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ORIGINAL EMPIRICAL RESEARCH

The desired level of market orientation and businessunit performance

Michael Song & Mark E. Parry

Received: 23 August 2007 /Accepted: 7 August 2008 /Published online: 8 January 2009# Academy of Marketing Science 2008

Abstract Existing studies of market orientation havehypothesized that the strength of the market orientation/ performance relationship depends on environmental varia- bles such as market turbulence, technological turbulence,and competitive intensity. To date most empirical studieshave failed to confirm these hypotheses; however, thesestudies (1) assumed that performance is a linear function of the achieved level of market orientation and (2) testedwhether environmental uncertainty moderates this relation-ship. A complementary explanation for the impact of environmental variables on a firm ’s market orientationarises from studies of organizational behavior that link theneed for coordination and control to environmental uncer-tainty and organizational strategy. Building on this per-spective, the authors argue that (1) environmentaluncertainty influences the desired level of market orienta-tion and (2) the gap between the desired and achievedlevels of market orientation influence business unit perfor-mance. The authors test these hypotheses with datacollected from multiple respondents in 308 US firms. Thedata analysis confirms that the desired level of market orientation is a function of market turbulence, competitiveintensity, technological turbulence, and innovation strategy.

In addition, the desired level of market orientation positively influences the achieved level. Finally, when theachieved level of market orientation is less than the desiredlevel, business unit performance is a negative function of the gap between the desired and achieved levels of market orientation.

Keywords Market orientation . Marketing strategy .Innovation strategy . Environmental moderators

In their seminal study of market orientation, Kohli andJaworski (1990) argued that the strength of the market orientation – performance relationship depends on environ-mental variables such as market turbulence, technologicalturbulence, and competitive intensity. Empirical tests of these environmental moderator hypotheses have yieldedmixed results. In a recent meta-analysis of the market orientation literature, Kirca et al. ( 2005) concluded that there was “ insignificant empirical evidence ” to substantiatethe hypotheses that market turbulence, competitive intensi-ty, or technological turbulence moderate the market orientation – performance relationship.

Importantly, prior research has examined the impact of environmental uncertainty under the assumption of aspecific functional relationship between market orientationand performance. In particular, existing studies (1) assumethat performance is a linear function of the achieved levelof market orientation and (2) test whether environmentaluncertainty moderates this relationship. In this paper weexamine an alternative set of linkages between environ-mental uncertainty and market orientation. We argue that environmental uncertainty influences managerial percep-tions of the desired level of market orientation, which inturn influences both the achieved level of market orienta-tion and business unit performance. Our framework implies

J. of the Acad. Mark. Sci. (2009) 37:144 – 160DOI 10.1007/s11747-008-0114-0

Michael Song and Mark E. Parry contributed equally to this research.

M. Song318 Bloch School, University of Missouri — Kansas City,5110 Cherry Street,Kansas City, MO 64110-2499, USAe-mail: songmi@umkc.edu

M. E. Parry ( * )321 Bloch School, University of Missouri — Kansas City,5110 Cherry Street,Kansas City, MO 64110-2499, USAe-mail: parryma@umkc.edu

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that managers should first assess the desired level of market orientation, based in part on the environmental uncertaintythat surrounds their firms, and then design their organiza-tions to achieve that desired level.

To test this reasoning, we use a two-stage data collection process in which we first obtain perceptions of the achievedlevel of market orientation from SBU managers and thencollect perceptions of the desired level of market orientationfrom each SBU manager ’s superior. Our analysis of thesedata indicate that the desired level of market orientation hasa significant incremental impact on the achieved level of market orientation. In addition, the gap between the desiredand achieved levels of market orientation adds significant explanatory power to models that regress performance onthe achieved level of market orientation. We also find that the desired level of market orientation is a positive functionof market turbulence, competitive intensity, technologicalturbulence, and innovation strategy. These results support the hypothesis that environmental uncertainty influences performance through its impact on managerial perceptionsof the desired level of market orientation.

The desired level of market orientation

Kohli and Jaworski ( 1990) define market orientation as “ theorganizationwide generation, dissemination, and respon-siveness to market intelligence ” (p. 3; see also Narver andSlater 1990). A significant body of empirical work suggeststhat market orientation has a positive impact on firm performance. In their recent meta-analysis of the market orientation literature, Kirca et al. ( 2005) concluded that,“ even though the implementation of market orientation maydemand resources, it generates profits over and above thecosts involved in its implementation while growingrevenues ” (p. 37).

To date, scholars have focused on the achieved level of market orientation, i.e., the set of behaviors through whichfirms collect, disseminate, and respond to market informa-tion. In contrast to this achieved level, the desired level of market orientation can be defined as the market orientationlevel that managers believe will maximize the performanceof their firm or business unit. Our focus on managerial beliefs is consistent with research on managerial mentalmodels, which emphasize the importance of the ways inwhich managers perceive market environments and processmarket information. As Day and Nedungadi ( 1994, p. 31)explain, “market environments are not unambiguous real-ities.” Instead, constructs like markets and competitiveforces are “ abstractions given meaning through processes of selective search and attention, selective perception, andsimplification ” (p. 31). These abstractions help managersdeal with overwhelming amounts of information and

manage environmental uncertainty, but they also reflect cognitive biases such as the tendency to rely on informationthat is easily available and consistent with existing beliefs(DeSarbo et al. 2006).

Given the selective nature of human information processing, managerial representations of the optimal levelof market orientation (i.e., the level that maximizes firm or SBU performance) are incomplete. While managers maynot have formed complex models of the market orientation – performance relationship, they do have an understanding of their company ’s current level of market orientation and thechanges needed to enhance performance. These perceptionsreflect the understanding executives have of customers andcompetitors. In summary, the mental models that managershave of the optimal level of market orientation areincomplete, reflecting the limits of managerial experience,attention, and processing capacity. For this reason, we focusour discussion on the manager ’s desired level of market orientation.

A review of the market orientation literature suggeststhat environmental uncertainty will influence managerial perceptions of the desired level of market orientation (Slater and Narver 1994; Kirca et al. 2005). For example, Kohliand Jaworski ( 1990, p. 15) observed that, “ under conditionsof limited competition, stable market preferences, techno-logically turbulent industries, and booming economies, amarket orientation may not be strongly related to business performance. ” The importance of environmental variablesto the desired level of market orientation is underscored bydiscussions of the impact of environmental instability onthe firm. Instability arises from unanticipated changes inconsumer preferences, competitor activities, government regulations, or technology. Such changes increase theimportance of adaptive skills; a failure to adapt risks theloss of current customers and a diminished capacity toexploit new opportunities (Lusch and Luczniak 1987).Firms that attempt to discern, anticipate, and react to suchchanges face challenging information processing tasks(Achol et al. 1983; Mintzberg 1979). To improve informa-tion processing in uncertain environments, firms developspecialized, differentiated departments that increase theneed for interdepartmental coordination and organization(Lawrence and Lorsch 1969; Khandwalla 1974). Suchorganizational responses tend to increase the importanceof disseminating market intelligence across functional boundaries.

Resource dependency theory has argued that functionalinterdependence rises when task uncertainty and complex-ity increase (Pfeffer and Salancik 1978). The perceivedimportance of information sharing is further enhanced byindividual difficulties in processing new information andgenerating relevant action alternatives (Aaker 1984; Weitzet al. 1986). These difficulties increase the perceived need

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for information dissemination and coordinated responses(Olson et al. 1995). Taken together, these argumentssuggest that the need for collecting, disseminating, andresponding to market intelligence (and thus the desiredlevel of market orientation) should increase when environ-mental uncertainty rises. Thus the level of environmentaluncertainty should have a direct impact on the desired levelof market orientation

In addition, to the extent that business performance is afunction of the gap between the desired and the actual levelof market orientation, environmental uncertainty will alsohave an indirect impact on business performance. Anumber of marketing scholars have argued that firm performance depends in part on the fit between the strategicactivities needed to implement a firm ’s marketing strategyand attributes of the firm ’s marketing organization (e.g.,Olson et al. 1995; Vorhies and Morgan 2003).1 Similarly,Gupta et al. (1986) hypothesized that a firm ’s level of innovation success depended on the gap between thedesired and achieved levels of R&D – marketing integration.In the next section we draw on these research streams todevelop a conceptual model that identifies the antecedentsof the desired level of market orientation and its impact onfirm performance.

Conceptual framework and research hypotheses

Recent research on market orientation has focused on thedefinition of the construct, the identification of antecedent variables, and the specification of co-determinants of business performance (Kirca et al. 2005). The model inFig. 1 reflects two important research streams in the market orientation literature. One stream of research has examinedthe top management, interdepartmental, and organizationalsystem variables identified by Kohli and Jaworski ( 1990)and Jaworski and Kohli ( 1993) as antecedent variables of the achieved level of market orientation. A second, relatedstream of research has examined the role of industry andfirm descriptors as co-determinants of business unit performance (e.g., Narver and Slater 1990; Slater and Narver 1994).

In addition to incorporating these research streams, themodel in Fig. 1 also identifies antecedents of the desiredlevel of market orientation and links this desired level to

both the achieved level of market orientation and to business unit performance. Following Kohli and Jaworski(1990, p. 3), we define the achieved level of market orientation to be the “organizationwide generation, dissem-ination, and response to market intelligence. ” Based on thediscussion in the previous section, we define the desiredlevel of market orientation to be the optimal level of market orientation as perceived by managers.

Determinants of the desired level of market orientation

Figure 1 links the desired level of market orientation toenvironmental uncertainty and to innovation strategy.Environmental uncertainty refers to the instability andunpredictability of the external environment (Ruekert et al.1985). Consistent with previous research (e.g., Kholi andJaworski 1990; Slater and Narver 1994), we distinguishamong three kinds of environmental uncertainty: market

turbulence, competitive intensity, and technological turbu-lence. Market turbulence refers to changes in the “compo-sition of customers and their preferences ” (Kohli andJaworski 1990, p. 14). When market turbulence is low,companies can rely on their existing knowledge of stablecustomer preferences to design effective marketing strate-gies. To the extent that the existing marketing mix fits knowncustomer preferences, few modifications in the marketingmix may be required over time. In contrast, when market turbulence is high, a high level of market orientation isneeded to help firms understand changes in customer preferences, design appropriate segmentation strategies,

and create new marketing programs that cater to these altered preferences (Grewal and Tansuhaj 2001; Subramanian andGopalakrishna 2001). Thus we hypothesize that:

H1: The higher the level of market turbulence, thehigher the desired level of market orientation.

Competitive intensity refers to the ability and willing-ness of competitors to alter marketing mix decisions inorder to gain competitive advantage. The need for a market orientation increases as competitive intensity rises (Houston1986; Noble et al. 2002). When competitive intensity ishigh, the potential availability of better purchase options

provides consumers with incentives to revisit and revisetheir existing decision rules. As a result, firms have a higher risk of losing existing customers, relative to situations inwhich the competitive environment is stable (Lusch andLaczniak 1987; Appiah-Adu 1997). Moreover, frequent changes in the marketing mix of competitive firms increasethe potential benefit of monitoring those changes (Han et al.1998). Thus we hypothesize that:

H2: The higher the level of competitive intensity, thehigher the desired level of market orientation.

1 Studies of individual decision-making also reveal the potentialvalue of distinguishing between the desired and achieved levels of adesired product attribute or consumption outcome. Examples includethe desired-point consumer preference model (e.g., Shocker andSrinivasan 1974; Kamakura and Sirvastava 1986; Lee et al. 2002),the SERVQUAL model (e.g., Parasuraman et al. 1985), and a number of job satisfaction models (for a review of this literature see Kristof 1996).

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Hypotheses H1 and H2 are consistent with the work of Miller (1988), who found that product innovation andaggressive marketing differentiation yielded the greatest benefits in markets characterized by high levels of competitive intensity and market turbulence, and with thefield research of Kohli and Jaworski ( 1990), who concludedthat, in markets characterized by limited competition andstable market preferences, “ a market orientation may not berelated strongly to business performance ” (p. 15). Under Hypotheses H1 and H2, this conclusion can be restated asfollows: when competition is limited and market prefer-ences are stable, the desired level of market orientation isrelatively low.

Technological turbulence refers to changes in the “entire process of transforming inputs to outputs and the delivery

of those outputs to the end customer ” (Kohli and Jaworski1990, p. 14). According to Jaworksi and Kohli ( 1993),firms that:

… work with nascent technologies that are undergoing

rapid change may be able to obtain a competitiveadvantage through technological innovation, therebydiminishing — but not eliminating — the importance of a market orientation. By contrast, organizations that work with stable (mature) technologies are relatively poorly positioned to leverage technology for gaining acompetitive advantage and must rely on market orientation to a greater extent (pp. 57 – 58).

Thus the desired level of market orientation is potentiallylower for firms that have the opportunity to establish a

The Achieved Level of Market Orientation (AMO)•Intelligence Generation•Intelligence Distribution•Market Responsiveness

Business UnitPerformance•ROI•Relative Market Share•Customer Retention•Overall Performance

Slater and Narver (1994)

Control Variables•Market Turbulence (-)•Competitive Intensity (-)•Technological Turbulence (-)•Relative Size (+)•Relative Costs (-)•Market Growth (+)

•Ease of Entry (-)•Buyer Power (-)

Market Environment•Market Turbulence (H1)•Competitive Intensity (H2)•Technical Turbulence (H3)

Innovation Strategy•Prospector•Analyzer•Defender•Reactor

Jaworski and Kohli’s (1993) Antecedents of Market-Orientation

Top Management•Top Management Emphasis (+)•Top Management Risk Aversion (-)

Interdepartmental Dynamics•Conflict (-)•Connectedness (+)

Organizational Systems•Formalization (-)•Centralization (-)•Departmentalization (-)

•Reward Systems (+)

The Desired Level of Market Orientation (DMO)•Intelligence Generation•Intelligence Distribution

•Market Responsiveness

New Constructs

(H4)

(H6)GAP•UNDER a

•OVER(H5)

a: UNDER = IMO – AMO when IMO > AMO and 0 otherwise. OVER = AMO – IMO when AMO > IMO and 0otherwise.

The Achieved Level of Market Orientation (AMO)•Intelligence Generation•Intelligence Distribution•Market Responsiveness

Business UnitPerformance•ROI•Relative Market Share•Customer Retention•Overall Performance

Slater and Narver (1994)

Control Variables•Market Turbulence (-)•Competitive Intensity (-)•Technological Turbulence (-)•Relative Size (+)•Relative Costs (-)•Market Growth (+)

•Ease of Entry (-)•Buyer Power (-)

Market Environment•Market Turbulence (H1)•Competitive Intensity (H2)•Technical Turbulence (H3)

Innovation Strategy•Prospector•Analyzer•Defender•Reactor

Jaworski and Kohli’s (1993) Antecedents of Market-Orientation

Top Management•Top Management Emphasis (+)•Top Management Risk Aversion (-)

Interdepartmental Dynamics•Conflict (-)•Connectedness (+)

Organizational Systems•Formalization (-)•Centralization (-)•Departmentalization (-)

•Reward Systems (+)

The Desired Level of Market Orientation (DMO)•Intelligence Generation•Intelligence Distribution

•Market Responsiveness

New Constructs

(H4)

(H6)GAP•UNDER a

•OVER(H5)

a: UNDER = IMO – AMO when IMO > AMO and 0 otherwise. OVER = AMO – IMO when AMO > IMO and 0otherwise.

Figure 1 The desired level of market orientation and firm performance.

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competitive advantage through technological innovation. Inaddition, consumer predictions of their responses to radicalinnovations are often unreliable (Tauber 1974). As a result,when technological turbulence is high, the relative impor-tance of certain kinds of market intelligence (e.g., consumer perceptions and preferences) will be lower than whentechnological turbulence is low. These considerationssuggest the following hypothesis:

H3: The lower the level of technological turbulence,the higher the desired level of market orientation.

The level of achieved market orientation

The market orientation literature has emphasized theimportant role of senior management in nurturing a market orientation. Conceptually this literature has distinguished between (1) the importance senior management places on amarket orientation and (2) the communication of that importance throughout the organization (e.g., Kohli andJaworski 1990; Webster 1988). Empirical work has focusedon the latter variable (for a summary see Kirca et al. 2005), but the commitment of senior managers is an “essential prerequisite ” (Kohli and Jaworski 1990, p. 7) and logically prior to communication of that commitment. For this reasonwe distinguish between the perceived importance of amarket orientation to senior managers (as reflected in their perceptions of the desired level of market orientation) andthe communication of those perceptions to subordinates.Consistent with the reasoning of Kohli and Jaworski(1990), we expect that communication of senior manage-ment perceptions will moderate the impact of those perceptions on the achieved level of market orientation.Thus we hypothesize that:

H4: The achieved level of market orientation as perceived by SBU managers is an increasingfunction of the desired level of market orientationas perceived by the SBU managers ’ superiors.

H5: The relationship between theachievedlevelof market orientation as perceived by SBU managers and thedesired level of market orientation as perceived by theSBU managers ’ superiors is moderated by the degree

to which senior management communicates its perceptions to SBU managers.

Market orientation and business performance

A substantial body of empirical work supports the proposition that a market orientation has a positive impact on firm performance (Kirca et al. 2005). Importantly, thesestudies cannot be used to argue that increases in market orientation are always beneficial, because the underlying

statistical models assume a linear relationship between theachieved level of market orientation and performance. Inessence, linear regression models assume that the desiredlevel of market orientation is infinite (because more market orientation is always better). This assumption can be tested by specifying a linear regression model that includes one of the following explanatory variables: (1) the square of market orientation or (2) the gap between the desired andachieved levels of market orientation. We will refer to thefirst alternative as the quadratic model of market orientationand the second alternative as the gap model of market orientation. In this sub-section we develop the theoreticalrationale for the second alternative.

Linking firm or SBU performance to the gap betweenthe ideal and achieved levels of market orientation isconsistent with existing models that relate various perfor-mance measures to the fit between two variables or variable profiles. For example, Gupta et al. ( 1986) hypothesized that innovation success was a function of the gap between thedesired and achieved levels of cross-functional integration.This hypothesis has important implications for models of market orientation, because the constructs of marketingorientation and cross-functional integration are closelyrelated. Both involve the sharing of information acrossfunctional boundaries and the development of coordinated,cross-functional responses to that information (Gupta et al.1986; Kohli and Jaworski 1990). A key difference is that the Gupta et al. model focuses on one cross-functionalinterface while the market orientation literature addressesmultiple interfaces.

A gap model is also consistent with studies that link firm performance to the fit between the strategic activitiesneeded to implement a firm ’s marketing strategy andattributes of the firm ’s marketing organization (e.g., Olsonet al. 2005; Walker and Ruekert 1987). In particular, studiesof the Miles and Snow typology have hypothesized that the prospector, analyzer, and reactor strategies require distinct sets of marketing activities with different implications for organizational design (Matsuno and Mentzer 2000; McKeeet al. 1989). This reasoning led Vorhies and Morgan ( 2003)to argue that, “ for each set of strategic characteristics, thereexists an desired set of organizational characteristics that yields superior performance ” (p. 101). In their empiricalwork the authors found that the gap between a firm ’sdesired and actual set of organizational characteristicssignificantly influenced marketing performance.

This research also suggests that underachieving (AMO<DMO) may have different performance implications thanoverachieving (AMO>DMO). In particular, underachievingshould affect revenue-based measures of effectiveness aswell as cost-based measures of efficiency (Vorhies andMorgan 2003). In contrast, the impact of overachieving willvary depending on whether a particular performance

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measure accounts for the costs of achieving that perfor-mance (Matsuno and Mentzer 2000). Because we havedefined the desired level of market orientation to be thelevel that maximizes profits, overachieving should reducemeasures like ROI that reflect both market performance andcosts. In contrast, over-achieving can have a positiveimpact on market performance measures that do not reflect relevant costs. For example, high levels of responsivenessto market intelligence might lead to product line expansionsthat increase relative market share and customer retention but reduce SBU profitability. Taken together, these consid-erations suggest the following hypotheses:

H6a: When the desired level of market orientationexceeds the achieved level (DMO>AMO), busi-ness unit performance is negatively related to thegap between the desired and achieved levels of market orientation.

H6b: When the achieved level of market orientation

exceeds the desired level (AMO>DMO), ROI isnegatively related to the gap between theachieved and desired levels of market orientation.

H6c: When the achieved level of market orientationexceeds the desired level (AMO>DMO, relativemarket share and customer retention are posi-tively related to the gap between the achievedand desired levels of market orientation.

Methodology

We collected data at the SBU level for two reasons. First,we wished to simplify the process of comparing our resultswith those obtained in earlier studies (e.g., Jaworski andKohli 1993; Slater and Narver 1994). Second, we hypoth-esized that SBU strategy was an antecedent variable of thedesired level of market orientation.

Field research procedure

Following the suggestions of Kohli and Jaworski ( 1990)and Churchill ( 1979), the empirical phase of our investiga-tion began with exploratory field research. Because noexisting literature discusses the desired level of market orientation and its antecedents, we conducted in-depthinterviews with 28 executives from eight SBUs in sixcompanies. Consistent with the recommendations of Eisen-hardt (1989) regarding theoretical sampling, we designedour sample so that each of the Miles and Snow strategytypes was represented by two SBUs.

The interviews, which ranged from 90 minutes to 3hours in length, followed a structured interview guide that addressed three topics: (1) the position and responsibility of

each interviewee, (2) the achieved level of market orienta-tion, and (3) the desired level of market orientation. A key portion of the interview focused on the interviewees ’evaluation of the Jaworski and Kohli ( 1993) model of theantecedents and consequences of the achieved level of market orientation. In general, interviewee responsesconfirmed the validity of the Jaworski – Kohli model.

We also asked the interviewees to define the desiredlevel of market orientation and to identify factors that influence this desired level. An analysis of intervieweeresponses yielded four antecedents of the desired level of market orientation: innovation strategy (including entrytiming), competitive environment, market/customer envi-ronments, and technology environments. These findingswere consistent with the variables that emerged from our literature review.

Survey instrument development

In our field interviews we found that senior executives weremore concerned with their SBUs ’ overall strategies and performances, while SBU managers were more familiar with the details of SBU operations. For this reason wedesigned two surveys for each SBU: one for the SBUmanager and one for his or her superior. Based on aliterature review and our field interviews, drafts of ques-tionnaires were constructed and pretested with executivesfrom the firms that participated in the field interviews.Respondents were encouraged to evaluate the constructsand items in the questionnaires, to suggest changes, and tocomment on related issues. After revising the question-naires, we followed the suggestions of Churchill ( 1979) andasked four researchers to classify the measurementsindependently and judge the validity of the constructs andmeasurement items. After a further set of revisions, thequestionnaires were again pretested with selected partic-ipants in our field research.

Data collection

Our sample frame consisted of a random sample of 800firms listed in Ward ’ s Business Directory of U.S. Privateand Public Companies . Because we had designed twodifferent questionnaires, responses from both informantswere necessary to get usable data for each SBU. For thisreason, we designed a multi-stage data collection procedurethat involved extensive pre-survey contact with eachorganization in order to select informants and (hopefully)increase the response rate. Following the suggestions of Phillips ( 1981), our selection of respondents was guided bytwo criteria: (1) the informant ’s knowledgeable of theresearch subject and (2) the informant ’s ability andwillingness to communicate with the researcher.

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− 0 . 2 1 *

− 0 . 4 1 *

− 0 . 1 5 * *

I n t e r d e p a r t m e n t a l c o n n e c t e d n e s s ( C O N N )

5 . 9 6

2 . 1 9

0 . 2 5 *

0 . 2 2 *

0 . 2 9 *

0 . 2 7 *

0 . 2 5 *

0 . 4 0 *

− 0 . 3 5 *

0 . 0 4

0 . 2 4 *

0 . 1 0 * * *

F o r m a l i z a t i o n ( F O R M )

3 . 0 5

2 . 7 6

− 0 . 2 2 *

− 0 . 1 4 * *

− 0 . 1 5 *

− 0 . 1 8 *

0 . 0 2

− 0 . 1 2 * *

0 . 2 2 *

− 0 . 0 3

− 0 . 1 6 *

0 . 0 2

C e n t r a l i z a t i o n ( C E N T )

6 . 1 6

2 . 6 8

0 . 0 0

− 0 . 0 8

− 0 . 0 7

− 0 . 0 6

0 . 1 1 * *

0 . 0 5

0 . 0 2

− 0 . 1 8 *

0 . 1 3 * *

0 . 3 6 *

D e p a r t m e n t a l i z a t i o n ( D E P T )

3 . 6 5

2 . 3 9

− 0 . 2 3 *

− 0 . 2 7 *

− 0 . 2 5 *

− 0 . 2 2 *

− 0 . 2 1 *

− 0 . 3 3 *

0 . 2 6 *

− 0 . 1 5

− 0 . 1 5 *

− 0 . 1 1 * *

R e w a r d s y s t e m ( R E W A R D )

5 . 4 2

2 . 5 6

0 . 3 3 *

0 . 3 5 *

0 . 3 8 *

0 . 3 8 *

0 . 3 4 *

0 . 5 4 *

− 0 . 4 3 *

0 . 1 9 *

0 . 4 4 *

− 0 . 0 3

R e l a t i v e s i z e ( R S I Z E )

6 . 5 8

2 . 0 9

0 . 1 7 *

0 . 2 0 *

0 . 2 1 *

0 . 2 9 *

0 . 2 5 *

0 . 3 7 *

− 0 . 2 8 *

0 . 1 0 * * *

0 . 3 5 *

0 . 1 4 * *

R e l a t i v e c o s t ( R C O S T )

5 . 9 3

2 . 7 9

0 . 2 7 *

0 . 2 6 *

0 . 2 3 *

0 . 2 7 *

0 . 2 9 *

0 . 4 4 *

− 0 . 3 4 *

0 . 1 5 *

0 . 6 1 *

0 . 0 1

M a r k e t g r o w t h ( M G R O )

5 . 7 3

3 . 6 7

0 . 0 4

0 . 0 2

− 0 . 0 4

− 0 . 0 4

− 0 . 1 6 *

− 0 . 1 3

0 . 0 2

0 . 0 2

− 0 . 1 2 * *

− 0 . 0 1

E a s e o f e n t r y ( E N T R Y )

4 . 7 3

3 . 7 3

− 0 . 3 0 *

− 0 . 1 9 *

− 0 . 1 3 * *

− 0 . 1 5 *

− 0 . 0 2

− 0 . 1 7 *

0 . 2 4 *

− 0 . 1 2 * *

− 0 . 1 2 * *

− 0 . 0 9

B u y e r p o w e r ( B P O W )

4 . 8 1

3 . 4 1

0 . 0 2

− 0 . 0 2

− 0 . 0 1

− 0 . 0 4

0 . 0 1

0 . 0 3

− 0 . 0 5

− 0 . 0 4

0 . 0 6

0 . 1 6 *

U N D E R = D M O

− A M O i f D M O > A M O a n d 0 o t h e r w i s e . O V E R = A M O

− D M O w h e n A M O > D M O a n d 0 o t h e r w i s e .

* p = 0 . 0 1 ; * * p = 0 . 0 5 ; * * * p = 0 . 1 .

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prospector, analyzer, or a defender. To assign an innovationstrategy to each firm, we presented the senior manager of each respondent firm with the McDaniel and Kolari ( 1987)summary of the Miles and Snow typology and asked eachsenior manager to classify the innovation strategy of thefocal business unit (see also Matsuno and Mentzer 2000).To assess the validity of this self-classification, we presented each business unit manager with the 11 itemsdeveloped by Conant et al. ( 1990) to measure innovationstrategy. To convert these 11 items into a statement about each SBU ’s strategic type, we used the scoring rulesemployed by Conant et al. We then confirmed that, for all308 SBUs in our sample, the senior manager ’s self-classified innovation strategy was identical to the innova-tion strategy implied by the business unit executive ’sresponses to the 11-item scale.

We used confirmatory factor analysis (CFA) to examinethe measurement properties of our scales. Given the number of scale items, we did not have enough observations to perform a single CFA that included all multi-item measures.For this reason, we performed five separate CFAs for sub-groups of variables. After deleting problematic items, the fit of each CFA model was acceptable. In particular, for eachCFA model, the CFI, GFI, and NFI fit statistics all exceeded0.95. We assessed discriminant validity in two ways: (1) wecomputed the analysis of the average variance extracted(AVE) for each construct (all AVEs exceeded 0.50) and (2)compared one-factor and two-factor CFA solutions for each possible pair of constructs within each group of variables.Both approaches indicated that our constructs possessedacceptable discriminant validity, which can also helpalleviate multicollinearity problems (Grewal et al. 2004).

Table 1c Variable means, standard deviations, and correlations ( n =308)

DEPT REWARD RSIZE RCOST MGRO ENTRY BPOW

Departmentalization (DEPT) 1.00Reward system (REWARD) −0.13** 1.00Relative size (RSIZE) −0.18* 0.30* 1.00Relative cost (RCOST) −0.12** 0.35* 0.46* 1.00Market growth (MGRO) −0.02 −0.24* −0.05 −0.13** 1.00Ease of entry (ENTRY) −0.13** −0.01 −0.08 −0.16* −0.16 a 1.00Buyer power (BPOW) 0.16* −0.01 0.13** 0.12** 0.14** −0.36* 1.00

* p=0.01; ** p=0.05; *** p=0.1.

Table 1b Variable means, standard deviations, and correlations ( n =308)

TECHTU PROS ANAL DEFD TME TME×DMO

RISK CONF CONN FORM CENT

Technological turbulence (TECHTU) 1.00Prospector (PROS) 0.08 1.00Analyzer (ANLZ) −0.09 −0.48* 1.00Defender (DEFD) −0.02 −0.39* −0.45* 1.00Top management emphasis (TME) −0.07 −0.02 0.01 −0.03 1.00DMO×TME −0.01 0.19* 0.04 −0.22* 0.87* 1.00Top management risk aversion(RISK)

0.12** −0.02 0.00 −0.05 0.53* 0.45* 1.00

Interdepartmental conflict (CONF) −0.08 −0.29* 0.08 0.20* −0.33* −0.38* −0.18* 1.00Interdepartmental connectedness(CONN)

−0.17* 0.14** 0.02 −0.06 0.19* 0.25* 0.05 −0.28* 1.00

Formalization (FORM) 0.30* −0.01 0.03 −0.01 −0.21* −0.15** 0.09 0.20* −0.41* 1.00Centralization (CENT) −0.43* −0.06 0.11 −0.07 0.46* 0.40* 0.13** −0.11*** 0.15* −0.29* 1.00Departmentalization (DEPT) −0.03 −0.20* 0.06 0.17* −0.17* −0.22* −0.14** 0.22* −0.08 −0.20* −0.07Reward system (REWARD) 0.15* 0.19* 0.03 −0.16* 0.27* 0.34* 0.07 −0.44* 0.42* −0.16* −0.07Relative size (RSIZE) 0.04 0.04 0.00 −0.05 0.51* 0.49* 0.54* −0.26* 0.31* 0.00 0.19*Relative cost (RCOST) 0.03 −0.07 0.04 −0.03 0.62* 0.58* 0.44* −0.36* 0.09 −0.07 0.16*

Market growth (MGRO) −0.19* −0.16* 0.01 0.13** 0.01 −0.06 −0.02 0.08 −0.02 −0.17* 0.17*Ease of entry (ENTRY) 0.38* 0.03 −0.06 −0.01 −0.27* −0.23* −0.15* 0.26* −0.22* 0.48* −0.36*Buyer power (BPOW) −0.29* −0.13** 0.08 −0.01 0.16* 0.14** 0.10*** 0.04 0.16* −0.36* 0.21*

* p=0.01; ** p=0.05; *** p=0.1.

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is consistent with the meta-analysis of Kirca et al. ( 2005).Importantly, relative to the coefficient of DMO (0.72), thecoefficient of TME (0.21) is significantly smaller ( F =70.65, df =1,298). These results indicate that, while verbalcommunication is important, it is not the only way inwhich senior management perceptions of DMO influenceAMO.

Model 2B contains the interaction term DMO×TME,which has an estimated coefficient of 0.05. The F test for theaddition of this term is significant ( F =4.97, df =1,297, p<0.05) and the coefficient of DMO×TME is positive (0.03)and significantly different from zero. A re-estimation of model 2B using SUR regression yielded similar results (seethe last column of Table 3). These results provide support for H5, which states that TME moderates the relationship between DMO and AMO. This support must be qualifieddue to the presence of multicollinearity. An application of the Belsley – Kuh – Welsch test indicates that the coefficient estimates for DMO, TME, and DMO*TME are significant-ly affected by multicollinearity (the highest varianceinflation factor is 32.97 and the highest condition index is88.05). Given this concern, we conclude our data provide partial support for Hypothesis H5. 4

The market orientation – performance relationship

Hypothesis 6 states that business unit performance is afunction of the gap between AMO and DMO. Becauseunderachieving (AMODMO), wedefined two gap variables:

Underachieve (UNDER)=DMO −AMO when DMO>AMO and 0 otherwise; andOverachieve (OVER)= AMO −DMO when AMO>DMO and 0 otherwise.

This formulation allows us to estimate what might betermed a generalized gap model . Conventional gap modelsconstrain the effects of underachieving and overachievingto be equal. This conventional gap model is a special caseof the generalized gap model estimated below, and we canuse conventional statistical tests to determine whether theimplied constraint is appropriate.

We regressed four business performance measures onAMO, UNDER, OVER, three environmental variables, andfive control variables used by Slater and Narver ( 1994) intheir study of market orientation and environmentalmoderators. Table 4 summarizes the series of OLSregressions used to evaluate Hypothesis 6. Model 3Acontains the three environmental and five control variables,while model 3B adds AMO. An incremental F -test led us toreject the restriction of the AMO coefficient to zero. Noticethat the overall fit of this model is comparable to that of the

4 We did not perform a subgroup analysis because this analysis “ is performed only when there is no pure moderator effect and nosignificant correlation between the hypothesized moderator and either the predictor or criterion variables ” (Slater and Narver 1994, p. 51; seealso Sharma et al. 1981). The correlation between TME and DMO issignificant ( p

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model estimated by Slater and Narver ( 1994), who obtainedadjusted R2 s ranging from 0.31 to 0.39. 5

Model 3C is identical to model 3B except for theaddition of AMO 2 . The addition of the squared termsignificantly improves model fit, but the increase inadjusted R2 s is small (the largest increase is 0.2, whichoccurs in the RMS regression). Moreover, multicollinearityappears to be a problem in these models: both AMO andAMO 2 have variance inflation factors greater than 26.

In model 3D, the AMO and AMO 2 terms have beenreplaced with the UNDER gap variable. The impact of thischange in model specification on overall fit is dramatic: theadjusted R2 s range from 0.67 to 0.81, significantly higher than the corresponding range (0.31 to 0.43) for model 3C.These results indicate that the gap model (model 3D)clearly dominates both the linear AMO model of prior research (model 3B) and a model that is quadratic in AMO(model 3C).

Model 3E adds the over variable to model 3D. In three of the performance regressions (relative market share, customer retention, and overall performance), this addition has noimpact on adjusted R2 and the incremental variable F -test isinsignificant. In the ROI equation the incremental variable F -test is significantly different from zero and the Belsley – Kuh – Welsh test (1980) indicates that multicollinearity is not a problem (all of the variance inflation factors are less than 2).Thus the ROI column in Table 5 reports the SUR coefficientsestimated from model 3E while the remaining columns present the SUR coefficients estimated from model 3D.

Consistent with Hypothesis 6a, the coefficient of UNDER is negative (ranging from −1.88 to −2.52) and significant ineach regression in Table 5. Contrary to Hypothesis 6b, thecoefficient of OVER is positive (0.81) and significant inthe ROI equation, but is significantly less in magnitudethan the coefficient of UNDER ( F =139.59, df =1,297). 6

Our results also do not support Hypothesis 6c, because thecoefficient of OVER is not significantly different from zeroin the RMS and CRR equations (see model 3E in Table 5).Importantly, in no case is the coefficient of OVER statistically equal to the coefficient of UNDER. Based onthese results, we conclude that a conventional gap model(one in which the impact of underachieving and over-achieving are equal) does not fit our data. These results must be treated with caution, because DMO>AMO in only about 13% (41/308) of our observations. For these reasonsHypotheses 6b and 6c merit further study in future research.

Discussion

We have described a model of market orientation that (1)specifies the antecedents of the desired level of market orientation, (2) identifies the desired level of market orienta-tion as an antecedent of the achieved level of market orientation, and (3) links business unit performance to thegap between the desired and achieved levels of market orientation. Our empirical analysis confirms the usefulnessof this conceptual framework for understanding the ante-cedents and consequences of a business unit ’s market orientation. In particular, the data examined here support the

5 The model estimated by Slater and Narver ( 1994 ; see Table 2 on p.52) included two variables that measured the achieved level of market orientation. These variables were based on measures developed by Narver and Slater ( 1990 ). 6 This F -test was computed using the OLS regression results.

Table 3 Antecedents of the achieved level of market orientation(n =308)

Model2A a

Model2B

Model 2B-SUR

Intercept −0.11 b 1.29* 2.19***(0.41) (0.75) (0.73)

Desired market 0.72*** 0.50*** 0.40***orientation (DMO) (0.04) (0.11) (0.10)

Top management 0.21*** −0.01 −0.06emphasis (TME) (0.04) (0.11) (0.11)

DMO×TME 0.03** 0.04***(0.02) (0.02)

Top management risk 0.03 0.02 0.02aversion (0.03) (0.03) (0.03)

Interdepartmental −0.15*** −0.14*** −0.16***conflict (0.03) (0.03) (0.03)

Interdepartmental 0.09*** 0.09*** 0.08***connectedness (0.03) (0.03) (0.03)

Formalization −0.04* −0.04* −0.03(0.02) (0.02) (0.02)

Centralization −0.12*** −0.13*** −0.13***(0.02) (0.02) (0.02)

Departmentalization −0.10*** −0.10*** −0.11***(0.02) (0.02) (0.02)

Reward system 0.09*** 0.10*** 0.10***orientation (0.02) (0.03) (0.02)

Overall F statistic 119.10*** 109.12***Adjusted R-square 0.78 0.78Incremental variable F test c 4.97**

a The first four columns of coefficient estimates were obtained usingOLS regression, while the last column was obtained using seeminglyunrelated regression (SUR). The SUR estimates were obtained byestimating a system of equations in which DMO, AMO, and RMS

were the dependent variables. b Table entries are unstandardized regression coefficient estimates andstandard deviations (in parentheses). All hypotheses were evaluatedusing a two-tailed test of significance.c The F -statistic in this row tests the hypothesis that the added variablein the model (relative to the model in the previous column) has acoefficient of zero.*** p=0.01; ** p=0.05; * p=0.1.

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following conclusions. First, the desired level of market orientation is positively related to perceived levels of market turbulence, competitive intensity, and technologicalturbulence. These findings are consistent with the idea that rapid or unanticipated changes in consumer preferences andcompetitor activities increase the importance of collecting,disseminating, and responding to market intelligence.However, our findings contradict the hypothesis that increases in technological turbulence decrease the impor-tance of a market orientation. One possible explanation for this result is that, when environmental turbulence is high,additional market information provides insights that canhelp managers evaluate the attractiveness of varioustechnological alternatives.

Second, consistent with the prescriptions of the market-ing orientation literature, the desired level of market orientation as perceived by senior managers is positivelyrelated to the achieved level of market orientation as perceived by SBU managers. Moreover, the magnitude of this relationship depends on the degree to which senior management emphasizes the importance of a market orientation within the firm.

Third, the impact on performance of a change in theachieved level of market orientation depends on the desired

level of market orientation. Our analysis indicates that,when the achieved level of market orientation is less thanthe desired level, business unit performance is negativelyrelated to the gap between the desired and achieved levelsof market orientation.

Managerial implications

Our results have several important managerial implications.First, Kohli and Jaworski ( 1990) concluded that “a market orientation may or may not be very desirable for a business,depending on the nature of its supply- and demand-sidefactors” (p. 15). Our results suggest that this conclusionmay be restated as follows: the desired level of market orientation for a business depends on its environment andits innovation strategy.

Second, Kohli and Jaworski ( 1990) observed that thefactors that “ foster or discourage ” a market orientation “ arelargely controllable by managers and therefore can bealtered by them to improve the market orientation of their organizations ” (p. 15). However, the findings reported hereindicate that key determinants of the desired level of market orientation such as competitive intensity and market turbulence lie largely outside the control of managers

Table 4 Performance regression results ( n =308)

Model Independent variables b Fit stati st ics Dependent variables a

ROI RMS CRR OVP

3A Control variables+ environmental turbulencevariables

Overall fit F statistic c 12.93*** 11.00*** 9.89 15.65***Adjusted R2 0.24 0.21 0.19 0.30

3B Control+ environmental turbulence variables +AMO Overall fit F stati st ic 22.58*** 18.34*** 16.13*** 26.73***Adjusted R2 0.39 0.34 0.31 0.43Incremental variable F -test d

74.38*** 59.76*** 52.61*** 81.61***

3C Control+ environmental turbulence variables +AMO+AMO 2

Overall fit F stati st ic 21.28*** 18.54*** 15.19*** 24.13***Adjusted R2 0.40 0.36 0.32 0.43Incremental variable F -test

6.12** 11.49*** 4.87** 0.87

3D Control+ environmental turbulence variables +UNDER

Overall fit F statist ic 142.72*** 76.75*** 136.68*** 83.42***Adjusted R2 0.81 0.69 0.80 0.67Incremental variable F -test

877.79*** 465.95*** 911.01*** 364.30***

3E Control+ environmental turbulence variables +UNDER+OVER

Overall fit F statist ic 131.61*** 68.93*** 123.31*** 64.08***Adjusted R2 0.81 0.69 0.80 0.67

Incremental variable F -test

6.77*** 0.28 1.39 0.43

a ROI=Return on Investment, RMS=Relative Market Share, CRR=Customer Retention Rate, OPF=Overall Performance. b UNDER=DMO −AMO when DMO>AMO and 0 otherwise. OVER = AMO −DMO when AMO > DMO and 0 otherwise (recall that AMO=Achieved Level of Market Orientation and DMO=Desired Level of Market Orientation).c The Overall Fit F statistic tests the hypothesis that all regression coefficients are zero.d This F -statistic tests the hypotheses that the coefficient of the added independent variable is zero. In Model 3B (3C), this F -statistic tests thehypothesis that the coefficient of AMO (AMO 2 ) is zero. In Model 3D (3E), this F -statistic tests the hypothesis that the coefficient of UNDER (OVER) is zero.*** p=0.01.

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within the firm. Thus decisions about the appropriate level of market orientation must reflect an internal consideration of thefirm’s innovation strategy and an external evaluation of market turbulence and competitive rivalry.

Third, our results suggest that increases in technologicalturbulence increase the desired level of market orientation.This finding has important implications for managers, whotend to respond to high levels of technological turbulence by focusing on changes in technology and the implicationsof those changes for their marketing mix (Hayes andWheelwright 1984) This focus, which reflects the desire of executives to manage the risks and uncertainties that arise

from technological turbulence, decreases the resourcesavailable for monitoring and responding to changes incustomer preferences (Glazer 1991; Slater and Narver 1994). Our findings suggest that managers should recon-sider the wisdom of allocating fewer resources to market intelligence when technological turbulence increases.

Fourth, we found that, the higher the desired level of market orientation, the greater the gap between the desiredand achieved levels of market orientation. Unfortunately, thecollection of additional market information creates difficultiesfor the individuals responsible for processing new informationand generating relevant action alternatives (Aaker 1984;Weitz et al. 1986). As a result, when the desired level of information rises, managers must make added efforts toensure that new information is disseminated across function-al boundaries and that functional responses to new informa-tion are coordinated (Olson et al. 1995).

Data limitations and directions for future research

Our conclusions must be qualified in several ways. First,our results reflect the analysis of correlations amongcontemporaneous variables. While such an analysis isconsistent with causal relationships implied by existingtheory, it cannot be used to establish those causal relation-ships. This limitation could be addressed by a longitudinalstudy (e.g., Noble et al. 2002), which we offer as anambitious goal for future research. In addition, our cross-sectional data do not permit us to evaluate the degree towhich environmental variables change over time. Our model implies that changes in environmental variablesshould alter the desired level of market orientation, and thisimplication could be tested with longitudinal data.

A second limitation involves the collection of data at the business-unit level of aggregation. We chose this level of aggregation to permit comparisons of our results with prior studies that have examined the moderating effect of environ-mental variables on the market orientation – performancerelationship. However, the resulting “global ” measures arenecessarily driven by respondent ’s perceptions of manyactivities over time with regard to a variety of products andcustomers. This raises the possibility that certain events mayhave a disproportionate influence on the respondents ’ global perceptions of their business units ’ market orientation. Thusfuture research might profitably examine whether the collec-tion of more disaggregate information can generate additionalinsights regarding the antecedents and consequences of amarket orientation.

Third, we tested our theoretical model by analyzing datacollected from U.S. firms. There are reasons to believe that the market orientation – performance relationship may varyacross national boundaries. For example, the level of government intervention in the economy may influence

Table 5 The market – orientation – performance relationship: SUR regressions ( n =308)

ROI a RMS CRR OPF

Intercept 8.19*** 7.76*** 8.11*** 6.30***(0.43) (0.54) (0.44) (0.50)

Underachieve(UNDER) c

−2.23*** −2.36*** −2.52*** −1.88***

(0.08) (0.10) (0.08) (0.09)Overachieve

(OVER)0.81***

(0.26)Market turbulence

−0.00 −0.04 −0.09** −0.07*

(0.03) (0.05) (0.04) (0.04)Competitive

intensity

−0.04 −0.01 0.03 0.05

(0.04) (0.05) (0.04) (0.04)Technological

turbulence0.04 0.08** 0.05 0.15***

(0.03) (0.04) (0.03) (0.03)

Relative size −0.11*** −0.05 −0.01 0.09**(0.04) (0.05) (0.04) (0.04)

Relat ive costs 0.02 −0.02 −0.09** −0.07*(0.03) (0.04) (0.03) (0.04)

Market growth 0.03* 0.04* −0.01 0.00(0.02) (0.02) (0.02) (0.02)

Ease of entry −0.09*** −0.03 0.04* −0.03(0.02) (0.03) (0.02) (0.03)

Buyer power −0.04* −0.05* −0.00 −0.04(0.02) (0.03) (0.02) (0.03)

Overall F statistic

131.61*** 76.75*** 136.68*** 71.28

Adjusted R-square

0.81 0.69 0.80 0.67

a ROI=Return on Investment, RMS=Relative Market Share, CRR=Customer Retention Rate, OPF=Overall Performance. b Table entries are unstandardized regression coefficient estimates andstandard deviations (in parentheses). All hypotheses were evaluatedusing a two-tailed test of significance.c UNDER=DMO −AMO when DMO>AMO and 0 otherwise. OVER=AMO −DMO when AMO>DMO and 0 otherwise (recall that AMO=Achieved Level of Market Orientation and DMO=Desired Level of Market Orientation).*** p=0.01; ** p=0.05; * p=0.1.

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the desired level of market orientation by affectingcompetitive intensity and technological turbulence (Quand Ennew 2005). Government intervention might alsoinfluence achieved levels of market orientation by con-straining a firm ’s ability to collect and respond to market intelligence (e.g., government restrictions on the collectionof consumer information in certain European countries).Finally, in some markets government intervention can beexpected to influence the benefits that a firm receives fromdeveloping a market orientation. For all of these reasons, ananalysis that examines the impact of market-specificgovernment intervention may shed additional light on theantecedents and consequences of a market orientation.

A review of our findings suggests additional paths for future research. First, Han, Kim, and Srivastava (1989)found that organizational innovativeness (i.e., the number of outside innovations adopted and implemented by the banks in their study) mediates the relationship between performance and the achieved level of market orientation.Importantly, this study measured the achieved level of market orientation using the Narver and Slater ( 1990) scale,which does not assess organizational responsiveness tomarket intelligence. In contrast, the scale used in this study(the MARKOR scale developed by Kohli, Jaworski, andKumar 1993) contains a sub-scale that assesses responsive-ness to market intelligence in a broad sense, but does not distinguish between innovative and non-innovativeresponses. Thus future research should examine the impact of the AMO – DMO gap on organizational innovativeness.

Second, prior research has emphasized the positive impact of senior management communicating the importance of amarket orientation throughout an organization (e.g., Jaworskiand Kohli 1993). The results reported here indicate that,when we control for this communication, senior manage-ment perceptions of the desired level of market orientationstill have a substantial incremental impact on the achievedlevel of market orientation. The mechanisms underlying thisrelationship are an important topic for future research.

Third, while existing research has hypothesized that increases in the level of technological uncertainty decreasethe importance of a market orientation (e.g., Kohli andJaworski 1990), we found a positive relationship in our empirical work. There are at least two possible explanationsfor this result. First, when technological turbulence is high,major innovations often come from outside the industriesserved by the firm (Utterback and Abernathy 1975;Christensen 1997). For this reason, high levels of techno-logical turbulence increase the firm ’s incentive to expandthe breadth of its information collection processes (Slater and Narver 1994). Second, firms often respond to highlevels of technological uncertainty by seeking allianceswith other firms (Teece 1992; Robertson and Gatignon1998). The increased importance of identifing and evaluat-

ing potential technology partners, along with the need totrack the alliance activity of competitive firms, can alsolead to an increase in firm ’s need for market information.The relative importance of these explanations for under-standing the positive relationship between technologicalturbulence and the ideal level of market orientation is animportant topic for future research.

Fourth, a surprising result in our analysis involvedHypotheses H6b and H6c, which predicted that achievingmore than the desired level of market orientation wouldhave (1) a negative impact on cost-based measures of efficiency like ROI and (2) a positive impact on customer- based measures of effectiveness like relative market shareand customer retention. An implicit assumption underlyingthese hypotheses was that an increase in market information processes beyond the desired level would only increasefirm costs. One possible explanation for our failure to findsupport for H6B and H6C involves the potentiallyfavorable impact of market information on product andmarketing costs. Perhaps an increase in market orientationenables the firm to reduce costs by accelerating itsdevelopment efforts, reducing production costs, and better targeting its marketing efforts. This possibility should beexplored in future research.

In summary, we believe that the research presented heremakes several important contributions. We have presented amodel of market orientation that specifies the antecedentsof the desired level of market orientation and links thisdesired level to both the achieved level of market orientation and to performance. Our empirical analysis, based on data collected from 308 US firms, provides strongsupport for the hypothesized model. Our results shouldhave relevance for both academicians and practitioners. In particular, we hope that our findings will be of considerableinterest and value to those executives who seek to establisha sustainable competitive advantage through the creationand sustenance of a market-driven organization.

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