Network of Practice, IT Knowledge Spillovers, and Productivity: Evidence from Enterprise Software

Network of Practice, IT Knowledge Spillovers, and Productivity: Evidence from Enterprise Software Peng Huang R.H. Smith School of Business University of Maryland College Park, MD 20742 huang@umd.edu Marco Ceccagnoli, Chris Forman and D. J. Wu Scheller College of Business Georgia Institute of Technology 800 West Peachtree Street, NW Atlanta, GA 30308 {marco.ceccagnoli;chris.forman;dj.wu}@scheller.gatech.edu Abstract Prior research on information technology (IT) spillover often uses spillover pools with undefined transmission mechanisms and ignores the direction of the spillovers. We introduce an alternative measurement of IT spillover derived from a different transmission pathway: knowledge transfer through interactions in an Internet-enabled network of practices where IT professionals exchange ideas and help each other resolve technical problems they encounter. Our method separates the effects of knowledge spillover from spillovers that are embedded in intermediate inputs. We show that IT knowledge spillovers through these networks contribute positively to firm productivity. The economic payoff of the spillovers is significant: by our estimate, one percent increase in the inward knowledge spillovers translates to a $0.46 million increase in added value for an average firm in our sample. Further, we find that IT spillovers complement a firm s investment in IT capital in the sense that the productivity of IT capital is increased with higher level of IT spillovers, but such complementarity does not exist for either non-it capital or non-it labor. We discuss the implications for research and practice.

1. Introduction As knowledge becomes one of the central sources of competitive advantage, the identification, acquisition and use of knowledge has generated sustained interest among researchers (Alcácer and Chung 2007). The non-rival, public good characteristics of knowledge imply that when external knowledge is not protected by intellectual property rights, it can be acquired through indirect means such as spillovers. For example, Romer (1986) argues that increasing economy-wide returns to innovation are likely to be the result of spillovers, even with decreasing firm-specific returns to innovation. A large body of literature has examined the role of knowledge spillovers in R&D (Ceccagnoli 2005, Eeckhout and Jovanovic 2002, Griliches 1992, Nadiri 1993, O'Mahony and Vecchi 2009). In contrast, IT knowledge spillovers have been examined only recently (Chang and Gurbaxani 2012, Cheng and Nault 2007, Han et al. 2011), with good reasons. First, for a long time, the IT productivity paradox (Brynjolfsson 1993) has plagued IS researchers due to the lack of robust and consistent measures of IT capital investment, and the fact that a large fraction of IT investment is spent on intangible assets that are not recorded on the balance sheet (Brynjolfsson et al. 2002). In addition, as Krugman (1991) pointed out, knowledge flows leave no paper trail by which they may be measured and tracked. The only exception to this is in the form of patent citations, which have been used by researchers to explore R&D spillovers in inventive activities (Jaffe and Trajtenberg 1999, Jaffe et al. 1993). In contrast, it is difficult to observe a similar linkage of IT knowledge spillovers as that in patent citations. Therefore, studies on IT spillovers have to rely on vaguely defined spillover pools, with little understanding of the underlying transmission mechanisms. Such methodological treatment is further complicated by the well-known issue of measurement errors in IT investments, which is likely to result in estimation bias in the magnitude of the spillover effect (Tambe and Hitt 2012b). This paper aims to provide yet another counterexample to Krugman s observation by identifying paper trails of IT knowledge spillovers through fine-grained data obtained from an Internet-enabled network of practices, where IT practitioners help each other solve technical problems and exchange knowledge and 1

ideas on the use of technologies. While prior research stresses the role of geographical proximity in knowledge spillovers (Jaffe et al. 1993, Keller 2002a), spillovers are increasingly taking place over virtual channels such as Internet. IT knowledge spillovers are especially susceptible to this path of transmission, as many large platform technology companies have established Internet-enabled professional communities of practice to facilitate knowledge exchange and self-support among technology users. IT knowledge spillovers are likely to enhance efficiency in the implementation, deployment, and use of the latest information technology, and therefore shift total factor productivity. That is, given the same amount of productive inputs such as labor, capital, and IT investment, firms that receive greater inward IT knowledge spillovers tend to produce more production output than firms that do not. We test this hypothesis by examining the knowledge spillovers that take place in the context of enterprise software, an information technology that represents a large fraction of firms IT investment and is accompanied by recent innovation in corporate IT (McAfee and Brynjolfsson 2008). Prior research shows that the adoption of enterprise software has significantly improved firm financial and operational performances (Hitt et al. 2002, McAfee 2002). We measure knowledge spillovers by tracking SAP Community Network forum activities of the members who are employees of the Fortune 1000 firms that have installed SAP software, and aggregate the individual-level knowledge exchange patterns to firm-level using selfrevealed information on their employers. We use a production function framework to examine the extent to which IT knowledge spillovers drive variations in total factor productivity by incorporating this spillover measure into the production function. There are several major results from our study. First, we find that the SAP community network has experienced rapid growth. For example, the total number of registered users has grown to around 268,000 in approximately 7 years. We also find that the community has been used by many users as a means of knowledge acquisition and peer-support through crowd sourcing (Howe 2008). For example, there have been over 1.8 million discussion threads (Q&A conversations) posted in the forums that focus on various technical and business process-related questions. On average, about a quarter of all the questions raised 2

by knowledge seekers are solved by the collective efforts of the community members, and the average time it takes to get a correct solution is between 3-5 days. Second, we find evidence that is consistent with interpreting these interactions as valuable IT spillovers. In particular, our results show that, among firms that use SAP software, those that experience an increase in knowledge inflows also experience an increase in productivity. The economic payoff of the IT knowledge spillovers is significant: by our estimates, a one percent increase in the inward knowledge spillovers translates to a 0.0086 percent, or $0.46 million increase in added value for an average firm in our sample. Our main analyses assume that changes in spillover inflows are uncorrelated with other unobserved factors that may be changing over time and influencing productivity. We probe this identification assumption through a series of falsification exercises and an instrumental variables analysis. In particular, we find that the identified spillover effect is absent for the rest of the firms in the Fortune 1000 that do not use SAP software. Further, IT spillovers complement a firm s investment in IT capital in the sense that the productivity of IT capital is increased with higher level of IT spillovers, but such complementarity does not exist for either non-it capital or non-it labor. Finally, the identified effects of IT spillovers are also robust to instrumental variables tests, in which we use variations in the SAP modules installed by different firms and longitudinal variation in the introduction of different forums on SAP community network to derive our IVs Third, we find that not all types of IT knowledge spillovers derived from Internet-based community networks are equally valuable to the firms. We differentiate between two types of IT knowledge: technical specialties knowledge and business functional knowledge. The first type of knowledge consists primarily of expertise in technical domains such as operating systems, programming languages, database management systems, networks, and telecommunication, while the second type emphasizes changes to organization and business processes that are enabled by IT. Our data suggest that members of the SAP community network raise fewer business functional questions than technical questions (827,401 vs. 1,000,949). In addition, questions related to business functions receive fewer responses from knowledge 3

contributors (4.55 vs. 4.81, p<0.01), and have a lower probability of receiving a correct answer from the community (0.226 vs. 0.262, p<0.01). As a result, firms in our sample on average receive more technical knowledge spillovers than business functional knowledge spillovers. In addition, we find that the productivity gains derived from IT knowledge spillover is primarily driven by its technical component instead of its business functional component, which highlights the limits of the extent to which firms can benefit from participating these Internet-enabled networks of expertise. The remainder of the paper is organized as follows: Section 2 presents a brief overview of the literature on knowledge spillovers in information technology and its relationship to productivity. Section 3 introduces the research context from which our measure of IT spillovers is derived. The data and empirical methods are introduced in Section 4. We present the results of data analyses in Section 5. In Section 6 we summarize the findings and discuss their implications. 2. Knowledge Spillovers, Information Technology, and Productivity The analysis of economic growth by Solow (1957) has sparked much interest in the search of factors that underlie the productivity residual, which represents the part of output growth that is not explained by the changes in factor inputs (Hulten 2001). Such factors include firm investment in R&D (Griliches 1979, 1986) and the advances of technology. A significant part of the literature on endogenous growth has started to examine not only the effects of firm s own investment in these factors but also their social returns to the rest of the economy in the form of spillovers (Romer 1986). Traditionally, empirical research that has examined the relationship between spillovers and productivity has focused primarily on R&D investment, which reveals positive spillovers at the firm (Griliches 1986, Jaffe 1986), industry (Goto and Suzuki 1989, Keller 2002b), and country levels (Coe and Helpman 1995, Madsen 2007). 1 In some cases, the estimates of the social return of R&D appear to be unusually high and exceed the internal return, especially when R&D embedded in upstream industries where intermediate inputs are purchased is 1 For comprehensive surveys, see Griliches (1992) and Nadiri (1993). 4

included in the specification of production functions (O'Mahony and Vecchi 2009). In addition, R&D spillovers are found to display high levels of technological proximity (Jaffe and Trajtenberg 1999, Jaffe 1986) and geographical proximity (Agrawal et al. 2006, Alcácer and Chung 2007, Griffith et al. 2006, Jaffe et al. 1993). There is a surge of empirical research in the spillovers of IT investment in recent years (Chang and Gurbaxani 2012, Cheng and Nault 2007, Cheng and Nault 2012, Han et al. 2011, Tambe and Hitt 2012b). For example, recent studies have shown that IT investments made by a firm s suppliers can benefit downstream customers by lowering the costs and improving the quality of intermediate inputs (Cheng and Nault 2007), and that investments by its customers can reduce transaction costs through improved information sharing and coordination (Cheng and Nault 2012). However, not all the firms benefit equally from IT spillovers; Han et al. (2011) document that two characteristics of downstream industries IT intensity and competitiveness influence the ability and motivation of firms to capture and appropriate IT spillovers from the firms in the upstream industry. Moreover, Chang and Gurbaxani (2012) find that a large fraction of IT spillover benefits are derived from trade relationships with the IT services industry and that IT-related spillovers generate a sustained contribution to productivity in the long run, in some cases persisting for over ten years in IT-intensive firms. There are several reasons behind this recent interest in IT spillovers. First, a series of studies on endogenous growth present evidence that support the hypothesis that information technology is behind the productivity growth in the late 1990s (Jorgenson et al. 2008, Jorgenson and Stiroh 1999, Jorgenson et al. 2000, Stiroh 2002b), and that it is the primary reason that explains the difference in the growth rates between US and most European countries (Bloom et al. 2012, Severgnini 2010). Second, while the IT productivity paradox has largely been resolved by using better data and improved methodology (Brynjolfsson and Hitt 1996, Brynjolfsson and Hitt 2003), studies in this area have often detected an unusually large output elasticity of IT (Brynjolfsson and Hitt 1995, Dewan and Min 1997), which tends to be substantially higher than its input share. In a neoclassical economics framework where all capital input 5

must be paid at their marginal product, the output elasticity of IT must equal to its input factor share. This contradiction leads to the hypothesis that at least some of the excess returns of IT are attributed to ITrelated spillovers (Stiroh 2002a). Although significant progress has been made in the research of IT spillovers, scholars in this field face several challenges. One such challenge is the inability to directly observe IT knowledge spillovers with clearly defined transmission mechanisms. While remedies for this issue exist in R&D spillovers, 2 there hasn t been any satisfactory solution to this problem in the spillovers of IT. In the absence of such data, researchers rely heavily on the use of aggregate spillover pools with undefined spillover transmission pathways. For example, Tambe and Hitt (2012b) observe that the measurements of IT spillover pools are usually calculated by aggregating IT capitals of other firms using either supply chain weights, industry weights, or trading weights. 3 This methodological treatment leads to several implications. First, it assumes that spillovers are nondirectional and ignores asymmetry of spillovers in the sense that all firms in the industry/region draw equal benefits from the contributions of all other firms. However, the endogenous growth literature maintains the view that knowledge flows exclusively from frontier firms to follower firms, which promotes inequality and results in free riding by followers (Eeckhout and Jovanovic 2002). More generally, this literature implies that the sizes of relevant spillover pools differ across firms (Atallah 2005). Empirically, results from Knott et al. (2009) support the notion that knowledge does have directionality and reject the hypothesis that spillovers are pooled. 2 For example, researchers of R&D spillovers have started to examine the actual mechanisms of knowledge transfer: some use patent citations to trace the source and destination of knowledge spillovers (Thompson 2006), while others seek to employ variations in alliance and inventor mobility (Rosenkopf and Almeida 2003). 3 The pooled approach is not unique to IT spillovers. For example, many studies on R&D spillovers implicitly assume that the technological distance between two firms is primarily determined by their industrial proximity, and therefore construct the spillover pool by calculating a weighted sum of R&D stocks of other firms within the same industry (Ornaghi 2006). 6

Second, the use of spillover pool has the limitation of being unable to separate rent spillover from knowledge spillover (Griliches 1979, 1992), although the differences between the two have been highlighted in prior literature. Rent spillover happens when factor inputs are purchased from other industries at a price that does not fully reflect the improvements in the quality (Griliches 1992, Severgnini 2010). For example, an upstream supplier s IT investment may improve the quality/variety of its product offerings or timeliness /convenience of its services, which are used by a downstream firm as intermediate inputs. If the upstream firm is unable to internalize the full return of its IT investment due to competition among suppliers, externalities result in the downstream industry in the form of spillovers. Knowledge spillovers, on the other hand, refers to IT-enabled innovations and practices that can be transferred to other firms through interactions over time (Han et al. 2011). To the extent that the owner of the knowledge cannot perfectly protect its invention through patent, and such knowledge is non-rival and non-excludable, it can be learned and replicated by others. Such spillovers may happen via a number of channels, such as learning, employee mobility, and leakage at trade conferences. As Griliches (1992) correctly points out, rent spillover is more of a consequence of conventional measurement problem rather than a true spillover effect. In addition, IT spillover researchers encounter another challenge that is quite unique to IT value studies: the lack of robust, consistently available measures of IT investment. Even in situations where data sets are available, the measurement errors in these IT data are rampant, in some cases constituting as high as 30-40% of the total variance (Tambe and Hitt 2012a). While this measurement error creates a downward bias on the estimate of the productivity of own IT investment, it creates a different issue when the mismeasured IT data is used to construct IT spillover pool, which often leads to significant overestimate of the spillover effects. This estimation bias is caused by the fact that the spillover pool constructed in this way (for example, industry weighted spillover pool) tends to be highly correlated with a firm s own IT investment, as the firms in the same industry often share common operating environment and technological opportunities, therefore make similar IT investments. Tambe and Hitt (2012b) provide a 7

formal analysis of this problem, and they present evidence that when such measurement errors are corrected using instrumental variable method, the magnitude of the estimate of IT spillovers is significantly reduced. The aforementioned challenges call for a better understanding of the mechanisms through which IT spillovers take place, as well as more accurate measurement of IT spillovers with carefully constructed spillover functions. In consideration of these issues, this study introduces an alternative measurement of IT spillovers derived from directly observable linkage of knowledge flow. In contrast to prior literature, we focus on a different spillover transmission pathway: knowledge transfer through interactions in virtual, Internet-enabled network of practices where IT professionals exchange ideas and help each other resolve technical problems they encounter. A unique characteristic of these online communities is that they have the capability to generate spillovers that are not bounded by geographic spaces, as they permit long distance transmission of technologies (Severgnini 2010). Recent research has theorized this communitybased model of knowledge creation and transfer as an evolutionary process of learning driven by criticism (Lee and Cole 2003), which may expand beyond the boundary of firms (O'Mahony and Ferraro 2007). Compared to a traditional closed model, such a model is said to result in faster and higher quality solutions to technical questions raised by members and greater variety of innovations (Füller et al. 2007). More importantly, with the advent of Internet and online communities, there is a possibility to track the paper trail of knowledge flows between IT professionals that participate in these communities. For example, using information on the collaborative relationships between open-source software (OSS) developers, Fershtman and Gandal construct a two-mode social network of OSS developers and projects, and link project success with contributor spillovers and project spillovers (Fershtman and Gandal 2011). We take this nascent line of research one step further to examine if the knowledge spillovers that take place in these online networks of practice between the IT professionals lead to the variation in the accumulation of IT knowledge capital of the firms that employ these professionals, and therefore influence the total factor productivity of the firms. Although several prior studies have shown that IT 8

investment may generate externalities (Chang and Gurbaxani 2012, Cheng and Nault 2007, Cheng and Nault 2012, Han et al. 2011), none of them have demonstrated that such externalities are due to pure IT knowledge spillovers in the sense of Griliches (1992). Compared to the spillover pool approach, our method has the advantage of identifying knowledge flow with observable source and destination of the spillovers in a way similar to that observed in patent citations for R&D spillovers. Therefore, our study aims to add to the recent empirical evidence on IT spillovers by separating the effect of IT knowledge spillovers on productivity from that of IT-related rent spillover, resulting in more robust identification. In addition, our work extends the current understanding of emerging online networks of practice by presenting evidence of their business value. For example, although there is anecdotal evidence that technology platform owner-sponsored online communities are often used for promoting innovation and peer-support among platform adopters, and they bear similarity to open source communities in many ways (Gorman and Fischer 2009, Von Hippel 1994), there has been a lack of studies that examine the extent to which user firms of the underlying technology platform benefit from participation in those online communities. Given that technology platform sponsors have invested heavily in building such online user communities and they were embraced by millions of members in some cases, the answer to this question is critical for the understanding of the economic payoff to the investment in these technologies. 3. Research Context Our research setting is the online community network run by SAP AG, the largest enterprise software vendor by revenue. As part of its platform strategy, SAP established its Internet-based community of innovation since 2004, with SAP developer network (SDN) and business process expert (BPX) as its two major modules. It serves as a resource repository and a platform for SAP users, developers, architects, consultants and integrators to collaborate and exchange knowledge on the adoption, implementation and customization of SAP solutions. SAP Community Network (SCN) hosts forums, expert blogs, a technical library, article downloads, a code sharing gallery, e-learning catalogs, wikis and other facilities through 9

which its members contribute their knowledge. All these web 2.0 technologies support open communication between active members of the community, which amount to over 268,000 registered users from 224 different countries as of 2010. We choose enterprise software as the background for measuring IT knowledge spillovers for several reasons. First, wide adoption of enterprise software such as enterprise resource planning (ERP), customer relationship management (CRM), and supply chain management (SCM) marks the start of a period of innovation in corporate IT, and it coincides with the productivity revival of the US in the mid- 1990s (McAfee and Brynjolfsson 2008). Research has shown that investment in enterprise software and its implementation makes a significant portion of a firm s overall IT spending (Brynjolfsson et al. 2002) in some cases accounting for as high as 75% of corporate IT investment (McAfee and Brynjolfsson 2008), and adoption of enterprise software is associated with significant improvement in firm financial and operational performance (Hitt et al. 2002). The use of corporate IT such as enterprise software multiplies the value of innovation in business processes, intensifies competition among rival firms, and drives economic growth (McAfee and Brynjolfsson 2008). Second, the adoption of complex IT platforms such as enterprise software often requires complementary, specialized knowledge to unlock their productivity. Enterprise software products are highly business process oriented and usually need to be tailored to fit business practices, where idiosyncratic local needs usually drive innovations in work places (Hitt et al. 2002, Von Hippel 2005). For example, implementation of the off-the-shelf enterprise software modules usually requires users to customize the configuration of a series of system parameters, modify existing business processes, choose specific add-on functions and features, and sometimes even devise specific tools to meet heterogeneous user needs. In addition, to facilitate the interoperability of the enterprise system with legacy IT infrastructure, or the seamless integration with the information systems of its partners, suppliers and customers, the end users often have to create workarounds and solutions to integrate various IS components. Accumulation of specialized knowledge is likely to result during this process of adaption and customization, and such knowledge is particularly susceptible to spillovers as 10

most of it is not protected by intellectual property rights. Third, although IT knowledge spillovers may include that of knowledge related to other technologies such as computer operating systems and personal productivity software, we choose not to include them in this study as they may be driven by the need of individual skill-building that is not necessarily linked to the accruement of knowledge capital of the firm that the individual works for. Unlike many other open source software communities, the knowledge learned from enterprise software communities is most likely to be applied to drive firm productivity, instead of being applied to pursue personal interests of the individuals. A unique feature of SAP community network is that its members knowledge contribution to the community can be quantified. To reward active members, SAP s online community adopts a contributor recognition program (CRP), which awards points to community members for each technical article, code sample, video, wiki contribution, forum post, and weblog authored. For example, in the case of forum discussion participation, points may be awarded for posting solutions in reply to existing discussion threads marked as questions, if the answer is deemed helpful by the person who asks the question. SAP publicly recognizes its most active members. For example, on the Top Contributors page, the top 50 contributors are listed in recognition of their contribution. On each discussion forum page, the top three contributors to that forum are listed, with their total reward points displayed. In addition, SAP identifies and provides special status to exceptional and high-value members by granting them the title of SAP Mentor. SAP mentors are offered unique opportunities for access to SAP senior management, early access to information on products and programs and greater visibility in the on-line communities as well as at SAP events such as the SAP Tech Ed conference. The participation in the community network is completely voluntarily and anyone can register as a member by providing basic personal information. One piece of such profile information is the company that employs the individual. Using this piece of information, it is possible to aggregate individual level knowledge contribution and exchange to the firms that employ these individuals, and to derive the knowledge flow patterns among firms (the definition of such knowledge flow will be introduced in the 11

next section). Other identifying information includes the country that the user comes from, her relationship to SAP, email address, phone number, expertise, and LinkedIn profile page, etc. Figure 1 presents a sample user profile. [Insert Figure 1 Here] To track knowledge flows between the members of SAP online community network, we focus on user interactions through the most frequently used communication format: the discussion forums. The primary purpose of the discussion forums is to provide an avenue for conversations between the community members so that they help each other solve problems that they encounter during the implementation, deployment and use of SAP software (Fahey et al. 2007). The forums are organized according to the domains of the relevant knowledge or expertise, each of which usually corresponds to a particular SAP software module, or the application of the software solutions in a particular industry. Examples of SAP forums include ERP manufacturing, product life cycle management, CRM-interaction center, and SAP for automotive solutions. Each discussion thread is initiated by a knowledge seeker, who posts a specific technical question in a topic forum of her choice. Knowledge contributors, on the other hand, post responses to the question and try to solve the problem. A discussion thread is comprised of a list of messages, and each message (either a question or an answer attempt) contains the information about the member who posts the message, the body of the message, and a time stamp. Once a correct answer (at the discretion of the knowledge seeker) is received, the discussion thread is closed. We developed a web scripting tool and obtained the complete history of SAP forum discussions from 2004 to 2010. The dataset includes about 1.8 million discussion threads with over 8 million messages posted in 243 topicspecific forums. Table 1 presents some summary statistics of the evolution of the SAP community network, including numbers of registered members, topic forums and the discussion threads posted in these forums. Overall, we find that the online community has experienced rapid growth since its establishment, attracting close to 268,000 registered users in just 7 years, although the growth rate has slowed recently. In addition, the discussion forums are heavily employed by the members of SCN, with 12

over 463,000 discussion threads generated in 2008 alone. Further, our data are consistent with earlier anecdotal evidences that online communities of practice are an effective means of using the wisdom of the crowd for peer support: on average, about a quarter of all the questions raised are solved by the collective effort of the community members, and the average time it takes to get a correct solution is between 3-5 days. [Insert Table 1 Here] 4. Data and Methods 4.1. Estimation models We adopt the production function approach and extend it by introducing our measurement of IT-related spillovers. A typical production function relates firm output to factors of input of production (Hulten 2001). For example, a simple form of three-factor Cobb-Douglas production function has been widely used in prior studies on IT productivity (Brynjolfsson and Hitt 1996, Dewan and Min 1997, Mittal and Nault 2009): (1) Where Y is the quantity of production output, K is the stock of non-it capital, L is the stock of labor, C is the stock of IT-capital, and A denotes the total factor productivity (TFP). TFP is defined as the output contribution that is not explained by the factor inputs and often interpreted as technological progress. In this case, the output elasticity of IT-capital,, represents the percentage increase in output due to a one percent increase in IT capital. To incorporate the role of knowledge spillovers, we consider the following modification of (1), which also exploit longitudinal variation in a way similar to that used in Thornton and Thompson (2001): (2) 13

where S denotes the measure of inward IT-related knowledge spillovers. In this equation, i and t index firm and time period, respectively. The Cobb-Douglas production function can be employed to estimate the factor productivities by implementing the following stochastic model: (3) OLS estimates of IT spillovers effects are likely to suffer from unobserved firm heterogeneities that are correlated with inward spillovers. To address this issue, we introduce a set of firm- and year- fixed effects to control for unobserved heterogeneities. This amounts to the modification of (3) (4) where I and T represent a set of firm fixed effects and year fixed effects, respectively. Our choice of the estimators follow closely the mainstream methods in the IS literature (Tambe and Hitt 2012a), which points out that the application of recent methodological advances (Arellano and Bond 1991; Levinsohn and Petrin 2003, Olley and Pakes 1996, Ackerberg et al. 2006) does not perform well on existing IT data sets. 4 4.2. Data We conduct the empirical tests by constructing a dataset of firms that form the SAP install base and are publicly traded. Our data come from a variety of sources. Particularly, we obtained the SAP installation data, with a detailed list of product modules that are sold to and installed at all its clients in the United States prior to the end of year 2004. In addition, we use the Harte Hanks Computer Intelligence (CI) 4 In their study of the impact of health IT on hospital productivity, Lee et al. (2012) report that parameter estimates for the IT variables using OLS and FE estimates are lower than modern methods including dynamic panel data approach (Arellano and Bond 1991, Arellano and Bover 1995), and approaches proposed by Blundell and Bond (1998, 2000), Ackerberg et al. (2006), Olley and Pakes (1996), or Levinsohn and Petrin (2003). While the goal of our study is not to compare various production function estimates, our use of OLS and FE estimates are consistent with most mainstream production function literature. 14

Technology database to collect firm-level IT investment data. The CI database records detailed information about IT infrastructure for most of the Fortune 1000 firms, which include data on the quantity of mainframes, peripheral, minicomputers, servers and PC systems, as well as other IT hardware stocks. CI database has been widely used by prior studies to investigate issues related to IT productivity (Chwelos et al. 2010). The CI data were matched with Standard and Poor s Compustat database to obtain financial information of the publicly traded companies. Using similar method of prior research (Brynjolfsson and Hitt 1996, Brynjolfsson and Hitt 2003, Chwelos et al. 2010, Dewan and Min 1997), we use the financial data to construct measures of production output, non-it capital stock and labor expenses. 4.2.1. Sample As we are primarily interested in the knowledge spillovers restricted to those associated with a particular platform technology enterprise software by SAP, our sample is chosen as the set of firms among the Fortune 1000 that had installed SAP product prior to the beginning of our sample period. The SAP community network was established around the end of 2003 and beginning of 2004, so we choose 2004 as the starting year of our analyses. Although we have complete data of CI Technology database from 2004 to 2009, the methods that are used to collect information on critical data items such as numbers of PC and Server owned by firms has been changed dramatically by Harte Hanks from year 2009. To maintain consistency of the measurement of IT capital over the sample years, we choose to exclude data from 2009, resulting in a five year sample period from 2004 to 2008. The sample is derived in several steps. First, we retrieve the set of firms that had ever made it into the Fortune 1000 list during 2004-2008, and match them to Compustat data. We then match these firms with CI Technology data and get the firms in the intersection of the two databases. Finally, using the list of SAP clients, we obtain those firms that had installed at least one SAP module prior to the end of year 2004. The final sample consists of 278 firms with 1264 observations over a 5-year period. 15

4.2.2 Variables IT Knowledge Spillovers We derive the variable of inward IT-related spillovers, S it, from forum conversations that took place on the SAP community network. Specifically, the rules of SAP reward program dictates that, for each question that is posted in a topic forum, the knowledge seeker may use her discretion to judge the quality of answers posted by knowledge contributors, and she can distribute 10 reward points to a user whose answer is deemed correct (at most 1 answer can be evaluated as correct), 6 points if very helpful (at most 2 answers), and 2 points if helpful (no limit on number of helpful answers). We use a crawler program to acquire the information on the user profiles for all the registered users of SAP Community Network, such as their names, addresses, companies, profession, email addresses, countries, personal websites, LinkedIn profiles, etc. Next, we select all the members that reside in the United States, and match them to companies in our sample by examining their company affiliations and domains of their email addresses. For each individual a who is an employee of firm i, we retrieve all the discussion threads that were initiated by a in year t, and examine the history of the answers posted by other forum members. If a received any correct, very helpful, or helpful answers in year t, the total number of reward points she gave to the knowledge contributors are used as a proxy for inward IT spillovers to a. The reward points were then aggregated across all the threads posted by a in year t to derive an individual level spillover variable, S ait. The firm level spillover variable is defined as the sum of spillover measures of all the individuals who are employees of the firm: IT Capital The measure of IT capital is derived from Computer Intelligence Technology database. The information in the database covers major categories of IT hardware investments made by Fortune 1000 firms, such as personal computing, systems and servers, networking, software, storage and managed services (Gu et al. 16

2008). We adopt the method used by Brynjofsson and Hitt (1995), Hitt and Brynjofsson (1996), and Dewan and Min (1997) that define the IT capital stock as the sum of computer capital and three times of IT labor. Inclusion of IT labor expense in the calculation of IT capital is justified by the fact that a large fraction of IT labor expenses is dedicated to the development of computer software, which is a capital good. The assumption that underlies this method is that the current IT labor spending is a good proxy of the IT labor expenses in the recent past, and IS staff stock depreciates fully in three years (Brynjolfsson and Hitt 1995). The first component of this variable is the market value of total PCs and Servers currently owned by the firm, converted to constant 2005 US dollars. To be specific, we collect market prices of PCs and Servers in the United States from two report series produced by Gartner Dataquest Market Statistics database: Gartner Worldwide Server Forecast and Gartner Worldwide PC Forecast from 2004 to 2008. These two report series present detailed statistics on the number of shipments, prices, vendor revenues and other related information about PC and Servers, broken down to the level of each geographic region and market segment. 5 Our market prices of PC and Server are calculated as the average user price across their respective market segments within the - United States. These prices are then multiplied by the quantities of PCs and Servers owned by the firms in our sample, which are retrieved from Harte Hanks CI Technology database, to derive the market value of the IT computer assets. Our approach of calculating the computer market value is similar to that in Brynjofsson and Hitt (2003). Finally, we deflate the market value by Bureau of Economic Analysis (BEA) Price Index for computers and peripherals. The second component of IT capital stock is IT-related labor expenses. CI Technology database provides the number of IT employees of the sample firms at the site level. We aggregate the site-level employee 5 Gartner Dataquest defines PC market segments as: desk-based, mobile, professional, and home. Server market segments are defined by CPU types, which include x86, IA64, RISC, and other. The database covers regions of Asia/Pacific, Eastern Europe, Latin America, Middle East & Africa, and West Europe. Several country level statistics are also available, which include United States, Canada, and Japan. 17

numbers to the firm level to derive the total number of IT-related employees hired by the firm. 6 IT labor prices are obtained from Occupational Employment and Wage Estimates series of Bureau of Labor Statistics (BLS) Occupational Employment Statistics (OES), and we use the mean annual wage of computer and mathematical occupations as the average labor price for IT employees. As the wage reported by OES series does not reflect benefits, we multiply the wage number by the ratio of total compensation to salary, which is obtained from BLS Employer Costs for Employee Compensation (ECEC) series. The IT labor expense is then deflated by BLS Employment Cost Index (ECI) for private industry workers. Production Output We follow prior literature (Brynjolfsson and Hitt 2003, Dewan and Min 1997) and use added value as the measure of production output, which equals deflated sales less deflated materials. Compared to sales, added value is said to be less noisy and more comparable across industry sectors (Dewan and Min 1997). Annual sales numbers are retrieved from Compustat, and we deflate them using industry-specific (at twodigit NAICS sector) price deflators from BEA Gross Output and Related Series by Industry. Materials are calculated by subtracting undeflated labor and related expenses (Compustat data item XLR) from undeflated total operating expenses (Compustat data item XOPR), and deflating by BLS Producer Price Index (PPI) for intermediate materials, supplies, and components. Non-IT Capital The calculation of total capital stock is similar to that in Brynjofsson and Hitt (2003) for ordinary capital. Specifically, the gross book value of capital stock (Property, Plant and Equipment (Total-Gross), Compustat data item PPEGT) is deflated by industry-specific capital investment deflator reported in BLS 6 CI database actually records a range of IT employees at each site. The ranges are defined as: 1-4, 5-9, 10-24, 25-49, 50-99, 100-249, 250-499, and 500 or more. For each range, we take the middle value of the range as the number of IT employees. 18

1987-2010 Detailed Capital Measures. 7 In order to apply the deflators, the average age of capital stock is calculated as the ratio of total accumulated depreciation (Compustat data item DPACT) to current depreciation (DP). We then subtract the deflated computer capital from deflated total capital to get the value of non-it capital. Non-IT Labor Consistent with prior studies on IT productivities (Bloom and Van Reenen 2007, Bresnahan et al. 2002, Brynjolfsson and Hitt 2003), total labor expense is either obtained directly from Compustat Labor and Related Expenses (data item XLR), or calculated as the product of a firm s reported number of employees (Compustat data item EMP) and industry-average labor cost per employee, and deflated by BLS Employment Cost Index (ECI) for private industry workers. Average labor cost per employee is obtained from National Sector NAICS Industry-Specific estimates series of BLS Occupational Employment Statistics (OES). To account for the fraction of benefits in total compensation, we multiply the wage number by the ratio of total compensation to salary, which is obtained from BLS Employer Costs for Employee Compensation (ECEC) series. Non-IT labor is defined as the difference between deflated total labor expense and IT labor expense. Table 2 reports the summary statistics of the variables. The average firm in the sample has sales of $16.43 billion, added value of $5.42 billion, and 40,736 employees, consistent with the fact that our sample being publicly-traded, Fortune 1,000 SAP adopters. In addition, the firms invest heavily in IT capital, which has a mean level of $100.59 million and maximum of $1.22 billion. Table 3 provides the correlation matrix among the key variables. [Insert Table 2 and Table 3 Here] 7 Retrieved from http://www.bls.gov/mfp/mprdload.htm 19

Table 4 presents a breakdown of the sample firms by vertical industries, which is based on 2-digit NACIS sectors. It is notable that firms in manufacturing industry account for the majority (66%) of the sample, followed by utilities firms (8%). [Insert Table 4 Here] 5. Results Three-Factor Productivity Analysis Although the primary objective of this work is to examine the role of IT knowledge spillovers on firm productivity, considering a large body of literature has centered on the role of IT capital investment in driving productivity growth (Brynjolfsson and Hitt 1995, 1996, Brynjolfsson and Hitt 2003, Dewan and Min 1997), we present a set of results in comparison with prior studies on IT productivity using the same theoretical framework. One of the reasons that we report this analysis is the lack of studies that present evidence on IT productivity using data from recent years, partly due to the lack of robust and consistent measures of IT investment. Although exceptions do exist (Chwelos et al. 2010, Tambe and Hitt 2012a), the ways of constructing the IT capital variable in these studies are usually different from earlier literature. 8 In addition, economists have raised the concern that returns from IT investments may have declined in recent years, implying that the stock of IT-enabled innovations is being depleted (Stiroh 2008). To make the results comparable to earlier work, the sample used in this exercise is chosen as the complete set of Fortune 1000 firms, instead of the one that we will use in the spillover analyses which only includes the SAP installed base. This selection criteria results in 991 firms with 4286 observations over years 2004-2008, for which we have complete production output and input data. In Column 1 of Table 5 we 8 For example, Tambe and Hitt (2012a) use IT personnel data derived from a job search website, while Chwelos et al. (2010) use hedonic regression to impute IT equipment price, which is different from the method employed by the CI database in earlier years. 20

present the baseline OLS regression of a 3 factor Cobb-Douglas production function, where we use robust standard errors that are clustered by firms. To control for heterogeneity in IT productivity across different industries, we create a set of industry dummies based on SIC codes, and run a test with these industry fixed-effects as controls. The results are presented in Column 2 of Table 5. In Column 3 we present the results of the OLS model with a set of year dummies that control for productivity shocks over different time periods, and in Column 4 the model includes both industry and year dummies. Results from panel data models, including fixed-effects and random-effects models correcting for unobserved firm heterogeneity, are presented in Column 5 and Column 6. We notice that our estimates of the output elasticity is similar to those in Brynjofsson and Hitt (2003), 9 but significantly lower than some of the other IT productivity studies (Dewan and Min 1997, Hitt and Brynjolfsson 1996), probably due to the different ways of constructing IT capital, different estimation models, or different sample periods. For example, Hitt and Brynjofsson (1996) use survey data to construct the measure of computer capital, while Dewan and Min (1997) adopt translog and CES-translog estimation models. [Insert Table 5 Here] Baseline Spillover Analyses We next turn to the primary variable of interest and consider the role of IT knowledge spillovers in driving the variations in total factor productivity. We add the variable of IT knowledge spillovers in addition to the usual production factors into the regression and use panel data model as the starting point of the analyses to control for unobserved firm heterogeneity. In Column 1 and 2 we report the result from fixed-effects and random-effects models, respectively. As we expect, the coefficients of the spillover term are significant in both models (p<0.05), indicating that firms with greater amount of inward IT spillovers 9 For comparison, Brynjofsson and Hitt report the output elasticity of IT capital ranging from 0.0085 (using one-year differences) to 0.0456 (using seven-year differences) in the semi-reduced-form model (Brynjolfsson and Hitt 2003, p. 800). 21

produce more output, given the same amount of investment in capital, labor and IT. Particularly, results from the fixed effects model imply that one percentage increase in the amount of inward spillovers is associated with 0.0086 percentage increase in the added value produced by a firm. Considering that the added value of an average firm in our sample is $5.421 billion, this translates into a $0.46 million increase in production output. We notice that the estimated output elasticity of IT capital using the SAP installed base as sample is lower (and sometimes insignificant) than the estimates in Table 5, where we use the complete set of Fortune 1000 firms as sample. Considering that the SAP installed base consists primarily of manufacturing firms, and that IT intensity 10 in the SAP sample is considerably lower than that of the rest of the Fortune 1000 sample (.033 vs..062, p<0.01), this is consistent with the observation from prior studies (Dewan and Min 1997) that the output elasticity of IT capital is lower in manufacturing industry than in service industry, and it is higher for IT intensive firms. 11 To explore the robustness of our findings, we consider several alternative explanations that may contradict our interpretation. One of such explanations is that the longitudinal variation in the spillover variable merely reflects a positive time trend, which is correlated with the increasing number of IT employees of the focal firm who participate in the SAP community network as it gains popularity. As a result, positive spillover would be driven by a passive learning effect due to the increasing number of registered users, which is unobserved in our model specification. If this is the case, when the cumulative number of registered users who are the firm s employees is added into the regression, the effect of our measure of IT spillovers should vanish. Our results from Column 3, in which we add this variable to the regression, indicate that this is not the case, and the magnitude of the spillover effect is actually even higher when we control for this variable. Another possible explanation is that the spillover effect is 10 IT intensity is defined as the ratio of IT capital to total capital. 11 We also run separate regressions using manufacturing and non-manufacturing subsamples, and find that IT capital output elasticity is indeed higher for non-manufacturing firms than manufacturing firms. 22