THE EFFECT OF LAGGARDS AMBIDEXTROUS LEARNING ON IMPROVING THE SPEED OF TECHNOLOGICAL CATCH-UP

Transcription

1 THE EFFECT OF LAGGARDS AMBIDEXTROUS LEARNING ON IMPROVING THE SPEED OF TECHNOLOGICAL CATCH-UP Martin Hong, Technology Management, Economics and Policy Program, Seoul National University Marco Kim, Technology Management, Economics and Policy Program, Seoul National University, Jina Kang, Seoul National University & Global Education Center for Engineers Abstract What are the major factors that improve the speed of technological catch-up among laggards? This research suggests the determinants of technological catch-up, focusing on the ambidextrous learning strategy (exploration and exploitation) of laggards. First, we analyze the relationship between the level of knowledge newness and the speed of technological catch-up in terms of exploration. Second, the relationship between the level of internal knowledge and the speed of technological catch-up in terms of exploitation is investigated. Finally, this research investigates how the level of M&A and alliance and R&D intensity affects the two causal relationships mentioned above. This paper highlights the importance of each type of learning in technological catch-up as well as the relevant supporting activities that promote better performance. Key Words: technological catch-up, exploration, exploitation, laggard, laggard s advantage

2 1. Introduction Today s rapidly changing technology and industrial environment requires flexible decision-making, high adaptation, and core competencies from firms. As the lifecycle of technology is getting shorter, and paradigm shift is becoming frequent, the modern industrial environment is not likely to be favorable to firms. Hence, several studies focused on determining the source of firms competitive advantage in this rapidly changing environment. One typical argument relating to competitive advantage is the effect of entry order (Lieberman & Montgomery, 1988; Kerin et al., 1992; Schnaars, 1994). Most research related to this idea was focused on the first-mover advantage (Carpenter & Nakamoto, 1990; Lilien & Yoon, 1990), but research on the advantage of laggards has also been discussed widely (Cho et al., 1998; Lee & Lim, 2001; Mathews, 2003). Lieberman and Montgomery (1988) explain the concept of the first-mover advantage through technological leadership, preemption of assets, and creation of buyer-switching costs. However, in another paper in ten years, they conclude that the effect of the entry order of firms is relative, and the first-mover advantage varies according to the product and the environment (Liberman & Montgomery, 1998). Meanwhile, laggards are examined based on the free-rider effect, efficacy of avoiding technological uncertainty, and the imitation strategy (Golder & Tellis, 1993; Song, Almeida, & Wu, 2003). The research on laggards focus on catch-up strategies and made their case out of developing countries such as South Korea, Taiwan, and China, which have been growing continuously since the 1990s (Mathews & Cho, 1999; Ernst & Kim, 2002; Mathews, 2003). Firms from these countries successfully caught up with leading firms with the aid of laggards advantage as well as through national and institutional support. Consequently, the post-catch-up performance of these firms exceeded that of the first movers (Lee & Lim, 2001; Hobday et al., 2004; Song et al., 2006). The catch-up strategy has been discussed in the field of economics and management studies, and interest on the topic has increased as well (Mathews, 2002; Lee, 2007; Rasiah, 2010). However, previous studies have some limitations. First, previous research is restricted to the case of developing countries although catch-up strategy is also applicable broadly to laggard firms that may belong to developed countries. Second, previous research is focused on case studies related to catch-up strategy although the field requires systematic, empirical analysis to determine strategic implications. Finally, previous research is focused only on whether laggards succeeded in catch-up although catch-up speed is another important factor in the area of catch-up studies.

3 Therefore, the present study attempts to overcome the limitations of previous ones by focusing on all kinds of laggards regardless of the industrialized level of certain countries. In addition, the present study is implemented empirically to verify significant factors that affect the speed of technological catch-up. Specifically, the level of searching for new knowledge and the level of utilizing internal knowledge are examined by verifying the relationship between these two types of learning and the speed of technological catch-up through US patent data. In addition, alliance and M&A, and R&D intensity are suggested as moderators that affect the aforementioned relationships to examine the effect of practical investment and learning knowledge. 2. The theoretical background and hypotheses 2.1. The definition of laggards Laggards must first be defined clearly prior to the further discussion of laggards strategy. Robinson et al. (1985) define first movers as firms that enter a new market faster than any others. Golder and Tellis (1993) also define first movers, which they determined to be firms that start to sell products for the first time. The classification of first and late movers is relatively clear in terms of entry time. However, this division may be ambiguous at times, most especially when a firm determines its entry time strategically. Therefore, Mathews (2002) complements the existing definition and suggested four different types of laggards: 1) Firms that made a late entry into certain fields because of historical needs. 2) Firms lacking in resources during the early stages. 3) Firms that aim to chase leading firms. 4) Firms that have a competitive advantage such as low cost. The present study follows the definition of laggards suggested by Mathews (2003). The definition of laggards is not merely restricted to firms in developing countries, as it also includes firms that lag behind first movers. Late movers must be clearly defined according to the purpose of the study, as the classification of first and late movers can vary according to entry time, financial scale, and the level of technological advancement. As this study is focused on technological catch-up, we therefore classify first movers and late movers according to the level of technological advancement in the related industrial fields. 2.2 The advantages of laggards and their strategy

4 The advantages of laggards are discussed in this section (Lieberman & Montgomery, 1988, 1998; Porter, 1985; Schnaars, 1994). First, laggards, as free riders, take advantage of the market opened by leading firms. Free riding in this particular scenario helps laggards succeed in the industry by simply following the strategy of leading firms effectively. Second, laggards can run their business with lower R&D and marketing cost through lower imitation cost, the free-rider effect, and through economies of scale. The imitation cost of laggards is known to be around 65% of the innovation cost of first movers. With these competitive advantages, laggards can employ diverse kinds of strategies. An and Lee (2010) re-established the strategy of late movers into two kinds of viewpoints. The first one is the strategy to gain a toehold through external environment in terms of industrial organization theory. The other one is catch-up strategy through internal mechanism of a firm in terms of resourcebased view. In terms of industrial organization theory, representative studies are conducted by Lieberman and Montgomery (1988) and Kerin et al. (1992). These studies explore the effect of the entry order as a first-mover advantage. In such a case, laggards attempt to weaken first movers advantage, such as their customer loyalty and technological advantage. Then, they seize any changes and mistakes of first movers to achieve effective catch-up (Golder & Tellis, 1993). On the one hand, the resource-based view is focused on laggards own abilities for resource creation. On the other hand, the industrial organization perspective stresses the response of laggards to the changes in the industrial environment. Thus, as March (1991) suggests, the core capability of firms is formed in the course of exploiting existing resources, combining them in innovative ways, and exploring new resources. This capability is referred to as the learning capability. In case of laggards, the imitation strategy, which is examined in previous studies, is absolutely effective for their success in the industrial field (Kim, 1998; Lee, 2005). Similarly, the strategy of laggards is summarized according to two important factors. The first one is the laggards response to the exterior environment, and the other is the laggards own learning capability Technological Catch-up Technological catch-up is a representative research field that focuses on the efforts exerted by laggards to reduce the technological gap between them and the first movers. Discussions relating to late-moving countries and laggards settle mainly on how firms in technologically backward coun-

5 tries chase the level of technology of developed countries, and on the reasons behind the differences in the catch-up performance among firms (Hwang, 2006). This discussion leads to the division of the stream of catch-up strategy into three different perspectives; product lifecycle perspective (Utterback & Abernathy, 1975; Kim, 1980; Lall, 1980; Dahlman, Westphal, & Kim, 1985), catch-up and innovation strategy under relatedness with the global environment (Hobday, 1995; Ernst, 2002; Ernst & Kim, 2002), and resource-based view that focuses on laggards accumulation of internal capabilities (Mathews, 2003; Mathews & Cho, 1999; Hwang, 1999). Most research on the area of catch-up targets the case of developing countries and their firms, especially the successful ones. South Korea, Taiwan, and China are some of the countries that are most frequently mentioned in this research stream. These countries achieved industrialization in the 1980s and the 1990s, and reduced the technological gap between them and developed countries in certain fields. According to Lee (2001, 2005), technological catch-up of laggards is classified into three types: 1) path-following catch-up, or following the path of leading firms as it stands, 2) leapfrogging catch-up, or omitting some steps from the path taken by leading firms, and 3) path-creating catch-up, or creating a path that is totally different from that of leading firms Hypotheses As mentioned above, catch-up, a research field relating to laggards strategy, and more specifically, technological catch-up discusses the technological gap between first movers and laggards. However, previous studies are not only case-centered, but are also restricted to developing countries such as South Korea, Taiwan, and China. Furthermore, abundant discussion is lacking in these studies despite the great importance of the speed of technological catch-up in terms of industry innovation. In relation to the limitations discussed here, the present research defines laggards as late movers lagging behind in their fields. In addition, the factors that affect the speed of technological catchup are investigated empirically using industrial data. Park (2006) mentions the effect of appropriability and accessibility on the factors of industrial regime. Using US patent data, he found that appropriability and accessibility have a positive impact on improving the speed of technological catch-up. In his study on the DRAM industry of Japan, South Korea, and Taiwan, Lee et al. (2011) suggest that late movers tend to acquire technological knowledge from leading firms whose gap is relatively small compared with other leading firms.

6 Previous studies describe above put forward the idea that laggards must have easy access to external knowledge and target leading firms with relatively small gaps to improve the speed technological catch-up. Barney (1986) suggests that the performance of firms depends on their forecasting capabilities. Future profits are not guaranteed if firms cannot predict market demand or the changes to the industrial environment. In addition, Katila (2002) proposes that firms can predict more accurate outcomes when they are in search for newer technology. As mentioned above, the core elements of laggards strategy are the response to external environmental change and the learning capability. We can conclude that these two elements correspond to knowledge exploration developed by recombining existing resources and developing new resources, and to capabilities through experimentation and broader search (March, 1991). In sum, laggards have an advantage in catching up to leading firms when they have greater access to new knowledge. This advantage is based on the exploration of the two types of learning. Thus, the newer the knowledge the laggards learn, the greater are their chances to catch-up to leading firms. The first hypothesis is as follows: H1. The degree of newness of the technologies found by technological laggards is positively related to the speed of technological catch-up. Kogut and Zander (1992) reveal that learning capability, which involves utilizing and combining internal knowledge, plays an important role in realizing environmental opportunities in the market. Trajtenberg et al. (1997) propose that firms can more likely monopolize profit from self-created knowledge and innovation if they possess a higher appropriability of knowledge. Hence, we can infer that the utilization of internal knowledge plays an important role in promoting the growth of laggards. In such a case, the speed of technological catch-up can be improved if the level of internal knowledge utilization is high. The second hypothesis is as follows: H2. The degree of internal knowledge utilized by technological laggards is positively related to the speed of technological catch-up. Hypotheses 1 and 2 relate to the two types of learning, namely, exploration and exploitation. We suggest that practical investments for laggards to promote the relationships between learning and the speed of technological catch-up are M&A and alliance as well as investment on R&D.

7 Previous research suggests that the more active the firms carry out a higher level of M&A and alliance, the more likely they are to internalize inter-firm transfer of resources and external resources and to improve performance (Cohen & Levinthal, 1990; Duyster & Hagedoorn, 2000). On the one hand, M&A and alliance with other firms reduce the time for developing technology and diversifying risks related to the development of new technology (Balakrishman, 1988; Morris, 1991; Shelton, 1998). Intense R&D, on the other hand, is known to support firms technological growth and to increase the level of internal knowledge utilization. Put together, M&A, alliance, and R&D intensity may allow laggards to catch-up to leading firms in terms of practical investment. Based on this idea, the following hypotheses are suggested: H3a. The level of M&A and alliance will promote the relationship between the newness of knowledge and the speed of technological catch-up. H3b. The level of R&D intensity will promote the relationship between the level of internal knowledge utilization and the speed of technological catch-up. 3. Data The present study explores the pharmaceutical industry. This industry is known as a typical hightech industry whose scientific foundation automatically leads to frequent patenting activities. In addition, this industry is highly related to major strategic alliance cases and M&A activities (Hagedoorn, 2002; Higgins & Rodriguez, 2006). In this respect, the pharmaceutical industry is relatively suitable for the purpose of this study. This study examines patent data to investigate exploration and exploitation activities. The patent has been well used in the study of innovation within firms (Ahuja & Katila, 2001; Benner & Tushman, 2002; Rosenkopf & Nerkar, 2001). Previous studies propose that the patent is a crucial indicator that represents knowledge creation and innovation capability (Ahuja & Lampert, 2001; Nerkar, 2003; Song et al., 2003). Based on this idea, researchers decide that patent stock would be a good way to examine the result of technological catch-up, and that data from the United States Patent and Trademark Office (USPTO) could be useful because of the diverse information available, including assignee, inventor, technological type, and citation (Albert et al., 1991). Present study investigates the speed of technological catch-up from 1985 to 1995 through patent data. During the study period, the number of patents in the pharmaceutical industry, as well as alliance and M&A activities, increased. Hence, this study period is suitable for the hypotheses formed for this study.

8 Practically, patent data were examined from the NBER Patent Database, and the level of M&A and alliance activities was investigated from the Bioscan Database. Compustat from the Wharton Research Data Services was used for the number of employees. Ultimately, the three kinds of data were combined. Variables of this research are summarized in Table 1. Table 1. Description for variables Type Name Description Dependent Variable Independent Variables Moderating Variables CAGR Compound annual growth rate of a sample firm from 1985 to 1995 Newness Mean backward citation lag of patents granted by a sample firm from 1985 to 1995 Self-citation Share of self-citations made by a sample firm from 1985 to 1995 Collab Number of collaboration activities carried by a sample firm from 1985 to 1995 R&D Average R&D expenditure of a sample firm from 1985 to 1995 Control Variables Citation AccuPatent Average number of citations made by a sample firm from 1985 to 1995 Accumulated granted patents that a sample firm owned prior to Empirical Model Specification For this study, a total of 82 global pharmaceutical firms were randomly selected, 23 of which were included in hypothesis testing. Ordinary least squares (OLS) regression analysis was conducted to understand the causality relation between dependent variables and independent variables and with the role of moderating variables. The conclusion of this study is shown in Table 2. Table 2. Descriptive Statistics and Correlations among the Variables Variables Mean SD. Citation

9 AccuPatent Newness ** Selfcitation ** CAGR RESULTS Results found in Table 3 that were examined using OLS regression suggest the effect of independent variables and the interaction terms between independent variables and moderating variables on dependent variables. Model 1 only included control variables, while Model 2 included control variables and independent variables. Finally, interaction term was added with the control variables and the independent variables in Model 3. All models were proved to be significant (p-value>0.05), while the number of adjusted R 2 was increased from model 1 to model 3. In the case of Model 2, newness the first independent variable had a significant (p<0.01) positive impact on the compound annual growth rate (CAGR). Newness is calculated by deducting the mean backward citation lag among patents of each firm registered from 1985 to 1995 from the period between 1963, the year when patents were started to be filed for the first time, and Thus, the greater the newness, the more firms cite new patent. If we interpret this relationship with dependent variables, we can deduce that when firms cite newer patents, the speed of technological catch-up increases. In other words, the speed of technological catch-up becomes faster than that of other firms when the newest patent is used. Self citation, which is the second variable, also has a positive impact on the CAGR (p<0.05). Self citation is defined as the share of citing one s own patent from 1985 to Thus, firms conduct exploitation by using a greater number of self-created patents. In other words, the speed of technological catch-up is faster than other firms when exploitation takes place. Model 3 discusses the effect of interaction terms, which are products of Newness and Collab, and Self citation and R&D. However, no significance was found. Even though the degree of freedom

10 was increased due to the increased number of variables, we suspect this insignificance is due to the smallness of data. The effect of interaction term will be further examined after making up for extra data. Table 3. Ordinary Least Square Regression Results for the CAGR Depend variable: Model 1 Model 2 Model 3 CAGR Coefficient S.E Coefficient S.E Coefficient S.E Control variables Citation ** * ** AccuPatent * *** Independent variables Newness 0.011*** *** Self-citation 0.318* ** Newness Collab Self-citation R&D N Adjusted R^ F-value 3.850** 7.240*** 7.716*** Notes: *p < 0.10; **p < 0.05; ***p < Conclusion & Limitations Technological catch-up has been discussed in diverse academic fields. However, previous research is restricted to the case of developing countries. Moreover, these studies focus on cases despite the need for a systematic and empirical analysis to draw a meaningful implication for laggard firms. Even more, the speed is an important factor in catching up to leading firms, previous research only focus on successes or failures.

11 Therefore, this research tries to overcome the limitations of previous research by focusing on all kinds of laggards in the industry regardless of the level of industrialization within that industry. In addition, this study empirically analyses the speed of technological catch-up in the pharmaceutical industry. The conclusions of this research detailed below provide guidelines for the laggard firms. First, to catch up to leading firms quickly, laggards must acquire new knowledge. Exploring new knowledge is especially critical. Second, laggards with a higher level of internal knowledge utilization are more likely to catch up. Finally, learning ability is critical in terms of exploration and exploitation. Furthermore, M&A, alliance, and R&D intensity play important roles in the catching up to leading firms. As with any study, this research has few limitations. First, this study only focuses on learning ability, which puts emphasis on resource-based view and keeps responding to external environment based on industrial organization off. Therefore, follow-up research may simultaneously consider the learning characteristics of the study and the external environment, and derive more meaningful outcomes. Second, since this research is intended for the pharmaceutical industry, further validation across other high-tech industry will be required to draw broader implications.

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