Product Strategy and Industrial Leadership: Firm and Country Effects in the Worldwide Floppy Disk Drive Industry
David G. McKendrick Durham University Anand Swaminathan Graduate School of Management University of California-Davis One Shields Avenue, 157 AOB4 Davis, CA 95616-8609 Phone: (530) 752-9916; E-mail:
[email protected] James B. Wade Rutgers University
January 2007
Product Strategy over the Firm Life Cycle and Industrial Leadership: Firm and Country Effects in the Worldwide Floppy Disk Drive Industry
Abstract We examine the effect of product strategy on firm performance in the worldwide floppy disk drive industry from its emergence in 1970 to 1998. We find that the effects of both product strategy and country of origin vary over the firm life cycle. Younger firms benefit from superior technology whereas older firms benefit from a broader product line. Firms that have an older portfolio of products experience higher failure rates especially at older ages. We find that Japanese firms experience lower failure rates both when they are very young and very old. Further analyses revealed that these effects result from having a younger portfolio of products and a broader product line at all ages. Japanese firms also catch up with firms from other regions in technological sophistication as they age. Industry leadership shifts from American and European firms to Japanese firms as the basis of competitive success changes from technological sophistication to possessing a broader product line of products that are frequently updated.
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Product-level dynamics are becoming of increasing interest to scholars of strategy and organizations. Researchers have followed two primary approaches to product competition. One is product demography, where vital rates of products, such as product longevity and entry rates, are the focus of interest. Greenstein & Wade (1998), for example, find that products in the commercial mainframe market are more likely to disappear as they get older. Bayus (1998) finds that product lifetimes in the personal computer industry are positively associated with recency of firm entry but not with recency of product entry. De Figueiredo & Kyle (2006) find that product density in the desktop laser printer industry is a major factor driving products out of market, while economies of scale and learning have only marginal effect on product disappearance. The second approach, and the one we follow in this paper, relates product characteristics or strategies—such as product variety, product density, innovativeness, overlap, new product introductions, and the like—to organizational performance. Sorenson (2000) studied all North American computer workstation manufacturers and their products, examining how the breadth of product portfolios affects organizational survival. Barnett and Freeman (2001) find that the simultaneous entry into multiple product categories increases the mortality of U.S. semiconductor manufacturers. Cottrell and Nault (2004) analyzed product variety and product scope in the microcomputer software industry between 1981 and 1986. Dowell (2006) studied all U.S. bicycle manufacturers and their products between 1993 and 1998, finding that survival rates are higher for firms with broad and complex product lines and which have a moderate degree of overlap with rivals. Khessina (2006) analyzed how firms’ product characteristics in the
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optical disk drive industry affect their own and competitor survival. She finds that aging products make firms weaker competitors and survivors, while innovative products make firms stronger survivors but also generate stronger competition, thereby lowering survival chances of all firms. Given the growing recent attention to the topic and the fact that product strategy features prominently in theories of strategic management (Porter, 1980), it is surprising that very few systematic empirical studies relate worldwide product competition to organizational performance over the life of an industry. To our knowledge, only Khessina’s (2006) research exploits data for all firms worldwide and their products over the entire history of an industry. Thus, such an investigation would improve our understanding not only of product management and organizational competition but also, potentially, of shifts in industrial leadership between nations. We attempt to do both in this paper by examining selection processes in a global industry—the worldwide floppy disk drive industry from its inception in 1970 to 1998. We examine product management and its effect on firm survival, paying particular attention to the number of products a firm offers, product age, and product innovativeness. We argue that these strategies have different implications for firm survival depending on the age of the firm. We also pay close attention to country of origin and attempt to tease out product strategy effects over the firm life-cycle from country effects in explaining a shift in industrial leadership. During our period of study, dominance in floppy disk drive production passed from the United States and Europe to Japan. IBM created and shipped the first FDD in 1970 as part of its 3740 data entry system, and US firms held over 95 percent of the market in 1975. They continued to lead the industry through 1984, after
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which the Japanese displaced them. Japanese dominance of the industry occurred very quickly: from 18 percent of the worldwide market in 1980 to 91 percent in 1988. The Japanese share of the market remained above 90 percent until 1995 when the last remaining US producer, Iomega, achieved widespread market acceptance with its Zip drive, reaching 20 percent of the market in 1997 (figure 1). [Figure 1 here] A similar trend is evident in the number of global competitors but with one exception (figure 2). Density of US producers peaked in 1983 with 25 firms, followed by the Japanese with 26 firms in 1985, and emerging market producers in 1987-1988 with 28. Although European firms were early entrants, their density (8 producers) and market share (16 percent) peaked in the late 1970s. The exception to the shifting market share story has to do with emerging market producers. If measuring industrial leadership by density, they briefly displaced the Japanese in the late 1980s. But if measuring leadership by market share, emerging market firms never got more than 6 percent of the market. [Figure 2 here] Thus, the Japanese quickly came to dominate the FDD industry and later held off a challenge from a large number of emerging market entrants. We think examining the product-level strategies of the firms in the market can provide some theoretical traction in explaining the transition in leadership from U.S. firms to those of Japan. We find that the effects of both product strategy and country of origin vary over the firm life-cycle. Younger firms benefit from superior technology whereas older firms benefit from a broader product line. Firms that have an older portfolio of products experience higher failure rates especially at older ages. We also find that Japanese firms
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experience lower failure rates both when they are very young and very old. Further analyses reveals that these effects result from having a younger portfolio of products and a broader product line at all ages. Japanese firms also catch up with firms from other regions in technological sophistication as they age. Industry leadership shifts from American and European firms to Japanese firms as the basis of competitive success changes from technological sophistication to possessing a broader product line of products that are frequently updated. Theory Product Strategies We focus on three product strategies: product line breadth, product innovation, and product vintage, which prior studies have found to be important for organizational performance. Firms that offer a large number of products are likely to experience superior performance for several reasons. First, a large number of products may offer a greater possibility of obtaining economies of scope—without sacrificing scale economies (Bailey and Friedlander, 1982; Lancaster, 1990; Randall and Ulrich, 2001). Some products may share key components and assets, enabling firms to develop product platforms for tailoring products to different customer segments (Gimeno and Woo, 1999; Cottrell and Nault, 2004). Second, assuming that consumers are heterogeneous, a broader product line may lead to greater sales as product variants appeal to specific groups of consumers (Perloff and Salop, 1985). Third, customers may prefer to acquire products from the same vendor, a form of scope economies in consumption (Cottrell, 2004). Fourth, a broad product line may preempt market entry by occupying vacant niches (Schmalensee, 1978). Finally, a broad product line is likely to lead to multimarket contact with competitors,
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reducing competition through mutual forbearance (Bernheim and Whinston, 1990, Barnett, 1993; Gimeno and Woo, 1999). Overall, there is fairly strong evidence of a positive effect of product line breadth on a firm’s market share (Robinson and Fornell, 1985; Kekre and Srinivasan, 1990) and its survival chances (Dowell and Swaminathan, 2000; Sorenson, 2000; Cottrell and Nault, 2004; Dowell, 2006). Scholars are also in broad agreement that product innovations improve firm performance, whether measured by market share, growth, or survival (Nelson and Winter, 1982; Tushman and Anderson, 1986; Dosi, 1988; Teece et al., 1997; Klepper, 2002). Nelson and Winter (1982) make innovation the central mechanism for firms to adapt to a changing environment—fitness is based heavily on a firm’s technological capabilities. Increasing returns from R&D precipitate a shakeout by causing entry to dry up and force smaller firms to exit (Klepper, 2002). Students of both strategic management and technical change similarly see sustainable competitive advantage as dependent on consistent innovation (Tushman and Romanelli, 1985; Tushman and Anderson, 1986; Lerner, 1997). Product line obsolescence also has an important impact on performance. Firms that rely on a product line consisting of older products are likely to exhibit poor performance. This could occur for one of two reasons. First, some firms could be stuck on technological trajectories that have since become obsolete and have difficulty shifting to new technological trajectories (Dosi, 1982, 1988). Second, other firms may be losers in the competitive process that have been relegated to an industry fringe consisting of mature products. In fast cycle industries, such as the FDD industry, this might be due to difficulties in new product development or keeping development teams together. As a
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result, these products occupy niches that are distant from the market center and thus contain fewer of the resources required to sustain organizations. Based on prior literature, therefore, we expect support for the following three propositions in our baseline model: Proposition 1: The greater the number of products offered by a firm, the lower is its mortality rate. Proposition 2: The closer to the technological frontier a firm is, the lower is its mortality rate. Proposition 3: The older the average age of a firm’s products, the higher is its mortality rate.
Product Strategies and Survival over the Firm Life-Cycle The influence of product strategy on performance likely varies over the firm lifecycle. Theories of age dependence suggest that firms compete on the basis of different capabilities at different stages in their lives. We argue that product management is one such capability and that firms can adopt different product strategies to overcome the disadvantages associated with age dependence. Young organizations generally have a higher hazard of mortality than older organizations—the so-called liability of newness. Stinchcombe (1965) suggested this was because younger organizations had less developed routines than older organizations, and organizations with more developed routines faced a lower risk of failure. If young firms suffer from a liability of newness, they must mitigate the threat by offering products that are closer to the technological frontier than older firms (Klepper, 2002). Young firms
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must necessarily use greater technological capabilities to displace leading incumbents and improve their survival chances (Abernathy and Utterback, 1978; Tushman and Anderson, 1986). Hypothesis 1: Closeness to the technological frontier will lower the failure rate of younger firms to a greater extent than older firms. Older firms, by contrast, benefit from experience and positional advantages, yet they face obsolescence as the environment shifts. Aligned with the environment at founding, organizations become less aligned as the environment drifts (Carroll, 1983; Barron, West, and Hannan, 1994). The organization then begins to show signs of obsolescence—an overall decline in appeal to potential customers, which in the case we are considering here can mean an older or narrower product portfolio. At the same time, however, higher capabilities generally lower mortality, while an organization’s capability rises with age (Nelson and Winter, 1982; Sorensen and Stuart, 2000; Hannan, Polos, and Carroll, 2007). This suggests that older organizations that maintain their capabilities and increase their level of engagement as they age are less likely to fail. Sorensen and Stuart (2000) speculate that older firms can overcome the disadvantages of age if they develop stronger relations with vendors and strategic partners or else if they acquire higher status and superior reputation. We think managing their product portfolios effectively can also be a mechanism for maintaining capabilities and increasing engagement. Although older firms may not need to offer the most innovative products because of their positional advantages, relative to younger firms offering greater numbers of products can help them benefit from economies of scope and information about the environment gathered through the occupation of a broad niche. Older firms can also make
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frequent product upgrades to help retain existing customers, which is less costly than acquiring new customers. This implies that older firms will benefit more than younger firms from a lower average product age. For these reasons, we hypothesize the following: Hypothesis 2: Greater product line breadth will lower the failure rate of older firms to a greater extent than younger firms. Hypothesis 3: Lower average product age will lower the failure rate of older firms to a greater extent than younger firms.
Product Strategies and Country of Origin In order for product strategies to affect national industrial leadership, they must satisfy at least one of two explanations. The first is that industrial leadership passes to the country whose firms change product strategies to fit the environment. The other is that industrial leadership passes to the country whose firms maintain stable differences in product strategies relative to those of other countries. We observed at the outset that leadership in floppy disk drive production passed to the Japanese, so our empirical puzzle is whether Japanese firms were able to change product strategies over their lifetimes or whether they consistently followed different product strategies than did their competitors. Although prior research doesn’t offer strong theoretical reasons for backing either explanation, some of the innovation and product development literature suggests that Japanese firms may be better at some strategies than others. Even so, estimation of the link between product strategies and country of origin has been indirect at best and generally not explicitly comparative. Thus, though informed by this research, our theorizing will necessarily be somewhat speculative.
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Some scholars (see e.g. Aoki and Rosenberg, 1987; Aoki, 1990) make specific distinctions between the innovative style and capabilities of Japanese and U.S. firms, and argue that Japanese firms possess a number of organizational, incentive and communication advantages over their Western counterparts that are conducive to product innovation and may enable them to shift their product strategies to suit changes in the industry’s environment. While Japanese firms are considered to be formidable product innovators (Kodama, 1991; Miyazaki, 1995), much of their innovative success during our period of observation relied on their “fast second” strategy (Mowery and Rosenberg, 1989), which refers to the ability of Japanese firms to monitor external developments and move quickly to introduce high-quality modifications at lower cost. In addition, the close contacts between R&D and other units in Japanese companies mean that “research undertaken will be more commercially relevant and the introduction of a new product into the production and marketing stages will be faster” (Odagiri and Goto, 1993: 107; Mansfield, 1988) than in U.S. firms. Kodama (1995) attributes their innovative success to the fact in Japan (1) top managers take an unusually long term view of product development and make a commitment to provide the necessary resources; (2) the national industry as a whole has a high degree of technology capability and thus a great capacity to absorb technology from other industries; and (3) compared with its Western rivals, the Japanese firm faces more intense domestic competition that motivates it to focus on customers’ needs. Research on the Japanese product development process tends to support these claims. Some research finds Japanese new product development process to be highly adaptive and responsive to the perceived external environment (Clark and Fujimoto,
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1991; Song and Parry, 1996, 1997b, 1999). Song and Montoya-Weiss (2001) argue that the key to Japanese new product development is the project team, which they say is partly a manifestation of the cultural tendency toward high uncertainty avoidance and collectivism. This results in a product development process that involves a great amount and variety of information exchange and functionally coordinated responses. The Japanese firm is said to be strong in testing and redesign and better at small product modifications based on careful engineering (Imai, Nonaka, and Takeuchi, 1985), making it well adapted for innovations along a predictable technological trajectory. Evidence from the automobile industry also suggests that Japanese competitiveness results in part from the greater involvement of suppliers in the early stages in product design (Clark, 1989; Clark and Fujimoto, 1991; Takeishi, 2002). This may explain their ability to introduce new models more quickly with fewer defects. Imai, Nonaka, and Takeuchi (1985) reach similar conclusions in their analysis of the copier, personal computer and camera industries. Sanderson and Uzumeri (1995) illustrate some of the effectiveness of Japanese product strategies, suggesting that Japanese companies may be better at product strategies in industries that call for rapid model replacement, model longevity and model variety. In their study of the personal portable stereo market, they note that Sony appeared to have greater market success than its competitors because it offered many more models and its products had significantly longer lives than those of its competitors. These arguments suggest that, unlike their competitors, Japanese firms would be likely to avoid some of the disabilities of positive age dependence by maintaining a
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younger and broader product portfolio as they get older, as well as the disabilities of the liabilities of newness by introducing more innovative products when they are younger: Hypothesis 4a: When compared to firms from other regions, Japanese firms will be closer to the technological frontier when younger and have greater product breadth and younger products when older. Implicit in the above hypothesis is the idea that Japanese firms owe their dominance in the floppy disk drive industry to superior adaptability; they are able to change their product strategies in ways that are appropriate to their stage in the firm life cycle. An alternative argument is that Japanese firms pursue the same product strategies over the firm life cycle, but derive benefits at different stages of their life cycle when they match environmental demands. There is some evidence to suggest that product strategies reflect stable national differences among firms. Organizations are imprinted by their initial environment (Stinchcombe, 1965). A wide range of studies in business history, political economy, international business, and organizational sociology stress the different organizational models embraced by firms in different nations (Chandler, 1990; North, 1981; Hamilton and Biggart, 1988; Kogut, 1992; Carroll, et al., 1988; Nee, 1992; Guillen, 1994, 2001; Biggart and Guillen, 1999). Collectively, this research suggests that firms involved in global competition begin their lives under very different legal, social and political environments and histories, all of which shape organizational forms, structures and practices. These cross-national differences can persist as firms engage in international competition or expand overseas (Doremus, et al., 1998). National level data on outward foreign investment indicate that firms from the same country tend to invest in
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neighboring countries or in those countries with which they have close political or cultural ties (UN, 1993). Franko (1978) contrasts the organizational structures of Continental European multinationals with those of their American counterparts. Mason and Encarnation (1994) find that compared with their competitors Japanese MNCs sell more manufactured goods through international trade than through local production. Hu (1995) contends that the differences between national “qualities” are likely to be more important than the differences between firms based in the same country, so that in international competition the advantages of nationality usually outweigh firm-specific ones. Duysters and Hagedoorn (2001) find strategic differences along national lines among companies in the global computer industry, while McKendrick (2001) observes differences in global strategies between US and Japanese firms in the hard disk drive industry.. Bartlett and Ghoshal (1989) also note the persistence of national characteristics in the global strategies of firms in the same industry. Overall this view advances the notion that managerial ideologies, cultural norms, and the national institutional environment accumulate to influence an organization’s choices and behavior. Moreover, these national styles of behavior persist for extended periods. Thus, we hypothesize: Hypothesis 4b: At all ages, Japanese firms will follow product strategies that exhibit stable differences from firms based in other regions.
Industry Background Throughout the history of the FDD industry, uncertainty existed about the introduction of any new product innovation, as well as uncertainty about the benefits of
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sticking with an existing product standard. Only in retrospect does standardization appear to penalize experimentation or exploration. Product strategies centered on serving particular markets with FDDs of different physical sizes (form factors and heights), minor variations within form factors, and higher capacity drives. IBM set the de facto standard with its data entry system, and for systems that must communicate with IBM mainframes. The original IBM standards included user storage of 242K bytes; a 7.8-inch diskette of mylar base magnetic tape protected within an 8-inch square plastic case with openings for the drive hub, read/write head and positional sensing; 77 concentric tracks on the diskette physically spaced .02083 inches apart for a track density of 48 tpi; 26 sectors; an encoding density of 3200 bpi; double frequency encoding; a rotation speed of 360 rpm; addressable units of 128 bytes; and a read/write head that contacts the diskette causing wear (and a media replacement sub-industry). Originally, before they were peripherals for personal computers, floppy drives were used for data entry, small business systems, intelligent terminals/remote batch terminals, point-of-sale systems, programmable calculators, word processing systems, control and test systems, and minicomputers. The greatest potential for competitors lay with IBM-compatible units since FDDs were an integral part of specific IBM products: the System 370 loader, 3330 controllers, 3740 data entry systems, and a host of other things. About 67% of the FDDs sold in 1975 were IBM-compatible in both drive and format. (Most of the non-IBM-compatible units used a different formatting or sectoring method.) The data entry segment was dominated by IBM with its 3740 and accounted for the largest share of drives, pegged at 38% in 1975, followed by intelligent and remote batch terminals, with 31% of the market.
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The next standard, the 5.25-inch floppy drive, gained acceptance without IBM influence. This 100Kbyte drive could not store on IBM format, nor accept an IBM floppy. It was created in response to market demand, requested by the word processor and terminal manufacturers, and supported by the hobbyists (Titherley, 1978). Despite the absence of any IBM influence, its inventors, Shugart Associates, shipped over 35,000 units within 12 months of starting production. The third standard, which prevailed for the remainder of the industry’s life, was the 3.5-inch floppy drive. Sony, which introduced the proprietary design 3.5-inch drive, began production in 1981. Both the 5.25-inch and the 3.5-inch competed for the same customers: desktop, workstation and electronic typewriter markets. At the time, many observers thought the 3.5-inch drive would find a hesitant OEM market unless Sony signed a strong second source. Although the Sony design got a big boost in 1982 when Hewlett-Packard Co. signed a $30 million contract, OEM uncertainty continued throughout the 1980s. Even as late as 1987, Compaq Computer, one of the largest PC manufacturers, decided that the huge installed base of 5.25-inch FDDs made it too risky to offer the smaller form factor FDDs with its machines. Iomega’s Zip drive was on its way to become a fourth standard in the late 1990s until it was sideswiped by flash memory technology. These memory sticks and thumb drives soon made floppies of all kinds obsolete. (This occurred after our final year of observation.) Even in the face of these various dominant designs, manufacturers experimented with both smaller form factors and non-standard higher capacity drives. Three wellregarded Japanese manufacturers--Matsushita Electrical Industrial, Hitachi, and Hitachi
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Maxell--introduced a 3-inch floppy drive and media less than a year after Sony’s introduction of its 3.5-inch FDD. The president of Maxell said, “We expect the new 3inch disk will capture about 30 percent to 50 percent of the market currently held by 51/4-inch disks within four to five years” (PR Newswire 1982). They organized 17 U.S. and Japanese companies into a group to challenge the Sony-led group, but their competing standard failed to gain much market traction. Similar fates awaited the 2.0inch, 2.5-inch, 2.6-inch, 3.25-inch, and 3.8-inch drives supported by various manufacturers. A number of companies also introduced larger capacity drives, reasoning that consumers would welcome additional storage. In the 8-inch form factor, for example, Burroughs’ introduced a 3-mb drive in 1979, roughly 10 times the capacity of the standard 8-inch drive. Two years later, Iomega, an IBM spinout shipped a 10mb 8-inch floppy drive. A modest leapfrogging of capacity persisted throughout the 1980s and 1990s, although high capacity products achieved little market penetration until the Zip drive appeared in the mid-1990s, including a persistent yet failed effort by two US firms, Brier Technology and Insite Peripherals, to introduce competing 20mb, 3.5-inch drives. Data and Methods Our data include the complete population of floppy disk drive manufacturers between the time the first FDD was shipped in 1970, and 1998. Data were gathered from market research reports, publicly available financial information, industry participants, and an extensive search of the business press. From these disparate sources, the life history of each FDD producer was compiled. These histories cover entry and exit dates, sales, presence in a given form factor, product technical specifications, acquisition
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history, and nationality for each company that made a floppy disk drive. The resulting database includes 123 organizations that manufactured floppy disk drives at any time or place over the period. The data cover 1046 organization years. Missing data on the technological characteristics of products reduced our sample size to 1010 observations. We measure size as sales of floppy disk drives in the current year. From 1976 onward Disk Trend provides yearly sales for firms that constitute on average 95% of all sales in the industry. Disk Trend then provides total industry sales for each year in each form factor. We used this information to distribute the remaining 5% of sales equally among the firms in our sample for which there were no firm-level sales data. Because the remaining sales were so small relative to the overall industry, using different assumptions to distribute the sales had little effect on the results. A categorical variable called de alio was assigned a value of one if the firm entered the market from another industry and zero if it was a start-up. For each year we calculated the number of floppy disk drive producers in the industry, and following Carroll and Hannan (1989), we included firm density at founding as a covariate in order to control for the effects of density delay. We also computed dummy variables indicating whether the producer was from the US, Japan, Europe an emerging market (Eastern Europe, South America or an Asian country other than Japan). US firms were the left-out category. Another dummy variable was constructed indicating whether the firm had any captive production that it used in its own products. In order to control for differences between form factor markets, we constructed dummy variables indicating whether the firm was in the 8-inch, 5.25-inch, 3.5-inch, or a smaller form
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factor. It was not necessary to exclude one of these categories since a firm could be in more than one form factor at a given time. Using our data on products we computed the number of products that a firm had in its product portfolio in a given year and the average age of these products. To obtain a measure of a firm’s position on the technological frontier, we computed a frontier ratio by dividing the maximum areal density of a firm’s products by the highest areal density of any product in the industry in that year. Following Sorenson (2000), we also controlled for the extent to which a firm turns over its products. Sorenson found evidence that this product level strategy reduced failure rates in the computer workstation market. This was computed as the total number of products removed from the market by a firm divided by the total number of products introduced by the firm up to that point in time. Method In order to analyze rates of organizational mortality, each organization’s life history was broken down into annual spells. Following recommendations by Petersen (1991), we assumed that when organizations fail, they do so at the midpoint of the year. If firms are merged or survive until 1998, the spells are treated as right-censored. Time varying covariates are updated at the beginning of each year. We estimate a firm specific mortality rate using a constant piece-rate model and include age and our other variables of interest as covariates. By examining survivor and hazard plots we determined that reasonable break points for the firm ages were 0-6 years,6-12 years, 12-18 years and 18 years and above. The model is specified as follows: r(t)=exp(βxn+άzt) B
B
B
B
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where r(t) is the instantaneous probability of failure for an organization at time t, zt is the B
B
B
B
vector of age pieces and xn is a vector of independent variables. B
B
Results Table 1 presents descriptive statistics and bivariate correlations between our variables. We test our hypotheses and investigate country effects in Table 2. Model 1 in Table 2 is our baseline model and shows that having high sales and being vertically integrated (captive sales) lowers the probability of failure. Contrary to density dependence predictions, we find no effects for density and density squared. Although the age pieces are not always significant they suggest that age increases the mortality rate up to 18 years and then decreases it. Consistent with propositions 1 and 3 the number of products in a firm’s portfolio decreases the failure rate, while firms with older vintages of products have higher chances of failure. While the coefficient for a firm’s position on the technological frontier is in the expected negative direction (proposition 2) it is not significant. For reasons of parsimony, we drop the squared term of density, density at birth, de-alio status and our measure of product culling, none of which have significant effects, from model 2 and use this as our baseline model. In analyses not shown here we found that the variables we dropped did not have a significant effect when included in the subsequent models 3 to 7. All of our results are substantively unchanged from model 1 to model 2. [Insert Tables 1 and 2 about here] In model 3 we test our product level hypotheses. In support of hypothesis 1, closeness to the technological frontier only lowers the failure rates of younger firms (those less than 6 years old). Indeed, for firms greater than 18 of age being closer to the
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frontier actually increases the failure rate. We also found support for hypothesis 2, namely that product breadth will lower the failure rate of older firms to a greater extent than younger firms. As can be seen in model 3, a firm’s number of products has no effect on the mortality rates of firms that are less than 12 years old but decreases the chance of failure for firms greater than 12 years old. And, as expected this negative effect is strongest for the very oldest firms. Hypothesis 3 suggested that lower average product age would be of the greatest benefit to older firms. We find mixed support for this hypothesis. As can be seen in model 3, average product age has positive effects on the hazard rates for younger firms (0-12 years) and very old (>18 years) firms. Consistent with our hypothesis, however, the strongest positive effect on the hazard rate from this variable is for firms greater than 18 years old. In model 4 we add country dummies and find that Japanese firms have the lowest chances of mortality. Interestingly, firms in emerging markets also have lower chances of failure relative to US firms. In models 5, 6 and 7, we add several different operationalizations of period effects to investigate the robustness of our results. Model 5 investigates whether firms born after the peak density in our sample (1984) had higher failure rates. As might be expected firms born after the shakeout period had lower chances of survival. Model 6 tests whether the industry became more hazardous as it matured, while model 7 examines whether the post shakeout period was more deleterious to a firm’s survival chances. Both of these variables had positive effects on the failure rates. Importantly, however, all our previous effects remain robust to these controls. We do find one anomalous effect in that firms with older product portfolios that are between 12 and 18 years old are slightly less likely to fail when industry age or our post shakeout dummy is included. However, this effect is quite weak
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and does not substantively alter our conclusion that maintaining young product portfolios is most critical for very young or very old firms. [Insert Table 3 About here] Earlier, we suggested that Japanese firms may employ more effective product strategies in industries like this one that call for rapid model replacement, model longevity and product variety. In Table 3 we investigate the extent to which Japanese firms had an advantage over the firm life cycle. Model 1 shows our base model with the country effects at different ages in the model. In model 2 we add in the main effects of the product strategies while in models 4 through 6 we control for being born before or after the peak density (the shakeout period), industry age, and the shakeout period, respectively. For reasons of parsimony we will discuss the specific results in model 4 which includes industry age and also controls for firm product-level strategies. The results in this model are not substantively different from models 3 and 5 which simply include different operationalizations of the period effects. Model 4 shows that Japanese firms that are from 0 to six years old are significantly less likely to fail than their US counterparts. Indeed in examining the coefficient on the Japanese dummy, we find that US firms of this young age are almost 5 times more likely to fail. This disadvantage increases by a factor of two for US firms that are 6 to 12 years old as compared to Japanese firms of that age. Interestingly, firms in emerging markets that are between 1 to 12 years old also have survival advantages over US firms of comparable ages, though this effect is somewhat smaller than the one for Japanese firms. Japanese firms from 12 to 18 years old have comparable failure rates to their US counterparts, but their advantage reappears once they attain an age of 18 years. Consistent with our expectations, average
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product age positively affects the failure rate while being close to the technological frontier reduces it. Interestingly the number of products, while in the expected negative direction, is not significant. While it would have been desirable to disentangle the effects of the product level strategies and the country effects by putting them both in the age time pieces, the models would not converge, possibly because of multicollinearity and reduced degrees of freedom. In addition, if Japanese firms are employing the appropriate firm strategies at different points in their life cycle, the country effects and product strategy effects might be difficult to distinguish between in these multivariate models. We explore this issue below. In Table 2 we showed that being on the technological frontier was most beneficial for younger firms and that having larger and younger product portfolios was more desirable for older firms. In Table 3 we showed that Japanese firms had survival advantages over US firms over most of their life cycle. One explanation for these effects is that, consistent with hypothesis 4a, Japanese firms will tend to adopt the optimal products strategies at different ages. More specifically, when compared to firms in other regions, Japanese firms will be closer to the technological frontier when younger and have larger numbers of products, as well as a younger product portfolio when older. While we could not test this directly in multivariate analyses, we can compare the product-level strategies of Japanese firms to other firms over their life cycle. [Insert Table 4 About Here] In Table 4, we examine whether the product strategies of Japanese firms are significantly different from those firms in other regions at different firm ages. We perform t-tests for differences in group means across countries. Each number in the table
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is the difference between the mean values of Japanese firms of a given age on that product level strategy and that of the comparison group from other regions. For example, for firms that are 0 to 6 years old, Japanese firms have on average 2.893 more products as compared to firms in all other regions and this difference is statistically significant. In examining the number of products across all ages we find that Japanese firms have significantly more products in their portfolio than US firms across all ages. Indeed, they offer consistently more products than any other region across all ages. We show this graphically in Figure 3. [Insert Figures 3, 4 and 5 About Here] Table 4 also reveals that Japanese firms are lower down on the technological frontier than their US counterparts until they are greater than 18 years old. At this point their products are more technologically sophisticated than those offered by US firms (see also Figure 4). In general, the average age of Japanese products is significantly lower than those offered by American firms, although this difference is not significant for firms that are less than 6 years old. Japanese product portfolios are also significantly younger than those firms in other regions (see also Figure 5). From the pattern observed in this table, we conclude (counter to hypothesis 4a) that Japanese firms do not appear to be optimizing their strategies as they age. Indeed, for the most part, consistent with Hypothesis 4b, Japanese appear to be following relatively stable product- level strategies across all ages. In general, Japanese firms offer greater numbers of younger products of lower technological sophistication than those offerings of US firms. What then, is the source of these country-level differences in product strategies? We address this issue in the discussion that follows.
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Discussion What accounts for the transition in industrial leadership in FDD production from U.S. to Japanese firms? We find that firm performance in the floppy disk drive industry is affected by both firm and country factors. At the firm-level product strategies including being close to the technological frontier, offering a broad portfolio of products, and maintaining a relatively young average age of those products, reduce the likelihood of firm exit. The effect of product strategies varies over the firm life cycle with technical product innovation being particularly important early in a firm’s life cycle and product scope and a rapid rate of new product introduction becoming more important as firms age. We also found differences across countries with Japanese firms outperforming firms from other regions both early and late in their life cycle. In addition, we found that this was not because they changed their product strategies as they aged, but because they benefited from technical product innovation at younger ages and from product scope and a rapid rate of new product introduction at older ages. We found strong evidence of stable differences in product strategy between Japanese firms and firms from other regions at all ages. The stable country-level differences in product strategies are striking and demand an explanation. At this point, we can speculate based upon findings from other studies, particularly in the domain of institutional theory and population-level learning. These theories would suggest that product level strategies are influenced by local reference groups. In the case of global competition, two sharply divergent portraits of international competitive behavior have emerged in research on the multinational corporation and
25
international political economy respectively. One portrait emphasizes the differences in global behavior between firms of different nationalities while the other focuses on the common set of pressures and incentives all firms in a given industry face, regardless of their nationality. These two portraits appear to stand in opposition to one another: one implies similarity in behavior among firms in the same industry, the other underscores national differences. How do the conforming pressures of industry or the counter influence of nationality exert an effect on international competition? One framework for evaluating this question is population-level learning (Miner and Haunschild, 1995). Using this interpretive framework, McKendrick (2001) found in the hard disk drive industry that Japanese and U.S. firms mimicked the global strategic behavior of other similar national firms; the salient within-country reference groups were young and small firms. A comparative institutional approach would suggest a similar interpretation (Biggart and Guillen, 1999; Guillen, 2001). Different countries have different indigenous sources of strength, which generate path-dependent actions. Social organization and organizational action are introduced or enacted only if they make sense with respect to preexisting organizational configurations and behaviors. The institutional environment in countries allows them to excel at specific activities, but only producers that leverage these national capacities benefit.
26
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31
0
.2
Market Share .4 .6
.8
1
Figure 1: Market Shares By Region
1970
1980
1990
2000
Year US European
Japanese Emerging Markets
0
10
Density
20
30
Figure 2: Firm Densities By Region
1970
1980
1990
2000
Year US European
Japanese Emerging Markets
32
0
Average Number of Products 5 10
15
Figure 3: Number of Products by Region by Firm Age
0
5
10
15
20
25
Age US European
Japanese Emerging Markets
0
Average Frontier Ratio .2 .4
.6
Figure 4: Frontier Ratio by Region by Firm Age
0
5
10
15
20
Age US European
Japanese Emerging Markets
25
0
Average Product Age 5 10
15
Figure 5: Average Product Age by Region by Firm Age
0
5
10
15
20
25
Age US European
Japanese Emerging Markets
40
TABLE 1 Descriptive Statistics Variable Var Mean S.D. 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 1 Age 5.85 5.01 1.00 2 Ln(Floppy Sales) 2.16 2.06 0.57 1.00 3 Number of Products 5.73 5.47 0.49 0.67 1.00 Average Age of Products 4 2.58 2.19 0.62 0.14 0.00 1.00 5 Frontier Ratio 0.22 0.26 -0.24 -0.13 -0.13 -0.35 1.00 6 8” Form Factor 0.47 0.50 0.02 -0.10 -0.06 0.16 0.31 1.00 7 5” Form factor 0.58 0.49 0.09 0.26 0.30 -0.08 -0.29 -0.33 1.00 8 3.5” Form Factor 0.34 0.47 0.42 0.49 0.51 0.01 -0.15 -0.41 0.05 1.00 9 Small Form Factor 0.06 0.25 -0.04 0.13 0.15 -0.12 -0.03 -0.12 -0.04 0.14 1.00 10 Captive sales 0.45 0.50 0.09 0.16 -0.03 0.07 0.00 0.24 -0.03 -0.07 -0.07 1.00 11 49.39 16.93 -0.11 0.10 0.15 -0.01 -0.46 -0.21 0.40 -0.03 0.15 0.02 1.00 Density 12 42.82 19.59 -0.27 -0.05 -0.09 -0.12 -0.38 -0.71 0.28 0.20 0.06 -0.24 0.41 1.00 Density at Birth 13 De Alio Entrant 0.84 0.37 0.15 0.16 0.13 0.08 -0.22 0.05 -0.03 0.11 0.07 0.33 0.05 -0.03 1.00 14 Culling 0.30 0.31 0.69 0.40 0.29 0.37 -0.23 -0.25 0.11 0.43 0.02 -0.03 -0.03 0.05 0.06 1.00 15 15.35 5.78 0.61 0.44 0.30 0.42 -0.46 -0.58 0.26 0.60 0.00 -0.16 0.14 0.49 0.06 0.60 1.00 Industry Age 16 Japanese Firm 0.37 0.48 0.34 0.45 0.44 -0.04 -0.11 -0.20 0.04 0.60 0.25 0.06 -0.04 0.03 0.28 0.31 0.30 1.00 17 European Firm 0.07 0.25 -0.10 -0.11 -0.07 -0.03 0.09 0.22 -0.06 -0.15 0.02 0.03 -0.05 -0.12 0.12 -0.13 -0.20 -0.21 1.00 18 Emerging Market 0.25 0.43 -0.17 -0.25 -0.27 0.06 -0.37 -0.27 0.26 -0.22 -0.14 -0.02 0.27 0.44 0.05 -0.10 0.20 -0.44 -0.16 1.00 19 Born After 1984 0.24 0.43 -0.29 -0.16 -0.18 -0.07 -0.23 -0.51 0.14 0.13 -0.11 -0.14 0.20 0.66 -0.03 -0.06 0.38 -0.11 -0.15 0.41 1.00 20 Year>1984 0.63 0.48 0.41 0.28 0.27 0.28 -0.46 -0.56 0.32 0.49 0.05 -0.16 0.45 0.53 0.05 0.47 0.80 0.21 -0.20 0.28 0.43 1.00 N=1010
TABLE 2
Product Strategy and the Failure of Floppy Disk Drive Producers (1)
(2)
(3)
(4)
(5)
(6)
(7)
Firm Age
-2.5243*
-2.0138**
-2.1491**
-0.9723
-1.2744
-2.7053**
-0.6759
(0-6 years)
(1.2288)
(0.6688)
(0.7862)
(0.8382)
(0.8232)
(0.9147)
(0.8146)
Firm Age
-1.9634+
-1.3431*
-1.5018
0.139
0.1187
-1.6325
0.4199
(6-12 years)
(1.1084)
(0.6754)
(1.0477)
(1.0227)
(0.9782)
(1.0879)
(0.9734)
Firm Age
-1.7582+
-0.9566
1.2534
2.5358*
2.7720*
0.6985
2.7439**
(12-18 years)
(1.0426)
(0.6970)
(1.0787)
(1.1217)
(1.1035)
(1.1108)
(1.0454)
Firm Age
-2.5220*
-1.5754+
-18.6759**
-16.6068**
-16.8305**
-17.6777**
-15.8051**
(>18 years)
(1.1336)
(0.8924)
(2.1518)
(2.1312)
(1.9905)
(1.9798)
(2.0346)
Firm Sales
-0.4436**
-0.4481**
-0.5273**
-0.5281**
-0.5436**
-0.5995**
-0.5640**
(thousands of dollars)
(0.0861)
(0.0872)
(0.0875)
(0.0804)
(0.0807)
(0.0896)
(0.0846)
Number of Products
-0.0948+
-0.0934+
(0.0504)
(0.0501)
0.1813**
0.1687**
(0.0624)
(0.0568)
-1.6478
-1.5552
(1.0810)
(0.9672)
-1.1037*
-0.9628**
-0.8735*
-1.1837**
-0.9611**
-0.6252+
-0.7872*
(0.4480)
(0.3588)
(0.3459)
(0.3075)
(0.3235)
(0.3510)
(0.3077)
Average Age of Products
Frontier Ratio
8" Form Factor
5" Form Factor
0.2791
0.2938
0.2268
0.4766
0.4761
0.4857
0.4666
(0.3367)
(0.3304)
(0.3541)
(0.3662)
(0.3581)
(0.3433)
(0.3513)
0.0974
0.0548
-0.0249
0.1193
-0.0458
-0.2991
-0.1939
(0.3889)
(0.3910)
(0.4143)
(0.3564)
(0.3514)
(0.4108)
(0.3919)
0.4471
0.5149
0.6691
1.1414*
1.3380**
1.3425*
1.3539**
(0.4743)
(0.4566)
(0.5216)
(0.5170)
(0.5184)
(0.5213)
(0.5245)
Captive Sales
-0.5674*
-0.5080*
-0.6136**
-0.4518+
-0.4576+
-0.2909
-0.3058
(=1 if Captive Sales)
(0.2438)
(0.2252)
(0.2331)
(0.2351)
(0.2431)
(0.2476)
(0.2483)
Density
0.0508
0.0091
0.0066
-0.0037
-0.003
-0.0021
-0.0209+
(0.0370)
(0.0087)
(0.0102)
(0.0106)
(0.0103)
(0.0100)
(0.0125)
Number of Products
-0.1024
-0.072
-0.0615
-0.0565
-0.064
(0-6 years)
(0.0929)
(0.0819)
(0.0837)
(0.0863)
(0.0863)
3.5" Form Factor
Small Form Factor
Density2
-0.0005 (0.0004)
Density at Birth
-0.0065 (0.0116)
De Alio Entrant
0.1052 (0.2801)
Culling
0.1369 (0.3790)
Number of Products
-0.0213
-0.0505
-0.0447
-0.0345
-0.0422
(6-12 years)
(0.0594)
(0.0626)
(0.0615)
(0.0640)
(0.0629)
Number of Products
-0.2997*
-0.2386+
-0.2305+
-0.2362*
-0.2437*
(12-18 years)
(0.1454)
(0.1250)
(0.1186)
(0.1199)
(0.1223)
Number of Products
-1.4938**
-1.4806**
-1.5956**
-1.4136**
-1.4077**
(>18 years)
(0.1694)
(0.1674)
(0.1642)
(0.1629)
(0.1650)
Average Age of Products
0.4392**
0.4519**
0.4272**
0.3335**
0.3776**
(0-6 years)
(0.1035)
(0.1116)
(0.1070)
(0.1151)
(0.1105)
Average Age of Products
0.2023*
0.1578+
0.1174
0.0466
0.0674
(6-12 years)
(0.0995)
(0.0928)
(0.0916)
(0.0990)
(0.0936)
Average Age of Products
-0.0244
-0.0744
-0.1172
-0.1729*
-0.1457+
(12-18 years)
(0.0728)
(0.0767)
(0.0785)
(0.0844)
(0.0799)
Average Age of Products
1.8493**
1.7374**
1.7525**
1.5447**
1.5936**
(>18 years)
(0.1733)
(0.1675)
(0.1586)
(0.1599)
(0.1588)
Frontier Ratio
-2.3336*
-4.0621**
-3.9585**
-3.6230**
-4.3078**
(0-6 years)
(1.1086)
(1.4387)
(1.3551)
(1.2798)
(1.4497)
Frontier Ratio
-0.6679
-2.3709
-2.5775
-2.2604
-2.2635
(6-12 years)
(1.7568)
(2.1680)
(2.0938)
(1.6920)
(1.8513)
Frontier Ratio
1.2363
-0.29
-0.2788
-0.5248
-0.601
(12-18 years)
(1.4714)
(1.6297)
(1.6478)
(1.6528)
(1.6507)
Frontier Ratio
5.1056**
4.8427**
4.9821**
4.4131**
4.4844**
(>18 years)
(1.5229)
(1.4563)
(1.3968)
(1.3619)
(1.3986)
-1.3543**
-1.4010**
-1.4768**
-1.4856**
(0.4325)
(0.4284)
(0.4335)
(0.4426)
Japanese Firm
European Firm
Emerging Market
0.5064
0.5747
0.4068
0.4798
(0.3793)
(0.3826)
(0.4287)
(0.4194)
-1.1760**
-1.3019**
-1.3787**
-1.3513**
(0.3221)
(0.3294)
(0.3298)
(0.3245)
Born After 1984
0.5951* (0.2772)
Industry Age
0.1190** (0.0461)
Year>1984
1.1973* (0.4962)
Observations
1010
1010
1010
1010
1010
1010
1010
Number of Events
98
98
98
98
98
98
98
Log Likelihood
-79.4
-80.3
-69.2
-57.1
-55.6
-54.4
-53.9
Robust standard errors in parentheses + significant at 10%; * significant at 5%; ** significant at 1%
1
TABLE 3
Country of Origin and the Failure of Floppy Disk Drive Producers*** (1)
(2)
(3)
(4)
(5)
Firm Age (0-6 years)
-1.8331** -0.4805
-1.3996+ (0.8423)
-1.6328+ (0.8483)
-3.1263** (1.1058)
-1.1215 (0.8440)
Firm Age (6-12 years)
0.1186 (0.5279)
-0.6705 (0.7797)
-0.7165 (0.7974)
-2.2784* (0.9775)
-0.3877 (0.7829)
Firm Age (12-18 years)
0.204 (0.5386)
-2.2024* (1.0162)
-2.0652* (1.0309)
-3.8263** (1.1587)
-1.8670+ (0.9962)
Firm Age (>18 years)
0.3615 (0.6125)
-1.8711+ (0.9585)
-1.7974+ (0.9929)
-3.7824** (1.2615)
-1.6779+ (0.9284)
Japanese Firm (0-6 years)
-1.2327* (0.4994)
-1.6409* (0.6378)
-1.6969** (0.6168)
-1.5030* (0.6251)
-1.6164** (0.6249)
Japanese Firm (6-12 years)
-1.9224** (0.6430)
-2.1919** (0.7674)
-2.2670** (0.7702)
-2.4297** (0.7820)
-2.3153** (0.7683)
Japanese Firm (12-18 years)
-0.2803 (0.4279)
1.2598+ (0.7233)
1.2512+ (0.7197)
0.814 (0.7935)
0.9267 (0.7406)
Japanese Firm (>18 years)
-14.9876** (0.7214)
-13.1466** (0.7999)
-14.5633** (0.8020)
-13.7676** (0.7733)
-15.0466** (0.7929)
European Firm (0-6 years)
0.7397** (0.2660)
0.6069 (0.4749)
0.7376 (0.4713)
0.6798 (0.4897)
0.6469 (0.4954)
European Firm (6-12 years)
0.4666 (0.4985)
0.113 (0.6352)
0.1188 (0.6094)
-0.1931 (0.6875)
0.0167 (0.6762)
European Firm (12-18 years)
2.0754** (0.6171)
3.3634** (0.9617)
3.2311** (0.9404)
3.2123** (0.9501)
3.2029** (0.9451)
Emerging Market (0-6 years)
-0.4523 (0.3491)
-1.2653** (0.4848)
-1.4465** (0.4847)
-1.4259** (0.4656)
-1.4319** (0.4768)
Emerging Market (6-12 years)
-0.7324* (0.3473)
-1.3456** (0.4262)
-1.4663** (0.4338)
-1.6475** (0.4667)
-1.5229** (0.4409)
Emerging Market (12-18 years)
-0.2752 (0.5583)
0.6576 (0.7753)
0.6711 (0.7542)
0.4352 (0.7856)
0.5012 (0.7651)
Firm Sales (thousands of dollars)
-0.5130** (0.0698)
-0.5296** (0.0891)
-0.5534** (0.0910)
-0.5989** (0.0971)
-0.5500** (0.0926)
8" Form Factor
-1.6585** (0.2976)
-1.5556** (0.3992)
-1.3338** (0.4150)
-0.9847* (0.4388)
-1.2352** (0.4006)
5" Form Factor
-0.3012 (0.2830)
0.4988 (0.3596)
0.5135 (0.3608)
0.4831 (0.3507)
0.4896 (0.3548)
3.5" Form Factor
-0.1263 (0.3346)
0.2508 (0.3980)
0.0909 (0.4037)
-0.2221 (0.4556)
-0.0352 (0.4278)
2
Small Form Factor
0.4034 (0.5458)
1.2689* (0.5694)
1.4729* (0.5735)
1.4487* (0.5887)
1.4456* (0.5678)
Captive Sales (=1 if any captive sales)
-0.3711* (0.1880)
-0.351 (0.2364)
-0.3493 (0.2432)
-0.1996 (0.2523)
-0.2253 (0.2492)
Density
0.0153+ (0.0078)
0.0082 (0.0094)
0.0088 (0.0095)
0.0077 (0.0092)
-0.0071 (0.0118)
-0.0775 (0.0501)
-0.0679 (0.0506)
-0.0637 (0.0518)
-0.0728 (0.0515)
0.2744**
0.2416**
0.1482+
0.1991**
(0.0633)
(0.0651)
(0.0818)
(0.0701)
-2.7448+ (1.5485)
-2.8662+ (1.4894)
-2.5539+ (1.3213)
-2.8977+ (1.5219)
Number of Products
Average Age of Products
Frontier Ratio
0.5685* (0.2814)
Born After 1984
0.1265* (0.0557)
Industry Age
1.0415* (0.5084)
Year >1984
Observations Number of Events Log Likelihood
1058 106 -94.5
1010 98 -60.2
1010 98 -58.7
*** Robust standard errors in parentheses. + significant at 10%; * significant at 5%; ** significant at 1%
3
1010 98 -57.3
1010 98 -57.7
Table 4
Number of Products
Product Strategies of Japanese Firms Vs. Firms from other Regions (t tests for differences in group means) All U.S.A Europe Emerging Regions Market Age
