is at the same time the information that is needed to make a rational decision as to whether or not to buy it; (3) the information necessary to inform a potential licensee of the value of IPRs may be sufficient for that firm to gain enough knowledge to proceed independently, so the pricing regime occasionally has to account for that risk. With the acceptance of IP marketplaces, different business models (regional and global) are emerging to facilitate an IP trade (Trappey et al., 2006). Thus, an IP sale must be augmented with the licenses, the service terms and conditions, knowledge resources, and product guarantees. The value of the IP depends on whether or not the provider will guarantee compliance to standards; provide good quality documentation, as well as customer service and support. Customer service is a critical part of the offering, as is the ability to provide legal assistance, negotiate and trade documents, and configure contracts through the use of templates that model standard business practices (Trappey et al., 2006). Mohr et al. (2009) summarized the factors which influence high technology pricing decisions include

5 C. Y. Huang et al.: Defining Pricing Strategies for Silicon Intellectual Properties of Late Coming Providers 51 Factors influencing value: Strategic value Level of protection of the technology Risk premium Scope of IPR rights Potential markets Competitive position Cost and time for R&D, production, marketing, etc. in exploiting the license Potential margins and revenues in exploiting the license Potential learning effects Impacts on other license deals Licensee value to buyer Ceiling price levels Buyer s Uncertain to lerance Regulations Strategies and tactics Finance, payment form Bargaining power Negotiator biases Factors influencing cost: R&D, engineering and production cost IPR cost Marketing cost Overhead Risk premium Costs related to license restrictions Feedback benefits Potential learning effect Strategic cost/benefit Impact on other license deals Licensee seller s cost Price Window Floor price levels Licensee seller s cost Sellers s Uncertain tolerance Regulations Strategies and tactics Finance, payment form Bargaining power Negotiator biases Source: Granstrand (2000) Fig. 1. A valuation and pricing model for patents and licenses short and volatile product life cycle, pressure on price/ performance ratio, network externalities, unit-one costs, customers perceptions of costs/benefits of new technology, competition, backward compatibility and derivatives, the Internet, Investment in R&D as well as rapid pace of change. Based on the summary of factors, Mohr et al. (2009) further proposed that a solid pricing strategy should consider three factors including cost, competition and customers. Valuation is the process of ascribing value to technology which is particularly crucial for the commercialization of early technologies, for licensing and for mergers and acquisitions (M&A) (Hane, 2005). As summarized by Granstrand (2000), the factors influencing valuation, and thus, pricing of licenses may be classified into two aspects, ceiling price levels and floor price levels, respectively. The higher floor price is related to total cost, including a portion of fixed R&D costs, while the lower floor price is related only to operating costs. The factors can further be illustrated by the following Fig A HYBRID MCDM FRAMEWORK FOR SIP PRICING STRATEGIES DEFINITIONS The analytic process for defining SIP pricing strategies is initiated by collecting the criteria for evaluating the SIP pricing strategies by using the Delphi method. Since any criteria to be derived by the Delphi may impact each other, the structure of the MCDM problem will be derived using the DEMATEL. The priorities of every criterion are based on the structure being derived by using the ANP. Finally, the VIKOR technique will be leveraged for calculating the compromise ranking of the alternatives, which are SIP pricing strategies. In summary, this evaluation framework consists of five main phases: (1) deriving criteria using the Delphi method; (2) building the structure of network relation map (NRM) among determinants by using the DEMATEL; (3) calculating the priorities of every determinant by the ANP based on the structure of NRM derived by using the DEMATEL in (2); and finally (4) ranking the priorities of SIP pricing strate-

6 52 International Journal of the Information Systems for Logistics and Management (IJISLM), Vol. 5, No. 2 (2010) Delphi DEMATEL ANP Define an SIP Pricing Strategy for a Late Coming Provider Define Determinants and Possible Pricing Strategies From Literature Review and Experts? Opinions Establish a Structure of the Decision Problem Derive Weights of Determinants Calculate Compromise Ranking by VIKOR Derive the Optimal SIP Pricing Strategies Fig. 2. An analytical framework for SIP pricing strategy definition gies with the VIKOR (see Fig. 2). 3.1 Delphic Oracle s Skills of Interpretation and Foresight The Delphi method originated in a series of studies conducted by the RAND Corporation in the 1950s (Jones and Hunter, 1995). The objective was to develop a technique to obtain the most reliable consensus from a group of experts (Dalkey and Helmer, 1963). While researchers have developed variations of the method since its introduction, Linstone and Turoff (1975) captured its common characteristics in the following description: Delphi may be characterized as a method for structuring a group communication process; so the process is effective in allowing a group of individuals, as a whole, to deal with a complex problem. To accomplish this structured communication, certain aspects should be provided: some feedback of individual contributions of information and knowledge; some assessment of the group judgment or viewpoint; some opportunity for individuals to revise their views; and some degree of anonymity for individual responses (Linstone and Turoff, 1975). The Delphi technique enables a large group of experts to be surveyed cheaply, usually by mail using a self-administered questionnaire (although computer communications also have been used), with few geographical limitations on the sample. Specific situations have included a round in which the participants meet to discuss the process and resolve any uncertainties or ambiguities in the wording of the questionnaire (Jones and Hunter, 1995). The Delphi method proceeds in a series of communication rounds, as follows: Round 1: Either the relevant individuals are invited to provide opinions on a specific matter, based upon their knowledge and experience, or the team undertaking the Delphi expresses opinions on a specific matter and selects suitable experts to participate in subsequent questionnaire rounds; these opinions are grouped together under a limited number of headings, and statements are drafted for circulation to all participants through a questionnaire (Jones and Hunter, 1995). Round 2: Participants rank their agreement with each statement in the questionnaire; the rankings then are summarized and included in a repeat version of the questionnaire (Jones and Hunter, 1995). Round 3: Participants re-rank their agreement with each statement in the questionnaire, and have the opportunity to change their score, in view of the group s response; the re-rankings are summarized and assessed for their degree of consensus: if an acceptable degree of consensus is obtained, the process may cease, with the final results then fed back to the participants; if not, this third round is repeated (Jones and Hunter, 1995). 3.2 DEMATEL Method The DEMATEL method was developed by the Battelle Geneva Institute (1) to analyze complex world problems dealing mainly with interactive man-model techniques; and (2) to evaluate qualitative and factorlinked aspects of societal problems (Gabus and Fontela, 1972). The applicability of the method is widespread ranging firm industrial planning and decision making to urban plan and design, regional environmental assessment, analysis of world problems, and so forth. Therefore, in this paper, the authors use DEMATEL not only to detect complex relationships and build a NRM of the criteria, but also to obtain the influence levels of each element over others; we then adopt these influence level values as the basis of the normalization supermatrix for determining ANP weights to obtain the relative importance. To apply the DEMATEL method smoothly, the authors refined the definitions based on above authors, and produced the essential definitions indicated below. The DEMATEL method is based upon graph theory, enabling us to plan and solve problems visually, so that we may divide multiple criteria into a relationship of cause and effect group, in order to better understand causal relationships. Directed graphs (also called digraphs) are more useful than directionless graphs, because digraphs will demonstrate the directed relationships of sub-systems. A digraph typically represents a communication net-

7 C. Y. Huang et al.: Defining Pricing Strategies for Silicon Intellectual Properties of Late Coming Providers 53 work, or a domination relationship between individuals, etc. Suppose a system contains a set of elements, S = [s 1, s 2,..., s n } and particular pair-wise relationships are determined for modeling, with respect to a mathematical relationship, MR. Next, portray the relationship MR as a direct-relation matrix that is indexed equally in both dimensions by elements from the set S. Then, extract the case for which the number 0 appears in the cell (i, j), if the entry is a positive integral that has the meaning of: the ordered pair (s i, s j ) is in the relationship MR; it has the kind of relationship regarding that element such that s i causes element s j. The digraph portrays a contextual relationship between the elements of the system, in which a numeral represents the strength of influence (Fig. 3). The elements s 1, s 2, s 3 and s 4 represent the factors that have relationships in Fig. 3. The number between factors is influence or influenced degree. For example, an arrow from s 1 to s 2 represents the fact that s 1 influences s 2 and its influenced degree is two. The DEMATEL method can convert the relationship between the causes and effects of criteria into an intelligible structural model of the system Hori and Shimizu (1999). Definition 1: The pair-wise comparison scale may be designated as eleven levels, where the scores 0, 1, 2,..., 10 represent the range from no influence to very high influence. Definition 2: The initial direct relation/influence matrix A is an n n matrix obtained by pair-wise comparisons, in terms of influences and directions between the determinants, in which a ij is denoted as the degree to which the i th determinant affects the j th INC. A = 2 s 3 s 1 3 Fig. 3. An example of the directed graph a 11 a 12 a 1n a 21 a 22 a 2n a n1 a n2 a 2n Definition 3: The normalized direct relation/influence matrix N can be obtained through Equations (1) and (2), in which all principal diagonal elements are equal to zero. 1 s 2 s 4 3 N = za. (1) where z = 1/ max 1 i n n Σ j =1 a ij. (2) In this case, N is called the normalized matrix. Since lim N k = [0]. k Definition 4: Then, the total relationship matrix T can be obtained using Equation (3), where I stands for the identity matrix. T = N + N N k = N(I N) -1, (3) where k and T is a total influence-related matrix; N is a direct influence matrix and N = [x ij ] n n ; lim (N N k ) stands for a indirect influence matrix k n n n and 0 x ij < 1 or 0 Σ x ij < 1, and only one Σ x ij or n Σ i =1 x ij Σ j =1 i =1 j =1 equal to 1 for i, j. So lim k N k = [0] n n. The (i, j) element t ij of matrix T denotes the direct and indirect influences of factor i on factor j. Definition 5: The row and column sums are separately denoted as r and c within the total-relation matrix T through Equations (4), (5), and (6). T = [t ij ], i, j {1, 2,..., n} (4) r = r i n 1 = c = c j 1 n = n Σ t ij j =1 n 1 n Σ t ij i =1 1 n (5) (6) where the r and c vectors denote the sums of the rows and columns, respectively. Definition 6: Suppose r i denotes the row sum of the i th row of matrix T. Then, r i is the sum of the influences dispatching from factor i to the other factors, both directly and indirectly. Suppose that c j denotes the column sum of the j th column of matrix T. Then, c j is the sum of the influences that factor i is receiving from the other factors. Furthermore, when i = j (i.e., the sum of the row sum and the column sum (r i + c i ) represents the index representing the strength of the influence, both dispatching and receiving), (r i + c i ) is the degree of the central role that factor i plays in the problem. If (r i c i ) is positive, then factor i primarily is dispatching influence upon the strength of other factors; and if (r i c i ) is negative, then factor i primarily is receiving influence from other factors (Tamura et al., 2002). 3.3 The ANP Method The ANP (Saaty, 1996) is the general form of the AHP (Saaty, 1980) which has been used in multicritieria

8 54 International Journal of the Information Systems for Logistics and Management (IJISLM), Vol. 5, No. 2 (2010) Control Control Criteria Criteria Goal Criteria Subcriteria A possible different network under each subcriterion of the control hierarchy Source: Saaty (1996) Fig. 4. The control hierarchy decision making to release the restriction of hierarchical structure (Huang et al., 2005). The AHP decomposes a problem into several levels that make up a hierarchy in which each decision element is supposed to be independent. The ANP extends the AHP to problems with dependence and feedback (Lee et al., 2009). In Fig. 3 shows an example of the network model in the ANP compared with a hierarchy in the AHP. According to Tsai and Chou (2009), the ANP, developed by Thomas L. Saaty, provides a way to input judgments and measurements to derive ratio scale priorities for the distribution of influence among the factors and groups of factors in the decision (Saaty, 2003). Karsak et al. (2002) pointed that the ANP consists of two stages: the first one is the construction of the network, and the second one is the calculation of the priorities of the elements. All of the interactions among the elements should be evaluated by pairwise comparisons so as to construct the framework of the problem. The ANP is a coupling of two parts. The first consists of a control hierarchy or network of criteria and subcriteria that control the interactions. The second is a network of influences among the elements and clusters. The network varies from criterion to criterion and a different supermatrix of limiting influence is computed for each control criterion. Finally, each of these supermatrices is weighted by the priority of its control criterion and the results are synthesized through addition for all the control criteria (Saaty, 1999). A control hierarchy is a hierarchy of criteria and subcriteria for which priorities are derived in the usual way with respect to the goal of the system being considered. The criteria are used to compare the components of a system, and the subcriteria are used to compare the elements. The criteria with respect to which influence is presented in individual supermatrices are called control criteria. Because all such influences obtained from the limits of the several supermatrices will be combined in order to obtain a measure of the priority of overall influences, the control criteria should be grouped in a structure to be used to derive priorities for them. These priorities will be used to weight the corresponding individual supermatrix limits and add. Analysis of priorities in a system can be thought of in terms of a control hierarchy with dependence among its bottom-level alternatives arranged as a network as shown in Fig. 4. Dependence can occur within the components and between them. A control hierarchy at the top may be replaced by a control network with dependence among its components, which are collections of elements whose functions derive from the synergy of their interaction and hence has a higher-order function not found in any single element. The criteria in the control hierarchy that are used for comparing the components are usually the major parent criteria whose subcriteria are used to compare the elements need to be more general than those of the elements because of the greater complexity of the components. A network connects the components of a decision system. According to size, there will be a system that is made up of subsystems, with each subsystem made up of components, and each component made up of elements. The elements in each component interact or have an influence on some or all of the elements of another component with respect to a property governing the interactions of the entire system, such as energy, capital, or political influence. Fig. 5 demonstrates a typical network. Those components which no arrow enters are known as

9 C. Y. Huang et al.: Defining Pricing Strategies for Silicon Intellectual Properties of Late Coming Providers 55 C 1 Source Component Source Component (Feedback loop) C 2 Outerdependence Intermediate Component (Transient State) C 3 Sink Component (Absorbing State) C 5 Intermediate Component (Recurrent State) C 4 Source: Saaty (1996) Innerdependence loop Fig. 5. Connections in a network source components such as C 1 and C 2. Those from which no arrow leaves are known as sink component such as C 5. Those components which arrows both enter and exit leave are known as transient components such as C 3 and C 4. In addition, C 3 and C 4 form a cycle of two components because they feed back and forth into each other. C 2 and C 4 have loops that connect them to themselves and are inner dependent. All other connections represent dependence between components which are thus known to be outer dependent. A component of a decision network which was derived by the DEMATEL method in Section 3.2 will be denoted by C h, h = 1,..., m, and assume that it has n h elements (determinants), which we denote by e h1, e h2,..., e hm. The influences of a given set of elements (determinants) in a component on any element in the decision system are represented by a ratio scale priority vector derived from paired comparisons of the comparative importance of one criterion and another criterion with respect to the interests or preferences of the decision makers. This relative importance value can be determined using a scale of 1-9 to represent equal importance to extreme importance (Saaty, 1996). The influence of elements (determinants) in the network on other elements (determinants) in that network can be represented in the following supermatrix:

10 56 International Journal of the Information Systems for Logistics and Management (IJISLM), Vol. 5, No. 2 (2010) A typical entry W ij in the supermatrix, is called a block of the supermatrix in the following form where each column of W ij is a principal eigenvector of the influence of the elements (determinants) in the i th component of the network on an element (determinants) in the j th component. Some of its entries may be zero corresponding to those elements (determinants) that have no influence. f 1 * f 1 c f c f * (ideal solution) (compromise solution) W i1 j 1 W i2 j 2 W i1 j n j (Feasible solution set) W ij = W i2 j 2 W i2 j 2 W i2 j n j W ini j 1 W ini j 2 W ini j n j After forming the supermatrix, the weighted supermatrix is derived by transforming all columns sum to unity exactly. This step is very much similar to the concept of the Markov chain in terms of ensuring that the sum of these probabilities of all states equals 1. Next, the weighted supermatrix is raised to limiting powers, such as Equation (7) to get the global priority vector or called weights (Huang et al., 2005). lim θ Wθ (7) In addition, if the supermatrix has the effect of cyclicity, the limiting supermatrix is not the only one. There are two or more limiting supermatrices in this situation, and the Cesaro sum would need to be calculated to get the priority. The Cesaro sum is formulated as follows. lim (1 ψ ν ) ν Σ W j ψ j =1 (8) to calculate the average effect of the limiting supermatrix (i.e. the average priority weights) where W j denotes the j th limiting supermatrix. Otherwise, the supermatrix would be raised to large powers to get the priority weights (Huang et al., 2005). The weights of the k th determinants derived by using the above ANP processes, namely ω k, k {1, 2,..., m}, will be used as inputs for summing up the grey coefficients of the k th determinant in Equation (12) in the following GRA analysis. 3.4 VIKOR Fig. 6. Ideal and Compromised Solutions VIKOR was proposed by Opricovic (1998) and is a method to develop for multi-criteria optimization of complex systems. It determines the compromise ranking list and the compromise solution obtained with the initial (given) weights (Sayadi et al., 2008). For example from Fig. 6, f * 1 (the ideal value of the first assessment criterion) and f * 2 (the ideal value of the second criterion) cannot reach f * (ideal solution) at the same time. And the compromised solution is a point on the curve. f c is closest to the ideal solution (f * ) among all non-inferior solutions. And therefore, the optimum compromised solution is: f c = f 1 c, f 2 c. The steps of applying VIKOR are shown as follows (Opricovic & Tzeng, 2004; Opricovic & Tzeng, 2007): Step 1: Determine the ideal (best) solution (f * 1) and the negative (worst) ideal solution (f i ) values of all criterion functions, i = 1, 2,..., n. If the ith function represents a benefit then: f * i = max f ij (9) j f i = min j f ij (10) Step 2: Compute the values S j and R j, j = 1, 2,..., J, by the relations n Σ S j = w i ( f i * f ij )/( f i * f ij ) i =1 (11) R j = max w i (f * i f ij )/(f * i f i ), j j = 1, 2,..., J (12) where w i are the weights of criteria, expressing their relative importance. Step 3: Compute the values Q j, j = 1, 2,..., J, by the relation Q j = v(s j S * )/(S S * ) + (1 v)(r j R * )/(R R * ) (13) where S * = min j R = max R j j f 2 c f 2 * S j, S = max j S j, R * = min j R j,

11 C. Y. Huang et al.: Defining Pricing Strategies for Silicon Intellectual Properties of Late Coming Providers 57 and v is introduced as a weight for the strategy of maximum group utility, whereas 1-v is the weight of the individual regret. Step 4: Rank the alternatives, sorting by the values S, Q and R, in decreasing order. The results are three ranking lists. Step 5: Propose as a compromise the alternative (a ) which is ranked the best by the measure Q (minimum) if the following two conditions are satisfied: C1. Acceptable advantage: Q(a ) Q(a ) D Q Where a is the alternative with second position in the ranking list by Q; DQ = 1/(J 1); J is the number of alternatives. C2. Acceptable stability in decision making: Alternative a must also be the best ranked by S or/and R. This compromise solution is stable within a decision making process, which could be: voting by majority rule (when v > 0.5 is needed), or by consensus v = 0.5, or with veto v < 0.5. Here, v is the weight of the decision making strategy the majority of criteria (or the maximum group utility ). If one of the conditions is not satisfied, then a set of compromise solutions is proposed, which consists of: Alternatives a and a if only condition C2 is not satisfied. Alternatives a, a,..., a (M) if condition C1 is not satisfied; and a (M) is determined by the relation Q(a (M) ) Q(a ) < DQ for maximum M (the positions of these alternatives are in closeness ). According to Tzeng (2005), the compromise solution is determined by the compromise-ranking method; the obtained compromise solution could be accepted by the decision makers because it provides maximum group utility of the majority (represented by min S, Eq. (11)), and minimum individual regret of the opponent (represented by min S, Eq. (12)). The VIKOR algorithm determines the weight stability intervals for the obtained compromise solution with the input weights given by the experts (Opricovic, 1998). The compromise-ranking method (VIKOR method) determines the compromise solution; the obtained compromise solution is acceptable to the decisionmakers because it provides a maximum group utility of the majority (represented by min S), and a minimum individual maximal regret of the opponent (represented by min Q). The model uses the DEMATEL and ANP procedures in Sections 3.2 and 3.3 to obtain the weights of criteria with dependence and feedback and uses the VIKOR method to obtain the compromise solution. 4. THE SIP INDUSTRY AND BUSINESS PROCESS SIP is the subset of intellectual assets that is legally protected. There are five major forms of SIP: patents, trademarks, copyrights, trade secrets, and semiconductor mask works (Sullivan et al., 2002). SIP is associated with the ownership of knowledge, expertise, innovation and resources that went into the creation of a specific hardware core and/or the software and/or firmware program that is required to perform a system function. Examples of SIP are: microprocessor and DSP cores, peripherals, dedicated function accelerators such as MPEG2 decoders and encoders, mixed signal technology, on chip DRAM, and Flash memory technology. Software/Firmware is intellectual property and may be delivered as an indivisible part of the hardware SIP or separately as a necessary system component. In this report we will differentiate between intellectual property that becomes part of silicon technology and other categories (Baron, 2000). SIP cores can be classified into soft, firm, and hard SIP. The difference is in the degree of flexibility of the SIP; soft SIP is in the form of RTL code while hard SIP is in GDSII format. Firm SIP is somewhere in between; it is usually presented as a net list with a set of additional views and information pertaining to physical design. Digital designs are commonly defined as RTL code or soft SIP while analog & mixed signal designs come in the form of hard SIP that has been designed and optimized for a specific application and technology (Keating, 1998). SIP is the subset of intellectual assets that is legally protected. SIP is a legal term describing legally protected intellectual assets as well as the different forms available. There are five major forms of SIP: patents, trademarks, copyrights, trade secrets, and semiconductor mask works (Sullivan et al., 2002). 4.1 The SIP Industry Fabless semiconductor design companies, professional SIP providers, IC EDA (electronic design automation) tool makers, design service providers, and professional semiconductor foundries are six major categories of providers of the SIP industry. IC design companies including fabless, IDMs (integrated design manufacturers), and system companies purchased SIPs from SIP providers. They integrate SIPs into their ICs, send the integrated ICs to mask houses for mask making, and finally, ask foundries or IDMs for IC manufacturing and ask professional assembly and testing houses for related backend works. The SIP industry infrastructure, including the above IC manufacturing procedures after SIP integrated into IC, is shown in Fig. 3.

12 58 International Journal of the Information Systems for Logistics and Management (IJISLM), Vol. 5, No. 2 (2010) Except for existing EDA tool makers and professional semiconductor foundries, there are several categories of SIP companies including professional SIP providers, Fabless IC design companies, design service providers, spin offs from big semiconductor companies, or alliances between major companies that need to share expenses and/or expect more support from EDA tool makers and software vendors due to increased volume. In-Stat estimates the commodity SIP market to be valued at $1038 million in 2004 and will reach $1826 million in 2008 (Fig. 1). According to Dataquest (2004), ARM, Synopsys, and Artisan (acquired by ARM in 2005) are the top three vendors, and account for 33 percent of the total SIP market. Vendors other than the top ten account for 38% of the market share. 4.2 Current SIP Business Models Ratford et al. (2003) summarized business models in the current SIP product marketplace, what is typical to expect from different providers, and considerations for determining the economic value of different SIP product types. The discussion of each business model includes the purpose of the particular business model, a definition of the payment options, typical structure of the fees paid, and most common SIP use scope for the SIP products Table 3 provides a summary of the principal attributes of business models for established providers and SIP products. A typical SIP purchase may involve elements of more than one business model. Another aspect of determining the economic value of an SIP product is related to the different fees for enabling the successful use of the SIP product. Table 4 provides the typical enabling components that usually represent a secondary revenue stream for providers. These are often structured as separate fees within the SIP License Agreement itself, or sometimes as a separate Statement of Work (SOW) or contract if there are specific needs that are non-standard. These components enable the use of the SIP product itself, the IC design in which the SIP product is instantiated, and specific needs relating to SIP product use and deployment Per use model In the per use model, the SIP purchase gives the buyer the right to use each SIP product within a defined SIP use scope. The fee paid may apply to a specific SIP product instance (such as a single use of a DSP core) or an SIP configuration containing several instantiations of the SIP product that may be treated as a single use. The payments in a per use model typically include an initial fee for the first use normally paid upon execution of the license agreement. If the SIP product requires some development work to meet the needs of the buyer s application, then the initial fee may be a percentage of the license fee with subsequent portions being paid upon a milestone or an acceptance event Time based model In the time-based model, the SIP purchase gives the buyer the right to use the SIP multiple times over a defined time period. The time period may be explicit, such as a fixed date, or implicit in that the use of SIP may be restricted to a specific process technology that will become obsolete over time. The time based model for SIP products usually provides the buyer with the right to design and manufacture with the SIP. In most SIP time-based models, there is no transfer of the underlying IP rights in the SIP product. The SIP use scope in the time-based model is generally unlimited. Buyers can use the SIP product as many times and in as many projects as they wish within the time limits of the SIP License Agreement Royalty based model An increasingly common model in the SIP products industry is the royalty based model. Here, the provider and the buyer agree to amortize a portion of the license fee over the life cycle of the end product and thereby share the risks and rewards of the SIP product s use. The royalty-based model may be appropriate for buyers who wish to minimize their up-front costs and SIP providers who are willing to accept a lesser amount upfront in return for potentially greater long-term rewards if the buyer s end product is successful. In the royalty-based model, there is an ongoing payment of consideration tied to some objectively quantifiable measure, such as the number of units sold or the number of wafers manufactured. The royalty-based model is most common in the licensing of highly differentiated SIP products and may include other parties to the agreement such as the foundry who manufactures the device instantiating the SIP. In some cases, royalties are paid by foundries, such as in the case of foundation SIP products; in other cases, royalties are paid by the end user such as in the case of processor SIP products and specialty memory SIP products Access model In the Access model, the buyer generally is granted access to an SIP product portfolio for a defined period. This model may be useful when the buyer does not know his design needs up front. It may also be attractive for design service groups that are seeking to widen the range of market application segments they support and thereby attract more design projects. The advantage of the access

13 C. Y. Huang et al.: Defining Pricing Strategies for Silicon Intellectual Properties of Late Coming Providers 59 model is that it allows the buyer lower cost access to a wide range of SIP products that may be used as and when needed. 4.3 Current Problems in SIP Transactions and Business Models Deciding whether to develop internally or source commercial SIP products depends on several factors, such as whether or not the SIP product meets specific project requirements, availability, specification, cost, number of sources, the reputation of suppliers, and number of foundries supported. Outsourcing is unlikely if the SIP product is seen by the potential buyer as a core competency or a key differentiator in his product, or if its use requires third-party access to the buyer s patents or trade secrets. To simplify the process of finding, evaluating, and purchasing SIP products, the above factors should be considered as a starting point in the planning stages of a design project (Ratford et al., 2003). There are two major categories of problems, technical and business, based on current SIP transaction business models and processes. For technical problems, SIP users have to resolve problems including SOC integration difficulty caused by SIP from a variety of sources, SIPs based on different EDA tool design environments and complicated design methodology, and finally, technical issues including modification and support but not limited to the need to be resolved from multiple vendors. Meanwhile, integration of SIPs needs expertise in each SIP area. There are always complicated business issues including license model and business terms, SIP evaluations, and legal issues to be resolved in each SIP transaction. For SIP license models, RTL, net list, hard macro, their deliverables, and license terms including license fee versus royalty, payment terms, check-in and verification procedures are of first concern to users. Meanwhile, essentials for SIP evaluations including documentation, simulation model, test chip, demo board, and development kit should also be reviewed before making a decision. Finally, complicated legal issues include obligation and rights, patent terms and conditions, termination and its effect, and liability. These are also issues to be reviewed in detail. In short, SIP transaction and integration is not a matter of purchasing the SIP, plugging it into the IC and having it work. On the contrary, there are complicated technical and business issues to be resolved in each SIP transaction based on the current SIP transaction process. 5. PRICING SIPS OF A LATE COMING SIP PROVIDER At first, three SIP experts with more than ten years of experiences and electronic engineering background were invited for defining the criteria for evaluating SIP pricing strategies, possible SIP pricing strategies as well as evaluate the weights versus each criterion and grade the strategies versus each criterion. The availability of the experts could be a limitation of this empirical study due to very limited SIP experts being available in Taiwan s semiconductor industry. By surveying the three SIP experts, twelve criteria for defining the SIP pricing strategy were derived as follows. (1) Substitution (c 1 ): If the customer s perception of the value of a company s product is at par with competition, the customer will be price sensitive. The uniqueness lowers price sensitivity; substitution effect also enhances price sensitivity (Saxena, 2002). (2) Function(s) (c 2 ): Product development plays an increasingly important role in the competitiveness of the companies basically through introduction of new technologies and product customization. Therefore the product development and engineering functions have an active role to play (Sorli and Stokic, 2009). (3) The leading position (c 3 ): The leading position of an SIP provider implies that the provider should search for opportunities for innovations or performance improvements that will increase the gap between them and their main competitors. The leading position of an SIP provider may enable the provider to price higher (Ward and Daniel, 2006). (4) Target market (c 4 ): A target market is the specific group of customers at whom the business aims its goods or services (Zimmerer and Scarborough, 1994). (5) Time to market (c 5 ): The time to market means the time being required to bring a new product to market problem (Saaksvuori and Immonen, 2008). (6) Scope (c 6 ): Scope of an SIP implies that the SIP can be sold in new and different markets. (7) Category of competition (c 7 ): Different category of competition includes perfect competition, monopolistic competition, oligopoly, and monopoly (Pride et al., 2009). (8) Demand elasticity (c 8 ): The demand for products is sensitive to changes in many variables, and each leads to a different elasticity measurement (Forgang and Einolf, 2006). (9) Patent (c 9 ): A patent is a document that defines the scope of patent rights to exclude others from making, using, or selling an invention that is the subject of the patent (Pienkos, 2004). (10) Technology uniqueness (c 10 ): The uniqueness of technology is that, once it has been invented, it cannot be un-invented (Kew and Stredwick, 2005).

14 60 International Journal of the Information Systems for Logistics and Management (IJISLM), Vol. 5, No. 2 (2010) Time to Market Scope Target Market Leading Position Function Unique Technology Elasticity of Demand Substition Competition Patent License Royalty Fig. 7. The NRM by DEMATEL (11) License fee (c 11 ): Licensee fee the payment given by the licensee to the licensor. The license fee can represent an advance against which future royalty obligations will be charged, or it can simply represent a form of signing bonus for the licensor. Such fees also can be scheduled as certain developmental or marketing milestone are reached (Smith and Parr, 2004). (12) Royalty (c 12 ): the cost, consideration, compensation, or price paid or incurred for a license (Dratler, 2006). Based on the criteria being defined, the relationships between the determinants for appropriate SIP pricing strategies can be derived by using the DEMATEL method. The network relation map (NRM) is shown below in Fig. 7 by using the direct/indirect matrix, T. The weights versus each determinant for SIP pricing strategies were derived via the ANP. The weights are demonstrated in Table 1. T = Table 1. Weights versus each criterion Criteria c 1 c 2 c 3 c 4 c 5 c 6 c 7 c 8 c 9 c 10 c 11 c 12 Weight (%)

15 C. Y. Huang et al.: Defining Pricing Strategies for Silicon Intellectual Properties of Late Coming Providers 61 Table 2. S, R and Q value from VIKOR Strategy s 1 s 2 s 3 s 4 s 5 s 6 s 7 s 8 s 9 S Score R Score Q Score The pricing strategies are based much on what objectives the company has set itself to achieve and firms can adopt a number of pricing strategies from following alternatives: (1) penetration: setting a low price to increase sales and market share (s 1 ); (2) skimming: setting an initial high price and then slowly lowers the price to make the product available to a wider market (s 2 ); (3) competition: setting a price in comparison with competitors (s 3 ); (4) product line: pricing different products within the same product range at different price points (s 4 ); (5) bundle: bundling a group of products at a reduced price (s 5 ); (6) psychological: considering the psychology of price and the positioning of price within the market place (s 6 ); (7) cost-plus: sale prices allowing the company to cover all costs associated with the production and sale of the products, and still make a reasonable profit from the effort (s 7 ); (8) premium pricing: setting a high price to reflect the exclusiveness of the product (s 8 ); (9) optional: selling optional extras along with the product to maximize its turnover (s 9 ). Based on the experts opinions, the above strategies can be prioritized by using the VIKOR. Calculation results of VIKOR are shown in Table DISCUSSION In this Section, issues regarding to both research methods and managerial implications will be discussed. Compared with the traditional Analytic Hierarchy Process (AHP), ANP provides a general framework to deal with decisions without making assumptions about the independence of higher-level criteria from lower level criteria. ANP fits the reality much better than AHP. In the real world decision problems, feedbacks and relationships exist between criteria. Apparently, ANP can analyze the decision problems more reasonable. In VIKOR, v was assumed to be 0.5. This assumption implies that both benefit maximization and regret minimization were taken into account simultaneously. Ranking results being derived by setting v to other values can be future research topics. For the managerial implications, the weights being derived by ANP (Table 2) implies that target market (c 4 ), category of competition (c 7 ), technology uniqueness (c 10 ) and license fee (c 11 ) are the most important issues to be considered while a SIP provider price an SIP. Further, scope, elasticity of demand, and patent are not important. Thus, an SIP provider should always be reminded on which market it is promoting a specific SIP, the status of its competitors providing like products, whether it has led competitors in technology and whether the license fee is priced appropriately so that customers can make the purchase decisions easily. For the analytic results, premium pricing strategy (s 8 ), competition pricing strategy (s 3 ) and optional pricing strategy (s 9 ) were ranked as the highest, which imply that the price is usually set as high to reflect the exclusiveness of the product. Further, providers usually try to maximize its turnover by selling optional extras along with the product. Meanwhile, SIP providers usually set prices in comparison with competitors. Further, penetration pricing strategy (s 1 ) and bundle pricing strategy got middle ranks, which imply that market share is not really critical to SIP vendors. SIP providers do not scramble for market by lower prices and bundle promotion. Finally, among nine alternatives, psychological pricing strategy (s 6 ) and cost-plus pricing strategy (s 7 ) has the lowest rank, which implies that cost or psychology related issues are not what SIP providers really care about. 7. CONCLUSIONS SIP pricing has become an important issue to be dressed in the SoC era. However, late comers usually priced their products by following the pricing strategy of the industry leaders. This research proposed a novel MCDM framework consisting of DEMATEL, ANP and VIKOR. Twelve criteria for evaluating SIP pricing criteria were derived. Nine usually seen pricing strategies were adopted as the alternatives. The empirical study based on industry experts concluded that four criteria including target market, competition, license fee and royalty are the most important ones while evaluating the SIP pricing strategies. Further, nine pricing strategies were ranked by VIKOR. Premium pricing strategy,

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