Elisabeth Nocker, Harry P. Bowen, Christian Stadler, Kurt Matzler ...... impact on firm performance (Tobin's Q) of our technological relatedness measure (Model ...
CAPTURING RELATEDNESS: COMPREHENSIVE MEASURES BASED ON SECONDARY DATA Elisabeth Nocker, Harry P. Bowen, Christian Stadler, Kurt Matzler British Journal of Management, Vol. 0, 1–17 (2015)
CAPTURING RELATEDNESS: COMPREHENSIVE MEASURES BASED ON SECONDARY DATA Abstract In this article we present new measures of technological and customer side relatedness constructed from widely available secondary data. Relatedness is a concept central to predicting the existence and nature of a relationship between corporate diversification and firm performance. Yet, finding appropriate measures has been an ongoing struggle. The widely used SIC-based entropy measure has low construct validity, and survey based measures are hard to replicate across firms and industries and over time. The measures we develop significantly outperform established measures in explaining variation in firm performance across firms and over time, and both sources of relatedness are found to be independent and significant explanations of firm performance.
CAPTURING RELATEDNESS: COMPREHENSIVE MEASURES BASED ON SECONDARY DATA A central theme in corporate strategy research is the existence and nature of a relationship between corporate diversification and firm performance (Chatterjee & Wernerfelt, 1991). Building on the seminal work of Andrews (Andrews, 1951), Ansoff (Ansoff, 1957, 1958, 1965), Chandler (Chandler, 1962) and Gort (Gort, 1962), an immense amount of conceptual and empirical work has been conducted on this topic (for a recent review see Hauschild & zu Knyphausen-Aufsess, 2013). At present, there is wide agreement that firms who diversify into “related” businesses (related diversifiers) outperform firms that diversify into “unrelated” businesses (unrelated diversifiers) (Palich, Cardinal & Miller, 2000). While the constructs of relatedness and related diversification (Rumelt, 1974; Whittington & Mayer, 2000) are central for understanding how diversification strategy impacts firm performance, divergence exists between the theoretical constructs and their operationalization (Robins & Wiersema, 1995). Theoretically, the construct of relatedness derives from the idea that a firm is able to realize economies of scope if the industries in which it operates are “similar” (Markides and Williamson, 1994; Seth, 1990). “Similarity” therefore implies relatedness, in that the same resources, technologies, skills, knowledge, and processes can be deployed across similar industries so as to realize economies of scope. In turn, economies of scope can arise from sharing a common resource (Porter, 1985).1 Such sharing is conceptualized as multidimensional (Stimpert & Duhaime, 1997; Tanriverdi & Venkatraman, 2005; Pehrsson, 2006b), and can arise at any point along a firm’s value chain.
For example, sharing technological know-how
(Markides & Williamson, 1994; Robins & Wiersema, 1995; Miller, 2004, 2006) or know-how The term “economies of scope” was first introduced by Panzar and Willing (1977). Economies of scope are said to exist when “‘joint production of two goods by one enterprise is less costly than the combined costs of production of two specialty firms” (Willing, 1979, p. 346). 1
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regarding customers/markets (Stimpert & Duhaime, 1997; Tanriverdi & Venkatraman, 2005; Pehrsson, 2006b). Yet, most measures (e.g. Neffke & Henning, 2013; Miller, 2004, 2006; Robins & Wiersema, 1995), including the widely used SIC based entropy measure (Jacquemine and Berry, 1979; Palepu, 1985) only focus on capturing similarities on the technology side of the firm’s value chain.
In empirical inquiry, this narrow focus, by not capturing sources of
relatedness at other points along a firm’s value chain, can lead to biased inferences as to the true nature of a relationship between diversification strategy and firm performance. To better align the conceptualization of relatedness with its empirical measurement, this paper develops new firm level measures of relatedness that capture the potential for economies of scope on both the technological and customer/market sides of a firm’s value chain. In developing our new measures, emphasis is placed not only on alignment with the theoretical construct of relatedness, but also ease of construction and ease of replication over time. This is an important contribution; ongoing attempts to develop better measures of relatedness highlight that scholars continue to struggle with operationalizing relatedness (e.g. Rumelt, 1974; Jacqemin and Berry, 1979; Palepu, 1985; Farjoun, 1994 and 1998; John and Harrison, 1999; Robins and Wiersema, 1995; Silverman, 1999; Miller, 2004 and 2006; Bryce and Winter, 2009; Neffke and Henning, 2013). As does the widely used entropy index (Jacqemin and Berry, 1979; Palepu, 1985), our measures assume that the realization of economies of scope is enabled when a firm operates in similar industries. Our measures capture the extent of relatedness at the firm level as the sales weighted average of the similarity between those industries in which a firm is active. While consistent with the commonly used SIC-based entropy index, our approach to assessing industry similarity does not rely on the judgment of those who created the SIC system (or any other
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industry classification systems such as the North American Industry Classification System (NAICS)) as to the extent of similarity (synergy potential) between any pair of industries. Instead, we measure the actual distance (similarity) between industries based on a set of industry variables that capture salient characteristics of the content and process of a given industry on both the technical and customer/market side likely to support resource transfer and sharing. This paper is not the first attempt to create easily replicable measures that capture relatedness across the value chain. A commendable recent effort is Bryce and Winter (2009). While their measure is easily replicable and generally applicable, a concern with their approach is their assumption that observed pattern of industries within a given corporate portfolio already reflect the achievement of relatedness. The assumption that activities within a portfolio are a priori related is problematic since it renders the concepts of relatedness and diversification (and its benefits) tautological (Neffke and Henning, 2013), and therefore sidesteps entirely the issue of construct validity by simply assuming it away.2 We avoid such issues by not assuming that a firm’s portfolio of industries is coherent. Instead, we measure the synergy potential of a firm based on the similarity of the characteristics of the industries in which a firm operates. This approach has the additional advantage that distances, and hence the similarity, between industries vary over time, reflecting changes in technologies and market characteristics. Overall, our intention is to offer a viable alternative to the SIC-based entropy measure. Despite its often mentioned limitations, the entropy measure of relatedness remains dominant, in part because data to construct alternative measures is often not widely available. This is not an issue faced by our proposed measures.
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Another concern with the Bryce and Winter (2009) measure is that it uses value added and not sales at the firm level to weight the importance of industry pairings at the firm level. This potentially confounds – or leaves unanswered - the underlying basis for the values of their index since value added can also include pure economic profit and it also varies with industry characteristics (e.g., capital-labour intensity).
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In what follows, we first explain the theoretical foundation of our measures followed by a brief review of established relatedness measures and then the presentation of our new measures. Following this, we present an analysis that compares our new measures to the most widely used measures in terms of their ability to explain variation in firm performance. This analysis not only assesses if our measures significantly outperform existing measures, it also indicates whether synergies on the technological and customer sides of a firm’s value chain are independent and statistically significant sources of relatedness that impact firm performance. THE CONCEPT OF RELATEDNESS Strategy scholars interested in corporate diversification and its implications for firm performance argue that a firm is able to realize economies of scope if the industries in which it operates are “similar” (Rummelt, 1974; Markides and Williamson, 1994; Palepu, 1985; Seth, 1990). “Similarity” therefore implies relatedness, in that the same resources, technologies, skills, knowledge, and processes can be deployed across similar industries so as to realize economies of scope. The argument that diversification into similar (i.e., related) industries offers an opportunity to realize economies of scope is well established (Palich, et al. 2000). The early work by Ansoff (1965) refers to several types of synergies (e.g., sales synergies) through the transfer of know-how across products. Later work by Rumelt (1982) mentions opportunities to exploit technical and managerial knowledge and skills. Finally, since synergies can arise at any point along a firm’s value chain (i.e., set of activities in which a firm is engaged), relatedness is conceptualized as multidimensional (Stimpert & Duhaime, 1997; Pehrsson, 2006b). For example, Tanriverdi and Venkatraman (2005), using survey data, find that product, customer, and manager knowledge do not, on their own, contribute to firm performance. Instead, these
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three dimensions contribute to performance synergistically, that is, from synergies (complementarities) that arise when all three types of knowledge are used together. Most prior attempts to measure relatedness, and the most widely used measures of relatedness, focus on only one dimension: e.g., product relatedness (Jacqemin and Berry, 1979; Palepu, 1985), technological relatedness (Robins and Wiersema, 1995; Silverman, 1999), marketing relatedness (Capron and Hulland, 1999), managerial relatedness (Ilinitch and Zeithaml, 1995), or human resource relatedness (Farjoun, 1994). Such one-dimensional approaches not only raise questions about content validity, but are counter to the view that relatedness is a multidimensional construct. As such, a valid measure should be as comprehensive as possible, and should in particular capture relatedness that may exist at different points in a firm’s value chain, i.e., capture synergy potentials that may arise in technological activities (Markides & Williamson, 1994; Robins & Wiersema, 1995; Miller, 2004, 2006) and in activities associated with a firm’s customers/markets (Stimpert & Duhaime, 1997; Tanriverdi & Venkatraman, 2005; Pehrsson, 2006b). In each dimension, similarities in terms of content (i.e. factual knowledge about the technology or market) and process will matter. ESTABLISHED MEASURES OF RELATEDNESS Table 1 lists established measures of relatedness and indicates for each measure issues regarding content validity, subjectivity, and replication. While categorical measures suffer from subjectivity, for the other measures the most common issue is their one-dimensionality. Survey based measures have sought to address the lack of dimensionality issue, but such measures are unique to the particular survey and are rarely if ever used by anyone other than the original authors of a given study. This highlights that survey based measures are difficult to replicate. In
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addition, they often apply to only a single year and often cover only a limited number of industries. -----------------------------------------Insert Table 1 about here -----------------------------------------Categorical Measures Based on Wrigley (1970), Rumelt (1974) developed a “categorical” measure of a firm’s “diversification type” based on the product-market attributes of a firm’s different businesses. Implementing Rumelt’s method requires the researcher to make a judgment as to a firm’s diversification type based on the information gathered about a firm’s businesses. Rumelt’s scheme has high content validity, and was shown to highly correlate with later SIC-based measures (Montgomery, 1982). Rumelt’s method focuses on characteristics of a firm’s products and markets, which raises the issue that process similarity might not be fully captured. However, the main concerns with Rumelt’s method are its subjectivity (i.e. relying on the judgment of a researcher) and the impracticality of constructing his measure over time and for large samples (Hamilton and Shergill, 1992). These concerns led to the development of objective and continuous measures of relatedness, the most common being based on the SIC system of industry classification or on an industry’s use of particular inputs. SIC Based Measures The most widely used SIC-based measure of relatedness is derived from the entropy index of total diversification (Jacqemin and Berry, 1979; Palepu, 1985). The entropy index is a measure of a firm’s “total” diversification across those 4-digit SIC industries in which the firm is
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active.3 When computed at the 2-digit SIC level, the resulting diversification index value is considered a measure of a firm’s degree of unrelated diversification. The degree of a firm’s related diversification is then computed as a residual value, by subtracting the value of a firm’s unrelated diversification from the value of its total diversification. Despite its widespread use, the related entropy measure is criticized for its reliance on the SIC (or any other industry classification) system. There are three main issues. First, the SIC system classifies products according to similarity of production methods (often in terms of the commonality of raw materials usage) and hence captures only the technological dimension of a firm’s activities (Davis and Duhaime, 1992).
Second, the measurement of unrelated
diversification rests on the arbitrary assumption that the “distance” between different 2-digit SIC categories is the same (e.g., Hall and St. John, 1994; Robins and Wiersema, 1995). As Bryce and Winter (2009) and Neffke and Henning (2013) point out, the hierarchical industry structure upon which the entropy measure relies does not represent an underlying relatedness scale. Finally, the SIC system is slow to incorporate new or emerging industries (although this concern has been partly remedied by replacing, in 1997, the SIC system with the NAICS). Input Based Measures The limitations of the SIC-based entropy measure led, starting in the 1990s, to new efforts that sought to instead capture relatedness in terms of intangible technological characteristics, or that operationalized industry relatedness as the similarity between industries in their use of the same type and proportion of human expertise (Farjoun, 1994 and 1998; John and Harrison, 1999; Robins and Wiersema, 1995; Silverman, 1999; Miller, 2004 and 2006). Farjoun (1994) captured In formal terms, total diversification is measured as TDk S ki log S ki1 , where TDk is total diversification of Nk
3
i 1
firm k, Ski is the share of k’s total sales derived from 4-digit SIC industry i, and Nk is the number of 4-digit SIC industries in which firm k is active.
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relatedness by measuring the similarity of human skills required by different industries. Robins and Wiersema (1995) captured relatedness in terms of shared technological know-how by measuring the similarity among industries in terms of their purchases of R&D across industries. John and Harrison (1999) similarly measured relatedness in terms of knowledge and skill transfers measured as the similarity among industries in their use of raw materials, product and process science and technology, and resource conversion process. Finally, like Robins and Wiersema (1995), Silverman (1999) and Miller (2004, 2006) captured technological relatedness in terms of shared technological know-how but used similarity among industries in patents rather than R&D purchases. While more focused on the underlying sources of relatedness, such efforts nonetheless operationalize relatedness only in terms of technological characteristics, and often adopt measures that are one-dimensional. The focus of prior studies on technological relatedness may be appropriate when addressing a particular question, but it is now widely agreed that relatedness is a multidimensional, and not one-dimensional, construct (Bryce and Winter, 2009; Pehrsson, 2006a,b; Stimpert & Duhaime, 1997).
Most attempts to identify sources of relatedness beyond the
technological side have used survey data. For example, Stimpert and Duhaime (1997) used survey data to conclude that managers think of relatedness as a construct consisting of productmarket relatedness (e.g., shared customers, shared distribution network) and at least one additional construct, described as differentiation relatedness (e.g., R&D, product design). Tsai (2000) used survey data to identify five broad categories of “strategic assets” as potential sources of relatedness: customer assets, channel assets, input assets, process assets, and market knowledge. Tanriverdi and Venkatraman (2005) used survey data to identify three types of knowledge resources as potential sources of synergy: product, customer and managerial
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relatedness. Finally, Pehrsson (2006a) used survey data to measure five factors of relatedness: product technology, general management skills, end customers, brand recognition and supply channel. While these efforts based on survey data have improved our understanding of the multidimensional nature of relatedness they nonetheless evidence a number of limitations. First, since each study relied on survey data it was assumed that managerial assessment of relatedness underlies strategic decisions, and hence that managerial perceptions of relatedness can be used as input for a given relatedness measures. However, the ambiguous nature of similarity judgments (Farjoun & Lai, 1997) implies that managers can have varying degrees of confidence in their own assessments of the nature of similarity between business units, which implies an inadequate understanding of the actual interrelationships between businesses (Pehrsson, 2006a). Second, the limitations of survey-based measures usually include low response rates (e.g. Stimpert & Duhaime, 1997) as well as non-response and key informant biases that can substantially limit reliability and external validity. Third, survey data is often collected at a single point in time which precludes testing for firm specific heterogeneity and other forms of endogeneity in the relationship between diversification strategy and firm performance. Finally, data collected from primary sources is usually proprietary and difficult to duplicate or update, making replication difficult if not impossible. To avoid the disadvantages of using primary data, the new measures proposed in this paper use readily available secondary data covering many firms and over time, making the measures both encompassing and replicable. Overall, established measures of relatedness create a dilemma for scholars: they can either use the common– yet widely disparaged - and easy to construct one-dimensional SICbased entropy measures or develop survey based measures which, to date, cannot be used to
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conduct the kind of large scale panel data analyses that has more become common in the literature in an effort to control for statistical issues, for example, firm level heterogeneity.
NEW MEASURES OF TECHNOLOGICAL AND MARKET RELATEDNESS Our new measures are intended to meet three criteria. First, they should adequately capture the potential of a firm to benefit from economies of scope. Second, they should be easily replicable and extendable over time as new data becomes available.
Third, they should
encompass as many firms and industries as possible. While survey based instruments have made substantial advances in terms of content validity, it is not possible for a survey approach to meet the second and third criteria. Our measures are therefore constructed from readily accessible secondary data. The basic assumption underlying our measures is that the greater the similarity between industries the greater is the potential for a firm to realize economies of scope. Unlike the SICbased entropy measure, we do not rely on the judgment of the creators of the SIC classification system to determine the synergy potential of different industries. Instead, we measure the actual “distance” (i.e., similarity) between industries based on a number of industry characteristics described later below. Finally, recognizing that relatedness can arise anywhere along a firm’s value chain, we develop separate relatedness measures for the technological and customer/market sides of the value chain. Our use of industry similarities to capture economies of scope at the firm level has sound theoretical justification. Lawrence and Lorsch (1967) argue that a firm’s external surroundings critically influence its knowledge development and information processing. Dess and Beard (1984) argue that industry environments vary in the stimuli they provide and thus different
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industries, as defined by their characteristics, require different sets of skills for success. In addition, firms which operate in industries with different industry characteristics occupy different external environments that lead to different linkages between actions and outcomes. This view is echoed by Cyert and March (1963), who argue that a difference in external environments is a critical source of problems that drive organizational search and change, and hence that business specific knowledge is both shaped by its external environment and applied in response to it. Accordingly, the effective use of knowledge across businesses is unlikely unless their external environments are similar. Stated differently, the knowledge, routines and capabilities embedded in an organization are more transferrable the more similar are the external environments of the businesses within a firm’s portfolio. Method Using the logic of industry similarities, we construct our firm level measures of technological and customer side relatedness in three steps. First, we identify industry characteristics on the technological and customer sides of a firm’s value chain that are believed likely to facilitate resource sharing. Second, for each set of characteristics (technological or customer), the extent of similarity between any pair of industries is then computed as the correlation between their vector of characteristics. Given N industries, the resulting pairwise correlation coefficients form an N×N matrix (P) of pairwise similarities (rij):
1 r12 K r1N r 1 K r2 N P = 21 M M M rij 1 rN 1 rN 2 K
where i, j = 1, …, N.
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The third and final step is to create our measures of relatedness at the firm level. To do this we follow the approach of Robins and Wiersema (1995) in which the similarity (rij) between each pair of industries is first weighted by a firm’s sales share in each industry:
Vijk Sik rij S kj rji Sik S kj rij , where rij = rji = similarity between industry i and j; and Sik ( S kj ) = share of firm k’s total sales derived from industry i(j). Since only similarity between distinct industries is of interest, values of Vijk for which i = j are excluded (equivalently, one sets Vijk 0 if i = j). Given this, the values
Vijk (i j) are then summed over all combinations of industries in which a firm is active to obtain a single measure of relatedness (Rk) among firm k’s portfolio of industries: Nk
Nk
Rk Vijk . i 1 j 1
In this expression, Vijk 0 when i = j and Nk is the number of industries in which firm k is active. Since the value of Rk depends on the total number of industries in which a firm is active, the correction of Robins and Wiersema (1995) is used to constraint Rk to lie between -1 and +1.4 In general, higher values of Rk indicate higher relatedness among a firm’s portfolio of industries. Industry Characteristics This section discusses our choice of industry level characteristics. As explained above, we assume that the greater the similarity between industries the greater is the potential to gain synergies. Such synergies relate to the similarity of knowledge and processes among businesses (Hauschild & zu Knyphausen-Aufsess, 2013; Tanriverdi & Venkatraman, 2005). For example, diversified firms operating in similar lines of business can minimize complexity and apply core As detailed in Robins and Wiersema (1995: p. 299), since a firm’s sales shares sum to one the value of Rk will equal (Nk -1) when all similarities rij take their maximum value (+1) and will equal -(Nk -1) when all similarities rij take their minimum value (-1). The correction is therefore to divide the value of Rk by |Nk -1|. 4
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skills appropriately (Ilinitch & Zeithaml, 1995). Synergy potentials both on the customer and technology side of the value chain depend on similarity in terms of content and process. Our challenge was to find industry characteristics that capture such similarities. Figure 1 shows that we capture distance between industries in terms of content using R&D employee intensity on the technological side and by using advertising intensity on the customer side of the value chain. To capture distance between industries in terms of process we identified two characteristics on each side of the value chain: capital and materials intensity for the technological side and customer type and distribution channel for the customer side of the value chain. -----------------------------------------Insert Figure 1 about here ------------------------------------------
Below we discuss our rationale for choosing these particular industry characteristics. Table 2 provides complete data definitions and sources. -----------------------------------------Insert Table 2 about here -----------------------------------------Technological Side Characteristics Capital Intensity. Industry capital intensity is measured by industry real net capital stock divided by total industry employment.
Industry capital intensity indicates the extent of
equipment relative to labor used in production and degree of production process automation; a low capital-labor ratio indicates low automation (Datta, Guthrie and Wright, 2005). Firms in industries with high versus low capital intensity gain different knowledge. Harrison, Hall and Nargundkar (1993) argue that firms with high capital intensity seek out acquisition targets with similarly high capital intensity as they are expected to have similar strategic characteristics 14
facilitating knowledge transfer. Firms in highly capital-intensive industries are also focused on leveraging their investments, cost efficiencies and economies of scale (Datta and Rajagopalan, 1998; Hambrick and Lei, 1985; Porter, 1985). Conversely, firms in low capital-intensive industries can use and share knowledge of human resource management, for example, employee motivation (e.g., Ramlall, 2004). In each case, the specialized knowledge obtained can be shared across businesses in industries with similar capital-labor ratios. Materials Intensity. Industry materials intensity is measured by the ratio of materials cost (less energy cost) to total industry sales. Similarity of industries in terms of their materials intensity captures knowledge associated with managing supply chains as well as vertical integration (Helm and Stumpp, 1999). In industries with high materials intensity, key subprocesses of supply chain management, such as selection and qualification of desired suppliers, logistics management, order processing, pricing, billing, rebates, etc., are of critical importance (Tikkanen, Lamberg, Parvinen and Kallunki, 2005). The establishment of operational excellence and thereby the lowering of costs and risks, as well as aligning inputs to maximize value creation through procurement, are important skills needed in high materials intensity contexts (Tikkanen et al., 2005). Similarity of industries in terms of their materials intensity therefore creates opportunities for knowledge transfer and synergies. R&D Employee Intensity. Industry R&D employee intensity is measured as the number of R&D scientists and engineers per 1,000 employees in the industry. 5 Harrison et al. (1993) argue that high R&D intensity, regardless of the reason, is likely to facilitate skill and knowledge transfer (e.g., through executive transfer and promotion) and D’Aveni et al. (2004) found that similarity in R&D intensity leads to cost efficiencies from economies of scope. Measuring the 5
In the management literature, R&D intensity usually refers to R&D spending per dollar of sales. We were unable to employ this measure since R&D spending data are sparse due to disclosure restrictions at our level of industry detail.
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prevalence of people whose task it is to create and transfer knowledge offers a more appropriate proxy for knowledge creation than does R&D spending. Scientists and engineers engaged in R&D also provides an indication of the degree of product/process sophistication (Silverman, 1999). Similarities between industries in terms of their R&D employee intensity allow a firm to leverage and share technological knowledge among its businesses (Montgomery and Hariharan, 1991). Kanzanjian and Drazin (1987) argue that, with respect to human capital, “relatedness is the relative distance between the knowledge needed to operate in the new domain and the degree of knowledge available in the current domain” (Kanzanjian and Drazin 1987; p. 347). Empirical research has also shown that skills are more easily leveraged when there are similarities in skill levels (Markides and Williamson, 1994) and in the ratio of staff across occupational categories (Chang, 1996; Farjoun, 1994). Hence, similarities in human resource profiles (in our case R&D employee intensity) should be favorable to relatedness. Customer Side Characteristics Customer Type. We identify two types of customers, defined as a firm’s most immediate customer: consumer and industrial. Several studies (Webster, 1978; Kotler, 1995; MacMilian, Hambrick and Day, 1982; Hambrick and Lei, 1985; Day, 2002) emphasize differences between consumer and industrial product businesses in terms of functional interdependence, product complexity, buyer-seller interdependence and buying process complexity. Similarities across a firm’s businesses in terms of type of customer allows a firm to share knowledge gained through interactions with one customer type with all its businesses that sell to that customer type. Capon et al. (1988) argue that, as different markets require different skills for success, a concentration on either consumer or industrial markets “allows for both economies of experience in key marketing and management skills, and for economies of scale in advertising, distribution and
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sales force” (Capon et al. 1988; p. 63). Capon et al. (1988) empirically confirm that firms who specialize in selling to only one market type (consumer or industrial) outperform firms that compete in both. Distribution Channel. We identify two distribution channels: wholesale and retail. Research indicates a number of important differences between wholesale and retail channels, including differences in the nature and importance of establishing relationships (Ford, 1993; Christy, Oliver and Penn, 1996). In addition, Markides and Williamson (1994) argue that the skills needed to build and manage distribution and dealer networks form the basis for a potentially important core competence and hence competitive advantage, and that economies of scope can be realized when business units share the same distribution system. Firms operating in industries that use the same distribution channel are therefore expected to more easily share and leverage knowledge of managerial processes and customer characteristics (Tanriverdi and Venkatraman, 2005). Advertising Intensity. Advertising intensity, measured by the ratio of industry advertising expenditure to industry sales, is indicative of the extent of product differentiation and hence the importance of branded products in an industry. Businesses with similar advertising intensity imply the potential for a firm to share its knowledge regarding branding, promotional, sales force and marketing (D’Aveni, Ravenscraft and Anderson, 2004; Montgomery and Hariharan, 1991). Montgomery and Hariharan (1991) find that high advertising intensity is associated with more diversification and interpret this as empirical evidence that marketing activities are transferable resources that provide an advantage in diversified firms. D’Aveni et al. (2004) argue that similarity of advertising intensity among units in a multi-business firm should lead to advertising
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efficiencies since first, advertising-related resources can be shared and second, the parent firm’s mental maps, measures, and incentives are aligned among the businesses. ASSESSMENT OF RELATEDNESS MEASURES To assess the merits of our relatedness measures relative to existing measures we estimate five alternative models, each with firm performance as the dependent variable and controls for firm size, industry capital intensity, industry profitability, and industry concentration: 6
Model 1: Related component of the entropy measure plus control variables; Model 2: Robins and Wiersema (1995) relatedness measure plus control variables; Model 3: Our measure of technology relatedness plus control variables; Model 4: Our measure of customer relatedness plus control variables; Model 5: Our measures of technology and customer relatedness plus control variables.
Each model is estimated in a panel dataset of 3,625 U.S. manufacturing firms covering 1984 to 2004.7 Each model is estimated using two-stage least squares, with firm-level fixed effects included to control for endogeneity arising from firm heterogeneity. 8 In total, the sample consists of 59,715 firm-year observations and includes both single and multi-business firms as samples restricted to multi-business companies alone may lead to estimation biases (Bowen and Wiersema, 2007). See Table 2 for definitions and data sources on each variable used in our estimations.
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Initial estimations also included firm leverage (debt to equity) as a control. This variable was dropped in subsequent estimations since it was not significant and the results with and without this variable were largely the same. 7 Firms were identified as being in manufacturing if their main line of business as reported in COMPUSTAT was an SIC code between 2000 and 3999. Due to a change in 1997 from the SIC to the NAICS system, a firm after 1997 was identified as belonging to the sample if its NAICS code was between 311111 and 339999. Pre-1997 SIC data was translated into NAICS codes using weights published by the U.S. Census Bureau. The weights are the fraction of each SIC industry that goes into each NAICS industry. Further details are available from the authors upon request. 8 The instrumental variables used were: annual growth rate of industry sales, industry R&D intensity (industry R&D expenditures relative to industry sales) and firm R&D intensity (firm R&D expenditures relative firm sales). These variables are a subset of those thought to determine technological and customer relatedness. Further details are available from the authors upon request.
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Table 3 presents summary statistics and correlations for all model variables. The pairwise correlations among all relatedness measures are significant (p < 0.05). Our measure of technological relatedness has a higher correlation with the Robins-Wiersema R&D measure than with the related entropy measure (0.165 vs. 0.062). While smaller in size, a similar pattern of correlations appears between our measure of customer relatedness, the Robins-Wiersema measure (0.149), and the related entropy measure (0.046). -----------------------------------------Insert Table 3 about here -----------------------------------------Table 4 presents results of estimating each of the five models when firm performance is measured as Tobin’s Q (columns labeled 1-5) or as Return on Assets (ROA) (columns labeled 610). For each model, unstandardized and standardized (beta) values of the estimated coefficient(s) on the relatedness variable(s) are shown, with the standardized value in parentheses. These beta coefficient values allow direct comparison across models as to the size of the effect on performance of a change in a given relatedness variable. -----------------------------------------Insert Table 4 about here -----------------------------------------In Table 4, each model is significant as indicated by significance of the overall F-statistic and each relatedness variable is positive and significant whether firm performance is measured as Tobin’s Q or ROA. For Tobin’s Q, the estimated beta coefficients for Model 5 indicate that the impact on performance of higher technological relatedness (29.2) is about 1.5 times that of higher customer relatedness (19.3).
For ROA, the impact on performance of higher
technological relatedness is about 5.7 times the impact of higher customer relatedness. Since
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both technological and customer relatedness are significant in Model 5, each source of relatedness has a significant and independent impact on firm performance. We use two methods to indicate which model (measure) the data prefer for explaining variation in firm performance: the Davidson-MacKinnon J-test (Davidson and MacKinnon, 1981, 1982) and the Akaike Information Criterion (AIC) (Akaike, 1973).
The Davidson-
MacKinnon J-test is a commonly used test for choosing among competing non-nested models. Essentially, the J-test examines if the variables in one model (the null model) are significant for explaining variation in the dependent variable when included in a second model (the alternative model) containing a different set of explanatory variables. The test is symmetric, in that each model serves once as the null model and once as the alternative. For this reason, the results of the J-test can be inconclusive as to the preferred model.9 The AIC also provides an indication of the extent to which the data prefer one model (measure) over another model, 10 with the model having the lowest value of the AIC being the more preferred as a representation of the underlying data (i.e., variation in firm performance). Negative AIC values are permissible, and models with a negative AIC value are preferred to models with a positive AIC value. We conducted the Davidson-MacKinnon J-test (results not shown) for two “alternative” models and two “null” models. The alternative models were: Model 3, which includes only Technological Relatedness as a variable and Model 5, which includes both Technological 9
The J-test uses values of the dependent variable predicted from the null model as an explanatory variable in the alternative model. The roles of the null and alternative models are then reversed (i.e., predicted values from the alternative model are used as an explanatory variable in the null model). The null model is rejected in favor of the alternative model if 1) the coefficient on the predicted value from the alternative model is significant in the null model and 2) the coefficient on the predicted value from the null model is not significant in the alternative model. If the reverse is found then the alternative model is rejected in favor of the null model. The results are inconclusive if the coefficient on the predicted value is significant in both models or not significant in either model. 10 The AIC is a measure of the information lost when a given model is used to represent the true but unknown data generating process (i.e., the true model). Comparing AIC values of two models can be interpreted to indicate the relative likelihood of one model relative to the other. For example, if model A has AIC = 2 and model B has AIC = 4 then the loss of information in using model B is twice (= 4/2) that of using model A to represent the true but unknown model. Use of the AIC is appropriate here since our models are non-nested and our estimation procedure (two-stage least squares) does not yield a value of R-square that is bounded between zero and one.
20
Relatedness and Customer Relatedness as variables. The null models were Model 1, which uses the Robins-Wiersema technological relatedness measure, and Model 2, which uses the Related Entropy measure. For both the ROA and Tobin’s Q models, the results unambiguously rejected the “null” model (either Model 1 or Model 2) in favor of the alternative model, either Model 3 or Model 5. Hence, in all cases, the models using our relatedness measures were preferred over the models that used established relatedness measures. We next considered model preference based on AIC values. Comparing first our measure of technological relatedness (Model 3) with the established technological relatedness measures (Models 1 and 2), the AIC values in Table 3 indicate overwhelming preference for our measure (Model 3); the widely used related entropy measure is consistently the worst model. These results concur with Robins and Wiersema (1995) finding that their R&D flows based measure of relatedness outperformed the related entropy measure for explaining firm performance. Yet, as indicated in Table 4, our technological relatedness measure substantially outperforms the Robins and Wiersema measure. This preference for our technological relatedness measure may reflect that it captures more possibilities for resource sharing than the Robins and Wiersema measure since our measure uses three rather than one underlying industry characteristic to capture the potential for resource sharing among a firm’s portfolio of industries. The preference for our technological relatedness measure over the established measures is also indicated by the relative size of the standardized coefficient in Model 3 versus that in Models 1 and 3. As indicated, the impact on firm performance (Tobin’s Q) of our technological relatedness measure (Model 3) is about 2 times that of the Robins and Wiersema measure (Model 2) and over 7 times the impact of the related entropy measure (Model 1).
21
Turning to customer side relatedness, we compare first the results in Table 4 for Model 4 and Model 3. As indicated, technological relatedness (Model 3) and customer relatedness (Model 4) have about the same impact on performance measured by Tobin’s Q. However, for ROA, the impact on performance of customer relatedness (Model 4) is about 87% (= 1 – 6.25/48.62) less than the impact of technological relatedness (Model 3). When these two sources of relatedness are considered together (Model 5), the results indicate that while the difference in the size of their impact on performance narrows, the size of the impact on performance of higher technological relatedness remains greater than the size of the impact of higher customer relatedness. In summary, the AIC comparisons and results of the more demanding J-test both indicate that the models using our measures of technological and customer relatedness are strictly preferred to those that use the Robins-Wiersema measure and the more common and widely used related entropy measure.11 DISCUSSION AND CONCLUSION This paper sought to address limitations in the construction and scope of established measures of relatedness, a core concept explaining the relationship between corporate diversification strategy and firm performance. Although conceptualized as arising from economies of scope that may be realized at any point along a firm’s value chain, most established measures of relatedness capture only technological side relatedness, and they are often constructed from data that is rare or difficult to replicate. Similarly, past efforts to construct measures of customer side relatedness have been ad hoc, often relying on survey data at a single point in time and covering a limited number of firms and industries. Overall, the established
We conducted a robustness check using a subsample of 100 firms for which we calculated Rumelt’s (1974) measure of related diversified firms. Using this measure to estimate an additional model, the results based on the AIC were substantively the same, with the preferred model again being model 5 which uses our measures of technological and customer relatedness. Results are available from the authors upon request. 11
22
measures of relatedness do not display adequate content validity and are difficult to replicate and extend over time. This paper proposed new measures of technological side and customer side relatedness constructed from widely available secondary data that encompasses a large number of firms and industries and that is available at different points in time. Unlike prior attempts to measure relatedness (e.g., Teece, 1980, 1982; Bryce and Winter, 2009), our approach avoids the risk of tautologically defining relatedness in terms of observed patterns of co-occurring industries. Hence, our approach is more in line with that of Neffke and Henning’s (2013) recent contribution on skills relatedness, but we go beyond their efforts since our measures are broader in scope and use data that is more widely available.12 Our new measures were evaluated and compared to existing relatedness measures in terms of their efficacy for explaining variation in performance across firms. The findings indicate that our measures are superior to the established and widely used measures of relatedness, suggesting that our new measures exhibit higher content validity than established measures for the purpose of capturing relatedness in general terms. From a methodological perspective, our measures are an attractive alternative to the widely used SIC-based entropy measure; the data required to construct our measures is no less widely available and, as found in this paper, our measures evidence greater alignment with the theoretical construct of relatedness. As such, our measures offer a more reliable and comprehensive foundation for advancing our understanding of diversification as a strategy and, in particular, its implications for firm performance. Two potentially important aspects not directly captured in our measures are knowledge which is tacit and the direct role that managers play in achieving synergies and hence economies 12
Neffke and Henning (2013, p.304) use data on cross-industry labor flows available exclusively for Sweden.
23
of scope. The resource based view (RBV) of the firm (Barney 1991) suggests intangibles, which include tacit knowledge, are a major source of competitive advantage, and this idea is commonly used to explain the existence of a positive relationship between related diversification and firm performance. While our measures do capture intangibles in the form of knowledge that arises from research and development activity or is related to particular market characteristics, our measures do not directly distinguish explicit from tacit knowledge. While of potential interest, distinguishing between explicit and tacit knowledge for the questions regarding firm diversification may not be important. Tacit knowledge is often deemed sticky and hard to transfer (Szulanski, 1996), and hence cannot be articulated and communicated across different businesses in the same manner as explicit knowledge, the latter often embedded in technologies and processes. For this reason, Tanriverdi and Venkatraman (2005) argue that tacit knowledge is not instrumental in the realization of economies of scope. Given this, the inability of our measures to distinguish explicit from tacit knowledge would not appear to be a limiting factor. The issue of the role of management in realizing economies of scope arises since two firms could have identical industry portfolios but only one may actually succeed in achieving the potential performance benefit associated with the measured relatedness. In principle, one could capture “managerial similarity” within our framework and hence embed the role of management directly in our measures. However, this could present a challenge since our measures capture similarity only at the industry level, not the firm level. One possible way forward might be to extend the work of Farjoun (1994), who used data on the occupational profiles of US industries to capture the distance between industries, and in a second step relatedness in terms of human skills. Another solution might be to account for the role of managers in a framework that is separate from the direct measurement of relatedness. For example, scholars interested in the role
24
of managers could use variables that capture this effect and then interact them with our relatedness measures to explore the role of different managerial capabilities, e.g. experience of a firm’s senior managers, in gaining performance. We also note a number of limitations. One obvious issue is that our measures, like all of the established measures, uses only data on manufacturing firms. While it is relatively easy to extend our overall approach to service industries, one can question whether the industry characteristics we have chosen, particularly on the technology side, would remain appropriate. A second issue is whether our industry characteristics capture customer and technological relatedness sufficiently among manufacturing firms. Since we measure similarity both in terms of content and process, we are confident that we offer a fairly comprehensive approach, i.e., we capture the main features. At the same time, we are aware that there may be unobservable factors that may confer “similarity” (e.g. subjective aspects or managerial cognition). On this point, we simply note that our measures involve a greater number of characteristics to capture relatedness than existing measures, and our measures do appear to offer substantial improvement over the existing and widely used measures. A third potential limitation is that we use a total of six industry characteristics to capture both technology and customer relatedness, but there might be additional characteristics one could consider. For example, we distinguish consumer from industrial customers but ignore the government as a customer type. This could matter for some industries, such as defense, which have strong relationships with governments that could be important for offering products other than weapons. This could also be particularly important in countries where government relations are a crucial success factor. As Palepu and Khanna (2000) note, firms in emerging economies may diversify to fill institutional voids and, as such, having strong government relations may
25
compensate for weak institutions in such economies. As they argue, this implies that emerging economy firms that enjoy such governmental relationships will expand their scope. Hence, including government as a customer type would capture a phenomenon of potential importance in some countries and industries. A final limitation of our approach is that similarities in one particular characteristic might over- or under-estimate the relatedness in this particular characteristic. For instance, paper and chemicals production are very capital intensive but use different capital goods. Similarly, both software development and pharmaceuticals involve much R&D but the overlap in technologies is somewhat limited. For such cases, we rely on Harrison et al.’s (1993) argument that a strong emphasis on R&D will result in similar strategic characteristics that facilitate the exchange of knowledge and skills. Combining different characteristics should therefore mitigate the issue related to one particular characteristic. In addition, our contribution lies less in the identification of relevant characteristics and more in providing an approach that encompasses both technology and customer relatedness. An outcomes or revealed structure based measure like that of Bryce and Winter (2009) might be able to circumvent such issues, but the tautology of their approach (Neffke and Henning, 2013) is a significant issue, particularly if one desires a measure that can be used to test the validity of theories of the basis for, and the financial impact of, corporate diversification strategy. In conclusion, the new measures of relatedness presented in this paper represent an incremental though important contribution. Among their virtues are ease of replication and extension as new data becomes available over time. Most importantly, by capturing relatedness on both the technological and customer sides of a firm’s value chain, our measures provide
26
greater construct validity compared to the most common and widely used measures of relatedness.
27
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Figure 1: Industry Characteristics used to determine relatedness
Knowledge Type
Technological Relatedness
Customer/Market Relatedness
Capital intensity
Customer type
Materials intensity
Distribution channel
R&D employee intensity
Advertising intensity
Process
Content
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Table 1: Established measures of relatedness Measure Categorical SIC Based
Input Based
Study Authors Wrigley (1970); Rumelt (1974) Jacqemin & Berry (1979); Palepu (1985)
Description Researcher judges how related are a firm’s businesses Degree of relatedness based on whether the 4-digit SIC industries in which a firm is active are in the same 2-digit SIC industry Similarity among industries based on similarity of R&D usage Similarity among industries based on patents
Issues - Subjective - Replication difficult - One-dimensional - Wrongly assumes same distance between all 2-digit industries - One-dimensional - Replication difficult; data availablity - One-dimensional - Data availablity
Similarity among industries based on human skills Relatedness determined by measuring existing portfolios of diversified firms
- One-dimensional - Data availablity - Construct validity issue: relatedness a priori assumed
Neffke & Henning (2013)
Skill relatedness determined based on cross-industry labor flows
- Not replicable outside Sweden - One-dimensional
Capron & Hulland (1999)
Survey determines market relatedness
Tsai (2000)
Five survey based factors determine relatedness
Tanriverdi & Venkatraman (2005)
Survey determines product, customer and managerial relatedness.
Pehrsson (2006a)
Five survey based factors determine relatedness
- One-dimensional - Relies on managerial judgment - Data availability - Relies on managerial judgment - Data availability - Relies on managerial judgment - Data availability - One-dimensional - Relies on managerial judgment - Data availability - Relies on managerial judgment - Data availability - Relies on managerial judgment - Data availability
Robins & Wiersema (1995) Silverman (1999); Miller (2004, 2006) Farjoun (1994) Bryce & Winter (2009)
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Table 2: Variable definitions and data sources Construct
Variable Customer Type
Customer relatedness
Distribution Channel Advertising Intensity Capital Intensity
Technological relatedness
Materials Intensity R&D Employee Intensity
Firm performance
Return on Assets (ROA) Tobin’s Q Firm Size Industry Capital Intensity
Control variables
Industry Profitability Industry Concentration
Definition % of industry sales destined for business or consumers in each 6-digit NAICS industry % of industry sales destined for wholesale or retail businesses in each 6-digit NAICS industry sum of advertising expenditures by all firms in a given industry divided by total industry sales in each 6-digit NAICS industry real net capital stock divided by total employment in each 6-digit NAICS industry material costs minus energy costs divided by industry sales in each 6-digit NAICS industry number of R&D scientists and engineers per 1000 employees in in each 6-digit NAICS industry profit before tax and exceptional items divided by total firm assets the market value of the firm’s outstanding stock and debt to the replacement cost of the firm's assets logarithm of a total firm sales real net industry capital stock divided by total employment in the 6-digit NAICS core industry of the firm Return on Assets (ROA) was aggregated in the 6-digit NAICS core industry of each firm 4-firm concentration ratio in the 6-digit NAICS core industry of the firm
Source “use tables” of benchmark I-O accounts, U.S. Bureau of Economic Analysis “use tables” of benchmark I-O accounts, U.S. Bureau of Economic Analysis COMPUSTAT NBER productivity database (Bartelsman and Gray, 1996) NBER productivity database (Bartelsman and Gray, 1996) and COMPUSTAT National Science Foundation (NSF) COMPUSTAT COMPUSTAT COMPUSTAT NBER productivity database (Bartelsman and Gray, 1996) COMPUSTAT U.S. Census of Manufacturers
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Industry Profitability
Industry Capital Intensity
Firm Size
Customer Relatedness
Technological Relatedness
Robins/Wiersema
Entropy (related)
Tobin's Q
Return on Assets
Table 3. Variable means, standard deviations and correlations.
Return on Assets
Mean 0.028
S.D. 0.001
Min -0.613
Max 0.795
1
Tobin's Q
1.419
0.019
0.341
9.389
0.17
1
Entropy (related)
0.089
0.012
0.000
0.723
0.062
0.021
1
Robins/Wiersema
0.289
0.004
-0.046
0.749
0.026
0.001
0.064
1
Technological Relatedness
0.796
0.085
-0.335
0.984
0.055
0.053
0.062
0.165
1
Customer Relatedness
0.705
0.050
-0.239
0.980
0.120
0.033
0.046
0.149
0.401
1
Firm Size
5.946
0.167
0.009
12.167
0.268
0.009
0.225
0.066
0.186
0.254
1
Industry Capital Intensity
1.092
1.293
0.042
27.700
0.015
0.033
0.049
0.032
0.107
0.018
0.166
1
Industry Profitability
0.240
0.195
-0.325
4.465
0.036
0.057
0.046
0.006
0.010
0.062
0.038
0.008
1
Industry Concentration
0.354
0.203
0.034
0.992
0.026
0.048
0.021
0.133
0.117
0.097
0.292
0.222
0.025
N = 59,715; a correlation exceeding 0.008 in absolute value is significant at p = 0.05
37
Table 4. Two-stage least squares results estimating firm performance measured as Tobin’s Q or Return on Assets Firm Performance Measured as Tobin’s Q Variable Related Entropy (SIC-based)
Firm Performance Measured as Return on Assets
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
Model 1
Model 2
Model 3
Model 4
Model 5
Model 1
Model 2
Model 3
Model 4
Model 5
3.053*** (1.928)
Robins-Wiersema Measure
0.344*** (4.128) 34.817*** (7.330)
Technological Relatedness
1.464*** (5.856) 3.465*** (15.501)
Customer Relatedness
6.531*** (29.218) 6.697*** (17.624)
7.319*** (19.261)
0.572*** (48.620)
1.206*** (102.510) 0.125*** (6.250)
0.357*** (17.850)
Firm Size
0.049***
0.354***
0.161**
0.268***
0.178***
0.011***
0.016***
0.056**
0.095***
0.057***
Industry Capital Intensity
0.050**
0.082***
0.083***
0.012*
0.066**
0.035**
0.015**
0.019**
0.011**
0.013**
Industry Profitability
0.015*
0.023**
0.035**
0.046*
0.081**
0.014***
0.053*
0.011***
0.015***
0.069***
Industry Concentration
0.021*
0.012***
0.038**
0.071**
0.014***
0.001*
0.019**
0.010**
0.027*
0.042*
F-statistic
10.18***
13.32***
11.98***
13.32***
10.78***
15.72***
Akaike information criterion (AIC)
21,389.39
39,372.47
-16,472.02
-19,835.68
14.64*** 2,448.24
13.24***
13.82***
18.80***
1,497.84
744.24
15,923.70
-3,825.45
-14,479.79
N= 59,715, * p < 0.1, **p < 0.05, ***p < 0.01 Standardized (beta) coefficients in parentheses; estimation uses firm fixed effects to account for unobserved firm heterogeneity.
38
