strategic schemas, strategic flexibility, and firm

Strategic Management JournalStrat. Mgmt. J., 28: 243–270 (2007)

Published online in Wiley InterScience (www.interscience.wiley.com) DOI: 10.1002/smj.576

Received 29 March 2004; Final revision received 11 July 2006

STRATEGIC SCHEMAS, STRATEGIC FLEXIBILITY,AND FIRM PERFORMANCE: THE MODERATINGROLE OF INDUSTRY CLOCKSPEED

SUCHETA NADKARNI1* and V. K. NARAYANAN2

1 College of Business Administration, University of Nebraska, Lincoln, Nebraska,U.S.A.2 LeBow College of Business, Drexel University, Philadelphia, Pennsylvania, U.S.A.

We examine the moderating effect of industry clockspeed on the relationship between strategicschemas, strategic flexibility and firm performance. We employ two key properties of strategicschemas: complexity and focus. Using a sample of 225 firms from 14 industries, we show that thepattern of relationships among the theoretical constructs is different in fast- and slow-clockspeedindustries. The results suggest that complexity of strategic schemas promotes strategic flexibilityand success in fast clockspeed industries, whereas focus of strategic schemas fosters strategicpersistence, which is effective in slow-clockspeed industries. Copyright 2007 John Wiley &Sons, Ltd.

Most of the managerial challenges at Dell Com-puter have to do with what we call velocity -speeding the pace of every element of our business.(Kevin Rollins, Vice-chairman of Dell ComputerCorporation, Harvard Business Review 1998: 76(2):81)

Speed of industry change is of great interest inboth the academic and practitioner-oriented lit-erature on strategic management (Bourgeois andEisenhardt, 1988; Brown and Eisenhardt, 1997;D’Aveni, 1994; Eisenhardt, 1989; Eisenhardt andMartin, 2000; Williams, 1994). Fast-paced indus-tries are characterized by rapid changes in prod-uct and process technologies and in competi-tors’ strategic actions, which make it difficult tobuild sustainable competitive advantage (Fines,1998; Williams, 1994). To survive in fast-pacedindustries, firms must embed flexibility in strate-gic actions (Eisenhardt, 1989). In contrast, firms

Keywords: strategic schemas; strategic flexibility; indus-try clockspeed*Correspondence to: Sucheta Nadkarni, College of BusinessAdministration, University of Nebraska, Lincoln, NE 68488-0491, U.S.A. E-mail: [email protected]

in slow-paced industries can achieve sustainablecompetitive advantage by gradually building andimproving core competencies (Fines, 1998;Williams, 1994). In short, strategic actions of firmsin fast-paced industries must be different fromthose of firms in slow-paced industries.

Despite the importance of the rate of industrychange, the literature in this area has been lim-ited in three ways. First, it has focused mainly onthe fit among environment, structure, and compet-itive actions as a key predictor of performance andhas largely ignored the role of managerial schemasin coping with industry change (Bogner and Barr,2000). Because managerial schemas drive strategicdecision making and thus competitive actions (Fioland O’Connor, 2003), the paucity of such studiesseverely limits our understanding of the strategicschemas needed for success in fast and slow pacedindustries. Second, recent literature on industrychange has focused primarily on fast-paced indus-tries (Brown and Eisenhardt, 1997) and compar-ative studies exploring systematic differences incognitive and strategic challenges faced by firmsin fast- and slow-paced industries are noticeably

Copyright 2007 John Wiley & Sons, Ltd.

244 S. Nadkarni and V. K. Narayanan

absent. Little is known about the implications ofthe fit between cognitive characteristics of strate-gic decision-makers and rate of industry changefor strategic actions and firm performance. Finally,there is some theoretical tension regarding thenature of knowledge required in fast-paced indus-tries. The managerial cognition literature suggeststhat firms need a complex and varied strategicknowledge to cope with the fast pace of change(Lant, Milliken, and Batra, 1992; Wally and Baum,1994; Weick, 1995), whereas the recent literaturecontends that a few simple, central guiding strate-gic routines are likely to be successful in fast-pacedindustries (Eisenhardt and Martin, 2000; Eisen-hardt and Sull, 2001). Few empirical studies haveaddressed this issue.

In this paper, we address these gaps by exam-ining the effects of cognitive differences in fast-and slow-paced industries and the strategic andperformance outcomes of the match between strat-egy schemas and rate of industry change (industryclockspeed). We draw on three distinct streamsof research to develop this complex set of rela-tionships. First, the literature on industry veloc-ity has underscored the difficulty of sustainingcompetitive advantage (D’Aveni, 1994; Eisenhardtand Martin, 2000) and learning from past actions(Bogner and Barr, 2000) in fast-paced industries.Second, the literature on strategic flexibility hasemphasized a contingency view, highlighting theneed for flexible strategic responses in fast-pacedindustries but for strategic persistence in slow-paced industries (Garg, Walters, and Priem, 2003;Kessler and Chakrabarti, 1996). Finally, we inte-grate the prescriptions from the managerial cog-nition literature (Bogner and Barr, 2000; Calori,Johnson, and Sarnin, 1994; Fiol and O’Connor,2003; Weick, 1995) with those from the industryvelocity literature (Eisenhardt and Martin, 2000) totheorize the nature of strategic schemas needed toimplement effective strategic actions in fast- andslow-paced industries and we show that the patternof relationships among the theoretical constructsdiffers between fast- and slow-paced industries.

Our study contributes to existing literature inthree ways. First, our study provides insights intodifferences in the nature of strategic schemasneeded to cope with fast- and slow-paced indus-tries, which not only provide cognitive prescrip-tions for firms in each industry context, but alsoaddress the tension in existing literatures on thenature of cognition required to cope in fast-paced

industries. Second, our empirical analysis exam-ines complex relationships between rate of indus-try change, strategic schemas, strategic flexibility,and firm performance, thus responding to calls forintegrative studies increasingly voiced in recent lit-erature (Huff, 1997; Walsh, 1995; Rajagopalan andSpreitzer, 1997). Third, we illustrate a valid andreliable method of standardizing strategic schemasso that they can be compared across firms andindustries. Lack of valid operationalizations ofstrategic schema has been a bottleneck in themanagerial and organizational cognition research(Huff, 1997; Walsh, 1995) and, as a result, most ofthe strategic schema research has been qualitativeand descriptive in nature, involving small samplesof closely related firms in a single industry (e.g.,Barr, Stimpert, and Huff, 1992; Barr and Huff,1997). The valid and reliable measures of strate-gic schema developed in our study may stimulatethe much-needed large sample, cross-industry, andcross-firm investigations of strategic schemas.

We organize the paper as follows: in the nextsection we define the central constructs in ourstudy. In the third section we present an overviewof our theoretical model and develop our hypothe-ses. The fourth section outlines the research meth-ods, and the fifth section reports the analyses andresults. In the final section, we discuss the resultsand outline their theoretical and practical implica-tions.

CONSTRUCT DEFINITIONS

Industry clockspeed

Definition

Fines (1998) introduced the concept of industryclockspeed to capture the rate of industry changedriven by endogenous factors (technological andcompetitive). He identified three facets of indus-try clockspeed: product, process, and organiza-tional. Product clockspeed represents new prod-uct introduction and product obsolescence rates.For example, the aircraft industry has experi-enced slow clockspeed because incumbent firmshave launched on average about two new prod-ucts per decade (777 and remodeled 737 in the1990s, 757 and 767 in the 1980s, 747 in the1970s). In contrast, the motion picture industry,in which studios turn out dozens of new products(movies) in a year, is considered fast clockspeed.

Copyright 2007 John Wiley & Sons, Ltd. Strat. Mgmt. J., 28: 243–270 (2007)DOI: 10.1002/smj

Strategic Schemas, Strategic Flexibility, and Firm Performance 245

Process clockspeed reflects the rates at which pro-cess technologies are replaced in an industry. Ahigh rate of change in the semiconductor indus-try has resulted from frequent replacements ofprocess technologies. For example, semiconduc-tor firms invest about a billion dollars in a waferfabrication plant and expect that plant to be essen-tially obsolete in less than 4 years. In contrast, therate of change has been slow in the automobileindustry, where replacement of process technolo-gies is less frequent; automobile firms expect abillion-dollar engine or an auto assembly plant toearn significant cash flow for 20 years. Organiza-tional clockspeed reflects the rate of change in thestrategic actions (e.g., mergers, acquisitions, inter-nal expansion, interorganizational alliances) andstructures (e.g., restructuring, and changes in topmanagement) of incumbent firms in an industry.Taken together, product, process, and organiza-tional clockspeeds reflect industry-level changesbased on the aggregate actions initiated by all theincumbent firms in the industry.

Industry clockspeed and facets of industry change

The literature identifies three distinct dimensionsof industry change: rate (frequency of changes andspan of intervals between changes in the relevantindustry variables) (Duncan, 1972; Bourgeois andEisenhardt, 1988; Fines, 1998; Jurkovich, 1974;Tung, 1979; Williams, 1994); turbulence (unpre-dictability and variation of change in industry vari-ables) (Boulding, 1971; Dess and Beard, 1984;Duncan, 1972; Fombrun and Ginsberg, 1990;Jurkovich, 1974; Perrow, 1972; Tung, 1979); andmagnitude (scope or size of change; e.g., incre-mental vs. radical changes, technological continu-ities vs. discontinuities) (Brown and Eisenhardt,1997; Jurkovich, 1974; McGahan, 2004; Tushmanand Anderson, 1987; Tushman and Romanelli,1984).

Industry clockspeed captures rate of industrychange, but not turbulence and magnitude. Forexample, Fines (1998) explains that both the cir-cuit board and the semiconductor industries expe-rienced high rates of change, but the changes in thesemiconductor industry were predictable and incre-mental, whereas the circuit board industry experi-enced large variations with the arrival of surfacemount technology, a discontinuous technologicalimprovement. Thus, the semiconductor industryexperienced high rate of change but low turbulence

and magnitude, whereas the circuit board industryexperienced high rate, magnitude, and turbulence.

Thus, it is important to recognize the conceptualdistinctions among these constructs in investigat-ing the strategic implications of industry change.With this in mind, we focus on industry clock-speed, or rate of industry change. This is becausethe bulk of the research on industry change hasfocused on environmental turbulence (Dess andBeard, 1984; Garg et al., 2003) and magnitude(Tushman and Romanelli, 1984; Tushman andAnderson, 1987), whereas research examining thestrategic challenges presented by rate of indus-try change is sparse. Moreover, as stated ear-lier, industry clockspeed captures changes that areendogenous to an industry (product, process, andorganizational clockspeeds). This is important forcognition research because incumbent firms playmore direct roles in shaping endogenous changesthan in shaping exogenous (e.g., regulations, reces-sion, and political) changes (Ferrier, 2001; Ferrier,Smith, and Grimm, 1999; Rindova and Fombrun,1999; McGahan, 2004; Porac, Thomas, and Baden-Fuller, 1989).

Strategic flexibility

Strategic flexibility refers to the ability to precipi-tate intentional changes and adapt to environmen-tal changes through continuous changes in currentstrategic actions, asset deployment, and invest-ment strategies (Aaker and Mascarenhas, 1984;Bahrami, 1992; Evans, 1991; Harrigan, 1985; Hitt,Keats, and DeMarie, 1998; Sanchez, 1995). Firmsrealize strategic flexibility through their strate-gic actions (Ansoff, 1988; Dixon, 1992; Evans,1991; Volberda, 1999), and flexible firms exhibitboth diversity in strategic responses and rapidshifts from one strategy to another (Sanchez, 1995;Slack, 1983).

The literature on strategy has discussed twomain aspects of strategic flexibility: resourcedeployment and competitive actions (D’Aveni,1994; Eisenhardt and Martin, 2000; Miller et al.,1996; Williams, 1994). Because organizations areinternalized structures for allocating resources(Williamson, 1975), the diversity and frequency ofshifts in patterns of resource deployment (Fombrunand Ginsberg, 1990) are critical to strategicflexibility. Indeed, flexibility in exploiting andcontrolling resources may explain why some firmsmove more quickly into new niches (Eisenhardt



and Martin, 2000; Fombrun and Ginsberg, 1990).Similarly, competitive actions are the meansthrough which firms establish and protect theirown advantage, as well as eroding the advantagesof competitors (Ferrier, 2001; Ferrier et al.,1999; Young, Smith, and Grimm, 1996). Thefrequency of new and diverse sets of competitiveactions (action complexity) that firms undertakedetermines their ability to change competitiveposture and respond quickly (Young et al., 1996).

We integrate the strategic flexibility and theindustry velocity literature to argue that flexibilitymay be more important in fast-changing indus-tries than in slow-changing industries (D’Aveni,1994; Eisenhardt and Martin, 2000; Sanchez, 1995;Young-Ybarra and Wiersema, 1999).

Strategic schema

A strategic schema (also called dominant logic,strategy frame, cognitive map, or belief structure)refers to the knowledge structures that top man-agers use in making strategic decisions (Daft andWeick, 1984; Fisk and Taylor, 1991; Huff, 1982;Lyles and Schwenk, 1992; Prahalad and Bettis,1986). Both the strategic choice (Child, 1972) andupper echelon views (Hambrick and Mason, 1984)argue that top managers bring together and inter-pret information for the firm as a whole. Manymay participate in scanning or data processing,but the point at which information converges andis interpreted for organization-level action is pre-sumed to be at the top manager level (Daft andWeick, 1984; Lyles and Schwenk, 1992; Prahaladand Bettis, 1986; Thomas, Clark, and Gioia, 1993).Thus, strategic schemas represent the knowledgeof strategic decision-makers (top managers) andnot other managers within the firm.

Strategic schemas are the lenses through whichstrategic decision-makers interpret information andtranslate it into organizational actions (Huff, 1982).Strategic schemas influence firm performance bypromoting effective strategic actions (Daft andWeick, 1984; Weick, 1995). The strategy literaturehas explicated three mechanisms by which strate-gic schemas influence strategic actions: scanning,diagnosis and choice of alternatives (Daft andWeick, 1984; Prahalad and Bettis, 1986; Thomaset al., 1993). First, strategic schemas act as fil-ters on the information that strategic managerspay attention to and consider relevant for strategyformulation. Second, strategic schemas influence

diagnosis by enabling decision-makers to postulatecause–effect relations amid ambiguous informa-tion (Dutton, Fahey, and Narayanan, 1983). Third,because diagnosis influences the choice of strate-gic actions, strategic schemas also influence firmresponses to environmental change and the typeand range of competitive behaviors. Effective firmresponses to environmental conditions are thus tiedto the strategic schemas managers use to organizestimuli and filter cues (Fiol and O’Connor, 2003;Thomas et al., 1993).

Two key characteristics of strategic schemasare most relevant to strategic flexibility: complex-ity (Baum and Wally, 2003; Wally and Baum,1994) and focus (Eden, Ackermann, and Cropper,1992). Complexity reflects the differentiation andintegration in a strategic schema (Walsh, 1995).Differentiation reflects the breadth or variety ofenvironmental, strategy, and organizational con-cepts embedded in the schema, whereas integra-tion reflects the degree of connectedness amongthese concepts. Thus, complex strategic schemasaccommodate a diverse set of alternative strat-egy solutions in strategic decision making. Greatercomplexity allows firms to notice and respond tomore stimuli, which in turn increases their adapt-ability (Ashby, 1956; Stabell, 1978; Weick, 1995).Recent research in technology has also emphasizedthat greater variety of knowledge affects creativityand innovation as well as the ability to implementnew ideas, thereby fostering rapid innovation andchange (Rodan and Galunic, 2004; Shane, 2000).

Focus reflects the degree to which a strategicschema is centralized around a few ‘core’ con-cepts (Eden et al., 1992; Porac and Rosa, 1996).In a focused schema, there is a clear distinctionbetween the core and peripheral sets of knowledgestructures. Core concepts, the central concepts in astrategic schema, generally develop through grad-ual elaboration and feedback over a long periodof time (Carley and Palmquist, 1992; Lyles andSchwenk, 1992; Prahalad and Bettis, 1986). Theyoften have a strong historical context and a sig-nificant depth of meaning to strategic decision-makers (Eden et al., 1992). Peripheral concepts,the knowledge structures outside the core set, pro-vide the means to support the core set of strategies(Gustafson and Reger, 1995; Lyles and Schwenk,1992). The depth and significance are thereforelower for peripheral concepts than for the core set.Focused schemas drive strategic decision making



that is hierarchical, in which managers focus atten-tion mainly around a narrow set of core strategyconcepts. The core set determines which periph-eral concepts managers draw on in decision mak-ing (Lyles and Schwenk, 1992). Thus, focusedschemas mainly promote a narrow set of deeplyrooted, tried-and-true strategic actions.

Cognition research has argued that complexityand focus represent distinct facets of strategicschemas (Eden et al., 1992). Recently, Nadkarniand Narayanan (2005) empirically demonstratedthe distinctness of these facets.

HYPOTHESES

We present our theoretical model in Figure 1. Thecognition literature suggests that strategic schemasinfluence firm performance through strategic ac-tions (Prahalad and Bettis, 1986; Weick, 1995;Walsh, 1995), whereas the contingency view instrategy proposes that the fit between strategicactions and the industry context predicts firmperformance (Burns and Stalker, 1961; Porter,1985). We integrate these views to propose that

complexity of strategic schemas will promotestrategic flexibility (Hypothesis 2), which is criticalto success in fast clockspeed industries (Hypoth-esis 1), whereas focus will promote strategic sta-bility (Hypothesis 3), which is likely to succeed inslow clockspeed industries (Hypothesis 1).

Strategic flexibility, firm performance, andindustry clockspeed

Fast- and slow-clockspeed industries differ in boththe potential for sustainable competitive advan-tage and the feasibility of feedback-based learning.In fast-clockspeed industries, building sustainablecompetitive advantage is difficult because firmscannot long protect existing products and pro-cesses (Eisenhardt and Martin, 2000; Williams,1994). Firms cannot sustain above-average profitsbased on a single established innovation or advan-tage in these environments. To survive in suchindustries, firms must introduce new products andprocess technologies faster (Nerkar and Roberts,2004; Cottrell and Nault, 2004) and carry out fre-quent strategic and organizational changes (Eisen-hardt and Martin, 2000; Fines, 1998). In contrast,

Figure 1. Theoretical model of our study



firms in slow-clockspeed industries can protectexisting core competencies and achieve sustainablecompetitive advantage by building isolating mech-anisms that retard imitation (Garg et al., 2003;Williams, 1994).

Second, fast-clockspeed industries severely limitthe potential for feedback-based learning. Firmscannot learn from past experience because strate-gic actions that have proven effective in the pastbecome outdated quickly, so that similar actionsno longer produce similar outcomes (Bogner andBarr, 2000; Eisenhardt and Martin, 2000). In slow-clockspeed industries, rates of technological andcompetitive changes are slow and past strategicactions are durable. Thus, firms in slow-clockspeedindustries can use performance as a signal to judgethe appropriateness of past actions and can use pastexperience in making current decisions.

These differences in the strategic challengesposed by fast- and slow-clockspeed industries havemajor implications for the relative emphasis onstrategic flexibility required of incumbent firmsin the two industry contexts. The industry veloc-ity as well as strategic change literatures pointto differences in the relative importance of flex-ibility in industries with high and low rates ofchange (Eisenhardt and Martin, 2000; Sanchez,1995; Young-Ybarra and Wiersema, 1999).

Strategic flexibility is likely to be superfluousand inefficient in slow-clockspeed industries, forthree reasons. First, because the rate of changeis low and competitive advantage is sustainablein these industries, frequent changes in resourcedeployment and competitive actions may usurp afirm’s established competitive advantages (Ferrier,2001; Garg et al., 2003). For example, frequentshifts in advertising strategies may destroy thecumulative benefits realized in the past. Similarly,introducing new process innovations frequentlymay damage established and efficient organiza-tional processes. Firms can rely on and protectexisting resources and competitive actions to main-tain competitive advantage, because managers areclear about the causal relations between their ownmarket actions and positive competitive outcomes(Ferrier, 2001). Thus, stable and status quo behav-ior are likely to be successful in slow-clockspeedindustries (Eisenhardt and Martin, 2000; Williams,1994). Second, because technological and compet-itive changes are few in these industries, feweropportunities may be available to firms to exploit(Garg et al., 2003). Finally, because competitive

advantages are durable, development of new prod-ucts and processes may prematurely shorten thelife cycles of its strong products (Garg et al., 2003;Kessler and Chakrabarti, 1996). Recent technologystudies also contend that high rates of new productintroductions can cannibalize the sales of exist-ing products, adversely affecting firm performance(Jones, 2003). Thus, firms in slow-clockspeedindustries would benefit from strategic persistencerather than flexibility.

The stable and persistent pattern of resourcedeployment may not work in fast-clockspeedindustries, where products, processes and compet-itive actions are changing rapidly and competi-tive advantage is short-lived. Stability in resourcedeployment may lock company resources intoproducts and processes that may become out-dated, adversely affecting performance (Nerkarand Roberts, 2004; Jones, 2003). Firms rely muchless on maintaining core competencies throughstatus quo behavior and much more on creatingsituation-specific new resources through change.Status quo behavior can even be a disadvan-tage if managers overgeneralize from past experi-ences (Argote, 1999). Thus, flexibility of resourcedeployment is likely to be successful in fast-clockspeed industries.

Moreover, competition in fast-changing indus-tries is likely to provide feedback that disruptsmanagers’ perceptions of causality between theirown market actions and positive competitive out-comes. Thus, to succeed in these industries, firmsneed to engage frequently in new and diverse com-petitive actions in the form of new product intro-ductions. For example, Cottrell and Nault (2004),in their study of the microcomputer software (fast-clockspeed) industry, found that increasing prod-uct variety through new product introductions waspositively related to firm performance, whereasreliance on existing products resulted in low firmperformance. Similarly, Nerkar and Roberts (2004)found that firms in fast-changing industries regu-larly introduced new products and market actionsin their efforts to sustain superior firm perfor-mance. Thus, competitive flexibility will yieldhigh returns in fast-clockspeed industries (Ferrier,2001).

Hypothesis 1: Industry clockspeed will moderatethe relationship between strategic flexibility andfirm performance.



Hypothesis 1a: Strategic flexibility will be pos-itively related to firm performance in a fast-clockspeed industry.

Hypothesis 1b: Strategic flexibility will be neg-atively related to firm performance in a slow-clockspeed industry.

Because strategic schemas affect resource andcompetitive action flexibility, the nature of strate-gic schemas needed for success in industries withhigh and low rates of change may also be different.

Strategic schema and strategic flexibility

Complexity of strategic schema

The managerial cognition literature suggests thatcomplex strategic schemas will foster strategicflexibility through broad scanning, speedy diag-nosis, and simultaneous consideration of strate-gic alternatives. Complex schemas promote broadscanning by reducing two major biases in strate-gic decision making: discounting and cognitiveinertia (Hodgkinson, 1997; Keisler and Sproull,1982; Reger and Palmer, 1996). The discountingbias occurs when managers take a narrow focusin identifying causes of specific events, ignoringimportant environmental variables; it arises fromthe gaps between the actual environmental condi-tions and the strategic schemas used to interpret theenvironment. In fast-changing industries, with con-tinuously changing environmental stimuli, greatercomplexity allows managers to notice and respondto more stimuli, reducing the gap between theenvironment and their interpretations of it (Bognerand Barr, 2000). As Weick suggests, ‘organizationswith access to more varied images will engage insensemaking that is more adaptive than will orga-nizations with more limited vocabularies’ (Weick,1995: 4). For example, major changes in competi-tive conditions will not create the needed momen-tum for strategic change unless managers recog-nize such changes (Fahey and Narayanan, 1989).Managers who fail to notice important environ-mental changes are unlikely to adjust the firm’sstrategic actions (Lant et al., 1992). Thus, complexschemas enable firms to develop a comprehen-sive awareness of new opportunities and hence todevelop new resources and to change their compet-itive posture quickly by promoting better inferenceof continuously shifting competitor moves.

Complexity also promotes strategic flexibilityby preventing firms from getting locked into cog-nitive inertia during strategic diagnosis and theconsideration-of-alternatives phase (Dutton et al.,1983; Lyles and Schwenk, 1992). Complex sche-mas increase the diversity of perspectives broughtto bear on strategic questions; this diversity pro-motes more extensive discussion of strategicchoices (Lant et al., 1992), reducing the likelihoodof cognitive inertia (Hodgkinson, 1997; Reger andPalmer, 1996) and status quo behavior (Miller andChen, 1996) that inhibit strategic flexibility. Com-plex schemas may thus enable a firm to absorbnew, situation-specific knowledge rapidly. In short,complexity aids in overcoming cognitive inertiaby increasing awareness of new knowledge andthe capacity to absorb it. This accommodation ofdiverse perspectives and multiple dominant logicsin complex schemas is likely to lead to strategicflexibility (Bahrami, 1992; Calori, Baden-Fuller,and Hunt, 2000; Volberda, 1999).

Hypothesis 2: Complexity of strategic schemaswill be positively related to strategic flexibility.

Focus

The clear distinction between the core and theperipheral sets of knowledge (Lyles and Schwenk,1992) evokes focused and hierarchical decisionmaking around the core concepts with a stronghistory. Although firms may have the vocabu-lary to recognize various stimuli and their rela-tionships, in fast-changing industries centralizationmay create an illusory causation bias in decisionmaking, wherein firms make false associations ofenvironmental events based on the core conceptsin their strategic schemas (Keisler and Sproull,1982). Illusory causation results from prematureor inappropriate causal inferences about new envi-ronmental stimuli, especially when core conceptsin the schemas draw managers’ attention to non-existent variables and relationships between vari-ables. Managers automatically infer new events byusing the core concepts rather than by conductinga formal directed search first. Focused schemasmay also lead to cognitive inertia, because cen-tral concepts, with their deep historical roots, aredifficult to discard (Carley and Palmquist, 1992).Focus may lock firms into known and historicallysuccessful strategic actions that will preclude themfrom absorbing new knowledge and experimenting



with new alternatives (Hodgkinson, 1997; Regerand Palmer, 1996), thus inhibiting flexibility.

Hypothesis 3: Focus of strategic schemas willbe negatively related to strategic flexibility.

METHOD

Sample

Selection of industries

We chose Fines’ (1998) classification to identifyfast- and slow-clockspeed industries, for two rea-sons. First, Fines’ (1998) classification is theoret-ically consistent with our definitions of rate ofindustry change. Second, recent studies have estab-lished the convergent, discriminant, and nomologi-cal validity of Fines’ (1998) measures (Mendelsonand Pillai, 1999). Fines’ (1998) measures of prod-uct, process, and organizational clockspeeds cap-ture the aggregate actions of all incumbent firmsin an industry rather than the actions of any sin-gle firm. Fines (1998) identified seven fast clock-speed industries (personal computers, computer-aided software engineering, toys and games, ath-letic footwear, semiconductors, movie and cosmet-ics) and nine slow clockspeed industries (commer-cial aircraft, military aircraft, tobacco, steel, ship-building, petrochemicals, paper, electricity, and

diamond mining). We did not include the elec-tricity industry, in which firms serve consumersin specific territories and in which the competitivecontext is therefore unique.

We identified these industries based on theirfour-digit Standard Industry Classification (SIC)codes. Use of this classification has been criti-cized because empirical results on the appropriatelevel of this classification (two-digit, three-digit,and four-digit) have been unclear and becausesome segments are more related in some industrygroups (two-digit level) than others. However, SICcodes have been used widely in research becausethey represent an objective and valid classificationmethod (Hall and St. John, 1994). Because thecommercial and military aircraft industries havethe same four-digit SIC code and because all majorcommercial aircraft firms also operate in the mil-itary aircraft segment of the industry, we chosethe aircraft industry, which includes both commer-cial and military aircraft firms. Table 1 shows thedifferences in the product, process, and organiza-tional clockspeeds of these industries as well astheir four-digit SIC codes.

We confirmed the differences in the measures ofindustry clockspeed for the 14 industries for ourtime frame. Because Fines (1998) defines prod-uct, process, and organizational clockspeed at theindustry level, we first identified all the firms in

Table 1. Description of high-velocity and low-velocity industries

Industry (SIC) Four-digitSIC code

Productclockspeed

Processclockspeed

Organizationalclockspeed

No. ofcompanies

Fast-clockspeed industries1. Personal computer 3571 <6 months 2–4 years 2–4 years 152. Computer-aided software engineering 7373 6 months 2–4 years 2–4 years 273. Semiconductor 3674 1–2 years 2–3 years 3–10 years 244. Movie industry 7812 <3 months <1 year 2–4 years 75. Athletic footwear 3149 <1 year 5–15 years 5–15 years 116. Toys and games 3944 <1 year 5–15 years 5–15 years 127. Cosmetics 2844 2–3 years 5–10 years 10–20 years 28Total 124Slow-clockspeed industries1. Aircraft 3721 10–20 years 5–30 years 20–30 years 132. Tobacco 2111, 2112 1–2 years 20–30 years 20–30 years 243. Steel 3324, 3325 20–40 years 10–20 years 50–100 years 114. Shipbuilding 3731 25–35 years 5–30 years 10–30 years 45. Petrochemicals 2911 10–20 years 20–40 years 20–40 years 316. Paper 2621 10–20 years 20–40 years 20–40 years 37. Diamond mining 1499 >100 years 20–30 years 50–100 years 15Total 101

Adapted from Fines (1998).



each of the seven fast-clockspeed (n = 178) andseven slow-clockspeed (n = 154) industries, basedon the four-digit numerical SIC codes assigned bythe U.S. government to business establishments toidentify the primary business of each firm from theCOMPUSTAT database. Following recent technol-ogy studies (Cottrell and Nault, 2004; Nerkar andRoberts, 2004), we used manufacturers’ catalogsand the U.S. Patent and Trade Office (USPTO)database to identify a list of new products intro-duced in each of the 14 industries between theyears 1980 and 1990. Consistent with previousstudies examining rates of new product introduc-tions (Jones, 2003; Mendelson and Pillai, 1999;Nerkar and Roberts, 2004), we did not weighthe new product introductions. We then measuredproduct clockspeed as average time span betweennew products introduced in the industry between1980 and 1990 (Mendelson and Pillai, 1999). Wemeasured process clockspeed by the average num-ber of years over which firms depreciated capitalequipment1 (Fines, 1998; Mendelson and Pillai,1999).

To measure organizational clockspeed, we cre-ated a list of 14 corporate strategic actions (e.g.,mergers and acquisitions, change in TMT, orga-nizational restructuring, and business spin-offs)based on prior literature on strategic management(Fombrun and Ginsberg, 1990; Hitt, Ireland, andHoskisson, 2002; Porter, 1985; Thompson andStrickland, 2000). We then analyzed the content ofnews headlines of all the firms in the 14 industries(between 1980 and 1990) to track new corporatestrategic actions introduced by all the incumbentfirms in the industry. We defined organizationalclockspeed as the average time span between newcorporate strategic actions introduced by all firmsin each industry. The Wilcoxon sign rank testshowed significant differences between fast- andslow-clockspeed industries on product (1990: Z =2.53, p < 0.01; 1992: Z = 2.79, p < 0.01), pro-cess (1990: Z = 3.95, p < 0.001; 1992: Z = 2.91,

1 We chose depreciation of capital equipment as an indicationof how frequently firms replace their capital equipment. Weacknowledge that depreciation rates may not always reflect thereplacements of capital equipment, and how often firms replacetheir equipment may not necessarily reflect process clockspeed.However, Fines (1998) has recommended this measure as ameasure of process clockspeed because it is based on publiclyavailable data and is generally consistent with the replacementpatterns of capital equipments. Moreover, our source of fast- andslow-clockspeed industries is Fines’ (1998) classification, whichhe developed through a comprehensive analysis of firms’ processinnovations.

p < 0.01), and organizational clockspeeds (1990:Z = 1.89, p < 0.05; 1992: Z = 2.12, p < 0.05).

We tested the unidimensionality of the product,process, and organizational clockspeed measuresusing exploratory factor analysis with varimaxrotation. The factor analyses yielded a single fac-tor (eigenvalue: 3.04) explaining 59 percent of thevariance. The factor loadings were product clock-speed 0.89, process clockspeed 0.94, and orga-nizational clockspeed 0.81. The three measuresalso showed high internal consistency (Cronbach’salpha 0.85).

Selection of firms

From the 154 firms in the seven slow-clockspeedand 178 firms in the seven fast-clockspeed indus-tries in the COMPUSTAT database, we employedthree criteria to select the sample. First, becauseexisting diversification may influence strategicschemas (Calori et al., 1994), we restricted thesample to single-industry firms—firms drawingmore than 70 percent of their revenue from theirdominant business (Rumelt, 1974). Second, vari-ation in strategic schemas and performance canresult from maturation of firms, rather than fromvariation in the industry clockspeed, a problemmore likely in younger than in older firms (Baronand Bielby, 1984). Because firms that are at least10 years old are adequately mature (Baron andBielby, 1984) and have well-developed cognition(Barr, 1998), we selected firms that were at least10 years old.

These criteria yielded 127 firms in the fast-clockspeed industry and 104 firms in the slow-clockspeed industry. However, we had to excludethree firms from the fast-clockspeed group andthree from the slow-clockspeed group, becauseindustry analysts considered the information intheir annual reports untrustworthy. This resulted ina final sample of 225 firms: 124 firms in the slow-and 101 firms in the fast-clockspeed industries.Table 1 also shows the number of firms selectedfrom each of the 14 industries.

Constructing strategic schemas

Timing

We collected the data on strategic schemas in 1990(expansion) and 1992 (contraction) (U.S. Depart-ment of Commerce, 1994). Contraction reflects a



decline in total output, income, employment, andtrade, whereas expansion reflects a rise in all ofthese. Because business cycle is an exogenousvariable, we attempted to reduce the potential biasassociated with relying on a single year by averag-ing the strategic schema measures in the 2 years.We also tested our theoretical model separately,using the 1990 and the 1992 data, to see if therewere any inconsistencies. Because the results for1990 and 1992 were consistent with those reportedin this study, we do not report the separate resultsfor 1990 and 1992 here.

Data source

Our source of data for building strategic schemasis the CEO’s letter to shareholders published by thecompanies in the annual report. Researchers haveused annual reports to identify corporate strategicactions (Bowman, 1978), to assess causal reason-ing within firms (Bettman and Weitz, 1983), and toexplain differences in joint ventures (Fiol, 1989).Annual reports provide an aggregated measureof strategic schemas (Schneider and Angelmar,1993). Nevertheless, we needed to address threepotential problems in the use of annual reports: theaccuracy of annual reports, time lags in reporting,and attribution bias.

Accuracy. Top decision-makers may play littlerole in preparing annual reports. However,Schwenk (1989) and Barr et al. (1992) argue thatthis document is important enough for top manage-ment to pay close attention to it, both in early sub-ject framing and later in word-level editing. Barret al. (1992), through informal conversation withtop executives, found that they do have consid-erable involvement in preparing communicationswith investors, particularly in times of poor per-formance. Fiol (1991), in her comparison of theannual report statements with internal companydocuments, found that annual reports did not dif-fer significantly from internal documents in broadstrategic issues and strategic facts.

To check for this content bias, we correlatedthe content of the CEO’s letter with the con-tent of management’s analysis in the 10K2 forms

2 The 10K form, which is required to be filed with the Securitiesand Exchange Commission (SEC) within 90 days of the com-pany’s fiscal year end, explains the company’s business activitiesfor the most recent 12-month operating period but contains more

for a random subsample of 30 firms—15 eachfrom fast- and slow-clockspeed industries. Strat-egy researchers as well as practitioners considerthe contents of the 10K report to be reliable. Basedon a comparative content analysis of multiple doc-umentary sources of strategy research, Glueck andWillis (1979) found 10K forms to be an objec-tive, consistent, and accurate documentary source.Moreover, investors have rated this report muchhigher than other sources of corporate informa-tion, including analysts’ reports (Investor RelationsBusiness, 2000).

We used two different indicators to match thecontents of the CEO’s letter with those of the man-agement’s discussion in the 10K forms. First, wedivided the number of common concepts betweenthe CEO’s letter and management’s discussion bythe total concepts in the CEO’s letter to repre-sent the ratio of convergence in concepts containedin the two documents. Cognition researchers haveused shared concepts in causal maps to repre-sent the convergence in thinking among differentindividuals (Carley and Palmquist, 1992). In ouranalysis, shared concepts across two different doc-uments point to the degree of convergence andconsistency between them. The shared concepts inthe two documents ranged from 55 percent to 78percent for the years 1990 (mean = 68%, S.D. =12%) and 1992 (mean = 65%, S.D. = 10%), sug-gesting an acceptable convergence between thetwo documents (Carley and Palmquist, 1992).

Second, we correlated the frequencies of codedconcepts in the CEO’s letter to shareholders withthe frequencies of coded concepts in the manage-ment’s discussion in the 10K forms. Frequencyof a concept in a text represents the concepts’relative importance (Knoke and Kuklinski, 1982).In our data, the high correlations (ranging from0.47, p < 0.05, to 0.74, p < 0.0001) between fre-quencies of concepts suggested satisfactory con-vergence in the relative emphasis given to differentconcepts.

Third, for the subset of 30 firms, we conductedshort (15- to 20-minute) telephone interviews withat least one senior executive (e.g., vice presi-dents and general managers) involved in the firms’strategic planning between 1990 and 1992. Weasked the senior executives to identify the core

details than an annual report, such as more thorough operatingand financial statistics, information on legal proceedings, andmanagement compensation.



elements of the company’s strategic plan or strate-gies that were central to the company’s strategicgoals and vision. We then compared the core strate-gies that the senior executives identified with themost frequently mentioned strategic concepts inthe CEO’s letter. We found significant convergencein the senior executives’ responses and frequencyof concepts in the CEO’s letter (ranging from 65%to 91%) for the 30 firms.

Finally, 15 financial and strategic consultantsfrom two ‘Big Five’ accounting firms rated all 231companies (each analyst rated 20–24 companies)for the trustworthiness of information disclosedin the annual reports, using a five-point scale(1 = Highly untrustworthy and 5 = Highly trust-worthy). The consultants chosen on the basis oftheir expertise in the financial and strategic areas,as well as their familiarity with our sampled indus-tries, had an average of 15.9 years of consultingexperience. At least two consultants were famil-iar with each sampled industry for our time frame(1990 and 1992). We asked the analysts to focuson both degree of misstatements (lies of commis-sion) as well as degree of strategic informationleft out (lies of omission) in their trustworthinessratings.3 Six out of 231 firms (four fast-clockspeedfirms and two low-velocity firms) received a ratingof ‘Highly untrustworthy’ (1) or ‘Untrustworthy’(2) and were consequently dropped from the sam-ple.4 As a result, the final sample consisted of 124firms from fast-clockspeed industries and 101 firmsfrom slow-clockspeed firms.

Time lags. The expression in an annual reportmay lag behind an actual strategic change.Although the CEO’s letter is written in the timeperiod of interest, the reporting of managerialactions may lag, especially for adverse organiza-tional events. We checked the time lag in reportingchanges in corporate strategic actions for a sub-set of 30 firms randomly drawn from our sample.

3 On lies of commission, the analysts gave 78 firms a ratingof 5 (highly trustworthy), 95 firms a rating of 4 (trustworthy),52 firms a rating of 3 (satisfactory), 2 firms a rating of 2(untrustworthy), and 4 firms a rating of 1 (highly untrustworthy).The analysts’ ratings of lies of omission were as follows: highlytrustworthy 91, trustworthy 98, satisfactory 36, untrustworthy 3,and highly untrustworthy 3. The same six firms got low ratingson both lies of commission and omission.4 We conducted the analysis both including the six firms andexcluding the six firms. The structural and path analyses includ-ing these six firms do not differ significantly from the resultsreported in the study and hence are not reported in the study.

We collected the announcements, in the Wall StreetJournal in 1990 and 1992, of 15 specific corporatestrategy events (such as change in top manage-ment, mergers, acquisitions, and divestitures) madeby the 30 firms in our subsample. We identifiedthe 15 corporate strategy events from the litera-ture on strategic management (Hitt et al., 2002).We then compared the date of an announcementin the Wall Street Journal with the mention ofthe event in the annual report. A time lag existedif a firm did not report an event in the annualreport in the same year it was announced in theWall Street Journal. For each company, we calcu-lated the percentage of ‘lagged’ events. None ofthe 30 firms had time lags for more than 15 per-cent of the events in either 1990 or 1992. Thustime lag was not a major problem for our sam-ple.

Attribution bias. Prior studies suggest that firmperformance may affect organizational cognitionbecause of attribution bias (Salancik and Meindl,1984). For example, firms attribute low firm per-formance to the external environment and highfirm performance to internal strategies and organi-zational actions. Huff and Schwenk (1990) arguethat environmental factors come into focus dur-ing times of bad performance because performancegaps challenge managers’ assumptions about theexternal world.

To assess the attribution bias, we standardizedperformance of the 30 randomly selected firmsby computing the z-scores (sales, ROI, and netincome) based on the mean performance of allfirms in a single industry (having a specific four-digit SIC code) for the years 1990 and 1992. Next,we classified the causes of major organizationaloutcomes identified in the CEO’s letter as eitherinternal or external. Internal causes included orga-nizational strategic actions, structures, and pro-cesses affecting firm performance, whereas exter-nal causes were macroeconomic (recession andwar) and industry-specific (industry sales, foreigncompetition, industry decline, and competition).We then calculated a ratio, external causes dividedby internal causes, and correlated this ratio with theindustry-specific z-scores of performance of the 30firms. We found no significant correlation betweenthe ratio of external to internal causes of organiza-tional outcomes and sales (0.14, n.s.), ROI (0.17),and (0.24, p < 0.10). Thus, attribution bias wasnot a major problem for our sample.



Causal mapping

Following Axelrod (1976), we traced causal mapsfrom the CEO’s letters to shareholders in theannual reports in a five-step procedure, as shownin Appendix 1. In the first step, we identified state-ments that clearly implied a cause–effect relation-ship. Examples of key words used in identifyingcausal statements included ‘if–then,’ ‘because,’‘so,’ ‘as.’ Two raters (not authors) independentlyidentified these causal statements, and we assessedtheir inter-rater reliability using Kendall’s coeffi-cient of concordance (W = 0.85).

In the second step, we separated the causalstatements identified in the first step into ‘causes’and ‘effects’ to build the ‘raw causal maps.’ Rawphrases in the text can be aggregated into gener-alized concepts, to move the coded text beyondexplicitly articulated ideas to implied or tacit ideas(Carley and Palmquist, 1992). Aggregation alsoreduces misclassification of concepts due to pecu-liar wording by individuals. This process involvesdeciding which part of the text to code, and whatwords to use in the coding scheme. Three expe-rienced coders (not authors) independently codedraw cause-and-effect phrases in the CEO lettersinto 214 common ‘raw concepts’ (W = 0.85) thatwere close in words as well as in meaning tothe raw phrases in the annual reports. To avoidcoder bias, text distortion, and comparative incon-gruence, the coders used the rule ‘denotation ratherthan interpretation.’ We used the ‘majority rule’ toresolve disagreements between coders (Carley andPalmquist, 1992). We also consulted industry andstrategy experts to ensure that the 214 conceptswere distinctive and at the same level of abstrac-tion.

In the third and fourth steps, we classified the214 raw concepts into 35 broad categories. Car-ley and Palmquist (1992) argue that generaliza-tion of similar concepts in documents makes theseconcepts comparable across individuals or firms.Such categorization is especially useful in studiesthat compare causal maps across different contexts,because it enables researchers to set up a commonbasis to compare diverse topical contexts (Faheyand Narayanan, 1989). In our study, we comparedthe causal maps of different firms in high- andlow-velocity industries. We used 35 broad cate-gories to compare different firms in each indus-try.

We used the exploratory filtering (also calledaggregation) suggested by Carley and Palmquist(1992) to generalize raw cause-and-effect conceptsinto broader categories. We validated the catego-rization using the sorting technique suggested byAnderson and Gerbing (1991). Three strategy pro-fessors and four industry analysts classified the 214concepts into 35 categories. We estimated inter-rater reliability by use of Kendall’s coefficient ofconcordance (W = 0.89). We used the majorityrule in categorizing concepts for which agreementwas not 100 percent (Carley and Palmquist, 1992).We classified concepts into categories agreed onby four or more raters. Additionally, we tied theemergent categories to the literature on strategicmanagement as another way of validating the clas-sification, a technique that is recommended fordeveloping categories that are distinct and uniformin breadth and abstraction (Carley and Palmquist,1992; Fahey and Narayanan, 1989). Theoreticalsources included a review of major textbooks onstrategy (Hitt et al., 2002; Thompson and Strick-land, 2000) and ‘classic’ books (Andrews, 1971;Ansoff, 1981; Porter, 1985; Schendel and Hofer,1979).

In the fifth step, two raters not involved in thestudy independently recast raw phrases into theconceptual categories (W = 0.79). We resolveddisagreements among raters in identifying causalstatements, as well as in coding of raw phrases,through discussion.

Measures

Complexity5

We used two established measures of complexity:comprehensiveness and connectedness (Carley andPalmquist, 1992; Calori et al., 1994; Eden et al.,1992; Knoke and Kuklinski, 1982). We measuredcomprehensiveness as the total number of con-cepts in a causal map (NC) and connectednessas the total number of linkages in a causal mapdivided by the total number of concepts in the map(NL/NC).

Focus5

We measured focus by two established network-based measures: centralization (Carley and

5 To strengthen the validity of the strategic schema measures,we tested the correlations between the schema measures derived



Palmquist, 1992; Eden et al., 1992; Freeman, 1978;Knoke and Kuklinski, 1982) and eigenvector cen-trality (Bonacich, 1972). In centralization, the cen-trality of each concept in the causal map is com-puted by adding the total number of concepts towhich a specific concept in the map is linked,either directly or indirectly. A decreasing weight(that is, a distance decay function) is assignedto each successive layer. For example, a conceptdirectly linked to the central concept may be givena weight of 1, concepts in the next layer a weightof 1/2, concepts in the subsequent layer a weightof 1/3, and so on. The centrality of a concept is theweighted average length of all the total paths thatlink it to other concepts in the map. Centrality ofthe causal map is the sum of the difference betweenthe centrality of the most central concept and thecentrality of all other concepts in the map, scaledby the total number of possible links between theconcepts in the map (Freeman, 1978; Knoke andKulinski, 1982):

CB =

N∑

i=1

(CB(p∗) − CB(pi))

N 3 − 4N 2 + 5N − 2

where CB(p∗) is the centrality of the most centralconcept in the causal map and N is the totalnumber of concepts in the causal map.

The second measure was Bonacich’s (1972)eigenvector measure of centrality, in which thecentrality of each concept is determined by thecentrality of the concept to which it is connected(Borgatti, Everett, and Freeman, 1992). The cen-trality of concept i (denoted ci) is given by ci =[α][

∑Aijcj ], where α is a parameter, Aij repre-

sents the links between concept i and other con-cepts in the causal map, and cj is the centralityof concepts to which i is linked. The centrality ofeach concept is therefore determined by the cen-trality of the vertices to which it is connected. Theparameter [α] is required to give the equations anon-trivial solution. The centrality of the network(causal map) is the eigenvector of the largest eigen-value of the network, standardized so that its length

from CEOs’ letters with the schema measures derived from the10K forms for a subsample of 30 firms. The strong correlationsbetween the measures derived from CEOs’ letters and the 10Kforms (comprehensiveness 0.88, connectedness 0.84, centraliza-tion 0.87, and eigenvector centrality 0.85) further strengthen thevalidity of these measures.

is equal to eigenvalue (for a detailed description ofthis measure see Borgatti et al., 1992; Bonacich,1972).

A high value on either measure of centralityimplies that a few core concepts dominate thecausal map. Figure 2 shows these three measuresfor the causal maps of Microsoft (high-velocityindustry) and Boeing (low-velocity industry). Boe-ing (with only 23 concepts) has a less comprehen-sive causal map than Microsoft (which has 33).Similarly, Microsoft has a more connected causalmap than Boeing, as suggested by the ratio oflinks to concepts in the map. Finally, the links inBoeing’s causal map focus on two concepts—cost-cutting capabilities and sales growth —reflecting ahigh degree of centralization. In contrast, no con-cept dominates the links in the strategic schema ofMicrosoft. Thus, Boeing has a more focused causalmap than Microsoft.

Strategic flexibility

We measured strategic flexibility by four measures:variety in resource deployment, shifts in resourcedeployment, competitive simplicity, and shifts incompetitive action (Aaker and Mascarenhas, 1984;Ferrier, 2001; Ferrier et al., 1999; Fombrun andGinsberg, 1990; Slack, 1983).

Following Fombrun and Ginsberg (1990), wemeasured variety in resource deployment by threeresource types: R&D intensity (R&D expendi-tures/sales), capital intensity (capital expenditures/sales), and advertising intensity (advertising expen-ditures/sales), with a 3-year lag (1993 and 1996).Although planning horizons vary across firms,most turnaround and strategic change researchersuse 3- to 4-year plans (Fombrun and Ginsberg,1990). Consequently, we picked 3 years as a rea-sonable time span within which important realloca-tion of resources should become evident. However,we reran our results using 2-, 3-, 4-, and 5-yearlags for the strategic flexibility variables. Becausewe did not find any significant differences in theresults for the different lags, we chose a 3-year lagfor our study, which is consistent with previousstrategy formulation research.

We used the coefficient of variation (σ /mean)(Dooley, Fowler, and Miller, 1996) across the threeareas to measure the variety in resources. A highcoefficient of variation suggests low range flexibil-ity, whereas a low coefficient of variation implieshigh range flexibility of resource deployment.



Figu

re2.

Apa

rtof

the

caus

alm

aps

ofM

icro

soft

and

Boe

ing



To measure shifts in resource deployment, wefirst computed the proportion of allocations toeach of the three resource types: Ra/RTL, whereRa is the dollar amount allocated to the athresource type and RTL is the total dollar amountspent on all the three resource categories (capi-tal, R&D, and advertising). Next, we computedthe absolute differences in the proportions of allo-cations to the three resource types across twotime periods— t1 (1990–93) and t2 (1993–96)—as|(Ra/RTL)t1 − (Ra/RTL)t2|. We then conducted anexploratory factor analysis to test the dimensional-ity of the shift variables. Exploratory factor anal-ysis yielded a single factor with an eigenvalue of3.48 explaining 67 percent of the variance. Thefactor loading of shifts in capital expenditures was0.83, in R&D expenditures 0.81, and in advertis-ing expenditures 0.87. Given the unidimensional-ity of the three shift measures, we summed theshifts in each of the three expenditures to get anoverall measure of shifts in resource deployment:∑ |(Ra/RTL)t1 − (Ra/RTL)t2|. Such aggregation ofchanges in individual resource allocation variablesbased on exploratory factor analyses results isconsistent with previous strategic change studies(Fombrun and Ginsberg, 1990).

To measure the extent to which a firm’s compet-itive attack consists of a broad range (comparedto a narrow range) of different action types, weused Ferrier et al.’s (1999) Herfindahl-type indexof competitive simplicity. First, following Ferrier(2001) and Ferrier et al. (1999), we used struc-tured content analysis to sort news headlines fromF&S Predicasts about each firm into competitiveaction events of different types in 1991 and 1993.We developed a list of keywords related to actioncategories similar to that used in prior studies ofcompetitive dynamics (e.g., Ferrier et al., 1999;Young et al., 1996) and strategic change (e.g., Lantet al., 1992). Using these keywords, two academicexperts not involved with the original keywordgeneration process separately coded a representa-tive sample (n = 300) of news headlines into sixcategories: pricing actions, marketing actions, newproduct actions, capacity-related actions, serviceactions, and overt signaling actions (W = 0.83).Following Ferrier (2001), we calculated the ratioof actions in each of the six action categoriesto total actions and squared each proportion. Wethen summed these squared proportions to arriveat the measure for competitive complexity, whichaccounts for the weighted diversity among all six

action types (Ferrier, 2001). Firms with low scorescarry out competitive attacks that typically con-sist of a broad range of action types; high scoressuggest that a firm typically carries out compet-itive attacks with few action types. Competitivesimplicity = ∑

(Na/NTL)2, where Na/NTL is the

share or proportion of competitive actions in theath category.

To measure shifts in competitive actions, we firstcomputed the absolute differences in the propor-tion of competitive actions in each of the six actioncategories across two time periods: t1 (1990–93)and t2 (1993–96), |(Na/NTL)t1 − (Na/NTL)t2|. Wethen conducted an exploratory factor analyses toconfirm the unidimensionality of the measures.As expected, shifts in the six action categoriesyielded a single factor with an eigenvalue of 3.29,explaining 62 percent of the variance. The fac-tor loadings for the six action categories rangedfrom 0.81 to 0.92. Given the unidimensionalityof the shift variables, we summed the changes inthe proportions of the competitive actions in eachcategory to compute the competitive shift mea-sure

∑ |(Na/NTL)t1 − (Na/NTL)t2|. Such aggrega-tion of individual competitive actions is consistentwith previous competitive dynamics studies (Fer-rier, 2001).

Firm performance

We used three measures of firm performance:sales growth, return on investment, and net incomegrowth (Venkataraman and Ramanujam, 1987),which we obtained from the COMPUSTAT data-base over 3 years. Because this study examinesthe influence of strategic schema on performance,we lagged performance measures by 1 year. Forthe year 1990, we measured sales growth and netincome as the moving averages of changes in thetwo variables from 1988 to 1991, 1989 to 1992,and 1990 to 1993, whereas for 1992 we measuredit as the moving average of the change from 1990to 1993, 1991 to 1994, and 1992 to 1995. We aver-aged the ROI measures 1991, 1992, and 1993 for1990, and 1993, 1994, and 1995 for 1992.

Control variables

We used five industry variables as con-trols—industry growth, R&D intensity, capitalintensity, advertising intensity, and industry con-centration, which affect strategic actions andfirm performance. Industry growth—measured as



percentage change in industry gross sales between1990 and 1992 (Dess and Beard, 1984)—reducesthe motivation to engage in aggressive strategicand competitive behavior (Ferrier, 2001). Slowgrowth often causes more intense competitionand lower profitability, which motivates strategicaggressiveness (Porter, 1985). High industry con-centration (the ratio of sales for the industry’s topfour companies to total industry sales) limits com-petitive actions among firms by creating high barri-ers to entry and yields higher profits to establishedfirms (Porter, 1985). High capital intensity (capi-tal expenditures divided by sales) requires firms toinvest heavily in long-term assets, which fosterspersistence in resource deployment and competi-tive behavior (Dess and Beard, 1984). High R&D(R&D expenditures divided by sales) and advertis-ing intensity (advertising expenditures divided bysales) encourage innovation and product differenti-ation, in which firms engage in diverse competitiveactions and aggressiveness in resource deployment(Rajagopalan and Datta, 1996).

To evaluate the moderating effect of industryclockspeed, we controlled the other two facets ofindustry change: turbulence and magnitude. Envi-ronmental turbulence was measured by use of avariation of the environmental instability measuredeveloped by Dess and Beard (1984). We calcu-lated turbulence by regressing a variable for eachyear on a variable for net industry sales (Bergh andLawless, 1998). We used 5 years of data for eachequation (net industry sales from 1984 through1989 to predict volatility in 1989 and net salesfrom 1986 through 1991 to predict volatility in1991). Following the equation yt = bo + b1t + at ,where y is industry sales, t is year, and a isthe residual, volatility was the standard error ofthe regression slope coefficient divided by averagesales. Larger values of volatility indicate greaterenvironmental turbulence.

To measure magnitude of industry change, wefirst obtained a list of technological and regu-latory changes in each industry, using a myr-iad of resources including trade- and industry-specific journals, and news headlines from busi-ness databases from 1980 to 1990. For each indus-try, we then selected three professors who spe-cialized in technologies driving changes in theindustry and who had worked on projects withfirms in that industry. We asked the professorsto rate the technological changes on a one- tofive-point Likert scale on two items: impact of the

technological change (1 = marginal, 5 = high) andscope of technological change (1 = incremental,5 = radical). For regulatory changes in each indus-try, we asked industry analysts to rate each regula-tory change on impact and scope. We averaged theratings of the professors for technological changes(W = 0.89) and industry analysts (0.85) for reg-ulatory changes. Cronbach’s alpha for the fourmeasures was 0.87.

We conducted a CFA using LISREL 8 ((Joreskogand Sorbom, 1993) of industry clockspeed, tur-bulence, and magnitude measures to confirm thedistinctness of the three facets of industry change.The three-factor model showed a very good fit withour data (χ 2 = 98.97; AGFI = 0.96; IFI = 0.94;and RMSEA = 0.03). The factor loadings for theindustry clockspeed measures ranged from 0.79to 0.91, for turbulence from 0.83 to 0.94, andfor magnitude from 0.77 to 0.85. Inter-factor cor-relations were clockspeed–turbulence 0.19, p <

0.10; clockspeed–magnitude 0.15, n.s.; and tur-bulence–magnitude 0.21, p < 0.10. These resultsconfirm theoretical contentions that the rate ofindustry change (captured by industry clockspeed)is distinct from both turbulence and magnitude ofindustry change (Duncan, 1972; Jurkovich, 1974).

We used firm size, age, and diversification ascontrols (Carley and Kauffer, 1993). We measuredfirm size by the logarithmic transformations of thetotal number of employees in the organization, firmage by the number of years from the foundingdate of the company to the two time periods ofthe study (1990 and 1992), and diversificationby the percentage sales that firms derived fromtheir primary business (Rumelt, 1974). Youngerand smaller firms are more dynamic and transientthan older and larger firms, which tend to becomebureaucratic (Miller and Chen, 1996; Fombrunand Ginsberg, 1990). Thus, younger and smallerfirms are likely to experiment with a wider varietyof resource deployment and competitive strategiesand frequently shift these strategies, whereas older,bigger firms are likely to focus on a few ‘triedand true’ strategic actions (Miller and Chen, 1996).Although our selected firms derived 70 percent oftheir sales from their primary business (Rumelt,1974), there was some variance in their levelof diversification (0 to 30%). Research suggeststhat diversified firms have more complex schemas(Calori et al., 1994) and greater variety in resourcedeployment (Miller and Chen, 1996) than single-business firms.



Figure 3. The empirical model used to test the relationship between complexity, focus, strategic flexibility, and firmperformance in fast and slow clockspeed sample groups

Analyses

We tested our theoretical model separately for thefast- and slow-clockspeed samples by use of multi-group comparisons in structural equation modelingand then assessed the differences in the structuralpaths linking strategic schemas, strategic flexibil-ity, and firm performance across the two clock-speed samples6 (Figure 3). We explain this proce-dure in detail in the following section.

ANALYSES AND RESULTS

We compared the structural model in Figure 3across fast- and slow-clockspeed industries through

6 All the hierarchical regression models supported our hypothe-ses. We thank two anonymous reviewers for suggesting the useof structural equation modeling for our analysis.

several steps, using LISREL 8 (Joreskog and Sor-bom, 1993).7 Because it is recommended thatcentered variables be used in the SEM analyses(Bollen and Paxton, 1998; Williams, Edwards, andVandenberg, 2003), we used standardized z-scoresof our study measures in the SEM analyses. First,we assessed the equivalence of the measurementvariables. To conduct multiple group comparisonsin SEM, it is essential to confirm that the variablesload on the a priori defined constructs in boththe fast- and slow-clockspeed samples (Bollen,

7 We tested the sensitivity of our results to the categorization ofindustry clockspeed using the leave-one-out resampling methodin which the model is retested by leaving out one subset (in ourcase one industry) at a time (Efron, 1982). We reran the SEManalyses 15 times, each time excluding one of the 15 industriesfrom the analyses to get a range of estimates for our model. Wedid not find any significant deviations in the results for any ofthe 15 iterations of analyses, which confirms the robustness ofour results to the categorization of industry clockspeed.



1989; Chan and Schmitt, 1997; Rigdon, Schu-macker, and Wothke, 1998). We show the resultsof these analyses in Table 3. The factor loadingsof the measurement variables range from 0.75 to0.92 in the fast-clockspeed industry sample andfrom 0.72 to 0.88 for the slow-clockspeed sam-ple. The high factor loadings confirm convergentand discriminant validity as well as the equiva-lence of construct measures across fast- and slow-clockspeed samples. The Cronbach’s alphas of theconstruct measures range from 0.79 to 0.83 in thefast-clockspeed and from 0.76 to 0.81 in the slow-clockspeed sample.

Second, we assessed the equivalence of thefactor structure across fast- and slow-clockspeedindustry groups, using unconstrained and con-strained models (Bollen, 1989; Chan and Schmitt,1997; Rigdon et al., 1998). These results are alsoshown in Table 2. In the unconstrained model,we allowed the factor structure to vary acrossfast- and slow-clockspeed samples by freely esti-mating the factor loadings and error variances ofthe measurement variables, as well as inter-factorstructural coefficients and variance–covariances.Following the recommendations of others (e.g.,Marsh, Balla, and McDonald, 1988; MacCallum

and Austin, 2000), we present several differentindices of the adequacy of model fit, as indicesdiffer in their specific assumptions. The chi-squarestatistic is widely used and thus we report it here;however, because it is greatly influenced by sam-ple size, other indices should also be consideredin assessing fit. For the adjusted goodness-of-fitindex (AGFI; Joreskog and Sorbom, 1993), andincremental fit index (IFI; Bollen, 1989), valuesvary between 0 and 1.0, and values of 0.90 andabove are conventionally considered to indicategood model fit (Hoyle, 1995). The root meansquare error of approximation (RMSEA) is a mea-sure of the discrepancy between the predicted andobserved covariance matrices per degree of free-dom; Brown and Cudek (1993) suggest a value of0.05 or less indicates a close fit, while values up to0.08 represent reasonable errors of approximation.The unconstrained multigroup model exhibited agood fit: χ 2 = 182.97, AGFI = 0.93, IFI = 0.94,and RMSEA = 0.05.

We compared the unconstrained model to a con-strained model, in which we set the factor struc-ture as invariant across fast- and slow-clockspeedsamples. We set the factor loadings and errorvariances of the measurement variables, as well

Table 2. Confirmatory factor analysis of the validity and reliability of construct measures

Constructs and measurement variables Standardized measurement variable-construct loadingsa

Fast-clockspeed model Slow-clockspeed model

ComplexityV1: Comprehensiveness 0.81 0.78V2: Connectedness 0.87 0.82Cronbach’s alpha 0.82 0.76FocusV3: Centralization 0.84 0.72V4: Bonacich eigenvector centrality 0.88 0.79Cronbach’s alpha 0.81 0.78Strategic flexibilityV5: Variety in resource deployment 0.84 0.72V6: Shifts in resource deployment 0.82 0.81V7: Competitive simplicity 0.79 0.77V8: Shifts in competitive simplicity 0.75 0.82Cronbach’s alpha 0.79 0.81Firm performanceV9: Sales growth 0.92 0.88V10: ROI 0.85 0.81V11: Net income growth 0.83 0.84Cronbach’s alpha 0.83 0.81Unconstrained model: χ 2 = 182.79, AGFI = 0.94, IFI = 0.94, RMSEA = 0.05Constrained model: χ 2 = 201.82, AGFI = 0.91, IFI = 0.92, RMSEA = 0.07

a All loadings are significant at p < 0.01



Table 3. SEM results of the structural paths of individ-ual models

Modelfit

Fast-clockspeedIndustries

Slow-clockspeedindustries

χ 2 (d.f. 101) 171.23 165.96AGFI 0.95 0.92IFI 0.94 0.91RMSEA 0.04 0.05γ11: Complexity →

Strategic flexibility0.44∗∗∗ 0.29∗

γ12: Focus → Strategicflexibility

−0.49∗∗∗ −0.27∗

β12: Strategic flexibility→ performance

0.52∗∗∗ −0.39∗∗

∗ p < 0.05; ∗∗ p < 0.01; ∗∗∗ p < 0.001

as inter-factor structural coefficients and vari-ance–covariances, as equal across groups. If themeasures and factor patterns are equivalent acrossfast- and slow-clockspeed groups, then the con-strained model should show a good fit: χ 2 =201.82, AGFI = 0.91, IFI = 0.92, and RMSEA =0.07. The good fit of the constrained model con-firms the factor structure invariance across the twoclockspeed groups.

Third, once the measurement and factor struc-ture invariance conditions were met, we testedthe model (strategic schema → strategic flexibility→ firm performance) shown in Figure 3 for eachgroup8 to compare the path coefficients across thetwo samples.

The model fit both the samples well, as is indi-cated by the fit indices (Table 3). The path coef-ficients from complexity and centrality to strate-gic flexibility are similar across the two clock-speed samples. Complexity was positively relatedto strategic flexibility, and centrality was nega-tively related to strategic flexibility in both fast-

8 We conducted two separate analyses to test whether otherindustry variables confounded differences in clockspeed. Firstwe found no significant differences in the industry growth(F = 1.54, n.s.), R&D intensity (F = 2.01, p < 0.10), capitalintensity (F = 1.29, n.s.), advertising intensity (F = 1.79, n.s.),industry concentration (F = 1.14, n.s.), turbulence (F = 1.89,n.s.), and magnitude of change (F = 1.67, n.s.) across the twoclockspeed groups, which suggests that other industry effectsdid not confound the moderating relationship of industry clock-speed. Second, we standardized our construct measures for eachindustry. We reran the path models using these industry-specificz-scores. The results of this analysis were consistent with theresults shown in Tables 3 and 4. Because it is recommended touse centered variables in SEM, we did not include these analysesin the paper.)

Table 4. Results of multigroup comparison

Constraint χ 2 difference

γ11: Complexity → Strategic flexibility 3.51†γ12: Focus → Strategic flexibility 2.94†β12: Strategic flexibility → performance 21.45∗∗∗

† p < 0.10; ∗∗∗ p < 0.001

and slow-clockspeed samples. These results sup-port Hypotheses 2 and 3. However, the path coef-ficient from strategic flexibility to firm perfor-mance differed across the two samples: it was 0.52(p < 0.001) for the fast-clockspeed industry sam-ple and −0.39 (p < 0.01) for the slow-clockspeedindustry sample. The positive relationship betweenflexibility and performance in the fast-clockspeedsample supports Hypothesis 2, whereas the neg-ative relationship between flexibility and perfor-mance for the slow-clockspeed sample supportsHypothesis 3.

Finally, we conducted multigroup simultane-ous path analysis (Calantone, Schmidt, and DiBenedetto, 1997; Rigdon et al., 1998) to test forsimilarities and differences in the relationshipsamong complexity, centrality, strategic flexibil-ity, and firm performance across the two industryclockspeed samples. The objective was to deter-mine whether the path coefficients were equalacross the two groups. To test which path coef-ficients were different, we used the multiple-groupcomparison method of LISREL (Joreskog and Sor-bom, 1993; Rigdon et al., 1998). We first con-strained one path to be equal across the threesamples and then freely estimated this path. Aninsignificant difference in chi-square between theconstrained and unconstrained models would sug-gest an equal path coefficient across the two sam-ples, whereas a significant difference would implythat the path coefficient is statistically differentbetween the two samples. The results are shownin Table 4. For the complexity-to-strategic flexibil-ity path, the difference in chi-square is 3.51 (p <

0.10). Similarly, for the centrality-to-strategic flex-ibility path, the difference in chi-square is 2.94(p < 0.10). Thus the differences in these relation-ships between fast- and slow-clockspeed samplesare not statistically significant. However, the dif-ference in chi-square is 21.45 (p < 0.001) forthe strategic flexibility-to-firm performance path.These results, which suggest significant differencesin the strategic flexibility → firm performance



path and confirm the moderating effect of indus-try clockspeed in the relationship between strategicflexibility and firm performance, support Hypoth-esis 1.

DISCUSSION

Studies examining the different challenges facedby firms in fast- and slow-paced industries froma cognitive perspective are noticeably absent inthe current literature. The existing literature, focus-ing mainly on competitive and economic predic-tors of performance (D’Aveni, 1994; Fines, 1998;Williams, 1994), suggests that firms in fast- andslow-clockspeed industries need to have differentcapabilities (Eisenhardt and Martin, 2000), pro-cesses of scanning (Garg et al., 2003), speeds ofdecision making (Bourgeois and Eisenhardt, 1988;Brown and Eisenhardt, 1997), strategic responsesand organization structures (D’Aveni, 1994; Fines,1998; Williams, 1994). Our study extends thisliterature by highlighting the role of strategicschemas in fast- and slow-clockspeed industries.

Strategic schema and firm performance

Integrating insights from the literatures on industryvelocity (Brown and Eisenhardt, 1997; D’Aveni,1994; Eisenhardt and Martin, 2000), strategic flex-ibility (Garg et al., 2003; Kessler and Chakrabarti,1996) and managerial cognition (Bogner and Barr,2000; Gustafson and Reger, 1995), we empiricallyexamined the complex linkages between industryclockspeed, strategic schemas, strategic action andperformance. In doing so, we responded to calls forundertaking such studies in strategic management(Huff, 1997; Rajgopalan and Speitzer, 1997). Ourresults extend the understanding of the relationshipbetween strategic schema and firm performance.

One of the most significant findings of our studywas the mediating role of strategic actions in therelationship between strategic schema and firmperformance. Our results suggest that complex-ity promotes strategic flexibility, which is suc-cessful in fast-clockspeed industries. In contrast,focus fosters strategic persistence, which is effec-tive in slow-clockspeed industries. These resultsare especially important because few empiricalstudies have (1) examined cross-industry differ-ences in cognition or (2) examined the relation-ship between cognition and firm performance. Our

results have prescriptive value in demonstrating thetype of strategic schemas needed in fast- and slow-clockspeed industries.

We acknowledge that our model tested onlyone aspect of the schema–performance relation-ship (strategic schema → strategic flexibility →firm performance). However, the cognition liter-ature suggests a recursive relationship betweenperformance and cognition (Chakravarthy, 1982;Weick, 1995). For example, past performance pro-vides feedback that firms use to classify as suc-cesses or failures the strategic actions that precededthe performance. This knowledge triggers changesin the existing causal attributions developed byfirms, implying a performance → strategic schemarelationship. Although examining this particularrelationship is beyond the scope of this paper, itis an important direction for future research. Espe-cially interesting is the role of industry velocityin how firms utilize past performance to changetheir schemas. Because firms in fast-clockspeedindustries focus on experimentation and innova-tion, we may find that these firms rely less stronglyon performance-based feedback than do slow-clockspeed firms, which emphasize efficiency. Atthe same time, as slow-clockspeed firms repeatedlyuse performance-based feedback to change theirschemas, they may encounter problems of inertia,whereby firms ignore environmental stimuli exceptthose that adversely affect performance (Hodgkin-son, 1997). In contrast, as fast-clockspeed firmsseek feedback through experimentation and inno-vation, using an act-first, think-later approach, theyare less likely to experience inertia.

Complexity of strategy schema

Our results provide interesting insights into theongoing theoretical tension in the nature ofknowledge required to succeed in fast- andslow-changing industries. One view suggests thatsimple knowledge-based principles and routinesthat consist of a few rules specifying theboundary conditions on managers’ decisions orindicating priorities are likely to succeed in high-velocity environments (Brown and Eisenhardt,1997; Burgelman, 1996; Eisenhardt and Martin,2000; Eisenhardt and Sull, 2001). This is becausesimple routines keep managers focused on broadlyimportant issues without locking them into specificbehaviors or the use of past experience that maybe inappropriate in view of the actions required



in a particular situation. Using simple routines,firms can rapidly create new, situation-specificknowledge by engaging in experimental actions,thereby compensating for limited, relevant existingknowledge. Moreover, simple routines also allowmanagers to focus on real-time information,which builds their knowledge of the marketplace,facilitates rapid grasp of changing situations, andalerts them early on to the need to adjust theiractions. In contrast, the managerial cognition viewsuggests that complex schemas foster effectiveadaptation in fast-changing industries (Bognerand Barr, 2000; Fiol and O’Connor, 2003; Lantet al., 1992; Weick, 1995). Our results support themanagerial cognition view, but not the industryvelocity literature. Two possible reasons exist forthese conflicting results.

First is the need to delineate industry veloc-ity and industry clockspeed. The industry velocityconstruct encompasses both rate and turbulenceof industry change (Eisenhardt and Martin, 2000).We focused on industry clockspeed, which cap-tures rate of industry change, but not turbulence. Amajor implication of our results is that the theoret-ical propositions of the industry velocity constructmay not apply to rate of industry change. Theliterature on industry change has emphasized thedistinctness of rate and turbulence (Duncan, 1972;Jurkovich, 1974; Tung, 1979). It is possible that thepattern of relationships between strategic schema,strategic action, and performance may be differ-ent in industries that are both turbulent and fastchanging.

Second, there is a need to distinguish strategicthinking from thinking at other levels. Eisenhardtand Martin (2000) discuss ‘simple routines’ indifferent areas of the firm, including manufactur-ing processes (Pisano, 1994) and product devel-opment processes (Brown and Eisenhardt, 1997;Eisenhardt and Tabizi, 1995). We focused primar-ily on strategic schemas—knowledge structuresused by top managers in strategic decision makingthat affect the entire firm (Daft and Weick, 1984;Huff, 1982; Thomas et al., 1993; Wally and Baum,1994). The strategic choice (Child, 1972) as wellas upper echelon (Hambrick and Mason, 1984;Finkelstein and Hambrick, 1996) theories haveemphasized that the strategic decision-making con-text is unique and distinct from other levels inthe firm. Thus, complex thinking may be morecritical at top levels than at other levels in thefirm (Jaques, 1998; Weick, 1995), because top

managers are the chief cognizers charged withmaking sense of the fast-changing external envi-ronment and with integrating diverse views withinthe firm (Calori et al., 1994; Finkelstein and Ham-brick, 1996). Thus, establishing effective and sim-ple routines at different levels of the firm mayrequire complex thinking on the part of strategicdecision-makers. For example, building effectivebut simple routines may require top managers toconsider a wide range of information, separate rel-evant information from irrelevant information, andconsider a broad range of solutions.

Future studies may want to build on the prelim-inary evidence provided by our study by formallytesting these contentions. For example, testing theinteraction effects of industry clockspeed and tur-bulence on the strategic schema → strategic action→ firm performance would be an important exten-sion of our study. Such a study may provide someinteresting insights on the differences in the pat-tern of relationships between firms that are high onclockspeed as well as turbulence, high on clock-speed but low on turbulence, low on clockspeedand high on turbulence, and low on both clock-speed and turbulence. Similarly, future studies maywant to compare strategic thinking at different lev-els of the firm to clarify the effectiveness of com-plexity in high-velocity industries.

Methodological implications

Our study outlines how strategic schemas can beoperationalized using causal maps. Using socialnetwork measures, we measured two key facetsof strategic schemas: complexity and focus. Draw-ing on the strategy and cognition literature, weargued that complexity and focus represent distinctfacets of strategic schemas. Our SEM results con-firmed the distinctness of the two facets. Moreover,our measures of complexity and centrality demon-strated high convergent and discriminant validity,as well as internal consistency (Table 3). Develop-ing valid and consistent strategic schema measuresis especially important, because lack of opera-tional measures has been a major bottleneck inmanagerial cognition research (Huff, 1982; Walsh,1995). Thus, future managerial cognition studiescan use these measures to investigate substantiverelationships between strategic schemas and otherimportant strategic action, environment, and indus-try constructs.

Our study also points to the viability of usingindustry clockspeed (Fines, 1998) to operationalize



rate of industry change. The exploratory factoranalyses confirmed the convergent validity andinternal consistency of product, process, and orga-nizational clockspeed measures. Another importantresult yielded by our CFA analyses is the distinct-ness of industry clockspeed from either turbulenceor magnitude of industry change. These resultsconfirm theoretical contentions that rate of indus-try change (captured by industry clockspeed) isdistinct from both turbulence and magnitude ofindustry change (Duncan, 1972; Jurkovich, 1974).A major implication is the need for separate test-ing of substantive relationships of each constructof industry change.

Practical implications

By using firm performance as a dependent vari-able, our study provides important prescriptionsfor practice about the importance of fit betweenstrategic schema and the speed of industry change.An important prescription is that to achieve higherperformance firms in high-clockspeed industriesfirms need to develop complex schemas that pro-mote strategic flexibility, whereas firms in slow-clockspeed industries need to develop focusedschemas that promote strategic stability. This hassignificant implications for the choice of top man-agement teams. Those of fast-clockspeed firmsneed to collectively constitute a complex schemafor achieving success. Such complexity can beimplemented through selection of the ‘right’ CEO(exemplified by Lou Gerstner in IBM) and seniormanagers by the board of directors, who shouldthen refrain from insisting on tight focus, giventhe speed of change in the industry. In slow-speed industries, focus is necessary and complexitymay not be particularly important. Indeed, focused,tightly controlled leadership may be the route tosuccess in slow-speed industries.

In conclusion, this study represents one of thefirst empirical works integrating industry, cogni-tion, strategic actions, and firm performance con-structs. We hope that it spurs additional researchthat improves our understanding of the set of issuesand relationships surrounding industry change,cognition, strategic actions, and firm performance.

ACKNOWLEDGEMENTS

The authors would like to thank Associate Editor,William Mitchell, and two anonymous reviewers

for their extensive and valuable comments thathelped develop this paper.

REFERENCES

Aaker DA, Mascarenhas B. 1984. The need for strategicflexibility. Journal of Business Strategy 5: 74–82.

Anderson JC, Gerbing DW. 1991. Predicting the perfor-mance of measures in a confirmatory factor analysiswith a pretest assessment of their substantive validi-ties. Journal of Applied Psychology 76: 732–740.

Andrews K. 1971. The Concept of Corporate Strategy .Richard D. Irwin: Homewood, IL.

Ansoff I. 1981. Strategic Management . Halstead Press:New York.

Ansoff I. 1988. Corporate Strategy . Penguin: London.Argote L. 1999. Organizational Learning: Creating,

Retaining and Transferring Knowledge. KluwerAcademic: Boston, MA.

Ashby WR. 1956. An Introduction to Cybernetics .Chapman & Hall: London.

Axelrod R. 1976. The Structure of Decision . PrincetonUniversity Press: Princeton, NJ.

Bahrami H. 1992. The emerging flexible organization:perspectives from Silicon Valley. California Manage-ment Review 34(4): 33–52.

Baron JN, Bielby WT. 1984. Bringing the firms back in:stratification, segmentation and organization of work.American Sociological Review 45: 737–765.

Barr PS. 1998. Adapting to unfamiliar environmentalevents: a look at the evolution of interpretation andits role in strategic change. Organization Science 9(6):644–669.

Barr P, Huff AS. 1997. Seeing isn’t believing: under-standing diversity in the timing of strategic response.Journal of Management Studies 34(3): 337–370.

Barr PS, Stimpert JL, Huff AS. 1992. Cognitive change,strategic action, and organizational renewal. StrategicManagement Journal , Summer Special Issue 13:15–36.

Baum J, Wally S. 2003. Strategic decision speed and firmperformance. Strategic Management Journal 24(11):1107–1130.

Bergh DD, Lawless MW. 1998. Portfolio restructuringand limits to hierarchical governance: the effects ofenvironmental uncertainty and diversification strategy.Organization Science 9(1): 87–102.

Bettman JR, Weitz A. 1983. Attributions in the boardroom: causal reasoning in corporate annual reports.Administrative Science Quarterly 28: 165–183.

Bogner WC, Barr PS. 2000. Making sense in hypercom-petitive environments: a cognitive explanation for thepersistence of high velocity competition. OrganizationScience 11(2): 212–226.

Bollen KA. 1989. Structural Equations with LatentVariables . Wiley: New York.

Bollen KA, Paxton P. 1998. Detection and determinantsof bias in subjective measures. American SociologicalReview 63: 465–478.



Bonacich P. 1972. Factoring and weighting approachesto status scores and clique identification. Journal ofMathematical Sociology 2: 113–120.

Borgatti S, Everett M, Freeman LC. 1992. UCINET IV,Version 1.0 . Analytic Technologies: Columbia, SC.

Boulding KE. 1971. The dodo did not make it: survivaland betterment. Bulletin of Atomic Scientist 27: 19–22.

Bourgeois LJ III, Eisenhardt KM. 1988. Strategic deci-sion processes in high velocity environments: fourcases in the microcomputer industry. Management Sci-ence 34: 816–835.

Bowman EH. 1978. Strategy, annual reports, andalchemy. California Management Review 20: 49–58.

Brown MW, Cudeck R. 1993. Alternative ways ofalternative fit. In Testing Structural Equation Models ,Bollen KA, Long S (eds). Sage: Newbury Park, CA;136–162.

Brown SL, Eisenhardt KM. 1997. The art of continuouschange: linking complexity theory and time-pacedevolution in relentlessly shifting organizations.Administrative Science Quarterly 42: 1–34.

Burgelman RA. 1996. A process model of strategic busi-ness exit: implications for an evolutionary perspectiveon strategy. Strategic Management Journal , SummerSpecial Issue 17: 193–214.

Burns T, Stalker GM. 1961. The Management ofInnovation . Tavistock: London.

Calantone RJ, Schmidt JB, Di Benedetto C. 1997. Newproduct activities and performance: the moderatingrole of environmental hostility. Journal of ProductInnovation Management 14(3): 179–189.

Calori R, Johnson G, Sarnin P. 1994. CEOs’ cognitivemaps and the scope of the organization. StrategicManagement Journal 15(6): 437–457.

Calori R, Baden-Fuller C, Hunt B. 2000. Managingchange of Novotel: back to the future. Long RangePlanning 33: 779–804.

Carley K, Kauffer DS. 1993. Semantic connectivity:an approach for analyzing semantic networks.Communication Theory 3: 183–213.

Carley K, Palmquist M. 1992. Extracting, representingand analyzing mental models. Social Forces 70:601–636.

Chakravarthy B. 1982. Adaptation: a promising metaphorfor strategic management. Academy of ManagementReview 7: 35–44.

Chan D, Schmitt N. 1997. Video-based versus paper-and-pencil method of assessment in situational judgmenttests: subgroup differences in test performanceand face validity perceptions. Journal of AppliedPsychology 82: 143–159.

Child J. 1972. Organizational structure, environment andperformance: the role of strategic choice. Sociology 6:1–22.

Cottrell T, Nault BA. 2004. Product variety and firmsurvival in the microcomputer software industry.Strategic Management Journal 25(10): 1005–1026.

D’Aveni R. 1994. Hypercompetition: Managing theDynamics of Strategic Maneuvering . Free Press: NewYork.

Daft RL, Weick KE. 1984. Toward a model oforganizations as interpretation systems. Academy ofManagement Review 9: 284–295.

Dess GG, Beard DW. 1984. Dimensions of organi-zational task environments. Administrative ScienceQuarterly 29: 52–73.

Dixon JR. 1992. Measuring manufacturing flexibility:an empirical investigation. European Journal ofOperations Research 60(2): 131–143.

Dooley RS, Fowler DM, Miller A. 1996. The benefitsof strategic homogeneity and strategic heterogeneity:theoretical and empirical evidence resolving pastdifferences. Strategic Management Journal 17(4):293–305.

Duncan RB. 1972. Characteristics of perceived environ-ment and perceived environmental uncertainty. Admin-istrative Science Quarterly 17(3): 313–327.

Dutton JL, Fahey L, Narayanan VK. 1983. Towardunderstanding strategic issue diagnosis. StrategicManagement Journal 4(4): 307–323.

Eden C, Ackermann F, Cropper S. 1992. The analysisof cause maps. Journal of Management Studies 29:309–324.

Efron B. 1982. The Jackknife, the Bootstrap and otherResampling Plans . SIAM: Philadelphia, PA.

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

Eisenhardt KM, Martin JA. 2000. Dynamic capabilities:what are they? Strategic Management Journal , SpecialIssue 21(10–11): 1105–1121.

Eisenhardt K, Sull D. 2001. What is strategy in the neweconomy? Harvard Business Review 79(1): 107–116.

Eisenhardt K, Tabizi BN. 1995. Accelerating adaptiveprocesses: product innovation in the global computerindustry. Administrative Science Quarterly 40(1):84–110.

Evans S. 1991. Strategic flexibility for high technologymaneuvers. Journal of Management Studies 28(1):69–89.

Fahey L, Narayanan VK. 1989. Linking changes inrevealed causal maps and environmental change: anempirical study. Journal of Management Studies 26:361–378.

Ferrier W. 2001. Navigating the competitive land-scape: the drivers and consequences of competitiveaggressiveness. Academy of Management Journal 44:858–877.

Ferrier W, Smith K, Grimm C. 1999. The role ofcompetitive action in market share erosion andindustry dethronement: a study of industry leadersand challenger. Academy of Management Journal 42:372–388.

Fines CH. 1998. Clockspeed: Winning Industry Controlin the Age of Temporary Advantage. Perseus: Reading,MA.

Finkelstein S, Hambrick D. 1996. Top Executives andtheir Effects on Organizations . West Publishing: StPaul, MN.

Fiol CM. 1989. A semiotic analysis of corporate lan-guage: organizational boundaries and joint venturing.Administrative Science Quarterly 34: 277–303.



Fiol CM. 1991. Managing culture as a competitiveresource: an identity-based view of sustainablecompetitive advantage. Journal of Management 17:191–211.

Fiol CM, O’Connor EJ. 2003. Waking up! Mindfulnessin the face of bandwagons. Academy of ManagementReview 28: 54–70.

Fisk ST, Taylor SE. 1991. Social Cognition (2nd edn).McGraw-Hill: New York.

Fombrun CJ, Ginsberg A. 1990. Shifting gears: enablingchange in corporate aggressiveness. Strategic Manage-ment Journal 11(4): 297–309.

Freeman LC. 1978. Centrality in social networks. I.Conceptual clarification. Social Networks 1: 215–239.

Garg V, Walters BA, Priem RL. 2003. Chief executivescanning emphases, environmental dynamism, andmanufacturing firm performance. Strategic Manage-ment Journal 24(8): 725–744.

Glueck WF, Willis R. 1979. Documentary sourcesand strategic management research. Academy ofManagement Review 4(1): 95–102.

Gustafson LT, Reger RK. 1995. Using organizationalidentity to achieve stability and change in highvelocity environments. Academy of ManagementJournal Best Papers Proceedings 464–468.

Hall EH, St. John CH. 1994. A methodological noteon diversity measurements. Strategic ManagementJournal 15(2): 153–168.

Hambrick DC, Mason PA. 1984. Upper echelons: theorganization as a reflection of its top managers.Academy of Management Review 9(2): 193–206.

Harrigan KR. 1985. Strategic Flexibility: A ManagementGuide for Changing Timing . Lexington Books:Lexington, MA.

Hitt M, Keats B, DeMarie S. 1998. Navigating in the newcompetitive landscape: building strategic flexibilityand competitive advantage in the 21st century.Academy of Management Executive 12(4): 22–43.

Hitt MA, Ireland RD, Hoskisson RE. 2002. StrategicManagement: Competitiveness and Globalization,Concepts and Cases (5th edn). South-Western CollegePublishing: Cincinnati, OH.

Hodgkinson G. 1997. Cognitive inertia in a turbulentmarket: the case of UK residential estate agents.Journal of Management Studies 34: 921–945.

Hoyle RH. (ed). 1995. Structural Equation Modeling .Sage: Thousand Oaks, CA.

Huff A.S. 1982. Industry influences on strategy reformu-lation. Strategic Management Journal 3(2): 119–131.

Huff AS. 1997. A current and future agenda for cognitiveresearch in organizations. Journal of ManagementStudies 34(6): 947–952.

Huff AS, Schwenk C. 1990. Bias and sensemaking ingood times and bad. In Mapping Strategic Thought ,Huff AS (ed). Wiley: Chichester; 81–108.

Investor Relations Business. 2000. Annuals remain topinfo source: Investors rate annual over financial mediaor analysts’ reports 5(7): 1–4.

Jaques E. 1998. Requisite Organization: A Total Systemfor Effective Managerial Organization and ManagerialLeadership for the 21st Century . Cason Hall:Arlington, VA.

Jones N. 2003. Competing after radical technologicalchange: the significance of product line managementstrategy. Strategic Management Journal 24(13):1265–1288.

Joreskog K, Sorbom D. 1993. LISREL 8: StructuralEquation Modeling with the SIMPLIS CommandLanguage. Scientific Software: Chicago, IL.

Jurkovich R. 1974. A core typology of organiza-tional environments, Administrative Science Quarterly19(3): 380–394.

Keisler S, Sproull L. 1982. Managerial response tochanging environments: perspectives on problemsensing from social cognition. Administrative ScienceQuarterly 27(4): 548–570.

Kessler EH, Chakrabarti AK. 1996. Innovation speed:a conceptual model of context, antecedents andoutcomes. Academy of Management Review 21:1143–1191.

Knoke K, Kuklinski JH. 1982. Network Analysis . Sage:Newbury Park, CA.

Lant TK, Milliken FJ, Batra B. 1992. The role ofmanagerial learning and interpretation in strategicpersistence and reorientation: an empirical exploration.Strategic Management Journal 13(8): 585–608.

Lyles MA, Schwenk CR. 1992. Top management,strategy and organizational knowledge structures.Journal of Management Studies 29: 155–174.

MacCallum RC, Austin JT. 2000. Applications of struc-tural equation modeling in psychological research.Annual Review of Psychology 51: 201–226.

Margetta J. 1998. The power of integration: an interviewwith Michael Dell. Harvard Business Review 76(2):72–85.

Marsh ML, Balla JR, McDonald RP. 1988. Goodness offit indices in confirmatory factor analysis: the effect ofsample size. Psychological Bulletin 103(3): 391–410.

McGahan A. 2004. How industries change. HarvardBusiness Review 82(10): 87–94.

Mendelson H, Pillai RR. 1999. Industry clockspeed: mea-surement and operational implications. Manufacturingand Service Operations Management 1: 1–20.

Miller D, Chen M. 1996. The simplicity of competitiverepertoires: an empirical analysis. Strategic Manage-ment Journal 17(6): 419–439.

Miller D, Lant TK, Milliken FJ, Korn HJ. 1996. Theevolution of strategic repertoires: exploring twomodels of organizational adaptation. Journal ofManagement 22: 863–888.

Nadkarni S, Narayanan VK. 2005. Validity of thestructural properties of text-based causal maps:an empirical assessment. Organizational ResearchMethods 8(1): 9–40.

Nerkar A, Roberts PW. 2004. Technological and product-market experience and the success of new productintroductions in the pharmaceutical industry. Strate-gic Management Journal , Special Issue 25(8–9):779–800.

Perrow C. 1972. Complex Organizations . Scott, Fores-man: Glenview, IL.

Pisano GP. 1994. Knowledge, integration, and thelocus of learning: an empirical analysis of process



development. Strategic Management Journal , WinterSpecial Issue 15: 85–100.

Porac JF, Rosa JA. 1996. In praise of managerial narrow-mindedness. Journal of Management Inquiry 5(1):35–42.

Porac JF, Thomas H, Baden-Fuller C. 1989. Competitivegroups as cognitive communities: the case of Scottishknitwear manufacturers. Journal of ManagementStudies 26: 397–416.

Porter M. 1985. Competitive Strategy . Free Press: NewYork.

Prahalad CK, Bettis RA. 1986. The dominant logic:a new linkage between diversity and performance.Strategic Management Journal 7(6): 485–501.

Rajagopalan N, Datta DK. 1996. CEO characteristics:does industry matter? Academy of ManagementJournal 39(1): 197–215.

Rajagopalan N, Spreitzer G. 1997. Toward theory ofstrategic change: a multi-lens perspective andintegrative framework. Academy of ManagementReview 22(1): 48–80.

Reger RK, Palmer TB. 1996. Managerial categorizationof competitors: using old maps to navigate newenvironments. Organization Science 7: 22–39.

Rigdon EE, Schumacker RE, Wothke W. 1998. Acomparative review of interaction and nonlinearmodeling. In Advanced Structural Equation Modeling ,Marcoulides GA, Schumacker RE (eds). Erlbaum:Mahwah, NJ; 1–16.

Rindova VP, Fombrun CJ. 1999. Constructing compet-itive advantage: the role of firm–constituent interac-tions. Strategic Management Journal 20(8): 691–710.

Rodan S, Galunic C. 2004. More than network structure:how knowledge heterogeneity influences managerialperformance and innovativeness. Strategic Manage-ment Journal 25(6): 541–563.

Rumelt R. 1974. Strategy, Structure and EconomicPerformance. Harvard University Press: Cambridge,MA.

Salancik GR, Meindl Jr. 1984. Corporate attributionsas strategic illusions of management control.Administrative Science Quarterly 29(2): 238–254.

Sanchez R. 1995. Strategic flexibility in productcompetition. Strategic Management Journal , SummerSpecial Issue 16: 135–159.

Schendel D, Hofer CW. 1979. Strategic Management: ANew View of Business Policy and Planning. Little,Brown: Boston, MA.

Schneider SC, Angelmar R. 1993. Cognition in organiza-tional analysis: who’s minding the store? OrganizationStudies 14: 347–374.

Schwenk C. 1989. Linking cognitive, organizational andpolitical factors in explaining strategic change. Journalof Management Studies 25: 41–56.

Shane S. 2000. Prior knowledge and discovery ofentrepreneurial activities. Organization Science 11(4):448–469.

Slack N. 1983. The flexibility as a manufacturingobjective. International Journal Operations andProduction Management 3(3): 4–13.

Stabell C. 1978. Integrative complexity of environmentperception and information use: an empirical

investigation. Organizational Behavior and HumanPerformance 22: 116–142.

Thomas JB, Clark SM, Gioia DA. 1993. Strategicsensemaking and organizational performance: linkagesamong scanning, interpretation, action and outcomes.Academy of Management Journal 36: 239–270.

Thompson A, Strickland A. 2000. Strategic Manage-ment: Concepts and Cases (10th edn). Irwin: Chicago,IL.

Tung R. 1979. Dimensions of organizational environ-ments: an exploratory study of their impact on orga-nization structure. Academy of Management Journal22(4): 672–693.

Tushman ML, Anderson P. 1987. Technological discon-tinuities and organizational environments. Administra-tive Science Quarterly 31: 439–465.

Tushman ML, Romanelli E. 1984. Organizational evo-lution: a metamorphosis model of convergence andreorientation. In Research in Organizational Behav-ior , Vol. 7, Cummings LL, Staw BM (eds). JAI Press:Greenwich, CT; 177–222.

U.S. Department of Commerce. 1994. U.S. Industry andTrade Outlook . International Trade Administration,USDC: Washington, D.C.

Venkataraman N, Ramanujan V. 1987. Measurement ofbusiness economic performance: an examination ofmethod convergence. Journal of Management 13:109–122.

Volberda HW. 1999. Building the Flexible Firm . OxfordUniversity Press: Oxford, U.K.

Wally S, Baum JR. 1994. Personal and structuraldeterminants of the pace of strategic decision making.Academy of Management Journal 37(4): 932–956.

Walsh JP. 1995. Managerial and organizational cognition:notes from a trip down memory lane. OrganizationScience 6: 280–321.

Weick KE. 1995. Sensemaking in Organizations . Sage:Thousand Oaks, CA.

Williams JR. 1994. Strategy and the search for rents: theevolution of diversity among firms. In FundamentalIssues in Strategy , Rumelt R, Schendel DE, Teece DJ(eds). Harvard Business School Press: Boston, MA;229–246.

Williams LJ, Edwards JR, Vandenberg RJ. 2003. Recentadvances in causal modeling methods for organiza-tional and management research. Journal of Manage-ment 29(6): 903–936.

Williamson OE. 1975. Markets and Hierarchies . FreePress: New York.

Young G, Smith K, Grimm C. 1996. ‘Austrian’ andindustrial organization perspectives on firm-levelcompetitive activity and performance. OrganizationScience 7: 243–254.

Young-Ybarra C, Wiersema M. 1999. Strategic flexibilityin information technology alliances: the influenceof transaction cost economics and social exchangetheory. Organization Science 10(4): 439–459.



APPENDIX

STEP 1

Identification of causalstatements

STEP 2Constructing rawcausal maps

STEP 3Developing raw concepts

STEP 4Develop theoreticalcoding scheme

STEP 5Recast raw conceptsinto coded causal map

Example of a causal statement:

'In order to meet evolving needs of our major customers, we mustachieve a rapid migration to our next-generation technology'

Cause Causal connector Effect

Raw phrase Raw concept

1. Meet evolving demands of ourmajor customers

Evolving customer demand

2. Achieve a rapid migration to our next-generation technology

Adapt to newtechnology

Coded causal map

Customer/market characteristics

New product-related strategic actions

In order toTo meet evolvingdemands of ourmajor customers

we must achieve arapid migration toour next-generationtechnology

Raw concept Coded category

1. Evolving customer demand Customer/market environment

2. Adapt to new technology New-product-related strategicactions

Appendix 1. An illustration of the five-step procedure of constructing causal maps



ENVIRONMENT (No. 1–7)

1. Macro-environment• Change in government administration• Change in government policies• Recession in the U.S.• Global recession• Persian Gulf War• International U.S. relations• Political conflicts and hostilities• Natural disaster/floods• Economic conditions• Asian financial crisis

2. New entrants/barriers to entry

• Capital intensity• R&D intensity• Industry concentration• Importance of brand name• Ease of exit• Economies of scale• Learning/experience effects• Profit margins• Access to technology and know-how• Resource requirements• Cost disadvantages independent of

size• Access to distribution channels

3. Drivers of industry change

• Change in long-term growth rate• Shifts in buyer demographics• Product innovation• Technological change • Market innovation• Entry or exit of major firms• Diffusion of technological know-how• Globalization of the industry• Changes in cost and efficiency• Emerging buyer preferences• Regulatory influences and

government policy changes• Changing societal concerns• Reductions in uncertainty and business

risk4. Customer/market environment• Diversity of market segments• Growth of specific markets• Evolving customer demand• Diverse customer needs within a

segment• Market fragmentation• Market convergence• Emerging market segments

5. Competition

• Imitators• Intellectual property rights• Increased competition from

alternative producers• Cooperative alliances of

competitors• Market saturation• Global competition• Decline in barriers to entry• New entrants

6. Substitute markets• Closeness of substitute products• Prices of substitute products• Performance of substitute products

7. Suppliers

• Scarcity of raw materials• Supplier dominance• Diversity of suppliers

CORPORATE STRATEGY (No. 8–14)8. Cooperative alliances

• Acquisition• Merger• Cooperative ties with international

companies• Cooperative agreement with domestic

competitors• Joint venture• Cooperative ties with suppliers

9. Portfolio analysis

• Complementary capabilities acrossacquired/merged business units

• Optimum and balanced business mix• Turnaround strategy• Consolidation• Divestiture/sale of business units• Business focus vs. diversification

10. TMT/corporate governance

• Change in CEO• Change board of directors• Change in EVP

11. Vision• Industry leadership• Definition of scope of business:

focus vs. diversity• Integrity• Customer focus• Business excellence• Continuous learning12. Internal growth:• Expansion of product lines• Non-traditional expansion

opportunities

13. Strategic objectives• Long-term growth• Business survival• Reduce cost• Increase in market share• Technology leadership• Global presence• Increased manufacturing efficiency• Increase in innovation• Improve profit margins

14. Financial objectives

• Increase in short-term sales• Earning per share• Strong cash flow• Increase in stock price• Increase in dividend• ROI

STRATEGIC ACTIONS (No. 15–19)

15. Service-related strategic actions

• Customized products• On-time product deliveries• Enhanced customer service

16. New-product-related actions

• New product introduction• Product development• R&D expenditures• Adapt to new technologies• Fast introduction of products• Product technology focus

17. Marketing strategic actions

• Dealer incentives• Alliances with dealers• Advertising• New channels of distribution• Brand promotion• Expansion of marketing programs

Appendix 2. Coding Scheme (Underlined concepts are coded categories whereas bulleted concepts are raw concepts)



53

18. Low cost/pricing actions

• Minimum tooling• Low inventory levels• Product delivery on time• Lower production cost• Powerful suppliers• Production rates• Economies of scale• Increase productivity• Lower waste

19. Capacity related strategic actions

• Production rates• Expansion of manufacturing capacity• Computerized manufacturing• New equipment and facilities• Capital expenditures• Increased outsourcing• Reallocation of existing capacity

RESOURCES (No. 20–25)20. Human capital resources• Experience• Intelligence• Insights of managers and workers• Production know-how• Articulate customer/market

knowledge• Loyalty• Entrepreneurial abilities• Risk-taking• Persistence• Motivated• Hard-working people• Pioneer spirits• Creativity

21. Organizational tangible resources• Format of reporting structure• Formal and informal planning• Controlling and coordinating system• Relations among groups within a firm• Informal and formal relations between a

firm and those in its environment• Flexibility in resource allocation• Powerful franchise• Full product line• Cost-cutting capabilities22. Technological resources

• Patents• Innovation infrastructure and capability• New technologies• Technological leadership

23. Physical capital resources

• Physical technology used in afirm

• Firm's plant and equipment• Geographic location• Access to raw materials24. Organizational intangibleresources• Brand names• Organizational reputation• Strong group culture• Shared knowledge• Trust and respect• Collegial spirit• Self-managed empowered

teams

25. Financial resources• Strong financial base• Financial flexibility

PERFORMANCE (No. 26–29)

26. Product performance• High quality• High reliability• Better customer support• Product value-added• Product versatility• Affordability/lower priced

27. Strategic performance• Customer satisfaction• Long-term growth• Increase in market share• Environmental improvement• Lower cost

28. Manufacturing performance• Efficient design and production systems• Short market cycle times• High quality of processes

29. Financial performance:• Higher profit margins• Higher shareholder value• Efficient cash flowSTRATEGIC

IMPLEMENTATION (No. 30–35)

30. Organizational structure

• Simplifying organizationalstructures

• Flat structures• Decentralized structures• Flexible structures• Deletion of layers of

management

31. People• Stock incentive program• Downsize• Longer employee contracts• Select, promote and train

employees

• Negotiations with workers• Cooperative efforts• Employment stability

32. Strategic processes• Simplification of business

processes• Promoting understanding of

long-term objectives• Improve resource use• Organize

33. Strategic controls• Communication• Strengthen control and

procedural systems• Reporting systems• Budgeting

34. Culture Building• Positive work climate• Cooperative programs• Strong leadership• Developing entrapreneurship

35. Strategic change• Steady, incremental movements of change• Restructuring• Reengineering• Adapting to change• Minimize disruption• Think differently• Willingness to embrace change• Change in attitude

Appendix 2. (Continued )


strategic schemas, strategic flexibility, and firm

Documents