67) National Semiconductor. 68) Nordson Corporation. 69) Northrop Grumman Corporation. 70) Owens Corning. 71) Owens-Illinois, Inc. 72) Pall Corporation.
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Patent Portfolio Diversity, Technology Strategy, and Firm Value Bou-Wen Lin, Chung-Jen Chen, and Hsueh-Liang Wu
Abstract—This paper investigates how the composition and diversity of a firm’s patent portfolio can create synergy and, thus, contribute to firm performance. To resolve two conflicting views on whether technology diversity or strategic focus can improve firm performance, we develop a scheme to measure the diversity of a patent portfolio at the two levels of broad technology diversity and core field diversity. In our framework, both views can be valid. The former argument is effective when the focal firm has very high technology stocks and profitability is used as a performance measure. The latter is true for a focal firm with above average technology stocks and where shareholder value is considered as a performance indicator. This paper highlights technology stocks as a moderator between the relationship of technology diversity and firm performance. Generally, a firm without very hightechnology stocks should concentrate its R&D resources on a specific technology field, and even within the core technology field the firm should stay focus on a small number of core technologies. Results support the competence-based view of the firm. Technology-based firms should develop a portfolio with a clear technology focus. This study lays the groundwork for future study on the interrelationships of technology strategy, patent portfolio, and long-term performance. Index Terms—Patent portfolio, strategy, synergy, technology diversity.
I. INTRODUCTION
T
HE MOST challenging task of managers in the current knowledge-based economy is to exploit the full value of corporate intellectual properties and to effectively accumulate and commercialize knowledge assets [1]–[3]. This challenge is actually not new for technology managers since their primary job is to manage corporate technological assets and to develop new technological capabilities. Textbooks on innovation management and technology strategy often start with the concept of technology portfolio and technological core competences [4]. One of the underlying assumptions of this field is that the portfolio of a firm’s technological assets and its complementary resources should be considered as an integrated whole and a synergistic effect is expected so that the value of a technology portfolio can add up to more than the sum of its separate parts. Henderson and Cockburn [5] tried to measure heterogeneous organizational competence by using patent data in the context
Manuscript received July 1, 2003; revised April 1, 2004 and April 1, 2005. Review of this manuscript was arranged by Department Editor A. S. Bean. This work was supported in part by the National Science Foundation, Taiwan, R.O.C. B.-W. Lin is with the Institute of Technology Management, National Tsing Hua University, Hsinchu 30013, Taiwan, R.O.C. (e-mail: bwlin@mx. nthu.edu.tw). C.-J. Chen and H.-L. Wu are with the Department of Business Administration, National Cheng Kung University, Tainan 701, Taiwan, R.O.C. Digital Object Identifier 10.1109/TEM.2005.861813
of pharmaceutical research. Based on patent data, Fleming and Sorenson [6] demonstrated that technology should be considered as a complex adaptive system. Several authors such as DeCarolis and Deeds [7] and Gittelman and Kogut [8] have conducted empirical studies using patent and financial data from biotechnology firms. Based on this prior work, two research questions deserve further empirical investigation: how can a technology portfolio create synergy, and what are the characteristics of a valuable patent portfolio? We concentrated here on the patent portfolio diversity and how it relates to firm performance. The resource-based view of the firm stresses that successful firms have the ability to identify, cultivate, and exploit core competencies that are the roots of sustainable competitive advantages [9]. In this sense, a firm should not be perceived as a collection of businesses, but rather as a portfolio of competencies and strategic assets. However, much of the strategic management literature investigates corporate diversification effects not from a competence perspective but from a product/market perspective. From a resource-based view, technology portfolios shape the capability bases for firms to generate a series of technological innovations. Investigating corporate diversification effects from “the roots of sustainable competitive advantages” can shed light on how a firm’s technology strategy could be integrated as an integral part of its corporate strategy. For a technology-based firm, its technology portfolio can characterize its competences and strategic assets. The R&D function is, therefore, increasingly being charged with the job of managing and restructuring the corporation’s technology portfolio [10]. As technological innovation increasingly plays the central role in today’s knowledge economy, building linkages between technology portfolio strategy, and firm performance deserves more theoretical and empirical work. This paper tries to fill the gap in the current literature by investigating: 1) the characteristics of a valuable technology portfolio and 2) the relationship between technology diversity strategy and firm value. II. LITERATURE REVIEW A. Technology Portfolio Strategy Sadowski and Roth [3] suggested that technology portfolio strategy lies at the heart of all business activities of a technology-based firm. Theories of portfolio management address the question of how a firm can reduce risk and tap into business opportunities by effectively holding a collection of different technologies, markets, or resources. Modern finance portfolio theory suggests that investors should diversify their portfolio of financial assets to reduce
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their risk, so that risk-adverse managers might apply the portfolio theory from the finance literature and therefore argue for unrelated technology diversification [3]. Amit and Livnat [11] also found that firms could adopt an unrelated diversification strategy characterized by low return and low risk. However, many strategic management scholars [12], [13] disagree with this view and suggest that firms and individuals should build technology portfolios strategically and the synergy created by the portfolios might be a major source of competitive advantage. Synergy creation rather than just risk reduction seems to be the most important goal for managing a technology portfolio. Therefore, Spradlin and Kutoloski [13] argued that efficient R&D portfolio management was paramount to adding shareholder value in technology-based firms. This points to the pressing need for a systematic investigation on how a firm can create additional value through its technology portfolio strategy. Long [14] regards patents as a signaling mechanism for technology firms to credibly publicize information. Through the processes of patent application, information asymmetries between patentees and observers can be reduced. It is possible that in some situations the informational function of patent application is actually more valuable to the rights holder than the substance of the rights. In particularly, technology-based firms can use patent portfolios to signal their future cash flows and competitive advantages. For example, Lang and Stulz [15] found that diversified firms have significantly lower average Tobin’s Q ratios than single-segment firms, suggesting that diversified firms are consistently valued less than specialized firms. Stakeholders could evaluate the signal sent by the firm’s technology portfolio in two dimensions: the composition and synergy effects of a firm’s patent portfolio. The former deals with the general property of the patent portfolio and uses simple statistics to describe the composition and general characteristics of the patent portfolio. The latter deals with the interrelatedness of portfolio technologies and possible portfolio synergy. In the literature, there are two very distinct views on how a technology portfolio can create synergy: by keeping well-diversified technologies to exploit business opportunities of many industries or by strategically focusing on a small number of technology fields. Some strategy scholars (e.g., [16] and [17]) view the creation of synergy out of interrelated businesses and strategic assets to be the essence of corporate-level strategy, arguing that without synergy a diversified company is little more than a mutual fund. On the one hand, Granstrand et al. [18, p. 8] suggest that corporate performance could be significantly improved by increasing technological diversity. They argue that the world’s largest, technologically active firms “invest beyond their distinctive core technological competencies in order to manage and coordinate technical change with their suppliers of components, equipment, and materials, as well as to explore and assess the major new opportunities emerging from the knowledge base.” Technology and market convergence seems to be the trend of today’s product innovation while products are becoming increasingly multitechnology and technologies are becoming increasingly multiproduct. On the other hand, the capability- based view of corporate strategy (e.g., [16] and [19]) suggests that firms should focus on their core competences and
concentrate only on technology fields that they can do best. Technology-based firms should deepen their technological knowledge in a small number of technology fields and by doing so synergy occurs because they can preempt potential entrants to those technology fields by constructing patent minefields to protect their core competences. B. Synergy Through Corporate Technology Diversity Granstrand et al. [18] stressed the importance of technological diversity in corporations by showing that world-class high-tech companies are highly diversified in their technological competencies, and that this diversity is increasing over time. Therefore, a firm’s long-term value can be improved through increasing diversity in its technology portfolio. Granstrand et al., therefore, argued for “distributed” rather than core technological competencies. In order to explore and exploit new opportunities emerging from scientific and technological breakthroughs, large firms must become multitechnology with broad technological assets. Several empirical studies [20], [21] have supported the positive relationship between the increase of corporate technology diversity and the growth of R&D expenditures and corporate sales. Gambardella and Torrisi [20] provide empirical evidence that corporate performance is positively related to technological diversification and they also find a positive relation between corporate performance and the technology-focused strategy of business operations. From a sample of 1528 firms, Singh et al. [21] found that diversified firms performed significantly better than single-segment firms. Therefore, technology-related business diversification can be a feasible strategy when corporate technological competencies can be matched with emerging business opportunities [18]. The performance effects of corporate technology diversity remain unclear despite a huge body of prior research, which “has yielded mixed results due to differing performance measures, diversification measures, samples, and time periods” [22:259]. Whether to diversify or not is one of the most challenging decisions a company can confront and little conventional wisdom is available to guide the managers. There might be dark sides of technology-related business diversification. For example, managers with larger debt capacity and access to free cash flow tend to undertake nonvalue maximizing investments, which can explain why a firm’s diversification strategy is often unfruitful. There might be costs involved in terms of overdiversification that can generate negative synergy and diseconomies of scope. The emphasis on technology diversity can fall into the overdiversification trap, where the increased R&D costs come from the costs of coordination and the heavy costs of integrating technological knowledge across disciplinary frontiers. In a more recent study, Jaffe and Lerner [23] found that the success of a national research laboratory is associated with avoiding technological diversification. Technological diversification beyond a certain level can yield fewer opportunities to gain synergies, and extensive diversification can have a detrimental impact on firm performance. Comment and Jarrell’s [24] study supports this view that “dediversified” firms enjoy subsequent improvements in stock market performance.
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C. Synergy Through Focusing on Core Technologies The capabilities-based theory provides valuable theoretical hypotheses that are testable within the diversification strategy literature [25]. The literature on core competences and capabilities broadens our understanding of a corporation’s resources, and points out the important role of corporate management in building such resources and ensuring that they are used to best advantage. Prahalad and Hamel [6] stress the core competences of the firm, arguing that the firm should be perceived a portfolio of competences and strategic assets. To manage the competence portfolio, managers must ensure that each part of the portfolio is integrated into and contributes to the core competences of the firm. The firm should nurture core competences and concentrate only on core business activities they can do best, especially, in R&D activities. From this perspective, portfolio synergy occurs because the firm can efficiently accumulate and deepen its technological knowledge through focusing only on a small number of core technology fields. By developing extensive patent minefields in certain core technology areas, a firm can protect its core technologies that its competitors could not easily patent around or overcome the technology barriers. Therefore, a technology portfolio with a clear and consistent strategic focus can create synergy. Some authors [26] suggest that a firm should “put all of its eggs in similar baskets” because synergy is expected while interrelated businesses can benefit from each other under unified governance mechanisms. Medcof [27, p. 59] further demonstrated that the explanatory power of the capabilities-based theory “could be significantly increased through the inclusion of a resource portfolio perspective and by the acknowledgment that imitation, as well as the pursuit of the unique can be a viable resource strategy.” Although the capabilities-based view of the firm has provided important new insights into corporate strategy, the measures and data typically have only a weak connection to capabilities-based theory.
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emerging from scientific and technological breakthroughs because of the likely economy of scope in technology commercialization and new product development across different industries. For example, the development of an excellent digital camera requires a diversified portfolio of technologies including optics, operating systems, mechanics, digital image processing, and electronics. In this paper, broad technology diversity (BTD) refers to the extent to which the technology firm diversifies its technological capability on a broadly defined first-level technology area. On the contrary, the arguments for core competence tend to see technology narrowly categorized at the second level. A firm can pursue an aggressive technology strategy characterized by a strong offensive posture together with the intensity of a firm’s R&D effort to outperform its rivals and to maintain technology leadership in the specific technology field. By focusing on a narrow technology field, the firm can build a dominant position and preempt its potential rivals. For example, Intel holds a huge number of patents on CPU technology, forming a patent minefield that its competitors cannot easily circumvent. High-performing technology-based firms often enjoy high profit margins because their dominant positions in specific technology areas can create long-lasting monopoly rents. Those firms can create synergy from their technology portfolios by focusing on core technology fields, while interrelated technologies can reinforce each other’s patent claims. In this paper, core field diversity (CFD) refers to the extent to which the technology firm diversifies its technological capability on a narrowly defined second-level technology area. Drawing from the two schools of thoughts on technology diversity and the corresponding two-level technology classification schemes, the following two tentative hypotheses are proposed. H1: The BTD of a firm’s technology portfolio will be positively associated with its performance. H2: The CFD of a firm’s technology portfolio will be negatively associated with its performance.
D. Two Levels of Technology Diversity The above two distinct views on how technology portfolio diversity can affect firm performance can be mitigated if we adopt a two-level hierarchical structure of technology classification schemes. For the first level, we broadly categorize technology into several main technology types, such as mechanics, electronics, and chemistry. The first-level classification schemes are often intuitive and are in line with academic disciplines or industrial classification schemes. Each of the first-level technology types can be classified into several second-level technology fields. Certainly, each of the second-level technologies can be further categorized into subfields. Widely used technology classification systems such as the International Patent Classification and U.S. Patent Classification, have their own hierarchical structures. With the hierarchical structure of technology classification in mind, we can understand that the arguments for technology diversity have the tendency to see technology broadly categorized at the first level. By maintaining a broad technology portfolio, large technology firms can explore and exploit new opportunities
III. RESEARCH DESIGN A. Sample Firms and Data The United States Patent and Trademark Office (USPTO) has a comprehensive database of U.S. patents that is publicly available and this USPTO database is a frequently used source of information for scientists, engineers, and managers to guide their R&D strategies. Furthermore, the database has been widely used in the technology and policy literature [8], [23], [28]. This paper greatly benefits from the NBER Patent Citation Data File [28] from which some of our variables were calculated. This file resulted from government-supported research efforts with a goal of being widely accessible for further research. The data file provides comprehensive information on the U.S. patents over the last three decades, including several measures constructed with the citation data, such as originality, generality, backward citation lags, and self-citation. The database
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also provides a venue to link to the COMPUSTAT database.1 From the NBER, we first identified the top 150 assignees that received more than 100 U.S. patents during a three-year period between 1985 and 1999 (15 years). We then eliminated government-owned institutions, foreign companies, or those companies without complete financial records in the COMPUSTAT database. Our final sample included 94 firms, as shown in the Appendix, representing large U.S. technology enterprises with sizeable patent portfolios. An interest in the factors influencing the technology portfolio strategy and performance outcomes of these large firms is warranted. We removed some of the firmyear data that had missing values and the resulting 1275 observations were used for the following data analysis. We gathered all financial data from the COMPUSTAT database to insure that industry, company, and business-unit data were comparable. The COMPUSTAT database is compiled by Standard & Poor’s and includes accounting and financial data for over 6000 public corporations that have their shares traded on the New York, American, NASDAQ, and over-the-counter stock exchanges. B. Return on Assets (ROAs) and Tobin’s Q A widely used measure for firm performance is return on assets (ROAs). This short-term performance measure is a useful indicator of how profitable a firm is relative to its total assets. It is displayed as a percentage calculated by dividing a firm’s annual earnings by its total assets. Tobin’s Q, defined as the ratio of the market value of a firm to the replacement cost of its assets, is an index commonly used to measure firm value and managerial quality [29]. Jose et al. [30] argued that Tobin’s Q is a good measure of firm performance and may include a variety of subjective characteristics. It represents the ratio of the market value of its assets to replacement costs of the firm’s assets, especially when dealing with the effects of various diversification measures. Tobin’s Q should approach unity in a competitive business environment while barriers to entry and exit are low. If competitive advantage can be maintained, a firm could consistently have a high Q-ratio relative to its competitors over a long period. Yet, if competitive advantage is transitory, a firm’s Q-ratio would gravitate back to a level consistent over time with the industry average. Unlike short-run corporate performance measures, Tobin’s Q-ratio represents a longer-run equilibrium measure capturing both risk and return dimensions. The Q-ratio reflects the market expectations of less quantifiable dimensions of performance [30], which reflects the portion of the firm’s intangible assets in addition to its tangible assets. Consequently, Tobin’s Q could be a good proxy for measuring intangible capital and for evaluating corporate strategy in the capital investment decision. Lindenberg and Ross’s procedures that are typically employed in the calculation of Tobin’s Q values are very complex and cumbersome, so that not “even the most dedicated of analysts would ever attempt to undertake them” [31:70]. Chung 1Note that since the NBER patent database does not handle corporate affiliations very well once the years move away from 1989 in either direction, the authors did not check the corporate affiliations. There could be data missing from the portfolios if the corporate affiliations listing the patentee as a public company are missing.
and Pruitt [31], therefore, proposed a simplified formula for approximating Tobin’s Q requiring only basic financial and accounting information. Approximate Q, which is defined below can explain at least 96.6% of the variability of Tobin’s Q. This paper adopts Chung and Pruitt’s approximate Q as a proxy for a firm’s long-term performance. The approximate , where MVE is the product of share price and common stock shares outstanding, PS is the liquidating value of outstanding preferred stocks, DEBT is the value of the firm’s short-term liabilities net of its short-term assets, plus the book value of the firm’s long-term debt, and TA is the total assets of the firm. Following previous studies examining the relationship among diversification strategies and performance, which have typically used measures that were three-, four-, or five-year averages [32], [33], we set our observation period to be five years. C. Control Variables We used several control variables to reduce possible variances since we drew sample firms of different sizes and technology categories. The relationship between firm size and innovation capability has long been a debated issue in the innovation literature [35]. Most empirical studies of firm performance include firm size as a control variable or an independent variable. Following this tradition, we used the logarithm of total assets as a control variable for firm size effects. Bettis and Hall [34] concluded that Rumelt’s findings might have been biased by the high returns of the pharmaceutical firms in his sample. The NBER database categorizes patents into six different technology categories: 1) Chemical; 2) Computer/Communication; 3) Drug/Medical; 4) Electrical/Electronic; 5) Mechanical; and 6) Others. Firms with different categories of technology assets can have distinct technology assets in terms of patent portfolios and technology strategies. Their performance implications can also differ. We counted the numbers of patents granted to the focal firms in each of six technology categories over a three-year period. Each firm was assigned to the specific technology category in which it had the largest number of patents. That is, each firm-year data observation was assigned to one of the six technology categories, tech1 through tech6. This categorical variable was then used to control for possible variances caused by the characteristics of a firm’s technology assets. One of the possible causes of inconsistent findings in the R&D performance literature would be due to using data from different time periods, in which general environmental conditions could have been very different. This study used data from 1985 to 1999, and we treated the data from the same firm in different years as repeated measures of the same subject. Therefore, the MIXED procedure of the SAS program was adopted and repeated measures statistical models with unconstrained covariance structure were specified. Another two control variables, R&D intensity and patents per assets, were measured as the firm’s R&D expenditures and number of patents received during the past three years both divided by its total assets. These two control variables were adopted because they represent the flows and stocks of the firm’s technology assets.
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D. Characteristics of a Technology Portfolio In order to ensure that our research findings were duplicable and comparable with previous empirical studies in the literature, we chose three relevant data items in the NBER patent database to describe the general characteristics of a patent portfolio. First, patent claims as they appear in the front page of each patent are the building blocks of patented invention. The average number of patent claims per patent is used as an indicator of the “scope” or “richness” of a firm’s patent portfolio [28]. Second, self-citations measure the average percentage of patent citations that the assignee cites its own previous patent inventions. This measure reflects the degree to which the inventions in a technology portfolio are unique, independent, and have less knowledge spillover. Finally, Hall et al. introduced a Herfindahl-type index of originality to measure the extent to which a patent cites previous patents that belong to a wide range of technological fields. The pioneering-advantage literature suggests a positive relationship between pioneering activities and firm performance. The success of pioneering may depend on industry factors, the market power of the pioneering firm, and the radicalness of innovation. On the other hand, radical technology may demand considerable learning on the part of the customers, and it has higher technological and market risk. We calculated those three patent portfolio measures by averaging the corresponding index values taken directly from the NBER Database. E. Patent Portfolio Diversity In this paper, we define two levels of technology diversity measures: BTD and CFD. We recognize that technology classification can be an elusive concept and that any classification scheme has both advantages and limitations. Defining technology categories into a two-level hierarchical system as in the NBER database can avoid the pitfalls of defining technology categories too broadly or narrowly and the NBER classification system fits well with our research hypotheses. We measured BTD or the degree to which a firm built a diversified repertoire of technology portfolio in six broadly defined technological categories as defined by the NBER database (Chemical, Computer/Communication, Drug/Medical, Electrical/Electronic, Mechanical, and Others). This measure adopts the Herfindahl-type index, which is widely used in the diversification literature to measure the level of diversification across industry categories [37]. We calculated the number of patents in a firm’s patent portfolio that belong to the six technology categories (Xi, to 6). Then
(1) CFD, or the degree to which a firm has built a dense repertoire of technology portfolio in its core technology category, is also a Herfindahl-type index. We define a firm’s core technology category as the NBER technology category in which the firm receives the largest portion of its patents, that is, the tech. The six technology nology category that has the largest technology subcatecategories have 6, 4, 4, 7, 6, and 9 gories, respectively. We then calculated the number of patents
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of the core technology category that belong to its corresponding technology subcategories as defined by the NBER database ( , to ). Then (2)
IV. RESULTS We adopted multilevel statistical methods for analyzing such repeated measures data [38]. The SAS MIXED procedure provides a good statistical tool to test our hypotheses with the specification of an unstructured covariance structure. To avoid undue multicollinearity between main effects and interaction effects and to facilitate the interpretation of main effects, we standardized [39] the variables involved in the interaction terms: technology stocks, BTD, and CFD. As shown in Table I, all four repeated measures models for each model). were highly significant ( Table II shows the description of the independent variables of the models. Models 1A and 2A include Tobin’s Q and ROAs, respectively, as independent variables where Tobin’s Q represents a measure of the firm’s long-term performance and ROA represents that of the firm’s short-term performance. However, these two models do not consider the interaction effects between BTD, CFD, and technology stocks. The results of Models 1A and 2A show that firms with different types of technology assets can have different levels of firm performance. Firms in computer/communication, drug/medical, and electrical/electronic industries generally enjoy higher levels of shareholder value and profitability. The relations between firm size and firm performance in Model 1A are negative and significant. In the high-tech industries, small firms might be more innovative and therefore have higher performance than large ones. The coefficient of firm size in Model 2A is also negative but insignificant. The negative effect of firm size on profitability may not be very significant. The coefficients of average claims in Models 1A and 2A are both insignificant. Since the contribution of average claims per patent to Tobin’s Q (intangible value) is not significant, valuable patents might not need to have many claims in them. The coefficient of average claims in Model 2A is negative and insignificant, so the average claims of a firm’s patents may not reflect on its short-term profitability. It is surprising that the coefficient of originality of a firm’s patent portfolio in Models 1A is negative and significant, which suggests that originality of a firm’s patent portfolio might not be a good predictor for firm performance. We assume that either the Herfindahl-type index introduced in the NBER patent data does not reflect the originality of a patent portfolio or the relation between technology originality and firm performance is insignificant. The coefficients of average self-citation ratios in Models 1A and 1B are both positive and highly significant, which suggests that firms with a technology portfolio highly related its well-protected core competences will have higher intellectual capital and be more profitable. High-performing firms tend to strengthen their existing knowledge bases to build technology portfolios that are unique, independent, and with
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TABLE I REPEATED MEASURES MODELS FOR TECHNOLOGY PORTFOLIO
TABLE II DESCRIPTION OF INDEPENDENT VARIABLES
less knowledge spillovers. The coefficient of technology stocks , which is positive in Model 1A is 0.0248 but insignificant. This suggests that firms that maintain large stocks of patents do not necessarily contribute shareholder
value, although some other factors may need to be taken into account. Furthermore, the coefficient of technology stocks in , which is negative Model 2A is 0.078 and insignificant. This result also indicates that technology stocks might not lead to profitability directly. To maintain large stocks of patents can be very expensive and the technology stocks may not be easily commercialized and become cash flows to contribute to the firm’s ROA in the short run. Thus, firms might need complementary assets such as marketing, and manufacturing capabilities to exploit the potential value of the technology stocks. A firm’s R&D intensity will contribute to its intellectual value (Tobin’s Q) in the long run but it does not necessarily reflected in its short-term performance since high R&D expenses can reduce its dividend-paying ability. The coefficients of R&D inand tensity in Models 1A and 2A are 0.132 1.924 , respectively, both of which are significant. Those results suggest that R&D investments are an effective strategy to trade short-term profitability for long-term intellectual assets. Although, Hypothesis 1 postulates that there is a positive relation between BTD and firm performance, the results of Models 1A and 2A do not support this hypothesis. The relation between BTD and Tobin’s Q is negative and significant, suggesting that technology diversification into broadly defined technology areas
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can reduce shareholder value. If a firm requires technologies falling outside of its core competence, it need not conduct internal R&D for them since licensing, strategic alliances and joint ventures might be more effective alternatives for technology sourcing. Similarly, we can infer that a diversified patent portfolio has less chance to dominate specific technology areas than a patent portfolio that is strategically focused on the firm’s core technology areas. A firm that has a dominant position in a specific technology area can charge a high price premium due to its patented products and, therefore, it can have higher ROA. The coefficients of CFD in Models 1A and 2A are both insignificant, which also does not support Hypothesis 2. By concentrating on a few core (narrowly defined) technological areas, technology-based firms can better utilize their limited R&D resources and effectively accumulate intellectual properties. Cultivating core competence in a small number of narrowly defined technology areas and becoming the world leader in those technology areas [6] enable a firm to develop a series of core products, and consequently leads to profitable businesses. Our sample contains only large technology firms that received more than 100 patents during the previous three years. Many of them received more than 1000 patents in a three-year time frame. It may be unrealistic to ask those technology giants to focus on specific small technology areas. Those inconsistent results to the two hypotheses make us suspect that there might be interaction effects between the two diversity measures and technology stocks. Therefore, we introduce two interaction terms into our repeated measures models as shown in Models 1B and 2B in Table I. Model 1B has results very similar to Model 1A except that the coefficient of CFD becomes negative and marginally signifis negative and sigicant, while the interaction term in Model nificant. In addition, the interaction term 1B is insignificant. Those results explain that Model 1A does not support Hypothesis 2 since there is a significant interaction effect between technology stocks and CFD. The negative coefficient suggests that for firms with low technology stocks, focusing on specific technology areas can increase shareholder value, while for high technology stock firms the relation between CFD and shareholder value is insignificant. Model 2B and Model 2A have almost the same results except that the interacis positive and significant. The positive tion term of interaction effect suggest that it is possible that the relation between BTD and ROA for firms with very high-technology stocks can be positive. That means that Hypothesis 1 can be sustained for firms with very high-technology stocks. Firms that accumulate large stocks of patents in diversified technology fields are able to capture unexpected new opportunities emerging from scientific and technological breakthroughs that large multitechnology firms with a broad technology portfolio can quickly integrate their existing knowledge to meet market needs. However, this kind of opportunity exists only on the short term and only for large companies with complementary resources. V. DISCUSSION OF FINDINGS This paper develops a framework to evaluate the performance implication of technology diversity and corporate patent port-
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folios. A statistical analysis of patent and financial data from a large sample U.S. large technology-based firms reveals several interesting findings. First, the interrelatedness of patents in a technology portfolio can be analyzed at two different levels: BTD and CFD. This two-level classification scheme help us to clarify confusing concepts and empirical results in the literature. We found a negative relation between corporate technology diversity and two performance measures (Tobin’s Q ratio and ROA) that BTD can have a negative impact on shareholders value. This result does not support the recent technology diversity literature [18], [20]. In addition, the relationship between the second level of technology diversity, CFD, and firm performance is insignificant, suggesting that focusing on core technological competences does not necessarily lead to competitive advantages for all firms. Second, the composition and characteristics of patents in a technology portfolio do affect corporate performance. A technology portfolio composed of high quality patents positively contributes to the firm’s performance. Average self-citation ratio is positively associated with both Tobin’s Q ratio and ROA, while the relationship between originality and Tobin’s Q is negative. The self-citation ratio is an indicator of the extent to which a firm continuously invests and accumulates extensive firm-specific knowledge in its core technology areas. We also found that patent portfolios in computer/communication, drug/medical and electronic/electronic fields are more valuable than those in other technology fields. These results suggest that a firm could enhance its performance by strategically adjusting the composition of its technology portfolio. R&D projects that have potential to generate high quality patents and to reinforce the company’s core technologies deserve more management attention and resources. The average number of patent claims may not be a good indicator for predicting the value of a patent. A patent with too many claims signal that it may not be able to effectively protect each of its claims because competitors can design around. Third, we can use both long-term shareholder value (Tobin’s Q) and short-term profitability (ROA) as performance measures for technology-intensive firms and the performance implication for the two types of measures can be different. For example, the increase of R&D intensity can reduce short-term profitability while it can increase shareholder value. Firm size can have a negative effect on firm growth potential (Tobin’s Q), while the relation between firm size and profitability is not significant. Finally, technology stocks play an important role in a firm’s technology portfolio strategy. Their direct effect on firm performance may not be significant but they interact with technology diversity. By including the interaction effects with the two levels of patent portfolio diversity, BTD and CFD, we can explain possible causes of inconsistent results in the literature regarding performance implication of technology diversity. It is seen that firms with very high technology stocks can enjoy higher profitability derived from technology diversity in broad technology areas to capture emerging business opportunities of technology integration. This supports the arguments for huge global multitechnology corporations to have distributed rather than distinctive core competencies [18]. On the other hand, our
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results also support the resource-based view of the firm that firms have to focus on core technology areas in order to create long-term shareholder value [19], especially for firms with high technology stocks. This study has several limitations. Some technology firms are more likely to protect their discoveries with patents, while others tend to use know-how or trade secrete to protect their intellectual properties. Similarly, some technologies are less likely to lead to patents than others. Our sample includes technology firms from a wide range of industries causing the potential problem of data biases. Another area of data biases comes with the 15-year time observation period. As a result, we employ a multilevel statistical technique to mitigate this problem. Over this long period, some firms can have changed considerably in terms of their technology and market strategies. Sampling those technology-based firms provided the opportunity to study, over a relatively long period, a large sample of firms for which extensive data was readily available and had been objectively evaluated. However, the most obvious drawback to using this sample of firms is that it raises questions about the generalizability of these findings to smaller high-tech firms. Larger technology-based firms obviously have far greater financial resources and higher legitimacy than the average technology firms, which could affect their size and composition of technology portfolios, as well as the quality of grant patents they awarded and their ability to invest on high risk R&D. Therefore, additional research is needed to determine whether the results found here would also apply in a more random sample of technology-based firms and whether similar insights could be gained regarding smaller, newly established firms. Although the use of collected archival data was a benefit of this study, it also represents a major limitation in investigating the impacts of organizational factors on firm performance. Further research is needed to examine the details implied by the results of this study. VI. CONCLUSION Our findings indicate several important theoretical and practical issues and have implications for firm strategy and investment decisions. First, this paper provides an integrative framework to explain two seeming inconsistent research streams on technology diversity. The multitechnology corporation literature argues for technology diversity whereas the capability-based view suggests that firms should focus on their core technology areas. In our framework, both views can be valid. The former argument is right when the firm has very high-technology stocks, and we use profitability as a performance measure. The latter is true for a firm with above average technology stocks and when shareholder value is considered as a performance indicator. This paper also highlights technology stocks as a moderator between the relationship of technology diversity and firm performance. Generally, our results suggest that for firms with extremely high-technology stocks, it is effective to diversify their technology resources to a wide spectrum of technology fields while within their core technology fields, they stay focused on a small number of core technologies. A firm without very high-technology stocks should concentrate its
R&D resources on a specific technology field, and even within the core technology field, the firm should stay focus on a small number of core technologies. However, a firm with limited technology stocks should concentrate its R&D resources on a specific technology field, while within its core technology field, the firm can concentrate on several emerging technologies. Second, a firm’s patent portfolio represents a rich information source to characterize its technology strategy. Composition and interrelatedness of its patent portfolio reveals a firm’s technology strategy and a series of quantifiable measures can be developed to represent the nature of a firm’s technology strategy. Therefore, researchers and managers can compare technology strategies across different firms using numerical scales. Those quantifiable measures can also serve as management tools for benchmarking and technology auditing. Most importantly, they are accountable in that they can be traced to verify whether the firm’s technology strategy is in line with its strategic intent. Those measures can assist managers in strategy formulation and portfolio management. Third, for technology firms, it can be effective to add a new dimension of corporate diversification strategy—the diversity of a firm’s technology portfolio. The diversity of a technology portfolio can be further divided into levels: BTD and CFD. This new dimension can help managers and researchers link technology strategy to the overall corporate strategy. We therefore developed two Herfindahl-type indexes for patent portfolios as proxies for the diversity of a firm’s technology capability to measure the relatedness of portfolio patents. The two Herfindahl-type indexes were used to explain how a portfolio of patents could have synergistic effects. The former is related to the technology diversity literature [18], whereas the latter is linked to the capability-based view of the firm [26]. Our results generally support the capability-based view of the firm. Fourth, a useful set of patent-based bibliometrics measures associated with the NBER patent data file can be used to describe the characteristics of composition of a patent portfolio. Our results indicate how a firm’s patent portfolio can contribute to its performance. Managers and researchers may find that they are useful tools for strategy formulation and patent portfolio management. Finally, managers should take into account the effect of technology portfolios on shareholder value when allocating R&D resources. Outstanding firms tend to conceive of themselves as a portfolio of competencies instead of a portfolio of businesses or product families. As discussed in the introduction section, technology portfolio strategy can profoundly influence firm value, and a firm’s technology portfolio strategy cannot be quickly changed in a short period of time. Therefore, it is important to have a long-term commitment on building a technology portfolio that is valuable and flexible. The findings of this study are significant in that they provide a better understanding of the technology portfolio of the representative large, diversified, established firms. Given these benefits, perhaps some of the most fruitful future opportunities for technology management research may lie in continued research on technology portfolios and R&D strategy.
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LIN et al.: PATENT PORTFOLIO DIVERSITY, TECHNOLOGY STRATEGY, AND FIRM VALUE
APPENDIX LIST OF THE 94 FIRMS 1) 2) 3) 4) 5) 6) 7) 8) 9) 10) 11) 12) 13) 14) 15) 16) 17) 18) 19) 20) 21) 22) 23) 24) 25) 26) 27) 28) 29) 30) 31) 32) 33) 34) 35) 36) 37) 38) 39) 40) 41) 42) 43) 44) 45) 46) 47) 48) 49) 50) 51) 52) 53)
Abbott Laboratories Advanced Micro Devices Air Products & Chemicals Allergan, Inc. Altera Corporation Amgen, Inc. Analog Devices Apple Computer, Inc. AT&T Corporation Avery Dennison Corporation Baker-Hughes, Inc. Baxter International Becton Dickinson & Company Black & Decker Corporation Boeing Company Borden Chemical, Inc. Bristol-Myers Squibb Brunswick Bristol Myers Squibb Caterpillar, Inc. Chiron Corporation Church & Dwight, Inc. Cirrus Logic, Inc. Colgate-Palmolive Company Corning, Inc. Cummins, Inc. Cypress Semiconductor Dana Corporation Deere & Company Dow Chemical DuPont (E I) De Nemou Eastman Chemical Company Eastman Kodak Company Eaton Corporation Emerson Electric Company Energy Conversion Dev. FMC Corporation Ford Motor Company GenCorp, Inc. Genentech, Inc. General Dynamics Corporation General Electric Company-PR General Motors Corporation Goodrich Corporation Goodyear Tire & Rubber Halliburton Company Harris Corporation Hercules, Inc. Hewlett-Packard Company Illinois Tool Works Integrated Device Tech Intel Corporation Intl Business Machines Intl Flavors & Fragrance
54) 55) 56) 57) 58) 59) 60) 61) 62) 63) 64) 65) 66) 67) 68) 69) 70) 71) 72) 73) 74) 75) 76) 77) 78) 79) 80) 81) 82) 83) 84) 85) 86) 87) 88) 89) 90) 91) 92) 93) 94)
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Intl Paper Company Johnson & Johnson Kimberly-Clark Corporation Lexmark Intl, Inc.-CL Lilly (ELI) & Company LSI Logic Corporation Lubrizol Corporation Medtronic, Inc. Merck & Company Micron Technology, Inc. Microsoft Corporation Molex, Inc. Motorola, Inc. National Semiconductor Nordson Corporation Northrop Grumman Corporation Owens Corning Owens-Illinois, Inc. Pall Corporation Pfizer, Inc. Pitney Bowes, Inc. PPG Industries, Inc. Praxair, Inc. Procter & Gamble Company Qualcomm, Inc. Quantum Corporation Raytheon Company Rohm & Haas Company Schering-Plough Schlumberger Ltd. Storage Technology CP Sun Microsystems, Inc. Symbol Technologies Tektronix, Inc. Texas Instruments, Inc. Textron, Inc. Unisys Corporation United Technologies Company Whirlpool Corporation Xerox Corporation Xilinx, Inc. ACKNOWLEDGMENT
The authors would like to thank S.-C. Hung and Y. Lee for helpful suggestions and comments. They appreciate the input of the Senior Editor, A. Bean, and the anonymous reviewers. REFERENCES [1] P. F. Drucker, “The discipline of innovation,” Harvard Bus. Rev., vol. 76, no. 6, pp. 149–157, 1998. [2] O. Granstrand and C. Oskarsson, “Technology diversification in MULTECH corporations,” IEEE Trans. Eng. Manage., vol. 41, no. 4, pp. 355–364, 1994. [3] M. Sadowski and A. Roth, “Technology leadership can pay off,” Res. Technol. Manage., vol. 42, no. 6, pp. 32–33, 1999. [4] R. Burgelman, M. Maidique, and S. Wheelwright, Strategic Management of Technology and Innovation. New York: McGraw-Hill, 2001.
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[5] R. Henderson and I. Cockburn, “Measuring competence? Exploring firm effects in pharmaceutical research,” Strategic Manage. J. (Special Issue), vol. 15, pp. 63–84, 1994. [6] L. Fleming and O. Sorenson, “Technology as a complex adaptive system: Evidence from patent data,” Res. Policy, vol. 30, no. 7, pp. 1019–1039, 2001. [7] D. M. DeCarolis and D. L. Deeds, “The impact of stocks and flows of organizational knowledge on firm performance: An empirical investigation of the biotechnology industry,” Strategic Manage. J., vol. 20, no. 10, pp. 953–968, 1999. [8] M. Gittelman and B. Kogut, “Does good science lead to valuable knowledge? Biotechnology firms and the evolutionary logic of citation patterns,” Manage. Sci., vol. 49, no. 4, pp. 366–382, 2003. [9] C. K. Prahalad and G. Hamel, “The core competence of the corporation,” Harvard Bus. Rev., vol. 68, no. 3, pp. 79–91, 1990. [10] F. Van Remoortere and F. P. Boer, “Globalization of technology: what it means for American industry,” Res. Technol. Manage., vol. 35, no. 4, pp. 8–9, 1992. [11] R. Amit and J. Livnat, “Diversification and the risk-return trade-off,” Acad. Manage. J., vol. 31, no. 1, pp. 154–166, 1988. [12] E. Norton, “Don’t manage your strategic acquisitions like stock portfolios,” Acad. Manage. Exec., vol. 8, no. 4, pp. 86–87, 1994. [13] M. Lubatkin and S. Chatterjee, “Extending modern portfolio theory into the domain of corporate diversification: Does it apply?,” Acad. Manage. J., vol. 37, pp. 109–136, 1994. [14] C. Long, “Patent signals,” The Univ. Chicago Law Rev., vol. 69, no. 2, pp. 625–679, 2002. [15] L. Lang and R. M. Stulz, “Tobin’s Q, corporate diversification, and firm performance,” J. Pol. Econ., vol. 102, no. 6, pp. 1248–1280, 1994. [16] M. E. Porter, Competitive Advantage. New York: Free Press, 1985. [17] R. M. Kanter, When Giants Learn to Dance. London, U.K.: Simon & Schuster, 1989. [18] O. Granstrand, P. Patel, and K. Pavitt, “Multitechnology corporations: Why they have distributed rather than distinctive core competencies,” Calif. Manage. Rev., vol. 39, no. 4, pp. 8–25, 1997. [19] J. B. Barney, “Firm resources and sustained competitive advantage,” J. Manage., vol. 17, pp. 99–120, 1991. [20] A. Gambardella and S. Torrisi, “Does technological convergence imply convergence in markets? Evidence from the electronics industry,” Res. Policy, vol. 27, no. 5, pp. 445–463, 1998. [21] M. Singh, I. Mathur, K. C. Gleason, and A. Etebari, “An empirical examination of the trend and performance implications of business diversification,” J. Bus. Econ. Studies, vol. 7, no. 2, pp. 25–51, 2001. [22] W. P. Lloyd and J. S. Jahera Jr., “Firm-diversification effects on performance as measured by Tobin’s Q,” Man. Decision Econ., vol. 15, no. 3, pp. 259–266, 1994. [23] A. B. Jaffe and J. Lerner, “Reinventing public R&D: Patent policy and the commercialization of national laboratory technologies,” Rand J. Econ., vol. 32, no. 1, pp. 167–198, 2001. [24] R. Comment and G. A. Jarrell, “Corporate focus and stock returns,” J. Fin. Econ., vol. 37, pp. 67–87, 1995. [25] T. Joseph and J. R. Pandian, “The capabilities-based view within the conversation of strategic management,” Strategic Manage. J., vol. 13, no. 5, pp. 363–380, 1992. [26] S. Chatterjee and M. Lubatkin, “Corporate mergers, homemade diversification, and changes in systematic risk,” Strategic Manage. J., vol. 11, no. 4, pp. 255–268, 1990. [27] J. W. Medcof, “The capabilities-based view and transnational technological strategy,” J. High Technol. Manage. Res., vol. 11, no. 1, pp. 59–74, 2000. [28] B. H. Hall, A. B. Jaffe, and M. Tratjenberg. (2001) The NBER patent citation data file: Lessons, insights and methodological tools. NBER Working Paper 8498. [Online]. Available: http://www.nber.org/patents/ [29] E. B. Lindenberg and S. A. Ross, “Tobin’s Q ratio and industrial organization,” J. Bus., pp. 1–32, Jan. 1981. [30] M. L. Jose, C. Lancaster, J. L. Stevens, and J. A. Jennings, “Stability of excellence: revealed patterns in Tobin’s Q-ratios,” J. Appl. Bus. Res., vol. 12, no. 2, pp. 83–94, 1996.
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Bou-Wen Lin received the Ph.D. degree in management of technology from the Rensselaer Polytechnic Institute, Troy, NY. His work experience includes engineering and project manager. He has served as an Associate Professor at the National Tsing Hua University, Hsinchu, Taiwan. His research papers have been published in R&D Management, Technological Forecasting and Social Change, International Journal of Project Management, and the International Journal of Human Resources Management. His current research interests are strategic alliances, patent portfolios, R&D management, and intellectual assets. Dr. Lin is a member of the Academy of Management.
Chung-Jen Chen received the Ph.D. degree in strategy and technology management from Rensselaer Polytechnic Institute, Troy, NY. He is an Associate Professor at the Graduate Institute of Business Administration, National Cheng Kung University, Tainan, Taiwan. He has published papers in Information & Management, International Journal of Technology Management, R&D Management, and other journals. He is a researcher in the fields of technology management, interfirm collaboration, and entrepreneurship.
Hsueh-Liang Wu received the Ph.D. degree in commerce from the University of Birmingham, Birmingham, U.K. He is an Assistant Professor at the Graduate Institute of Business Administration, National Cheng Kung University, Tainan, Taiwan. He has published papers in the Journal of Organizational Change Management, the Journal of Policy Modeling, Technological Forecast and Social Change, and other journals. He is a researcher in the fields of strategic management of policy and technology.
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