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Strategic Management JournalStrat. Mgmt. J., 20: 445465 (1999)

TOP MANAGEMENT TEAM DIVERSITY, GROUP

PROCESS, AND STRATEGIC CONSENSUS

DON KNIGHT1*, CRAIG L. PEARCE2, KEN G. SMITH1, JUDY D. OLIAN1,

HENRY P. SIMS1, KEN A. SMITH3 AND PATRICK FLOOD41The Robert H. Smith School of Business, University of Maryland at College Park,College Park, Maryland, U.S.A.2Belk College of Business, University of North CarolinaCharlotte, Charlotte, NorthCarolina, U.S.A.3School of Management, Syracuse University, Syracuse, New York, U.S.A.4University of Limerick, Plassey Technological Park, Limerick, Ireland

This study integrated concepts from upper echelons, group process and social cognition theoriesto investigate how demographic diversity and group processes influence strategic consensuswithin the top management team (TMT), where strategic consensus is defined as the degree towhich individual mental models of strategy overlap. Data from 76 high-technology firms in theUnited States and Ireland were used to examine three alternative models. The results showedthat while demographic diversity alone did have effects on strategic consensus the overall fitof the model was not strong. Adding two intervening group process variables, interpersonalconflict and agreement-seeking, to the model greatly improved the overall relationship withstrategic consensus. For the most part, TMT diversity had negative effects on strategic consensus.The model with superior fit showed both direct and indirect effects of diversity on strategicconsensus. Copyright 1999 John Wiley & Sons, Ltd.

Diversity in groups and teams is often portrayedas a positive force leading to effective functioningof the team. Diversity supposedly leads to greatervariance in ideas, creativity, and innovation, thusgenerating better group performance (Cox, 1993;Jackson, May and Whitney, 1995). In the popularpress, diversity is almost always synonymouswith gender or ethnic diversity. Research onorganizational work groups, however, has focusedon other forms of diversity including differencesin age, education, firm tenure, and functional ortechnical background (Jackson et al., 1995).

Key words: top management teams; diversity; consen-sus; group process* Correspondence to: D. Knight, The Robert H. Smith Schoolof Business, University of Maryland, College Park, MD20742, U.S.A.

CCC 01432095/99/05044521 $17.50 Received 27 September 1995Copyright 1999 John Wiley & Sons, Ltd. Final revision received 13 July 1998

Cross-functional teams, for example, are by defi-nition designed with deliberate differences indemographic diversity and technical specialization(Ancona and Caldwell, 1992).

In this research, we investigate the effects ofdemographic diversity within top managementteams (TMTs) on group process and strategicconsensus. Two related streams of research pro-vide the basis for this study. First, upper echelonstheory (Hambrick and Mason, 1984; Finkelsteinand Hambrick, 1996) links observable demo-graphic characteristics of top executives to a va-riety of organizational processes and outcomes.Second, group process theory illustrates howgroup interpersonal processes work to influencevarious group and/or organizational outcomessuch as firm performance (Shaw, 1981). By inte-grating these two research streams we hope to


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446 D. Knight et al.

provide a richer understanding of the combinedeffects of TMT diversity and group processes.

TMT diversity and group process are used inthis research to predict strategic consensus. Thebuilding of strategic consensus is generallyaccepted as one of the first steps in the strategy

formation process (Dess and Origer, 1987; Niel-son, 1981; Lyles, 1981; Bourgeois, 1980, 1985;West and Schwenk, 1996). For example, Whyte(1989: 41) notes that the first task of all decision-making groups is to produce consensus from theinitial preferences of its members. Similarly,Floyd and Wooldridge (1992: 27) contend thatthe successful implementation of strategy requiresthat managers are acting on a common set ofstrategic priorities. Studies of strategic consensushave either focused on its link to organizationalperformance (Bourgeois, 1980, 1985; Dess, 1983,

1987; West and Schwenk, 1996), or on the proc-ess of consensus formation (Priem, Harrison andMuir, 1995; Schweiger, Sandberg, and Ragan,1986; Cosier and Rechner, 1985). In this research,we focus on the process of consensus formation;that is, we treat TMT strategic consensus as adependent variable.

We advance the strategic consensus literatureby conceptually and empirically linking strategicconsensus to managerial cognition (Walsh, 1995).More specifically, we utilize a measure of eachteam members interpretation of the business-

level strategy of his/her firm as a representationof that team members mental model of the firmsstrategy. According to Barr, Stimpert, and Huff(1992: 16), mental models consist of conceptsand relationships an individual uses to understandvarious situations or environments. In this paper,we use the term mental models to refer to clustersof related, though not necessarily causallyorganized, concepts. When we aggregate individ-ual mental models of strategy to the group level,we assess the level of strategic consensus, whichis the extent to which the individual team mem-bers mental models of strategy overlap. Linkingthe concept of strategic consensus to the underly-ing mental models of strategy allows us to explorethe extent to which shared cognitions of strategyare influenced by TMT diversity and TMT groupprocess. Such relationships have long beenassumed (Shaw, 1981; Hambrick and Mason,1984), but, to our knowledge, not widely tested,in the literature.

The central research question in this study is:

Copyright 1999 John Wiley & Sons, Ltd. Strat. Mgmt. J., 20: 445465 (1999)

How do TMT demographic diversity and groupprocesses relate to strategic consensus? Theresearch question is broken into two parts. First,how well do measures of demographic diversityalone explain strategic consensus and, second,how does the inclusion of group process measures

affect the model? We propose and test four alter-native models of how demographic diversity andgroup process might work to influence the degreeof TMT consensus about firm strategy. The sam-ple for this research included top managementteams from 83 high-technology firms in theUnited States and Ireland.

THEORETICAL BACKGROUND

Strategic consensus as a mental model

Hambrick and Mason (1984) argue that thepsychological and cognitive characteristics under-lying observable demographic measures are criti-cal to the groups processes and subsequentdecisions. This is consistent with a growing bodyof research on managerial cognition (see Walsh,1995, for a summary) which suggests that man-agers mental models will influence the decisionsthey make (Day and Lord, 1992).

Mental models are similar to knowledge struc-tures (Walsh, 1995), schema (Fiske and Taylor,1984; Ireland et al., 1987; Sims and Gioia, 1986),

and implicit theories (Brief and Downey, 1983).With regard to managers, Mintzberg (1973: 183)observes that it is the power of his mentalmodels that determines to a great extent theeffectiveness of his decisions. Kiesler andSproull (1982: 557) assert that managers operateon mental representations of the world and thoserepresentations are likely to be of historicalenvironments rather than of current ones. Priorresearch has shown mental models to be relatedto strategies (Day and Lord, 1992), strategicactions, and performance (Thomas, Clark, andGioia, 1993), and interpretations of and responsesto strategic issues (Dutton and Dukerich, 1991).

Scholars have also posited that mental modelscan operate on the group level and have usedterms such as shared cognition, team mentalmodel (Klimoski and Mohammed, 1994), collec-tive cognitive map (Axelrod, 1976) or dominantlogic (Prahalad and Bettis, 1986) to describe thisphenomenon. Here we use the term strategicconsensus to represent the shared cognitions


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Diversity, Group Process and Consensus 447

among team members. This term mainly refersto agreement or overlap among individual teammembers mental models of strategy, but doesnot necessarily imply a deliberative consensus-seeking process.

We contend that each top-level manager will

have a mental model or perception of the strategicconcepts and their interrelationships that his/herfirm uses in an attempt to manage its environ-ment. For example, each manager will have amental model of the role that various elements(such as innovation, costs, or service) play in thefirms overall strategy. Moreover, we argue thatthese mental models may vary among TMT mem-bers within a given firm. Accordingly, we esti-mate each managers mental model or perceptionof his/her firms strategy from the responseshe/she made to a broad set of 48 questions

regarding the firms strategy. This is an indirectmeasure of individual mental models. That is, wedid not directly elicit the specific mental modelof strategy from each subject, but we used a setof terms common in the organizational strategyliterature and among executives to develop aquantitative measure of firm strategy.

We use these individual measures to createa group-level measure that we called strategicconsensus. We then investigate how TMT diver-sity and group processes relate to the strategicconsensus of the TMT. Our contention is that

strategic consensus can result either because TMTmembers have similar backgrounds (e.g., lowdiversity), and/or because effective group proc-esses have been utilized to resolve differences inindividual mental models of strategy. Althoughthere are other factors that might lead to strategicconsensus (such as the fact that managers in afirm are exposed to relatively similar stimuli fromboth inside and outside the firm), we focus in thisresearch on the specific effects of demographicdiversity and group processes.

Demographic diversity

Upper echelons theory builds on the idea of thedominant coalition (Cyert and March, 1963) topropose that executives influence organizationalperformance through the decisions they make(Hambrick and Mason, 1984). Upper echelonstheory suggests that executives will makedecisions that are consistent with their cognitivebase (Hambrick and Mason, 1984) or executive


orientation (Finkelstein and Hambrick, 1996),which consists of two elements: psychologicalcharacteristics (including values, cognitive mod-els, and other personality factors) and observableexperiences. A fundamental principle of upperechelons theory is that observable experiences

(i.e., demographic measures) are systematicallyrelated to the psychological and cognitiveelements of executive orientation. Upper echelonsresearch employs the use of observable demo-graphic characteristics as proxy measures ofexecutive orientation. Executive orientation worksthrough a perceptual or filtering process thatresults in what is called managerial perceptions(Hambrick and Mason, 1984) or construed reality(Finklestein and Hambrick, 1996). Managerialperceptions, in turn, influence strategic choicesand executive actions.

Research using this theoretical framework haslinked the demographic characteristics of topmanagers and/or the demographic diversity of theTMT to a variety of organizational outcomesincluding performance (Keck, 1991; Hambrickand DAveni, 1992; Michel and Hambrick, 1992;OReilly and Flatt, 1989; Smith et al., 1994),strategy (Finkelstein and Hambrick, 1990; Micheland Hambrick, 1992), strategic change (Grimmand Smith, 1991; Wiersema and Bantel, 1992),management turnover (Wagner, Pfeffer and ORe-illy, 1984), and organizational innovation (Bantel

and Jackson, 1989; OReilly and Flatt, 1989;Smith et al., 1993).

Studies have also found that demographicdiversity can influence group processes. In fact,diversity can influence group processes in contra-dictory directions. For example, diversity has beenshown to have negative effects on both groupcohesion (Katz, 1982; Lott and Lott, 1961; ORe-illy, Caldwell, and Barnett, 1989) and the fre-quency or quantity of communication (Smith etal., 1994; Wagner, Pfeffer and OReilly, 1984).In addition, diversity tends to lead to increasedconflict within the group (Eisenhardt andSchoonhoven, 1990; Wagner et al., 1984) and toincreased political activity (Pfeffer, 1981). How-ever, diversity can also lead to enhanced creativ-ity and innovation by generating greater variancein decision-making alternatives (Cox, 1993; Jack-son et al., 1995). Consistent with prior TMTresearch (e.g. Eisenhardt and Schoonhoven, 1990;Hambrick and DAveni, 1992; Keck, 1991;Michel and Hambrick, 1992; Smith et al., 1994),


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we used measures of diversity in functional back-ground, age, education, and employment tenureas indicators of TMT diversity, and investigatedhow TMT diversity was related to group proc-esses and to the level of strategic consensuswithin the TMT.

Group processes

Previous TMT studies have examined therelationships between organizational performanceand a variety of group processes. These includecomprehensiveness in the strategic decision-making process (Fredrickson, 1984; Fredricksonand Iaquinto, 1989; Fredrickson and Mitchell,1984), speed in decision-making processes(Eisenhardt, 1989; Flood et al., 1997), and polit-ical behavior within top management teams

(Eisenhardt and Bourgeois, 1988). Group proc-esses have also been shown to intervene in therelationship between diversity and firm perform-ance (Smith et al., 1994). The central argumentsbehind the study of group processes pertain eitherto group processes that provide greater efficiency(e.g., reducing costs or increasing speed indecision-making) or greater effectiveness (e.g.,making better decisions). We contend that groupprocesses are likely to influence the level ofstrategic consensus within a TMT, and examinetwo important group processes from previous

research: interpersonal conflict and agreement-seeking.

Consistent with prior research, we define inter-personal conflict as conflict that relates toemotional or personal relationships betweenpeople (Amason, 1996) as opposed to conflictthat is task oriented in nature. While previousstudies have indicated that there can be differentkinds of conflict (Amason, 1996; Cosier andRose, 1977; Jehn, 1992; Pelled, 1996; Pondy,1969; Priem and Price, 1991), we chose to focusonly on interpersonal conflict in this research.Interpersonal conflict (called social or affectiveconflict in some research) can have negativeconsequences for an organization. For example,Amason (1996) found that affective conflict wassignificantly and negatively related to bothdecision quality and affective acceptance of thedecision. Jehn (1995, 1997) found that membersatisfaction with the group was negatively relatedto social conflict among group members. Accord-ingly, we built on this research and examined the


impact of interpersonal conflict on the level ofstrategic consensus within the TMT.

We define agreement-seeking behaviors asthose that are intended to produce consensus oragreement among TMT members regarding firmstrategy. Gero (1985) found that group members

have more confidence in decisions reachedthrough consensual decision techniques (i.e.,agreement-seeking techniques) than in thoseresulting from higher conflict techniques. Previousstudies (Schweiger et al., 1986; Schweiger, Sand-berg, and Rechner, 1989) have shown that groupsusing structured methods of task-oriented conflict(such as dialectical inquiry or devils advocacy)during decision-making produced higher-qualityrecommendations than groups using agreement-seeking processes. However, agreement-seekingprocesses led to greater member satisfaction with

the group and higher acceptance of their groupsdecisions. These findings indicate that the typeof group process used to manage or direct conflictcan influence not only the quality of the decisionbut the affective responses of group members aswell. Because some groups may actively engagein agreement-seeking behaviors (such as not mak-ing final decisions until all members of the groupagree), the present study built on this researchstream and examined the influence of agreement-seeking styles on strategic consensus within theTMT.

TMT diversity, group processes and strategic

consensus: Alternative models

Recall, the research question of this study is:How do TMT demographic diversity and groupprocess relate to strategic consensus? A reviewof the literature suggests alternative explanationsor paths. We treat these alternative explanationsas three alternative or more finely elaboratedmodels (as portrayed in Figure 1).

Pfeffer (1983: 348) contends that demographyis an important causal variable that affects anumber of intervening variables and processes,and through them, a number of organizationaloutcomes. Pfeffer (1983) also questions whetherthe study of the intervening processes will explainany incremental variation in the dependent vari-ables beyond what could be explained by demo-graphic measures alone. Thus, Pfeffer (1983)would contend that the addition of group processvariables would not improve the explanation of


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Figure 1. Alternative models relating TMT diversity, group processes, and strategic consensus

strategic consensus. Also, demographic variablesmay directly influence strategic consensus in

addition to any influence expressed through groupprocesses. Consistent with Pfeffer (1983), the firstpart of the research question is how well meas-ures of demographic diversity alone explain stra-tegic consensus, and we first examine whetherthere is a direct relationship between demographicdiversity and strategic consensus without con-sidering the effects of group processes. We labelthis model the direct effects model in Figure 1.

Finkelstein and Hambrick (1996) and Priem(1990) suggest that demographic diversity willbe negatively related to strategic consensus.Demographic diversity within a TMT reflects dif-ferences in experiences among its members,which should result in differences in individualsmental models. Differences in mental modelsshould, in turn, be reflected in differences in theway that individual TMT members characterizeor understand firm strategy. For example, Irelandet al., (1987) posit that people of similar age arelikely to have similar values and beliefs becausetheir life experiences are similar. Similarly,


Pfeffer (1983) suggests that individuals from dif-ferent age cohorts will have significantly different

values and perspectives. Hambrick, Cho, andChen suggest that demographic heterogeneity willlead to dispersion in the groups perspective(1996: 664). This dispersion in perspectives (orconstrued reality as Finkelstein and Hambrick,1996, term it), which may arise from differentlife or unique organizational experiences, make itless likely that members of a TMT will have thesame mental models of firm strategy.

More specifically, if cognitions or mental mod-els of the individual manager are based on his/herpast experiences and values as Kiesler and Sproull(1982) suggest, then differences in experienceshould result in differences in mental models.Thus, if past experiences are represented by asurrogate measuredemographywe expect dif-ferences in demography to be related to differ-ences in managers cognitions. For example, amarketing vice-president might have a differentmental model of organizational strategy than afinance vice-president (e.g., First on the marketvs. Profits through tight cost control). Therefore,


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the literature suggests a direct effects modelwhereby diversity in functional position, age, edu-cation, and employment tenure will be negativelyrelated to the level of strategic consensus.

In contrast to Pfeffer, other research (Gist,Locke, and Taylor, 1987; Smith et al., 1994)

suggests that the study of intervening group proc-esses is important. With regard to strategic con-sensus, Langfield-Smith (1992) proposes thatsocial (i.e., group) processes will have animportant effect on the development of sharedcognitive maps. Ford and Baucus (1987) suggestthat individual cognitive interpretations will beshaped by the personal contexts in which theindividual operates. Even if Pfeffer (1983) iscorrect, the study of potentially intervening groupprocesses is still important as their effects on theorganization may be easier to control or alter

than the effects that arise from the groups demo-graphic characteristics. In addition, group proc-esses may, in fact, be useful in overcoming someof the potentially negative consequences of demo-graphic diversity. Therefore, we also examine aset of intervening models to assess whetherinclusion of two group process measuresinterpersonal conflict and agreement-seekingimproves the explanation of strategic consensus,and, if so, which intervening model is mostappropriate to the data.

If group processes influence the cognitions of

TMT members, then it is reasonable to believethat interpersonal conflict within the TMT maydiminish shared cognitions about firm strategy. Infact, as Jehn (1995: 257) notes, conflict has beendefined as perceptions by the parties involvedthat they hold discrepant views. Minkes(1994: 80) defined conflict as imperfect compati-bility of views which necessarily follows fromthe variety of human beings. This suggests thatdifferences in mental models or construed realitiescannot exist without conflict. Research on groupcohesiveness (Shaw, 1981), which might be inter-preted as the inverse of interpersonal conflict,suggests that interpersonal conflict may reducestrategic consensus. Moreover, while interpersonalconflict may provoke disagreements about whatfirm strategy should be, it is also likely to resultin different perceptions or interpretations aboutwhat current firm strategy actually is. Conflictthat is personal or emotional in nature may resultin or even create disagreements about a widerange of issues. This may be particularly true


when dealing with firm strategy since it involvesa high degree of uncertainty and ambiguity(Eisenhardt, Kahwajy, and Bourgeois, 1997).Accordingly, we expect that interpersonal conflictamong TMT members will be negatively relatedto strategic consensus. That is, when interpersonal

conflict is high within the TMT the differencesin perceptions or mental models of what thefirms current strategy is will be greater andstrategic consensus as we measure it will belower.

Another important group process identified inprevious research is the manner in which groupsmake decisions. As noted above, prior studies(Schweiger et al., 1986, 1989) found that groupsusing agreement-seeking behaviors achievedhigher levels of consensus than groups usingprocesses that incorporated structured systems of

task-oriented conflict. Consistent with this litera-ture, we expected that the use of agreement-seeking behaviors by TMTs (i.e., behaviorsintended to foster agreement among teammembers) would be positively related to strategicconsensus (i.e., there would be greater similarityamong team members interpretations or mentalmodels of strategy).

Since prior research offers little direction onthe precise relationships among the variables, weanalyze two possible intervening models. Model2, designated as the partially mediated model,

assumes that demographic diversity will haveboth direct and indirect effects on the level ofstrategic consensus. Model 3, which is labeledthe fully mediated model, assumes that TMTdemographic diversity will have no direct effecton strategic consensus but will directly affect theintervening group processes which, in turn, willaffect strategic consensus.

With regard to the intervening models, thequestion of relationships between the TMT demo-graphic measures and the intervening group proc-esses, and relationships between the two groupprocess measures (interpersonal conflict andagreement-seeking) must be addressed. In general,we expect demographic diversity will be posi-tively related to interpersonal conflict within theteam. This is consistent with prior research suchas that of Zenger and Lawrence (1989), whofound that heterogeneity can lead to conflict.Similarly, with regard to agreement-seeking, wepropose that demographic diversity will be nega-tively related to the use of agreement-seeking


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behaviors within the TMT. This is also consistentwith prior research. For example, heterogeneityhas been found to impede other group processes,such as communication between team members(OReillyet al., 1989, Smith et al., 1994), whichcause us to believe that it will have a negative

impact on the use of agreement-seekingbehaviors.Finally, the question of a relationship, if any,

and the direction of that relationship betweeninterpersonal conflict and agreement-seeking mustbe addressed. It is reasonable to expect that inter-personal conflict and agreement-seeking would berelated. However, there remains the question ofexactly how these two group processes arerelated. In this research, we proceed with theexpectation that interpersonal conflict wouldlikely precede and affect the degree to which

TMT members are willing to adopt agreement-seeking decision processes within the team. Thisis consistent with other researchers, such as Jehn(1997: 531), who suggests that relationship con-flicts interfere with task-related effort. Accord-ingly, we expect that interpersonal conflict willbe negatively related to the use of agreement-seeking behaviors within a TMT, since agree-ment-seeking is a task-related undertaking. Wealso expect that agreement-seeking behavior willintervene in the relationship between interpersonalconflict and strategic consensus.

METHOD

Sample

The sample for this study consisted of the TMTsof 83 high-technology firms located in the mid-Atlantic region of the United States and a groupof subsidiaries of U.S. multinational firmsoperating in Ireland. Data used in this researchwere developed from CEO interviews and fromcomprehensive questionnaires that were com-pleted by the members of the top managementteam at each company. The companies includedin the sample were involved in a variety oftechnology-related businesses including infor-mation technology, research (biotechnology andaerospace), hazardous waste management, anddefense. The sample included both large andsmall firms, both publicly and privately owned,and, in the Irish sample, subsidiaries of U.S.multinational companies.


The U.S. sample contained 56 companies from114 originally contacted. An almanac profilinghigh-tech or technology-intensive firms that werepart of a technology and research and develop-ment consortium was the original source used toidentify the firms. Forty-seven firms were dropped

from the sample after initial contacts for a varietyof reasons, including unwillingness to participate,mistaken identification (e.g., were low-technology), mergers, diversification, or becausethe firm had gone out of business. Interviewswere conducted with the CEOs of the remaining67 firms (an initial response rate of 59%).

The personal interviews served two purposes.First, it allowed the researcher to explain morefully the goals of the study and to obtain theCEOs approval and endorsement of the study.The study design called for the CEO to identify

each of the team members and for each teammember to complete a questionnaire. Second, aspart of the interview, the CEO initiated a memoto each top management team member, whichrequested participation in the study and servedto endorse the study, increasing the likelihoodof participation.

Usable responses were received from 53 of the67 CEOs interviewed, for a participation rate of79 percent of the final population (47% of thoseoriginally selected). The size of companies in thesample, measured in gross sales, ranged from

$200,000 to $162 million. Mean sales were$29 million, with a standard deviation of$32.5 million and a median of $17.8 million. Themean size of firms in number of employees was357, with a standard deviation of 395, while themedian number of employees was 225.

The companies that did not participate in thestudy were involved in similar kinds of businessesto the firms included in the final sample. A one-way analysis of variance on gross sales indicatedthat responding firms were not significantly differ-ent from nonresponding firms in terms of size(F= 1.85; p 0.18; N= 114).

Hambrick and Masons (1984) upper echelonstheory suggested that researchers can identifymembers of a top management team simply byequating executive titles with membership in theteam, and some recent studies of top managementteams have used this approach (Norburn andBirley, 1988; Tushman, Virany, and Romanelli,1989; OReilly and Flatt, 1989; Keck, 1991).However, to more closely approximate Cyert and


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Marchs (1963) notion of the dominant coalition,we asked each CEO to identify the members ofhis or her real top management team. Of thoseidentified, 78 percent were also officers of thecorporation.

All members of the top management team,

including the CEO, were asked to complete ques-tionnaires. From the 286 questionnaires requestedfrom team members, a total of 230 usable oneswere returned. Eighty percent of the team mem-bers who were asked to complete the question-naire did so, and the average number of question-naires returned per firm was 4.5.

In the Irish sample, 60 percent of the com-panies contacted agreed to participate, yielding afinal sample of 26 companies. From these 26companies, 98 usable questionnaires werereturned (approximately 3.77 per firm). Combin-

ing the samples resulted in an overall responserate of 45 percent across both subsamples.

The questionnaires consisted of a group ofdemographic and process-oriented questions, anda series of questions designed to assess variousaspects of each executives mental model of firmstrategy. The respondents were members of thecompanys TMT, which included the CEO. Theunit of analysis was the TMT.

Demographic diversity variables

Four measures of TMT diversity, drawn fromprior studies in upper echelons theory, were usedin this study. To measure functional diversity,team members were asked to indicate the func-tional category that most closely represented theirbackground; functional diversity was calculatedin terms of Blaus (1977) heterogeneity index:(1 i2), where i is the proportion of the groupin the ith category. A high score on this indexindicates variability in the functional responsi-bilities among team members or functional diver-sity, while a low score represents greater func-tional homogeneity.

Age diversity was computed as the coefficientof variation in age of the members of the team.Educational diversity was computed as the coef-ficient of variation of the number of years ofpostsecondary education across team members.Employment tenure diversity was the coefficientof variation on the number of months each teammember had been employed by their current com-pany.


Group process variables

Two group process measures were assessed inthis research: interpersonal conflict and agreementseeking. Interpersonal conflict was defined as con-flict that was emotional or person oriented in

nature rather than task or issue oriented (Amason,1996). It was measured with a five-item, Likert-type scale where one represented definitely nottrue and five represented definitely true. Thequestions included items such as, The membersof the TMG get along together very well andRelationships between members of the TMG arebest described as winlose; if he/she wins, Ilose. Cronbachs alpha for the interpersonal con-flict scale was 0.84. (See Appendix 1 for the listof items included in this measure.)

Agreement-seeking was defined as the degree

to which TMT members worked together in orderto achieve consensus or reach agreement on stra-tegic issues. It was also measured using a five-item, Likert-type scale where one representeddefinitely not true and five represented defi-nitely true. The questions included items suchas, When making decisions, the TMG workshard to reach a decision, and TMG decisionsare not final until all members agree that thedecision is acceptable to them. Cronbachs alphafor this six-item scale was 0.79. (See Appendix1 for the list of items included in this measure.)

To assess the appropriateness of aggregatingindividual responses to the team level, weemployed two techniques recommended by otherresearchers. First, the James, Demaree, and Wolf(1984) r(WG(J)) procedure was used. This pro-cedure produces a measure of agreement amongrespondents and it provides the justification foraggregation (Koslowski and Hattrup, 1992: 162).The r(WG(J)) for the interpersonal conflict scalewas 0.97 and the r(WG(J)) for the agreement-seeking scale was 0.96, both indicating high lev-els of agreement within teams, thus suggestingthat aggregation is appropriate. Second, using thetechnique recommended by Danserau, Alutto, andYammarino (1984) we performed a within-and-between analysis (WABA). Generally, the ratio ofthe calculated eta-squared between to eta-squaredwithin should be greater than 1 in order to justifyaggregating individual level responses. Theratio for the interpersonal conflict scale was1.046 and for the agreement-seeking scale was1.009. Therefore, our data met the criteria for


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aggregation using two commonly accepted tech-niques.

Strategic consensus

Strategic consensus was an outcome measure of

similarity among TMT members interpretationsabout the firms strategic orientation. In essence,the measure attempted to determine the degree towhich the top management team shared a com-mon mental model with regard to the currentstrategy of the organization.

The strategic consensus variable was createdfrom responses to 48 Likert-type questionsregarding firm strategy asked of each participant.Our conception of strategic consensus was verybroad and informed by the strategic managementliterature (e.g., Miles and Snow, 1978; Porter,

1980) and TMT interviews. We used a very broadconceptualization to capture the great variety ofviewpoints in the firms strategy that are rep-resented in both the conceptual and empiricalstrategic management literature and in the view-points of executives. From strategic managementscholars, such as Porter (1980), we included thefirms emphasis on costs, price, market differen-tiation, and product/service differentiation. FromMiles and Snow (1978), we added the firmsemphasis on risk, innovation, and proactiveness.Research of MacMillan, McCaffrey, and Van

Wijk (1985) and Chen, Smith, and Grimm (1992)provided dimensions of competitive timing offirms strategic actions. From Eisenhardt (1989)we included the speed of the firms strategicbehavior. Interviews with executives participatingin the study provided additional insights intostrategic variables. Examples of these itemsincluded the extent to which the firm emphasizedtotal quality, product specialization, customiza-tion, branding, after sales support, and an accentu-ation on strategic change. Each item was an-swered using a five-item Likert response formatwith anchors ranging from one to five, where oneindicated definitely not true and five indicateddefinitely true. (See Appendix 2 for the list ofitems included in this measure.)

The computation of the final variable was gen-erally consistent with the previous work of Bour-geois (1980) and Dess (1987) and was done asfollows. First, within each team, a standard devi-ation was calculated from the responses for eachof the 48 items. Next, these 48 standard devi-


ations were summed within each team. Since wehad named the variable strategic consensus,which reflects agreement, we reversed the direc-tion of this score by multiplying by 1. Thus,for the final metric, high scores implied consensus(i.e., high similarity among TMT members men-

tal models), while low scores implied lack ofconsensus. This measure, that we called strategicconsensus, was an indirect estimate of the degreeto which top management shared a mental modelwith regard to the strategy of the organization. Itis important to note that the measure was not anobjective measure of the actual strategy, butrather was a surrogate measure of the extent towhich the top management team possessed ashared viewpointa common mental modelabout the strategy.

Data analysis

Structural equation modeling using LISREL(version 8.12a) was used to determine whetherthe pattern of relationships observed among thevariables was consistent with any of the fouralternative models proposed above. The alterna-tive models examined in this research representeda set of fully nested models. Since we wereinterested in determining if one of the four alter-native models was superior to the others, struc-tural equation modeling was particularly appropri-

ate for this research. Unlike some other analyticaltechniques, structural equation modeling allowsfor assessment of the fit of the data to thehypothesized model(s), and, especially importantfor us, a comparison of fit among alternativemodels can be made.

Joreskog and Sorbom (1993: 26) suggestedusing the following formula to compute the mini-mum sample size for estimation of the asymptoticcovariance matrices:

k( k 1)

2

where k equals the number of variables. Themaximum number of variables in this researchwas 8, resulting in a recommended minimumsample size of 38substantially smaller than ourfinal sample size of 76 cases.

An additional underlying assumption ofmaximum likelihood estimation is that all vari-ables are normally distributed (Hayduk, 1987;


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Table 1. Means, standard deviations, and intercorrelations (N= 76)

Variable Mean S.D. 1 2 3 4 5 6 7 8

1. Interpersonal conflict 2.401 0.533 1.002. Agreement seeking 3.246 0.538 0.62** 1.003. Strategic consensus 36.397 6.463 0.11 0.026* 1.00

4. Location 0.737 0.443 0.03 0.00 0.07 1.005. Functional diversitya 0.175 0.133 0.26* 0.12 0.024* 0.04 1.006. Age diversity 0.157 0.068 0.046 0.01850.13 0.17 0.19 1.007. Educational diversityb 0.469 0.252 0.03 0.06 0.21* 0.19* 0.03 0.12 1.008. Employment tenure diversity 0.600 0.307 0.13 0.09 0.02 0.32** 0.18 0.09 0.01 1.00

p 0.10; *p 0.05; **p 0.01aThis variable was transformed using a power transformation. The descriptive statistics are pre-transformation.bThis variable was transformed using a natural log transformation. The descriptive statistics are pre-transformation.

Tabachnick and Fidell, 1996). To address this,we screened all variables and found that two

variables did not satisfy this condition. In accord-ance with standard procedures (Hayduk, 1987;Tabachnick and Fidell, 1996) we did transfor-mations of these two variables. Education diver-sity was transformed using a logarithmic functionand functional diversity was transformed using apower transformation. Following the transfor-mations, both met all conditions for normality.

Finally, in order to test for systematic differ-ences between the two subsamples, location wasincluded as a control variable, and was enteredas a dummy variable with U.S. companies coded

as one and Irish companies coded as zero.

RESULTS

Descriptive statistics for the variables in thisstudy and the correlation matrix of transformedvariables are provided in Table 1.

The first research question was how well demo-graphic diversity alone explained the level ofstrategic consensus within the TMT. Four meas-ures of demographic diversity were used withlocation included as a control variable, and theresults for the direct effects model are presentedin Figure 2. Although all relevant paths weretested, only those paths that were significant wereincluded in the figure. Of the four measures ofdemographic diversity, functional diversity, edu-cational diversity, and employment tenure diver-sity were significant, and with the exception ofemployment tenure diversity the direction of therelationships was negative, as expected. Location,


which was included to test for systematic differ-ences between the U.S. and Irish samples, was

significant, indicating that the level of consensuswas lower for U.S. firms. The squared multiplecorrelation (analogous to an R2) for this modelwas 0.17.1 However, the fit statistics for thisdirect effects model (summarized in Table 2)indicated that the data did not fit the hypothesizedmodel well. Therefore, although several of therelationships were significant and in the expecteddirection, the direct effects model did not rep-resent the data well.

We next addressed the question of whetherincluding intervening group process variables

would increase explanatory power. The secondgroup of models posited that the effects of demo-graphic diversity on the level of strategic consen-sus would be mediated, either partially or fully,by interpersonal conflict and agreement-seeking.Accordingly, we examined a set of fully nestedmodels with respect to the effects of TMT diver-sity and group processes on strategic consensus.LISREL facilitated the examination of these alter-native tests of the research question by allowingcomparisons of fit between the competinghypothesized models. Three mediated models areshown in Figures 35. For clarity of inter-pretation, only the significant paths were indicatedalthough all relevant paths were tested.

Figure 3 represents a partially mediated model,in which both direct and indirect effects of allthe variables on strategic consensus were allowed.

1 For all models, we tested alternative calculations of themeasures (i.e., standard deviation instead of coefficient ofvariation) and the results were similar.


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Figure 2. Direct effects model

Table 2. Statistics from structural equation modeling

Model d.f. 2 GFI AGFI AIC CFI

Direct effects model 13 56.40a 0.87 0.63 102.40 0.48Partially mediated model 1 1.19b 1.00 0.86 71.19 1.00Fully mediated model (A) 6 13.75c 0.96 0.76 73.75 0.91Fully mediated model (B) 11 18.67d 0.94 0.82 68.67 0.91

ap = 0.00000023; bp =0.27; cp= 0.033; dp = 0.067

Table 3. Squared multiple correlations for all models

Model Strategic consensus Agreement seeking Interpersonal conflict

Direct effects model 0.17 NA NAPartially mediated model 0.21 0.43 0.11Fully mediated model (A) 0.07 0.43 0.11Fully mediated model (B) 0.07 0.39 0.11



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Figure 3. Partially mediated model

Figure 4, Model A, reflects a model where demo-graphic diversity was allowed to influence bothgroup process variables, but was not allowed adirect effect on strategic consensus. Figure 5,Model B, portrays a model where demographicdiversity was allowed to influence only the groupprocess variable of interpersonal conflict. We pro-posed these alternative models to investigatewhich represented the best fit with the data. Forall of the intervening models we considered thereverse relationship between interpersonal conflictand agreement-seeking although we believed thatthe indicated direction made the most sense. Themodels including the reversed path did not fit thedata as well as the models presented.

As noted previously, one advantage of struc-tural equation modeling is that a variety of tech-niques may be used to assess the appropriatenessof hypothesized models and to compare the fitamong alternative models. LISREL generates a


variety of tests that can be used to assess fit.Since no one test has been judged superior tothe others, we calculated and compared severalso that we could more adequately assess whichmodel was the best. The chi-square statistic,goodness of fit index (GFI), adjusted goodnessof fit index (AGFI), the Akaike Information Cri-terion (AIC) (Akaike, 1973), and the comparativefit index (CFI) were used to assess and comparethe fit of the direct effects model and the threehypothesized group process models. These sta-tistics are presented in Table 2 for all four mod-els. With the chi-square test, the preferred out-come is that the chi-square is nonsignificant.When using the GFI, the AGFI, or the CFI, thecloser to one the better the fit of the model. Withregard to the AIC, the lower the number, thebetter the fit.

Overall, the fit statistics indicated that all threeintervening models fit the data. Thus, one con-


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Figure 4. Fully mediated model A

clusion that could be drawn was that group proc-esses did, in fact, intervene in the relationshipbetween TMT diversity and played a significantrole in the level of strategic consensus withinthe TMT.

We also wanted to determine which interveningmodel was the best representation of the actualrelationships between TMT diversity, group proc-esses and strategic consensus since these issueshave not been fully examined in prior research.To do this, we compared the fit of the threeintervening models to see which was superior.Based on the indicators in Table 2, it was clearthat the best model overall was the partiallymediated model. The chi-square statisticassociated with the partially mediated model was1.19 with 1 degree of freedom (p = 0.27), thegoodness of fit index was 1.00, the adjustedgoodness of fit index was 0.86, the AIC was71.19 and the CFI was 1.00, all indicating that


the proposed model fit the sample data well. Inaddition, the partially mediated model was beston three of the four tests of fit, and second beston the remaining test. This indicated that thepartially mediated model was more appropriateto the data than either of the fully mediatedmodels or the direct effects model. In addition,the squared multiple correlations for strategic con-sensus, agreement seeking and interpersonal con-flict (presented in Table 3) clearly indicated thatthe partially mediated model was superior to theothers in terms of the amount of variance explain-ed.

Because it was the best-fitting model, wefocused our attention on the partially mediatedmodel to interpret the details. As shown in Figure3, both TMT diversity and group processes hadsignificant impacts on strategic consensus. Withregard to the demographic measures of diversity,functional diversity and educational diversity were


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Figure 5. Fully mediated model B

negatively related to strategic consensus, asexpected. Contrary to our expectations, employ-ment tenure diversity was positively related tostrategic consensus, and age diversity was notsignificantly related to strategic consensus.Finally, location was positively related to strategicconsensus, indicating that consensus was higherin the Irish firms. In addition, as we proposed,age diversity had a negative impact on agreement-seeking (i.e., age diversity reduced team effortsat agreement), while functional diversity had apositive relationship to interpersonal conflict (i.e.,as functional diversity increased interpersonalconflict also increased). None of the otherrelationships between demographic diversity andgroup processes were significant. The results withregard to the group process variables were asexpected. That is, interpersonal conflict had anegative relationship with agreement-seekingbehavior, and, in turn, agreement-seeking had a


positive relationship to strategic consensus. Thus,overall, diversity was found to have both directand indirect effects on strategic consensus, andthese effects were primarily negative as proposed.

DISCUSSION

This research examined the relationships betweendemographic diversity, group processes, and stra-tegic consensus in a sample of 76 high-technologyfirms in the United States and Ireland. Ourconceptualization was inspired by upper echelonstheory (Hambrick and Mason, 1984), the groupprocess literature (Shaw, 1981; Stogdill, 1959),and the social cognition literature (Sims andGioia, 1986; Walsh, 1995). By treating strategicconsensus as representative of underlying TMTmental models, we cast some initial light onfactors that can influence the content of managers


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mental models and the degree to which thatcontent is shared among members of a TMT.

This research makes several important contri-butions. First, by demonstrating systematicrelationships between demographic measures andone measure of executive cognition, we validate

an important assumption of upper echelonstheory. Second, our results support the contentionof other researchers that demographic diversitywould be negatively related to consensus.Although not all of our results were as we pro-posed, the general impact of diversity on strategicconsensus was negative. Third, our results indi-cate that some dimensions of diversity do influ-ence group processes. Finally, the results suggestthat group processes do, in fact, add importantinformation about strategic consensus beyondwhat demographic measures alone explain.

These findings lend some weight to Walshs(1995) discussion of the formation of managerialcognitions, in which he posits that individualsknowledge structures are formed on the basis ofboth differences in origins (e.g., experience ordemographic background) and differences in themore immediate environmental context (e.g.,group process). This research suggests that differ-ences in origins (i.e., demographics) may leadmanagers to interpret firm strategy differently,thus resulting in a lack of agreement in TMTmental models about the firms strategic orien-

tation. The issue of group processes is somewhatless clear since group processes are shared withina specific TMT. This study does suggest thatgroup processes play an important role in shapinga managers mental models of his/her firms strat-egy. Although group processes are shared byTMT members, they may still result in somedifferences in mental models between individuals.For example, all team members may not perceivethe group processes similarly due to differences inexperience (i.e., demography). In addition, groupprocesses may influence shared cognitions differ-ently due to differences in values among themembers or to the affective nature of the parti-cular group process (e.g., interpersonal conflict).

More specifically, the first research questionaddressed how well demographic measures ofdiversity alone explained the level of strategicconsensus within the TMT. Of the four diversitymeasures, functional diversity and educationaldiversity were significant, and the direction ofboth relationships was negative, as expected.


However, the direct effects model, which includedonly measures of demographic diversity, wasunsatisfactory as a whole, since the fit statisticsindicated that the hypothesized model did not fitthe data well.

The second research goal of this study was to

explore the nature of the relationships amongteam diversity, group processes, and strategic con-sensus. We tested a series of three fully nestedmodels in order to determine whether a partiallymediated or fully mediated model best fit thedata. The results showed that the partiallymediated model provided the best empirical rep-resentation of the data. That is, the model thatincluded both direct and indirect effects ofdemography on strategic consensus was superior.The indirect effects showed the influence ofdemography on strategic consensus was partially

mediated by group processes. Generally, with oneexception, diversity had a negative effect on stra-tegic consensus.

However, although we proposed that our meas-ures of demographic diversity would affect theintervening group processes and strategic consen-sus similarly, we found that this was not alwaystrue. For example, although we proposed that allmeasures of diversity would be negatively relatedto strategic consensus, in the best-fitting model(the partially mediated model) employment tenurediversity had a positive effect on strategic consen-

sus, contrary to our expectations. In addition,we proposed that all measures of demographicdiversity would be positively related to inter-personal conflict and negatively related to agree-ment-seeking behaviors within the teams. How-ever, in each case only one of the demographicmeasures had a significant influence on eithergroup process variable (although in both cases thedirection of the relationship was as we expected).

The question we must address is why we didnot find all of the relationships that we proposed.There are several possible explanations includingthe nature and size of our sample or the specificoperationalizations of variables that we used.Another possible explanation is that the effectsof diversity on executive cognitions and groupprocesses may not be uniform as we first pro-posed. One possible explanation is that there areother attributes or characteristics of demographicmeasures that differentiate them. For example,Pelled (1996) suggests that two characteristics(visibility and job-relatedness) may determine the


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strength of a particular type of diversity withregard to its effect on conflict. It is entirelyreasonable to believe that there are other attributesof demographic characteristics that may alter thenature of the relationship with either a cognitiveconstruct such as strategic consensus or with

group processes. The possibility that all diversitymay not be equal, either in direction or strengthof effect, suggests a promising area for futureresearch.

This research has implications for the study ofdiversity within organizations and how it can bemanaged more effectively. One important findingis that some aspects of diversity may increaseinterpersonal conflict that, in turn, has a stronglynegative impact on strategic consensus by reduc-ing the use of agreement-seeking behaviors. Forexample, in our sample, functional diversity had

a significant and positive effect on interpersonalconflict within the team. Therefore, one area forfuture research would be to examine interpersonalconflict more closely in order to understand whatadditional factors influence interpersonal conflictamong TMT members.

As noted above, prior research has determinedthat there are also different kinds of conflict.Another promising area for future research mightinvestigate whether other kinds of conflict alsoinfluence strategic consensus. In addition to dif-ferent kinds of conflict there may also be different

dimensions, all of which might affect other groupprocesses or strategic consensus. For example,Jehn (1997) suggests that conflict has four dimen-sions and reports that these dimensions can influ-ence other effects of conflict. Future researchshould examine some of these other dimensionsto assess their impact on strategic consensus.

The finding that consensus was higher in theIrish teams suggests another interesting area forresearch. Perhaps this reflects the influence ofstrongly held cultural beliefs that tend to encour-age members to reach consensus. This is partic-ularly interesting in light of the fact that therewere no significant differences between the U.S.and Irish teams on either of the group processmeasures (i.e., interpersonal conflict or agree-ment-seeking). Since cultural influences might bedue to any number of factors (e.g., nationality,regional factors, religious beliefs) further researchinto the potential effects of culture on consensuswould be valuable.

Future research might also examine whether


direct interventions intended to encourage team-work and cooperation within the TMT mightmitigate any negative effects of diversity. Forexample, our results suggest that leadership prac-tices that discourage interpersonal conflict andencourage agreement seeking could increase stra-

tegic consensus. Indeed, Katzenbach (1997) advo-cates the creation of certain team conditions toencourage agreement-seeking behavior, includingestablishing mutual accountability, and allowingteam members to shape collectively the workproducts. Similarly, HR policies designed to pro-mote team cohesion might enhance the effective-ness of diverse TMTs by drawing on the uniqueinsights of individual team members while mini-mizing the potential for interpersonal conflict.Perhaps one of the most important implicationsof this study is that future research into questions

of diversity and its effects on organizations musttake a broad perspective if the complex inter-actions of these demographic variables with eachother and group processes are to be understoodand effectively managed.

Another interesting extension of this researchcould involve the influence of power differentialswithin the top management team on strategicconsensus. It is entirely possible that the powerdistribution among TMT members might have astrong effect on strategic consensus, group proc-esses within the team, or both. That is, would

broad empowerment of the TMT enhance or di-minish TMT consensus? Related to this wouldbe whether the kind of leadership employed bythe CEO affects either the processes within theteam or the level of strategic consensus.

One potential limitation of this research is thatwe used a questionnaire-based method as anindirect surrogate measure of the mental modelsof strategy. We took several steps to ensure thatour measure was well grounded with the truecontent of real mental models of strategy. Ourconceptualization was based on 48 different itemsgleaned from an extensive review of the strategyliterature and through pilot tests with executives.The main advantage of this is that a quantitativeassessment of agreement within 76 separate topmanagement teams can be accomplished. Thisquantitative approach would not be possible withmany direct cognitive elicitation methods. Still,for future research we suggest that an interestingextension of this study would be to employ moredirect methods that would entail nonquantitative


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cognitive elicitation to provide a more directprobe of the actual strategic mental models ofindividual members of the top management team.

The influence of functional diversity is inter-esting, but indicates another potential limitationof this study. Functional diversity might be mod-

eled in many different ways. For example, Hittand Tyler (1991) and Walsh (1988) have sug-gested that senior executives often do not have asingle, clearly identifiable functional track in theircareers, and, in fact, differences in career tracksmight make a difference in the way executivescharacterize firm strategy. While the results ofthis research tend to support the importance offunctional position, we need to explore otherpossible measures of the construct, such aswhether differences in prior career tracks contrib-ute to this effect.

Our results indicate that organizations need tobe aware of the potential problems created byincreased diversity among top executives. How-ever, we believe that it would be a mistake toassume that the potential difficulties identifiedhere imply that firms should avoid diversity.Rather, CEOs and other executives need to beaware of the potential pitfalls and take activesteps to counter any possible negative conse-quences. Diverse values and perspectives maybring challenges with regard to managing groupprocesses or promoting shared mental models of

firm strategy. Nevertheless, diversity is also likelyto have positive effects such as increasing theenvironmental scanning capacity of the team orincreasing the range of potential actions that thegroup considers when making strategic decisions.Also important, these results indicate thatresearchers and managers should be sensitive notonly to the direct effects of diversity on groupor organizational outcomes, but also to ways inwhich diversity may operate through interveninggroup processes. Excluding group processes fromfuture research creates the possibility thatrelationships may be misinterpreted, which hascritical implications for our understanding ofTMTs.

In summary, this research provides importantinsights into the antecedents of TMT strategicconsensus, and helps illuminate potential newresearch directions with regard to the linkbetween TMT consensus and firm performance.As Priem (1990) suggests, increasing our under-standing of the antecedents of consensus can help


us better understand the link between consensusand performance, which has important impli-cations for management. This study providesanother link in the chain of understanding ofTMT consensus and provides the basis for con-tinued research and theory building with regard

to the role of the top management team.

ACKNOWLEDGEMENTS

The authors would like to thank Rhonda K.Reger, Edward L. Fink, Cormac MacFhionnlaoichand two anonymous reviewers for their helpfulcomments on earlier drafts of this paper. Theauthors would also like to thank Sarah Moore,Mike Morley and Phillip ORegan for their assis-tance in gathering the Irish data for this study

and the University of Limerick Foundation forfinancial support.

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APPENDIX 1: VARIABLECONSTRUCTIONS

Agreement seeking (alpha = 0.79)

1. TMG decisions are not final until all the mem-

bers agree that the decision is acceptable tothem.

2. Everyones input is incorporated into mostimportant company decisions.

3. The TMG believes that taking more time toreach consensus on a strategic decision is gen-erally worth it.

4. When final decisions are reached, it is commonfor at least one member of the TMG to beunhappy with the decision (reverse coded).

5. All the members of the TMG are committedto achieving the companys goals.

6. When making decisions, the TMG works hardto reach a decision.

Interpersonal conflict (alpha = 0.84)

1. The members of the TMG get along welltogether very well (reverse coded).

2. Relationships between members of the TMGare best described as winlose; if he/shewins, I lose.


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agement team demography and corporate strategicchange, Academy of Management Journal, 35,pp. 91121.

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3. The members of the TMG are always ready tocooperate and help each other (reverse coded).

4. The members of the TMG really stick together(reverse coded).

5. Relationships between members of the TMG

are positive and rewarding (reverse coded).

APPENDIX 2: STRATEGICCONSENSUS

1. Our company believes that unstable, rapidlychanging environments provide more oppor-tunities than threats.

2. Our company specifically identifies the causesof problems before making important stra-tegic decisions.

3. Our number one priority is lowest cost rela-tive to our competition.

4. Our company is more reactive than proactive.5. The speed with which we develop products

relative to our competition is an importantpriority for this company.

6. Most of our products and services competein lower-priced markets.

7. Our company places strong emphasis on R&D, technological leadership, and innovation.


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8. Our company places a great deal of emphasison long-term (over 5 years) goals and strate-gies.

9. Our company makes investments with lowerreturns but higher probabilities of success.

10. Our company develops an exhaustive set of

alternatives before making important man-agement decisions.11. There is a bird-in-the-hand emphasis on the

short term in making management decisions.12. We work hard in each area of the organi-

zation at maintaining the lowest cost possible.13. Our companys products are sold to very

broadly defined, unspecialized markets.14. Our competitors consider us to be fast in

responding to their actions (e.g., price cutsor new product introductions).

15. We strictly enforce our Quality Control speci-

fications.16. Our company more often introduces com-

pletely new products and services rather thansimply modifying existing lines.

17. Our company seeks advice from outsideexperts to help us make important strategicdecisions.

18. Our company makes its profits by deliveringabove-average quality goods and services andthen charging more for them.

19. We formally monitor our product quality:where it is good and where it needs improve-

ment.20. Our company frequently assesses the long-

term implications of change in technologyrelevant to the products and services weoffer.

21. Our company frontloads production by build-ing capacity ahead of sales.

22. From start to finish we develop productsfaster than our competitors.

23. Our company seeks advice from all the firmsfunctional areas when making important stra-tegic decisions.

24. Taking advantage of economies of scale isan important goal of this company.

25. The quality of our products and services farexceeds that of our competitors.

26. Our company focuses its products and ser-vices to meet the specialized needs of selectclients; we dont try to be all things toall people.

27. Our company is extremely thorough in itsevaluation of strategic alternatives.

28. Product quality is not the most importantpriority of this firm.

29. When we see a business opportunity, wecan seize that opportunity quicker than our

competitors can.30. Our company anticipates how our competitorsmight respond to our strategic actions.

31. Our company emphasizes marketing tried andtrue products and services.

32. Our company invests in high-risk projectswith chances of very high returns.

33. Our company introduces more new productsand services each year than our closest com-petitors.

34. Our company frequently assesses long-termforecasts of sales, profits, and the nature

of markets.35. Our brand name is an extremely valuable

marketing asset.36. Most of our products and services compete

in higher-priced markets.37. Our company lets someone else break new

ground and only moves into a market onceit has been proven profitable.

38. Our company makes dramatic rather thanminor changes in our product lines.

39. Our company will spend whatever it takes toarrive at a good decision.

40. Overall, our customers rate the quality of ourproducts and services as excellent.

41. Our sales force is faster than our competitorsin responding to the needs of customers.

42. Using our experience to cut costs is animportant goal of this company.

43. Employees are rewarded for high-qualitywork.

44. Our company makes its profit by selling largequantities of goods and services at the lowestpossible price.

45. Our companys products are sold to narrowlydefined, specialized markets.

46. Our company emphasizes after-sales support,such as telephone hot lines, field techniciansand strong warranties.

47. Our company works hard at building a strongproduct or brand image.

48. Our number one priority is innovation, rela-tive to our competitors.

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