Exploration and exploitation in product innovation - CiteSeerX

Industrial and Corporate Change Advance Access published May 26, 2007 Industrial and Corporate Change, pp. 1 of 31 doi:10.1093/icc/dtm013

Exploration and exploitation in product innovation Henrich R. Greve

A central theoretical problem in organizational evolution is how organizations acquire new capabilities. Organizational exploitation of current capabilities often reduces exploration of new capabilities, resulting in a short-term bias in organizational adaptation (March, 1991). In addition, problemistic search and slack search have different consequences for exploration and exploitation because exploration has greater risk and less routinization. Exploration and exploitation are also affected by organizational momentum (Kelly and Amburgey, 1991) and direct competition from exploitation to exploration (March, 1991). These propositions are tested using data on innovations in shipbuilding between 1972 and 2000.

Exploration and exploitation are fundamental activities of organizations and other adaptive systems (March, 1991). Organizational exploration is search for new knowledge, use of unfamiliar technologies, and creation of products with unknown demand. Because these activities do not reliably and quickly produce revenue, exploration has uncertain and distant benefits. Exploitation is use and refinement of existing knowledge, technologies, and products, and has more certain and proximate benefits. Exploration and exploitation both draw resources, and thus resource constraints require organizations to make tradeoffs between them (Levinthal and March, 1993). Organizations appear to have difficulty making these tradeoffs. Exploration and exploitation rely on different organizational routines and capabilities (Lewin et al., 1999; Galunic and Eisenhardt, 2001; Benner and Tushman, 2003), so it is easier to specialize in one of them than to efficiently perform a mixture of both. In technology development, exploitation of existing stocks of knowledge appears to reduce the incentives for exploring new knowledge and possibly even the ability to do so (Levinthal and March, 1981; Tushman and Anderson, 1986; Leonard-Barton, 1995; Christensen and Bower, 1996). Despite the difficulties in combining exploration and exploitation, theory and empirical evidence suggests that too little of either exploration or exploitation reduces performance (Levinthal and March, 1993;

ß The Author 2007. Published by Oxford University Press on behalf of Associazione ICC. All rights reserved.

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Katila and Ahuja, 2002; Fagiolo and Dosi, 2003; He and Wong, 2004). This indicates a need to learn more about how organizations shift between exploration and exploitation. Some of the earliest theoretical work relevant to exploration and exploitation is the behavioral theory of the firm (Cyert and March, 1963), which posits problemistic search and slack search as drivers of organizational learning and change in general and innovations specifically. Later work on organizational learning has added much theory and evidence (see reviews by Levitt and March, 1988; Miner and Mezias, 1996; Schulz, 2002; Argote and Greve, 2007), and has proposed additional ways in which organizational learning may affect innovations. These include the propositions that routinization causes repetition of organizational changes (Kelly and Amburgey, 1991) and that scarce resources and pursuit of proximate awards cause exploitation to drive out exploration (March, 1991). These propositions are a good foundation, but added theoretical and empirical work is needed to assess which mechanisms affect organizational exploration and exploitation through making innovations. First, these propositions are so general that they do not specify the type of organizational change that is predicted, so it is easier to predict that low performance will lead to some sort of change than to predict that it will lead to exploration. For example, low performance leads to downsizing (Ahmadjian and Robinson, 2001), which seems contrary to exploration. Second, theory of routines will not predict exploration well if exploration events are too unique to become routinized. Currently only the proposition that exploitation competes with exploration gives a direct prediction on how exploration and exploitation interact, but consideration of whether the other theoretical mechanisms have unequal effects on exploration and exploitation may yield additional predictions. One contribution of this article is to develop theory on how performance, slack, and routines affect exploration and exploitation, including the proposition that the greater risk and diversity of exploration innovations cause them to be affected differently from exploitation innovations. Third, the evidence on extant propositions is scarce with respect to organizational innovations, though there is evidence from other forms of organizational change. There is little research examining performance effects on innovations (Bolton, 1993; Greve, 2003), but on some other outcomes such as risk taking and strategic changes (Bromiley, 1991; Lant et al., 1992; Nickel and Rodriguez, 2002; Miller and Chen, 2004). Most research on organizational slack has looked for effects on R&D or patenting (Kamien and Schwartz, 1982; Antonelli, 1989), which are intermediate steps in the process of developing innovations. The well-known difficulties of getting innovations based on R&D projects selected for product launch (Burgelman and Sayles, 1986; Dougherty and Heller, 1994) caution against drawing strong conclusions on innovativeness from R&D evidence. Routinization of organizational change has been investigated through mergers and acquisitions, alliances, and other strategic changes (Amburgey and Miner, 1992; Amburgey et al., 1993; Lavie and

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Rosenkopf, 2006), but not through innovations. A second contribution of this article is to empirically test these propositions using innovation data.

1. Theory and Hypotheses 1.1 Innovations as exploration and exploitation Many writers emphasize the uncertainty involved in developing innovative products and launching them in the market (Burgelman and Sayles, 1986; Van de Ven et al., 1999), suggesting that innovations are a form of organizational exploration. Much product development hews closely to the current knowledge of the firm (Griffin, 1997), however, it is important to distinguish explorative innovations from those that exploit current knowledge. One point of departure is the distinction between incremental innovations, which advance existing technology, and radical innovations, which develop new technology (Dewar and Dutton, 1986). Radical innovations fit the definition of exploration because development of new technology is a form of knowledge development (Rosenkopf and Nerkar, 2001; Benner and Tushman, 2003). Despite its merits, the definition is not immediately applicable to firm-level research on exploration and exploitation. First, it defines innovations as radical with respect to the industry as a whole, which ignores differences in the knowledge held by a focal firm with respect to a given innovation. Indeed, the finding that a firm’s depth of knowledge predicts early adoption of both incremental and radical innovations (Dewar and Dutton, 1986) can be explained by prior knowledge of an innovative technology allowing early adoption. Second, it does not sufficiently take into account the link from the knowledge held by the firm prior to developing the innovation and knowledge that must be generated in order to develop it. An innovation is more explorative when the firm had less advance knowledge to assess the probability of successfully developing the innovation and launching it in the market. To make this judgment, researchers have emphasized novelty in the technological and market domain as a useful criterion (Rosenkopf and Nerkar, 2001; Benner and Tushman, 2002; 2003). This is because innovations that are technologically very different from existing products have lengthy and unpredictable development durations (McDonough, III, 1993), and innovations that address unfamiliar markets have unpredictable market success (Christensen and Bower, 1996). Thus, we may define the extent of exploration in an innovation launch as its technological and market novelty for the focal firm. Firms can choose not only the extent to which they seek to innovate, but also whether to emphasize exploitation innovations or exploration innovations. Exploitation innovations that apply known technology are frequent in firms that design and customize products based on customer requests and in mass-producing firms with product development processes geared towards the interests of

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customers (Burgelman and Sayles, 1986), but firms also develop exploration innovations through learning new technologies (Rosenkopf and Nerkar, 2001). In the shipbuilding industry, which is the context of this study, container ships without hatch covers were an innovation that improved container handling in ports. They were different from the earlier container ships with hatch covers, but did not require new technology because the engineering problems in building them were familiar from other types of ships without hatch covers, and were thus an exploitation innovation. On the other hand, aluminum honeycomb hulls were designed as a replacement of conventional steel hulls for smaller vessels, but design and manufacture of aluminum honeycomb hull involves new skills for shipbuilding firms, who are familiar with the manufacturing and seagoing properties of steel.

1.2 Balancing exploration and exploitation Many have argued the benefits of balancing exploration and exploitation (March, 1991; Levinthal and March, 1993; Benner and Tushman, 2003). A preference for exploration results in excessive costs of failed experiments and insufficient rewards from successful ones. A preference for exploitation may not be harmful in the short run, or even in the long run if the environment is stable, but it reduces the organization’s ability to discover opportunities and respond to environmental changes. Thus, a common expectation is that a balance of exploration and exploitation is preferable, though the costs of insufficient exploration may not be apparent in the short run. Empirical work has supported this proposition (Katila and Ahuja, 2002; He and Wong, 2004). The arguments for why a balance might be beneficial stand in stark contrast to arguments on the difficulty of maintaining a balance between exploration and exploitation. Exploration in technological domains requires greater diversity of knowledge than exploitation, and hence a different set of capabilities (Nonaka and Takeuchi, 1995). Exploration calls for less attention to the current organizational strategy, lower conformity to current organizational practices, and less emphasis on leveraging current strengths (Burgelman, 1991; March, 1991; Dougherty and Heller, 1994; Leonard-Barton, 1995). An emphasis on routine development and refinement benefits exploitation but will suppress exploration (Benner and Tushman, 2002). Experienced top management teams favor exploitation over exploration (Beckman, 2006). These pressures towards specialization are sufficiently strong to raise the question of how an organization might balance—or mix—exploration and exploitation. If exploration and exploitation do not coexist easily, as these arguments suggest, then some form of decoupling between them is needed. Two possible alternatives are organizational decoupling by having specialized subunits or temporal decoupling through switching (Levinthal and March, 1993; Gupta et al., 2006). Decoupling across subunits is possible at some cost, though the need for


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separate management approaches in the same organization will introduce tensions (Benner and Tushman, 2002). Decoupling over time requires the organization to be capable of switching between two sets of behaviors, including one (exploration) that is presumably used less often (Burgelman, 2002). Both approaches to mixing the two are possible and will produce outcomes that appear similar, as it is difficult to tell whether an observed mix of exploration and exploitation over time is a result of temporal decoupling or of organizational decoupling with independent launches of exploration and exploitation innovations. There are scraps of evidence on organizations that mostly exploit but sometimes explore as well. Organizations with insufficient capabilities to explore new opportunities can obtain them from the outside (Cattani, 2005), with alliances a likely approach when the necessary capabilities are dispersed across organizations (Beckman et al., 2004; Lavie and Rosenkopf, 2006). Product development teams with diverse capabilities are effective at both exploration and exploitation, suggesting that the underlying capabilities are less important than the goal of exploring or exploiting (Taylor and Greve, 2006). However, there is still modest evidence to support either the balancing or the specialization hypothesis, so we are left with the contention that a balance is possible but probably difficult to maintain. The hypotheses derived here concern pressures towards exploration and exploitation, and assume that balance or specialization is a result of the relative strength of the countervailing forces. Two features of exploration innovations are important for deriving the hypotheses. First, exploration innovations are diverse. It is difficult to obtain new technology in a routine and repeated manner, and even applying existing technology to make qualitatively new products is a process that likely differs every time. As one moves towards exploitation, the innovations become more homogeneous. For example, the ease of loading and discharging ships without hatch covers or with hatch covers that extend the entire width of the deck suggests the potential for applying such designs to other ship types. As another example, a firm that has made an engine of record-breaking fuel efficiency will find it natural to pursue the next record-breaking engine through the same development process and to market it to the same customers. This difference matters for theory of routine behavior. Second, exploration innovations are riskier than exploitation innovations because they require acquisition of new knowledge, which is a difference that matters for theory of risk taking. Keeping these two differences in mind, we can examine whether learning rules have different consequences for exploration and exploitation innovations. The learning rules that will be investigated are problemistic search (Cyert and March, 1963: 120–121), slack search (Cyert and March, 1963: 279), organizational momentum (Kelly and Amburgey, 1991), and the exploitation bias (March, 1991). These learning rules are fundamental and have proven successful in predicting

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organizational change, but their consequences for exploration and exploitation have not been sufficiently investigated.

1.3 Problemistic search and innovations Bounded rationality reasoning predicts that decision makers determine goal variables and set aspiration levels for each goal variable so that they can use a simple decision rule: search sequentially through the alternatives until finding one that satisfies the aspiration levels (March and Simon, 1958). When managers experience performance below the aspiration level, they initiate problemistic search for remedial actions (Cyert and March, 1963: 120–123). As a result, performance below the aspiration level predicts a wide range of actions that managers view as consequential for performance, such as strategic reorientation (Lant et al., 1992), market entry (Greve, 1998; McDonald and Westphal, 2003), and innovations (Bolton, 1993; Greve, 2003). The basic form of this argument applies equally to exploration and exploitation innovations and can thus not predict changes in the balance of the two, but a simple extension leads to such a prediction. It has been observed that many innovations that organizations develop are not launched because of perceived lack of fit with the current strategy (Dougherty, 1992; Dougherty and Heller, 1994). This observation leads to two suggestions. First, lack of fit with the current strategy characterizes exploration innovations, suggesting that organizations that do not explore in actual product launches do explore in the development process. Second, the role of managerial evaluations of strategy fit in halting launches of exploration innovations suggest that organizations could turn sharply towards exploration if managers viewed the existing strategy less favorably or accepted more risk. Actions that depart from the current strategy will have less known consequences and thus be seen as risky, and low performance causes increased propensity to take risky actions (Kahneman and Tversky, 1979; Bromiley, 1991; March and Shapira, 1992), making exploration innovations benefit disproportionately from low performance. Hence, performance should have a greater effect on exploration innovations: Hypothesis 1a: When performance relative to the aspiration level decreases, the rates of launching exploration innovations and exploitation innovations increase. Hypothesis 1b: Performance relative to the aspiration level has a stronger effect on exploration innovations than on exploitation innovations.

1.4 Slack search and innovations Slack resources are argued to be an important determinant of innovations (Jelinek and Schoonhoven, 1990; Schoonhoven et al., 1990). Organizations with excess resources engage in slack search, which is search for “innovations that would not be


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approved in the face of scarcity but have strong subunit support” (Cyert and March, 1963: 279). Unlike problemistic search, slack search is unrelated to immediate problems and guided mainly by the interests of the individuals and groups engaged in search. Slack search is thus closely associated with organizational exploration (March, 1991). Organizations with slack resources have greater opportunities for experimentation and laxer performance monitoring, both of which are needed to make exploration innovations (Lounamaa and March, 1987). Slack in the form of administrative resources beyond what is necessary for the short-term operation and maintenance of the organization is called absorbed slack (Singh, 1986). Facilities for R&D, staff specialized for development purposes, and time for development activities among other staff are examples of absorbed slack useful for developing innovations. Absorbed slack less useful for developing innovations includes large administrations, costly facilities, and high wage levels, so absorbed slack only increases the supply of innovations if some of it is directed to innovation development. Slack in the form of financial reserves is called unabsorbed slack (Singh, 1986). Unabsorbed slack is not directly helpful in the development of innovations, but it affects the decision to continue R&D projects because great financial resources lead to laxer performance monitoring of uncertain projects. Strict performance monitoring can cause new activities to be aborted before the organization has accumulated enough experience to know whether they will eventually improve its performance (Lounamaa and March, 1987), which can cause premature termination of R&D projects. As argued previously, exploration innovations have high risk of termination as a result of perceived lack of fit with the current strategy (Dougherty, 1992) and high risk (Singh, 1986; Howell and Higgins, 1990), so an innovation development process geared towards risk reduction and strategy congruence may weed them out even before the launch stage. Organizational slack can save explorative development processes in the form of unauthorized research (Jelinek and Schoonhoven, 1990; Burgelman, 1991), leading to the suggestion that slack increases the development of exploration innovations. However, this prediction cannot be made for exploration innovations only, as low levels of slack could lead to termination of projects to develop exploitation innovations as well. Thus, we predict that slack search increases the rates of both exploration and exploitation innovations, but that the effect on exploration innovations is greater: Hypothesis 2a: When absorbed and unabsorbed slack resources increase, the rates of launching exploration innovations and exploitation innovations increase. Hypothesis 2b: Absorbed and unabsorbed slack resources have a stronger effect on exploration innovations than on exploitation innovations.

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1.5 Routinization of innovative direction Organizations build up routines when activities are performed repeatedly (Nelson and Winter, 1982), and this routinization offers productivity advantages such as those seen in learning-curve effects on the unit cost of production (Argote, 1999), causing actions performed recently to be more efficiently executed. Also, major managerial actions are subject to interpretation and self-attribution that give actions recently performed greater prominence in decision making (March et al., 1991). These mechanisms make repetition more likely than novelty and give greater short-term rewards to repeated actions than to new ones (Levinthal and March, 1981). The result is organizational momentum, or the tendency to repeat previous actions (Miller and Friesen, 1982; Kelly and Amburgey, 1991). Organizational momentum is seen as one of the processes favoring exploitation over exploration because momentum is caused by positive feedback from repetition of known actions (Kelly and Amburgey, 1991). The homogeneity of exploitation innovations may result in greater ease of routinizing them, but some organizations also develop routines for making exploration innovations (Jelinek and Schoonhoven, 1990). Thus, an organization is more likely to make an exploitation innovation the more recently it has made an exploitation innovation, and is more likely to make an exploration innovation the more recently it has made an exploration innovation. Conversely, organizational routines atrophy with disuse (Argote, 1999), making it less likely that organizations will innovate when they have not done so for a while. A parsimonious way of formalizing this argument is to let the likelihood of an exploitation innovation and an exploration innovation, respectively, depend on the duration since the last time the same kind of innovation has been made (Amburgey et al., 1993). The weakening of routines through disuse implies that the effect of duration is negative; making an innovation less likely the more time has passed since the previous one. These predictions can be made: Hypothesis 3a: The rate of launching exploration innovations decreases with the duration since the last exploration innovation. Hypothesis 3b: The rate of launching exploitation innovations decreases with the duration since the last exploitation innovation.

1.6 Exploitation bias and innovative direction Exploration and exploitation compete for a limited pool of resources, leaving managers with a choice of which to support more strongly (March, 1991). Moreover, exploitation and exploration differ in attention patterns because an organization that explores seeks to learn from experiments performed by subunits, while an organization that exploits seeks to make each subunit conduct activities that draw on the existing organizational knowledge (March, 1991). An important mechanism to support exploitation is cancellation of R&D projects that diverge from the


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organizational strategy and transfer of resources to strategy-congruent projects (Burgelman, 1991; Dougherty and Heller, 1994). How the tradeoff between exploration and exploitation is made differs among organizations, and may systematically depend on their prior record of exploration and exploitation, as hypothesized earlier. In addition to this mechanism, theoretical work has proposed that the tradeoff tends to be made in favor of exploiting more intensely because exploitation has greater and more certain benefits in the short run, which is a feedback pattern that easily draws attention and is rewarded by managerial incentives (Levinthal and March, 1993). Thus, the competition for resources is asymmetric, with exploitation innovations harming exploration innovations but not the other way around. The result is that performing exploitation not only improves the organizational routines for exploitation and increases the likelihood that exploitation will be performed again; it also reduces the resources available for exploration. Thus the prediction is: Hypothesis 4: The rate of launching exploration innovations decreases with the proportion of recent exploitation innovations.

1.7 Integration and theoretical problems Through its prediction that resources allow generation of innovations, the theory of slack search seems to posit the opposite effect on innovations as that of problemistic search. On closer examination, the theories differ because problemistic search is a theory of perceived performance shortfalls, while slack is one of excess resources. An organization with reserves built up over a period of time but with recent performance below its managers’ aspiration levels would simultaneously have resources necessary for innovating and a perceived need to innovate, and would be predicted to have a high likelihood of launching innovations. An organization with low reserves may perform above the expectations of its managers, and would be predicted to have a very low likelihood of launching innovations. One may still ask whether these mechanisms are different only in principle, but in reality are correlated because slack resources diminish as a result of low performance and increase as a result of high performance. There are two reasons for slack and performance to have low correlation. The first is that slack is a stock of resources that is incrementally changed by the resource flow from the performance. If firm differences in stocks are large compared with the temporal variation in resource flows, then performance and slack will not be highly correlated. The second is that the evaluation of performance by comparing with an aspiration level decouples subjective evaluations of success from resource flows (Levinthal and March, 1981). While these relations both reduce the correlation of slack and performance, it is an empirical question whether they are distinct in a given study context.

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Problemistic search also has a potential conflict with the theory of threat rigidity, which argues that low performance may be viewed as a threat, leading to risk aversion and inability to act (Staw et al., 1981). Theoretical work has sought to resolve this conflict by determining the conditions under which low performance is seen as a threat to the existence of the organization or as a repairable problem (March and Shapira, 1992; Mone et al., 1998). This resolution fits well with evidence that problemistic search is usually found in organizations with low profitability, but threat rigidity has been observed in organizations close to bankruptcy or with extremely low profitability (Wiseman and Bromiley, 1996; Ketchen and Palmer, 1999; Ferrier et al., 2002; Miller and Chen, 2004). Hence, the prediction that innovations become more likely as the performance declines should hold for most performance levels, but may be counteracted by threat rigidity for very low performance. Finally, an unresolved theoretical question is whether the increased rate of change in low-performing organizations is a result of problemistic search (Cyert and March, 1963), risk taking (Singh, 1986; Bromiley, 1991), or a combination of the two (Greve, 1998). The causes are difficult to distinguish because problemistic search and risk taking make the same prediction as long as changing is more risky than not changing. Investigation of exploration and exploitation innovations gives leverage for discovering the role of risk, as the prediction that the effect on exploration innovations is greater relies solely on theory of risk taking. Hence, if risk taking is an important explanation for the reactions to low performance, the findings from analyzing exploration innovation and exploitation innovations should diverge. If risk taking is not important, and thus problemistic search alone accounts for the effect, then the findings for exploration innovations and exploitation innovations should be similar. Clearly, these hypotheses can supply evidence relevant to important theoretical disputes.

2. Methods 2.1 Japanese shipbuilding This study uses data on the Japanese shipbuilding industry from 1971 to 2000. The Japanese shipbuilding industry built its competitiveness in the 1950s and early 60s through a combination of government financing of work-in-process inventories, firms’ attention to production efficiency, and anticipation of demand changes such as the tanker boom (Blumental, 1976; Chida and Davies, 1990). Modern production facilities gave Japanese shipbuilders unrivaled labor productivity, and large docks allowed them to dominate the supertanker market. Rapid delivery times were also a source of competitive advantage, and were achieved through effective production management and an avoidance of large order reserves (Chida and Davies, 1990). Finally, innovations were used to increase the competitive


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18000 16000 14000

1000 GT

12000

Japan Korea China Americas W. Europe E. Europe Others

10000 8000 6000 4000 2000

04

02

20

00

20

20

96 98 19

94

19

92

19

90

19

88

19

86

19

84

19

82

19

80

19

78

19

76

19

74

19

72

19

70

19

68

19

66

19

19

19

64

0

Year

Figure 1 World ship deliveries by nation or region of production.

strength and create new market niches of large and high technology vessels (Chida and Davies, 1990). Ships are investment goods used in the production of transportation services. They have highly flexible life-times: 20 years is often used as a rough estimate of the lifetime of ships, but they can be scrapped earlier if the shipping firm does not see prospects for transportation rates high enough to justify continued operations, and can continue operating for at least a decade more if high rates justify the higher insurance, maintenance, and repair costs of old ships. The result of these product characteristics is an extremely variable demand, as shown in Figure 1. Because the Japanese shipbuilding industry was the world’s largest when the oil shock of 1972 caused the shipbuilding market to collapse, it (along with Western Europe) took most of the production cut. Western European production never recovered to past levels as a result of large capacity cuts. Japanese shipbuilders took a different approach to this crisis. Shipbuilding became the target of Ministry of Technology and Industry policies for structural adjustment of declining industries in the late 1970s, which included plans to cut capacity and to merge some shipbuilders. The firms were able to negotiate joint capacity cuts, but the ministry’s merger plan failed because the target firms resisted (Strath, 1994). After the oil crisis passed, the shipbuilders were left to fend for themselves, as policy attention turned to industries thought to have greater growth potential (Patrick, 1986). The shipbuilders kept their production capacity roughly constant after the initial round of cuts and used ship repairs and non-shipbuilding business to maintain activity. The average annual growth of production facilities in Japan fell from 11% during the most intense expansion from 1967 to 1973 to 4% in the decade

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Table 1 Selected firm characteristics over time Firms

Mean

SD

Minimum

Maximum

Employees

13

19,511

22,180

851

75,330

Sales

13

115,927

135,173

7407

385,155

463,689

21,326

1,606,831

0.007

0.205

1973

Order Res.

13

419,289

ROA

13

0.046

Employees

12

17,010

19,218

947

63,771

Sales Order Res.

12 12

374,471 461,546

446,380 625,944

16,842 14,182

1,274,862 2,050,309

ROA

12

0.061

0.148

0.443

0.118

Employees

11

16,012

16,357

914

51,763

Sales

11

566,591

652,820

19,393

1,999,745

0.055

1979

1985

Order Res.

11

713,695

925,378

16,430

3,120,241

ROA

11

0.036

0.045

0.057

0.135

1991 Employees

11

10,973

13,329

615

44,272

Sales

11

570,163

719,633

23,492

2,327,067

Order Res.

11

799,005

1,174,480

30,644

4,051,316

ROA

11

0.052

0.037

0.038

0.094

Employees

11

9698

12,029

547

40,829

Sales

11

653,215

805,666

25,363

2,733,765

Order Res. ROA

11 11

986,165 0.045

1,544,477 0.031

29,770 0.003

5,342,736 0.093

1997

following the oil crisis and 1% in the next decade.1 However, employment levels were cut throughout this period. As a result of these decisions, Japanese shipbuilding firms captured much of the growth in the brief recovery in the first half of the 1980s and the sustained recovery thereafter, but also took most of the production cuts in the intervening recession. Possibly as a result of the order cancellations after the oil shock, Japanese firms built multi-year order reserves when the shipbuilding market recovered, just as their foreign competitors did. Table 1 shows how selected firm characteristics changed in 6-year intervals starting in 1973. Throughout this period, the Japanese shipbuilders could do little to counter competition from South Korea. Korean labor rates were one-third of the Japanese until 1990, and South Korean shipbuilders had modern facilities, high productivity, 1

Author’s calculation from establishment-level data on production facility value.


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and government support. Recently, Chinese shipbuilders, which have yet-lower labor rates, have expanded greatly, but are technologically behind the Japanese and South Korean ones. The addition of shipbuilding capacity in China and elsewhere has kept prices of most ship categories constant or even slightly declining after 19952 despite the great increase in demand, but the shipbuilders have still been generally profitable in this period as a result of the high capacity utilization. The idea of consolidation through mergers resurfaced in year 2000 as two different groups of Japanese shipbuilders announced that they were in talks to form alliances with a final goal of merging (Hitachi-NKK and IHI-Sumitomo-Kawasaki-Mitsui; with industry leader Mitsubishi notably absent from both groups). The first merger to be realized was IHI and Sumitomo in October 2002.

2.2 Data The data contain 13 firms, of which 11 were active throughout the study period, while two disappeared through mergers in 1978 and 1985, respectively. These are all the Japanese shipbuilding firms listed on the Tokyo and Osaka stock exchanges, and all are present in the international market for oceangoing ships. The firms range in size from the giant Mitsubishi Heavy Industries with more than 70,000 employees to Hashihama Shipbuilding with about 800 employees. Earlier work found that the Japanese shipbuilders launched innovations in response to performance below the aspiration level, but did not find effects of slack resources (Greve, 2003). That work did not distinguish between exploration innovations and exploitation innovations, however, and estimated Poisson models of the number of innovations per year, which does not allow testing of how the rate of making innovations depends on the duration since the last innovation. Thus, the hypotheses presented here have not seen previous testing.

2.3 Dependent variables Innovations were identified by reading the monthly journals New Technology Japan and Techno Japan from 1971 through 2000. The use of technology journals as data sources reflects a tradeoff between the characteristics of alternative data sources on innovations. Innovations are listed in the journals, when they meet the industry’s definition of a technological innovation and are thus subject to external review. The innovations in these journals are launched as products, unlike patents, which are sometimes made just to record and protect a technological advance not yet viewed as ready for use in a product. Because risk taking and change of routines is important in the theory predicting exploration, mere recording of a technological advance is a less appropriate dependent variable than the launch 2

E.g., as reflected in the ship prices shown in Table 1.6.2 in Shipping Statistics Yearbook by the Institute of Shipping Economics and Logistics.

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of a product innovation. Finally, firm sources such as annual reports lack external review of the innovativeness of new technologies, so they are vulnerable to self-presentation concerns. Classification of the innovations was based on the written description of each innovation as follows: (i) Exploration innovations were innovations that the sources described as involving development of new technology or application of existing technology not earlier used by the focal firm. Thus, Mitsubishi developing a semisubmerged catamaran hull for high-speed passenger traffic for the first time in the world and Ishikawajima-Harima developing a navigational simulator for the first time in Japan were exploration innovations. This coding identified 70 exploration innovations. (ii) All remaining innovations, 203 in total, were exploitation innovations that did not involve the firm learning or developing new technology. The frequency of exploitation innovations in the data reflects the ability of shipbuilders to reconfigure existing technologies to provide new products, as in the container ship example mentioned earlier and new marine engine designs by various shipbuilders. Figure 2 shows innovation event plots in which each innovation is placed in a time line, and exploration and exploitation innovations are separated. The plots are shown for three shipbuilders in the data: Mitsubishi, Mitsui, and Hitachi. These have more than the average number of innovations, but this helps demonstrate two features of their innovation plots that are typical of the firms in the data. First, the rate of innovation is sensitive to the economic conditions, as there are many more innovations in the troubled early period than in the high-demand later period. The analysis will show that this difference is accounted for by the effect of firm profitability on innovativeness. Second, there is serial correlation in the type of innovation launched. Exploitation innovations come in batches, and to some extent this seems to occur for exploration innovations as well. The latter observation is important for the theoretical arguments made earlier, because it supports the idea of temporal specialization in exploration or exploitation rather than mixing of the two. To provide a formal test, Table 2 shows a simple analysis of the likelihood of switching innovation type. Each innovation is an observation, and the dependent variable is whether the innovation is of a different type (exploration or exploitation) than the previous. Firms are less likely to switch from an exploitation innovation to an exploration innovation than the other way around, as one would expect if exploitation drives out exploration. However, the relative scarcity of exploration innovations can also explain this finding. There is not a significant effect of the duration since the last innovation, though the positive sign suggests that switching is least likely shortly after making an innovation. This is similar to the tendency of self-reinforcing exploration and exploitation observed in alliance formation (Lavie and Rosenkopf, 2006). Later analysis will examine the firmlevel determinant of each type of innovation.


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Exploitation Exploration

Innovation

Mitsubishi

01jan1972

01jan1980

01jan1988

01jan1996

Date

Innovation

Exploration Exploitation

Hitachi

01jan1972

01jan1980

01jan1988

01jan1996

Date

Innovation

Exploration Exploitation

Mitsui

01jan1972

01jan1980

01jan1988

01jan1996

Date

Figure 2 Innovation event plots.

2.4 Independent variables Nikkei corporation’s NEEDS database of corporate accounts was used to download accounting measures of performance and slack. Performance was measured through return on assets (ROA), defined as the operating income divided by the total assets. Return on assets is theoretically appealing because it is an estimate of economic rents

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Table 2 Logit model of switching innovation type Variable

Estimate

Previous innovation is exploitation

2.133**

Odds Ratio 0.118

(0.319) Time since previous innovation

0.239

1.270

(0.157) Constant

0.648 (0.368)

Log likelihood Likelihood ratio test

146.598 53.1**

Standard errors are shown in parentheses. 258 observations. **P50.01; two-sided tests.

and is salient in managerial performance evaluation. It is the preferred measure in work on performance effects on organizational change and risk taking (Bromiley, 1991; Lant et al., 1992; Wiseman and Bromiley, 1996; Miller and Chen, 2004). Return on equity is less preferred because it blends profitability from operations with the debt–equity mix. Other measures that have seen less use are return on sales (Audia et al., 2000) and various industry-specific performance measures (Miller and Chen, 1994; Greve, 1998). Following Cyert and March (1963: 123), the performance (P) was compared with an aspiration level (A) computed as a mixture of a social and a historical aspiration level. The social aspiration (SA) is the average of the other firms’ performance, calculated as the average ROA of all shipbuilders in the data except the focal firm. The historical aspiration (HA) level is a weighted average of the previous-year historical aspiration level and the previous-year performance of the same firm. Assuming a1 and a2 be weights, the formulae are: Ati ¼ a1 SAti þ ð1 a1 ÞHAti X SAti ¼ P =ðN 1Þ t;j j6¼i HAti ¼ a2 HAt1;i þ ð1 a2 ÞPt1;i Here, t is a time subscript and i/j are firm subscripts. The weights were estimated by searching over all parameter values in increments of 0.1 and taking the combination giving the highest model log likelihood. This procedure yielded a1 ¼ 0.8 and a2 ¼ 0.2, which means that the industry average of performance had a weight of 0.8, the firm performance in the previous year had a weight of 0.16, and the historical aspiration level in the previous year had a weight of 0.04. The performance was split


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into the two variables performance above the aspiration level and performance below the aspiration level to allow a greater effect of performance above the aspiration level, as predicted by performance feedback theory (Greve, 1998). Absorbed slack is measured as the ratio of selling, general, and administrative expenses to Sales, and unabsorbed slack is measured as the ratio of quick assets (cash and marketable securities) to liabilities (Bourgeois, 1981; Singh, 1986). These measures use accounting data to capture real and financial assets in excess of what is needed for regular operations, and thus available for search (Bourgeois, 1981). The theoretical fit and easy replicability across different samples have made them leading measures in research on slack (Bromiley, 1991; Nohria and Gulati, 1996; Wiseman and Catanach, 1997).3 These slack measures are easiest to interpret when the organizations are involved in similar forms of business, as they measure excess resources without adjusting for the normal resource requirement for a given business. Because innovations are developed over a period of time, the slack measures are expressed as averages over the four preceding years. Organizational momentum was captured by specifying a hazard rate model in which the rate depends on the duration since the previous event of the same type (Amburgey et al., 1993). The prediction is negative duration dependence, which means that the event is more likely shortly after the previous occurrence. In addition, for each type of innovation, an indicator variable was set to one until the first time a firm was observed making an innovation of that type in the study period, and was set to zero thereafter. This variable is an adjustment for the left-censored observations, which have unknown values of the duration since the last innovation. In preliminary analyses, an alternative approach of deleting all left-censored observations was tried, and found to yield the same results. The models entering an indicator for left-censored observations are displayed because they use more of the available data. The proportion of all firm innovations in the four most recent years that were exploitation innovations was included to model whether exploitation drives out exploration. In other not to lose four full years of observations in the start, the observations from 1972 through 1974 used all available years of innovations instead of the full four years to compute this proportion. The data for 1971 were only used to initialize the innovation variables. To ensure that the findings were not vulnerable to changes in the specification, other specifications were also tried, but these (6- and 8-year proportions, a log ratio, and the average of time-discounted innovation counts) did not provide better fit or different results. 3

Alternative measures of slack exist, along with some overlapping terminology. For example, current assets divided by current liabilities is called available slack, and sales and general administrative expenses divided by sales is also known as recoverable slack (Bromiley, 1991).

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Table 3 Descriptive statistics and correlation coefficients Variable

Mean

SD

1

2

Innovations in

13.298 9.576

1.00

8.770 1.458

0.17

3

4

5

6

7

8

9

industry Log employees Freight rates Average customer

1.00

10.565 3.743 0.32 0.10 3.382 0.944 0.03

1.00

0.14 0.04

1.00

size Performance,

0.013 0.028 0.06 0.06

0.09 0.12

1.00

over aspiration Performance,

0.016 0.035

0.02

0.16 0.10

0.14

0.21

1.00

0.48

0.12 0.04

0.08

under aspiration Absorbed slack

0.072 0.028 0.41

Unabsorbed slack

0.600 0.096

0.52 0.23 0.22 0.05 0.21 0.05 0.46 1.00

0.24

1.00

Exploitation

0.206 0.405

0.04 0.62 0.09 0.03

0.09 0.04 0.39 0.26 1.00

0.423 0.494

0.09 0.83 0.08 0.12

0.10 0.13 0.76 0.36 0.58 1.00

indicator Exploration indicator Exploitation

0.846 0.239 0.11 0.47 0.01

0.01 0.00 0.09 0.41 0.09 0.30 0.55

proportion

4061 observations.

Firms engaged in technological races may launch innovations as a response to innovations made by other firms. To capture this effect, the number of innovations made by other Japanese shipbuilding firms in the previous year is entered. To capture the effect of firm size on innovation capability, the logarithm of the number of workers was entered. Asset value was also considered as a size variable, but correlated highly with the number of workers. To control for the effect of economic trends in shipbuilding on R&D and innovation, a number of industry measures were entered in exploratory runs, such as the finished tonnage completed by the Japanese shipbuilders, the growth in shipping income, and various freight rates. Of these, the Gulf–Europe freight rate proved to be the best predictor and was retained in the analysis. Freight rates predict shipbuilding because they are high when there is scarcity of shipping capacity. Finally, large customers may induce the shipbuilder to make innovations as a result of their greater product development capabilities and more specialized needs (Dyer and Singh, 1998; Darr and Talmud, 2003; Ingram and Lifschitz, 2006). To control for this effect, the average customer size, specified as the logarithm of the average number of ships owned by customers to whom it delivered ships, was entered as a control variable. The independent variables were lagged by 1 year.


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Table 3 displays the mean, SD, and correlation matrix of the independent variables. Some variables have medium to high correlation, but preliminary analyses with subsets of these variables indicated that the estimates were stable. In addition to the correlations reported in the table, two correlations coefficients of interest are those of the return on assets (not adjusted for aspiration levels) with absorbed slack and unabsorbed slack. These are 0.05 and 0.23, respectively, showing that the returns in a given year are practically unrelated to the organizational slack. Thus, performance and slack are both conceptually and empirically distinct.

2.5 Statistical model The analysis was performed as a continuous-time event history model with two events—exploration innovation and exploitation innovation. These were modeled as a competing risk model with a Gompertz specification of the rate and duration since the last innovation of the same type as the time variable. The Gompertz model specifies the rate (r) as a function of time (t) and covariates (X) with associated coefficients () as follows: rt ¼ e X e t The Gompertz model can be fit to a rate that declines over time (which would support H3a and H3b), one that is constant, or one that increases over time (which would contradict H3a and H3b) depending on whether the duration parameter is negative, zero, or positive. Seemingly unrelated regression was used to obtain significance tests of coefficient differences across the outcomes, as required by hypotheses 1b and 2b.4 The data file split spells monthly, in order to update the covariates, and the event times were precise to the nearest month. The data contained three pairs of tied event times (two innovations by the same firm in the same month), which were separated by letting them occur at a half-month interval. The estimation was done with Stata 9.0.

3. Results Table 4 presents the results of the analysis. It contains three sets of estimates. Model 1 omits the duration dependence and the proportion of exploitation innovations, and is thus similar to the model specification in Greve (2003) except that it is a hazard rate model instead of a count model, and it separates exploration innovations and exploitation innovations instead of combining them.5 For exploitation innovations, performance relative to the aspiration level has a negative and significant effect as 4

The simpler test of mean equality of two normal distributions is incorrect because the coefficient estimates are correlated with each other.

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5 This model is estimated using the exponential distribution, which is the same as a Gompertz model with the duration dependence constrained to be zero.

Table 4 Event history models of innovations (1) Model Outcome Log employees Log age Freight rates Innovations in industry Average customer size Performance – aspiration level

Exploit 1.047**

Exploit

Explore

0.862** (0.139)

0.681

2.310*

(0.777)

(0.922)

(0.848)

0.104**

0.012

(0.024)

(0.025)

(0.022)

(0.023)

(0.023)

(0.024)

0.007 (0.012)

0.023 (0.015)

0.003 (0.012)

0.016 (0.012)

0.001 (0.012)

0.016 (0.013)

0.091

0.187y

0.076

(0.094)

(0.096)

(0.098)

(0.109)

(0.112)

(0.106)

34.041**

9.394*

31.985**

9.733*

31.909**

0.015

10.047* (4.384) 2.399

(10.475) 26.634**

(4.756) 3.358

(0.279) 2.210** (0.763) 0.101**

0.158

(8.635) 28.159**

0.836**

Explore

1.120**

0.891

0.881**

Exploit

(0.214)

(when over aspiration level) (when under aspiration level)

Explore

(3)

(0.120)

Performance – aspiration level Absorbed slack

(2)

(0.170) 0.900 (0.841) 0.012

0.088

(4.720) 3.459

0.879 (0.280) 2.206** (0.730) 0.101**

0.158

(8.389) 28.167**

(3.087)

(7.809)

(2.888)

(6.369)

(2.804)

(6.559)

3.462 (4.366)

10.726y (6.525)

2.461 (3.205)

8.303 (6.561)

1.616 (3.368)

8.301 (6.560)

Unabsorbed slack

4.329** (1.254)

1.894 (1.936)

Exploitation indicator

3.813** (1.173)

0.876 (1.601)

0.748

0.781

(1.118)

(1.090)

Exploration indicator Duration since last exploitation innovation

3.849** (1.198)

0.816

0.820

(0.889)

(0.910)

0.0104** (0.0037)

0.0109** (0.0039) 0.0092

0.0093

(0.0059)

Exploitation proportion

(0.0065) 0.408 (0.539)

Constant

13.457** (4.209)

Events Log pseudolikelihood Likelihood ratio Chi-square Degrees of freedom

195 253.233 252.228** 9

7.009

9.950*

3.875

9.299*

(4.867)

(4.684)

(4.997)

(4.598)

67 101.981 116.4469** 9

195 249.028 260.638** 11

67 99.727 120.9549** 11

Robust standard errors adjusted for clustering by firm are in parentheses. 4061 observations. yP50.10; *P50.105; **P50.01; two-sided tests.

195 248.451 261.792** 12

0.024 (0.449) 3.877 (4.987) 67 99.727 120.9549** 12


Duration since last exploration innovation

0.871 (1.621)

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H. R. Greve

predicted, but only above the aspiration level. For exploration innovations, performance relative to the aspiration has a negative and significant effect above the aspiration level, and a coefficient twice the size of that for exploitation innovations, and a positive and significant effect below the aspiration level. The results for exploitation innovations are exactly as those in Greve (2003), but the positive effect of performance below the aspiration level on exploration innovations is a new finding. It suggests that the greater risk of exploration innovations makes these less likely for firms with very low performance, giving the greatest likelihood of exploration innovations for firms with performance near the aspiration level. For exploitation innovations, unabsorbed slack has a positive and significant effect. This finding is different from Greve (2003), who found no effect of slack. The difference in results suggests that financial resources are more important for exploitation innovations. Model 2 introduces the duration dependence variables, and is used to test hypotheses 1 through 3. Hypothesis 1a is tested by the coefficient estimates for performance minus aspiration levels, which show the same results as in model 1. The effects on exploitation are both negative as predicted, and the coefficient above the aspiration level is significant, in support of hypothesis 1a. The effect on exploration is negative and significant above the aspiration level, in support of hypothesis 1a. Below the aspiration level, the effect on exploration is positive and significant, which contradicts hypothesis 1a and shows that this more risky outcome is less likely in firms with very low performance. Hypothesis 1b is tested by seemingly unrelated regression Chi-square tests of equality of the coefficients for performance across the exploration and exploitation. Such tests were performed, but were not significant for performance above the aspiration level despite the difference in effect magnitude. The coefficients of performance below the aspiration level were significantly different at the 5% level, consistent with hypothesis 1b. Hypothesis 2a is tested by the coefficients for slack. The findings show lack of support for an effect of absorbed slack on exploitation innovations, as before. The coefficient for unabsorbed slack is positive and significant for exploitation, supporting hypothesis 2a and indicating that financial resources help organizations make exploitation innovations. For exploration innovations, neither slack variable has significant effect. The Chi-square test for difference of coefficients used for assessing hypothesis 2b found no significant difference of the effect of slack on the two outcomes, so despite the different patterns of significance we cannot conclude that slack affects exploration and exploitation differently. Hypothesis 3a is tested by the duration coefficient of exploitation, and is supported by the negative and significant negative coefficient estimates. Hypothesis 3b is tested by the duration coefficient of exploration, and fails to receive support because the coefficient is insignificant. The exploitation coefficient shows the predicted momentum from previous exploitation innovations, resulting in higher rates of making exploitation innovations soon after a previous one than after some


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time has passed. The duration coefficient is –0.0104, which implies that a firm that lets 12 months lapse since the last exploitation innovation will have a rate of innovating that drops to exp(12 0.0104) ¼ 88.3% of the original rate. This is an appreciable decline, but it is smaller than losses from production stoppages, which have been estimated to as much as 90% in a year (Argote et al., 1990). In model 3, the proportion of exploitation innovations has been added to capture the effect of competition between exploration and exploitation. Hypothesis 4 predicts a negative effect for exploration, and also implies that no significant effect be seen for exploitation. However, both coefficient estimates are insignificant, and fail to support the hypothesis. Compared with model 2, model 3 has unchanged findings on the variables testing hypotheses 1 through 3. This is important with respect to the findings on duration, which could potentially be affected by entering the new variable of exploitation proportion. Various models were estimated to test the sensitivity of the estimates to changes in the model specification. First, replacing absorbed slack with research and development intensity gave no effect of R&D intensity on innovations and unchanged results for the other variables. Second, estimating performance and slack variables separately (but with all other variables entered) led to unchanged results. Performance and slack appear to explain different parts of the variance in the rate of innovation. Third, estimating models with frailty effects of firms (similar to random effects) showed that the frailty effects were not significant, and showed only minor changes in parameter estimates.

4. Discussion The vulnerability of exploration is fundamental issue in organizational adaptation. “Compared to returns from exploitation, returns from exploration are systematically less certain, more remote in time, and organizationally more distant from the locus of action and adaptation” (March, 1991: 73). Organizational routines such as local search naturally lead to exploitation (Stuart and Podolny, 1996), and managerial practices such as the search for production efficiency (Benner and Tushman, 2002) and strategic consistency (Dougherty and Heller, 1994) have the same effect. Current theory of exploration and exploitation raises the question of how organizations manage to explore at all. The theory developed here uses the lower risk and greater routinization of exploitation to predict that exploration innovations are more sensitive to performance and slack and less affected by momentum. Although the findings suggested some differences between exploitation and exploration innovations, the similarities are more salient. Exploration innovations were scarcer than exploitation innovations in the data, but were generated by similar processes. This is different from current theory, which specifies that low performance

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initially triggers local search and thus exploitation, but persistent problems cause expansion of search and potentially lead to exploration (Cyert and March, 1963; Grinyer and McKiernan, 1990; Katila and Ahuja, 2002). The findings of this study suggest that reductions in performance significantly increased the rate of making exploration innovations as well as that of exploitation innovations, and at the same time. Managers solving problems do turn to exploitation as a solution, but also try exploration. Problemistic search thus offers an explanation for why organizations that usually exploit will sometimes try exploration. The analyses showed that unabsorbed (financial) slack affected exploitation innovations. This finding is perhaps surprising, and suggests that financial reserves affects managerial decision making so strongly that they increase innovation launch rates. Money in the bank obviously does not create innovations, but it does influence managerial perceptions of risk. This finding is consistent with work showing that risk taking is increased by unabsorbed slack, but not by absorbed slack (Singh, 1986), and suggests a role of risk preferences in the launching of innovations. The findings suggest that some organizations build up routines for exploration that lead them to be overall more innovative than other organizations (Miller and Friesen, 1982). The analyses showed decay in the rate of exploitation innovations, when the duration since the last one increased, as routinization of innovations would predict. The rates of decay for exploration innovations were approximately the same as those for exploitation innovations, but not significant, so it is difficult to tell whether these innovation types are routinized differently. Routinization theory predicts repetition of innovation patterns, which explains both cross-sectional differences of firm innovation rates and temporal clustering of individual firms’ exploration and exploitation innovations. The analysis did not show that exploitation reduces exploration rates, and hence failed to support the direct tradeoff between exploration and exploitation that theory has suggested (March, 1991). Overall the findings show that organizational learning theory explains how organizations adjust the rates of exploration and exploitation innovations, but it does better in predicting the overall level of each one than in predicting shifts between them. The study has some limitations that suggest a need for further work. First, the data set is not sufficiently large that we can be sure that the failure to find differential effects on exploration and exploitation is because the effects are not there, or because the effects are too weak to be distinguished in the data. This problem is intrinsic to the study of innovations, which are often rare events. A larger study might find differential effects between these determinants of exploration and exploitation innovations, and would give better indication of whether direct competition from exploitation to exploration occurs. Second, the single-industry research design leaves doubts about the generalizability of the findings. There are industries with a higher baseline pace of innovation because they use technologies that are in rapid development, as in


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computing and telecommunications. Without further investigation we cannot be sure that the organizational search processes seen in such industries are the same as those observed here, so investigation of these hypotheses in industries with fastpaced technological change should be a high priority. Finally, exploration and exploitation are concepts that extend to the market behavior of firms as well as to their technology behaviors. It is not clear that studies can generalize across these two outcomes, although from a theoretical viewpoint, symmetric findings are expected. When investigating exploration and exploitation in markets, operationalizing the degree of exploration in a given action (such as a market entry) is probably at least as difficult as when investigating exploration and exploitation in technology. However, it would be highly valuable to investigate the determinants of exploration and exploitation in markets. There are clear opportunities for further research to extend our knowledge of the drivers of exploration and exploitation. It would be useful to expand the search for variables that shift the balance between exploration and exploitation, and to perform similar tests of their joint effects on exploration innovation and exploitation innovations. For example, an important topic in current research is how the network positions of firms affect their innovativeness (Baum et al., 2000; Tsai, 2001; Ruef, 2002). It seems likely that network position affects the type of innovation firms make as well as the overall level of innovativeness. Firms with diverse networks through structural holes or diverse contacts may more easily obtain the knowledge necessary to make exploration innovations (Burt, 2004). However, innovation rates are not just determined by knowledge entering organizations, but also by how knowledge is turned into innovations that are launched as products (Fiol, 1996). Much work has sought to identify internal organizational routines that efficiently generate innovations, as well as factors that prevent successful innovation development (Burgelman and Sayles, 1986; LeonardBarton, 1995; Van de Ven et al., 1999). This work should be extended to also identify factors that lead to more or less explorative innovations. The present findings showed rather slow decay of innovation rates, implying that the ability to innovate is embedded in relatively stable organizational structures and routines. Discovering exactly what these structures and routines are requires direct examination of the process. The findings suggested a large influence of managerial decision making on organizational innovations, as earlier work has done (Dougherty, 1992; Dougherty and Heller, 1994). Specifically, managerial problem solving and risk taking seem to account for the effects of performance relative to aspirations and unabsorbed slack. This suggests that examination of additional managerial characteristics that may influence organizational innovativeness will be fruitful. Clearly, characteristics that predict willingness to accept applications of new knowledge would be particularly important for exploration innovations. One conjecture is that management teams with greater cognitive diversity will have greater willingness to launch exploration

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innovations because a diverse base of knowledge and interpretation improves the ability to analyze such innovations (De Dreu and West, 2001). Though the concepts of exploration and exploitation have deep roots in organizational theory, empirical research distinguishing these types of innovations is still in its infancy. This study and other recent empirical work have shown that organizational theory can give testable predictions on how organizations choose between exploration and exploitation, and that these predictions have some empirical success. It seems likely that further investigation of organizational exploration and exploitation will help develop the theory of organizational adaptation to uncertain environments.

Acknowledgements I am grateful for financial support from Japan’s Ministry of Education and for helpful comments from Wesley Cohen, Jerker Denrell, Tai-Young Kim, Arie Lewin, Torben Pedersen, Sim Sitkin, Bilian N. Sullivan, Anand Swaminathan, Alva Taylor, Udo Zander, Xueguang Zhou, and seminar participants at Copenhagen Business School, Duke University, Hong Kong University of Science and Technology, and the Prince Bertil Symposium in Stockholm.

Address for correspondence Henrich R. Greve, Norwegian School of Management BI, 0442 Oslo, Norway. e-mail: [email protected]

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