a multidimensional conceptualization of environmental velocity

A MULTIDIMENSIONAL CONCEPTUALIZATIONOF ENVIRONMENTAL VELOCITY

IAN P. MCCARTHYTHOMAS B. LAWRENCE

BRIAN WIXTEDBRIAN R. GORDON

Simon Fraser University

Environmental velocity has emerged as an important concept but remains theoreti-cally underdeveloped, particularly with respect to its multidimensionality. In re-sponse, we develop a framework that examines the variations in velocity acrossmultiple dimensions of the environment (homology) and the causal linkages betweenthose velocities (coupling). We then propose four velocity regimes based on differentpatterns of homology and coupling and argue that the conditions of each regime haveimportant implications for organizations.

Environmental velocity1 has become an im-portant concept for characterizing the conditionsof organizational environments. Bourgeois andEisenhardt (1988) introduced this concept to themanagement literature in their study of strate-gic decision making in the microcomputer in-dustry. They described this industry as a “high-velocity environment”— one characterized by“rapid and discontinuous change in demand,competitors, technology and/or regulation, suchthat information is often inaccurate, unavail-able, or obsolete” (Bourgeois & Eisenhardt, 1988:816). From the perspective that the environmentis a source of information that managers use tomaintain or modify their organizations (Aldrich,1979, Scott, 1981), velocity has important impli-cations for organizations. Studies have found,for example, that success in high-velocity indus-tries is related to fast, formal strategic decision-making processes (Eisenhardt, 1989; Judge &Miller, 1991); high levels of team and process

integration (Smith et al., 1994); rapid organiza-tional adaptation and fast product innovation(Eisenhardt & Tabrizi, 1995); and the use of heu-ristic reasoning processes (Oliver & Roos, 2005).More generally, research on velocity has shownthat it affects how managers interpret their en-vironments (Nadkarni & Barr, 2008; Nadkarni &Narayanan, 2007a), further highlighting the ef-fects of environmental dynamism on key orga-nizational members (Dess & Beard, 1984).

A common feature of the treatment of environ-mental velocity in the literature has been theuse of singular categorical descriptors to char-acterize industries—most typically as “low,”“moderate,” or “high” velocity (e.g., Bourgeois &Eisenhardt, 1988; Eisenhardt, 1989; Eisenhardt& Tabrizi, 1995; Judge & Miller, 1991; Nadkarni &Narayanan, 2007a,b). Although Bourgeois andEisenhardt (1988) defined environmental veloc-ity in terms of change (rate and direction) inmultiple dimensions (demand, competitors,technology, and regulation), research on veloc-ity has tended to overlook its multidimensional-ity, instead assuming that a single velocity canbe determined by aggregating the paces ofchange across all the dimensions of an organi-zation’s environment. This assumption over-looks the fact that environmental velocity is avector quantity jointly defined by two attributes(the rate and the direction of change) and thatorganizational environments are composed ofmultiple dimensions, each of which may be as-sociated with a distinct rate and direction ofchange.

We are grateful to associate editor Mason Carpenter andthree anonymous reviewers for their helpful and construc-tive comments. The development of this paper also benefitedfrom comments from Joel Baum, Danny Breznitz, SebastianFixson, Mark Freel, Rick Iverson, Danny Miller, DaveThomas, Andrew von Nordenflycht, Mark Wexler, CarstenZimmermann, and seminar participants at Simon FraserUniversity and the 2008 INFORMS Organization Science Pa-per Development Workshop. We are also grateful to theCanadian Social Sciences and Humanities Research Coun-cil for funding that supported this research.

1 To increase the paper’s readability, we use the termsenvironmental velocity and velocity interchangeably.

� Academy of Management Review2010, Vol. 35, No. 4, 604–626.

604Copyright of the Academy of Management, all rights reserved. Contents may not be copied, emailed, posted to a listserv, or otherwise transmitted without the copyrightholder’s express written permission. Users may print, download, or email articles for individual use only.

In this paper we aim to advance understand-ing of environmental velocity by developing atheoretical framework that articulates its multi-dimensionality and by exploring the implica-tions of this framework for understanding theorganization-environment relationship. We ar-gue that while there may be cases in whichorganizational environments can be accuratelyspecified by a single descriptor (e.g., high veloc-ity), a multidimensional conceptualizationopens up a number of opportunities. First, itprovides a basis for more fine-grained descrip-tions of the patterns of change that occur inorganizational environments. An understandingof a firm’s environmental velocity as composedof multiple, distinct rates and directions ofchange across multiple dimensions allows us tomove beyond characterizations of industries ashigh or low velocity and the assumption that alldimensions change at similar rates and in sim-ilar directions (Bourgeois & Eisenhardt, 1988;Eisenhardt, 1989; Judge & Miller, 1991; Smith etal., 1994). Perhaps most important in this regard,a multidimensional conceptualization allowsfor an examination of the relationships amongthe dimensions of velocity, which we argue canhave a profound impact on organizations.

Second, a multidimensional conceptualiza-tion of velocity offers a foundation for more con-sistent operationalizations of the construct,which would help improve the reliability andvalidity of research that employs it. Our reviewof the environmental velocity literature indi-cates a reliance on singular descriptors of ve-locity, which has led to inconsistent operation-alizations of the construct. Thus, while it hassometimes been claimed that people can recog-nize a high-velocity environment when they seeone (Judge & Miller, 1991), the different ways thatthe velocity of the same industry has been cat-egorized by different researchers would seem toindicate otherwise. Such inconsistencies may bedue to focusing on one or two particularly sa-lient velocity dimensions or to combining datafor multiple velocity dimensions without consid-ering the aggregation errors that can occur if thedimensions do not perfectly covary.

Finally, by understanding that the environ-ments of organizations have multiple, distinctvelocities, it is possible to identify different pat-terns of environmental velocity whose condi-tions affect organizations in ways that go be-yond the insights that have emerged from

studies characterizing velocity as simply high orlow. Specifically, we explain how the multidi-mensionality of velocity can affect the degree towhich an organization’s activities will be en-trained and adjusted over time. We then high-light how these implications apply to two pro-cesses that have been central to prior researchon velocity: strategic decision making and newproduct development.

Our exclusive focus on environmental velocitydiffers from prior research that has sought tocharacterize organizational environments interms of a set of core properties—most com-monly some variation of complexity, dynamism,and munificence (Aldrich, 1979; Dess & Beard,1984; Scott, 1981). In pursuing this aim, we rec-ognize the trade-offs among generalizability, ac-curacy, and simplicity (Blalock, 1982) inherent inexamining one aspect of the environment indepth while bracketing other important environ-mental dimensions. Research focused on thegeneral organizational environment has strivedfor “high levels of simplicity and generalizabil-ity, with a corresponding sacrifice of accuracy”(Dess & Rasheed, 1991: 703). This approach hasbeen characterized as “collapsing” the hetero-geneity of the environment into a more parsimo-nious set of properties (Keats & Hitt, 1988). Incontrast, we focus on a single specific aspect ofenvironmental dynamism—velocity—and ex-plore in detail its dimensions, how the velocitiesof these dimensions vary and interact, and theconsequences of those differences and interac-tions. Our approach follows other studies thathave examined specific environmental con-structs, such as uncertainty (Milliken, 1987) andmunificence (Castrogiovanni, 1991). An impor-tant consequence of focusing on a single aspectof the environment is that any normative or pre-dictive claims we make must be made with ce-teris paribus restrictions placed on them. This,of course, complicates the application of suchclaims in research or practice but also allows adeeper examination of specific phenomena (Pi-etroski & Rey, 1995).

We present our arguments as follows. First,we review the concept of environmental velocityas it has been developed in management re-search, focusing on the opportunities that thiswork presents for developing a multidimen-sional conceptualization. Second, we presentour framework by defining several fundamentaldimensions of the organizational environment

2010 605McCarthy, Lawrence, Wixted, and Gordon

and defining the key aspects of velocity—therate and direction of change—for each dimen-sion. Third, we examine the potential relation-ships among velocity dimensions (such as prod-ucts and technology) by introducing threeconcepts: (1) “velocity homology,” which is thedegree to which velocity dimensions have sim-ilar rates and directions of change at a point intime; (2) “velocity coupling,” which is the degreeto which the velocities of different dimensionsaffect one another over time; and (3) “velocityregimes,” which represent patterns of velocityhomology and velocity coupling. Fourth, we ex-plore the implications of our framework for or-ganization-environment relationships and forstrategic decision making and new productdevelopment.

ENVIRONMENTAL VELOCITY INMANAGEMENT RESEARCH

In physics, velocity refers to the rate of dis-placement or movement of a body in a particulardirection. Thus, it is a vector quantity jointlydefined by two distinct attributes: the rate ofchange and the direction of change. The defini-tion of high-velocity environments articulatedby Bourgeois and Eisenhardt (1988) capturedthese two attributes, referring to rapid and dis-continuous change in multiple dimensions ofthe environment, such as demand, competitors,technology, and regulation. The notion of highvelocity provided an evocative way to charac-terize the fast-moving, high-technology industrythat was the context of their studies, and it com-plemented a number of similar but conceptuallydistinct environmental constructs, including dy-namism (Baum & Wally, 2003; Dess & Beard,1984; Lawrence & Lorsch, 1967), turbulence (Em-ery & Trist, 1965; Terreberry, 1968), and hypertur-bulence (McCann & Selsky, 1984). More recently,environmental velocity has been used in con-junction with or as a synonym for other relatedenvironmental constructs, such as “clockspeed”(i.e., the speed of change in an industry; Fine,1998; Nadkarni & Narayanan, 2007a,b) and hy-percompetition (Bogner & Barr, 2000; D’Aveni,1994).

Table 1 lists some of the major studies in stra-tegic management and organization theory inwhich the concept of environmental velocityplays a central role. For each study the tabledelineates the phenomenon of interest, the in-

dustry context, the level (high, moderate, or low)of velocity considered, and the measures em-ployed (if any). Looking across these studies, weidentify three themes that characterize much ofthe existing research in the area and provide themotivation for the theoretical framework that wedevelop.

First, existing studies have predominantly fo-cused on high-velocity environments, with lim-ited attention to other potential patterns of ve-locity. Consequently, we know relatively little,for instance, about the velocity-related chal-lenges faced by firms operating in low-velocityenvironments, where the slow pace of changemay be associated with protracted developmentlead times, long decision horizons, and rela-tively infrequent feedback. Also, and more gen-erally, the focus on high-velocity environmentsmay be a significant factor in the treatment ofvelocity in terms of singular categorical descrip-tors; the term high-velocity environment itselfseems to imply that multiple dimensions of theenvironment (e.g., products, markets, technol-ogy) combine nonproblematically to produce asingle, cumulative, high level of velocity. Whilethis may be true in some cases, it is not clearthat it applies broadly across firms andindustries.

Second, high-velocity environments are oftenpresented as synonymous with high-technologyindustries, perhaps because Bourgeois andEisenhardt’s initial study focused on the earlymicrocomputer industry. Industries have beencategorized as high velocity simply becausethey are technology intensive (Smith et al., 1994)or are built around an evolving scientific base(Eisenhardt & Tabrizi, 1995), regardless ofwhether other environmental dimensions ex-hibit low or modest rates of change or relativelycontinuous directions. Judge and Miller (1991),for instance, identified the biotechnology indus-try as high velocity, despite its relatively longproduct development lead times and productlife cycles (both ten to twenty years).

Finally, existing research tends to lack an ex-plicit measurement model or justification for thecategorization of specific organizational con-texts or industries. Instead, researchers declarethat they are studying high-velocity environ-ments and reiterate Bourgeois and Eisenhardt’s(1988) original definition without significant ex-planation or direct evidence (the studies byJudge and Miller [1991] and Nadkarni and Barr

606 OctoberAcademy of Management Review

[2008] representing notable exceptions). Thisvariation in the extent to which velocity hasbeen operationalized has resulted in some coun-terintuitive and inconsistent categorizations ofindustry velocity. Studies of health care, for in-stance, have labeled those environments asboth high velocity (Stepanovich & Uhrig, 1999)and moderate velocity (Judge & Miller, 1991).Furthermore, our understanding of velocity andits effects across industry contexts has largelyfocused on only one attribute of velocity—therate of change—since prior research has tendedto use measures associated with the clockspeedof an industry (e.g., Nadkarni & Narayanan,2007a; Oliver & Roos, 2005; Smith et al., 1994) or

has equated velocity with the speed at whichnew opportunities emerge (Davis, Eisenhardt, &Bingham, 2009).

Looking across these themes, we see that re-search on environmental velocity has providedinteresting and influential insights, particularlyinto the nature of organizational processes op-erating in fast-changing, high-technology in-dustries. We suggest, however, that the con-struct itself requires a more fine-grainedexamination, since existing research tends toassume that it can be adequately representedby an aggregation of the rates of change acrossdifferent environmental dimensions or by a fo-cus on change in only one dimension of the

TABLE 1Environmental Velocity in Management Research

Example StudiesManagement/OrganizationPhenomena

Level of Velocity(Industry Context) Conceptualization of Velocity

Velocity MeasuresUsed

Bourgeois &Eisenhardt (1988)

Pace and style of strategicdecision making

High (microcomputerindustry)

Uniform change in the rate anddirection of demand,competition, technology, andregulation

Illustrative statisticsand examples

Eisenhardt &Bourgeois (1988)

Politics of strategic decisionmaking

High (microcomputerindustry)

As per Bourgeois & Eisenhardt(1988)


Eisenhardt (1989) Rapid strategic decision making High (microcomputerindustry)



Judge & Miller(1991)

Antecedents and outcomes ofdecision speed

High (biotechnology),medium (hospital),and low (textile)

Aggregation of industry growthand perceived pace oftechnological, regulatory,and competitive change

Industry data andsurvey data fromfirms

Smith et al. (1994) The effect of team demographyand team process

High (informational,electrical,biomedical,environmental)

Rate of change in product,demand, and competition

Illustrative statistics

Eisenhardt &Tabrizi (1995)

Rapid organizational adaptationand fast product innovation

High (computer) As per Bourgeois & Eisenhardt(1988)


Brown &Eisenhardt (1997)

Continuous organization change High (computer) As per Bourgeois & Eisenhardt(1988)


Stepanovich &Uhrig (1999)

Strategic decision-makingpractices

High (health care) Rate of change in demand,competition, technology, andregulations

An illustrativeexample

Bogner & Barr(2000)

Cognitive and sensemakingabilities

High (IT) A form of hypercompetition None

Oliver & Roos(2005)

Team-based decision making High (toys and ITtools)

Rate of change and the timeavailable to make decisions

None

Brauer & Schmidt(2006)

Temporal development of afirm’s strategyimplementation

High and low(industries notspecified)

A form of dynamism andvolatility

Industry marketreturns data

Davis & Shirato(2007)

A firm’s propensity to launchWorld Trade Organizationactions

High (computer),medium (auto), andlow (steel)

The number of product linesand the rate of productturnover

R&D expenditure/total revenue

Nadkarni &Narayanan(2007a)

How cognitive construction byfirms drives industry velocity

High (computers,toys) and low(aircraft, steel)

Rate of change (clockspeed) forproduct, process, and organi-zational dimensions

Industry clockspeeds

Nadkarni &Narayanan(2007b)

Relationship between strategicschemas and strategicflexibility

High (computers,toys) and low(aircraft, steel)

The rate of industry change(clockspeed)

Industry clockspeeds

Nadkarni & Barr(2008)

How velocity affects managerialcognition, which in turnaffects the relationshipbetween industry context andstrategic action

High (semiconductor,cosmetic) and low(aircraft,petrochemical)


A review of existingliterature andmatching usingindustry attributes

Davis, Eisenhardt,& Bingham(2009)

The performance and structuralimplications of velocity

High and low(conceptualsimulation model)

The speed or rate at which newopportunities emerge in theenvironment

A Poissondistribution of newopportunities


environment to the exclusion of others. In con-trast, we believe that a multidimensional con-ceptualization of velocity would provide astronger foundation for clarifying and opera-tionalizing its characteristics and for betterunderstanding its diversity and impacts onorganizations.

ENVIRONMENTAL VELOCITY AS AMULTIDIMENSIONAL CONCEPT

The core understanding of environmental ve-locity that we propose is that organizational en-vironments are composed of multiple dimen-sions, each of which is associated with its ownrate and direction of change. This simple notion,we argue, has profound effects on how we un-derstand and research velocity and on the or-ganizational reactions to velocity we expect andprescribe. In this section we begin to constructour theoretical framework, first by defining thebasic concepts of rate of change and direction ofchange as they apply to the organizational en-vironment in general, and then by describinghow these basic concepts apply to some primarydimensions of the organizational environment.

The Rate and Direction of Change

Environmental velocity is a vector quantitydefined by the rate and direction of change ex-hibited by one or more dimensions of the orga-nizational environment over a specified period.The rate of change is the amount of change in adimension of the environment over a specifiedperiod of time, synonymous with such conceptsas pace, speed, clock rate, or frequency ofchange. The direction of change, while oftenmentioned in studies citing Bourgeois andEisenhardt’s (1988) definition, has attracted rel-atively little attention beyond that. One possiblereason for this is the relative difficulty of de-scribing the direction of environmental change.Whereas the velocity of a physical object can bedescribed simply as moving eastward at 50 km/hr, similarly straightforward descriptions of thedirection of change of an organizational envi-ronment are not so obvious. This is particularlythe case when we consider the direction ofchange across different industry dimensions,such as products, technology, and regulation,the direction of each of which could be de-scribed in numerous distinct ways.

In order to describe the direction of change ina way that allows comparison across industrydimensions, we follow Bourgeois and Eisen-hardt (1988), who suggest that the direction ofchange varies in terms of its degree of continu-ity-discontinuity. They argue that continuouschange represents an extension of past devel-opment (e.g., continuously faster computer tech-nology), whereas discontinuous change repre-sents a shift in direction (the move from film todigital photography, or the shifts that occur infashion industries). Discontinuities, therefore,can be represented by inflection points in thetrajectories that describe change in a dimensionover time (e.g., technology price-performancecurves or demand curves for specific products).

To more fully articulate a continuum of con-tinuous-discontinuous change, we draw onWholey and Brittain’s (1989) three-part concep-tualization of environmental variation, arguingthat the direction of change is discontinuous tothe extent that shifts in the trajectory of changeare more recurrent, with greater amplitude andwith greater unpredictability over a period oftime. This approach helps us distinguish be-tween relatively regular, predictable (e.g., sea-sonal) variations in environmental velocity andirregular types of change that are more difficultto predict and, consequently, more challengingin terms of organizational responses (Milliken,1987). We suggest that such variations in thecontinuity-discontinuity of a velocity dimen-sion’s trajectory allow for the use of structuralequation modeling (Kline, 2004) and differencescores (Edwards, 1994) to produce growth modelsthat measure transitions in change over time(Bliese, Chan, & Ployhart, 2007; Singer & Willett,2003).

Furthermore, to operationalize the rate anddirection of change of each velocity dimension,we suggest that the measures will require scaleuniformity to allow the relative differences be-tween the dimensions to be compared and cor-related (Downey, Hellriegel, & Slocum, 1975; Mil-liken, 1987). To achieve this, we suggest that therate and direction of change will be some formof scalar measure (e.g., change/time). Therefore,even though what is changing will vary for eachof the dimensions, their relative rates and direc-tions of change can be determined and com-pared by using the same period of time for thedifferent dimensions (i.e., new products per yearand changes in product direction per year).


Dimensions of Environmental Velocity

The second way in which we break down theconcept of environmental velocity is in terms ofthe dimensions of the organizational environ-ment that are changing. While the dimensionsof the environment that are salient for any par-ticular study will vary according to the specificsof the research project, there are several thathave been widely used in prior research on or-ganizational environments. We use the four di-mensions suggested by Bourgeois and Eisen-hardt (1988)—demand, competitors, technology,and regulation—and to this list we add a fifthdimension—products. We do this because priorresearch on environmental velocity has tendedto merge the technology and product dimen-sions, and we argue that they often have dissim-ilar rates and directions of change, which makesseparating them important for our purposes.Archibugi and Pianta (1996) point to the impor-tance of this distinction when they argue thatproduct changes need not be technical but canalso include changes in the aesthetic, branding,or pricing features of a product. Our discussionof environmental dimensions is not meant to beexhaustive; rather, it is meant to highlight theheterogeneity of environmental dimensions thatmotivates our development of a multidimen-sional conceptualization of velocity.

Technological velocity. Technological velocityis the rate and direction of change in the pro-

duction processes and component technologiesthat underlie a specific industrial context, suchas float glass technology in glass manufactur-ing, genetic engineering in the biotechnologyindustry, and rolling mills in metals processing.See Table 2 for a summary of the definitions foreach of the velocity dimensions on which wefocus.

The rate of technological change is theamount of change in those technologies over aspecific time period, including the creation ofnew technologies, the refinement of existingtechnologies, and the recombination of compo-nent technologies. The rate of technologicalchange varies dramatically across industries.Drawing on patents as an indicator of the rate oftechnological change, one can argue, for in-stance, that the electronics industry exhibits amore rapid rate of technological change thandoes the oil industry. In 2006, rankings for thenumber of patents granted in the United Statesshowed that the top five positions were held byelectronics companies, whereas the oil industryfirms Shell and Exxon occupied positions 126and 139, respectively (IFI, 2008). Although sometechnological change is either not patentable ornot patented for strategic reasons, the rate ofpatenting can nevertheless provide a useful in-dication of the technological rate of changesince it is a relatively direct and publicly avail-able indicator of the proprietary technological

TABLE 2Environmental Velocity: Dimension Definitions and Example Measures

Definition/ExampleMeasures Technological Product Demand Regulatory Competitive

Velocity dimensiondefinition

The rate and directionof change in theproductionprocesses andcomponenttechnologies thatunderlie a specificindustrial context

The rate and directionof change in newproductintroductions andproductenhancements

The rate and directionof change in thewillingness andability of the marketto pay for goodsand services

The rate and directionof change in lawsand regulations thataffect an industry

The rate and directionof change in thestructure ofcompetition withinan industry

Example measuresof the rate ofchange in thedimension

The number of newpatents andcopyrights grantedin a given period

The number of newproducts introducedin a given period(i.e., productclockspeed)

The change inindustry sales in agiven period

The number of newand amended lawsand/or regulationsintroduced in agiven period

The change inindustry populationsize and density(i.e., number andsize of firms) in agiven period

Example measuresof the directionof change in thedimension

The changes in thedirection of therelationshipbetween the priceand technicalperformance oftechnology in agiven period

The change in thenature of productfeatures asperceived by themarket in a givenperiod

The change in thetrend (e.g., growthversus decline) andnature (e.g.,personal versusimpersonal) ofdemand in a givenperiod

The change in thenature and scope ofthe control providedby new laws andregulations in agiven period

The change inindustry growthtrends (e.g., growthversus decline) in agiven period


outputs of an industry (Archibugi & Pianta, 1996;Griliches, 1990).

The direction of technological change refers tothe trajectories along which technological ad-vancements take place (Abernathy & Clark,1985; Dosi, 1982; Tushman & Anderson, 1986). Dis-tinguishing between continuous and discontin-uous directions of technological change is mosteasily understood in terms of performance/pricecurves. Continuous technological change in-volves a series of improvements that enhancethe performance of the technology (e.g., ad-vances in photographic film technology focusedon improving contrast quality, light sensitivity,and speed). Such changes move a technologysmoothly along a performance/price curve, usu-ally at a decreasing rate, thus creating a con-cave downward performance/price curve. Incontrast, discontinuous technological changeinvolves “architectural” (Henderson & Clark,1990) or “radical” innovations that “dramaticallyadvance an industry’s price vs. performancefrontier” (Anderson & Tushman, 1990: 604). Theseinnovations temporarily alter the shape of theperformance/price curve such that it becomesconcave upward until the immediate benefits ofthe innovation are exhausted.

Product velocity. This dimension is the rateand direction of new product introductions andproduct enhancements. We define products asany combination of ideas, services, and goodsoffered to the market (Kotler, 1984). The rate ofchange in products can vary tremendouslyacross industries and across market segmentswithin an industry. In terms of the former, Fine(1998) and Nadkarni and Narayanan (2007a,b)show that the movie, toy, and athletic footwearindustries have relatively high rates of productchange (new products launched every three tosix months), whereas the aircraft, petrochemi-cal, and paper industries have low rates of prod-uct change (new products launched every ten totwenty years).

The direction of change for products can bedescribed as continuous when new product in-troductions represent improvements on previ-ously important product attributes, and discon-tinuous when the new products introducefundamentally new attributes for consumerchoice. Adner and Levinthal’s (2001) study of thepersonal computer industry between 1974 and1998 provides an example of relatively continu-ous product change, with only two major inflec-

tion points with respect to price (in 1981 and1988) and no major inflection points with respectto performance. In contrast, fashion products,such as clothing, music, and travel, all changefrequently through the creation of new productsand the transformation and repackaging of ex-isting ones. Such variations in product changeacross industries are associated with differ-ences in the complexity, risk, and impact of theproduct change.

While velocity research has often lumped to-gether product and technological velocities, ourdefinitions of their rates and directions ofchange illustrate the importance of distinguish-ing between them. Over the past several de-cades, for example, the underlying materialsand production processes in the automobileindustry have changed more rapidly and dis-continuously than have the end products them-selves. In contrast, textile production technolo-gies have changed more slowly andcontinuously than the fashion products they areused to create.

Demand velocity. Demand velocity is the rateand direction of change of the willingness andability of the market to pay for goods or services,including changes in the number and types oftransactions and market segments. The rate ofchange in demand varies tremendously acrossindustries, with some experiencing rapidgrowth or decline and others facing steadygrowth for years. Such variance is influenced bya wide range of factors, including changes intaste, new rival products, substitutes, comple-ments, changes in relative prices, business cy-cle fluctuations, and switching costs. Empiricalresearch has used summary industry sales fig-ures as an indicator of the rate of change indemand (e.g., Bourgeois & Eisenhardt, 1988).

The direction of change for demand is contin-uous when there is a steady progression of in-creasing or decreasing sales to a consistent setof consumers. Conversely, change in the direc-tion of demand is discontinuous when there arefrequent, significant, unpredictable shifts in thegrowth, decline, or steady state of demand, or aradical change in the segments that composethe overall market. For example, demand veloc-ity in the U.S. restaurant industry from 1970 to1995 was relatively continuous, with sales gainsmade nearly every year during that period (Har-rington, 2001). In contrast, the demand for com-modities, such as copper and gold, can be


highly volatile owing to a wide range of macro-economic influences, exemplifying the case of adiscontinuous demand velocity. Similarly, theNintendo Corporation created discontinuouschange in the demographics of demand since itsWii games console appealed to nontraditionalmarket segments, such as families, women, andolder people.

Regulatory velocity. We define regulatory ve-locity as the rate and direction of change in theregulations and/or laws that directly affect thefirm or industry under consideration. This in-cludes government action (e.g., changes in laws,regulations, and polices) and industry self-regulation (e.g., voluntary standards and codes).It is a dimension that can open or close markets,present organizations with compliance costs,and necessitate strategic shifts in practices. Therate of regulatory change is a function of thecreation of new laws or regulations, or changesto existing laws or regulations, in a time period.It can vary greatly across industrial, national,and historical contexts, and it often depends onother factors, such as technology (e.g., regula-tions for stem cell research), business scandals(e.g., the Enron scandal), health and safety is-sues (e.g., mad cow disease), and demographicshifts (e.g., an increase in the retiredpopulation).

The direction of change in regulation is con-tinuous to the degree that new regulations re-semble the old in scope, form, or substantiveareas of concern, and it is discontinuous to thedegree that they address new issues, focus ondifferent kinds of behaviors, or employ new prin-ciples. For example, the U.S. airline industryfrom 1938 to 1975 experienced changes in regu-lations that were relatively continuous, in thatthe Civil Aeronautics Board (CAB) restrictedprices, flight frequency, and flight capacity(Vietor, 1990). Then, in 1975, the direction of reg-ulatory change changed as the CAB began ex-perimenting with limited deregulation, and in1978 the industry was completely deregulatedand the CAB abolished.

Competitive velocity. Competitive velocity isthe rate and direction of change in the structuraldeterminants of industry profitability (Barney,1986; Porter, 1980). Its rate of change is, in part, afunction of the entrance and exit of industryrivals (Hannan & Carroll, 1992), as well as thespeed with which firms respond to competitors’strategic moves or other shifts in the environ-

ment (Bowman & Gatignon, 1995). Such mea-sures describe the overall pace at which thecompetitive conditions that define an industryare changing—a factor that has been shown toinfluence firm performance across a wide rangeof industries, including the automotive (Hannan,Carroll, Dundon, & Torres, 1995), computer (Hen-derson, 1999), and insurance (Ranger-Moore,1997) industries.

The direction of change in competitive struc-ture involves continuity-discontinuity with re-spect to the value chain in an industry (Jaco-bides & Winter, 2005), the nature of rivals (Porter,1980; Schumpeter, 1950), or changes in marketcontestability (Hatten & Hatten, 1987). Change incompetitive structure is continuous to the de-gree that these characteristics remain constantand stable over time. Conversely, the change indirection in competitive structure is discontinu-ous to the degree that industry value chains arein flux (Jacobides, 2005) and existing bases ofcompetition are challenged by firms introducingnew products, pioneering new markets orsources of supply, or implementing new meansof production (Schumpeter, 1950).

RELATIONSHIPS AMONG VELOCITYDIMENSIONS: VELOCITY HOMOLOGY,

VELOCITY COUPLING, ANDVELOCITY REGIMES

An important benefit of a multidimensionalconceptualization of environmental velocity isthe potential it provides to examine the differ-ences and relationships among the velocities ofdifferent dimensions. To that end, we introducethree concepts: (1) velocity homology—the rela-tive similarity among the rates and directions ofchange of different dimensions; (2) velocity cou-pling—the degree to which the velocities of dif-ferent dimensions are causally connected; and(3) velocity regimes—the different patterns ofenvironmental velocity that emerge from varia-tions in velocity homology and velocitycoupling.

Velocity Homology

The term homology was coined by the paleon-tologist Richard Owen (1843) to explain the mor-phological similarities among organisms. It hasbeen used by management scholars to describethe degree to which two phenomena are similar


(Chen, Bliese, & Mathieu, 2005; Glick, 1985; Han-lon, 2004) and is consistent with the homogene-ity-heterogeneity aspect of environmental com-plexity (Aldrich, 1979; Dess & Beard, 1984). In ourframework, velocity homology is the degree towhich the rates and directions of change of dif-ferent dimensions are similar to each other overa period of time. Thus, “high homology” de-scribes a condition in which the velocities ofdifferent dimensions in a given environment ex-hibit relatively similar rates and directions ofchange, whereas “low homology” describes rel-atively dissimilar rates and directions ofchange.

To help explain velocity homology, we presenta map of the velocities of different dimensions,with the rate of change and the direction ofchange on each axis (see Figure 1). With thisimage of velocity (based on the fashion apparelindustry example we present in the followingsections), homology is represented by the close-ness of the points. Thus, low homology (as is thecase in Figure 1) is represented by relativelyspread out points, and high homology would berepresented by relatively tightly clusteredpoints. To operationalize this concept of homol-ogy, we suggest using distance measures andmethods, such as cluster analysis (Ketchen &

Shook, 1996), factor analysis (Segars & Grover,1993), and multidimensional scaling (Cox & Cox,2001), all of which are considered suitable forassessing interdimension similarity in constructcomposition (Harrison & Klein, 2007; Law, Wong,& Mobley, 1998).

An assumption of a highly homologous set ofvelocities typified much of the early work onhigh-velocity environments, in which industriessuch as microcomputers were characterized by“rapid and discontinuous change” across multi-ple dimensions (Bourgeois & Eisenhardt, 1988:816). An assumption of high homology carriedover to subsequent studies, with limited consid-eration of the degree to which homology mightvary across firms and industries. Most studiesseem to have aggregated the velocities of differ-ent dimensions, regardless of the varianceamong these dimensions, thereby assumingsimilarity (i.e., high homology in our terms)among the velocities of different environmentaldimensions. Consequently, we know relativelylittle about the conditions and effects of low-homology environments, where the velocityproperties of a firm’s multiple environmental di-mensions are highly dissimilar.

To illustrate and clarify the concept of homol-ogy, we present the example of the apparel in-

FIGURE 1Fashion Apparel Industry Example

Rate of change

Direction ofchange

Product

TechnologicalRegulatory

Demand

Competitive

HighLow

Continuous

Discontinuous

Key: The solid lines indicate tight coupling and the dashed lines loose coupling.


dustry and focus on the industry segment in-volved in the design and supply of seasonalfashion apparel. This includes brands sold pri-marily through own-brand stores (e.g., Gap,Zara, and American Apparel) and brands soldthrough a mixture of own-brand stores and in-dependent stores (e.g., Armani, Benetton, andLevi’s). We chose this industry because aca-demic studies and business reports suggest thatfrom 1985 through 2005 the velocities of differentdimensions in this industry spanned a diverserange of rates and directions of change (Djelic &Ainamo, 1999; The Economist, 2005; Jacobides &Billinger, 2006; Taplin & Winterton, 1995).

Beginning with the product dimension, thissegment of fashion retailing is associated witha relatively high rate of change and a moder-ately discontinuous direction. This is illustratedby the operations of Zara, one of Europe’s lead-ing fashion brands. Zara launches some 11,000new products annually, most of which are com-pletely new products as perceived by the cus-tomer and typically take only five weeks fromdesign to retail store (The Economist, 2005). Evencasual fashion houses, such as Sweden’sHennes & Mauritz (H&M) and the Americanchain Gap, roll out between 2,000 and 4,000 prod-ucts each year. Moreover, the rate of change inproducts has increased, with the emergence of“fast fashion” as a dominant strategy for massmarket designers/retailers (Doeringer & Crean,2006). We argue that the direction of productchange is moderately discontinuous, becausealthough these firms launch many new prod-ucts, they represent a mix of new items andextensions of existing products. This view isconsistent with studies of the rate and directionof change in women’s formal wear (Lowe &Lowe, 1990).

The technologies that underpin the fashionindustry have been changing rapidly over thepast twenty years (cf. Richardson, 1996) but at arelatively slower rate than changes in fashionproducts. Although manufacturing technologyin the apparel industry has remained stable fornearly a century (Audet & Safadi, 2004), therehave been advances in the manufacture of tex-tiles, as well as in communication and informa-tion technologies, that have facilitated the moveto quick response (Forza & Vinelli, 1997) and fastfashion (Doeringer & Crean, 2006) strategies infashion design and retailing. The direction ofthese changes has been relatively continuous

over the past twenty or so years—toward greaterautomation and efficiency in textile manufac-turing, more rapid response to customer de-mands, and more efficient communication andcoordination in fashion design and retailing(Doeringer & Crean, 2006; The Economist, 2005).

In contrast to product and technological veloc-ities, regulatory change in this industry has, forthe past two decades, occurred relatively slowlyand continuously. The regulation that affectsthis industry most significantly is directed at themanufacture of clothing and the protection ofconsumer rights, both of which have changedslowly over that period. With respect to the man-ufacture of garments, the Multi Fibre Arrange-ment (MFA) was introduced in 1974 as a short-term measure to govern world trade in textilesand garments, imposing quotas on the amountdeveloping countries could export to developedcountries (Spinanger, 1999). This regulation un-derwent only minor modifications until it ex-pired in 2005 (Audet & Safadi, 2004). National-level regulation tends to focus on labor andemployment standards. In response to the shiftof clothing manufacturing from developed toemerging economies, the governments of West-ern nations have been reluctant to further regu-late (and potentially stifle) clothing manufactur-ing, much of which occurs as home-based work(Ng, 2007).

Change in demand for fashion apparel has,for the past twenty years, occurred moderatelyslowly, with a high degree of discontinuity. Re-searchers argue that the fashion industry ischaracterized by low to moderate levels of pos-itive sales growth each year (Nueno & Quelch,1998), with occasional major demographic andlifestyle shifts and changes in customer prefer-ences (Danneels, 2003; Siggelkow, 2001). Al-though the direction of change in demand forfashion has oscillated between relative stabilityand discontinuity over the last 150 years (Djelic& Ainamo, 1999), the past 20-year period hasbeen associated with customers becoming moredemanding, arbitrary, and heterogeneous(Djelic & Ainamo, 1999; The Economist, 2005).

The competitive velocity of the fashion indus-try has long fascinated observers. In recentyears it has altered as increased cost pressureshave led firms to engage in rapid-fire attemptsto source the lowest-cost materials and to movelabor-intensive aspects of the value chain tocountries with lower costs. The industry has also


experienced constant shifts in the major centersof production (Dosi, Freeman, & Fabiani, 1994).By way of example, U.S. employment levels inthis sector in 2002 were a third of what they werein the early 1980s (Doeringer & Crean, 2006). Theintersection of cost pressures and the increasingrate of change in consumer preference and de-mand has led to significant shifts in firms’ strat-egies, particularly speeding up the supply chain(Richardson, 1996) and altering organizationalstructures and boundaries (Djelic & Ainamo1999; Jacobides & Billinger, 2006; Siggelkow,2001). Such conditions characterize change thatis both moderately rapid and continuous innature.

The fashion industry points to two importantissues with respect to understanding homologyamong environmental velocity dimensions.First, it highlights that the organizational envi-ronment is composed of a number of distinctdimensions, each of which is defined by its ownrate and direction of change—or velocity. Sec-ond, we see that there are significant differ-ences in the rates and directions of change (lowhomology) across the five dimensions that wehave considered. This makes the idea of de-scribing the industry as having a single veloc-ity, whether based on an “average” across di-mensions or on the velocity of whicheverdimension might be considered most important,misleading both to researchers attempting tounderstand the industry and to managers need-ing to make strategic decisions.

Velocity Coupling

A second important aspect of the relationshipbetween velocity dimensions is the degree towhich and the ways in which they interact overtime. We examine these interactions through theconcept of coupling. This is the degree to whichelements of a system, including product compo-nents (Baldwin & Clark, 1997; Sanchez & Ma-honey, 1996), individuals (DiTomaso, 2001), or-ganizational subunits (Meyer & Rowan, 1977;Weick, 1976, 1982), and organizations (Afuah,2001; Brusoni, Prencipe, & Pavitt, 2001), are caus-ally linked to each other (Orton & Weick, 1990;Weick, 1976). In our framework velocity couplingis the degree to which the velocities of differentdimensions in an organizational environmentare causally connected—the degree to which a

change in the velocity of one dimension causesa change in the velocity of another.

Weick (1976) defined loosely coupled systemsas those in which the properties of constitutiveelements are relatively independent, whereasthe properties of elements in tightly coupled sys-tems are strongly mutually dependent. Weick(1982) further argued that loose coupling in-volves causal effects that are relatively periodic,occasional, and negligible, whereas tight cou-pling involves relatively continuous, constant,and significant causal effects. Thus, we de-scribe the velocities of different dimensions of afirm’s environment as loosely coupled whenchanges in the velocity of one dimension (e.g.,technology velocity) have relatively little imme-diate, direct impact on the velocities of otherdimensions (e.g., product velocity), and we de-scribe them as tightly coupled when the rela-tionship between the velocities of different di-mensions involve significant immediate, directcausal effects. To determine the degree of cou-pling between velocity dimensions, we suggestusing structural equation modeling (Kline, 2004),which is recommended for operationalizing co-variance between construct variables (Law etal., 1998).

Although coupling and homology both de-scribe the relationships among velocity dimen-sions, they are separate, distinguishable as-pects of those relationships. The velocitiesof different dimensions can have high levels ofinterdependence (coupling), regardless ofwhether they exhibit similar rates and direc-tions of change (homology). Homology is a first-order property of velocity, describing the simi-larity among velocities over a period of time. Incontrast, coupling is a second-order property,describing the degree to which changes in thevelocity of a dimension affect the velocity ofanother dimension over the same specified pe-riod of time. The distinction between homologyand coupling is observable in the biotechnologyindustry, which experiences high rates and dis-continuous directions of technological changebut relatively slow, continuous regulatory andproduct velocities (Zollo, Reuer, & Singh, 2002).While these dimensions have very different ve-locities (low homology), there is evidence to sug-gest that they are relatively tightly coupled. Thisis illustrated by the impacts of the 2001 U.S.regulation on stem cell research, which re-stricted research to twenty-one stem cell lines (a


family of constantly dividing cells) and, in turn,limited the rate and direction of U.S. stem cellresearch activity (i.e., technological velocity) rel-ative to other countries. In 2009 this regulationwas overturned, permitting research on up to1,000 new stem cell lines, allowing “U.S. humanembryonic stem-cell research to thrive at last”(Hayden, 2009: 130).

We again draw on the fashion apparel indus-try to illustrate the idea of coupling among ve-locity dimensions. Beginning with products,changes in the velocity of this dimension havebeen attributed to increases in the adoption ofnew communications, design, and manufactur-ing technologies, suggesting a relatively tightcoupling between product and technological ve-locity dimensions. Perhaps most significant,changes in the direction of technology have im-proved the ability of fashion apparel firms togather market feedback and, thus, to developnew product offerings at a faster rate (Jacobides& Billinger, 2006; Kraut, Steinfield, Chan, Butler,& Hoag, 1999; Richardson, 1996). Similarly, thevelocity of demand has been tightly coupled toproduct velocity over the past two decades: in-dustry observers argue that the perceived newarbitrariness of customer demand has forcedfashion organizations to frequently engage inlarge-scale market explorations (Cammet, 2006;Jacobides & Billinger, 2006). In contrast, there islittle evidence of a strong relationship betweenproduct velocity and competitive velocity. Prod-uct velocity appears to be primarily driven bychanges in market demand and the product in-novation programs of existing organizations ex-ploiting those changes, as opposed to a flow ofnew entrants (Cammet, 2006).

In terms of the velocity of regulation in thisindustry, there is evidence that it is tightly cou-pled to the velocities of competition, demand,and products, with changes in internationaltrade regulations (Spinanger, 1999) and domes-tic labor standards (Ng, 2007) leading to increas-ing imports from developing economies, bothcreating and satisfying the demand for cheaperfashion products. Similarly, the velocities ofcompetition and demand appear to be tightlycoupled, with firms in this industry attemptingto predict and adapt to what Siggelkow (2001)calls “fit-destroying changes” that can signifi-cantly alter their competitive positions. There isalso tight coupling between the velocity of tech-nology and the velocity of demand. For example,

in their study of the U.S. fashion apparel indus-try in the 1980s, Abernathy, Dunlop, Hammond,and Weil (1999) explain how changes in demandled to “lean retailing,” which, in turn, requiredfirms to drastically alter their information andproduction technologies to enable new workingpractices. In contrast, there is little evidence tosuggest that changes in the velocity of technol-ogy for the fashion industry will affect or areaffected by changes in the velocities of compe-tition or regulation.

In this illustrative example (see Figure 1), weargue that seven of ten possible dyadic connec-tions among velocity dimensions are relativelytightly coupled (designated by solid lines) suchthat changes in the velocity of one dimensionwill affect the velocity of another. We have ar-gued that the three other connections areloosely coupled, as indicated by the dottedlines. Thus, although not all of the velocity di-mensions of the fashion industry exhibit strongcausal connections to each other, we suggestthat this industry can be described as a rela-tively tightly coupled environment. Any assign-ment of such a category is somewhat arbitrarywithout a formal measurement of coupling, sofor now we follow work on modular (loosely cou-pled) and integrated (tightly coupled) organiza-tional forms that suggests that when at least 50percent of the system elements are tightly cou-pled to each other, the system can be consideredtightly coupled (Schilling & Steensma, 2001).

Velocity Regimes

We propose the concept of a velocity regimeas a way to describe the pattern of velocity ho-mology and velocity coupling within an organi-zational environment. Although both these char-acteristics of velocity vary continuously, wefocus on combinations of high or low homologyand tight or loose coupling to more clearly illus-trate how they vary and the effects of thesevariations. The result is a typology (see Figure 2)with four distinct velocity regimes that repre-sent ideal types, rather than an exhaustive tax-onomy of velocity conditions. To illustrate andvisualize the degrees of homology and couplingthat characterize each regime, we have embed-ded a variation of Figure 1 into each cell ofFigure 2. Like Figure 1, these embedded figurespresent illustrative sets of velocities, the rela-


tive positions of which indicate their rates anddirections of change for different dimensions.

The first velocity regime in our typology oc-curs when environmental dimensions are highlyhomologous and loosely coupled to each other.We call this the “simple velocity regime” be-cause it has similar rates and directions ofchange across all dimensions. Thus, regardlessof whether these dimensions are all changingslowly and continuously or rapidly and discon-tinuously, we argue that it is the relative unifor-mity of the change in strategic information thatmakes the environment relatively analyzable(Daft & Weick, 1984). Furthermore, because thevelocities of the multiple dimensions are looselycoupled, they are free to vary independently sothat changes in the velocity of one dimensionare unlikely to affect the velocities of otherdimensions.

An example of a simple velocity regime is theU.K. tableware industry from the mid 1950s tothe late 1970s. During this period, this industrywas exposed to changes in regulations, de-mand, product, technology, and competition thatwere all relatively slow and continuous in na-ture (Imrie, 1989; Rowley, 1992). At the same time,this industry had relatively loose couplingamong velocity dimensions. For example, whenchange did occur in the velocity of the productdimension during the 1970s, due to an increasein the rate at which product variety and customi-zation changed, the only other velocity dimen-sion to be affected was technology, wherebychanges in the flexibility of production machin-ery altered at a similar rate (Carroll, Cooke,Hassard, & Marchington, 2002; Day, Burnett, For-rester, & Hassard, 2000). This combination ofhigh homology and loosely coupled dimension

FIGURE 2Environmental Velocity Regimes

Velocityhomology

Tight

Loose

HighLow

Velocitycoupling

Direction of change

Rate of change

Conflicted velocity regime Integrated velocity regime

Direction of change

Direction of change

Rate of change

Rate of change

P

D

Divergent velocity regime

C

T

R

Simple velocity regime

C

PT

R D

CT

R

CT

R

CT

RDirection of change

Rate of change

C

PT

R D

CT

R

CT

R

CT

R

P

D

C

T

R

Key: T � technological velocity, R � regulatory velocity, D � demand velocity, C � competitive velocity, and P � productvelocity. The solid lines indicate tight coupling and the dashed lines loose coupling.


velocities created an environment that analystsand scholars described as being uniformly sta-ble, consistent, and regular in nature (Imrie,1989).

The second environmental velocity regime inour typology occurs when the velocities of dif-ferent dimensions are highly homologous andtightly coupled. This creates what we call an“integrated velocity regime.” This regime is in-tegrated in two senses: the velocity attributesof each dimension (i.e., rates and directions ofchange) are very similar, and the velocities ofthe dimensions are highly interdependent oneach other for a period of time. The tight cou-pling differentiates this regime from the simpleregime, presenting managers with the complextask of monitoring and responding to causallyconnected changes in a velocity. This is whatAldrich (1979: 77) calls the “everything’s relatedsyndrome,” where a change in the velocity ofone dimension reverberates throughout the ve-locities of other dimensions. Together, theseconditions create an environment that is bestunderstood as having, at least for a time, a sin-gle overarching velocity. Moreover, if all the di-mensions are changing rapidly and discontinu-ously, this situation will be exemplified by the“high-velocity” industries that have dominatedresearch on environmental velocity.

Consequently, an example of an integratedvelocity regime is the global computer industryfrom approximately 1982 to 1995. During this pe-riod, which is known as the third era of theindustry, the microprocessor and personal com-puter were invented (Malerba, Nelson, Orsenigo,& Winter, 1999), and most of the environmentaldimensions were changing rapidly and in a dis-continuous direction. Firms were frequently en-tering and exiting the industry, as well as form-ing and breaking alliances with each other(Bresnahan & Malerba, 1999; Langlois, 1990).Technological substitution in hardware andsoftware was a frequent occurrence, resulting inregular product innovations (Bourgeois & Eisen-hardt, 1988; Brown & Eisenhardt, 1997). WhileEisenhardt and colleagues clearly argued thatsuch conditions equated to multiple velocitiesundergoing similar “rapid and discontinuouschange,” we suggest there was also a signifi-cant level of interdependence among the veloc-ities of these dimensions. For example, studieshave explained how the velocity of competitionaffected the rate at which new technologies and

products were developed, which, in turn, af-fected the rate at which new market segmentswere created (Bresnahan & Malerba, 1999; Lan-glois, 1990). This coupling among dimensionsalso brought about the wholesale change in thevelocities that occurred around 1995 as the in-dustry began its fourth era—the age of the net-work (Malerba et al., 1999).

The third velocity regime, which we call the“divergent velocity regime,” has a set of dissim-ilar and loosely coupled velocities, so firms facediverse and possibly contradictory environmen-tal conditions. This potentially makes the envi-ronment more difficult to analyze, because somedimensions change slowly and continuously—generating modest amounts of information—while other dimensions change rapidly and dis-continuously—producing large quantities ofinformation that quickly becomes inaccurate orobsolete. This set of dissimilar velocities pre-sents diverse temporal demands on the informa-tion processing and sensemaking abilities ofmanagers. The relatively loose coupling amongthese dissimilar velocities, however, somewhatlessens the challenge of monitoring and re-sponding to environmental conditions, becausechanges in the velocities of different dimensionsare relatively independent, limiting the poten-tial for rapid, widespread change in the flows ofstrategic information.

An example industry of this regime would bethe U.S. flat glass manufacturing industry from1955 to 1975. During this period, the environmen-tal dimensions for this industry had very differ-ent and unconnected velocities. The technology—float glass production methods—that wasdeveloped to produce flat glass was adoptedrelatively quickly during this period comparedto other process technology innovations (Teece,2000). It was also a discontinuous change thatrevolutionized how flat glass was made, withproductivity gains approaching 300 percent asthe need for grinding the glass was eliminated(Anderson & Tushman, 1990). This led to signifi-cant price/performance improvements so thatfloat glass products replaced existing flat glassproducts in a relatively rapid and continuousfashion, rising from 30 million square feet peryear of glass in 1960 to 1,730 million square feetper year of glass in 1973 (Bethke, 1973). Becausethis change in demand was generated by exist-ing producers for existing automotive and con-struction customers, the pace and direction of


competitive change remained relatively slowand continuous in nature. The only significantregulatory event for this industry was that theU.S. Tariff Commission and Treasury more fre-quently cited foreign producers for dumping flatglass on the U.S. market at prices lower thanthose in their own markets (Bethke, 1973). Thislink between the rate of government action andthe increase in production capacity from thenew technology appears to be the only majorinterdependency between the different veloci-ties of the dimensions for this industry duringthis period.

The final velocity regime we propose is com-posed of dimensions whose velocities are rela-tively dissimilar and tightly coupled. We callthis the “conflicted velocity regime,” since orga-nizations operating with such a regime will ex-perience diverse and potentially contradictoryvelocities that are also highly interdependent.As in the case of the divergent regime, the lowlevel of homology among velocity dimensions inthe conflicted velocity regime leads to condi-tions that are, as a whole, inconsistent and rel-atively unanalyzable. However, the tight cou-pling among these heterogeneous velocitiesincreases the difficulty associated with track-ing, understanding, and responding to changesin the conditions of this regime, because thecausal variation makes the environment rela-tively unstable over time. Although neglected inthe velocity literature, we believe that this kindof velocity regime may be quite common. Ourexample of the fashion industry since the mid1980s illustrates the dynamics associated withthe conflicted velocity regime. We argued thatthe rates and direction of change in this industryspan a diverse range. We further argued thatthis industry’s environmental dimensions arerelatively tightly coupled. Such conditions de-fine an environment with a set of dimensionsthat are not only changing dissimilarly but arealso highly interdependent.

ORGANIZATIONAL ANDSTRATEGIC IMPLICATIONS

The importance of environmental velocity isdue to the impacts it has on key organizationaland strategic processes. Thus, in this section weexamine how a multidimensional conceptual-ization of environmental velocity would affectour understanding of these impacts. We explore

the implications of velocity homology and veloc-ity coupling in terms of their general impacts onorganizing and on the processes of strategic de-cision making and new product development.

Implications of Velocity Homology

We argue that the notion of velocity homol-ogy significantly affects how we need to thinkabout the relationship between an organizationand the temporal characteristics of its environ-ment. The dominant notion that has emergedover the past two decades in the velocity litera-ture, and more broadly in research on time andorganizations, has been the importance of orga-nizations operating “in time” with their environ-ments and in synchrony across their subunitsand activities. This is the view of research onorganizational “entrainment” (Ancona & Chong,1996; McGrath, Kelly, & Machatka, 1984; Perez-Nordtvedt, Payne, Short, & Kedia, 2008), whichargues that “functional groups not only must be[internally] entrained with each other for theorganization to work, there must also be exter-nal entrainment, at both the subsystem and sys-tem levels, to ensure adaptation to the environ-ment” (Ancona & Chong, 1996: 19). The impact ofexternal entrainment on performance is echoedin research on high-velocity industries, whichargues that organizational performance in suchenvironments is associated with rapid decisionmaking (Eisenhardt, 1989) and fast new productdevelopment (Eisenhardt & Tabrizi, 1995;Schoonhoven, Eisenhardt, & Lyman, 1990). Intheir discussion of “timepacing,” Eisenhardt andBrown (1998) provide examples of the impor-tance of external entrainment, including thehousehold goods manufacturer that timed itsproduct launch cycles to key retailers’ shelfplanning cycles and, thus, was able to win moreshelf space.

Our multidimensional conceptualization ofvelocity suggests that temporal alignment be-tween an organization’s operations and its en-vironment is critically important but that varia-tions in homology create significant limits to thesynchronization of activities within firms (inter-nal entrainment). If the velocities associatedwith different environmental dimensions aresimilar, as in our high-homology regimes (sim-ple and integrated), then it is appropriate toentrain the pace and direction of all organiza-tional activities to this uniform environmental


velocity. This will be a relatively simple situa-tion to manage. However, if the dimension ve-locities differ significantly, as in our low-homology regimes (conflicted and divergent),then the situation will be more difficult to man-age. This is because the task of entraining or-ganizational activities with dissimilar dimen-sion velocities will lead to heterogeneous sets ofpaces and directions of activities within firms.Such differences create challenges for firms, in-cluding potential incoherence among subunitsand activities, fragmented internal informationflows, and the breakdown of issue capture andanalysis across intraorganizational boundaries.Furthermore, managers who understand thatchanges in velocity homology conditions can beboth endogenous and exogenous in nature willhave not only the option of reactively entrainingtheir organizations to their environment but alsothe option of trying to alter the speed and direc-tion of change in specific environmental dimen-sions to suit their organization. Firms might, forexample, lobby to influence the rate at and di-rection in which legislators develop laws andregulations (i.e., shape what is regulated/deregulated in an industry and the pace atwhich regulatory reform occurs), or undertakemarketing activities to influence changes indemand.

A central theme of research on environmentalvelocity has been its effect on strategic decisionmaking—those “infrequent decisions made bythe top leaders of an organization that criticallyaffect organizational health and survival”(Eisenhardt & Zbaracki, 1992: 17). Following ourgeneral argument regarding the impact of ve-locity homology, we argue that variations in ho-mology reward strategic decision-making activ-ities that are individually entrained with thevelocity of their relevant environmental dimen-sion. Thus, more effective strategic decisionmaking in high-homology regimes (simple andintegrated) will involve a set of activities withsimilar paces and directions. Such internal con-sistency will provide benefits in terms of greaterefficiency and lowered task conflict (Gherardi &Strati, 1988). In contrast, strategic decision mak-ing in low-homology regimes (conflicted and di-vergent) will be more effective when the paceand direction of strategic decision-making ac-tivities are dissimilar, because they are tailoredto their relevant but distinct dimensionvelocities.

A second key strategic process that illustratesthe implications of velocity homology is newproduct development—the set of activities thattransforms ideas, needs, and opportunities intonew marketable products (Cooper, 1990). Previ-ous research has shown the value of rapid newproduct development in high-velocity industries(Eisenhardt & Tabrizi, 1995) but leaves open thequestion of how this might change if we incor-porated a multidimensional conception of envi-ronmental velocity. Although new product de-velopment processes may seem to be primarilylinked to the product dimension of the organiza-tional environment, they cut across a widerange of organizational functions, including re-search, development, design, manufacturing, le-gal, marketing, and sales. Consequently, eachof these different new product development ac-tivities collects, interprets, and applies relevantinformation from different dimensions of the or-ganization’s environment. Thus, the contributionof each function to new product development islikely to be more effective when that function isentrained with the environmental dimension forwhich it is more directly responsible. The abilityof marketing, for instance, to effectively contrib-ute to the development of new products dependson its being entrained with the velocity of de-mand. This means that different new productdevelopment functions may need to operate atdifferent speeds and in different directions inorder to ensure process-environment entrain-ment. Again, this can potentially create signifi-cant organizational challenges in terms of coor-dination and integration across the stages of thenew product development process.

Implications of Velocity Coupling

We argue that the notion of velocity couplingsignificantly affects how we think about the sta-bility of velocity conditions and impacts howorganizations coordinate changes in the paceand direction of their internal activities. Previ-ous research has tended to treat environmentalvelocity not only as a unidimensional conceptbut as a relatively stable feature of organiza-tional environments. In contrast, we argue thatvariations in velocity coupling will lead to im-portant differences in the stability of the velocityconditions of environments. For firms operatingin tightly coupled environments, a change in thevelocity of any one dimension (e.g., technology)


will have a broad impact on the velocity condi-tions of the regime, through its effects on thevelocities of the other dimensions to which it iscoupled (e.g., products, demand, competition).This suggests that regimes with tight velocitycoupling (integrated and conflicted) will haverelatively unstable velocity conditions. This ar-gument follows research on coupling in bothorganizational environments and organizationsthat has shown that tight coupling among ele-ments of a system increases the instability ofthat system (Aldrich, 1979; Dess & Beard, 1984;Terreberry, 1968). An important facet of this in-stability is the rhythms through which it occurs.The impacts of changes in the velocity of onedimension on the velocities of other dimensionsare unlikely to occur instantaneously but,rather, over time, as the social and technologi-cal mechanisms that connect the dimensionsare sequentially triggered and exert theirimpact.

We argue that the environmental instabilityand sequencing of changes associated withtight coupling provide an advantage to certainfirms over others. In particular, tightly coupledregimes (integrated and conflicted) will rewardfirms that employ mechanisms that sensitizethem to velocity changes and allow them to rap-idly and effectively shift the paces of their inter-nal operations. Typical mechanisms could in-clude strategic scanning systems that managersuse to monitor and respond to changes in theirenvironments (Aguilar, 1967; Daft & Weick, 1984)and “interactive control systems” (Simons, 1994)to promote external reflection and internal com-munication and action. These mechanisms areanalogous to other traditional organizational in-tegration (Lawrence & Lorsch, 1967) and bound-ary-spanning (Galbraith, 1973) mechanisms, butwith a focus on coordinating change in the paceand direction of organizational activities tomatch temporal instability in the environment.

Moreover, sequenced changes in velocitiesprovide an advantage to firms that recognizethese causal connections and are consequentlyable to anticipate sequences of velocitychanges. For example, increases in human ge-netic engineering technology in the late 1990sled geneticists and government agencies to callfor more regulation to control the developmentand application of this technology. Those firmsthat anticipated the connection between techno-logical velocity and regulatory velocity proac-

tively planned and shifted the velocities of theirresearch advocacy units to better link with theactivities of patient advocacy groups. Thesechanges helped the industry to garner the pub-lic support necessary to overturn regulations(Campbell, 2009).

Achieving this sequenced change in the paceand direction of organizational activities wouldinvolve the use of time-based mechanisms.These include scheduling and project deadlines,informationtechnologiesthatalignorganization-al activities, and resource allocation rules thatspecify the time to be spent on decision tasks(McGrath, 1991).

As with velocity homology, changes in veloc-ity coupling may stem from external conditions,or it may be that managers are able to increaseor decrease the causal connections among ve-locity dimensions in order to create strategicadvantage for their firms. One strategy to affectvelocity coupling is to alter the degree of mod-ularity in products (Baldwin & Clark, 1997;Sanchez & Mahoney, 1996), technologies (Yaya-varam & Ahuja, 2008), organizations (Meyer &Rowan, 1977; Weick, 1976, 1982), or interorgani-zational networks and supply chains (Afuah,2001; Brusoni et al., 2001). Such changes can af-fect the overall coupling among environmentaldimensions, particularly if they establish newcompetitive standards. Furthermore, suchchanges can be hard to attain and thereforedifficult to imitate, thus creating a competitiveadvantage. Shimano, for example, became thedominant supplier of bicycle drive train compo-nents (shifters, chains, derailleurs, etc.) by de-veloping high-performing, tightly coupled com-ponent systems that changed the nature of thenew product development and production func-tions for their customers, as well as the nature ofend-user demand. Shimano’s strategy alteredthe pace and direction of multiple velocity di-mensions for the bicycle industry and has beencredited with helping Shimano gain almost 90percent of the drive train market for mountainbicycles (Fixson & Park, 2008).

The effects of velocity coupling on how orga-nizations coordinate their activities can also beillustrated by considering strategic decisionmaking and new product development pro-cesses. For strategic decision making, coordina-tion is an issue of social cognition within topmanagement teams (Forbes & Milliken, 1999),which we argue is significantly affected by the


“temporal orientation” of a team. A temporalorientation is a cognitive concept that describeshow individuals and teams conceive of time—as“monochronic,” a unified phenomenon that mo-tivates attention to individual events in serialfashion, or as “polychronic,” a heterogeneousphenomenon that necessitates simultaneous at-tention to multiple events (Ancona, Okhuysen, &Perlow, 2001; Bluedorn & Denhardt, 1988; Hall,1959). We argue that strategic decision makingin tightly coupled regimes would benefit from apolychronic orientation on the part of top man-agement teams so that team members share aview of time as malleable and unstructured.This would help them to simultaneously coordi-nate strategic decision-making velocities and topay continuous partial attention to a broad setof issues (Stone, 2007). In contrast, in looselycoupled regimes the benefits of multitasking,monitoring, and simultaneously adjusting to thevelocities of different dimensions are lower.Such situations, we argue, reward a mono-chronic temporal orientation that leads seniormanagement teams to engage in strategic deci-sion making in a relatively independent man-ner, focusing on one issue at a time.

For new product development processes, theimpact of velocity coupling rests on the ability offirms to recognize and predict the conditionsunder which a new product will be launched.The instability associated with tightly coupledregimes (integrated and conflicted) influencesthe effectiveness of different process controlframeworks that help ensure that the right typeof product innovation is launched at the righttime (McCarthy, Tsinopoulos, Allen, & Rose-Anderssen, 2006). “Linear” new product develop-ment frameworks conceive of the process as aseries of relatively discrete, sequential stages,with team members at each stage making deci-sions (go forward, kill the project, put the projecton hold, etc.) about the progress and outputs ofthe process (McCarthy et al., 2006). These frame-works include the waterfall model (Royce, 1970)and the stage-gate method (Cooper, 1990), whichassume and impose structures or “scaffolds”that restrict the amount of iterative feedback.We argue that such linear frameworks are bestsuited to new product development processesthat operate in loosely coupled velocity regimesin which the activities within the new productdevelopment process are relatively discrete,

with changes in their paces and directions hav-ing limited impacts on each other.

In contrast, “recursive” new product develop-ment frameworks conceive of the process as asystem of interconnected, overlapping activitiesthat generate iterative and nonlinear behaviorsover time (McCarthy et al., 2006). These includeKline and Rosenberg’s (1986) chain-linked modeland Eisenhardt and Tabrizi’s (1995) experientialmodel, both of which, we argue, are suited totightly coupled velocity regimes because theyfacilitate improvisation and flexibility. Thesecapabilities help managers of the process tofocus on and accommodate both the greater in-stability and more turbulent information flowsassociated with these velocity regimes.

CONCLUSION

In the paper’s introduction we suggested thata multidimensional conceptualization of envi-ronmental velocity presented three importantopportunities to advance research in the area.First, we argued that it would allow a morefine-grained examination of environmental ve-locity so as to better understand the diversity ofthis construct across different organizationalcontexts. In our discussions of several indus-tries, including fashion, tableware, computers,and flat glass, we have shown that characteriz-ing these environments simply as high or lowvelocity overlooks the fact that environmentalvelocity is composed of multiple dimensions,each with a distinct velocity.

Second, we argued that a multidimensionalapproach to velocity could lead to more reliableand, thus, more valid empirical research by of-fering a basis for more consistent operation-alizations of velocity. Consequently, with ourframework we have urged researchers to con-sider both the rate and direction of change formultiple pertinent dimensions of the organiza-tional environment. This reveals homology andcoupling relationships among the velocity di-mensions, which describe the different velocityregimes we propose. These concepts provide abasis to better specify environmental velocityand use appropriate operationalizations to mea-sure its diversity. This, in turn, helps avoid in-appropriate aggregations and inconsistent usesof the velocity construct.

Third, we suggested that a multidimensionalconceptualization of environmental velocity and


the conditions of our proposed velocity regimescould provide insights into organizational andstrategic processes beyond what has been pos-sible with a unidimensional concept. To thisend, we have explored some general implica-tions for organizations that follow from velocityhomology and velocity coupling, along withmore specific implications for two key pro-cesses: strategic decision making and new prod-uct development. We have explained how vari-ations in velocity homology influence thedegree to which a firm’s activities or subunitswill be synchronized (internal entrainment) asthey seek to operate in time with their respectivedimensions of the environment (external en-trainment). We have also described how varia-tions in velocity coupling affect the need fororganizations to recognize the stability of theirvelocity regime and anticipate sequences ofchanges in the velocities of their environmentaldimensions.

In summary, the challenges of high-velocityenvironments have captured the attention ofmanagers and scholars. However, the multidi-mensional nature of the velocity construct andits effects have not been explored. Our workbuilds on contingency approaches to organiza-tion-environment relations and work on timeand organizations. To these traditions it offers amore nuanced understanding of one aspect ofchange in organizational environments, and iturges researchers to examine both the complex-ity and diversity of the construct and its effectson organizations.

REFERENCES

Abernathy, H. F., Dunlop, J. T., Hammond, J. H., & Weil, D.1999. A stitch in time: Lean retailing and the transforma-tion of manufacturing—Lessons from the apparel andtextile industries. New York: Oxford University Press.

Abernathy, W. J., & Clark, K. B. 1985. Innovation: Mapping thewinds of creative destruction. Research Policy, 14: 3–22.

Adner, R., & Levinthal, D. 2001. Demand heterogeneity andtechnology evolution: Implications for product processinnovations. Management Science, 47: 611–628.

Afuah, A. 2001. Dynamic boundaries of the firm: Are firmsbetter off being vertically integrated in the face of atechnological change? Academy of Management Jour-nal, 44: 1211–1228.

Aguilar, F. J. 1967. Scanning the business environment. NewYork: Macmillan.

Aldrich, H. E. 1979. Organizations and environments. Engle-wood Cliffs, NJ: Prentice-Hall.

Ancona, D. G., & Chong, C. L. 1996. Entrainment: Pace, cycle,and rhythm in organizational behavior. Research in Or-ganizational Behavior, 18: 251–284.

Ancona, D. G., Okhuysen, G. A., & Perlow, L. A. 2001. Takingtime to integrate temporal research. Academy of Man-agement Review, 26: 512–529.

Anderson, P., & Tushman, M. L. 1990. Technological discon-tinuities and dominant designs: A cyclical model oftechnological change. Administrative Science Quar-terly, 35: 604–633.

Archibugi, D., & Pianta, M. 1996. Measuring technologicalchange through patents and innovation surveys. Tech-novation, 16: 451–468.

Audet, D., & Safadi, R. 2004. A new world map in textiles andclothing: Adjusting to change. Paris: OECD.

Baldwin, C., & Clark, K. 1997. Managing in an age of modu-larity. Harvard Business Review, 75(5): 84–93.

Barney, J. B. 1986. Types of competition and the theory ofstrategy: Toward an integrative framework. Academy ofManagement Review, 11: 791–800.

Baum, J., & Wally, S. 2003. Strategic decision speed and firmperformance. Strategic Management Journal, 24: 1107–1129.

Bethke, W. M. 1973. The outlook for the flat glass industry.Financial Analysts Journal, 29(1): 41–92.

Blalock, H. M., Jr. 1982. Conceptualization and measurementin the social sciences. Beverly Hills, CA: Sage.

Bliese, P. D., Chan, D., & Ployhart, R. E. 2007. Multilevelmethods: Future directions in measurement, longitudi-nal analyses, and nonnormal outcomes. OrganizationalResearch Methods, 10: 551–563.

Bluedorn, A. C., & Denhardt, R. B. 1988. Time and organiza-tions. Journal of Management, 14: 299–320.

Bogner, W. C., & Barr, P. S. 2000. Making sense in hypercom-petitive environments: A cognitive explanation for thepersistence of high velocity competition. OrganizationScience, 11: 212–226.

Bourgeois, L. J., III, & Eisenhardt, K. M. 1988. Strategic deci-sion processes in high velocity environments: Fourcases in the microcomputer industry. Management Sci-ence, 34: 816–835.

Bowman, D., & Gatignon, H. 1995. Determinants of competitorresponse time to a new product introduction. Journal ofMarketing Research, 32: 42–53.

Brauer, M., & Schmidt, S. M. 2006. Exploring strategy imple-mentation consistency over time: The moderating ef-fects of industry velocity and firm performance. Journalof Management and Governance, 10: 205–226.

Bresnahan, T., & Malerba, F. 1999. Industrial dynamics andthe evolution of firms’ and nations’ competitive capabil-ities in the world computer industry. In D. Mowery &R. Nelson (Eds.), The sources of industrial leadership:Studies of seven industries: 79–132. Cambridge: Cam-bridge University Press.

Brown, S. L., & Eisenhardt, K. M. 1997. The art of continuouschange: Linking complexity theory and time-paced evo-


lution in relentlessly shifting organizations. Administra-tive Science Quarterly, 42: 1–34.

Brusoni, S., Prencipe, A., & Pavitt, K. 2001. Knowledge spe-cialization, organizational coupling, and the boundariesof the firm: Why do firms know more than they make?Administrative Science Quarterly, 46: 597–621.

Cammet, M. 2006. Development and the changing dynamicsof global production: Global value chains and localclusters in apparel manufacturing. Competition andChange, 10: 23–48.

Campbell, P. 2009. By common consent. Nature, March 12: 125.

Carroll, M., Cooke, F. L., Hassard, J., & Marchington, M. 2002.The strategic management of outsourcing in the UKtableware industry. Competition and Change, 6: 327–343.

Castrogiovanni, G. J. 1991. Environmental munificence: Atheoretical assessment. Academy of Management Re-view, 16: 542–565.

Chen, G., Bliese, P. D., & Mathieu, J. E. 2005. Conceptualframework and statistical procedures for delineatingand testing multilevel theories of homology. Organiza-tional Research Methods, 8: 375–409.

Cooper, R. G. 1990. Winning at new products. Reading, MA:Addison-Wesley.

Cox, T. F., & Cox, M. A. A. 2001. Multidimensional scaling.Monographs on Statistics and Applied Probability, 88:303.

Daft, R. L., & Weick, K. E. 1984. Toward a model of organiza-tions as interpretation systems. Academy of Manage-ment Review, 9: 284–295.

Danneels, E. 2003. Tight-loose coupling with customers: Theenactment of customer orientation. Strategic Manage-ment Journal, 24: 559–577.

D’Aveni, R. A. 1994. Hypercompetition: Managing the dynam-ics of strategic maneuvering. New York: Free Press.

Davis, C., & Shirato, Y. 2007. Firms, governments and WTOadjudication: Japan’s selection of WTO disputes. WorldPolitics, 59: 274–313.

Davis, J. P., Eisenhardt, K. M., & Bingham, C. B. 2009. Optimalstructure, market dynamism, and the strategy of simplerules. Administrative Science Quarterly, 54: 413–452.

Day, M., Burnett, J., Forrester, P. L., & Hassard, J. 2000. Brit-ain’s last industrial district? A case study of ceramicsproduction. International Journal of Production Econom-ics, 65: 5–15.

Dess, G. G., & Beard, D. W. 1984. Dimensions of organization-al task environments. Administrative Science Quarterly,29: 52–73.

Dess, G. G., & Rasheed, A. M. A. 1991. Conceptualizing andmeasuring organizational environments: A critique andsuggestions. Journal of Management, 17: 701–710.

DiTomaso, N. 2001. The loose coupling of jobs: The subcon-tracting of everyone? In I. Berg & A. L. Kalleberg (Eds.),Sourcebook of labor markets: Evolving structures andprocesses: 247–270. New York: Kluwer Academic/Plenum.

Djelic, M. L., & Ainamo, A. 1999. The coevolution of neworganizational forms in the fashion industry: A histori-

cal and comparative study of France, Italy, and theUnited States. Organization Science, 10: 622–637.

Doeringer, P., & Crean, S. 2006. Can fast fashion save the USapparel industry? Socioeconomic Review, 4: 353–377.

Dosi, G. 1982. Technological paradigms and technologicaltrajectories. Research Policy, 11: 147–162.

Dosi, G., Freeman, C., & Fabiani, S. 1994. The process ofeconomic development: Introducing some stylized factsand theories on technologies, firms and institutions. In-dustrial and Corporate Change, 3: 1–45.

Downey, H. K., Hellriegel, D., & Slocum, J. W. 1975. Environ-mental uncertainty: The construct and its application.Administrative Science Quarterly, 20: 613–629.

The Economist. 2005. The future of fast fashion. June 18: 57–58.

Edwards, J. R. 1994. Regression analysis as an alternative todifference scores. Journal of Management, 20: 683–690.

Eisenhardt, K. M. 1989. Making fast strategic decisions inhigh-velocity environments. Academy of ManagementJournal, 32: 543–576.

Eisenhardt, K. M., & Bourgeois, L. J., III. 1988. Politics ofstrategic decision making in high-velocity environ-ments: Toward a midrange theory. Academy of Manage-ment Journal, 31: 737–770.

Eisenhardt, K. M., & Brown, S. L. 1998. Time pacing: Compet-ing in markets that won’t stand still. Harvard BusinessReview, 76(2): 59–69.

Eisenhardt, K. M., & Tabrizi, B. N. 1995. Accelerating adaptiveprocesses: Product innovation in the global computerindustry. Administrative Science Quarterly, 40: 84–110.

Eisenhardt, K. M., & Zbaracki, M. J. 1992. Strategic decisionmaking. Strategic Management Journal, 13(Winter Spe-cial Issue): 17–37.

Emery, F. E., & Trist, E. L. 1965. The causal texture of orga-nizational environments. Human Relations, 18: 21–32.

Fine, C. 1998. Clockspeed: Winning industry control in theage of temporary advantage. Reading, MA: Perseus.

Fixson, S. K., & Park, J. K. 2008. The power of integrality:Linkages between product architecture, innovation, andindustry structure. Research Policy, 37: 1296–1316.

Forbes, D. P., & Milliken, F. J. 1999. Cognition and corporategovernance: Understanding boards of directors as stra-tegic decision-making groups. Academy of Manage-ment Review, 24: 489–505.

Forza, C., & Vinelli, A. 1997. Quick response in the textile-apparel industry and the support of information technol-ogies. Integrated Manufacturing Systems, 8(3): 125–136.

Galbraith, J. R. 1973. Designing complex organizations. Read-ing, MA: Addison-Wesley.

Gherardi, S., & Strati, A. 1988. The temporal dimension oforganizational studies. Organization Studies, 9: 149–164.

Glick, W. H. 1985. Conceptualizing and measuring organiza-tional and psychological climate: Pitfalls in multilevelresearch. Academy of Management Review, 10: 601–616.

Griliches, Z. 1990. Patent statistics as economic indicators: Asurvey. Journal of Economic Literature, 28: 1661–1797.


Hall, M. 1959. The theory of groups. New York: Macmillan.

Hanlon, G. 2004. Institutional forms and organizational struc-tures: Homology, trust and reputational capital in pro-fessional service firms. Organization, 11: 187–210.

Hannan, M. T., & Carroll, G. 1992. Dynamics of organizationalpopulations. New York: Oxford University Press.

Hannan, M. T., Carroll, G. R., Dundon, E. A., & Torres, J. C.1995. Organizational evolution in a multinational con-text: Entries of automobile manufacturers in Belgium,Britain, France, Germany, and Italy. American Sociolog-ical Review, 60: 509–528.

Harrington, R. 2001. Environmental uncertainty within thehospitality industry: Exploring the measure of dyna-mism and complexity between restaurant segments.Journal of Hospitality & Tourism Research, 25: 386–398.

Harrison, D. A., & Klein, K. J. 2007. What’s the difference?Diversity constructs as separation, variety, or disparityin organizations. Academy of Management Review, 32:1199–1228.

Hatten K. J., & Hatten M. L. 1987. Strategic groups, asymmet-rical mobility barriers and contestability. Strategic Man-agement Journal, 8: 329–342.

Hayden, E. C. 2009. Obama overturns stem-cell ban: Presi-dent’s executive order will allow US human embryonicstem-cell research to thrive at last. Nature, March 12:130–131.

Henderson, A. D. 1999. Firm strategy and age dependence: Acontingent view of the liabilities of newness, adoles-cence, and obsolescence. Administrative Science Quar-terly, 44: 281–284.

Henderson, R. M., & Clark, K. B. 1990. Architectural innova-tion: The reconfiguration of existing product technolo-gies and the failure of established firms. AdministrativeScience Quarterly, 35: 9–30.

IFI. 2008. IFI patent intelligence announces 2007’s topU.S. patent assignees. http://www.ificlaims.com/IFI%20Patent%20Release%201-9-08.htm, January 2008.

Imrie, R. F. 1989. Industrial restructuring, labour, and local-ity: The case of the British pottery industry. Environmentand Planning A, 21: 3–26.

Jacobides, M. G. 2005. Industry change through vertical dis-integration: How and why markets emerged in mortgagebanking. Academy of Management Journal, 48: 465–498.

Jacobides, M. G., & Billinger, S. 2006. Designing the bound-aries of the firm: From “make, buy, or ally” to the dy-namic benefits of vertical architecture. OrganizationScience, 17: 249–261.

Jacobides, M. G., & Winter, S. G. 2005. The co-evolution ofcapabilities and transaction costs: Explaining the insti-tutional structure of production. Strategic ManagementJournal, 26: 395–413.

Judge, W. Q., & Miller, A. 1991. Antecedents and outcomes ofdecision speed in different environmental contexts.Academy of Management Journal, 34: 449–463.

Keats, B., & Hitt, M. 1988. A causal model of linkages amongenvironmental dimensions, macro-organizational char-

acteristics, and performance. Academy of ManagementJournal, 31: 570–598.

Ketchen, D. J., & Shook, C. L. 1996. The application of clusteranalysis in strategic management research: An analy-sis and critique. Strategic Management Journal, 17: 441–458.

Kline, J., & Rosenberg, N. 1986. An overview of innovation. InR. Landau & N. Rosenberg (Eds.), The positive sum strat-egy: Harnessing technology for economic growth: 275–305. Washington, DC: National Academy Press.

Kline, R. B. 2004. Principles and practice of structural equa-tion modeling. New York: Guilford Press.

Kotler, P. 1984. Marketing management: Analysis, planning,and control. Englewood Cliffs, NJ: Prentice-Hall.

Kraut, R., Steinfield, C., Chan, A. P., Butler, B., & Hoag, A.1999. Coordination and virtualization: The role of elec-tronic networks and personal relationships. Organiza-tion Science, 10: 722–740.

Langlois, R. N. 1990. Creating external capabilities: Innova-tion and vertical disintegration in the microcomputerindustry. Business and Economic History, 19: 93–102.

Law, K. S., Wong, C.-S., & Mobley, W. 1998. Toward a taxon-omy of multidimensional constructs. Academy of Man-agement Review, 23: 741–755.

Lawrence, P. R., & Lorsch, J. W. 1967. Organization and envi-ronment: Managing differentiation and integration. Bos-ton: Harvard University Press.

Lowe E. D., & Lowe, J. W. G. 1990. Velocity of the fashionprocess in women’s formal evening dress, 1789–1980.Clothing and Textiles Research Journal, 9: 1–50.

Malerba, F., Nelson, R., Orsenigo, L., & Winter, S. 1999. His-tory-friendly models of industry evolution: The computerindustry. Industrial and Corporate Change, 8: 3–40.

McCann, J. E., & Selsky, J. 1984. Hyperturbulence and theemergence of type 5 environments. Academy of Man-agement Review, 9: 460–470.

McCarthy, I. P., Tsinopoulos, C., Allen, P. M., & Rose-Anderssen, C. 2006. New product development as a com-plex adaptive system of decisions. Journal of ProductInnovation Management, 23: 437–456.

McGrath, J. E. 1991. Time, interaction, and performance (TIP):A theory of groups. Small Group Research, 22: 147–174.

McGrath, J. E., Kelly, J. R., & Machatka, D. E. 1984. The socialpsychology of time: Entrainment of behavior in socialand organizational settings. Applied Social PsychologyAnnual, 5: 21–44.

Meyer, J. W., & Rowan, B. 1977. Institutionalized organiza-tions: Formal structure as myth and ceremony. Ameri-can Journal of Sociology, 83: 340–363.

Milliken, F. J. 1987. Three types of perceived uncertaintyabout the environment: State, effect, and response un-certainty. Academy of Management Review, 12: 133–143.

Nadkarni, S., & Barr, P. S. 2008. Environmental context, man-agerial cognition, and strategic action: An integratedview. Strategic Management Journal, 29: 1395–1427.

Nadkarni, S., & Narayanan, V. K. 2007a. The evolution of


collective strategy frames in high- and low-velocity in-dustries. Organization Science, 18: 688–710.

Nadkarni, S., & Narayanan, V. K. 2007b. Strategic schemas,strategic flexibility, and firm performance: The moder-ating role of industry clockspeed. Strategic Manage-ment Journal, 28: 243–270.

Ng, R. 2007. Garment production in Canada: Social and po-litical implications. Studies in Political Economy,79(Spring): 193–211.

Nueno, J. L., & Quelch, J. A. 1998. The mass marketing ofluxury. Business Horizons, 46(6): 61–68.

Oliver, D., & Roos, J. 2005. Decision making in high-velocityenvironments: The importance of guiding principles. Or-ganization Studies, 26: 889–913.

Orton, J. D., & Weick, K. E. 1990. Loosely coupled systems: Areconceptualization. Academy of Management Review,15: 203–223.

Owen, R. 1843. Lectures on the comparative anatomy andphysiology of the invertebrate animals. London: Long-man, Brown, Green & Longmans.

Perez-Nordtvedt, L., Payne, G. T., Short, J. C., & Kedia, B. L.2008. An entrainment-based model of temporal organi-zational fit, misfit, and performance. Organization Sci-ence, 19: 785–801.

Pietroski, P., & Rey, G. 1995. When other things aren’t equal:Saving ceteris paribus laws from vacuity. British Journalfor the Philosophy of Science, 46: 81–110.

Porter, M. E. 1980. Competitive strategy: Techniques for ana-lyzing industries and competitors. New York: Free Press.

Ranger-Moore, J. 1997. Bigger may be better, but is olderwiser? Organizational age and size in the New York lifeinsurance industry. American Sociological Review, 62:903–920.

Richardson, G. B. 1996. The organization of industry. In P. J.Buckley & J. Michie (Eds.), Firms, organizations and con-tracts: A reader in industrial organization: 59–75. NewYork: Oxford University Press.

Rowley, C. 1992. The illusion of flexible specialization: Thecase of the domestic ware sector of the British ceramicsindustry. New Technology, Work and Employment, 9:127–129.

Royce, W. 1970. Managing the development of large softwaresystems. Proceedings of IEEE, 26(August): 1–9.

Sanchez, R., & Mahoney, J. T. 1996. Modularity, flexibility,and knowledge management in product and organiza-tion design. Strategic Management Journal, 17: 63–76.

Schilling, M. A., & Steensma, K. H. 2001. The use of modularorganizational forms: An industry-level analysis. Acad-emy of Management Journal, 44: 1149–1168.

Schoonhoven, C. B., Eisenhardt, K. M., & Lyman, K. 1990.Speeding products to market: Waiting time to first prod-uct introduction in new firms. Administrative ScienceQuarterly, 35: 177–207.

Schumpeter, J. A. 1950. Capitalism, socialism and democracy.New York: Harper Brothers.

Scott, W. R. 1981. Organizations: Rational, natural, and opensystems. Englewood Cliffs, NJ: Prentice-Hall.

Segars, A. H., & Grover, V. 1993. Re-examining perceivedease of use and usefulness: A confirmatory factor anal-ysis. MIS Quarterly, 17: 517–525.

Siggelkow, N. 2001. Change in the presence of fit: The rise,the fall, and the renaissance of Liz Claiborne. Academyof Management Journal, 44: 838–857.

Simons, R. 1994. How new top managers use control systemsas levers of strategic renewal. Strategic ManagementJournal, 15: 169–189.

Singer, J. D., & Willett, J. B. 2003. Applied longitudinal dataanalysis: Modeling change and event occurrence. NewYork: Oxford University Press.

Smith, K. G., Smith, K. K, Olian, J. D, Sims, H. P., Jr., O’Bannon,D. P., & Scully, J. A. 1994. Top management team demog-raphy and process: The role of social integration and com-munication. Administrative Science Quarterly, 39: 412–438.

Spinanger, D. 1999. Textiles beyond the MFA phase-out.World Economy, 22: 455–476.

Stepanovich, P. L., & Uhrig, J. D. 1999. Decision making inhigh-velocity environments: Implications for healthcare.Journal of Healthcare Management, 44: 197–205.

Stone, L. 2007. Living with continuous partial attention. Har-vard Business Review, 85(2): 28.

Taplin, I. M., & Winterton, J. 1995. New clothes from oldtechniques: Restructuring and flexibility in the US andUK clothing industries. Industrial and CorporateChange, 4: 615–638.

Teece, D. J. 2000. Managing intellectual capital: Organiza-tional, strategic, and policy dimensions. New York: Ox-ford University Press.

Terreberry, S. 1968. The evolution of organizational environ-ments. Administrative Science Quarterly, 12: 590–613.

Tushman, M. L., & Anderson, P. 1986. Technological discon-tinuities and organizational environments. Administra-tive Science Quarterly, 31: 439–465.

Vietor, R. H. K. 1990. Contrived competition: Airline regula-tion and deregulation, 1925–1988. Business History Re-view, 64(1): 61–108.

Weick, K. E. 1976. Educational organizations as loosely coupledsystems. Administrative Science Quarterly, 21: 1–19.

Weick, K. E. 1982. Management of organizational changeamong loosely coupled elements. In P. S. Goodman (Ed.),Change in organizations: 375–408. San Francisco: Jos-sey-Bass.

Wholey, D. R., & Brittain, J. 1989. Characterizing environmen-tal variation. Academy of Management Journal, 32: 867–882.

Yayavaram, S., & Ahuja, G. 2008. Decomposability in knowl-edge structures and its impact on the usefulness ofinventions and knowledge-base malleability. Adminis-trative Science Quarterly, 53: 333–362.

Zollo, M., Reuer, J. J., & Singh, H. 2002. Interorganizationalroutines and performance in strategic alliances. Orga-nization Science, 13: 701–713.


Ian P. McCarthy ([email protected]) is the Canada Research Chair in Technologyand Operations Management in the Faculty of Business Administration at SimonFraser University. He received his Ph.D. in industrial engineering from the Universityof Sheffield. His research deals with organizational taxonomy, organizational design,operations management, and innovation management.

Thomas B. Lawrence ([email protected]) is the Weyerhaeuser Professor of ChangeManagement and director of the CMA Centre for Strategic Change and PerformanceMeasurement at Simon Fraser University. He received his Ph.D. in organizationalanalysis from the University of Alberta. His research focuses on institutions andagency in organizations and organizational fields.

Brian Wixted ([email protected]) is a research fellow at the Centre for PolicyResearch on Science and Technology at Simon Fraser University. He received hisPh.D. from the University of Western Sydney. His research interests include theinternational geography of sectoral innovation systems and the governance of pub-licly funded research systems.

Brian R. Gordon ([email protected]) is a doctoral candidate in business administration atSimon Fraser University. His research interests include knowledge and knowledgecreation processes in strategy, entrepreneurship, and innovation.


a multidimensional conceptualization of environmental velocity

Business

administrative

oxford university

administrative

simon fraser

tive science

strategic

stem cell

stem cell