Who joins the Platform? The Case of the RFID Business Ecosystem

Transcription

1 Who joins the Platform? The Case of the RFID Business Ecosystem Anne Quaadgras Information Systems Department, School of Management, Boston University Abstract Today, many knowledge-based technology applications form a business ecosystem: a set of complex products and services made by multiple firms in which no firm is dominant. For this paper the emerging radio frequency ID (RFID) ecosystem was built based on firms alliance announcements, and propositions around the behavior of large, multi-line technology firms in this network were analyzed. The RFID network is used to empirically show that absorptive capacity, and exploration vs. exploitation theories may explain some behavior of large firms. Specifically, a propensity to form alliances in general makes it more likely large firms will join the RFID ecosystem, and more exploratory firms join earlier. Greater availability of slack resources also leads to the formation of more alliances in the network. The ecosystem perspective and these results may influence alliance decisions of firms entering into high cost technological innovations. 1. Introduction In June 2003 Wal-Mart said it planned to require its top 100 suppliers to put radio-frequency identification (RFID) tags on shipping crates and pallets by January [1]. By April 2004, it was clear this deadline would not be met by most of them [2]. Why is RFID so hard? RFID is not a single, simple piece of technology, but requires millions of tags containing standardized, coded data, and thousands of tag readers. These, in turn, must transmit relevant data to multiple software applications, including middleware, databases, legacy systems and new applications, that must interoperate in order to effectively manage the supply chain. An RFID tag is made up of a microchip attached to an antenna. Tags draw power from the reader, which sends out radio waves that induce a current in the tag's antenna. Tags can be read at distances up to about 10 feet and require no maintenance. Tags are inexpensive (approaching 35 cents today, with a goal of 5 cents) and prices are falling rapidly. Tags are encoded with an electronic product code (EPC) that identifies the version number (so standards can be adjusted), the manufacturer, the product, and the item serial number. Readers can read the tags and pass the information on to computer systems running specialized technology (e.g. Savant) which act as the nervous system of the network. The platform also includes methods for identifying objects (object name servers), markup languages (PML), and control methods. (For more, see aboutthetech_indepthlook.asp.) No single firm manufactures all of the relevant technologies, can set the standards, or can coordinate and integrate all the required components, software, and services. RFID is a classic example of a technology supported by an ecosystem of companies trying to develop a platform, in which no single firm dominates, no single standard covers all requirements, and no single path to success exists [3]. That makes RFID an interesting case study of firm behavior. Who competes in the RFID space? What characteristics do they share? How do they compete, or cooperate? And what does this mean for would-be participants? Or users? Going beyond this specific case, interesting questions include: How does an ecosystem evolve? How do platforms grow out of ecosystems? How do established firms adapt in the face of new technologies? These are some of the questions that motivate this line of research. In this paper I build and analyze the emerging RFID ecosystem based on announcements of alliances among firms, and analyze propositions around the behavior of large, multi-line technology firms in this innovative, technology-based ecosystem. Drawing the preliminary diagrams of this network made it clear some established firms seemed to be much more involved than others (see Figure 1). This observation leads to the key questions in this paper: why these firms, and not others? and what about these firms made them join earlier, or to be more centrally positioned? 1

2 Theories used in this paper are March s exploration/exploitation processes [4], as well as Cohen and Levinthal s theories of absorptive capacity [5], to ground observations around the behavior of the large, established firms who have joined the RFID network as of mid-april, The firms in the RFID ecosystem are conceptualized as a social network, and the analysis is based on social network analysis concepts, as well as more traditional variance analyses. Key propositions around who joins the network, and when are: 1. A propensity to form alliances in general leads large, established firms to be more likely to join the RFID ecosystem. 2. Exploratory firms are more likely to join the RFID network earlier. 3. Availability of slack resources to form alliances leads to greater centrality in the RFID ecosystem. Understanding the structure and evolution of the network can help technology firms decide when and how to enter, and may help users determine who are likely to be the better partners. The paper is organized as follows: Section 2 consists of a literature review, covering platforms, networks, network analysis methods, absorptive capacity and exploration/exploitation. Section 3 develops some propositions based on this literature. Section 4 describes the analysis process, including building the network based on third party data sources, and the process of determining relevant measures. Section 5 covers the empirical results, and section 6 consists of discussion, conclusions, and next steps. 2. Literature review The RFID alliance network (or ecosystem) is interesting from an information systems (IS) perspective for multiple reasons. First, it is an old technology with a brand new use (tagging retail products and other high volume items), making it interesting to study how firms interact based on the technology and related technical components. This allows us to empirically study concepts such as appropriation of value, as well as the impact of the technology specifics (e.g. hardware, middleware, software, services) on the network of firms, to, for example, determine whether competition occurs at the system level or component level [6]. Second, it is a terrific case study for innovation in information systems, including studying how established firms branch out into this new industry, and how startups fare with alliances and cross-boundary innovation, following, for example, [7, 8] who studied firms in the semiconductor industry. Third, the RFID ecosystem may develop a platform, similar to other technology platforms such as operating systems, and so can be used to confirm or disconfirm various theories around platforms; see e.g. [9, 10] Platforms The word platform has entered into common use, but often in a vaguely defined way. Breshnahan and Greenstein [10] describe a platform in general as "a bundle of standard components around which buyers and sellers coordinate efforts." They also note that "the nexus of compatibility standards between hardware and software is the hallmark of a platform". Gawer and Cusumano [9] define a high-tech platform as: an evolving system made of interdependent pieces that can each be innovated upon. Both definitions highlight the interdependency of products and services, and the ability for multiple actors to innovate, by focusing on each component independently. Implicitly platforms have a network connotation, where each product or service is a node that is in some way connected to the other nodes. Henderson and Kulatilaka [11] are both broader and more precise. They define a platform as: "a set of capabilities used by multiple parties in a manner that: 1. Creates options value through design efficiency and flexibility, 2. Creates network effects that include both connectivity and effects due to a complementary system of goods and services, and 3. Has explicit architectural control points influenced by the platform investors." Capabilities include people, process, technology, and organization, making it far broader than technology alone. They also conceive of a platform as a system of interacting components. RFID tags, with the attendant tag readers, computer hardware and software, and related services qualify as a platform in this sense. Network effects come in two forms. Either a user benefits directly from the existence of other uses, such as with a telephone, or the user benefits indirectly due to the existence of complementary products, such as a PC owner who benefits when a large base of PC s results in wide availability of software [12]. Clearly, users of RFID tags (e.g. retailers) benefit when all their products are RFID enabled, by for example, improving inventory management; similarly, producers of goods tagged with RFID s benefit when the entire supply chain can use the tags to track the goods to the point of sale rather than just to the loading dock. Based on all these definitions, and the characteristics of RFIDs, the RFID ecosystem is likely to mature into a platform Networks and network analysis methods The RFID ecosystem is amenable to network analysis because it is, in essence, a network. A network is a set of nodes and a set of relationships that links two or more nodes to each other. [13]. Network theory and methods 2

3 can be used to describe many types of networks, including technical systems, networks of firms producing components of a system, social networks of developers or users of a system, etc. Much of network theory and related mathematical methods is based on graph theory and other mathematical methods [14, 15]. How is the RFID ecosystem a network? Clearly a platform consists of multiple components that are linked in a coherent whole. The nodes may be applications and the links information flows, or the nodes may be firms who created the components, and the links partnerships among those firms. The network may consist of developers or users as nodes, and links of information or knowledge transfer. Using the technologies of the eocsystem (in this case, RFID s) we can build a network of the players who participate in building and using the platform, specifically through joint development in alliances. This is the network we analyze in this paper. The key network analysis method used is based on centrality measures. The notion of centrality in networks is highly intuitive, but there are many types of centrality, each calculated in a different way. [See 15, Chapter 5]. One family of notions defines centrality as being as close to as many others as possible. Degree centrality, which enumerates the number of links to others, exemplifies this idea. Another intuitive notion of being central is the degree to which one is between others, and thus central to ensuring communications reach all parts of the network, or has the ability to control information or other flows. Why does centrality matter? A more central player has more access to knowledge, a key to innovation success [16], [17]. A player with more ties is more likely to succeed [18]. Burt posits that structural holes, based on betweenness centrality, are key to competitive success in a knowledge-intensive arena because of the unique knowledge and brokerage capabilities a central player is likely to have [19] Why alliances? There are multiple theories for the formation of alliances. Gulati [20] classified them into three broad categories based on a review of reasons for forming joint ventures by Kogut [21]: transaction costs resulting from small numbers bargaining, strategic behavior by firms trying to enhance their competitive position or market power, or a search for knowledge, learning, or capabilities. Technology structure may also drive the need for alliances. Stuart [7] makes a start at this with his analysis of technological crowding, which is a quite general concept, focusing on technological similarity as evidenced by patent classes. Several authors have argued alliances are key to survival for small firms [22], as well as important strategies for incumbent firms facing rapid technological change [23]. A need for technological diversity (but not too much) as found by Sampson [24] may apply to the RFID network as well. She found that both the degree of diversity and the way in which alliances are organized matter to the success of the alliance. Her work was at the dyad level. She grounds the effect of organization type on link performance in both transaction cost economics (TCE) and knowledge based theories (e.g. [25], [26]) Why a network perspective on alliances? Multiple streams of research exist that focus on various aspects of interorganizational alliance networks, with empirical work covering several industries. Below is a summary of the most relevant ones that motivate this stream of research. Gulati s 1998 [20] review paper establishes a social network analysis perspective to study alliances. He shows that the social network in which players are embedded shapes the alliance network that forms. It is a dynamic process and the network is endogenous. He raises five provoking issues about alliance networks, including their formation, governance choices, evolution, performance, and performance of firms who enter. The focus in this paper is on formation and evolution, as the RFID network has not matured enough to have many performance measures. Gulati also showed that dyadic studies are insufficient for understanding who enters. Competence at alliances is based on prior experience with alliances. He also focuses on TCE (competitiveness); however, this is not yet an issue for RFID s as they are a new market and application. A series of interesting empirical studies focuses on explaining whether it is social capital or structural holes that affect firm performance in the network more. These include Gulati, Walker Kogut & Shan [18], Ahuja [27], and some of Powell s work (e.g. [28]). The RFID network would make a good test case, as it is already clear that the network structure is both rich and varied. Social capital provides access, timing, and referrals, improving the chances that an effective alliance will be formed at an opportune time. The control potential of structural holes was found to be less important. Gulati s work focuses on embeddedness as the source of information and trust, and the reason firms continue to ally with those they already know. He, as well as Kogut, Shan, and Walker have found that firms that had more prior alliances, were more centrally situated in the alliance network, or had more focused networks were more likely to enter into new alliances, and with greater frequency. Similar findings have been reported for the influence of firm centrality on forming new alliances in 3

4 biotech [28], and semiconductor firms [29]. The details vary, but all show that embeddedness (i.e. location in the network, and structure of the network) matters. This is expected to be true for the RFID network also. Walker, Kogut & Shan [18] justify the use of formal agreements as the link, noting that formal agreements are the most salient and reliable indicator of resource and information sharing between firms, and they are the origin of information regarding a firm s cooperative strategy. Most formal alliances are also publicly announced as press releases, making them easy to find through services such as Lexis-Nexis. They also identify five control variables to understand differences in startups behavior. Their list of control variables includes firm size, firm experience in cooperative relationships (i.e. prior alliances), whether the firm is public, concentration of partners across regions, and the average number of relationships of partners Absorptive capacity and capabilities For the purposes of this paper, Cohen & Levinthal s [5] key insight is that the ability of a firm to recognize the value of new external information, assimilate it and apply it to commercial ends is critical to its innovative capabilities, and is largely a function of the firm's level of prior related knowledge. They also note that absorptive capacity and innovative performance are path dependent, in that lack of investment early on may foreclose future development in a given area. Thus firms that want to be in the RFID network both need prior related knowledge, and should get in early to maximize innovative performance. Kogut & Zander [25] develop a more dynamic view of how firms create new knowledge. They build up this dynamic perspective by suggesting firms learn new skills by recombining current capabilities. What a firm has done before tends to predict what it can do in the future. In this sense firm's cumulative knowledge provides options to expand in new but uncertain markets in the future. This supports the idea that more diverse firms are more likely to be able to both recognize and take advantage of the opportunities of a new technology such as RFID Exploration and exploitation March [4] describes and models the relation between the exploration of new possibilities and exploitation of old certainties, both with respect to learning and resource allocation. He finds that adaptive processes, by focusing on exploitation, are more effective in the short run, but can be self-destructive in the long run. Thus exploration behavior is important for long-term firm success. Rosenkopf & Nerkar [30] refine and apply March s ideas empirically as boundary-spanning search by organizations. That is, organizations that attempt to learn by crossing firm and/or technology boundaries are engaging in exploration, and this behavior has a strong impact on subsequent innovation in the industry. Thus firms that wish to benefit from RFID should engage in this network via multiple types of relationships. 3. Proposition development and testing This paper focuses on two of Gulati s five questions posed in his 1998 review paper: Which firms enter, and how does the network evolve over time? Gulati believes that embeddedness in the social network determines subsequent actions; i.e. a firm with many ties will form more ties. Subsequent work will attempt to show whether this is true in RFID s. In technology industries, much of new learning is the result of knowledge spillovers; no firm develops complex technology from scratch, on its own. Much of this knowledge can be learned through formal and informal alliances with other firms in the industry, be they focused startups or other suppliers. It is expected that certain characteristics make it more likely for large, diversified, technologically sophisticated firms to join a nascent network in a technology that may be peripheral to their current focus. These firms are clearly engaging in exploration, a key component of effective innovation [4], but certain firm-level characteristics make them more likely to do so, as noted by several authors discussed in Section 2. One factor that seems relevant is their prior capability at forming alliances, as evidenced by their past number of alliances; potentially adjusted for alliance type and partner type. Cohen and Levinthal s absorptive capacity, as well as resource-based theory around organizational routines and capabilities (e.g. [25] or [31]) certainly support this view. So, hypothesis is: H1: A propensity to form alliances in general leads large firms to be more likely to join the RFID ecosystem. Cohen and Levinthal s [5] work on absorptive capacity states that firms that invest more in R&D are able to assimilate more new information. The RFID industry integrates multiple technologies across SIC codes. Thus, large firms that invest more in R&D, are both more likely to be interested and more likely to be able to absorb the new knowledge created in RFID s. March s notion of exploration vs. exploitation implies that firms that are exploratory tend to be more diverse, and focus more on research and development than do exploitative firms. Similarly, firms that engage in R&D in general (as evidenced by relative patenting intensity) should be more likely to be involved in the RFID network. Exploration can be operationalized as diversity in SIC codes, tendency to patent, and R&D spending. 4

5 Combining these theoretical ideas leads to: H2: Exploratory firms are more likely to join the RFID network earlier. Availability of slack resources also permits firms to be more exploratory and thus to form more alliances [e.g. 32]. Slack resources can be operationalized as employee availability, e.g. by relatively lower revenues per employee, or by higher excess cash. Thus: H3: Availability of slack resources to form alliances leads to greater centrality in the RFID ecosystem. 4. Analysis Data description and building the networks from the data The RFID network was built based on formal alliance announcements in the RFID industry using two sources of data: the SDC database of alliances, and a direct search of all newswire reports relating to RFID alliances by specified firms between 1999 and April 23, This industry consists of a number of established players (mostly publicly held) as well as a number of smaller, focused firms that specialize in some aspects of RFID s. Although RFID s themselves are not new, the platform combining tags, readers, software, and services for tracking individual consumer goods in the supply chain has only recently become practical. Membership in the MIT AutoID center as of October 2003 was used as the source of firms likely to be in the network. The MIT AutoID center was founded in 1999, with the goal of establishing standards for RFID tags and related infrastructure, so the search for alliances starts from 1/1/1999. The database was searched for all announcements involving RFID, alliances, and these firms. In addition, the database was searched for other announcements involving all of the partners of the first set. Drawing the preliminary diagrams of the network made it clear some established firms seemed to be much more involved than others (see Figure 1). These diagrams led to the questions: why these firms, and not others? and what about these firms made them join earlier, or become more central? To answer the first question, a set of potential large firms that could join the network was needed. The Mergent database was used to find the largest (by revenue) firms in the industry (i.e. firms with SIC codes relevant to the RFID platform, see Appendix A) for which data was available. This list of 76 firms contains 28 large firms that have joined the RFID network, and 48 that have not. The analysis seeks to explain why these two sets of firms behave differently with respect to this new technology platform. The second question required network analysis to determine multiple measures of centrality, as well as additional data on joining date and R&D expenditures. Figures and network analysis were done using Pajek, [33], freeware for analyzing large networks, and Ucinet [34]. Regressions were done in SAS Dependent variables Network membership: is 1 if a firm is in the network, 0 if it is not. Entry year: is in the form of (n 1998), where n is the year. That is, entry year for a firm joining in 1999 is 1; for a firm joining in 2004 it is 6. There have been no announcements about the ending of any RFID alliances, or of the demise of any partner firms. Centrality: measures of degree and betweenness centrality in the final network (all alliances announced as of April 23, 2004). Prior work, e.g. [28] has shown that closeness centrality tends not to be significant Independent variables For the test set of large firms the following data was collected: Other alliances: all alliances in the SDC database, other than RFID, formed between 1/11999 and 3/20/2004. Diversity: count of the number of SIC codes under which the firm is listed, Mergent database. Patents: patent count in the USPTO database of granted patents with applications recorded between 1/1/98 and 1/1/2002. Employees: total in fiscal 2003, Mergent database. R&D: total R&D expenditures in fiscal 2003, from Compustat, annual reports, and SEC filings. Revenue: fiscal 2003: from Mergent. Revenue per employee and R&D spending per employee were calculated from this data Regression procedures H1 was tested using a logistic regression on the 76 large firms to determine which independent variables predict membership in the network by April A version was also run on a subset of 56 firms for which R&D data was available. H1a: Probability of membership = function of: diversity, other alliances, patents, revenue, employees, and revenue per employee H1b: Probability of membership = function of: diversity, other alliances, patents, revenue, employees, revenue per employee, R&D, R&D per employee H2 and H3 were tested via separate OLS multivariate regressions using only the 24 large firms that joined the 5

6 network by April 2004 AND that had R&D spending data available: H2: entry year = function of: diversity, alliances, patents, employees, revenue, R&D, revenue per employee, R&D per employee. H3a: degree centrality = function of: diversity, alliances, patents, employees, revenue, R&D, revenue per employee, R&D per employee. H3b: betweenness centrality = function of: diversity, alliances, patents, employees, revenue, R&D, revenue per employee, R&D per employee. The literature on network analysis of alliances includes many more complex regression models. Popular ones that may be appropriate include McFadden s discrete choice model, which is a variant of the conditional logit model. Powell et al used it with k-components to develop and test alternative logics of attachment. [35]. Stuart used three different estimation methods (all with similar results) including random effects Poisson models, negative binomial models, and hazard of alliance formation using continuous time event history analysis [7] but as these analyses are exploratory it is best to start simple. 5. Results 5.1. Description of the data and network The RFID network began with just 13 firms being part of alliances in 1999, rising to 191 firms by April The data consists of 150 alliance announcements. The network as of April 2004 is shown in Figure 1. In the figure, firms are gray-scale coded by type of firm: white (1) is RFID-specific firms (mostly small, many startups). Pale gray (2) is related specialist technology firms, including supply-chain firms, firms that make various label readers, and specialized software firms. They did not start out as RFID firms but may find themselves with RFID as a major line of business. Black (3) are the large firms that are the focal point of this study. They are diversified and RFID s are not their core business. Medium gray (4) are user firms, including retailers, pharmaceuticals, and consumer goods companies. Dark gray (5) are other types of firms, including standards-setting bodies such as UCC and EAN. Figure 1. RFID alliances, 4/2004 6

7 Table 1. Regression Analysis Results Model/Hypothesis: Variable H1a H1b H2 H3a H3b Dependent variable member? member? entry year degree centrality N Diversity ns ns P=.0002 ns ns Allies P=.013 P=.005 ns ns ns Patents ns ns ns ns ns Revenue ns ns ns.02 ns Employees ns ns ns.005 ns Rev/emp P=.11 P=.12 ns ns P=.15 R&D Not used ns ns ns ns R&D/emp Not used Ns ns ns ns Ln (allies) ns Ns ns P=.04 P=.03 Model Chi Adjusted R ns = not significant betweenness centrality 5.2. Results Table 1 shows the level of the significant variables for each analysis. These were determined from stepwise regressions, with a minimum significance level of 0.2. These results for H1 show that the main predictor for being in the RFID network by April 2004 is the number of other alliances a large firm has formed since The point estimate for allies in H1a is.0193, which means that a firm with 100 alliances is almost 7 times as likely to be in the network as a firm with only 1 alliance (e (100*.0193) = 6.89). This is in line with Cohen and Levinthal, as well as other resource-based theories. Revenues per employee, which can be interpreted as a measure of slack resources or human resource availability, is next most significant, with a negative parameter, in line with exploration characteristics. Surprisingly, and contrary to the theories, R&D spending (model H1b), diversity, and patenting activity (both models) seems not to be significant at all in predicting which firms join. For H1a, the model chisquare is 13.9, p =.0009, and c=.67, showing this model explains the data fairly well (For the smaller data set c=.73, probably because there is less noise in the data firms that report R&D are more similar to each other). In conclusion, H1 holds: the number of other alliances a large firm significantly raises the probability it will join the RFID network. Models for H2 and H3, analyzing how the large firms behave in their network activity, also lead to some interesting findings. Diversity is the only significant explanatory variable for when firms join. It has a negative parameter, meaning that more diverse firms that join do so earlier than less diverse firms, in line with March s explore/exploit dichotomy. Patent count and R&D spending per employee were also negatively correlated with joining year (see Table 3), though not significant. That is, a firm with a higher patent count tends to join earlier, in line with absorptive capacity theory. Where large firms position themselves in the network in terms of degree centrality (number of direct ties) and betweenness centrality (brokerage/control potential) is harder to predict. For degree centrality, the significant independent variables are revenues (negative parameter), and employees (positive parameter). This may imply that perhaps more employees are available for managing alliances rather than directly generating revenue. Log of number of allies is also significant. The parameter for log of number of allies is positive, as expected. Similar to the reasoning for hypothesis 1, more experience with alliances leads to more alliance formation. This suggests that both slack and experience may lead firms to form more alliances once they ve joined the network. Betweenness also seems to depend on the log of number of allies, and, less significantly on revenues per employee. However, the adjusted R 2 for these models is low, so we can neither accept nor reject hypotheses about large firms network position; clearly more data, or other analytical tools, are needed. Correlation analyses for H1 (Table2) show that number of alliances, number of patents, revenues, and number of employees are all significantly correlated with each other, and diversity is correlated with revenues and employee levels; but as noted earlier, none of these were significant in predicting who joins the RFID network. 7

8 Table 2: Correlations for logit analysis of entry into RFID network Diversity Allies Patents Revenue Empl. Rev. per employee RFID.13.34**.19.23*.22* -.08 Diversity **.41** -.12 Allies.62**.67**.48**.22* Patents.64**.48**.14 Revenues.83**.21 Employees -.15 (n=76; *p<.05, **p<.01) Table 3: Correlations for firm evolution analyses Degre Betwe Divers Allies Patent Reven Empl. Rev/ R&D R&D/ e enness ity s ue empl Empl. Joined -.42* ** * Degree.64** * Betweennes s Diversity * ** Allies.69**.76**.60**.49*.79**.32 Patents.88**.79**.09.75** -.06 Revenues.94**.17.85** -.06 Employees ** -.22 Rev/emp.41*.68** R&D.34 (n=24, *p<.05; **p<.01) The correlations for the firm evolution analyses (H2 and H3) are shown in Table 3. As expected, for the dependent variables, joining year and degree show a significant negative correlation (the earlier you join, the more time you have to form alliances). Degree and betweenness show very significant positive correlation. However, none of the other variables correlate strongly with betweenness, though number of employees does correlate significantly with degree (number of alliances). Again there is a strong correlation among allies, revenues, patents and employees, all of which are strong functions of size. Surprisingly, revenue per employee and R&D per employee do not correlate well with any of the other variables. Another interesting correlation: R&D per employee is positively correlated with joining year (i.e. later joiners tend to do more R&D per employee), whereas total R&D is insignificantly but negatively associated with joining year, as would be expected from absorptive capacity theory. It is possible that attention must be divided between doing R&D and joining the RFID network, or that these are substitutes. 6. Discussion, conclusions, and next steps The contributions of this paper include the following: 1. Developing a method for, and building the RFID alliance network and related test data based on publicly available data, and 2. Developing and testing an empirical model based on theories of absorptive capacity and exploration/ exploitation to try to explain the behavior of large, diverse firms with respect to joining the network. The results lead to some preliminary conclusions: experience with alliances makes it easier for firms to explore and get involved with a new ecosystems that depends on alliances for success, while predicting more detailed firm exploratory behavior in a new ecosystem is much harder with only third-party information. The results are suggestive, also, for what they do not say. Patenting, revenues, and R&D investment do not seem to be significant predictors for large firms behavior, contrary to theoretical expectations. However, this needs to be examined more closely. Implications for managers include having a way to gain a deeper understanding of the current ecosystem and trends using this paper s method to build and analyze a business network, as well as once again pointing out the importance of absorptive capacity, slack, and the need for a consistent set of behaviors in multiple areas to be able to take advantage of new technologically based opportunities. To be able to appropriate the innovation, a firm has to be in the game. Managers of technology firms need to understand not just the technology and the components, but the structure and dynamics of the ecosystem to determine where and how they want to compete. Managers of user firms need to understand the ecosystem, especially for a new unproven technology, to determine which firms may make the better partners or suppliers. Building this network and performing these analyses are just the first step in better understanding firm behavior in technology ecosystems. The RFID business is still in early stages, with small firms showing very little revenue and most having no profit, and no clear platform having emerged yet. So first, we will continue to build the network, and reanalyze the hypotheses as announcements are made. At current rates we may easily see another 60 announcements this year. At the aggregate level, R&D spending did not appear significant. This seems to be at odds a wide variety of research on the impact of R&D on absorptive capacity. However, the data is highly aggregated and may be neither precise nor consistent. More disaggregated analyses (including interviews with industry players) may help. Similarly, a more precise analysis of patenting is needed, to be closer to the explore/exploit dichotomy used by Rosenkopf [30] to try to relate network behavior more closely to exploration tendencies of the firms. As the network grows and matures it will be possible to classify links and analyze the resulting networks 8

9 separately, as, for example, Owen-Smith and Powell [17] do. They classify a link as R&D, licensing, marketing, or production, and analyze the resulting networks independently. Coalition links will also be included separately, as these seem to be very different in scope and participants from other types of alliances, focusing often on standards or a way to bundle a set of products. Another view of the network is more technological: the RFID ecosystem can be divided into distinct components (e.g. hardware-software-services, or in more detail) and it will be possible to see whether it is evolving towards systems or component competition [6]. Finally, as the network expands it will be possible to analyze it in new ways, using additional centrality and other measures, and as sales and profit information begins to emerge, the performance of the firms in the network can also be analyzed. 7. Appendix List of SIC codes relevant to RFIDs: SIC code SIC Description 3571 Electronic Computers 3572 Computer Storage Devices 3577 Computer Peripheral Equipment, NEC 3651 Household Audio and Video Equipment 3674 Semiconductors and Related Devices 3679 Electronic Components, Not Elsewhere Classified 7371 Computer Programming Services 7372 Prepackaged Software 7373 Computer Integrated Systems Design 7374 Computer Processing & Data Preparation and Processing 7375 Information Retrieval Services 7376 Computer Facilities Management Services 7377 Computer Rental and Leasing 7379 Computer Related Services, Not Elsewhere Classified 7389 Business Services, Not Elsewhere Classified 8. References [1] J. V. a. B. Brewin, "Wal-Mart Backs RFID Technology. Will require suppliers to use 'smart' tags by 2005," in Computerworld, [2] C. Sliwa, "Analysts: Wal-Mart RFID deadline could be tough to meet," in Computerworld, [3] M. Iansiti and R. Levien, The Keystone Advantage: What the New Dynamics of Business Ecosystems Mean for Strategy, Innovation, and Sustainability. Boston, MA: Harvard Business School Press, [4] J. G. March, "Exploration and Exploitation in Organizational Learning," Organization Science, vol. 2, [5] W. M. Cohen and D. A. Levinthal, "Absorptive Capacity: A New Perspective on Learning and Innovation," ASQ, vol. 35, pp , [6] J. Farrell, H. K. Monroe, and G. Saloner, "The Vertical Organization of Industry: Systems Competition vs. Component Competition," Journal of Economics and Management Strategy, vol. 7, pp , [7] T. E. Stuart, "Network positions and propensities to collaborate: an investigation of strategic alliance formation in a high-technology industry," Administrative Science Quarterly, vol. 43, pp , [8] T. E. Stuart, "Interorganizational alliances and the performance of firms: a study of growth and innovation rates in a high-technology industry," Strategic Management Journal, vol. 21, pp , [9] A. Gawer and M. A. Cusumano, Platform Leadership: How Intel, Microsoft, and Cisco Drive Industry Innovation. Cambridge, MA: Harvard Business School Press, [10] T. F. Breshnahan and S. Greenstein, "Technological competition and the structure of the computer industry," The Journal of Industrial Economics, vol. 47, pp. 1-40, [11] J. C. Henderson and N. Kulatilaka, "The Concept of Platforms," in Intelligence at the Edge. Forthcoming, [12] M. L. Katz and C. Shapiro, "Systems competition and network effects," Journal of Economic Perspectives, vol. 8, pp , [13] A.-L. Barabasi, Linked : the new science of networks. Cambridge, MA: Perseus Pub., [14] R. Albert and A.-L. Barabasi, "Statistical mechanics of complex networks," Reviews of Modern Physics, vol. 74, pp , [15] S. Wasserman and K. Faust, Social Network Analysis: Methods and Applications. New York: Cambridge University Press, [16] G. Ahuja, "The duality of collaboration: inducements and opportunities in the formation of interfirm linkages," Strategic Management Journal, vol. 21, pp , [17] J. Owen-Smith and W. W. Powell, "Knowledge Networks as Channels and Conduits: the Effects of Spillovers in the Boston Biotechnology Community," Organization Science, vol. forthcoming,

10 [18] G. Walker, B. Kogut, and W. Shan, "Social capital, structural holes and the formation of an industry network," Organization Science, vol. 8, pp , [19] R. S. Burt, "Chapter 1: The Social structure of competition," in Structural Holes: The social structure of competition. Cambridge, MA: Harvard University Press, 1982, pp [20] R. Gulati, "Alliances and networks," Strategic Management Journal, vol. 19, pp , [21] B. Kogut, "Joint ventures: theoretical and empirical perspectives," Strategic Management Journal, vol. 9, pp , [32] J. D. Thompson, Organizations in action; social science bases of administrative theory. New York: McGraw- Hill, [33] V. Batagelj and A. Mrvar, "Pajek - Program for Large Network Analysis," [34] S. P. Borgatti, M. G. Everett, and L. C. Freeman, Ucinet for Windows: Software for Social Network Analysis. Harvard: Analytic Technologies, [35] W. W. Powell, D. R. White, K. W. Koput, and J. Owen-Smith, "Network Dynamics and Field Evolution: the growth of interorganizational collaboration in the life sciences," American Journal of Sociology, vol. forthcoming, [22] B. GomesCasseres, "Alliance strategies of small firms," Small Business Economics, vol. 9, pp , [23] M. Iansiti, F. W. McFarlan, and G. Westerman, "Leveraging the incumbent's advantage," Mit Sloan Management Review, vol. 44, pp. 58-+, [24] R. C. Sampson, "R&D Alliances & Firm Performance: The Impact of Technological Diversity and Alliance Organization on Innovation," Working paper, [25] B. Kogut and U. Zander, "Knowledge of the firm, combinative capabilities, and the replication of technology," Organization Science, vol. 3, pp , [26] K. J. Arrow, "Economic welfare and the allocation of resources for invention," in The Rate and Direction of Inventive Activity, R. R. Nelson, Ed. Princeton, NJ: Princeton University Press, [27] G. Ahuja, "Collaboration networks, structural holes, and innovation: a longitudinal study," Administrative Science Quarterly, vol. 45, pp , [28] W. W. Powell, K. W. Koput, and L. Smith-Doerr, "Interorganizational Collaboration and the Locus of Innovation: Networks of Learning in Biotechnology," Administrative Science Quarterly, vol. 41, pp , [29] J. M. Podolny and T. E. Stuart, "A role-based ecology of technological change," American Journal of Sociology, vol. 100, pp , [30] L. Rosenkopf and A. Nerkar, "Beyond Local Search: Boundary-spanning, exploration, and impact in the optical disk industry," Strategic Management Journal, vol. 22, pp , [31] K. R. Conner and C. K. Prahalad, "A Resource-based Theory of the Firm: Knowledge vs. Opportunism," Organization Science, vol. 7, pp ,