Effects of Customer and Innovation Asset Configuration Strategies on Firm Performance

8/3/2019 Effects of Customer and Innovation Asset Configuration Strategies on Firm Performance

1/17

Journal of Marketing Research

Vol. XLVIII (June 2011), 587602

*Eric (Er) Fang is Assistant Professor of Marketing and James F. ToweyFaculty Fellow, College of Business, University of Illinois atUrbanaChampaign (e-mail: [email protected]). Robert W. Palmatier isAssociate Professor of Marketing, John C. Narver Endowed Professor inBusiness Administration, Michael G. Foster School of Business, Univer-sity of Washington (e-mail: [email protected]). Rajdeep Gre-wal is Irving & Irene Bard Professor of Marketing and Associate ResearchDirector, Institute for the Study of Business Markets Smeal College ofBusiness, Pennsylvania State University (e-mail: [email protected]). Chris-tine Moorman served as associate editor for this article.

Eric (Er) Fang, robErt W. PalmatiEr, rajdEEP grEWal*

b use sses e p fpefe. P ese s sy exe ese sses ef ee s suse ewee e effes f sse ep

ee pes sse e ss ffee sees.Us fu ey e esue-se ew f e f, eus ppse w ese sses e fuee pefeepes ep e euse ese feues efewee e sses e ey ee / pppe ue weepye. Ep esus f w suese us sey e us py f suey f se ese pefe s es we fs epy fusus eep use sses eep use sses. i s, f pefe yeeses e pesee f eepeep ssefus. te effe f fu sees f pefes s ypy ee y se ees.

Keywords: fu ey, esue-se ew, e sses,sse e, sse ep,

Effes f cuse i assecfu Sees FPefe

2011, American Marketing Association

ISSN: 0022-2437 (print), 1547-7193 (electronic) 587

The business enterprise has two and only two basic

functions: marketing and innovation. Marketing andinnovation produce results; all the rest are costs.

(Drucker 1954, p. 144)

This often-cited quotation, offered more than half a cen-tury ago, still rings true among academics and business

managers who recognize the importance of customer and

innovation assets that often emanate from marketing activi-

ties (Srivastava, Shervani, and Fahey 1998). Yet neitherform of assets appears on a firms balance sheet because

they are intangible and often difficult to measure. As a

result, firms continue to struggle to understand and docu-ment the link between assets and firm performance to pro-vide support for the necessity of marketing investments(Srinivasan et al. 2009). Most research has examined theseassets at the firm level, even though a few recent studieshave demonstrated that disaggregating product pipelineportfolios or technical knowledge into depth andbreadth dimensions provides important insights into theunderlying mechanisms for how intangible assets createshareholder value (Grewal et al. 2008; Prabhu, Chandy, and

Ellis 2005). We build on these studies by developing andtesting a model that proposes that how customer and inno-vation assets interact to influence performance depends onboth depth and breadth because these features reflectwhether the assets are likely to create and/or appropriatevalue when deployed. Thus, the research question we studyis how a firms configuration of customer and innovationassets influences the firms financial performance and per-formance variability.

To develop our conceptual model, we rely on two theo-retical perspectives used to understand the link betweenassets and firm performancethe resource-based view


2/17

(RBV) of the firm (Barney 1991; Conner 1991) and config-uration theory (Siggelkow 2002; Vorhies and Morgan2003). The RBV offers a framework for understanding howfirm assets generate sustainable competitive advantages(Barney 1991), and configuration theory provides a per-spective for understanding how the arrangement of organi-zational assets (internal fit) and alignment of those assetswith the environment (external fit) affect performance(Siggelkow 2002); thus, we study environmental dynamismas a moderator.

Consistent with extant research in both theoreticaldomains regarding the importance of the focus and scope ofa firms assets in building sustainable competitive advan-tage, we disaggregate customer and innovation assets intotwo categories that we posit are critical for understandingassets ability to create and/or capture value (Grewal et al.2008; Prabhu, Chandy, and Ellis 2005): depth and breadth.Asset depth refers to the focus and intensity of an asset andis critical for creating value because deep assets tend to berare, unique, and hard to duplicate. Asset breadth capturesthe diversity and scope of the asset, and in addition to creat-ing value, it is especially important for capturing value over

time because broad assets provide diverse, expansive con-texts for extracting value from unique offerings.If the depth and breadth of customer and innovation

assets represent the fundamental building blocks ofassets, firm performance may depend on the internalarrangement or architecture of those assets, as well astheir fit with the environment (Siggelkow 2002; Teece,Pisano, and Shuen 1997). We refer to the architecture ofcustomer and innovation assets as the firms asset configu-ration strategy. Our theoretical framework and empiricalresults suggest that a more is better view toward assets isnot optimal; rather, customer and innovation assets must beviewed from a configuration or portfolio perspective thatrecognizes the underlying mechanisms that operate among

these assets, as well as their fit with the environment.We evaluate the effects of asset configuration strategies

on firm performance and performance variability in twostudies. In Study 1, we test our model using secondary dataand integrate customer depth and breadth measures withinnovation depth and breadth measures, which we then linkto firm performance and performance variability. In Study 2,we test our model using multi-item measures of key constructsin a survey of senior managers, which increases confidencein the underlying theoretical rationale of our predictions.

We find that customer and innovation assets interact andhave differential effects on performance and performancevariability. Therefore, different asset configuration strategiesfunction better to improve performance than to reduce per-

formance variability. Specifically, deep innovationbroad cus-tomer and deep customerbroad innovation asset-leveragingstrategies lead to the best firm performance. In contrast,firm performance variability decreases for asset diversifica-tion (broadbroad) and asset concentration (deepdeep) con-figuration strategies. Our analysis also demonstrates the useof configuration theory as a lens for viewing the influenceof assets on performance. Both internal fit among assets andtheir fit with the external environment affect performanceand performance variability. For example, a deep customerbroad innovation asset configuration strategy has a greaterimpact on performance, and an asset diversification config-

uration strategy has a greater suppression effect on perform-

ance variability when environmental dynamism increases.

These results suggest the effects of some configuration

strategies on outcomes increase in dynamic environments.

Finally, our research extends extant literature on market-ing and technology linkages (Dutta, Narasimhan, and Rajiv

1999; Moorman and Slotegraaf 1999) by examining how

different aspects of customer and innovation assets lead to

different performance outcomes. Our results reveal theimportance of disaggregating assets into the depth and

breadth components of both customer and innovation assets

to understand how marketing activities affect firm perform-ance and performance variability through their interdepen-

dent value-creating and value-capturing mechanisms. Com-

ponent-level interactions would be difficult, if not

impossible, to detect at higher levels of aggregation (e.g.,

firm level). We also build on Vorhies and Morgans (2003)

use of configuration theory, in that we apply a similar per-

spective to understand the effect of internal fit among assetcomponents and their external fit with the environment on

outcomes.

CONCEPTUAL BACKGROUNDLinking Assets and Firm Performance: The RBV and

Configuration Theory

In the past decade, research in marketing has demon-

strated that a firms assetsthat is, any items of value

owned or controlled by a firm capable of creating value

(Barney 1991), such as brands (Rao, Agarwal, and Dahlhoff

2004) and customer relationships (Palmatier 2008), have a

direct influence on financial outcomes. However, focusingsolely on performance is less meaningful for employees and

customers, who are also interested in firm survival and per-

formance variability. Consistent with classic cash flow

arguments and financial theory (Markowitz 1987), we

investigate a firms financial performance and performancevariability.

We attempt to determine the influence of two of the mostimportant assets of a firmcustomer and innovation

assetson firm performance (Drucker 1954; Srivastava,

Shervani, and Fahey 1998). We are particularly interested in

how the configuration of assets affects firms financial out-

comes. We rely on the RBV of the firm to provide insights

into the link between a firms assets and performance; this

well-recognized framework indicates how firm assets gen-erate sustainable competitive advantages (Barney 1991).

Specifically, when firms have assets or bundles of assets

that are valuable, rare, inimitable, and nonsubstitutable

(VRIN), they can generate competitive advantages andabove-average financial performance.

Consistent with extant RBV research (Prabhu, Chandy,and Ellis 2005), we propose that customer and innovation

assets typically have positive effects on firm performance

because, even as stand-alone assets, they often meet VRIN

criteria, but additional refinements could help clarify the

underlying mechanisms by which they create and capture

value. We propose that customer and innovation assets

should be disaggregated across depth and breadth dimen-sions to isolate their underlying value-generating mecha-

nisms and relative effects on performance.

588 joUrnal oF markEting rESEarch, jUnE 2011


3/17

cuse i asse cfu 589

Asset depth refers to the focus and intensity of an assetand is critical for creating value because deep assets tend tobe rare, unique, and hard to duplicate (Szulanski 1996). Forexample, firms with deep customer assets often have, com-pared with the competition, closer customer relationships,greater loyalty, and deeper knowledge about customersdesires and behaviors, which is typically reflected in ahigher share of sales in their served market segments(Palmatier 2008). A close customer-to-firm relationship isdifficult for competitors to copy because it takes time andeffort and because it is difficult for firms to gain access tokey decision makers (Colgate and Danaher 2000). Similarly,firms with deep innovation assets often have unique knowl-edge in their innovation space and technology-specificexpertise regarding product applications and process insights,which in many industries is reflected in a substantial patentpresence in their core technology areas (Prabhu, Chandy,and Ellis 2005). This technology-specific knowledge andrelated link to applications and processes make it difficult(e.g., due to patent protection) for competitors to offer sub-stitutes (Dierickx and Cool 1989).

Asset breadth captures the diversity and scope of the

asset, and in addition to creating value, it is especiallyimportant for capturing value over time because broadassets provide diverse, expansive contexts for extractingvalue from unique offerings (Bierly and Chakrabarti 1996).Asset breadth can help a firm generate a competitive advan-tage through performance enhancements by combiningunique knowledge or capabilities from diverse customersegments or technology areas, which can result in hard-to-duplicate insights (Barney 1991). For example, a firm withbroad customer assets may have unique access to andknowledge across different customer segments, enabling thefirm to identify a common trend or need across multiplesegments that may not be noticeable or even economicallyviable to a single-segment competitor, thus allowing the

firm to be the first to launch a new product across thesemultiple segments.

Moreover, while a firms deep customer or innovationassets can create value by satisfying the VRIN criteriathrough unique combinations of diverse perspectives, assetbreadth may be more important in terms of enhancing per-formance by helping firms appropriate the value createdfrom deep assets. For example, a new product technologycreated by a firm as a result of its deep innovation assets,while beneficial to the firm in the space for which it wasdeveloped, could generate additional benefits if the firmcould sell the new technology to multiple customer seg-ments as a result of its broad customer asset base.

Thus, disaggregating customer and innovation assets across

depth and breadth dimensions both isolates the dimensionof an asset most critical to enhancing performance (depth orbreadth) and helps explain how these dimensions worktogether in creating and appropriating value. More specifi-cally, if we consider asset depth and breadth as underlyingbuilding blocks that represent a firms key value creationand appropriation mechanisms, configuration theory holdsthat performance ultimately depends on their arrangement(Siggelkow 2002). Extending configuration theory to ourasset framework, we suggest that an optimal arrangement ofassets creates (1) internal fit between innovation and cus-tomer asset depth and breadth and (2) external fit between

the arrangement of innovation and customer asset depth andbreadth and the environment. A firm must create both inter-nal coherence, or fit among its innovation and customerassets, to create and appropriate value and external fit withthe environment to capture value because the value creationand value-capturing mechanisms of assets are not independ-ent but rather interact and are contingent on the externalenvironment (Kabadayi, Eyuboglu, and Thomas 2007).

For descriptive and expositional purposes, we identifyfour asset configuration strategies, which we propose havedifferential effects on a firms performance and perform-ance variability (see Figure 1): (1) deep customerbroadinnovation asset-leveraging strategy, (2) deep innovationbroad customer asset-leveraging strategy, (3), asset diversi-fication strategy (broadbroad), and (4) asset concentrationstrategy (deepdeep). However, we also note that assetdepth and breadth are continuous variables, and firms fallon continua across the four configuration strategies.

HYPOTHESES

Effect of Configuration Strategies on Firm Performance

Deep customerbroad innovation asset-leveraging strat-

egy. We propose that deep customer and broad innovationassets should have complementary effects on performance.Deep customer assets lead to an in-depth understanding ofcustomer demands and preferences, which provide firmswith ideas about unique ways to develop and deliver valueto customers. For example, firms with an above-averageshare in a specific market segment (deep customer assets)gain a valuable, rare, and hard-to-duplicate position thatenables them to understand the segments trends and prod-uct needs, as well as gain feedback about new products/ser-vices. Such knowledge benefits generate performanceenhancements through new product insights, quicker adap-tation to changing conditions, and more successful productlaunches.

A firm may leverage or capture knowledge benefits cre-ated from deep customer assets better when it also possessesbroad innovation assets. Firms with a breadth of innovationassets can better evaluate, assimilate, and respond to theopportunities that stem from their deep customer assetsbecause innovation asset breadth provides them with broadand diverse technology portfolios from which they can draw(Sorescu, Chandy, and Prabhu 2003). In turn, a firm candeploy its diverse technology portfolio to create diverseproducts/services, as well as unique or customized products/services that meet idiosyncratic customer needs, which willhelp the firm achieve greater sales, lower price sensitivity,and higher profit margins (Sorescu, Chandy, and Prabhu

2003). In contrast, if a firm learns about some new require-ment of a specific customer segment but lacks the technolo-gies to address this need, it cannot meet this emerging cus-tomer need.

We propose that a firm can leverage deep customerassets, which results in valuable and unique knowledge,when those assets are complemented with broad innovationassets, thus increasing sales and profit margins by matchingcustomers needs with new and improved technology solu-tions. For example, Apple enjoys enhanced performance bypursuing a deep customerbroad innovation asset-leveragingstrategy in which it consistently provides new and unique


4/17

Fue 1EFFEctS oF cUStomEr and innovation aSSEt conFigUration StratEgiES on Firm PErFor

' '' ' ' ' ' ' ' ' '

422.51,".2'6--&,-'

"#$%&'()!*$$(%!!

+(,%-!

.

.

.

.

.

.

.

"#$%&'()!*$$(%!

:)(*+%-!

/00&5*%2&0!*$$(%!

+(,%-!

/00&5*%2&0!*$$(%!

:)(*+%-!

/$-,.0&%'6--&,-'

6--&,-'/.27"#$%1,".2'8,%1,"&-'

/0+#$%)1!!

+10*'2$'!

:;


5/17

cuse i asse cfu 591

products to an entrenched group of existing customers by

leveraging its broad technology portfolio (e.g., hardware,

wireless, software).

Deep innovationbroad customer asset-leveraging strat-

egy. Similarly, deep innovation assets and broad customer

assets should have complementary effects on performance.

Deep innovation assets reflect an intense knowledge and

understanding of specific technologies and provide firms

with rare and hard-to-duplicate insights into technologicalcapabilities, trends, and trade-offs in a specific technology

area. Extant research has expounded on a wide range of

benefits resulting from innovation asset depth, including

enhanced abilities to manage innovation effectively, to

deploy technologies at lower cost, and to create value

(Bierly and Chakrabarti 1996; Prabhu, Chandy, and Ellis

2005). Moreover, innovation asset depth helps firms avoid

investing in projects that will not succeed (i.e., better ability

to filter bad projects).1

A firm might best leverage or capture the benefits of deep

innovation assets when it combines them with a broad and

diverse customer portfolio because deep knowledge of a

specific technology can create a competitive advantage and

performance enhancements only if it produces products and

services that are aligned with actual customer needs. Match-

ing deep innovation assets with broad customer assets

enables the firm to increase its sales by applying its deep

innovation assets to match demand from diverse customer

segments and to reduce its costs and increase profit margins

by exploiting common technology assets across different

segments. In contrast, a firm that pushes the boundary in a

specific technology area and develops a new offering with

unique performance capabilities may be unable to extract

value (sales and profits) if it has only a narrow portfolio of

similar customers, who may not need the application of the

unique technology. Thus, we propose that a firm can lever-

age its deep innovation assets, which results in valuable andunique technological insights, when it complements them

with broad customer assets by matching its specific technol-

ogy portfolio to a broad, diverse set of customers. For

example, Canon pursues a deep innovationbroad customer

asset-leveraging strategy in which it offers better, unique

products by leveraging its deep sensor technology knowl-

edge across a broad and diverse set of customers in the

copier, digital camera, and industrial product markets.

Both deep customerbroad innovation and deep innova-

tionbroad customer asset-leveraging strategies represent

complementary configurations, in which the internal fit

between different components of assets (i.e., rare and hard-

to-duplicate deep assets with multiple contexts throughbroad assets) work together to generate and capture value;

thus, firms that pursue these configuration strategies should

perform better than the firms that pursue other configura-

tion strategies.

H1: The interaction between customer asset depth and innova-

tion asset breadth increases firm performance.

H2: The interaction between customer asset breadth and inno-

vation asset depth increases firm performance.

Effect of Configuration Strategies on Firm Performance

Variability

Asset diversification strategy. Research supports thepremise that performance variability decreases as firmsbroaden either customer or innovation assets because firmshedge risks through such diversification (Evans 1991). Forexample, a loss of sales to one customer can be offset by anincrease in sales to another customer; similarly, a failure in

one technology area can be mitigated by a success in anotherarea. However, we advance this argument by proposing thatfirms gain additional variability suppression benefits whenthey broaden different asset bases rather than the same assetbase. We refer to broadening across both customer and tech-nology areas as an asset diversification strategy.

The interaction between customer and innovation breadthmay suppress performance variability because the two assetclasses are relatively independent, so changes in customersand innovations are more likely to be compensatory thanchanges within the same asset class (Markowitz 1987). Forexample, Microsoft experiences minimal performance vari-ability because it uses an asset diversification strategy withboth broad customer and broad innovation assets. A funda-

mental shift in information technology could negativelyaffect much of Microsofts technology portfolio, but thecompensatory diversification benefits from Microsoftsbroad customer base would be relatively unaffected.

Asset concentration strategy. Similarly, deep customerand deep innovation assets can work together to reduce per-formance variability. Increasing asset depth provides firmswith more knowledge about and insight into future trends,which helps them make better decisions by anticipating andadapting to changing conditions, resulting in less performancevariability (Srivastava, Shervani, and Fahey 1998). How-ever, deep insight into just one asset area (e.g., customer)offers no protection against unforeseen changes in a differ-ent area (e.g., technology), so firms remain susceptible todramatic performance shifts. However, if a firm has bothdeep customer and deep innovation assets, it possessesinsight and foresight for both major sources of uncertainty.We propose that firms that can monitor, evaluate, and trian-gulate across both sources of uncertainty are better at antici-pating and adapting, which should smooth their perform-ance variability by improving their decision making. Forexample, firms often develop new products and services bypredicting and matching future customer trends with thelikely trajectory of different technologies (Christensen1997). The likely success of this match of external customerneeds with internal technologies depends mainly on theaccuracy and depth of the firms relevant knowledge across

the two domains. We expect an interactive effect: Specifi-cally, with external and internal insights, the firms offeringis more likely to succeed, but good technology insights caneasily be undermined by poor customer insights, and viceversa (Day and Nedungadi 1994).

A deepdeep asset configuration strategy, or asset con-centration strategy, mirrors Mark Twains risk-reductionadvice: Put all your eggs in the one basket andwatch thatbasket (Stevenson 1948, p. 672). By reducing risk throughin-depth attention, the firm can anticipate and minimize theeffects of change. For example, Boeing focuses its innova-tion assets predominantly on aerospace technologies to1We thank an anonymous reviewer for this suggestion.


6/17

serve a narrow group of airline customers. Thus, Boeingdoes not reduce risk by diversifying across customers andtechnologies but rather centers its attention on and uses itsresultant insights for anticipating and adapting to potentialchanges in either.2

H3: The interaction between customer asset breadth and inno-vation asset breadth reduces firm performance variability.

H4: The interaction between customer asset depth and innova-

tion asset depth reduces firm performance variability.

Effect of Industry Dynamism on the ConfigurationStrategies and Firm Outcome Links

We capture the effects of external environmental changesusing industry dynamism, which we define as the extentto which industry demand changes rapidly and unpre-dictably (Jaworski and Kohli 1990). Configuration theoryargues that a strategys effect on performance depends onits fit with the external environment (Siggelkow 2002).Similarly, the RBV suggests that the effects of specific assetbundles or asset configurations on performance are bestleveraged in dynamic environments (Teece, Pisano, andShuen 1997).

The positive effect of deep customerbroad innovationand deep innovationbroad customer asset-leveragingstrategies on firm performance should be greater in dynamicthan in stable environments. As we have argued, the posi-tive effect of deepbroad complementary asset configura-tions stems from the combination of value created from theknowledge benefits of deep assets and the value extractionbenefits of broad assets. Because dynamic industries arecharacterized by frequent, difficult-to-predict changes incustomer needs and technologies, dynamic environmentsprovide firms that have deepbroad asset configurationswith more opportunities to match their deep knowledgeacross broad contexts to enhance performance. In a stable

industry, even firms without deep insights can copy the mar-ket leaders offering, so deepbroad configurations offerrelatively small advantages in stable markets. However, indynamic environments with constantly changing customerneeds, there are many opportunities for leveraging deepinsights across diverse situations (Jaworski and Kohli1990), which provide an advantage to firms withdeepbroad asset configurations because they can achievemore new offerings from unique customertechnologymatches.

The variability suppression benefits described for firmsthat use an asset diversification strategy also should payhigher dividends in dynamic than in stable environments.Dynamic environments present more and greater magni-

tudes of contingencies than stable environments, so build-ing redundancies through broad customer and broad inno-vation portfolios to reduce performance swings should bemore significant in dynamic industries (Evans 1991). Com-

pared with a stable environment, in a dynamic environment,a firm without deep insight into customers or technologiesmay be blindsided by rapidly changing customer needs ortechnology trends. We propose that the variability suppres-sion benefits of the asset concentration strategy (deepdeep)derived from in-depth knowledge and attention, whichallows the firm to anticipate and minimize the effects ofchange, is more effective as industry dynamism increases.

H5: The positive effect of (a) deep customerbroad innovationand (b) deep innovationbroad customer asset-leveragingstrategies on firm performance increases with industrydynamism.

H6: The negative effect of (a) asset diversification and (b) assetconcentration strategies on firm performance variabilityincreases with industry dynamism.

STUDY 1

We test our conceptual model in two complementaryempirical studies. In the first study, we collected data froma variety of secondary sources, including COMPUSTAT, theCenter for Research in Security Prices (CRSP), and patentdata, to capture the depth and breadth measures of customer

and innovation assets, as well as measures of financial per-formance and variability. The sampling frame includedfirms that compete predominantly in the high-tech indus-tries, such as software development (Standard IndustrialClassification [SIC] 7374), semiconductor (SIC 3672),computers and related products (SIC 35713577), and elec-tronic equipment (SIC 3600), during 1990 to 2005.

In the second study, we measured the key constructsusing multi-item measures in a survey of senior managersfrom high-tech firms. Thus, while both studies test the sameconceptual model (Figure 1), Study 1s use of secondarydata increases the external validity of our results, whileStudy 2s use of multi-item scales increases the internal

validity of our findings. In Table 1, we describe the con-structs, definitions, measures, and data sources for bothstudies.

Measures

Firm performance and performance variability. To mea-sure firm performance and performance variability, werelied on stock prices, which are forward looking, integratemultiple performance dimensions (sales, cash flow), and aredifficult for managers to manipulate (Fang, Palmatier, andSteenkamp 2008). In particular, we used stock marketreturns as a measure of firm performance and idiosyncraticrisk as a measure of performance variability (Srinivasan andHanssens 2009). In equilibrium, systematic risk reflects the

extent to which a stocks return changes when the overallmarket changes; it cannot be eliminated through diversifica-tion. The amount of risk that remains after accounting forsystematic risk equals idiosyncratic risk.

With Equation 1, we used a four-factor model to calcu-late shareholder returns using daily stock return data (Srini-vasan and Hanssens 2009). The four-factor capital assetpricing model recognizes many factors that may affect stockvaluations, such as differences in returns between large-capand small-cap portfolios (size risk factor) and between highversus low book-to-market stocks (value risk factor), as wellas a systematic risk factor (beta) and a momentum factor:


2We only offer hypotheses for the effect of configuration strategies onperformance and performance variability when we have a specific theoreti-cal rationale to support an interaction effect. For example, we do not havea theoretical rationale to expect deepbroad or broaddeep asset configura-tions to influence performance variability, because there is no mechanismfor leveraging the variability-reducing effects of diversification and infor-mation access across these two configurations. However, for completeness,we report the results of these nonhypothesized interactions.


7/17

cuse i asse cfu 593

(1) Ri,t Rf,t = a0 + b (Rm,t Rf,t) + spSMBt + hpHMLt

+ upUMDt+ ei,t,

where Ri,t is the stock return for firm i in month t, Rf,t is therisk-free rate of return in month t, Rm,t (market factor) is the

average market rate of return in month t, SMBt (size factor)is the return on a value-weighted portfolio of small stocks

less the return of big stocks in month t, HMLt (value factor)

is the return on a value-weighted portfolio of high book-to-

market stocks less the return on a value-weighted portfolioof low book-to-market stocks at month t, and UMDt(momentum factor) is the average return on two high-prior-

return portfolios less the average return on two low-prior-return portfolios at month t.

The data source for the four-factor financial model wasKenneth Frenchs Web site (http://mba.tuck.dartmouth.edu/

pages/faculty/ken.french/). We obtained data for Rit from

the University of Chicagos CRSP database. Thus, we esti-mated a four-factor model for each firm in each year to cap-

ture shareholder returns as a0 (alpha) and idiosyncratic riskas the variance of ei,t.

Customer assets. To determine the breadth (diversity and

scope) and depth (focus and intensity) of a firms customer

assets, we used data from the COMPUSTAT Business Seg-

ment database, which provides firm sales revenues for dif-

ferent business operating segments, defined by their four-

digit SIC codes. We used five-year windows to calculate the

customer asset measures. For example, customer assets for

2005 depended on the firms sales revenues in its specific

business operating segments and the overall industry sales

revenues of the pertinent four-digit SIC for 20012005.The calculation of customer asset breadth, as we show in

Equation 2, uses pj,c = nj,c/Snj,c to represent the proportionof a firms sales revenue in segment j relative to overall

sales revenue. We squared each p and took the sum over all

business segments. Customer asset breadth equaled 0 when

a firms sales all occurred in a single business segment (low

customer asset breadth) but moved toward 1 when the firm

spread its sales over many business segments (high cus-

tomer asset breadth), indicating greater diversity and scope

in the firms customer portfolio:

te 1

conStrUctS, mEaSUrEmEntS, and data SoUrcES

Constructs Definitions Study 1 Measures (Data Sources) Study 2 Measures (Data Sources)a

Performance Overall firm performance Shareholder return (CRSP and Kenneth Frenchdata library)

Three-item measure of firm profit margin/returnon assets/return on equity (customer survey)

Performance variabil ity Variabili ty in firmperformance

Idiosyncratic risk (CRSP and Kenneth Frenchdata library)

Three-item measure of stability of firm profitmargin/return on assets/return on equity

(customer survey)

Customer asset breadth Diversity and scope of thefirms customer portfolio

Entropy measure of firm sales revenue indifferent business segments (COMPUSTAT

Business Segment)

Four-item measure of customer-asset breadth(customer survey)

Innovation asset breadth Diversity and scope of thefirms technology portfolio

Entropy measure of granted patents in differentnational classes (Delphion Patent Database)

Four-item measure of innovation-asset breadth(customer survey)

Customer asset depth Focus and intensity of thefirms customer portfolio

Average ratio of sales revenue to industry salesacross different business segments(COMPUSTAT Business Segment)

Four-item measure of customer-asset depth(customer survey)

Innovation asset depth Focus and intensity of thefirms technology portfolio

Average ratio of granted patents to all patents ineach national class across different national

classes (Delphion Patent Database)

Four-item measure of innovation-asset depth(customer survey)

Industry competitiveness Intensity of competitiverivalry within an industry

Herfindahl index of firms primary industrysales revenue (COMPUSTAT)

Five-item measure adapted from Jaworski andKohli (1993) (customer survey)

Industry dynamism Intensity of change andenvironmental turbulence

within an industry

The standard deviation of sales in firmsprimary industry across the prior five years,divided by mean value of industry sales for

those years (COMPUSTAT)

Five-item measure adapted from Jaworski andKohli (1993) (customer survey)

Industry growth Rate of sales growth of anindustry

Slope coefficient of sales in firms primaryindustry across the prior five years, divided by

mean value of industry sales for those years(COMPUSTAT)

Three-item measure of industry growth(customer survey)

Marketing intensity Relative level of spendingon sales and marketing

within a selling firm

Marketing expenditure/total asset(COMPUSTAT)

Percentage annual marketing investment to firmtotal assets (customer survey)

R&D intensity Relative level of spendingon R&D by the firm

R&D expenditure/total asset (COMPUSTAT) Percentage annual R&D investment to firm totalassets (customer survey)

Firm size Number of peopleemployed by the firm

Log-transformation of number of employees(COMPUSTAT)

Log-transformation of number of employees(customer survey)

aSee the Appendix for the scale items used in the survey.


8/17

We measured customer asset depth as the average ratio ofa firms sales revenue to the industrys overall sales revenueacross each operating segment. For example, Hewlett-Packard operates in multiple segments (imaging and print-ing group [SIC 3577], personal systems group [SIC 3571],

services [SIC 7373], and financial services [SIC 6141]). Foreach of these segments, we calculated the ratio of Hewlett-Packards revenue to the overall sales revenues in the seg-ment across all firms and then averaged these ratios acrossits multiple segments. Thus, the measure provided an indi-cation of the focus or intensity of the firms customer assets.

Innovation assets. To measure a firms innovation assetdepth and breadth, we relied on a secondary data source thatsummarized patents granted to firms. In high-tech indus-tries, firms often use patents to protect their innovationknowledge; therefore, patents offer good proxies of innova-tion assets. We obtained patent data from Thomson Scien-tific Delphion. Furthermore, we considered patents grantedby the U.S. Patent and Trademark Office from 1990 to

2005. We used a five-year window to measure the firmsinnovation assets, so to capture innovation assets in 2005,we examined the firms granted patents filed between 2001and 2005, inclusive. Finally, to construct a meaningfulmeasure of innovation assets, we deleted firms with insuffi-cient granted patents (i.e., fewer than ten).

To calculate innovation asset breadth, we relied on prin-ciples underpinning concentration index measures, such asthe Herfindahl index, and the granted patents nationalclasses. The patent record contained primary nationalclasses, which the U.S. Patent and Trademark Office uses tocategorize patents into different subject areas. For firm isbreadth measure in year t, we calculated the number ofnational classes (c) of patents it filed between (t 4) and t

and denoted the number of times its patents fell intonational class j as nj,c (j = 1, , c). Then, pj,c = nj,c/Snj,c rep-resented the proportion of national class j compared withthe cumulative occurrence of all patents. We squared each pand took the sum over all national classes. Because we wereinterested in an index of breadth, not concentration, we sub-tracted this sum from 1 (see Equation 3). Innovation assetbreadth equaled 0 when a firms granted patents all occurredin a single class and moved toward 1 when the firm spreadits patents over many national classes. Similar to customerasset depth, the innovation asset depth measure used theaverage ratio of a firms filed patents in each national classto the overall number of patents in that class across allnational classes.

As a robustness check for using patent data as an indica-tor of innovation assets, we collected data about firm innov-ativeness from Fortunes Most Admired Company list.We were able to match 432 observations with our data setand run regressions of innovation asset depth and breadth,firm size, profits, industry dynamism, and competition, onfirm innovativeness obtained from the Fortune database.The results indicated that both innovation depth and breadth

( ) .,3 12

1

Innovation asset breadth pj cj

c

=

=

( ) .,2 12

1

Customer asset breadth pj cj

c

=

=

had positive effects (p < .01) on firm innovativeness,increasing our confidence in our measures.

Industry dynamism. To calculate industry dynamism, wedivided the standard deviation of sales in the firms productindustry (four-digit SIC code) across the prior five years bythe mean value of industry sales for those years (Fang,Palmatier, and Steenkamp 2008).

Control variables. We included several time-varying con-trol variables in our model. At the industry level, we con-trolled for industry competitiveness, growth, and size. Forindustry competitiveness, we used a Herfindahl index, inwhich we squared each firms market share and took thesum over all firms in the industry. Because we were inter-ested in industry competitiveness, not concentration, wesubtracted the sum from 1 for our measure. Industry growthrepresented growth in sales for the overall industry duringthe previous five years. Finally, we measured industry sizeas the log-transformation of overall sales in the firms pri-mary industry.

At the firm level, we controlled for marketing andresearch-and-development (R&D) intensity, firm size, andfirm profitability. Marketing intensity was the firms mar-

keting expenditure divided by its total assets. For R&Dintensity, we divided the firms R&D expenditures by itstotal assets. Firm size was the log-transformation of thenumber of employees; firm profitability was the return onsales.

Analysis

To link a firms customer and innovation assets to per-formance and performance variability, we used a one-yeartime lag between measures to control for endogeneity(Boulding and Staelin 1995). We obtained 2857 observa-tions that represented an unbalanced, cross-sectional (n =348) time series (t = 11). (Because we used five-year peri-ods to create the innovation and customer assets and a one-

year lag, we lost six years of data.) In Table 2, we summa-rize the descriptive statistics for all measures, pooled acrossfirms and time. We mean-centered all variables to aid inter-pretations. The variance inflation factors for all variableswere less than 3.5.

Our data reveal a panel structure, with a time series ofobservations for multiple firms, so we paid special attentionto several estimation issues. First, shareholder return andidiosyncratic risk may be nonstationary, which could biasestimates. However, the statistically significant panel unitroot test (c2 = 12.12,p < .01) indicated that shareholderreturn was stationary (Cameron and Trivedi 2005). Idiosyn-cratic risk also was stationary, according to the statisticallysignificant panel unit root test (c2 = 19.15,p < .01). Sec-

ond, we checked for first-order serial correlation in errors;the tests rejected the hypothesis of no first-order serial cor-relation (p < .01) for the estimations of shareholder returnsand idiosyncratic risk, in support of our inclusion of anautoregressive (AR1) disturbance term. Third, we checkedfor cross-sectional dependence among error terms byemploying a CD test (for shareholder return, 24.46,p < .01;for idiosyncratic risk, 19.20,p < .01) and Freess test (forshareholder return, 6.87,p < .01; for idiosyncratic risk, 5.63,p < .01), both of which rejected the null hypothesis of cross-sectional dependence. Consistent with Chandy, Prabhu, andAntia (2003) and Morgan and Rego (2006), we also com-



9/17

te 2dEScriPtivE StatiSticS and corrElationS

M SD Correlations

Constructs Study 1 Study 2 Study 1 Study 2 1 2 3 4 5 6 7 8

1. Performance .0005 4.88 .0022 1.02 1.00 .22 .24 .20 .17 .19 .09 .12. Performance variability .0009 4.12 .0016 1.14 .16 1.00 .16 .21 .17 .23 .14 .03. Customer asset breadth .34 4.29 .22 1.11 .04 .09 1.00 .09 .39 .22 .09 .14. Innovation asset breadth .49 4.03 .18 .98 .08 .02 .03 1.00 .11 .42 .04 .05. Customer asset depth .05 4.55 .08 1.03 .13 .03 .29 .05 1.00 .18 .11 .06. Innovation asset depth .02 5.01 .03 1.32 .01 .03 .06 .23 .02 1.00 .05 .07. Industry competitiveness .72 5.35 .23 1.27 .02 .04 .03 .06 .11 .08 1.00 .28. Industry dynamism .14 5.25 .10 1.15 .01 .03 .01 .03 .01 .07 .14 1.09. Industry growth .13 5.01 .19 1.17 .02 .04 .08 .09 .06 .03 .10 .0

10. Marketing intensity .19 .10 .30 .38 .02 .20 .07 .10 .03 .03 .02 .011. R&D intensity .14 .09 .22 .24 .07 .18 .03 .02 .19 .17 .04 .012. Firm size 2.46 2.03 1.01 .44 .15 .50 .25 .15 .19 .21 .09 .0

Notes: Correlations for Study 1 (2) are reported below (above) the diagonal. Study 1 (2) r > .03 (.13), significant atp < .05.


10/17

puted the White test statistic and BreuschPagan statistics;both tests fail to reject the null hypotheses of no het-eroskedasticity, indicating that heteroskedasticity is not aproblem for either performance or performance variability.3

Fourth, in estimating the time-series cross-sectional data,we controlled for unobserved firm-specific effects. Includ-ing firm-specific effects also reduces serial correlation in theerrors (Cameron and Trivedi 2005). Therefore, we conductedHausmans test to determine whether we should model theunobserved effects as fixed or random effects. Hausmanstest was significant (p < .05), so we estimated the fixed-effects model for performance and performance variabilityusing a least square dummy variable estimator. The specifi-cation of this fixed effects AR1 model is as follows:

(4) Performance/Performance variabilityi,t + 1 =

u + ai + b1Customer asset breadthit + b2Innovation asset breadthit

+ b3Customer asset depthit + b4Innovation asset depthit

+ b5Customer asset depthit Innovation asset breadthit

+ b6Innovation asset depthit Customer asset breadthit

+ b7Customer asset breadthit Innovation asset breadthit

+ b8Customer asset depthit Innovation asset depthit

+ b9Industry dynamismit Customer asset depthit

Innovation asset breadthit + b10Industry dynamismit

Innovation asset depthit Customer asset breadthit

+ b11Industry dynamismit Customer asset breadthit

Innovation asset breadthit + b12Industry dynamismit

Customer asset depthit Innovation asset depthit

+ b13Industry competitionit + b14Industry dynamismit

+ b15Industry growthit + b16Marketing intensityit

+ b17R&D intensityit + b18Firm sizeit + eit,

where u is the overall constant; ai are firm-specific fixedeffects; eit = error term, such that eit = rei(t 1) + vit, and vitis iid normal distributed with a mean of zero.

Results

We report the results of Study 1 in Table 3. For both per-formance and performance variability, as hypothesized inter-actions are added to the models, incremental R-square indi-cates a significant improvement in variance explained (i.e.,Models 13; Models 46). Furthermore, for both models,

we found support for performance persistence as indicatedby the autocorrelation coefficient for performance (r = .21,p < .01) and performance volatility (r = .21,p < .01). Withregard to the effects of asset configuration strategies on per-formance, the results of Model 3 indicated that the inter-action between deep customer and broad innovation assetshad positive effects on firm performance (b = .14,p < .10),providing marginal support of H1. We found support for H2

because the interaction between deep innovation and broadcustomer assets had a positive effect on firm performance(b = .16,p < .05). In terms of the effects of configurationstrategies on firm performance variability, the results inModel 6 indicated that the interaction between deep cus-tomer and deep innovation assets had a negative effect onfirm performance variability (b = .18,p < .05), in supportof H4. However, we must reject H3 because the interactionbetween broad innovation and broad customer assets wasnot significant.

Regarding the moderating effect of industry dynamism,we found support for H5a; the interaction between the deepcustomerbroad innovation configuration strategy andindustry dynamism positively affected the performance (b =.20, p < .01). However, H5b did not receive support; theinteraction between the deep innovationbroad customerconfiguration strategy and industry dynamism was not sig-nificant. We found support for H6a in the increased negativeeffect of an asset diversification strategy on performancevariability when dynamism increased (b = .25,p < .01). Incontrast, the interaction between asset concentration strategyand dynamism was not significant, in conflict with H 6b.

Overall, our final models explain approximately 10.1% ofthe variance in firm performance (shareholder return) andapproximately 42.1% in performance variability (idiosyn-cratic risk). Note that our model explains about four timesmore variance in performance variability versus perform-ance, which, while consistent with previous research (e.g.,Tellis and Johnson 2007), suggests that shareholder returnis affected by many other factors besides idiosyncratic risk.Further research should explore additional variables toincrease the explanatory power of these models.

Robustness Analysis

To enhance confidence in our results, we conducted severalrobustness tests to evaluate (1) a two-year lag between inde-

pendent and dependent variables, (2) alternative measures offirm performance and performance variability, (3) an alterna-tive measure of customer asset depth, and (4) two alternativemeasures of innovation asset depth and breadth. As we detailin the Web Appendix (see http://www.marketingpower.com/jmrjune11), the overall patterns held across these addi-tional analyses, adding to our confidence in the results.

STUDY 2

Primary Survey Data Collection

As a supplement to Study 1, in Study 2, we relied on pri-mary survey data to validate our model. Rather than usingsecondary data such as sales and firm patents as proxies for

customer and innovation assets, we measured the key con-structs using multi-item measures in a survey of senior man-agers to increase confidence in the underlying theoreticalrationale of our predictions. The small sample size and largenumber of parameters to be estimated means that we cannottest the moderating role of dynamism on the linkage betweenasset configurations and performance.

The sampling frame consisted of 450 firms in a variety ofhigh-tech industries. We obtained the names and contactinformation of senior executives from two commercialmailing lists and then contacted and prequalified each exec-utive by telephone. Of this initial list, 268 executives met


3As Greene (2003) suggests, we also used a White heteroskedasticityconsistent covariance estimator with ordinary least squares estimation tocontrol for heteroskedasticity; the results are consistent.
http://www.marketingpower.com/jmrjune11http://www.marketingpower.com/jmrjune11http://www.marketingpower.com/jmrjune11http://www.marketingpower.com/jmrjune11


11/17


12/17

the prescreening criteria and agreed to participate. Eachqualified executive received a cover letter, a survey ques-tionnaire, and a stamped return envelope. After follow-uptelephone calls and a second wave of mailing two weeksafter the first wave, we received 182 responses, of which167 were usable after we eliminated responses with toomany missing values (more than 5%) or inadequate levelsof informant knowledge or involvement in the firms strate-gic decision processes (less than 4 on a seven-point scale).The average knowledge level of respondents was 6.1, andthe average involvement level was 5.7, both on seven-pointscales. Our response rate was 37.1%, and the respondentsincluded vice presidents of marketing, senior vice presi-dents, senior marketing managers, project managers, andproduct managers. We found no significant differencesbetween early versus late respondents.

Measurement

The firm performance measure assessed return on assets,return on equity, and profit margins; the performance vari-ability measure included the stability of the firms return onassets, return on equity, and profit margins during the previ-

ous five years (reverse coded). In the Appendix, we providea complete list of the measurement items for Study 2.We used four seven-point Likert scale items, anchored by

strongly disagree (1) and strongly agree (7), to assesscustomer and innovation asset depth and breadth. We againincluded several control variables. First, we controlled forindustry competiveness, dynamism, and growth with multi-item measures. Second, we asked the respondents to indi-cate the percentage of their marketing and R&D expendi-tures relative to their firms overall assets to control formarketing and R&D intensity. Third, we controlled for firmsize.

We assessed the validity of our multi-item measures intwo steps. First, we estimated our measurement model by

restricting each item to load on its a priori specified factorand allowing the factors to correlate. The overall modelindicated acceptable fit indexes: c2(108) = 243.04, normedfit index = .95, goodness-of-fit index = .93, confirmatory fitindex = .96, and root mean squared error of approximation =.04. As we report in the Appendix, each factor loading waspositive and significant at the .01 level, and all constructsindicated acceptable coefficient alphas. Thus, our measuresdisplayed acceptable unidimensionality and convergentvalidity.

Second, we used a series of nested confirmatory factormodel comparisons between pairs of constructs in themodel to assess whether chi-square differences existedwhen we freed the correlations between the latent variables

compared with when we constrained them to 1.0. The vari-ous chi-square difference tests were all significant and pro-vided evidence of discriminant validity. In addition, theaverage variance extracted was greater than the squared cor-relation between the two constructs, in further support ofdiscriminant validity. Because both our independent anddependent variables came from the same source, commonmethod bias could pose a potential threat (Podsakoff et al.2003); thus, we conducted Harmans single factor test. Thelargest factor accounted for 28% of the variance; further-more, the rotated factor loading matrix showed that theitems for each latent construct loaded on a single factor

while items for different constructs loaded on different fac-tors (e.g., the four items for customer asset breadth loadedon one latent construct, the items for customer asset depthloaded on the second latent construct). We also confirmedthat none of the significance levels of correlations amongindependent and dependent variables changed when we par-tialed out common method bias using an unrelated markervariable (Lendell and Whitney 2001). Thus, on the basis ofthese tests, we do not believe that common method bias is aserious issue. Note that our multimethod approach helpsfurther alleviate this common methods concern.

Analysis and Results

We used a moderated regression analysis to test ourhypotheses with least squares; we mean-centered allvariables to improve the interpretability of regression coef-ficients. We verified the standard regression assumptionsusing the RESET test and heteroskedasticity using theBreuschPagan test. In addition, the variance inflation fac-tor statistic was less than 4.

We summarize the results from Study 2 in Table 4. First, theinteraction between customer asset depth and innovation asset

breadth (deep customerbroad innovation asset-leveragingstrategy) was positively associated with firm performance(b = .21, p < .05), in support of H1, and the interactionbetween innovation asset depth and customer asset breadth(deep innovationbroad customer asset-leveraging strategy)was positively associated with performance (b = .27,p

Documents

Effects of Customer and Innovation Asset Configuration Strategies on Firm Performance