On the importance of matching strategic behavior and target market selection to business strategy in high-tech markets

ORIGINAL EMPIRICAL RESEARCH

On the importance of matching strategic behavior and targetmarket selection to business strategy in high-tech markets

Stanley F. Slater & G. Tomas M. Hult & Eric M. Olson

Received: 15 August 2006 /Accepted: 17 August 2006 /Published online: 3 February 2007# Academy of Marketing Science 2007

Abstract Business strategy is fundamentally concernedwith the actions required to create superior customer valuein the firm’s target markets with the ultimate goal ofachieving superior performance. Marketing theory suggeststhat two critical marketing activities required to achieve thisend are: (1) the adoption of appropriate strategic behaviors(i.e., customer-oriented, competitor-oriented, technology-oriented) and (2) targeting of the appropriate market segments(i.e., innovators, early adopters, early majority, late majority,laggards). This study builds on prior research which demon-strates that the strategic behavior—firm performance relation-ship is contingent on the firm’s strategy by examining thisrelationship in high tech markets and by considering theincremental contribution of appropriate target market selec-tion. Responses from 160 senior marketing managers in high-tech firms reveal strong support for our framework. Thus, thisstudy provides useful guidance to executives and managers inhigh-tech firms regarding the steps that they should take toincrease their probability of success.

Keywords High-tech . Customer orientation .

Competitor orientation . Technology orientation .

Market segments . Business strategy . Performance

Strategic market management requires understanding emer-gent market patterns and making decisions that lead to thecreation of economic value (Dickson, Farris, &Verbeke,2001). In this paper we present a study, conducted in high-tech markets, that examines the performance implicationsof matching strategic behavior, target market selection, andbusiness strategy.

The theoretical foundation for this study lies in evolu-tionary economics. Schumpeter (1934) proposed thatmacroeconomic equilibrium is perpetually destroyed byentrepreneurs’ innovations. A successful introduction of aninnovation disturbs the normal flow of economic lifebecause it forces some of the already existing technologiesand means of production to lose their positions within theeconomy. Nelson and Winter (1982) focused on the issue ofchanges in technology and routines. They proposed that ifthe change occurs constantly in the economy, then somekind of evolutionary process must be in play.

A consensus is emerging that the evolution of product-markets is the result of a confluence of a variety of market,technological, and competitive forces (Lambkin & Day,1989). The strategy story here is exploration (technologicalinnovation) followed by imitative market making followedby exploitation (cost or differentiation-based) (c.f., Dicksonet al., 2001). Thus, the evolution that we envision is thatProspectors introduce new technologies into high-techmarkets while Analyzers seek to understand the reasonsfor Prospectors’ successes and failures, and improve on theProspectors’ offerings (Dickson, 1992; Lambkin & Day,1989). Defenders, both Low Cost and Differentiated, are

J. of the Acad. Mark. Sci. (2007) 35:5–17DOI 10.1007/s11747-006-0002-4

S. F. Slater (*)College of Business, Colorado State University,Fort Collins, CO 80523-1278, USAe-mail: [email protected]

G. T. M. HultCenter for International Business Education and Research,Marketing & Supply Management, Eli Broad Graduate SchoolManagement, Michigan State University,East Lansing, MI 48824-1121, USAe-mail: [email protected]

E. M. OlsonMarketing and Strategic Management, College of Businessand Administration, University of Colorado—Colorado Springs,Colorado Springs, CO 80918, USAe-mail: [email protected]

defending a consumer franchise and are hence more riskaverse and are late followers who take advantage ofrespectively fixed-cost structure and employee servicemotivation feedback effects (Dickson et al., 2001).

We first describe three strategic learning behaviors thathigh-tech firms engage in to generate knowledge abouttheir market environment. We then move to the markettargeting decision as it represents the foundation for thefirm’s marketing strategy (e.g., Dickson & Ginter, 1987). Wedescribe four generic business strategies (i.e., Prospectors,Analyzers, Low Cost Defenders, and DifferentiatedDefenders) that effectively summarize the strategic deci-sions that managers make in the pursuit of competitiveadvantage, and offer hypotheses for the strategic behaviorsthat managers of those strategies should engage in and thesegments of innovation adopters they should target. Wethen describe the research design and discuss the results.We conclude with suggestions for future research andimplications for managers.

Strategic learning behavior

To make sense of complex environments, managers focustheir learning efforts on the market forces that are mostsalient to the achievement of competitive advantage (Day &Nedungadi, 1994). This is critical in high-tech markets dueto the turbulence and dynamism that characterizes them.Without the ability to simplify, structure, and focus theirlearning efforts, managers would suffer from “paralysis byanalysis.” Gatignon and Xuereb (1997; see also Zhou, Yim,& Tse, 2005) argued that the three most important sets ofstrategic learning behaviors in high-tech markets aresubsumed under customer orientation, competitor orienta-tion and technological orientation.

Customer orientation

Customer needs often change rapidly and unpredictably inhigh-tech markets. As such, no information is moreimportant to firms competing in high-tech markets thancustomer information as this information shapes scienceinto commercial product or service (Leonard-Barton, 1995).Customer-oriented businesses engage in the organization-wide development of and responsiveness to informationabout the expressed and latent needs of current andpotential customers (Kohli & Jaworski, 1990; Slater &Narver, 1998). Due to its market-sensing and customer-relating capabilities, the customer-oriented business shouldbe well positioned to anticipate customer need evolutionand to respond through the development of new customervalue-focused capabilities and the addition of valuableproducts and services (Day, 1994).

Competitor orientation

A second characteristic of high-tech markets is competitivedynamism. Competitive dynamism refers to changes in thecompetitive landscape: who are your competitors now andtomorrow, what are their product offerings, and how aretheir strategies changing? A competitor orientation isrevealed through the priority placed on in-depth assessmentof a set of existing and potential competitors. As such, thecompetitor assessment focuses on understanding targetedcompetitors’ goals, strategies, offerings, resources, andcapabilities (Porter, 1980) and the organization-wide dis-semination of the information generated from this assess-ment (Kohli & Jaworski, 1990). The goal for the business isto match, if not exceed, competitors’ strengths.

Technological orientation

Technological uncertainty is the third primary characteristicand is concerned with the lack of clear standards for newinnovations in a market (Shapiro & Varian, 1999) and withthe speed with which the technology is adopted in aproduct-market (Glazer & Weiss, 1993). It is based on “notknowing whether the technology—or the company provid-ing it—can deliver on its promise to meet specific needs”(Moriarty, 1989). Gatignon and Xuereb (1997, p. 78) definetechnological orientation as “the ability and the will toacquire a substantial technological background.” Techno-logical background refers to the firm’s technical knowl-edge. Technology orientation also means that the companyis able to use its technical knowledge to create a newtechnical solution in order to address the needs of itscustomers. Technology orientation includes behaviors suchas substantial investment in R&D, use of sophisticatedtechnologies in new product development, rapid integrationof new technologies, and pro-active acquisition of newtechnologies and generation of new product ideas.

Market segmentation

“Market segmentation is a state of demand heterogeneitysuch that the total market demand can be disaggregated intosegments with distinct demand functions. Each firm’sdefinition, framing, and characterization of this demandheterogeneity will likely be unique and form the basis for thefirm’s marketing strategy,” (Dickson & Ginter, 1987, p. 5).

One approach to segmenting markets for high-tech prod-ucts is based on the categories of innovation adopters. Thetwo dominant typologies of innovation adopters are based onthe work of Bass (1969) and Rogers (1995). The majordifference between the two models is that Rogers assumesthat the percentage of adopters in each category is constant

6 J. of the Acad. Mark. Sci. (2007) 35:5–17

across innovations while the Bass model is innovationspecific (Mahajan, Muller, & Bass, 1990). This differenceis not germane to our study. Both models (see also Moore,1991) break adopters into five categories (i.e., innovators,early adopters, early majority, late majority, and laggards).

The early market for innovative products is comprised ofboth innovators and early adopters. Innovators are buyerswho appreciate innovation for its own sake and aremotivated by the idea of being a change agent in theirreference group. They are willing to tolerate initial glitchesand problems that may accompany any innovation justcoming to market and are willing to develop makeshiftsolutions to such problems.

Early adopters look to use innovation to achieve arevolutionary improvement. These buyers are attracted byhigh-risk, high-reward projects, and because they envisiongreat gains from adopting innovation, they are not veryprice sensitive. Customers in the early market typicallydemand personalized solutions and quick-response, highlyqualified sales and support.

Rather than looking for revolutionary changes, the earlymajority is motivated by evolutionary changes to gainproductivity enhancements. They are averse to disruptivechange and, as such, want proven applications, reliableservice, and results. They are the bulwark of the main-stream market.

The late majority are risk averse and technology shy;they are price sensitive and need completely preassembled,bulletproof solutions. They adopt innovation just to stayeven and often rely on trusted advisers to help them makesense of technology.

Finally, laggards prefer only to maintain the status quo.They tend not to believe that innovation can enhanceproductivity and resist new technology purchases. The onlyway they might buy is if they believe that all their otheralternatives are worse and that the cost justification isabsolutely solid.

Among the virtues of this model of innovation adoptionis that it enables marketers to think dynamically aboutconfigurations of strategy and behavior, and their influenceon performance (Lambkin & Day, 1989) as we describe inthe next section.

The performance impact of strategic behaviorand market targeting in the context of business strategy

Business strategy is concerned with how businesses pursuecompetitive advantage. The two dominant frameworks ofbusiness strategy (Walker & Ruekert, 1987) are the Milesand Snow typology and the Porter typology. Miles andSnow (1978) developed a comprehensive framework thataddresses how organizations define and approach their

product-market domains and construct structures andprocesses to achieve competitive advantage in thosedomains. They identified four archetypes of how firmsaddress these issues. Prospectors seek to locate and exploitnew product and market opportunities while Defendersattempt to seal off a portion of the total market to create astable set of products and customers. Analyzers occupy anintermediate position by following Prospectors into newproduct-market domains while simultaneously protecting astable set of products and customers. A fourth type, theReactor, does not have a consistent response to theentrepreneurial problem. Porter (1980) proposed thatstrategy is a product of how the firm creates customervalue (differentiation or low cost) and how it defines scopeof market coverage (focused or market-wide).

Walker and Ruekert (1987) synthesized these frame-works in a typology consisting of Prospectors, Low CostDefenders, and Differentiated Defenders. Slater and Olson(2000, 2001) utilized and found support for the distinctionbetween Low Cost Defenders and Differentiated Defenders.However, they also retained the Analyzer strategy type asnumerous studies have demonstrated the validity of thisstrategy. Thus, this study utilizes the Slater and Olsontypology. Due to the low proportion of self-reportedReactors in this study and their lack of a consistent strategy,we do not consider Reactors in this study (e.g., Miles &Snow, 1978).

Prospectors

Prospectors are the most proactive and innovative of thestrategy types. Exploration for new opportunities is acentral theme in the literature on innovation (March,1991). Exploration may take the form of “outside-in”processes, that is customer-oriented behaviors, or of“inside-out” processes, purely R&D driven innovation.Merely listening to customers or using traditional researchtechniques such as surveys and focus groups can inhibitinnovation, constraining it to ideas that customers canenvision and articulate—which may lead to safe, but bland,offerings. Customers are not always able to articulate theirneeds. Customers have needs of which they are not aware.They are real, but not yet in the customers’ awareness(Slater & Narver, 1998). Thus, to develop new products,Prospectors may closely observe customers’ use of productsor services in normal routines (Leonard & Rayport, 1997).They also may work closely with lead users who recognizea need in advance of the majority of the market (Herstatt &von Hippel, 1992).

An assumption generally subscribed to in evolutionaryeconomics is that innovations arise from developments intechnological knowledge (Nelson & Winter, 1982). Thesetechnological innovations create new market opportunities

J. of the Acad. Mark. Sci. (2007) 35:5–17 7

while simultaneously transforming demand in many exist-ing product markets. From this perspective, the marketprimarily influences selection among competing technolo-gies and the course of the technology after its inception.Thus, a technological orientation should be positivelyrelated to success for Prospectors (Miles & Snow, 1978)since R&D frequently drives development of these radicalinnovations (Olson, Walker, & Ruekert, 1995; Walker &Ruekert, 1987).

Marketers in Prospector firms should be aware of thetechnological capabilities of the firm when communicatingwith the market while R&D should be customer-orientedwhen creating new products as well as developing coretechnologies. In addition, when attempting to transformcutting edge technologies into products or services, Pros-pectors may not even recognize who their competitors orpotential competitors are. Thus, Prospectors should dem-onstrate more concern with customers and with technologythat continuously pushes product and market boundariesthan with competitors (Walker & Ruekert, 1987).

As the strategic orientation of Prospectors is to pursuenew product and market opportunities, it follows that theyshould target the innovator and early adopter segments.Buyers in these segments do not require a total solution totheir problems. Prospectors are neither totally effective norefficient at developing total customer solutions (Walker &Ruekert, 1987). Thus, we predict that in Prospectororganizations:

HP A positive relationship exists between (a) customerorientation and performance, (b) technological orientationand performance, (c) targeting innovators and performance,and (d) targeting early adopters and performance.

Analyzers

The key to success for Analyzers is to simultaneously bringout either improved or less expensive versions of productsintroduced by Prospectors while defending core marketsand products. Analyzers (followers) can be as successful asProspectors (early entrants) if they learn about customers’preferences from Prospectors’ successful and unsuccessfulefforts (e.g., Golder & Tellis, 1993) and limit their newproduct introductions to categories that have already shownpromise in the market place. Thus, Analyzers shouldclosely monitor customer reactions to Prospectors’ offer-ings as well as competitors’ activities, successes, andfailures. In other words, while customers are certainlyimportant to Analyzers, monitoring competitors’ actions isalso important to the success of Analyzers. Furthermore,market success for Analyzers is based on imitation ratherthan on technological innovation (Miles & Snow, 1978).

As imitators, Analyzers may observe buyer behavior inthe innovator segment but will not target their offerings tothis segment. However, the knowledge gained by observingbuyer behavior in the innovator segment may informproduct development and marketing efforts in the earlyadopter segment. The early adopter segment is whereProspectors and Analyzers are most likely to competehead-to-head. Analyzers are also interested in the main-stream market, as represented by the early majoritysegment, and have the capability to compete successfullythere (Slater & Olson, 2001). Thus we predict that inAnalyzer organizations:

HA A positive relationship exists between (a) customerorientation and performance, (b) competitor orientation andperformance, (c) targeting early adopters and performance,and (d) targeting the early majority and performance.

Low cost defenders

The key to success for Low Cost Defenders is to providequality products or services at the lowest overall cost.While Low Cost Defenders will have less technologicallysophisticated product lines than firms pursuing otherbusiness strategies, technological advances that result inprocess innovations are critical to their overall success(Walker & Ruekert, 1987).

Consistent with their objective of achieving a low costposition, the external focus of Low Cost Defendersemphasize is a competitor orientation. Competitors serveas a benchmark against which prices, costs, and perfor-mance can be compared. Low Cost Defenders do notrequire a sophisticated customer learning or customerlinking capability because their target market is comprisedof price-sensitive buyers.

Two drivers of low cost are experience effects andeconomies of scale. Cost reductions through cumulativeexperience are most likely to be achieved by takingadvantage of the experiences of competitors and a highgrowth rate. Low Cost Defenders are best able to takeadvantage of the experience of Prospectors and Analyzersafter the product technology has matured and standardshave emerged that reduce customer risk. This is most likelyto occur when targeting the early majority segment.Economies of scale will be achieved through successfulpenetration of the mass market, with the early and latemajority segments being the mass market. Thus, we predictthat in Low Cost Defender organizations:

HLCD A positive relationship exists between (a) compet-itor orientation and performance, (b) technological orienta-tion and performance, (c) targeting the early majority and


performance, and (d) targeting the late majority andperformance.

Differentiated defenders

The key to success for Differentiated Defenders is toprovide premium service and/or the highest qualityproducts to market segments that value and are willingto pay for them. The Differentiated Defender’s focus is onmaintaining its position in established (early and latemajority) markets (Walker & Ruekert, 1987). The Differ-entiated Defender’s value proposition is based on a nuancedunderstanding of its customers. Differentiated Defendersare skilled at segmenting the early and late majoritymarkets to identify those segments that value superiorquality and service (Slater & Olson, 2001). Consequently,the most successful Differentiated Defenders will empha-size customer-oriented behaviors. While this does notmean that they ignore competitors or do not engage inproduct or service innovation, these are not primaryactivities. Thus we predict that in Differentiated Defenderorganizations:

HDD A positive relationship exists between (a) customerorientation and performance, (b) targeting the early major-ity and performance, and (c) targeting the late majority andperformance.

Research design

Data collection process and the study sample

We focused this study on high-technology manufacturingand service firms operating in 20 different R&D-intensiveindustries as defined by the U.S. Bureau of Labor Statistics,in SIC categories 20 and 30, to provide a reasonably similarcontext for respondents but also to be broad enough for theresults to be generalizable. We purchased a commercialmailing list of 1,450 senior marketing managers inbusinesses with 500 or more employees operating in theseindustries. In collecting the data, we followed the guide-lines by Huber and Power (1985) on how to obtain highquality data from key informants. A key informant design iscommon in studies of marketing strategy (e.g., Slater &Olson, 2001; Vorhies & Morgan, 2005) and studies ofstrategic behavior (e.g., Day & Nedungadi, 1994; Gatignon& Xuereb, 1997). Senior marketing managers were selectedas key informants because they should be knowledgeableabout strategic behavior, target markets, business strategy,and overall firm performance.

Questionnaires were sent to the 1,450 senior marketingmanagers along with a personal letter that provided a briefintroduction and a general explanation of the intent of thestudy, a questionnaire, and a postage-paid return envelope.The questionnaire defined the meaning of business unit andasked each respondent to refer to either the largest SBU inthe organization or the one they were most familiar withwhen answering the questions. Four weeks after the initialmailing, a follow-up mailing was sent out with a duplicatecopy of the questionnaire and a return envelope. Wereceived 160 usable responses (Prospectors: 55, Analyzers:45, Low Cost Defenders: 23, Differentiated Defenders: 30,Reactors: 7) that, after accounting for undeliverables,constituted a 12% response rate. Approximately two-thirdsof responses were received after the first mailing with theremaining responses arriving after the second mailing.

Although non-response bias is always a concern insurvey research, this response rate is within the range oftypical response rates for strategic marketing studies (e.g.,Gatignon & Xuereb, 1997; Homburg & Pflesser, 2000).Furthermore, significant differences between late respond-ers and early responders would indicate the presence ofnon-response bias. We found no significant differencesbetween early and late responders at the 0.05 level onfifteen key measures.

As a group, the respondents averaged 22 years workingin their respective industries and 16 years within theircurrent organizations. In addition, the respondents indicatedan average of 4.68 (on a scale from 1=low to 5=high)when asked about the extent of their involvement in theprocess of formulating marketing strategies; 4.56 whenasked about their knowledge of marketing issues withintheir SBU; and 4.33 when asked about their knowledge ofmarketing issues within their industry. Thus, the keyinformants sampled appear to be knowledgeable regardingthe issues studied in our research.

Description of the measures

All constructs with the exception of strategy type aremeasured with multi-item scales (contact the first author forthe measures). The items were placed randomly in the finalinstrument to avoid order bias. We employed the Likertmethod of summated ratings in scale construction. Thethree strategic behavior scales were adapted from estab-lished scales, including: customer orientation (Narver,Slater, & MacLachlan, 2004), competitor orientation(Narver & Slater, 1990), and technological orientation(Gatignon & Xuereb, 1997).

Market targeting was measured with five new scalesrepresenting emphasis placed on targeting innovators, earlyadopters, early majority, late majority, and laggards.Following Churchill (1979), we first specified the domain


of the constructs from Rogers (1995) and Moore (1991).Based on a thorough review of Moore (1991, 1995), Rogers(1995), and Wiefels (2002), we generated a pool of 54items that capture the domains of the five constructs. Eachstatement was reviewed to insure that its meaning was clear.

We adapted Jaworski and Kohli’s (1993) measures ofmarket turbulence (customer product preference change),competitive hostility (price competition), and technologicalturbulence to control for the effect of industry structure onperformance. Those constructs are measured with three,two, and five indicators respectively.

We assessed strategy type using the self-typing para-graph approach that is commonly used in strategicmarketing research (e.g., Matsuno & Mentzer, 2000;Vorhies & Morgan, 2003). Several studies (e.g., Conant,Mokwa, & Varadarajan 1990; James & Hatten, 1995) havedemonstrated that this is a valid measurement approach. Weuse the descriptions from Slater and Olson (2000) todiscriminate between the Low Cost and DifferentiatedDefender types. As a check on the validity of the self-typing classification scheme, we analyzed differences inrevenues, customer product preference change and techno-logical turbulence across the strategy types. We expected aDefender > Prospector ordering for revenues and aProspector > Analyzer > Defender ordering for theenvironmental variables. We found that Defenders hadgreater revenues than Prospectors, a Prospector > Analyzer >Defender ordering for technological turbulence, and that

customer product preference change was greater forProspectors and Analyzers than for Defenders, providingsupport for the self-typing approach.

We followed the lead of other marketing strategyresearchers (e.g., Jaworski & Kohli, 1993; Olson et a1.,1995) and utilized a global measure of firm performancebecause of its relevance regardless of the nature of thecontextual influences. As Ittner and Larcker (1997, p.17)note, “overall perceived performance should encompass notonly the organization’s performance on the precedingdimensions (return on assets, return on sales, and salesgrowth), but also any other financial and non-financialgoals that may be important to the organization.” In a recentstudy, Morgan, Kaleka, and Katsikeas (2004) found astrong correlation between objective performance data andsubjective assessments of performance by key informants,which supports the validity of perceptual data.

Measurement purification

Tables 1 and 2 report the results of the measurementanalyses. Table 1 summarizes the constructs’ means,standard deviations, variances extracted, composite reli-abilities, factor loadings as well as the overall CFA model’sfit indices. Table 2 reports the correlations and sharedvariances among the constructs. Overall, the 12 reflectivescales and their purified 55 items were found to be reliableand valid in the context of this study.

Table 1 Summary statistics of the measurement analyses (n=160)

Constructs No. of itemsin scale

Mean Standard deviation Variance extracted Composite reliability Factor loadingsa

Innovators 5 3.01 0.52 0.40 0.76 0.57–0.77Early adopters 7 3.16 0.57 0.51 0.88 0.61–0.78Early majority 9 3.64 0.57 0.60 0.90 0.74–0.86Late majority 4 3.40 0.63 0.52 0.81 0.67–0.76Laggards 4 3.02 0.70 0.50 0.80 0.61–0.88Customer orientation 3 3.59 0.62 0.46 0.72 0.63–0.74Competitor orientation 4 3.65 0.71 0.53 0.81 0.57–0.88Technological orientation 5 3.39 0.61 0.51 0.84 0.63–0.82Customer product preferencechange

3 3.24 0.67 0.35 0.61 0.52–0.70

Price competition 2 3.82 0.79 0.50 0.66 0.69–0.72Technological turbulence 5 3.49 0.72 0.53 0.85 0.64–0.85Performance 4 3.49 0.84 0.63 0.87 0.69–0.88

Fit Statistics:χ2 =2,450.06df=1,364Delta2=0.91RNI=0.91CFI=0.91TLI=0.91RMSEA=0.06a All factor loadings are significant at the p<0.01 level.


Following the data collection, we tested the dimension-ality, reliability, and validity of the scales. Given therelatively small sample size (n=160) and the battery ofitems used to measure the various constructs, we factoranalyzed each construct separately to remove problematicitems and followed with an assessment of the remainingitems in one confirmatory factor analysis using LISREL 8.72(Jöreskog, S. Du Toit, & M. Du Toit, 2000). In removingitems from a scale, we followed suggestions by Andersonand Gerbing (1988) regarding maintaining conceptualintegrity and explanatory power while also incorporatingstatistical considerations associated with reliability andvalidity. For the overall CFAwe used the DELTA2 (Bollen,1989), RNI (McDonald & Marsh, 1990), CFI (Bentler,1990), TLI (Tucker & Lewis, 1973), and RMSEA (Steiger& Lind, 1980) fit indices to evaluate the measurementmodel. This measurement process resulted in us keeping 55of the 100 original items included in the survey. The refinedset of 55 items resulted in acceptable fit statistics (χ2=2,450.06, df=1,364, DELTA2=0.91, RNI=0.91, CFI=0.91,TLI=0.91, and RMSEA=0.06; See Table 1 for completeresults). In addition, the 55 items were found to be reliableand valid when evaluated based on each item’s errorvariance, modification index, and residual covariation (e.g.,Fornell & Larcker, 1981; Jöreskog et al., 2000).

Next, we calculated composite reliability for each scaleusing the procedures outlined by Fornell and Larcker (1981).The composite reliabilities for the 12 scales ranged from 0.61to 0.90, with factor loadings ranging from 0.52 to 0.88 (p<0.01) (See Table 1 for complete results). The technologicalorientation and customer product preference change scales fellbelow the commonly used threshold of 0.70, the innovator andcustomer orientation scales fell between 0.70 and 0.80, and theremaining eight scales were equal to or exceeded 0.80.

We assessed discriminant validity using two differentmethods. First, we assessed the average variance extracted

(AVE) for each construct, and verified that the AVE washigher than the corresponding shared variance for all possiblepairs of constructs (Anderson & Gerbing, 1988). The averagevariances extracted ranged from 0.35 to 0.63, and the sharedvariances ranged from 0.00 to 0.34 (Tables 1 and 2). Second,we tested discriminant validity via the test advocated byAnderson (1987) and Bagozzi and Phillips (1982). In thistest, all pairs of constructs were analyzed in a series of two-factor CFA models. Each model was run twice —onceconstraining the ϕ coefficient to 1.0 and once allowing ϕ tovary freely. Using a χ2-difference test on the paired nestedmodels (Anderson & Gerbing, 1988), we found that thecritical value (Δχ2

Δdf¼1ð Þ>3:84) was exceeded in all cases(the lowest Δχ2

Δdf¼1ð Þ ¼ 9:38 was found between customerproduct preference change and price competition).

We employed a confirmatory factor-analytic approach toHarmon’s one-factor test (e.g., Sanchez & Brock, 1996) toassess whether common method bias (CMB) wouldconstitute a problem in the testing and interpretation ofthe results. The rationale for this test is that if CMB poses aserious threat, a single latent factor would account for allmanifest variables (Podsakoff & Organ, 1986) as opposedto the a priori specified measurement model. As such, aworse fit for the one-factor model means that CMB is notsignificant enough to warrant concern (Sanchez, Korbin, &Viscarra, 1995). In our case, the one-factor model yielded aχ2=4,841.58 with 1,430 degrees of freedom (comparedwith the χ2=2,450.06 and df=1,364 for the measurementmodel). Thus, CMB is not a serious threat in the context ofthis study’s use of the measures.

Results

We tested the hypotheses using OLS regression within eachof the four strategy subgroups. This is the preferred

Table 2 Correlations and shared variances (n=160)

I EA EM LM L CO COM TO MKT CH TECH PERF

I – 0.18 0.01 0.04 0.10 0.01 0.00 0.11 0.08 0.00 0.01 0.02EA 0.42 – 0.04 0.02 0.17 0.07 0.00 0.24 0.07 0.01 0.14 0.21EM −0.11 0.20 – 0.04 0.00 0.16 0.17 0.01 0.03 0.00 0.10 0.21LM 0.19 −0.15 0.21 – 0.20 0.02 0.01 0.00 0.02 0.00 0.00 0.04L −0.32 −0.41 0.01 0.45 – 0.01 0.00 0.03 0.04 0.01 0.04 0.00CO 0.10 0.27 0.40 0.13 −0.10 – 0.22 0.10 0.08 0.00 0.21 0.12COM −0.06 0.07 0.41 0.12 −0.03 0.47 – 0.04 0.02 0.01 0.10 0.06TO 0.33 0.49 0.11 0.06 −0.16 0.32 0.20 – 0.12 0.00 0.26 0.08MKT 0.29 0.26 0.17 −0.15 −0.21 0.28 0.13 0.35 – 0.12 0.34 0.00CH −0.03 −0.08 0.03 −0.05 0.09 0.02 0.11 0.01 0.34 – 0.08 0.01TECH 0.10 0.37 0.31 −0.04 −0.20 0.46 0.31 0.51 0.58 0.28 – 0.01PERF 0.13 0.46 0.46 0.19 −0.01 0.35 0.25 0.29 0.06 −0.12 0.11 –

Correlations are included below the diagonal and shared variances are included above the diagonal. All correlations above 0.16 are significant atp<0.05.


technique when the moderator variable is categorical(Sharma, Durand, & Gur-Arie, 1981). One-tailed tests wereused for the directional hypotheses and two-tailed testswere used for all other relationships.

Given that each subgroup has a relatively small samplesize, we conducted a “power analysis,” as suggested by J.Cohen, P. Cohen, West, and Aiken (2003), to determine theprobability of finding the sample R2 to be greater than zerowith α=0.01 for each strategy type. We achieved excellentstatistical power (β>0.99, p<0.01) in each subgroup aswell as in the overall sample (R2-range: 0.75–0.96;Adjusted R2-range: 0.64–0.89; see Table 3). However, thepower to detect a significant relationship at the variablelevel is considerably lower due to the small subgroupsamples. Thus, we use p≤0.10 as our Type I error rateinstead of the more conservative p≤0.05 Type I error rate.We believe this strikes a reasonable balance betweencommitting a Type I error and a Type II error (Sawyer &Ball, 1981).

Additionally, for all models, the Variance InflationFactors (VIF) were lower than 10.0 except for the pricecompetition variable (VIF=10.70) and the technologicalorientation variable (VIF=12.49) in the “low-cost defender”model. Thus, multicollinearity does not appear to systemat-ically affect variables in the models.

Prospectors In the analysis of the Prospector model, wefound positive effects of technological orientation (β=0.25,

t=2.22, p<0.05), targeting innovators (β=0.33, t=2.63, p<0.05), and targeting early adopters (β=0.28, t=2.27, p<0.05) on performance. We also found a negative effect oftargeting the early majority (β=−0.21, t=3.49, p<0.05) onperformance, a relationship that was not hypothesized.Overall, the model had an adjusted R2=0.73. Thus, HPb,HPc, and HPd could not be rejected in the analysis.

Analyzers In the analysis of the Analyzer model, we foundpositive effects of targeting early adopters (β=0.46, t=3.08,p<0.01) and targeting the early majority (β=0.40, t=2.51,p<0.01). Although the regression results for Analyzersindicate that competitor orientation is not a significantpredictor of performance, this is actually a classic case ofredundancy. Each semi-partial correlation, and thecorresponding β is less than the simple correlation betweencompetitor orientation and performance. This is becausecompetitor orientation and targeting the early majority sharevariance and influence. Cohen et al.’s (2003) recommendedsolution to the problem of describing an IV’s participationin determining R is given by the partial correlationcoefficient and its square. In this case, the squared partialcorrelation between competitor orientation and performanceafter controlling for the influence of all IVs with theexception of targeting the early majority is 0.15 which isstatistically significant (p<0.05). Overall, the model had anadjusted R2=0.64. Based on the analysis, we cannot rejectHAb, HAc, or HAd.

Table 3 Standardized regression results with performance as the criterion variable for the four viable strategy types

Predictor variables Prospectors(n=55)

Analyzers(n=45)

Low cost defenders(n=23)

Differentiateddefenders (n=30)

Revenues (log) 0.06 0.01 0.29 0.20Customer product preference change 0.06 0.11 −0.47* 0.03Price competition −0.17 −0.08 −1.03** −0.20*Technological turbulence 0.05 −0.22 0.30* 0.02Customer orientation 0.09 −0.01 0.03 0.55**Competitor orientation −0.09 0.12*,a 0.52** 0.00Technological orientation 0.25** 0.15 1.17** 0.10Innovators 0.33** −0.16 −0.18* 0.00Early adopters 0.28** 0.46** 0.23* −0.28*Early majority −0.21** 0.40** 0.48** 0.43**Late majority 0.05 −0.13 −0.39*,b 0.20*,a

Laggards 0.03 0.19 0.91** −0.27*R2 0.80 0.75 0.96 0.90Adjusted R2 0.73 0.64 0.89 0.82F-value 11.76** 6.40** 13.16** 10.70**Effect size β>0.99** β>0.99** β>0.99** β>0.99**

** p<0.01, * p<0.05. Reactors (n=7) were excluded from the overall analysis. One-tailed tests were used for directional relationships and two-tailed tests were used for all others relationships. The effect size (i.e., “power of each subgroup model”) was determined using the procedures byCohen et al. (2003, p. 92); for each subgroup, we find adequate statistical power in the samples to conduct the analysis of the included variables.a redundancy effectb net suppressor effect


Low Cost Defenders In the analysis of the Low Cost-Defender model, we found negative relationships betweencustomer product preference change (β=−0.47, t=2.36, p<0.05) and performance, and between price competition (β=−1.03, t=4.06, p<0.01) and performance, and a positiverelationship between technological turbulence (β=0.30, t=2.24, p<0.05) and performance. The signs of the coef-ficients for competitor orientation (β=0.52, t=3.74, p<0.01) and technological orientation (β=1.17, t=4.05, p<0.01) were positive and significant. We found positive andsignificant relationships between targeting the early major-ity (β=0.48, t=4.02, p<0.01) and performance, andbetween targeting laggards (β=0.91, t=4.62, p<0.01) andperformance. The coefficient for targeting the late majoritywas negative and significant (β=−0.39, t=1.60, p<0.10).However, this seems to be a classic example of netsuppression where targeting the late majority is positivelycorrelated with performance (r=0.81, p<0.01), but has anegative regression coefficient. In this case, the squaredpartial correlation between targeting the late majority andperformance after controlling for the influence of all IVswith the exception of targeting the early majority andtargeting laggards is 0.33 which is statistically significant(p<0.05). Overall, the model had an adjusted R2=0.89.Given these results, HLCDa, HLCDb, and HLCDc couldnot be rejected in the analysis, and we do not reject HLCDdgiven the results of the partial correlation analysis.

Differentiated defenders In the analysis of the Differentiated-Defender model, we found a negative relationship betweenprice competition (β=−0.20, t=1.82, p<0.10), and positiverelationships between customer orientation (β=0.55, t=3.84, p<0.01) and performance, and between targeting theearly majority (β=0.43, t=2.46, p<0.05) and performance.We found no relationship between targeting the latemajority and performance. However, as was the situationwith Analyzers, this too seems to be a case of redundancy.When we computed the squared partial correlation betweentargeting the late majority and performance after controllingfor the influence of all IVs with the exception of targetingthe early majority, we found a value of 0.36 which issignificant (p<0.05). We also found a negative effect oftargeting laggards (β=−0.27, t=2.11, p<0.05). Overall, themodel had an adjusted R2=0.82. Thus, HDDa, HDDb, andHDDc could not be rejected in the analysis.

Before discussing the findings, we address some caveatsregarding this study. First, the study utilizes a cross-sectional design; thus, inferences about causality shouldnot be drawn. Second, we utilize a single respondent—keyinformant—design. Ideally, we would obtain responsesfrom multiple informants in each SBU. However, basedon our analysis, common method variance does not seem to

be a threat to the internal validity of the study. Third, whilethe response rate is within the typical range for studies suchas this and non-response bias does not seem to be aproblem, we clearly would have preferred a higher responserate. Despite these limitations, this study provides usefulguidance to scholars and managers regarding appropriatestrategic behavior and target market selection in the hightech sector.

Discussion and implications

The results of this study are quite intriguing for severalreasons. First, we find support for 13 out of our 15hypotheses. Second, the explanatory power of the modelsis quite high with the adjusted R2 for each subgroup at orabove 0.60. Third, market targeting which, to the best ofour knowledge, has not been studied empirically, addssignificantly to the explanatory of the models. Finally, thepattern of results for each strategy type holds somesurprises which we discuss now.

The most interesting results from the Prospector analysiswere the findings of no relationship between customerorientation and performance and a negative relationshipbetween targeting the early majority and performance.Previous research has found a significant and positiverelationship between customer orientation and performancefor Prospectors (Olson, Slater, & Hult, 2005). Why is it thatwe find no such relationship in this study of firmscompeting in high-tech markets?

One possibility is that a customer orientation improvesperformance only in markets where demand uncertainty ishigh but detracts from performance when demand uncer-tainty is low as found by Gatignon and Xuereb (1997). Todetermine whether uncertainty moderates the customerorientation—performance relationship for Prospectors, weconducted a post hoc analysis. After mean centering thevariables, we computed a multiplicative interaction term forcustomer orientation and customer product preferencechange. We then regressed performance on the original setof independent variables plus the interaction term. Wefound a positive relationship between the interaction term(β=0.21, t=2.18, p<0.05) and performance. R2 increasedby 2.4%, significant at p<0.05 (F change=4.74). Thus, acustomer orientation appears to be positively related toperformance for Prospectors when uncertainty is high.

The finding of a negative relationship between targetingthe early majority and performance is consistent withMoore’s (1991) proposition that innovative firms (i.e.,Prospectors) have difficulty “crossing the chasm” betweenthe early adopter and early majority market segments. Thechasm exists because critical differences between the early


adopter and early majority segments cause them to adopt atdifferent rates, make cross-market segment communicationextremely difficult regarding technological innovations,and, more critically, the marketing strategies firms use toeffectively reach the early market for technology innova-tions do not address the very different needs of the main-stream market. Thus, Prospectors who excel at exploitingnew product and market opportunities, have a difficult timereaching out to more mainstream customers to successfullycommercialize their technological innovations.

What then are the implications for managers of Pros-pector businesses? First, they should place a high priorityon developing customer sensing and relating capabilities,staying ahead or abreast of technological developments andusing these to supply new technology based solutions fortheir customers’ expressed and latent needs. While ourresults indicate that customer orientation is related toperformance only in markets characterized by a high degreeof market preference change, this makes perfect sense froma positive feedback perspective.

When there is high rate of change in customer prefer-ences, customer orientation involving willingness to exper-iment, market sensing capabilities and fast implementationskills increase and as they increase they drive even morechanges in customer preferences (Dickson, 1992). But evenin times of relative market calm it is in the Prospector’s bestinterests to prepare for market change by developing andmaintaining customer sensing and relating capabilities (e.g.,D’Aveni, 1994). Dickson et al., (2001) precisely describesuch a major prospecting dynamic, how a within firm marketsurveillance feedback effect can lead to increasing returns oninvestment in market sensing rather than decreasing returns.Good Prospectors get ever better at prospecting. But themomentum of the customer sensing capability needs to bemaintained because if it starts to slip the feedback effect willdrive it down (see Dickson et al., 2001, Figure 3).

Second, managers of Prospector companies might drawon the lessons of the evolutionary economics simulationliterature (Nelson & Winter, 1982). Nelson and Winterfound that most of the time, imitators with major collateraldistribution assets and brand reputation come to dominatetheir more innovative rivals. The imitator “analyzes” theinnovator’s success and the more quickly the innovator’sadvantage can be imitated (and appropriated) by the largerfirm that can rapidly diffuse their imitation across themarket at low cost, the faster the larger analyzer withmarketing collateral assets in place wins out (see Nelson &Winter, 1982). The Analyzer and Prospector in our studyare equivalent to Nelson and Winter’s Imitator andInnovator companies in their simulations. If the Analyzeralmost always beat the Prospector because of competitivedynamic advantages then it makes sense for a Prospector tocombat the Analyzer’s collateral asset advantages through a

business alliance of some form, but most particularly onethat reaches and penetrates the early majority and latemajority market.

The most surprising result for Analyzers is that customerorientation is not significantly related to performance.Again, previous research has found a significant andpositive relationship between customer orientation andperformance for Analyzers (Olson et al., 2005). Why is itthat we find no such relationship in this study of firmscompeting in high-tech markets? It may be that Analyzersdo not benefit from a customer orientation because theyderive sufficient benefit from copying the successful effortsof Prospectors.

Aside from developing strong competitor analysis capabil-ities, what should managers in Analyzer firms do to enhancetheir chances for success? First, they should recognize that theearly majority is comprised of many segments and identify thebest “beachhead” (Moore, 1995), the target market fromwhich to pursue the mainstream market. A good beachheadrequires that customers have a single, compelling, “musthave” reason to buy that can be addressed by the capabilitiesof the firm. Marketing capabilities of successful Analyzersrelative to Prospectors include (Slater & Olson, 2001) abilityto offer lower prices, intensive distribution, and a lesseremphasis on product innovation.

Low Cost Defenders are successful in the early majority,late majority, and laggard markets that are characterized byincreasing emphasis on price competition. They are able tooffer lower prices than businesses employing other strate-gies (Slater & Olson, 2001) because of their internal/costorientation (Olson et al., 2005), their competitor orientationthat allows them to benchmark their value chains, and theirtechnology orientation that leads to process improvement.Moreover, the most successful Low Cost Defenders havethe lowest marketing expenses due to placing the lowestemphasis on marketing research, product innovation,promotion, and distribution management of all of thestrategy types (Slater & Olson, 2001).

As we hypothesized, success for Differentiated Defend-ers is associated with a customer orientation, and withtargeting the early and late majority markets. The mostsuccessful Differentiated Defenders conduct extensivemarketing research to identify the market segments thatvalue innovative products and services and that are willingto pay premium prices for them. They promote theirproducts/services extensively and use an internal salesforce to control their message (Slater & Olson, 2001). Notsurprising is the finding of a negative relationship betweentargeting laggards and performance. Laggards are technol-ogy skeptics who prefer to maintain the status quo andpurchase only when they believe that the cost justificationis absolutely solid. Thus, they would be unlikely to pay thepremium prices that Differentiated Defenders require.


Suggestions for future research

It is difficult to study market dynamics in a cross-sectionalstudy. Our research design, data, and analytical approachcould be interpreted as our suggesting that all of the categoriesof innovation adopters and strategy types will exist simulta-neously in a market. However, the evolutionary perspective towhich we subscribe argues that by the time Defenders enter amarket, Prospectors will either have morphed into a differentstrategy type or will have moved into adjacent markets ordeveloped new products that make new markets. In that case,there exist two technology standards that compete againsteach other. In this case, Prospectors who represent the newtechnology will be competing against Defenders who repre-sent the old technology. However, our objective is not topredict the path of market evolution but to provide insight intothe predictors of success for the different strategy types. Thenext phase in this research stream would utilize archival datato study these phenomena as a market develops and matures(e.g., Christensen, 1997).

Another issue is whether the Miles and Snow typology isa valid vehicle for studying these types of phenomena. Onthe one hand, several studies (e.g., James & Hatten, 1995;Slater & Olson, 2001) have provided evidence of the val-idity of this classification scheme. A refinement that mightincrease the validity of the typology would be to view theresponse to the entrepreneurial problem in two dimensionalspace instead the Prospector, Analyzer, Defender “continu-um of adjustment strategies” (Miles & Snow, 1978, p. 68).The second dimension would closely parallel learning strat-egies. The first learning strategy emphasizes learning throughexploration and experimentation. The second strategy empha-sizes vicarious learning, learning by observing and analyzingrivals and their successes and failures. The third strategyemphasizes learning through exploitation manifested ascontinuous improvement based a succession of incrementalimprovements (e.g., March, 1991). While all three of theselearning strategies are essential for organizations, theycompete for scarce resources. Consequently, managers makeexplicit and implicit choices among them based on theirperceived value to the organization. Thus, a revised typologywould be based on the interaction between adjustmentstrategy and learning strategy. Testing the validity of thisapproach to organizational classification would represent thefirst substantial refinement to the typology since Walker andRuekert (1987).1

Conclusion

The results of this study suggest that, based on theirstrategy type, successful firms develop skill sets for specific

situations (e.g., success with early market customers). Forexample, Prospectors are the most likely to possess theresources and capabilities to develop the radical innovationsthat address needs in the innovator and early adoptersegments. Conversely, Analyzers and Defenders are morelikely to develop incremental and process innovations thatenable them to address mainstream market needs.

However, to be successful across a range of innovations(both radical and incremental, and product and process),firms must diversify their skill sets. In essence, thecapability to develop contradictory skill sets is what willallow Prospectors to successfully cross the chasm and allowAnalyzers and Defenders to overcome the innovator’sdilemma (Christensen, 1997). Specifically, for Prospectorsto cross the chasm and move into mainstream markets, theymust develop Analyzer-like marketing capabilities andstrive for cost reductions, or partner with firms that alreadypossess those capabilities. And for Analyzers and Defendersto successfully develop and commercialize radical innova-tions, they must develop some of the marketing andtechnological resources and capabilities of Prospectors, andbe willing to cannibalize sales from existing product lines.

At first glance this recommendation might be construedas simply a call for the elimination of a unique strategy.After all, if all groups share the same skill sets what is thereto differentiate their approach to achieving sustainablecompetitive advantage? But this interpretation would beincorrect. Rather, this recommendation focuses on theevolution of strategy. Just as today’s high technologyofferings spawn tomorrow’s mass-marketed generics,today’s skills in bringing innovative ideas to the marketmust give way to tomorrow’s need to generate costefficiencies and expand distribution and customer appeal.While, it is beyond the scope of this study to determinehow that process is best pursued, the strength of thefindings in this study serve to suggest that future researchinto the relationship between innovation adoption andstrategy evolution is warranted.

Acknowledgement The authors gratefully acknowledge the supportof the Center for International Business Education and Research atMichigan State University (MSU-CIBER) and the College of BusinessAdministration at University of Colorado—Colorado Springs forfinancial assistance. We also acknowledge the many helpful commentsand suggestions of two anonymous reviewers.

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Documents

On the importance of matching strategic behavior and target market selection to business strategy in high-tech markets