Competing in the Era of Emergent Architecture: The Case ... - CiteSeerX

Proceedings of the 40th Hawaii International Conference on System Sciences - 2007

Competing in the Era of Emergent Architecture: The Case of Packaged Software Industry Bala Iyer TOIM division Babson College Babson Hall Babson Park, MA 02457 [email protected]

Chi-Hyon Lee School of Management George Mason University Enterprise Hall 208 Fairfax, VA 22030 [email protected]

Abstract The firms that comprise the prepackaged software industry form a complex system engaged in systemsbased competition. This complex system survives and grows because it follows emergent design principles notably articulated by Herbert Simon. In particular, complex systems form stable subsystems – clusters – that can be described (in this industry) as a stack. In this research, we study the evolution of the software industry using data on packaged software development firms over 13 years (1990-2002) across functional markets. We show that by exploiting complementarities based on the emergent architecture, firms can outperform competitors that use complementarities that are based on the espoused architecture/stack, which outperform those that ignore architecture altogether.

1

Introduction

Technology Strategy Researchers are generally interested in the interplay between the evolution of technologies and their impact on firm and industry performance[1-3]. Understanding the topography of the industry is an important element in managerial decision making[4]. In our work, we consider an industry to be an example of a complex system that has no single point of control - its leadership is often divided[5]. To understand the complex system, we examine the interdependencies between its components – the firms, in this case. We call the pattern of interdependencies the industry’s architecture. In general, architecture can be espoused – as viewed by a designer – or emergent – the architecture “as is.” In complex, emergent systems lacking a central control, the notion of an espoused design is tenuous. We substitute the “industry analyst” for the designer and his or her abstraction of what is (or what should be) for the espoused design. Understanding the emergent architecture is far more difficult because it requires examining the architecture as it is. However, we are led to believe it exists due to the insights of Simon [6] who argues that complex systems are built

David Dreyfus School of Management Boston University Boston, MA 02215 [email protected]

of stable subsystems. It is these stable subsystems and the nature of there interconnection that emerges and is the subject of our interests. The managerial question is which perspective to take. If the analyst’s perspective is as good as an understanding of the emergent architecture, then the manager can dispense with the effort of detailed analysis and defer to the industry analysts. A second research stream of general interest to practitioners and researchers alike is the role of complementarities and network effects in strategy development. The general question is when should a firm “stick to its knitting” and when should it diversify into complementary markets. These two research streams are two sides of the same coin in the software industry. Software products are often complementary because they are used together and form the basis of systems-based competition[5]. The complementarities lead to interdependencies, which are the bases for the emergent industry architecture. In this paper we explore the espoused and emergent architecture, product complementarity, and its impact on firm performance. We found that while product complementarity, espoused and emergent architecture were good predictors of firm performance, emergent architecture was the best determinant. The rest of our paper is organized as follows. We begin with an overview of how complementarities have influenced the software industry. In section 3 and 4 we describe espoused and emergent product architecture. Section 5 shows how we use market boundaries to find architecture. Subsequently, we describe our methods – including data, measures and modeling specifications – and present our results.

2

Complementarities in software

In markets characterized by systems-based competition in which customers must purchase bundles of products, often from multiple vendors, value is derived from complementary products. In simple terms, a complementary product is one that enhances the value of another product when the two of them are used in conjunction by end-users. For example, in the

Proceedings of the 40th Annual Hawaii International Conference on System Sciences (HICSS'07) 0-7695-2755-8/07 $20.00 © 2007 1530-1605/07 $20.00 © 2007 IEEE

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software industry database products and operating systems are complementary. A database product cannot even be used without an operating system; thus, the existence of operating systems increases the value of the database product. Similarly, the existence of database products drives the sales of hardware and operating systems. In systems-based industries two or more components made by different manufacturers using different technologies may have to be interoperable. This need for interoperability leads to the creation of standards, which facilitates the creation of additional complementary products. Network effects across markets [7, 8]result in higher valuation for products with larger complementary markets and create incentives for producers of a particular good to enter the markets for complements. There are many ways in which firms can exploit complementarities to derive competitive advantages and create and appropriate value. Companies that produce highly complementary components may want to merge or vertically integrate if customers value a more reliable systems integration supplied by a single provider or if they want to quickly gain market share. Alternatively, companies can form alliances and standards committees to facilitate tighter integration at a strategic or technological level. Companies can use either their installed base, or the installed base of complementary components, to leverage and promote growth. Companies use acquisitions as a way to exploit complementarities, gain market share, and acquire technology. Companies also make acquisitions in a complementary market with the purpose of foreclosing competitors in that market. The “winner takes all” nature of software economics gives firms that achieve major platform status massive profit pools from which to invest in adjacent software categories.

3 3.1

Espoused stack complementarity The stack

The espoused industry architecture is often presented as an analog of the software stack. In the early days of the software industry, a set of vertically integrated companies producing everything that a consumer needed (e,g., DEC, IBM and Wang) delivered the entire stack. As described by Andy Grove [9], somewhere around the late 80’s a transition from vertical integration to horizontal layers occurred. As a result of this transition, we moved from a single firm offering end-to-end services to modular clusters [10] or stacks populated by specialist firms. The industry stack divides activities into layers that are complementary to each other [11, 12]. Today, just as was the case during the era of vertical integration, firms can deliver products that support

most (if not all) layers of the stack. For example, consumers can buy chipsets, assembled computers, operating systems (AIX), middleware (Websphere), applications (CRM) or services (Global consulting) from IBM. The main difference in the era of stacks is that IBM provides these products with loose coupling and with open interfaces between them. As a result, consumers of these services have the option to mix and match IBM’s products with those provided by other vendors. In the earlier era, this was not possible – a consumer had to pick a vendor and buy all required services from them. According to Lou Gerstner, former CEO of IBM, most companies specialize in one or a few layers and rely on other companies to offer complementary components. Each of these components is layered above or below the other, and communicates through more or less standard interfaces, with closer layers being more related to each other than layers that are further apart in the stack. Each layer is dependent on the layer below to deliver the promised functionality. This arrangement works well for vendors as they have to simply focus on what they do best and leave the rest to other product vendors. Lower layers and their components such as hardware and network services are often referred to as operating platforms and are fast becoming commodities. They have well defined interfaces with well defined terms of trade (prices). Firms build competencies on top of these lower layers by carefully selecting application packages and middleware packages and then launch business services on top of the application layer.

3.2

Complementarity

When firms have products that work within adjacent layers of the espoused architecture they are said to be highly complementary. If they have products that represent layers of the stack that are further form one another, they are less complementary. Database management systems and database applications are highly complementary. Database applications and operating systems are assumed to be less so. Firms can choose to participate in developing and marketing of complementary products or they may allow third-party developers to provide complementary products. Historically, large firms have developed complementary products in-house to ensure that the product interfaces are properly utilized and incremental profits appropriated [13]. Based on the resource-based views of the firm [14, 15], the use of complementary factors of production across multiple business units—should lead to economies of scope and improved firm performance. For example, software firms that reuse the same

Proceedings of the 40th Annual Hawaii International Conference on System Sciences (HICSS'07) 0-7695-2755-8/07 $20.00 © 2007

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software code in multiple software products should gain economies of scope in software development and perform better than software firms that write new code for each new product. Moreover, the firm can leverage their complementary assets - sales force, customer support departments, installed base, and their understanding of customer requirements. The previous paragraph describes how firms can exploit production-side synergies. Related products can also exploit consumption side synergies. When the set of products serve the needs of the same customer base, it is said to have consumption-side synergies. This type of synergy allows the producer to reduce customer acquisition costs by cross-selling products to the same customer base. Customers benefit by a set of interoperable products with familiar user interfaces. Our core assertion is that firms that have the necessary assets, resources and skills to develop and market complementary products will outperform those that don’t. They will take advantage of production-side and consumption-side synergies to improve their performance. More formally: Hypothesis-1: Espoused stack complementarity of a firm’s products has a positive effect on firm performance.

4

Emergent stack complementarity

According to Simon, complex systems evolve through a process that leads to near decomposability. Complex systems are built on stable components. In this paper the complex system is the software industry itself. The imperfect modularity and interdependency between products leads to interdependencies between firms as they cope with emerging technology. Of course, the reverse is also true: the components that make up a system may also reflect the organizational or industrial design that creates it [16]. There is a close relationship between the structure of designs and the economic structures – firms and markets – that emerge to realize them [10]. A set of firms and markets that support the evolution of a modular design is called a modular cluster [10]. Modularity, a general systems concept, has been proposed as a design principle to manage and understand complex systems. Schilling [17] refers to modularity as a continuum that describes the degree to which a system’s components may be separated and recombined. Modularity refers to both the tightness of coupling between components, and the degree to which the “rules” of the system architecture allow (or prohibit) the mixing and matching of components. The tightness or looseness of the coupling and the freedom imposed by the “rules” influences the existence and strength of the interdependencies between firms. Each firm in the marketplace learns to huddle together as an industry and take cues from their fellow

producers who face the same uncertainty of buyers and who need to offer differentiated products filling distinct niches. As these firms create and operate as actors, they socially construct their markets in response to uncertainty [18]. Since each firm within the industry will try to find a niche for itself by satisfying needs of particular sets of customers and differentiating from the rest, the architecture of the system will be reflected in the market structure of the industry [10, 18]. Hypothesis-2: Emergent stack complementarity of a firm’s products has a positive effect on firm performance The emergent industry architecture is potentially more nuanced and intricate than the espoused, abstracted architecture. It reflects the actual behavior of producers and consumers. The logic of complementarity described in the previous section holds for the emergent architecture as well: firms perform better by reaping economies of scope and scale by selling complementary products. Our argument is that by tuning a firm’s product portfolio to reflect the emergent architecture, the firm will perform better than it otherwise would. More formally: Hypothesis-3: Emergent stack complementarity of a firm's products is a better determinant of firm performance than the espoused stack complementarity of a firm's products.

5

Markets, Layers, and Market Boundaries

The task of finding architecture in the software industry is highly related to prior research that has addressed the related issues of market definition and market boundaries. A market is clearly a complex system resulting from interactions between its constituent parts. We embrace, in this paper, the notion that a market is a social structure resulting from complex and dynamic patterns of selling and buying among firms and customers, respectively [19-21]. Not only can markets and the boundaries that separate them be differentiated and determined by identifying differences in buying and selling patterns [21] but Burt [20] argues that “to the extent that the producers of one commodity and producers of another have identical suppliers and identical consumers, they are competitors in the same market (p. 358).” Competition among firms in a market exists because they have common consumers. Researchers have since extended these concepts by emphasizing the bounded rationality of the actors that interact in a market. The search for competitive advantage on the part of a firm is the result of the actions of people in firms. Decision makers form models of the market system, with special attention to the actions of competitors and consumers, and (re)act accordingly [22, 23]. Simon [24] has argued that:


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“The bounded rationality of humans does not allow us to grasp the complex situations that provide the environments for our actions in their entirety. The first step in rational action is to focus attention on some specific (strategic) aspects of the total situation and to form a model of the situation in terms of the aspects that lie in that focus of attention (p. 37)” In other words, markets should be defined with respect to the focus of attention of the actors and the models they use to compete [22, 23]. One result of incorporating bounded rationality into the definition of a market and determination of its boundary is that a market may span product, service, geographic, and other pertinent spaces [23]. Firms are not atomistic, single product actors that compete in a single niche. Firms may, in fact, compete in multiple markets. Moreover, as firms repeatedly interact with other firms and customers, a shared definition of what constitute a market, among the markets constituent parts and competitive relationships, emerges. It is this shared model of markets that defines the boundaries of a market and not an exogenous static definition per se [22]. For example, Burt and Carlton [21] conclude from their analysis of the network boundaries of 77 American markets that some of them can be combined into 7 classes. Brooks [23] analyzed patient flows to 97 hospitals from the zip codes contained within a 10 county area of San Francisco Bay area. Results indicate a firms market did not coincide with their espoused markets - statutory boundaries such as cities or counties. Similar results are echoed by other researchers in a wide range of industries [25, 26].

6 6.1

Methods Sample and Data

The data for this study were collected and assembled by International Data Corporation (IDC). IDC data has been used extensively in academic research, by industry analysts, and by the US Department of Justice’s litigation with Microsoft. Consequently, IDC data are arguably one of the most exhaustive and complete data of the software industry. These data meet the general requirement in that they are at a finer level of granularity than the eventually defined markets (Brooks 1995). The database tracks the sales of independent software vendors (ISV) that sell software products to consumers (i.e., people and other companies). Specifically, the IDC database contains over 260,000 unique ISV/operating system (OS)/product/ geographic region/year/sales combinations spanning the years 1990 to 2002. Overall, IDC tracks about 1200 ISVs, 10 operating systems (e.g., Windows and Unix), and 90 product categories (e.g., accounting, content management,

database management), and 6 geographic regions (e.g., North America) as of 2002. Each ISV/ operating system (OS)/functional offering/year/sales represents a unique transaction between ISV and customers. Each observation, with its associated sales, represents a competitive outcome stemming from the actions and reactions of firms to each other and with respect to consumers and their needs. Within the subsequent sections, we refer to product categories simply as products. For example, two firms may sell different applications (e.g., Oracle and SqlServer), but for the purpose of this paper the firms sell the same product (i.e., database management).

6.2

Dependent variable

We use sales growth to measure firm performance. Sales growth is a financial measure commonly used to assess firm performance in management studies [27]. Industry analysts also use sales growth as a key metric in valuing software firms [28]. We introduce a oneyear lag in the performance measure to assess how the independent variables and controls in year t impact firm performance in year t+1. The range of the time variable t is 1991 to 2001 – we omit 1990 and 2002 from the sample because they are needed for control and dependent variables. Firm sales growthi,t+1. Firm i’s sales growth is computed as the sum of the natural logarithm of the ratio of firm i’s t+1 and t sales of product j weighted by the firm’s percentage of sales in product j:

∑p

ij , t

ln (xij ,t +1 xij ,t ) where xij,t is firm i’s sales in

j

product j for year t and pij is the proportion of firm i’s sales from product j. Although firm growth can be computed differently, it is best denoted by the log of the revenues in year t+1 divided by total revenues in year t [29].

6.3

Independent variables

A key set of measures in this paper are the extent to which pairs of products are complementary. We argue that to the extent two products are highly complementary, firms that sell one product will also sell the other. To the extent that complementarity between a pair of products is low, a firm that sells one product may not necessarily sell the other. To measure product complementarity, we reference recent studies that focus on within-industry diversification [30-32]. We compute coefficients of similarity, rjj′, for every pair of products j and j′ for all firms in year t by using Sohn’s [33] similarity metric.  ∑ xijk ,t min xijk ,t , xij ′k ,t    r jj ′,t = ∑ p jk ,t  i . 2 xijk ,t k ∑   i  


(

)

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Xijk,t represents firm i’s sales of product j, on operating system k, in year t. Pjk,t represents the proportion of product j’s sales of all products sold on platform k in year t. The range of rjj′,t is zero to one. It is zero if two products exhibit no complementarities and one if they are perfectly complementary. This approach has been used to infer underlying resource similarities and complementarities of industries or products from the sales distributions of populations of firms across industries or products [20, 34, 35]. Lemelin [36] argues that complementary in use of products is one way to recognize the relatedness of products and markets. Firm product complementarityi,t. This variable is our measure of the extent to which a specific firm sells its complementary products when complementarity is defined as above. It is the normalized sum of the similarity between every pair of firm i’s products weighted by the firm’s proportion of sales in those products.

∑r

FirmProdCompi,t =

jj′| j ≠ j′

jj′,t

( pij ,t + pij′,t )

Niproduct −1 ,t

, where

rjj′,t and pij,t are described above, and Ni,tproduct is the number of products offered by the firm in year i. It normalizes the variable and removes double-counting bias [15]. Firm product complementarityi,t ignores stack layers. However, our argument is that industries selforganize into non-random typologies in order to manage complexity. Specifically, they form stacks – interdependent clusters or layers. In order to test for the existence of layers, and to test their predictive value on firm performance, we calculated two measures of stack complementarity. The measures for stack complementarity are calculated in an analogous fashion to the Firm product complementarityi,t variable. However, we redefine product j to be a stack layer. All of the IDC product categories that are identified as belonging to a stack layer are combined by summing their sales. Thus, a firm that sells to only one layer of the stack will have a stack complementary value of zero. The stack complementarity measures are calculated with the following formula:

∑r

StackCompi,t =

jj′| j ≠ j′

jj′ ,t

( pij ,t + pij′,t )

Nilayer −1 ,t

where rjj′,t

and pij,t are described above (except j now refers to layer because all product sales within a layer are summed), and Ni,tlayer is the number of layers offered by the firm in year i (to normalize and control for double counting bias).

The difference between the two stack complementarity measures is the method used to identify the layers. For the espoused stack layers we used a panel of experts to put different products into layers. In the case of emergent stack layers we used network cluster analysis to classify products into groups. The measures are described next. Espoused stack complementarityi,t. This is defined as the similarity metric (described earlier) for the espoused stack layers, j, firm i sells into in time t. The calculation for stack layer complementarity is described above. To compute the metric we aggregated the sales of each firm’s product category sales to create variable, j, containing sales by espoused stack layer. To create the espoused stack layer definitions we enlisted the assistance of a panel of industry expertise. We asked each expert to group products into layers (i.e., hardware, operating systems, middleware, groupware, etc.). The experts grouped the products into nine distinct groups (layers). We then used these nine layers to compute the firm i’s sales by layer j in time period t. Table 2 shows the results of this espoused grouping. Emergent stack complementarityi,t. This is defined as the similarity metric (described earlier) for the emergent stack layers, j, firm i sells into in time t. The calculation for stack layer complementarity is described above. To compute the metric we aggregated the sales of each firm’s product category sales to create variable, j, containing sales by emergent stack layer. To create the emergent stack layer definitions we use recent advances in graph partitioning to arrive at a dynamic clustering of product categories. These clusters represent the emergent, actual layering of products. The sum of sales by emergent layer becomes variable j when computing the similarity metric. To determine the emergent layers we defined a graph in which each product category is a node, j, connected by an arc with value 1.0 - rjj′,t . Thus, two products with high complementarity will have a low arc value. The goal is to then find a partition of the nodes that maximizes between subgroup differences and minimizes within subgroup differences. Our partitioning criterion is similar to the one described in Freidman [37], and has been suggested as the best performing stopping rule for clustering [38]. Unfortunately, grouping N elements into M (≤N) disjoint groups is NP-hard. The computation to find the best partition quickly becomes intractable. Other methods involves spectral methods (e.g., [39]), semidefinite programming relaxations (e.g., [40]), and nondeterministic approximation methods such as genetic algorithms and neural networks. Recently, interest has been renewed in flow-based cut clustering methods. These algorithms have enjoyed


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success at grouping similar search terms for use in internet search engines. Because the partitioning algorithm we use only works on symmetric graphs and the similarity metric is asymmetric, we sum the in and out arcs to create a symmetric, weighted graph. The partitioning algorithm is based on Gomory-Hu [41] min-cut spanning trees and is described by Flake [42]. Although all partitioning methods are not examined, those that are have good properties [43]. Table 3 shows the results of this emergent layering. For each year, we use the layers identified by the algorithm to aggregate sales and then calculate the Espoused stack complementarityi,t variable.

6.4

Control variables

To address alternative explanations for our findings, we include key controls that are likely to have a bearing on the dependent variable. To minimize potential endogeneity concerns, we also include controls that are likely to influence both independent and dependent variables of the study. The diversity of a firm’s operations (i.e., the extent to which a firm’s products are spread across distinct OS platforms and geographic regions) may impact firm performance [44]. We control for diversity by computing the entropy measure of diversification across platforms and geographic regions. Platform diversityi,t for firm i in year t is ∑kpik,tln(1/pik,t) where pik,t is the proportion of a firm i’s sales in platform k in year t. Geographic diversityi,t for firm i in year t is ∑lpil,t ln(1/pil,t) where pil,t is the proportion of a firm i’s sales in geographic region l in year t. Positioning across growing or declining markets, platforms, and geographic regions can influence the sales growth of a firm. Thus, we control for sales growth in the platforms, markets, and geographic regions supported by the firm. Platform growthi,t for firm i in year t is the sum, over all platforms, of the growth of platform k (i.e., Growthk,t) between year t-1 and t multiplied by the firm’s proportion of sales in that platform (i.e., ∑kpik,t Growthk,t) where pik,t is the proportion of firm i’s sales in platform k in year t, Growthk,t = ln(xk,t / xk,t-1), and xk,t is the total sales of platform k. Similarly, Product Growthi,t for firm i in year t is the sum, over all markets, of the growth of product market j (i.e., Growthj,t) multiplied by the firm’s proportion of sales in that product market (i.e., ∑jpij,t Growthj,t). Finally, Geographic Growthi,t for firm i in year t is the sum, over all geographic regions, of the growth of region l (i.e., Growthl,t) multiplied by the firm’s proportion of sales in that region (i.e., ∑lpil,t Growthl,t). Firm size can influence the performance of firms. Large firms offer more products [45], have more synergy exploitation opportunities, and suffer more from managerial diseconomies [46]. We control for

firm i’s size in year t by taking the natural logarithm of the firm’s total sales. Firm age is associated with within-industry diversification strategies and the performance of firms [32]. Thus, we control firm i’s age in year t. We measure firm age by counting the number of years since the firm’s founding or the first year in which the firm generated sales in the software industry. A firm’s initial performance advantages can influence subsequent performance of the firm as well. Thus, we control for a firm’s growth, as well as other unobserved firm heterogeneity, by including a lagged dependent variable, Firm sales growthi,t, in the model. Finally, we include a set of indictor variables to control for industry and period specific effects. Summary statistics of our sample can be found in Table 1.

6.5

Statistical Method and Analysis

We test the hypotheses using a cross sectional time series or panel design. The sample contains 4,392 distinct firm-years with approximately 5.0 observation years per firm. The design repeatedly measures firm performance and covariates, which includes a lagged performance measure. Under these conditions, ordinary least squares (OLS) may result in biased and inefficient estimates [47]. We thus use the generalized estimating equations (GEE) approach [48, 49] that has been used in prior studies [47, 50]. GEE is a flexible estimation procedure that addresses within firm correlation and heterogeneity and thus results in more efficient and unbiased parameters than OLS. Specifically, GEE estimators are asymptotically normal and consistent given an arbitrary correlation among observations [48, 49]. Because of its flexibility, a set of options must be specified prior to performing estimations. We use a Gaussian distribution for the dependent variable, an identity link function and an unstructured working correlation matrix. We also performed estimations using an exchangeable working correlation matrix, which denotes an equal correlation model and is equivalent to a random effects estimation with consistent results. We report the more conservative results obtained from the unstructured working matrix; we use a sandwich variance estimator for correcting standard errors.

7

Results

Table 4 presents Stata coefficient estimations from the GEE regression for sales growth. Since no substantial multi-colinearity issues were present in the sample, we omit the correlation matrix to save space (sample descriptors and zero-order correlation table is available on request). We estimate five models. Model 1 is the baseline model and only contains controls. Model 2 adds the covariate for the product


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complementarity – IDC product categories. Model 3 builds on Model 1 and adds the covariate for espoused stack complementarity. Model 4 builds on Model 1 and adds the covariate for emergent stack complementarity. Model 5 adds product, espoused stack, and emergent stack complementarity covarites together. All five models are individually significant and models 2 to 4 significantly increase the overall fit. Models 2-4 results suggest significant effects. We use Model 3 for testing our first hypothesis (H1) on espoused complementarity. The main effect is significant and in the expected direction (coefficient: 0.1, p < 0.05). We then tested the hypothesis, using Model 4, on emergent architecture complementarity (H2). The main effect is significant and in the expected direction (coefficient: 0.1, p