when are technologies disruptive? a demand-based …

Strategic Management JournalStrat. Mgmt. J., 23: 667–688 (2002)

Published online 28 March 2002 in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/smj.246

WHEN ARE TECHNOLOGIES DISRUPTIVE? ADEMAND-BASED VIEW OF THE EMERGENCE OFCOMPETITION

RON ADNER*INSEAD, Fontainebleau Cedex, France

By identifying the possibility that technologies with inferior performance can displace establishedincumbents, the notion of disruptive technologies, pioneered by Christensen (1997), has had aprofound effect on the way in which scholars and managers approach technology competition.While the phenomenon of disruptive technologies has been well documented, the underlyingtheoretical drivers of technology disruption are less well understood. This article identifiesthe demand conditions that enable disruptive dynamics. By examining how consumers evaluatetechnology and how this evaluation changes as performance improves, it offers new theoreticalinsight into the impact of the structure of the demand environment on competitive dynamics. Twonew constructs—preference overlap and preference symmetry—are introduced to characterizethe relationships among the preferences of different market segments. The article presents aformal model that examines how these relationships lead to the emergence of different competitiveregimes. The model is analyzed using computer simulation. The theory and model resultshold implications for understanding the dynamics of disruptive technologies and suggest newindicators for assessing disruptive threats. Copyright 2002 John Wiley & Sons, Ltd.

From S-curves (Foster, 1986), to technologytrajectories (Dosi, 1982) to punctuated equilibria(Tushman and Anderson 1986), the dominantview in technology strategy has been that thedisplacement of established firms and technologiesby new firms and technologies is driven bythe superior performance offered by newcomersand established players’ difficulties in matchingtheir performance and capabilities. By identifyingthe possibility that technologies with inferiorperformance can displace established incumbents,the notion of disruptive technologies, pioneeredby Christensen (1997), has had a profoundeffect on the way in which scholars andmanagers alike approach technology competitionand has prompted a reassessment of the ways in

Key words: disruptive technologies; demand hetero-geneity; technology strategy; technology evolution*Correspondence to: R. Adner, INSEAD, Boulevard de Con-stance, 77305 Fontainebleau Cedex, France.

which firms approach technological threats andopportunities.

While the phenomenon of disruptive technolo-gies has been well documented, the underlyingtheoretical drivers of technology disruption are lesswell understood. Understanding the conditions thatgive rise to technology disruptions, however, isfundamental to assessing the pervasiveness of thephenomenon and for guiding strategic responsesto potentially disruptive threats. Indeed, without atheoretical underpinning with which to answer thequestion of ‘When are technologies disruptive?’ itis impossible to make ex ante distinctions betweendisruptive technologies and technologies that aremerely inferior.

This article identifies the demand conditions thatenable disruptive dynamics. By examining howconsumers evaluate technology and how this eval-uation changes as performance improves, it offersnew theoretical insight into the impact of the struc-ture of the demand environment on competitive

Copyright 2002 John Wiley & Sons, Ltd. Received 1 December 1999Final revision received 16 November 2001

668 R. Adner

dynamics. Two new constructs—preference over-lap and preference symmetry—are introduced tocharacterize the relationships among the prefer-ences of different market segments. The articlepresents a formal model that examines how theserelationships lead to the emergence of differentcompetitive regimes. The model is analyzed usingcomputer simulation. The theory and model resultshold implications for understanding the dynamicsof disruptive technologies and suggest new indica-tors for assessing disruptive threats.

TECHNOLOGY COMPETITION ANDDISRUPTIVE TECHNOLOGIES

Explanations of technology competition outcomeshave tended to focus on the supply-side interac-tions of firms and technologies. At the technologylevel, outcomes have often been attributed to theexhaustion of the incumbent technology’s devel-opment trajectory (Foster, 1986; Utterback andAbernathy, 1975) or the entrant’s outright superi-ority. At the firm level, the challenge of managingdisplacement threats has often been attributed toan unwillingness to cannibalize existing technol-ogy investments (Kamien and Schwartz, 1982);organizational inertia (Hannan and Freeman, 1977;Tushman and Romanelli, 1985); and the inabilityto adopt the necessary skills needed to engage inthe new technology (Henderson and Clark, 1990;Leonard-Barton, 1992).

Closer examination of technology competition,however, reveals that technology transitions arenot necessarily due to the incumbent technol-ogy’s inherent limits (Christensen, 1992; Cooperand Schendel, 1976; Henderson, 1995), the newtechnology’s ability to provide superior perfor-mance (Christensen, 1997; Levinthal, 1998), orincumbents’ inability to master new skills (Bowerand Christensen, 1995). While these factors areimportant, numerous cases of innovative incum-bents who did not suffer from these handicaps, yetnonetheless mismanaged the challenge of techno-logical transition (Smith and Cooper, 1988; Smithand Alexander, 1988; Christensen and Rosen-bloom, 1995), suggest the need for additionalexplanations and argue that new insight can begained by considering the broader environment inwhich technologies compete (Afuah and Bahram,1995; Afuah, 2000).

Studies exploring the impact of market demandon development strategies offer a complementaryset of explanations that highlight the influenceof consumer needs on technology developmentat the level of technology projects (von Hippel,1988; Lynn, Morone, and Paulson, 1996), businessstrategy (Kim and Mauborgne, 1997; Day, 1990;MacMillan and McGrath, 2000) and the broaderevolution of technological trajectories (Abernathyand Clark, 1985; Malerba, 1985; Christensen,1997; Sutton, 1998; Malerba et al., 1999; Tripsas,2001; Adner and Levinthal, 2001).

The most influential expression of a demand-side role in technology competition has been Chris-tensen’s examination of disruptive technologies.Disruptive technologies are technologies that intro-duce a different performance package from main-stream technologies and are inferior to mainstreamtechnologies along the dimensions of performancethat are most important to mainstream customers.As such, in their early development they only serveniche segments that value their nonstandard per-formance attributes. Subsequently, further devel-opment raises the disruptive technology’s perfor-mance on the focal mainstream attributes to a levelsufficient to satisfy mainstream customers. Whileimproved, the performance of the disruptive tech-nology remains inferior to the performance offeredby the established mainstream technology, whichitself is improving as well. Technology disruptionoccurs when, despite its inferior performance onfocal attributes, the new technology displaces themainstream technology from the mainstream mar-ket. Christensen plots the performance-providedand performance-demanded trajectories for differ-ent technologies and market segments, and showsthat technology disruptions occur when these tra-jectories intersect. He documents these dynamicsin numerous contexts, including hard disk drives,earthmoving equipment, retail stores and motorcontrols (Christensen, 1997).1

1 Christensen’s most prominent illustration of disruptive tech-nologies draws on research in the hard disk drive industry.Accordingly, the hard disk drive example is used to illustratethe arguments made in this paper. Christensen observed thatnew generations of disk drive technology were first adopted inniche markets which valued the new functionalities they offered.For example, 3.5-inch hard disk drive technology found anearly home among notebook computer users who appreciatedits reduced weight, size, and power consumption. With further(sustaining) development, 3.5-inch hard drives then went on todisrupt the desktop market, replacing the incumbent 5.25-inchtechnology. Thus, 3.5-inch hard disk drives displaced 5.25-inch

Copyright 2002 John Wiley & Sons, Ltd. Strat. Mgmt. J., 23: 667–688 (2002)

When are Technologies Disruptive? 669

The dynamics of disruptive technologies are thuscharacterized by three aspects: incumbent tech-nologies that are displaced from the mainstreammarket by technologies that underperform them onthe performance dimensions that are most impor-tant to mainstream consumers; mainstream con-sumers who shift their purchases to products basedin the invading technology, even though thoseproducts offer inferior performance on key perfor-mance dimensions; and incumbent firms that donot react to disruptive technologies in a timelymanner.

Christensen explains these dynamics as resultingfrom the interaction of resource dependence (Pfef-fer and Salancik, 1978) and performance oversup-ply. He argues that the resource dependence ofincumbents on their most demanding customersguides investments towards enhancing focal main-stream performance features. Because the incum-bent technology offers superior performance onthese dimensions, incumbent firms’ investmentsare directed towards extending the existing tech-nology, rather than the (potentially) disruptivetechnological opportunity. Incumbents have anadditional incentive to ignore disruptive technolo-gies because, with their lower performance, theyappeal to the low-end, low-profit portion of themainstream market. In contrast, entrant firms,whose decisions are not constrained by an existingcustomer base and whose technology offers infe-rior performance on the focal mainstream dimen-sions, are forced to identify consumers who valuethe new features offered by the new technologyand support its further development.

Christensen introduces the idea of ‘performanceoversupply’ to explain the mainstream consumers’decision to adopt the disruptive technology in theface of superior incumbent technology. The prin-ciple of performance oversupply states that onceconsumers’ requirements for a specific functionalattribute are met, evaluation shifts to place greateremphasis on attributes that were initially consid-ered secondary or tertiary. By this logic, when thecapacity provided by 5.25-inch hard disk drives

drives from the mainstream desktop market, despite offeringlower storage capacity, slower data access speeds, and beingmore costly on a dollar-per-megabyte basis. Further, those firmsthat dominated the desktop market with 5.25-inch technologydid not manage the transition to 3.5-inch technology and weredisplaced by entrant firms employing the new technology. Notethat the context is internal hard disk drives, not removable floppydisks, and that compatibility and disk portability (as opposed tocomputer portability) are not significant concerns for consumers.

exceeded the requirements of desktop consumers,these consumers began to place greater weight onattributes such as size and weight in their purchas-ing decision and thus chose 3.5-inch hard drivesdespite their lower capacity and higher cost permegabyte.

This explanation of the drivers of technol-ogy disruption leaves several issues unresolved.Clearly the different functional package offeredby the disruptive technology plays a role in itsadoption in the initial niche market. However,the newfound prominence of previously marginalattributes seems an incomplete explanation foradoption in the mainstream market—why woulddesktop users care about weight or size of inter-nal hard disk drives? Similarly, much has beenmade of the performance and price/performancedisadvantages of disruptive technologies relativeto their mainstream rivals, but the explicit roles ofprice and performance in driving disruptions haveremained underexplored. Finally, while the promi-nence of the performance-provided–performance-demanded relationship highlights the critical roleof the demand environment in shaping disruptivecompetition, the demand-side factors that drive theemergence of competition remain largely unstud-ied. For both researchers and managers, resolvingthese issues is a necessary condition for definingthe boundaries of disruptive technologies.

A DEMAND-BASED VIEW OFTECHNOLOGY COMPETITION

This article develops a demand-based view oftechnology competition that resolves these openquestions surrounding disruptive technologies byformally modeling the role of the demand environ-ment in shaping competitive dynamics. The struc-ture of demand is characterized by two elementsof the relationship between market segment prefer-ences: preference overlap and preference symme-try. Preference overlap refers to the extent to whichdevelopment activity that is valued in one seg-ment is also valued in another segment. Preferencesymmetry refers to the symmetry of this overlap,the relative size of the functional ‘shadows’ thatsegments cast on each other. The article presentsa model that examines how technology improve-ments can blur the boundaries that divide marketsegment and lead to different competitive environ-ments. It shows that the extent to which demand


670 R. Adner

heterogeneity is masked depends on consumers’marginal utility from performance improvements,which dictates their willingness to pay for prod-uct enhancements, and on the relationship betweenthe functional preferences of the different marketsegments.

The model is used to examine how prefer-ence overlap and preference symmetry interactto affect the emergence of competition. Control-ling for supply-side asymmetries such as initialresource endowments and technological potential,the model examines the nature of three distinctcompetitive regimes that arise under varying con-figurations of demand: competitive isolation, inwhich technologies do not interact throughout thecourse of their evolution; competitive convergence,in which technologies evolve to compete head-onfor the same consumer groups; and competitivedisruption, in which one technology cedes domi-nance of its home market to its rival.

As technology development progresses, con-sumers’ performance requirements are met, andthen exceeded, by their home technology. Asperformance continues to surpass consumers’requirements, consumers’ willingness to payfor improvements decreases, opening the doorfor lower-priced, lower-performance (disruptive)offers to capture these consumers. As the overlapbetween market segments’ preferences increases,firms have greater incentives to enter rivals’ mar-kets. When preference overlap is asymmetric, thefirm whose technology casts a larger performanceshadow on its rival’s market, and whose technol-ogy is therefore relevant to a larger number ofconsumers, has greater incentive to invade, tradingprice for volume. The invaded firm, confronting asmaller set of potential users, chooses to exploitexisting opportunities in the uncontested portionof its market segment rather than engage in pricecompetition with its rival.

The results of the analysis highlight the relation-ships among consumer preferences and consumers’willingness to pay for performance improvementsas key factors that give rise to technology dis-ruptions. The analysis reveals the dynamics bywhich the underlying heterogeneity in market pref-erences, which initially acts to separate market seg-ments and attenuate competition, is progressivelymasked as technology improvements exceed con-sumer requirements. The results also identify theincreasing importance of unit price in determin-ing consumers’ choices as technology performance

progresses beyond their requirements. This find-ing offers an alternative to performance oversupplyin explaining the consumer adoption of disrup-tive technologies and points to price trajectoriesas useful complements to performance trajectoriesin identifying disruptive threats. These findingsare supported by data from the hard disk driveindustry.2

The remainder of the paper is structured asfollows: first, a conceptual characterization ofdemand and demand structure is presented. Thischaracterization is then used to develop a for-mal model for examining how demand structureinfluences competitive interactions. The model isanalyzed using computer simulation and the sim-ulation results are presented and interpreted. Thepaper concludes with a discussion of the resultsand their implications for understanding disruptivetechnologies and, more broadly, the evolution ofcompetition.

DEMAND STRUCTURE

To understand the influence of demand structure onthe emergence of competition we must character-ize the demand environment in which technologiescompete. To do this we model the behaviors ofconsumers as individual decision-makers and asmembers of market segments. Consumers are char-acterized by the way they trade off performance ondifferent functional attributes, their willingness topay for performance improvements, and the min-imum performance threshold that a product mustreach if it is to be of value to a given consumer.Market segments are composed of consumers withthe same performance trade-offs and are identifiedby value trajectories, which characterize the trade-off. The focus of the analysis is the relationshipbetween market segment’s value trajectories andon the emergence of competitive isolation, con-vergence and disruption.

2 Adner and Zemsky (2001) develop a formal game theoreticmodel that builds on the analysis in this paper and generalizessome of its results. They formally characterize the breakdownof competitive isolation and examine the additional effects ofasymmetric costs, segment sizes and number of firms using eachtechnology.



Individual consumers: thresholds, utilities anddiminishing returns

The notion of thresholds, defined as critical per-formance levels that must be met for an offer-ing to become relevant to a decision set, iswell established in the social sciences (Granovet-ter, 1978; Varian, 1978; David, 1969; McFadden,1986; Meyer and Kahn, 1991). We distinguishbetween two types of thresholds. A consumer’sfunctional threshold specifies the minimum levelof performance below which a consumer will notaccept a product regardless of its price. Functionalthresholds are determined in part by inherent taskrequirements and in part by context. Thus, a prod-uct that falls below one consumer’s functionalthreshold may well be acceptable to another con-sumer with a different functional threshold.

As product performance improves beyond thefunctional threshold, a consumer’s relative pref-erences among the possible functional attributesimpact the benefit she derives from the product.Functional benefit is thus a function of both theproduct’s objective performance on each func-tional attribute (e.g., speed, capacity, reliabil-ity) and the consumer’s trade-offs among theseattributes.3 For example, while a vehicle that cancarry 50 passengers with a maximum speed of30 miles per hour may satisfy the functionalthresholds of both a private driver and a publictransportation driver, the latter will derive greaterfunctional benefit from the offer. Thus, functionalrequirements indicate initial thresholds while rel-ative preferences dictate consumers’ evaluation ofperformance improvement. In this regard, Chris-tensen’s trajectories of performance demanded bydifferent market segments can be interpreted asplots of average segment functional thresholds.

Net utility thresholds incorporate price into theconsumer’s decision function, specifying the high-est price a consumer will pay for a product that justmeets her functional threshold. Here too, we mayexpect to find heterogeneity among consumers,

3 This conceptualization follows a long tradition of work inmarketing, decision science, and economics (Griliches, 1961;Lancaster, 1979; Green and Wind, 1973; Trajtenberg, 1990)that suggests that consumers have relative preferences forproduct characteristics and that consumer choice can be usefullyconceived as the maximization of utility measured in terms ofthe functional characteristics that are embodied in their productchoices. The treatment of preferences for goods as being derivedfrom preferences for collections of characteristics lies at theheart of established techniques such as hedonic analysis, conjointanalysis, and multidimensional logit models of brand choice.

even those with similar functional preferences.Differences in consumers’ willingness to pay maybe driven by differential budget constraints. Theymay also be driven by nonbudgetary considera-tions; for example, differences in the ability ofthe customer to exploit the product. Such het-erogeneity may stem from the customer’s inter-nal resources (Barney, 1986), capabilities (Amitand Schoemaker, 1993) or human capital (Becker,1962) (e.g., an efficient programmer is able toderive more benefit from a given computer systemthan can an inefficient programmer). Alternatively,differences in consumers’ willingness to pay maystem from the scale at which the buyer can applythe product. A customer who can apply the producttoward the production of a good that he can sellto a large downstream customer base will be will-ing to pay more for the product than a customerwith a smaller customer base. Finally, differencesin willingness to pay may reflect variation in theavailability and presence of a substitute product orservice. A consumer who has previously investedin a substitute good will benefit from the new prod-uct only to the extent of the product’s relativeperformance improvement over the existing sub-stitute. A similar consumer, not in possession of asubstitute, will value the product on the basis ofthe absolute benefit it provides.

While consumers have a minimum thresholdfor acceptable performance, there is no analogousboundary that specifies a maximum limit to thefunctional performance that a consumer would bewilling to accept.4 At the same time, consumersface decreasing marginal utility from increasesin functionality beyond their requirements (Meyerand Johnson, 1995). Correspondingly, it is reason-able to assume that consumers show a positive,but decreasing, willingness to pay for improve-ments beyond their requirements. Even if con-sumers place little value on performance differ-ences at sufficiently high absolute levels of func-tionality they will still, all else being equal, choosethe more advanced product.5

4 For the purposes of this paper increased functional performanceis treated as a purely a positive feature.5 Using a related model to study patterns of technology innova-tion, Adner and Levinthal (2001) find that competing firms maycontinue to enhance product performance beyond consumers’needs, even when such enhancements have little effect on con-sumers’ willingness to pay, as a form of nonprice differentiation.


672 R. Adner

Market segments: Value trajectories and thestructure of demand

Market segments are composed of consumers withsimilar functional preferences. Segment membersmay have heterogeneous functional and net util-ity thresholds. As a useful shorthand we definesegments’ value trajectories as characterizing themember consumers’ relative preferences for func-tional attributes. The relationship between valuetrajectories maps the structure of demand. Theoverlap between value trajectories and the symme-try of this overlap indicates the differential marketincentives firms face in choosing their innovationactivities, which in turn drive the emergence andevolution of competition. These relationships aredeveloped formally in the next section.

As illustrated in Figure 1, the value trajectoryidentifies the direction of propagation of a mar-ket segment’s indifference curves as they progresstoward higher utility levels.6 Consumers’ evalua-tion of products’ functional performance is deter-mined according to the products’ projection ontothe value trajectory—the farther out on the valuetrajectory the projection, the greater the product’sutility to the consumer.

Consider the following simplified example fromthe market for information storage products.

6 The value trajectory is the gradient of the Cobb-Douglas utilitycurve. It is thus the vector that minimizes the level of totalfunctional attainment required to attain a specified utility level.In Lancastrian terms it is defined by the vector which minimizesthe characteristic resources required to attain a given utility level;that is, the vector along which the compensating function is equalto unity (Lancaster, 1979, 1991).

Figure 2 shows hypothetical value trajectories forconsumers in the market for desktop personalcomputers (PCs), who value storage capacity muchmore than portability; consumers in the marketfor personal digital assistants (PDAs), who valueportability much more than storage capacity; andconsumers in the market for notebook computers(NCs), who value capacity and portability in equalmeasure.

The preference overlap between these segmentsis the degree of similarity between their functionalpreferences, which is graphically reflected in thedifference between their trajectories. The greaterthe preference overlap, the closer the value trajec-tories, and the greater the segments’ agreement onthe level of product performance. As illustrated inFigure 2(a), whereas the PC group derives a util-ity level of 3 from product A and a utility level of1.4 from product B, the PDA group derives a util-ity level of 1.4 from product A and 3 from productB. As preference overlap increases, as illustrated inFigure 2(b) for the PC and NC groups, these evalu-ations converge. Here, the PC group’s derived util-ities of 3 and 2.7 from products A and C, respec-tively, are in greater agreement with the utilitiesof 1.6 and 3 derived by the NC group. Preferenceoverlap is thus a measure of the extent to whichone market segment’s satisfaction with a givenproduct’s functionality is indicative of the satisfac-tion of another group—the performance shadowcast on a segment’s value trajectory by progressalong its counterpart’s trajectory. When individualfirms initially pursue different market segments,the segments’ preference overlap is an indicator

Fu

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ion

al a

ttri

bu

te Y

Functional attribute X

Value Trajectory

Figure 1. Indifference curves and a value trajectory



Portability

Sto

rag

e C

apac

ity

PDAValueTrajectory

PCValueTrajectory

Upda = 3Upda = 2Upda = 1

Upc = 2

Upc = 3

Upc = 1

45°

Product A

Product B

Portability

Sto

rag

e C

apac

ity

PC ValueTrajectory

NC ValueTrajectory

Unc = 3

Unc = 2

Unc = 1

Upc = 3

Upc = 2

Upc = 1

45°

Product A

Product C

(a)

(b)

Figure 2. (a) Small, symmetric preferences overlap. (b) Large, asymmetric preference overlap

of the ease with which the firms can invade othermarket niches.

While the magnitude of preferences overlapspeaks to the absolute degree of preference similar-ity, preference symmetry refers to the relative valueeach segment places on performance improve-ments along another segment’s value trajectory.When preference overlap is symmetric the util-ity which one group derives from a given levelof performance along the others’ trajectory is thesame for both groups. In the case of the PC andPDA groups, each derives a functional utility level

of 3 when the other drives a level of 1.4. Whenpreferences are not symmetric, a product posi-tioned at a given distance along one segment’svalue trajectory provides a different level of utilityto members of the other segments than a prod-uct positioned at the same distance along the othersegment’s value trajectory provides to members ofthe first segment. In the case of the PC and NCgroups, for a utility level of 3 along its counter-part’s value trajectory, the PC group will derive autility of 2.7, while for a utility level of 3 along thePC group’s value trajectory the NC group derives


674 R. Adner

a utility of only 1.6. For firms initially pursuingdifferent market segments, the symmetry of prefer-ence overlap plays a critical role in structuring theirdifferential incentives for pursuing other segments.

Conceptualizing demand according to con-sumers’ requirements and preferences suggestsa demand landscape that coevolves with tech-nology development: as consumers’ requirementsare exceeded, they derive positive but decreasingmarginal utility from further performance improve-ments. This satisfaction leads to the need forgreater and greater performance increases to sup-port a given increase in utility, which is reflectedin a decreasing willingness to pay for a given levelof performance improvement. Stated differently, asperformance exceeds requirements, price increasesin importance. As developed below, these changesin consumers’ valuation of performance improve-ments lead to changes in firms’ innovation incen-tives, which in turn affect consumers’ decisions infuture periods.

MODEL STRUCTURE

The model follows in the Lancastrian tradition ofconceptualizing the market space along attributedimensions. It extends previous applications ofcharacteristics demand models by examining howthe relationship between the value trajectories ofdiscrete market segments affects firms’ develop-ment choices throughout a technology’s evolutionand how these factors affect the emergence of com-petitive dynamics. The model is used to examinethe behavior of two firms pursuing independenttechnology initiatives in a market with two con-sumer segments.

The model structure has two basic components:a characterization of consumers and consumerpreferences, which comprises the market, and amechanism by which products move through thismarket space. The market space is defined by twofunctional dimensions and by a price dimension.7

In every period, single-product firms make devel-opment decisions that affect the location of theirproduct technology in this space; consumers, inturn, respond to the product offerings by purchas-ing either a unit quantity of the product or makingno purchase at all.

7 The model is presented using two functional dimensions, butcan be extended in a straightforward manner to incorporatehigher-dimensional spaces.

The model assumes repeat purchasing behaviorsuch that the entire population of consumersconsiders making a single unit purchase in everyperiod. Consumer preferences are stable overtime, but, consumer purchase decisions may varyas product offerings change over time. Themodel assumes that there are no switching costs,consumption externalities, capacity constraints,economies of scale, or price discrimination.Consumers are assumed to have a one perioddecision horizon and to be well informed, withperfect information about product performance.8

Given a choice among a set of acceptable products,a consumer will select the product that meets herthresholds, improves her utility over her previouspurchase, and maximizes her utility in the presentperiod. In every period the entire population ofconsumers is exposed to the available products andeach consumer selects her own best choice.

Consumer choice

In the model individual consumers are character-ized by their threshold requirements and their rel-ative preferences for improvements beyond theserequirements. Each individual, i, has a net util-ity threshold, Ui0, which a product must meet tobe considered for purchase. An individual derivesutility from a given product offering according tothe functional benefit she derives from the productand from its price. Functional benefit is determinedby the functionality of the product in excess of theconsumer’s functional threshold requirements, Fi0,and by the consumer’s relative preference for each

8 The model makes the simplifying assumption that consumersare perfectly informed regarding product performance. Becausethe interest is in the qualitative pattern of behavior, consumeruncertainty would be a relevant factor if it were to affect firms’development decisions in a nonsystematic way. Given, ex ante,no compelling reason to bias consumers’ assessment of productperformance in either the positive or negative direction, the errorin the assessment would have to be modeled as a symmetricdistribution around the true value. If consumers are assumed tobe risk neutral, and the error random, the expected actions of thepopulations would mirror the fully informed case. If consumersare assumed to be uniformly risk averse (risk seeking), theirfunctional requirements would shift up (down) to compensatefor the uncertainty. Such a shift, while affecting the absolutevalues associated with the observed outcomes, would not affectthe qualitative nature of the results. The more complex case ofheterogeneous risk preferences is beyond the scope of the currentdiscussion.



dimension of functional performance, γ .9 The con-sumer’s trade-off between functional benefit andprice is α.

The market space is defined by two functionaldimensions, X and Y . Bij is the functional benefitderived by consumer, i, with functional thresholdrequirements of Fix and Fiy , from product j , whichoffers functionality Fj , with components Fjx andFjy .

Bij = Bi(Fj ) =

(FjX − FiX)γ (FjY − FiY )1−γ

+1 if (FjX − FiX),

(FjY − FiY ) ≥ 10 otherwise

(1)

where 0 < γ < 1.The utility, Uij , that consumer i derives from

product j is specified as a Cobb-Douglas utilityfunction which trades off product price and func-tionality10:

Uij = Ui(Fj, Pj ) = (Bij )α(1/Pj )

1−α (2)

where 0 < α < 1.Consumers will reject any product that does not

meet both their functional and net utility thresh-olds, requiring both Bij ≥ 1, and Uij ≥ Ui0. ThusPi0, the price a consumer will be willing to pay fora product that just meets his functional threshold,(Bij = 1), is:

Pi0 = (Ui0)1/(α−1) (3)

Pij , the maximum price a consumer would bewilling to pay for a product that exceeds hisfunctionality requirements, is thus:

Pij = Pi0(Bij )α/(1−α) (4)

As discussed above, consumers value functional-ity improvements beyond their threshold require-ments, ∂Uij/∂Bij > 0, but at a decreasing rate,

9 Fi0 is a vector which specifies consumer i’s minimum func-tional attainment required on each functional dimension. Graph-ically, it is the position along an individual’s value trajectorythat a product must pass to become decision relevant.10 Note that [log U ]/(1 − α) = α/(1 − α) log B − log P . Hencethe model is a monotonic variant on the utility function ofstandard vertical differentiation models U = KB − P (Tirole,1988). The current model differs from the standard model inthat it incorporates multiple functional dimensions, minimumthresholds for functionality and diminishing returns to functionalimprovements.

∂2Uij/∂B2ij < 0. This valuation is reflected in

consumers’ decreasing willingness to pay forimprovements, (∂Pij/∂Bij > 0, ∂2Pij/∂B2

ij < 0),when α < 0.5.

Demand structure

In the model the essential aspects of demand struc-ture derive from the relationship between con-sumers’ relative functional preferences, γ . Con-sumers belong to one of two market segments.All members of a market segment, m, have thesame relative functional preferences γm. The valueof γm specifies a market segment’s value trajec-tory. Graphically, for two functional dimensions,the degree measure of the value trajectory is 90°

(1 − γ ).For two market segments A and B, whose

derived utility from two functional attributes X andY is described by:

UA = (FX)γA(FY )1−γA (5a)

UB = (FX)γB (FY )1−γB (5b)

where 0 ≤ γA, γB ≤ 1.Preference overlap is defined as:

Preference overlap = 1 − |γA − γB | (6)

The greater the preference overlap, the greater thesegments’ agreement on the rank ordering of alter-natives. When preference overlap is zero, the seg-ments’ preferences are entirely divergent, so thatprogress along one segment’s value trajectory hasno impact on the other segment’s assessment ofthe product’s functional benefit. When preferenceoverlap is unity, the value trajectories coincide andthe two segments have identical functional prefer-ences, essentially behaving as a single segment.

Preference symmetry, the extent to which onesegment’s preferences project onto the others’preferences, is defined as:

Preference symmetry = |0.5 − γA| − |0.5 − γB |(7)

When the preference symmetry measure is zero,preferences are symmetric and unit progress oneach segment’s value trajectory yields equal func-tional benefit to consumers in the other segment.11

11 Implicit in this discussion is the assumption that the dis-tance between indifference curves is cardinal, which allows


676 R. Adner

When the preference symmetry measure is positive(negative), the projection of segment B’s value tra-jectory on segment A’s value trajectory is greater(less) than A’s is on B’s, and a unit advancealong B’s value trajectory will yield greater func-tional benefits to consumers in segment A thanvice versa.

Technology development

In the model, technology initiatives are charac-terized by their performance on each functionaldimension and by a production cost. Technologiesare developed by firms. Firms introduce productsto the market on the basis of their technology posi-tions, with the goal of maximizing profits in thecurrent period. Each firm is initially endowed witha technology that has initial functional and costcharacteristics. In the course of the simulation,firms develop their technologies in response tothe opportunities they perceive in the marketplace.Technology development is locally constrained,and therefore path dependent, but globally uncon-strained regarding the absolute limit of technologyprogress. As described below, in every period firmscan engage in product innovation, process inno-vation, or can choose to forgo the opportunityto innovate. Following the characterization usedin previous analytical models (Cohen and Klep-per, 1996; Klepper, 1996), the effects of productand process innovations are reflected in changesin product functional performance and in prod-uct cost.

Product innovation enhances performance alongthe functional dimensions by a fixed Euclideandistance in market space, Fprod, and leads to afixed production cost increase, Cprod. In the spiritof an evolutionary approach, Fprod is relativelysmall in comparison to the range of functionalperformance demanded in the market (max Fi0 −min Fi0), such that numerous product developmentattempts are required if a firm is to satisfy the

for comparisons regarding positions and advances along val-uation trajectories. While unconventional in classical demandtheory, Lancaster speaks of the desirability of being able tomake quantitative utility comparisons across goods (Lancaster,1991: 158–159). Because his closed-form models do not allowfor the simultaneous consideration of final utilities for multipleconsumers, he opts for a ‘second best’ approach which proxy’sresource content for derived utility. The simulation methodologyused to examine the current model allows, in the spirit of Lan-caster’s stated intent, for the explicit consideration of cardinalutilities, and such utilities are therefore used.

functional requirements of all consumers in themarket. As discussed below, the allocation of effortalong functional dimensions, �Fx and �Fy , isdetermined according to a local search of themarket opportunities presented to the firm.

A product innovation affects product j as:

Fj,t+1 = (FjX,t + �FX), (FjY,t + �FY ) (8a)

where [(�FX)2 + (�FY )2]1/2 = |Fprod|

Cj,t+1 = Cj,t + Cprod (8b)

The choice of relative allocation of improvementdetermines the technology’s development trajec-tory. While this trajectory can be altered in everyperiod, the firm is charged a development costagainst profits that is proportional to the shift intrajectory.

Process innovation leaves product functionalityunchanged while lowering the cost of productionby a constant percentage, �c. Thus, a processinnovation affects product j as:

Fj,t+1 = Fj,t (9a)

Cj,t+1 = Cj,t (1 − �c) (9b)

Firms can pursue one innovation per period. A firmcan pursue either innovation mode in any periodand for as many periods as desired. Further, thereis no uncertainty as to the success of an innovationattempt.12 Firms are charged an innovation cost, I ,in every period in which they choose to innovate.

In the model, firms choose innovative activity onthe basis of local, profit-maximizing search. Firmssearch their local market environments to predictconsumer reaction to changes that are attainablethrough a single product or process innovation.The model assumes that firms are fully informedregarding consumers’ responses to pricing and thatfirms can, given their product’s performance andproduction costs, determine the price point whichwill yield greatest period profit. Firms are unableto evaluate potential consumer demand for changes

12 Clearly, eliminating uncertainty regarding innovative out-comes is a strong simplification. However, as was the case withconsumer uncertainty, because there is no justification, ex ante,to bias expectations, the addition of an error term would notaffect the qualitative behaviors that are of concern here.



that result beyond a single development action.13

Firms are unable to predict their rival’s develop-ment activity. Firms become aware of their rival’sactivities through their reflection in consumer pur-chasing decisions—consumers who were expectedto purchase but did not. Firms’ expectations ofmarket response is thus determined by their prod-uct’s functional state, the magnitude of innovationthat can be executed in a single period, the firm’spricing decision, and the product offers of the pre-vious period.14

In the evaluation of potential product innova-tion, the firm must determine the allocation of itsdevelopment efforts among the functional dimen-sions and determine a price point at which to offerthe product to the market. The firm has a profitexpectation for each possible action that is deter-mined by its production costs, development costs,and predicted market feedback. Similarly, the firmhas a profit expectation for potential process inno-vation by predicting market response to differentprice points given its reduced production costs.The firm commits to the innovation activity that isexpected to yield the highest profits for the ensu-ing period. If the expected profits from innovationare not greater than the realized profits from theprevious period, the firm will forgo innovation forthe period.

Model analysis

The model is used to examine the behavior oftwo firms pursuing independent technology initia-tives in a market with two consumer segments.The analysis explores how changing the preferenceoverlap and preference symmetry of the segment’svalue trajectories affects the emergence and evolu-tion of competition. The model is analyzed usinga computer simulation programmed in Pascal.

13 To the extent that firms engage in additional market research,these efforts would be reflected in a search radius that wouldextend beyond their immediate development opportunities. Suchmarket research would affect behavior when the relative prefer-ences of more distant consumers differ from the preferences oftheir local counterparts.14 This model of local, profit-maximizing search speaks to invest-ments that are driven by immediate market opportunities ratherthan those driven by visionary, long-term goals. Thus, it doesnot speak to activities with very long-term investment horizonssuch as pharmaceutical R&D or visionary technology bets madewithout expectation of any short-term return.

The first procedures of the simulation initializethe population of consumers and the initial charac-teristics of the product technologies. The consumerpopulation is initialized by specifying each con-sumer’s minimum functionality and utility require-ments. The market segments are specified by theirvalue trajectories. The range of consumers’ min-imum functional thresholds is such that approxi-mately 20 product innovation attempts are neces-sary to span the distance between the minimumrequirements of the least and most demandingcustomer in the market. Consumers are randomlydrawn from a uniform distribution along this range.Consumer preferences and requirements remainconstant throughout the simulation, but buyingbehavior changes as firms’ development activitieschange the product technologies’ characteristics.Each firm’s technology is initially positioned alongthe value trajectory of one of the market segments.The specification of value trajectories and initialproduct technologies is discussed in the resultssection.

After initialization, the following sequence ofevents is repeated until the market dynamics reacha steady state.15 First, each firm engages in localsearch to determine the profit expectations for eachpossible development action. Second, the firmscommit to the activity that will yield the highestexpected profit. Third, every consumer indepen-dently evaluates the available product offeringsand decides on purchase as above. Finally, mar-ket outcomes are tallied and firms realize theiractual market pay-offs. In the representative resultsdiscussed below the market space is seeded with250 consumers who are evenly distributed betweentwo market segments. Individual consumers’ func-tional requirements, |Fi0|, are uniformly distributedalong the range from 5 to 25. Each firm’s tech-nology is initially seeded along a segment’s valuetrajectory with |Fj | set at 7 and initial productioncost 1.7. These settings provide for an initial iso-lation between the technologies. In the absence ofsome distance between their initial positions thefirms face identical market landscapes and there-fore always engage in convergent competition.

For all presented results, α = 0.2, F prod = 1,Cprod = 0.1, �c = 0.05. While changing parametervalues affects the absolute magnitudes and rates of

15 Steady state is defined as having been reached when com-bined sales for both firms remain unchanged for 15 consecutiveperiods.


678 R. Adner

behaviors, the qualitative patterns of competitionare robust. Similarly, the qualitative results arerobust with regard to the stochastic seeding ofthe consumer population. The results are presentedin terms of the development activities of twofirms, Firm 1 and Firm 2, that act in a marketspace with two consumer segments, segment A andsegment B. At the start of the simulation, Firm 1’stechnology position is aligned with segment A’svalue trajectory and Firm 2’s technology positionis aligned with segment B’s value trajectory.

RESULTS

The simulation explores how the structure ofdemand influences the emergence and extent ofcompetition over consumer segments in the mar-ket. On the supply side we are interested in thetechnologies’ functional development and pricingdecisions, while on the demand side we are inter-ested in the degree of firms’ penetration into mar-ket segments, in terms of both satisfaction of con-sumers’ requirements and consumers’ actual pur-chasing decisions.

Figure 3 presents a stylized summary of the rela-tionships between preference overlap, preferencesymmetry and the competitive regimes to whichthey lead. Holding one value trajectory fixed, thefigure maps the competitive regimes that arise

as a second value trajectory is varied.16 Threequalitatively distinct dynamics emerge during thecourse of the analysis. Under demand conditions oflow preference overlap, the development dynam-ics lead to competitive isolation, a pure partition-ing of the market between the technologies, suchthat each focuses exclusively on its own segment.As preference overlap increases, isolation breaksdown and the development dynamics lead to theemergence of two distinct classes of competi-tion. When segment preferences are symmetric weobserve competitive convergence, in which eachtechnology’s development is directed at expandingits appeal not only in its own home market but inits rival’s as well. When segment preferences areasymmetric we observe competitive disruption, inwhich one firm maintains dominance of its homemarket while displacing its rival from the rival’smarket.

As a technology’s performance advances, it sur-passes the requirements of a growing number of

16 The objective of the model is to provide a qualitative repre-sentation of the competitive patterns of interest, rather than aquantitative replication of particular settings. In this spirit, theresults are presented in terms of three representative demandconfigurations and the dynamics. Because the precise (numeri-cal) location of the boundaries between the regimes is dependenton specific parameter values as well as the random seeding ofthe consumer population it is not a focus of this analysis. Theinterested reader is referred to Adner and Zemsky (2001), whichpresents closed-form expressions for the breakdown of isolation.

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consumers in the home market and, with suffi-cient preference overlap, it begins to satisfy andsurpass the requirements of consumers in the for-eign market. When preferences are asymmetric, thefirm whose home market casts the larger functionalshadow faces greater marginal incentives to pur-sue consumers outside its home market because itsoffer appeals to a larger number of consumers. Asthe invading firm pursues consumers at the low endof its rival’s segment with low-priced offerings,the invaded firm can either defend its position atthe low end through price reductions or focus onits own high-end consumers with higher price andperformance offers. From the invaded segment’sperspective the appeal of the invading technology,which offers neither higher performance nor higherprice/performance value, is due to its lower unitprice.

Demand structure and competitive isolation

Competitive isolation, in which each technologyis sold only in its home market, is characteris-tic when the preference overlap is small. Figure 4shows representative simulation results for thelow overlap case in which the two market seg-ments have functional preference parameters γA =0.1 and γB = 0.9. This case characterizes thePDA–desktop segments for hard disk drives inthe stylized example above. Initially, technologyinitiatives satisfy the functional requirements ofonly their home segment and development pro-ceeds along a path that matches that segment’s

value trajectory. Because of the wide difference inrelative preferences, technology development fol-lowing one segment’s value trajectory does notlead to significant improvement along the othersegment’s criteria. Firms’ development and pric-ing decisions are thus substantially determinedby the requirements of their home segments. Asdevelopment proceeds, and functional attainmentincreases, the firms are able to satisfy the func-tional requirements of the lower end of the foreignmarket segment. By this point, however, the appealof pursuing these customers is limited by twofactors: (1) its rival offers a higher-functionalityproduct; and (2) to reach these customers, giventhe functionality of the existing alternative, thefirm would need to lower prices well below thelevel it is able to charge its existing customers.Thus, while consumers in the foreign segment arefunctionally visible, their price requirements makethem unattractive to the firm given its profit oppor-tunities in its home market. As a result, neitherfirm registers any sales to consumers outside of itshome segment (Figure 4).

Demand structure and technology competition

As the overlap between value trajectories increases,the satisfaction of its home segment’s functionaldemands leads each firm towards earlier satisfac-tion of the functional requirements of consumersin the foreign segment. Firms face incentives topursue higher-end consumers within their homesegment and, simultaneously, are tempted by the

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680 R. Adner

presence of a new set of consumers. Becausedemand for functionality matures earlier at lowerfunctional levels, lower-end consumers providethe entry point for foreign technologies to cap-ture market share with lower prices. Competi-tion emerges when the volume of relevant con-sumers in the foreign segment is sufficient to offsetthe price reductions required to attract these con-sumers.

Competitive convergence

When preference overlap is symmetric, both firmsface broadly similar market landscapes. Figure 5shows representative results for market condi-tions with γA = 0.6 and γB = 0.4. With sufficientpreference overlap and technology development,one firm finds the marginal consumer in the for-eign segment that has the effect of redirecting itsdevelopment efforts and pricing policies towards

invasion.17 The transition to convergence is evi-dent in Figure 5(a), which shows the sales of eachfirm to each segment. Firm 2 is first to penetrate itsrival’s segment. Key to Firm 2’s ability to attractconsumers from segment A, given the presence ofFirm 1’s functionally superior product (from theperspective of all segment A consumers), is theconsumers’ decreasing marginal utility from per-formance improvements which is reflected in theirdecreasing willingness to pay for improvementsbeyond the minimum requirements. By period 10,the performance provided by both firms more thandoubles the threshold requirements of their lowest-end consumers. Decreasing willingness to pay,which effectively increases the importance of pricedifferentiation as functionality improves, allows

17 In the simulation the specific location of consumers in thespace, idiosyncratic for each run, determines the firm will be firstto redirect its activities to penetrate its rival’s segment. Thus,while the dynamics are identical across all runs, the specificidentity of the firm varies by run.

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Firm 2’s functionally inferior product to be per-ceived as the better value by lower-end consumersin segment A, whose requirements are satisfiedby both alternatives. Figure 5(b) shows the firms’price and performance positions over time.18

Firm 2’s redirection of innovation effortstowards price affects the innovation incentives thatFirm 1 faces. Firm 1 is faced with the optionof matching its rival’s price cuts, or holdingback from following suit, exploiting the presenceof high-end consumers who value its functionalsuperiority (along their value trajectory) and whowill support its higher prices. Note that withevery development step, Firm 1’s product offerbecomes relevant to a larger set of consumers insegment B. Thus, even with a focus on its homesegment’s value trajectory, Firm 1’s incentives

18 In all figures functional performance is measured as products’Euclidean distance from the origin, |Fj |.

to pursue segment B customers increase. Theresulting dynamic of competitive convergence, inwhich both firms pursue each other’s markets,is quite stark in the later periods shown inFigure 5(a).19

Competitive disruption

When the preference overlap between segments isnot symmetric, progress along the different valuetrajectories leads firms to view different demandlandscapes. Figure 6 shows representative resultsfor market conditions with γA = 0.9 and γB = 0.5.

19 The ‘trivial’ case of convergent competition is whenheterogeneity in either the demand or supply environments iseliminated. Thus, under conditions of high preference overlap,the technologies compete for what increasingly resembles asingle market segment, giving rise to competitive convergence.Similarly, when the initial positions of the technologies aresufficiently close, the distinction between the two technologiesdisappears, also giving rise to competitive convergence.

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682 R. Adner

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This case characterizes the desktop computer (seg-ment A)–notebook computer (segment B) mar-kets for hard disk drives discussed above. Undersuch asymmetrical preference conditions, progressalong the value trajectory of segment B leads tofaster natural progress along the value trajectoryof segment A than vice versa.

Figure 6(a) shows the number of consumers ineach segment whose functional requirements aresatisfied by each firm’s technology. In period 6,Firm 2’s product becomes functionally relevant tothe segment A consumer with the lowest functionalrequirements. As shown in Figure 6(b), Firm 2gains its first customer from segment A in period23. This first sale outside its home segments corre-sponds to a price decrease, evident in Figure 6(c),which shows the price and absolute performance,Fj , of each firm’s offering.

Figure 6(d) graphs the price/performance andperformance curves for each firm’s offering as

measured along segment A’s value trajectory. Sig-nificantly, it shows that at the time that Firm 2 ismaking inroads into segment A, its product offerslower performance and a worse price/performancelevel than does Firm 1’s product.20 The dynamicsbehind this disruption are further examined in theDiscussion section.

While progress along segment A’s value tra-jectory does eventually allow Firm 1 to satisfysegment B’s functional requirements (period 98in Figure 6a), it lags Firm 2 and is effectivelyprecluded from pursuing the segment. Because ofthe preference asymmetries between segments A

and B, Firm 1 is fighting an uneven battle formarket share. At equal rates of innovation, fol-lowing segment A’s value trajectory will attract

20 Firm 2’s price reduction ultimately provides for a more attrac-tive price/performance offer (still at a lower absolute perfor-mance level) in period 74 and beyond but, as seen in the figures,this transition does not affect its market performance.



fewer consumers from segment B than vice versa.Further, as Firm 2 begins to address the functionalrequirements of segment A, Firm 1 faces pressureto justify its price to its existing high-end cus-tomers and responds to this competitive threat byfocusing on providing increasing levels of perfor-mance. Firm 1 is thus driven further and furtherupmarket by a combination of market incentivesand competitive threats.

MODELS, SIMULATIONS, ANDREALITY

Drawing on Christensen’s rich inductive research,this paper presents a model that strives to offera parsimonious explanation for the dynamics oftechnology disruptions. The intent of the modelis to focus on the essential demand-side driversthat shape the emergence of competition, partic-ularly competitive disruption. As such, the modeldoes not seek to dispute the importance of factorssuch as resource allocation, organization design,or resource dependence. Rather, by isolating theeffects of demand structure on competition, themodel should aid future research to more pre-cisely consider the impact of such complementaryfactors.

The value of formalizing the model is thatit brings to light implicit links and assumptionsthat lie unexplored in the inductive work (cf.Nelson and Winter, 1982; Saloner 1991; Malerbaet al., 1999). Having identified what he believesare the key features driving the phenomenon,the modeler is forced to make explicit therelationships between these elements. It wasthrough this process of explication that many ofthe most interesting links in the present modelwere revealed. For example, in approaching themodel, it was evident that consumers’ reservationprices and product quality improvements wouldbe important factors to include. In characterizingthe evolutionary rules of the model, however,it became clear that these two factors mustbe linked—how does reservation price changewith quality improvement? Recognizing theimplications of consumers’ decreasing marginalreturns from performance improvements for theirwillingness to pay for new products turned outto be one of the most powerful realizationsof the model. Similarly, building the modelrequired characterizing the utility functions of

different market segments according to theirrelative preferences for functional attributes. Thisposed the novel requirement of relating segmentpreferences to each other, and resulted in thecreation of the grammar of preference overlapand preference symmetry to characterize demandstructure.

The model is a focusing tool that helps interpretand organize the richness of empirical observation.In turn, the model’s structure and results raisefurther questions for empirical exploration—e.g.,what is the actual ‘shape’ of decreasing marginalreturns; how does the way firms and consumersperceive preference overlap affect their definitionof industry structure and industry boundaries; areprice reductions subject to decreasing returns in theway functional improvements are; can firms affectthe onset of decreasing returns to their customers?Even as they provide some logical closure onpuzzles raised by earlier observations, the model’simplications provide impetus for further empiricalinvestigation.

The model is, by definition, a simplification.In the present model, the simplifying choices aredriven by the goal of creating a baseline modelthat isolates the influence of demand structure onthe emergence of competition. While factors suchas asymmetric firm capabilities, asymmetric mar-ket segment sizes, and economies of scale couldbe incorporated into the model structure, theyhave been excluded from the present discussionto reduce the complexity involved in interpret-ing drivers of the model results.21 The structureof the current model does not lend itself to theexploration of firm-level decisions, such as deci-sions to invest in alternative technology opportuni-ties. The incorporation of factors such as resourceallocation and resource dependence, which oper-ate at the intrafirm level, would require a differ-ent modeling approach that pays explicit atten-tion to the internal dynamics of decision mak-ing and cognition. While unexamined here, therelationships between market size, organizationstructure, credit allocation, and managerial incen-tives certainly offer a rich context for modelingexplorations.22

21 See Adner and Zemsky (2001) for a game theoretic examina-tion of these relationships.22 See Sastry (1997) and Gavetti and Levinthal (2000) for illus-trative examples of how simulation models can be applied toexplore intraorganizational change processes.


684 R. Adner

DISCUSSION

The model developed in this article explores theinfluence of the structure of consumer demandon technology rivalry. By examining heterogene-ity in consumer requirements and preferences, andby relating these factors across market segments,the model offers a basic grammar for character-izing the structure of demand and offers a newperspective on the emergence of competition. Astechnologies develop they increasingly satisfy con-sumers within their target market segments andmay also become relevant to consumers in othersegments. Categorizing demand structure accord-ing to preference overlap and symmetry highlightsthe differential market incentives that firms faceas their technologies advance. When consumersface diminishing marginal returns to performanceimprovements, technologies that offer lower rela-tive performance at lower price become increas-ingly attractive. In the face of continued perfor-mance improvements, this dynamic may lead to ablurring of the market’s underlying heterogeneitywhich, in turn, influences the emergence of differ-ent competitive regimes.

The model sheds light on the important phe-nomenon of disruptive technologies (Christensen,1997). Consider Christensen’s explanation of whydesktop computer users opted to purchase 3.5-inchhard disk drives when they offered lower capac-ity at a worse dollar-per-megabyte value than their5.25-inch rivals:

Why did the 3.5 inch drive so decisively conquerthe desktop PC market? A standard economic guessmight be that the 3.5-inch format represented amore cost-effective architecture: If there were nolonger any meaningful differentiation between twotypes of products (both had adequate capacity),price competition would intensify. This was notthe case here, however. Indeed, computer mak-ers had to pay, on average, 20 percent more permegabyte to use 3.5-inch drives, and yet they still[italics in original] flocked to the product. More-over, computer manufacturers opted for the costlierdrive while facing fierce price competition in theirown product markets. Why?Performance oversupply triggered a change in thebasis of competition. Once demand for capacitywas satiated, other attributes [size; power consump-tion], whose performance had not yet satisfied mar-ket demands, came to be more highly valued . . .(Christensen, 1997: 166–167)

But why should desktop users, who had neverbefore been concerned with issues such as power

consumption, and for whom energy costs representa negligible expense, suddenly choose to give upcapacity for reduced energy use? The answer is notimmediately apparent.

The current model offers an alternative expla-nation—that the desktop users were not choos-ing 3.5-inch hard disk drives due to their newattributes, but rather for their lower price—theirlower price as measured not on a dollar permegabyte basis but rather on the basis of unitprice. That is, consumers with sufficiently satisfiedfunctional requirements are more concerned withdifferences in absolute price than with differencesin price/performance points.

In support of this proposition, consider Figure 7,which shows the volume weighted average priceof hard disk drives from 1984 to 1990, the yearsspanning the critical substitution period for 3.5-inch for 5.25-inch drives. Throughout this period,the unit price of the 3.5-inch drives is below thatof 5.25-inch drives.

Viewed through a demand-based lens, thetechnology dynamics observed in the hard diskindustry can be interpreted as follows: preferenceasymmetries are fundamental to shaping firms’incentives in a way that leads to the technologydynamics observed in the hard disk driveindustry. Following the arguments made here,the critical factor driving the displacement of3.5-inch for 5.25-inch hard drives was not thelatent desire of desktop users for smaller, moreenergy-efficient disk drives; rather, it was thatalong with these other features, notebook usersvalued improvements in capacity—the attributeconsidered most critical by desktop users. Becausethe evaluation criteria of desktop users weresubsumed by the criteria of notebook users, thedevelopers of 3.5-inch technology, following thenotebook segment value trajectory, quite naturallybegan to satisfy users in the desktop segment. Assuch, facing higher volumes in the broader market,the 3.5-inch firms had greater incentives to pursuea low price strategy.

Having achieved sufficient storage capacity tomeet the minimum requirements of some low-enddesktop users, 3.5-inch technology firms still hadto contend with a 5.25-inch technology that offeredconsumers greater absolute capacity at a betterprice/performance ratio. The current analysis sug-gests that the essential aspect of consumer choicewhich allows for disruptive displacement maybe consumers’ decreasing marginal utility from



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performance improvements beyond their require-ments, rather than a new found appreciation forpreviously marginal attributes. This decreasingmarginal utility translates into a decreasing will-ingness to pay for improvements. Thus a criticalfactor in consumers’ decisions, once their require-ments are met, is absolute price—while 5.25-inch technology was cheaper in price/performanceterms, because the absolute performance of 5.25-inch drives was much higher than 3.5-inch drives,5.25-inch drives were more expensive on a unitbasis.23

The logic of decreasing marginal utility com-plements the notion of performance oversupply.It presents an even more fundamental aspect ofconsumer choice in that it accounts for changingbehavior in the absence of the introduction of newattributes. Further, this logic suggests that informa-tion about the state of demand can be ascertainednot only by observing the relative valuation ofattributes, but also by observing consumers’ will-ingness to pay for the total product at it evolves andimproves. It also suggests that the drivers of thedisruptive technology’s adoption differ betweenits initial home segment and the mainstream; adifference which may have implications for how

23 Christensen states that incumbents have higher cost structuresthan entering rivals so that they need higher margins to survive.In the context of the model, this speaks to asymmetries infirms’ initial cost positions and their abilities to engage in cost-reducing process innovations. To the extent that incumbents’process innovation options are limited, they will have an evenharder time holding on to less demanding consumers and losemarket share at the low end of the market.

firms should organize for market entry (Moore,1991).

At early stages of technology development,before performance is sufficient to satisfy con-sumer requirements, the issue of price is irrelevant.At later stages, when consumers’ performancerequirements are well satisfied and their willing-ness to pay for additional performance improve-ments has diminished, performance gains lose theirefficacy as competitive actions. It is during themiddle part of development that price/performanceis a critical factor. Technology disruptions are morelikely in this later stage, in which consumers maybe willing to accept a worse price/performanceoffering if its absolute price is sufficiently low.This dynamic, driven by consumers’ decreasingmarginal returns from performance improvements,speaks to the increasing importance of price astechnologies surpass consumers’ requirements.

This logic informs the response to the ques-tion of ‘When are technologies disruptive?’ andhelps distinguish between inferior technologiesand disruptive threats. Christensen suggests thatdisruptive threats can be recognized by identify-ing the point of intersection between performanceprovided by a new technology and performancedemanded by the existing consumers. This advicecan now be further refined: first, the degree ofpreference overlap between the new technology’sexisting customers and the incumbent’s segmentspecifies the magnitude of the impact that sustain-ing innovations along the preference lines of onesegment will have on consumer evaluation in the


686 R. Adner

other segment. Second, the degree of preferenceasymmetry specifies firms’ differential incentivesto compete for new market segments. Finally, crit-ical to a disruptive outcome is the price at whichthe invader offers its product. The attacking firmmust have the incentive and ability to offer itstechnically inferior, yet nonetheless satisfactoryproduct at a sufficiently lower unit price to con-sumers than its rival (e.g., 3.5-inch disk drivesdisplaced 5.25-inch drives, but notebook comput-ers have not displaced desktops from their coremarkets even though they offer sufficient process-ing power along with benefits such as smallersize, lower weight, and lower power consumption).While disruption is enabled by sufficient perfor-mance, it is enacted by price. Thus, to identifypotential disruptions, managers should plot not justperformance-provided and performance-demandedcurves, but also the price trajectories of the com-peting offers.

A demand-based view of the emergence ofcompetition

In examining the development of firm capabilities(Wernerfelt, 1984; Teece, Pisano, and Shuen, 1997)and the nature of firm competition (Porter, 1980;Hamel and Prahalad, 1994), the strategy literaturehas tended to exhibit a ‘supply-side’ bias. Focusedon firms’ activities and interactions, the literaturehas largely overlooked the role of the demandenvironment in which these interactions take place.The demand context, however, affects both theimmediate success of firms’ activities as well asthe nature of future activities. A demand-basedview, focused on how firms’ offers are evaluatedin the market, complements the resource-basedview that has focused on how firms create theseoffers.

The demand landscape shapes the opportunitystructure that firms face and affects individualfirms’ incentives to innovate. Firms’ innovationactivities, in turn, affect consumers’ expectationsand, through these expectations, the demand envi-ronment faced by rival firms. Of particular interestis how, as a technology’s performance begins toaddress the needs of consumers in multiple mar-ket segments, the distinction between these seg-ments is blurred, leading to radical changes infirms’ competitive positions. By focusing on theinteraction between firms and their environments,a demand-based view of competition presents a

complementary approach to examining many ofthe dynamics that are of interest to the strategyfield and offers a link between the evolution offirms’ individual activities and the evolution ofcompetition.

ACKNOWLEDGEMENTS

I thank Javier Gimeno, Rebecca Henderson, DanielLevinthal, Marvin Lieberman, Christoph Loch,Subramanian Rangan, Christophe Van Den Bulte,Sidney Winter, Peter Zemsky, Christoph Zott,Associate Editor Will Mitchell, the anonymousSMJ reviewers, and seminar participants atINSEAD, Harvard, and Informs for their valuablecomments. I am also grateful to Roger Bohn, theInformation Storage Industry Center at UC SanDiego and Disk/Trend Report for sharing their harddisk drive data.

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