Are There First-Mover Advantages in B2B Ecommerce Technologies?

Are there First-Mover Advantages in B2B eCommerce Technologies? Gezinus J. Hidding Loyola University Chicago [email protected]

Abstract Recently,“dot-com” startups relied on the well-known concept of “first-mover advantage” to justify huge marketing expenses and large financial losses. The idea was that once the firm had gathered customers, it would keep those customers because it had a first-mover advantage. But was that assumption valid? We analyzed whether first-mover advantage actually occurred in 19 Information Technology product categories that enable B2B eCommerce (and 6 that enable B2C eCommerce). Consistent with earlier studies in other industries, we found that, in half the product categories, the current leaders were among the first three entrants. However, we identified an important refinement. In at least 80% of the product categories in our sample, the first movers lost their initial advantage. This paper presents detailed results, including pioneer lead times and leader lag. It also presents implications for strategic management practitioners, as well as suggestions for further research.

1. Introduction Recently, eCommerce technology startups, a.k.a. “dotcoms”, relied on the decades-old concept of “first-mover advantage” to justify huge upfront marketing expenses and large financial losses. The idea was that once a startup company had gathered customers, it would keep those customers because it had a first-mover advantage. This paper deals mostly with B2B eCommerce enabling technologies, as opposed to conventional B2B topics, such as manufacturing, supply chain management, or distribution of physical goods. By “eCommerce” we mean Internet/web-based businesses that provide a product, online auctions. (Our view of product includes physical goods, services and information [18].) By “technology” we mean Information Technology (“I.T.”), including hardware, software (tools) and information services, e.g., routers, Collaborative Filtering software, and Internet Service Providers. Note that, aside from hardware, I.T. development does not revolve around manufacturing, storing or distributing physical goods. Note that

Jeffrey R. Williams Carnegie Mellon University [email protected] information technologies may constitute B2B industries themselves when I.T. businesses provide technology products to other businesses. In this paper, we focus on 19 eCommerce technology product categories related to business-to-business (“B2B”), some of which include business-to-consumer (“B2C”). In addition, we consider 6 eCommerce technology product categories related to B2C only.

1.1 Literature review First-mover advantages have been found in traditional industries. Indeed, much has been written over the years about first-mover advantage and the impact of the order in which firms enter a market. Seminal papers summarizing the literature include [15, 16, 29]. Most studies analyzed traditional industries based on consumer goods or industrial products when eCommerce was obviously not prevalent. First movers had substantially higher market shares in industrial product (i.e., B2B) industries [20] and also in consumer product industries [21]. First movers were also found to have high profit levels over several decades [17]. By implication, first-mover advantages may be important in contemporary supply chains and online marketplaces. New technologies may affect first-mover advantages. B2B eCommerce strategies may have to be (re-)aligned with a firm’s overall business strategies. However, B2B eCommerce technologies may be different from traditional B2B industries in important ways such as the pace of change in the industry or increasing returns to adoption. If first-mover advantages occurred in traditional industries, why should they (not) occur in eCommerce technology industries? Previous research provides arguments for either position. Fierce price competition, enabled by near-zero marginal costs, (such as exhibited in many eCommerce technology markets) has been modeled in BertrandStackelberg games [6]. In such games, the advantage ultimately goes to second-movers. Certain eCommerce technology industries involve positive demand-side network externalities (of adoption), i.e., a product is worth more to a customer if other customers already have the product. Examples of such products are online marketplaces and p.c. operating systems. For industries

Proceedings of the 36th Hawaii International Conference on System Sciences (HICSS’03)

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with such network externalities, the advantage eventually goes to the follower under certain conditions modeled in [10]. Indeed, in several industries with network externalities, the current leaders were not first-movers, but followers. For example, AOL online service, Windows Operating System, and eBay auctions. However, previous research also provides arguments that eCommerce technology first movers might sustain their initial advantage. For industries with network externalities, once a first mover has established a large network, the potential demand for a second mover’s product lies far below that of the first mover [17]. First movers keep the advantage under certain conditions modeled in [10]. The literature contains a few empirical studies about first-mover advantage or order-entry effects in eCommerce technology industries in particular, or even Information Technology related industries in general. They generally indicated first-mover advantages. In a study of the brewing, long-distance telecommunications and personal computer industries, the first two entrants achieved greater shareholder returns (at product introduction) than late movers and the durability of firstmover advantages eroded with imitation [14]. In wireless cellular telephone services, first movers had higher market share [9]. First mover advantage in the minicomputer industry was more prevalent than in the personal computer industry [23]. In the Metal Oxide Semiconductor (MOS) industry, the first manufacturers to introduce new designs retained the largest market shares. Interestingly, the first manufacturers to introduce a new design a new density or bit level did not retain the largest market shares [26].

1.2 Overview of this paper Given arguments and data for and against first-mover advantage as presented above, it is not clear at all whether to expect or how to understand first-mover advantage in B2B eCommerce technology markets. Is first-mover advantage the rule or the exception? In order to find out, we analyzed the history of 19 eCommerce technology product categories related to B2B (and also 6 related to B2C) to determine whether or not the first movers are currently still the industry leaders. We also analyzed statistics regarding how long the first mover was alone at first in the market, the lag between the time of entry of the first mover and that of the leader, and the first movers’ 5- and 10- year survival rates. This remainder of this paper first describes our research methodology, followed by the results of our study, and a discussion of the results. This paper also discussed the implications of our results for strategic

management practitioners. Finally, having concluded that first movers generally were not the current leaders in eCommerce technologies, the paper presents suggestions that further research be done as to why the first movers lost and why the current leaders won.

2. Research methodology All of the previous empirical studies we found suffer from one or more methodology issues (several of which were also raised in [15, 16]) regarding first-mover advantage studies in general: First, there was imprecision in determining which firm was “first.” The exact source of the data in [9] is not clear. One study [23] analyzed data about (only) U.S. firms that were “publicly held at the time of entry” and another [14] studied (only) firms that needed to be traded on “U.S. stock exchanges.” These two studies did not include data about firms that were private or pre-IPO (at the time of entry). However, pre-IPO firms could have been first movers. Such studies suffer likely from “survivor bias,” i.e., the sampling bias that includes only the surviving firms (that are currently successful), and omit the early firms that are now obscure or disappeared altogether [15]. In first-mover studies in general, the source is often PIMS data. However, several papers, e.g., [13, 27], note that PIMS data suffers from imprecision in which firm was “first.” PIMS data, based on self-reports, identify “early” entry, but not necessarily the “first” entry. Second, there were issues with the measure(s) for advantage. One study [14] analyzed abnormal deviations in shareholder returns only within 3-5 days of announcements of product introduction by other firms, hardly a long-term view of advantage. Others analyzed market share [9, 26]. However, studies using market share as the metric for advantage have found more firstmover advantages than when other metrics were used (e.g., relative returns), see the meta-analysis in [29]. In other words, the choice of measure for advantage may bias the research results. One study [23] seems to have used first-mover advantage as an exogenous variable, instead of establishing any first-mover advantage endogenously. Third, each study analyzed only very few industries. The largest number of industries in one sample was three (in [14), making it difficult, if not impossible, to generalize the results to a larger part of the economy.

2.1 Historical analysis We set out to avoid the methodology issues described above. Similar to [26], we studied industry publications


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to determine which firm was first, second, etc. into a new industry. We located and evaluated documents of the past, particularly those written around the same time that new products or firms emerged. The documents we found were often of two types: Articles that appeared in the trade or business press at or after the time of market entry, and industry monographs that detail the history of particular product categories. This research methodology is called “historical analysis,” and has been followed in earlier research into pioneer advantage [7, 28]. One of our principal aims of using historical analysis was to eliminate survivor bias. Historical analysis may resolve the question of “first”, but not of “first mover,” for which alternative definitions have been offered [7, p. 159]: • Inventor is the firm that develops patents or important technologies in a new product category. (Product category is a group of products such that consumers consider them substitutable and distinct from those in another product category.) • Product pioneer is the first firm to develop a working model or sample in a new product category. • Market pioneer is the first firm to sell in a new product category. These definitions are relevant for this study. For example, several Internet technologies (e.g., search engines, and, indeed, the world-wide web itself) were invented by university researchers. The first working model was then developed in a university research lab as the product pioneer, and later sold (i.e., a commercial transaction) by a for-profit company that was then deemed as the market pioneer. Similar to [7, 28], we considered the first mover to be the market pioneer. The definition of product category raises the important, but difficult, issue of substitutability ([16]), which is also very relevant for this study. For example, search engines (one of the product categories we studied) initially came in two different types: (1) “Directory” technology, as in humans finding and cataloguing web sites and (2) “Spider/crawler” technology, which is software that searches the web autonomously to collect web sites. Are these different technologies for searching URL’s two product categories or one? Even though they are clearly different, we consider them substitutes from the customer perspective: They require similar actions on the part of the user, and generate similar results. Over time, most search engine web sites combined spider/crawler technology and directories. As mentioned above, market share has often been used as a metric for “advantage,” despite the methodological issues cited earlier. There can also be practical problems with interpreting market share as strategic success, particularly in eCommerce technology

product categories. Firms (e.g., dot-coms) may have had large market shares (possibly of small and unstable markets) as well as large financial losses, making it difficult, if not impossible, to sustain any advantage. To avoid such methodological and practical issues, we analyzed, when possible, different measures of advantage. We analyzed profits (following [15]), where possible. However, profits for certain product categories may not be published, e.g., when the firm is not public, or when results for a particular product category of a public firm are reported separately. Even if profit information was available, it was not necessarily very informative as to the strength of leadership. For example, in Internet B2B market places, all of the four leading firms had substantial losses at the time we studied the category (Spring 2002). The leader, CommerceOne, was the firm that lost the least. If profit information was not available, we analyzed market capitalization, if the firm was publicly traded. Of course, market capitalization is relevant only for single-product firms. If market capitalization information was unavailable, we analyzed revenues and market share. Only as a last resort did we analyze the number of customer/end-users/web site visits. Revenues, market shares and/or number of customers/web site visits for a specific product category may not be published with sufficient reliability, particularly for private firms. Clearly, such measurement issues could have a great impact on our results and conclusions. However, it turned out that the results and conclusions of our study were quite insensitive to such measurement issues. Of course, for firms to have any advantage eventually, they must be surviving. Following the standard convention in the strategy literature, we considered a company to be “surviving” if it continued to exist (to date) as an independent entity. We considered companies that went bankrupt, ceased operations, or were bought up by other companies, as non-surviving or “failed.”

2.2 First-mover advantage definition For each product category, we performed historical analysis (as described above) to determine who was the first mover/ market pioneer and who were the first several followers. Using information such as SEC-10K reports, reports from market research firms, and/or recent articles in the business press, it was also determined who was the leader in that product category (when we finished the research into the product category). We defined first-mover advantage to exist in a product category if the first mover was still the leader (at the time the research into the product category was


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completed). We concluded that there was no first-mover advantage if the first mover was no longer the leader. Of course, first movers that are no longer the leader may still have been successful regarding other objectives, for example, cash out after some period of time. Such other objectives fell outside the scope of the research described in this paper.

2.3 The sample We analyzed 19 product categories related to B2B eCommerce technology. They are listed in the Appendix. They are organized in several groups, which are not particularly important to our analysis. They merely serve to present generally familiar groupings of products, such as infrastructure, software, etc. Overall, the product categories were, at the time of the study, on average 14 years old. Each product category was carefully defined such that buyers would consider the products within it substitutable and distinct from those in another product category. Similarly, we also analyzed 6 product categories related to B2C eCommerce, which are also shown in the Appendix. It is important to note that our sample, while not subject to survivor bias within the product categories, is still subject to survivor bias of the overall product categories, as we only focused on product categories that survived. The exception may be the “online delivery” product category (with former players such as Kozmo), which is much less prominent that when we studied it. The data gathering was performed by Master’s students at the business school of the first author. Most students studied two product categories as an independent study project. A few others studied one product category on a “volunteer” basis. Each student was asked to select the product category(-ies) of their own choosing, provided it was eCommerce technology that is potentially subject to network externalities. Alternatively, the students could select the product category(-ies) represented by one or more companies from the Internet-100 Index of companies as published by the US newspaper USA Today (which was paired down to the Internet-50 Index after the start of this research project). This selection method reduces sampling bias that might otherwise have been introduced by the authors. Note that we are not claiming that “strong” network externalities in our sample are definitely present; they are simply possible.

3. Results The table in the Appendix shows for each product category the first mover and the month (if available) and

year of market entry. Similarly, it shows the second mover and the current leader, and finally the whether or not the first mover was the leader at the time the analysis of the product category was completed.

3.1 First-movers generally do not win; Followers do In one product category, Enterprise Resource Planning, the first mover (SAP) is still the leader. In at least 16 (out of 19) B2B eCommerce technology product categories, there was no sustained first-mover advantage. For two product categories, we have to date been unable to determine first-mover or follower-advantage: In multiprotocol routers, Cisco is the current leader. The first mover was either Cisco or Proteon, but we could not determine who of these two was first. In client-side web applets, JAVA (and related software, such as components) was first, but it isn’t clear whether it is currently the leader or not. As an added complication, Sun Microsystems put JAVA in the public domain, so it is arguably no longer a product owned by a firm that can charge for it. If one assumes first-mover advantage in these two categories, then there would have been firstmover advantage in 3 out of 19 B2B product categories. For one other product category the conclusion regarding first-mover advantage depends on the definition of the product category. If search engines were considered two separate product categories (“directory” based search engines versus spider/crawler search engines), then in directory based search engines there would have been a first-mover advantage: Yahoo, the current leader, was the first mover. In spider/crawler search engines there still would not have been first mover advantage: Webcrawler, the first mover, is currently not a leader, in fact it was bought by AOL and later by Excite. However, we consider the two types of search engine technologies substitutable from the customer/user’s point of view, i.e., one product category. Webcrawler was the first mover, Yahoo the current a leader., i.e., no firstmover advantage. If one disagrees with our interpretation regarding substitutability, then there was first-mover advantage in 2 out of 20 product categories, or 4 out of 20 including the two product categories for which we were unable to determine first-mover or follower advantage. Similarly, for the B2C categories in our sample, no first mover is currently the leader. However, if “online consumer auctions” were considered to be two separate product categories (“B2C” and person-to-person (“P2P”) auctions), then in P2P auctions there would have been a first-mover advantage: Ebay, the current leader, was the


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first mover. Hence in at most 1 out 7 B2C categories is the first mover still the leader. Across B2B and B2C categories combined, in at least 1 out of 25 and at most 5 out of 27 product categories we studied there may have been a sustained first-mover advantage. Thus, our results to date provide a reasonable basis for our overall conclusion: Sustained first-mover advantage in eCommerce technology product categories is largely illusionary. It is interesting to note that, given our results to date, any errors of omission in our research would not weaken our overall conclusion, but instead strengthen it. Suppose that, for any particular product category, we had unwittingly omitted a firm that entered before the one we identified as the first mover. Given that we found many product categories with no first-mover advantage, correcting errors of omission would leave the overall conclusion unchanged or even strengthened because some doubts would be lessened.

3.2 First movers were alone for about one year By definition, the first-mover has the advantage (a temporary monopoly) at least until the second entrant. The lag between first and second entry (“pioneer lead time”) varied widely in our sample. In personal computers, the two earliest entrants announced during the same industry conference in April of 1977. The maximum pioneer lead time was 108 months, i.e., 9 years in ERP software. Across 16 B2B product categories for which we had relevant data, the average pioneer lead time was 17 months and the median 8 months. Across B2B and B2C product categories combined, the average pioneer lead time was 15 months (n=21) and the median 10 months.

3.3 One-third of the first-movers survived This study indicates that few first movers in eCommerce technology remain leaders. What happened to the first movers? To date 5 (out of 18 B2B categories for which we had data), or roughly 30%, survive to date as an independent firm, with 4 as current non-leaders in their industry. Two-thirds (13) failed: 12 first movers, or about 66%, were bought up by others (1 by the current leader in that product category and 11 by current nonleaders). One, or about 6%, went out of business. Failure occurred on average after 90 months with a median of 66 months, i.e., around 7 years. In this context it should be noted the “failure” could include the sale of a first mover at a profit to its founders or financiers. Across the total sample, i.e., with B2C

product categories added in, (n=23) these results remain substantially the same. We analyzed the 5-year and 10-year survival rates of the first movers similar to [22, 28]. In 18 product categories for which we had data, roughly two-thirds (13) of the first movers survived the first five years, but onethird (5) did not. In the 14 product categories in our sample that existed at least ten years (and for which we had data), the result was the opposite: roughly two-thirds (9) did not survive and one-third (5) did. Note that, in general, first movers survive probably less often that these numbers indicate, because we only studied product categories that survived. Across the total sample, i.e., with B2C product categories added in, the results are similar.

3.4 Current leaders entered on average 2-4 years after the first mover For eCommerce technology product categories with no first-mover advantage, we analyzed the “leader lag”: the time between entry of the first mover and of the current leader. In our sample, across 17 B2B product categories for which we had relevant data, the average leader lag was 43 months, and the median was 26 months. The minimum was 1 month and the maximum was 13 years and 6 months. Across a total of 21 product categories (B2B and B2C) the results are substantially the same.

3.5 Half of the current leaders were fast follower The current leader in 9 (out of 16) B2B product categories for which we had data was what we call a “fast follower”: the 2nd entrant (in 4 product categories), the 3rd entrant (in 2 categories), or the 4th entrant (in 3 categories). These fast followers entered on average 23 months after the first mover (median = 12 months, min. = 1 month, maximum = 74 months). In the other seven product categories, the current leader was fifth to market or later. When combining the B2B and the B2C product categories for which we had data (n=21), 10, i.e., roughly half of the current leaders were either second-to-market or third-to-market. They entered on average in 19 months after the first mover (median = 11 months, min. = 1 month, maximum = 72 months).

4. Discussion 4.1 Early entrants = First movers + fast followers


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Studies that have found significant early-entrant advantages include the meta-analysis studies in [13, 27, 29]. This study has similar results. An early entrant became leader in about half the industries. However, historical analysis enabled us to distinguish early followers from first movers, which revealed an important refinement: First movers rarely sustained leadership in their product category, but fast-followers are leaders in half of the (surviving) product categories. Perhaps a first mover is like a first system: As Fred Brooks said “Plan to throw one away, you will anyway” [4]. The first mover demonstrates a new idea, which points the way for many followers to improve upon quickly. Indeed, half of the current leaders who were second or third to market entered within a year after the first mover. I.T. products often evolve significantly within a few years. Thus, it appears that the fast followers appeared during the first product’s life cycle. The other half of fast-following current leaders in our sample entered after one year (and often within six years), which is still fast as compared with traditional product’s life cycles and consistent with fast-cycle competition [8, 31] in the initial stages of a new technology market. Perhaps because of the similarity in research methodology, our findings are similar to those by Golder and Tellis [7, 28]. They studied consumer product categories, including several dating back to the mid- to late 1800’s, and found that 11 percent of product pioneers were market leaders at the time of their study. In product categories we studied, we find at least 3 percent and at most 20 percent with a first-mover advantage. Golder and Tellis found that the proportion of current leaders who were “early market leaders” was roughly half. Although their definition of “early market leaders” differs from our definition of “fast followers,” it is interesting to note that the proportion of current leaders in our sample who were “fast followers” is also roughly half. It is perhaps not surprising that the average leader lag of 2-4 years in our sample is shorter than the average 11 years leader lag in consumer product categories created after World War II and the 26 years leader lag in consumer product categories created before World War II, as reported in [7]. An average leader lag of 2-4 years in eCommerce technology products created since 1977 is an indication that competition is generally getting more intense and advantage is more fleeting. It is also consistent with the notion that the pace of change in technology markets is faster than in traditional markets such as for industrial or consumer products.

4.2 Resource-Based Theory is not a basis for firstmover I.T. advantage The conceptual basis underlying previous research into first-mover advantage in traditional industries is the Resource-Based View (RBV), as argued by Lieberman and Montgomery [16] in a reflection on their awardwinning paper [15]. They argue that “every applied study of first-mover advantages provides evidence on the accumulation of resources and capabilities by market entrants.” Their focus is on how a firm’s order of entry influences any accumulation of superior resources, and, conversely, how a firm’s resources influence the order of entry. We find this focus on RBV rather counterintuitive. RBV logic, initially proposed in [30] and further advanced in, e.g., [3], holds that when resources cannot be copied cost-effectively (diffusion) and when they are unevenly divided across competitors (heterogeneity), a firm can expect an advantage to be sustainable. However, if resources can be copied by competitors (freerider effects) then resource-based theory predicts that any advantage is temporary. Generally, copying of eCommerce technology products, and I.T. products in general, is fairly easy. For example, the Windows Graphical User Interface (GUI) was copied from the Apple MacIntosh O.S. GUI, which in turn was copied from the Xerox Lisa GUI. Or, key people of the first mover start working for a later entrant, e.g., Mitch Kapor moved from VisiCalc to Lotus. Confirming the RBV logic in these cases, advantages were temporary. Generally, I.T.-based assets ought to be inherently easier to copy unless they are supported or shielded in some way by other, more durable assets such as a strong (pre-existing brand), economies of organizational scale (as for Wal-Mart) or possibly network externalities beyond a “tipping point” [8, 31]. First movers in eCommerce technology, particularly when they are start up firms as opposed to ventures by established firms, generally will not yet have accumulated such isolating mechanisms or complementary assets. In such situations, resource-based theory predicts that first-mover advantages should dissipate quickly, which is what we found in our sample. Consequently, the RBV appears to be a theoretical basis to explain follower advantages but not first-mover advantages, at least in eCommerce technology industries.

4.3 Increasing Returns is (also) not a theoretical basis for first-mover I.T. advantage Many eCommerce technology companies operate in markets that are at least potentially subject to the theory


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of increasing returns [2], in particular due to positive (demand-side) network externalities (of adoption) [25]. Positive demand-side network externalities hold that a product is more valuable to a customer the more customers have already bought the product. Network externalities can be direct (i.e., involving one product), or indirect (i.e., involving two products, e.g., hardware and software) [11]. An example of direct network externalities is eBay. The more (potential) customers on eBay, the more valuable eBay’s service is to a next customer. An example of indirect network externalities is the Windows Operating System. The larger the installed base of Windows, the more vendors will want to write software for Windows. With more software titles available, customers derive even more benefit from the dominant operating system. Thus, advantages get even bigger over time and all significant competitors might disappear from the market. Eventually, strong network externalities may lead to “tipping” [1, p. 23], i.e., “winner takes all.” The incumbent can maintain leadership of the product category over the lifetime of product demand. However, typically in the early market stage, many eCommerce technology product categories are characterized by great uncertainty as to what the dominant design will be. Many different technologies appear in a short time (a la Cambrian explosion). Customer preferences evolve dramatically in short time periods. It is difficult, if not impossible, for firms to determine how a market will evolve. As argued in [1, Ch. 2] argues, there is a high probability that one winner will eventually emerge in markets with strong network effects, but a low probability of successfully predicting beforehand whether it will be a fast follower or the first mover.

4.4 Advantages seem to extend into the next product generation A study of “disruptive” technologies [5] found that new entrants and new leaders emerged in each next product generation. The leader in a previous generation generally did not sustain its advantage into a next generation. We had not explicitly gathered data for our sample to analyze any such effect. However, from data that we do have in our sample, it seems that leaders in several product generations did sustain their advantage into the next generation. Examples are Spreadsheets, Multiprotocol routers, Optical data transmission equipment, B2B CRM application suites and PDAs. Perhaps this is because performance of a next generation of eCommerce technology is higher, not lower (as is true with disruptive

technologies). Hence, most eCommerce technology may not be “disruptive” in the Christensen sense. Instead, product performance of the next generation of eCommerce technology products, generally compatible with the prior generation, is arguably higher than of the previous generation. We might call such technology products “extending” technologies. In several product categories we have observed that products are extended by integrating more and more modules that were previously sold as standalone products. For example, spreadsheets are now part of productivity suites, the Internet Explorer web browser is part of the Windows operating system, MRP modules are now part of ERP packages, and document management functionalities combined with web publishing are now part of content management software. Particularly when compatibility with the previous product generation(s) is maintained, “staircase” strategies, when exploited beyond the tipping point, appear to allow the leader to keep the advantage through subsequent generations [31].

5. Implications for strategic management Based on our sample, we conclude that sustained firstmover advantage is largely illusionary in eCommerce technology product categories. This general finding has several implications: eCommerce technology firms wishing to build a longterm advantage do not need to rush into new markets seeking to be first. It seems more advantageous to follow, i.e., to delay entry, at least if the objective is to be the market leader eventually. Large companies seem to do this, either because of deliberate decision making to that end, or because of inertia in understanding the potential of new markets or in preparing to enter the new market at large scale. Startups trying to become longterm leaders in new markets may be better off taking their time, understanding customer needs and developing a solid business plan. Of course, startups may have other objectives for being first: To cash-out and exit after several years (which, in our sample, roughly twothirds of the first movers did), to influence standards that complement existing products, to run experiments to understand customer preferences, or to influence competitor’s behavior. Nevertheless, our study suggests that firms should not delay entry for very long: The current leaders in our sample entered the market quite fast after the first mover, often with a few years. Venture capitalists may consider funding eCommerce technology firms that follow fast. At least based on our sample, not funding the first mover in a new product category may be good, rather than bad. Advantages of investing in followers include less uncertainty about the


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“dominant design” or about customer requirements, as the market for a new idea is validated (or not) by the first mover. Of course, while we counted a first mover that was sold to another company as non-surviving (and hence as “failed”), the sale may still have yielded a nice financial return on the investment by the original owners. Alternatively, venture capitalists could fund the first three followers (perhaps as passive investors avoiding conflicts of interest), which might give them a 50% probability of funding the eventual leader, at least in surviving product categories.

6. Further research As usual, this study generates more questions than it answers. Some relevant new research questions are described next.

6.1 Is there a “law” of follower advantage? As discussed earlier, this study found that fastfollowers won in roughly half the industries studied, possibly because a dominant design was established when the second or third entrant appeared, particularly with network externalities causing dynamic lock-in to set in. We have not investigated this possibility, partly given the similarity of our results to those by Golder and Tellis [7, 28]. They studied consumer products dating as far back as the 1800’s. This raises at least two questions: Could the same (network) externalities be at work in both industries? After all, even traditional industries may have been subject to network externalities, particularly in the early phases of the industry, as new product standards became established. A second question is whether other industries, e.g., industrial products, exhibit similar fistmover disadvantages and follower advantages to the ones we observed. Analyzing other industries using historical analysis may reveal whether or not there is a “law” of (fast-) follower advantage.

6.2 Why do first movers not keep the advantage? Our sample demonstrates that first movers generally do not sustain their initial advantage. Why? First movers may have disadvantages, i.e., reasons that first movers loose their advantage and later entrants eventually win [15]. For example: (1) Free-rider effects (later entrants imitating the innovator): In R&D, learning-based productivity improvement, or attracting skilled labor. Such free-rider effects may be enhanced by complementary assets of an imitator. (2) Resolution of technological or market uncertainty, i.e., emergence of a “dominant design.” (3) Technology or customer needs

shift, which are difficult for incumbents to discern and/or react to, see also [5]. (4) Organizational inertia, due to dedicated fixed assets (particularly if they have not been fully depreciated), reluctance to cannibalize existing products, reluctance to antagonize existing sales channels, or inflexibility and sluggishness of a large firm. In addition, particularly for eCommerce technology products, first movers may not be able to sustain their initial advantage because they did not (attempt to) exploit network externalities effectively. For example, Apple did not license the MacIntosh specifications (with the exception of a short period of time). Consequently, other firms could not build clones, add-ons, etc. which arguably kept MacIntosh from becoming the dominant p.c. platform. Or as one colleague put it to us “It’s not about who moves first – it’s about who gets to the tipping point first.”

6.3 Which followers get the advantage and why? Various researchers have proposed different factors that may characterize followers who eventually get the advantage. For example: (1) Late entrants that are “innovative” outsell pioneers, [24, 28], (2) Interaction effects between service quality and entry order influenced eventual success [27], (3) The winner possessed certain resources such as a direct sales force [23], (4) Followers that win may envision the eventual mass market [28] and commit a large amount of financial resources. Other possibilities are that current leaders were simply better in pricing, market segmentation, in lowering the costs of goods sold and marketing/sales expenses (Dell), or in establishing a partnership with other firms (Microsoft/IBM). (We are grateful to two anonymous reviewers for these suggestions.) As B2B industries becomes increasingly information rich, it will be important to determine what role that such factors play in sustaining advantage in eCommerce technology products.

7. Acknowledgements The authors thank Loyola University Chicago for the 2002 summer research grant to support this research project. They also appreciate initial discussions with Chris Sorensen that sparked this research, the research support of the following graduate students of the Graduate School of Business of Loyola University Chicago: Andrew Cathlina, Matt Cottrell, Robin Edinson, Patrick Eslick, Vince Eslick, Catherine Fan, Matt Fortier, Meredith Heller, Thomas Kuncheria, Jaime Melendez, John Nimesheim, Dyuti Patankar, Anhad


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Singh, Thomas Thalakottu, Bart Wiacek, Nora Wideikis, as well as Rebecca Sedam for providing tireless library support.

8. References [1] W.B. Arthur, Increasing Returns and Path Dependency in the Economy, The University of Michigan Press, Ann Arbor, MI, 1994. [2] W.B. Arthur, “Increasing Returns and the New World of Business”, Harvard Business Review, July-August 1996, pp. 100-109. [3] J.B. Barney, Gaining and sustaining Competitive Advantage, Addison-Wesley, Reading, MA, 1997. [4] F. Brooks, The Mythical Man Month, Addison-Wesley, 1975. [5] C. Christensen, The Innovator’s Dilemma, Harvard Business School Press, Boston, MA, 1997. [6] R. Gardner, Games for Business and Economics, John Wiley and Sons, 1995. [7] P.N. Golder, and G.J. Tellis, “Pioneer Advantage: Marketing Logic or Marketing Legend”, Journal of Marketing Research, Vol. XXX, May 1993, pp. 158-170. [8] G.J. Hidding, “Sustaining Strategic IT Advantage in the Information Age", Journal of Strategic Information Systems, Volume 10, 2001, pp. 201-222. [9] G. Kalyanaram, and R. Gurumurthy, “Market Entry Strategies: Pioneers Versus Late Arrivals”, Strategy & Business, Third Quarter 1998, pp. 74-84. [10] M.L. Katz, and C. Shapiro, ”Technology Adoption in the Presence of Network Externalities”, Journal of Political Economy, Vol. 94, No. 4, 1986, pp. 822-841. [11] M.L. Katz, and C. Shapiro, Systems Competition and Network Effects”, The Journal of Economic Perspectives, Vol. 8, Issue 2, Spring 1994, pp. 93-115. [12] P.W. Keen, Competing in Time: Using Telecommunications for Competitive Advantage, Ballinger Publishing Company, Cambridge, MA, 1988. [13] R.A. Kerin, P. Rajan Varadarajan, and R.A. Peterson, “First-Mover Advantage: A Synthesis, Conceptual Framework, and Research Propositions”, Journal of Marketing, Vol. 56, October 1992, pp. 33-52. [14] H. Lee, K.G. Smith, C.M. Grimm, and A. Schomburg, “Timing, Order, and Durability of New Product Advantages with Imitation”, Strategic Management Journal, Vol. 21, 2000, pp. 23-30. [15] M.B. Lieberman, and D.B. Montgomery, “First-Mover Advantages”, Strategic Management Journal, Vol. 9, 1988, pp. 41-58. [16] M.B. Lieberman, and D.B. Montgomery, “First-mover (Dis)Advantages: Retrospective and Link with the ResourceBased View”, Strategic Management Journal, Vol. 19, 1998, pp. 1111-1125.

[17] D.C. Mueller, “First-mover Advantages and Path Dependence”, International Journal of Industrial Organization, Vol. 15, 1997, pp. 827-850. [18] G.S. Nezlek, and G.J. Hidding, “An Investigation into Differences in the Business Practices of Information Industries”, Human Systems Management, Vol. 20, No. 2, 2001, 71-81. [19] W.C. Patterson, “First-Mover Advantage: The Opportunity Curve”, Journal of Management Studies, Vol. 30, No. 5, September 1993, pp. 759-777. [20] W.T. Robinson, “Sources of Market Pioneer Advantages: The Case of Industrial Goods Industries”, Journal of Marketing Research, Vol. XXV, February 1988, pp. 87-94. [21] W.T. Robinson, and C. Fornell, “Sources of Market Pioneer Advantages in Consumer Goods Industries”, Journal of Marketing Research, Vol. XXII, 1985, pp. 305-317. [22] W.T. Robinson, and S. Min, “Is the First to Market the First to Fail? Empirical Evidence for Industrial Goods Businesses”, Journal of Marketing Research, February 2002. [23] T.S. Schoenecker, and A.C. Cooper, “The Role of Firm Resources and Organizational Attributes in Determining Entry Timing: A Cross-Industry Study”, Strategic Management Journal, Vol. 19, 1998, pp. 1127-1143. [24] V. Shankar, G.S. Carpenter, and L. Krishnamurthi, “Late Mover Advantage: How Innovative Late Entrants Outsell Pioneers”, Journal of Marketing Research, Vol. XXXV, February 1998, pp. 54-70. [25] C. Shapiro, and H.R. Varian, Information Rules, Harvard Business School Press, Boston, MA, 1999. [26] F.C. Spital, “Gaining Market Share Advantage in the Semiconductor Industry by Lead Time in Innovation”, in Research on Technological Innovation, Management and Policy, (ed. R. Rosenbloom) Vol. 1, JAI Press, Greenwich, CT, 1983, pp. 55-67. [27] D.M. Szymanski, D.M. Troy, L.C. Bharadwaj, and G. Sundar, “Order of Entry and Business Performance: An Empirical Synthesis and Reexamination”, Journal of Marketing, Vol., 59, No. 4, 1995, pp. 17-33. [28] G.J. Tellis, and P.N. Golder, ”First to Market, First to Fail? Real Causes of Enduring Market Leadership”, Sloan Management Review, Vol. 37, Winter 1996, pp. 65-75. [29] P.A. VanderWerf, and J.F. Mahon, “Meta-Analysis of the Impact of Research Methods on Findings of First-Mover Advantage”, Management Science, Vol. 43, No. 11, November 1997, pp. 1510-1519. [30] B. Wernerfelt, “The Resource-Based View of the Firm”, Strategic Management Journal, Vol. 5, 1984, pp. 171-180. [31] J.R. Williams, Renewable Advantage, The Free Press, New York, NY, 1998.


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Appendix: Overview of results Group

Product Category

First Mover (entry) B2B B2B CRM Applications Suites Florida InforManagement Services (12/91) B2B Internet B2B Market Places Vertical.net (8/95) B2B/B2C Price comparison PriceWatch.com (8/95) B2B/B2C Search Engines Web Crawler (4/94) Hardware Optical transmission equipment GTE (4/77) Hardware PDA GridPad (89) Hardware Personal Computers Commodore, Apple (4/77) Hardware Routers Cisco, Proteon? Infrastructure Online Service Providers Trintex/Prodigy (2/88) Infrastructure Short Message Service Centers Aldiscon (6/91) Software Software

Client-side Web Applets Collaborative Filtering

Software

Content Management software

Java (5/95) Firefly (3/95)

Software Software

OfficeSmith (7/83) Enterprise Resource Planning SAP (6/79) Online Market Places Software Tradex (1/96)

Software

Public Key Cryptography

Software Software

Spreadsheets Web Advertising Management

Software

Web browsers

B2C B2C B2C

Online Consumer Auctions Online Delivery Online Grocery Delivery

B2C

Online Travel Reservations

B2C

Personal Income Tax Software

B2C

Online job boards

Second Mover (entry) Aurum (5/92)

Current Leader First-Mover (entry) Advantage? Siebel (10/94) No

IBEX-GBA (9/95) Netbuyer.com (10/96) Yahoo (8/94)

FreeMarkets (12/95) MySimon (4/98)

No

Yahoo (8/94)

No

Lucent (5/77) Newton (92) Commodore, Apple (4/77)

Lucent (5/77) Palm Pilot (96) Dell (6/84)

No No No

Cisco (84) AOL (94)

? No

Logica (8/97)

No

Java or Active X NetPerceptions (3/96) Vignette (1/97)

? No

AOL (94) Vodafone or Sema (12/92) NetPerceptions (3/96) InterLeaf (1/84)

JDEdwards (6/88) SAP (6/79) Moai (6/96) CommerceOne (3/97) RSA Comsafe-X Verisign (3/85) (3/85)

GTE-Telecom Phasor Code Encryption System (6/84) VisiCalc (10/79) Super Calc (6/82) AdForce (6/94) Interactive Media Sales (4/95) Spry Mosaic Netscape Navigator (12/94) (8/94) OnSale (5/95) Ebay.com (9/95) Streamline (96) Kozmo (97) Peapod (96) NetGrocer.com (97) Travelshopper Eaasy Sabre TWA (1/85) (12/85) Personal Tax HowardSoft Plan – Aardvark (6/80) (10/79) OCC (4/93) Monster.com (4/94)


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MS Excel (6/85) DoubleClick (1/96) Internet Explorer (8/95) Ebay.com (9/95) ? NetGrocer.com (97) Expedia (10/96)

No

No Yes No No

No No No No No No No

TurboTax – Intuit (6/84)

No

Monster.com (4/94)

No