Individual Problem-Solving Styles and Attitudes Toward ...

Creativity Research JournalVolume 3 (1) 22-32 (1990)

Individual Problem-Solving Styles

and Attitudes Toward Divergent Thinking

Before and After Training

Min Basadur

McMaster UniversityMitsuru Wakabayashi

Nagoya UniversityGeorge B. Graen

University of Cincinnati

ABSTRACT: A field experiment was conductedto examine the mediating effect of individualcreative problem-solving style on the impact oftraining in creative thinking. This intensivehands-on training emphasized a specific three-phase process which synchronizes divergence andconvergence in problem-finding, problem-solv-ing, and implementation. Two attitudes asso-ciated with divergent thinking were measuredbefore and after training. The sample was com-prised of a mixture of organizational membersrepresenting both managers (n = 90) and non-managers (n = 66) and a variety of functionalspecialties, hierarchical levels, and types of busi-ness organizations. The most significant findingwas that the optimizer style of creative problem-solving improved more than the other three styles(generator, conceptualizer, and implementor) onmeasures of both creative thinking attitudes.

Improving organizational creativity is es-pecially important in these times of rapid

change and competitive pressure for in-creased innovation. Basadur, Graen, andGreen (1982) demonstrated that withtraining it is possible to improve creativeproblem-solving attitudes, practices, andperformance in an applied research or-ganization. Basadur, Graen, and Scandura(1986) reported similar training effects withmanufacturing engineers. In both studies,the training effects extended back to thejob. Such training emphasizes the value of(a) synchronizing both divergent and con-vergent thinking skills, and (b) using thesynchronization repeatedly in each of threedistinctly different phases of the process:problem-finding, problem-solving, and so-lution implementation.

Training in this type of multi phaseprocess of creative problem-solving is im-portant because many people need crea-

Correspondence and requests for reprints should be sent toMin Basadur, Faculty of Business, McMaster University, 1280Main Street West, Hamilton, Ontario, Canada L8S 4M4.

22 Creativity Research Journal

tive, non-programmed thinking, and yetour formal educational system and culturedo not teach or nurture such cognitive skills.Rather, they do much to suppress them(Altemeyer, 1966; Doktor, 1970; Kirton,1976; MacKinnon, 1977). Training canprovide participants with awareness,understanding, and skill in the importantcognitive techniques and processes. Thesetechniques and processes have been de-veloped over years of experience (Basa-dur, 1987) and are embodied in the three-phase process. The training not only pre-sents these cognitive techniques and proc-esses, but more importantly requires par-ticipants to practice them. Indeed, thesuccess of this training may be attributedto its intensive emphasis on such concretepractice.

This approach appears sufficient tochange the long-held attitudinal and cog-nitive processes which interfere with di-vergent thinking. In an earlier field study,Rickards (1975) emphasized the difficultyand importance of changing such attitudesand processes if creative performance is tobe increased. Basadur and Finkbeiner(1985) demonstrated that the attitudinaland cognitive facets of divergent thinkingare related to one another. They also iden-tified and presented scales to measure twospecific attitudes associated with divergentthinking: the preference for ideation (ac-tive divergence), and the tendency to makepremature critical evaluations of ideas(premature convergence). High active diver-gence includes aggressively generating largequantities of ideas, building onto other ideasto create additional ideas, and deliberatelythinking up many radical, seemingly im-possible ideas. Low premature conver-gence includes deliberately suppressing theurge to judge or analyze a fledgling idea.When people are trained to use low pre-


mature convergence and high active di-vergence in concert, more and better ideasresult (Basadur & Finkbeiner, 1985).

The present study investigated the pos-sibility that training effects on these twoattitudes may differ according to individ-ual differences in style of creative prob-lem-solving. If two people differ in theirstyle of creative problem-solving, they maydiffer in which phases of the multiphaseprocess of creative problem-solving theyprefer. Basadur, Wakabayashi, and Craen(in press) identified four styles of creativeproblem-solving distinguished by four dif-ferent combinations of opposing methodsof gaining knowledge (experiencing vs.thinking) and using knowledge (ideationvs. evaluation). The four styles favor dif-ferent phases of the creative problem-solv-ing process. For the present study, it wasexpected that people with different dOI11-

inant styles would react differentially totraining.

The training is geared more heavily to-ward improving skills and attitudes ofideation (divergent thinking) than the skillsand attitudes of evaluation (convergentthinking). The training is also geared heavilytoward the intensive practice of techniquesand processes of divergent thinking ratherthan abstract discussion. This assumes thatconvergent thinking and abstract learninghave been emphasized previously in mostpeople's formal training. One goal is toinduce participants to synchronize and de-velop an improved balance of both diver-gent and convergent thinking. Another goalis to induce participants to value feelingprocesses as well as thinking processes inlearning and problem-solving, and tounderstand better how attitudinal proc-esses affect cognitive processes in creativeproblem solving.

Thus, it was expected that this training

Creativity Research Journal 23

M. Basadur, M. Wakabayashi, and G. B. Graen

program would have a stronger impact onindividuals whose dominant creative prob-lem-solving style is the opposite to thetraining emphasis (that is, the combinationof a preference for obtaining knowledgeby detached, abstract thinking rather thandirect, concrete experiencing, and a pref-erence for using knowledge for evaluationrather than ideational purposes). This isthe optimizer (thinking-evaluation) style. Theother three styles are called generator (ex-periencing-ideation), conceptualizer (think-ing -ideation), and implementor (ex perienc-ing-evaluation). As denoted by hypothesesHI;, and H III below, compared to partici-pants with the other styles, participants withthe optimizer style of creative problem-solving were expected to be most suscep-tible to the message delivered in the train-ing. Individuals who are already more atease with divergent thinking and withlearning by practical experience would dis-cover fewer new and exciting revelationsthan all others.

For effective creative problem-solvingby individuals, groups, and organizations,both ways of learning and both ways ofusing knowledge need to be well done(Basadur & Finkbeiner, 1985). Because oureducational and societal systems have notemphasized concrete learning and diver-gent thinking, it is important to try to bal-ance things with the training. The optim-izer style provides the greatest opportunityto move groups and organizations towardthat balance. The training must, therefore,be especially capable of delivering effectsto the optimizer stylet to make the greateststrides toward the balance. To this end, thetraining in the three-phase process of thisstudy is hands-on and step-by-step, and isdesigned to demystify creative problem-solving. When participants see that suchnew methods actually work, the mystery is

removed. The design allows for partICl-pants to make such discoveries during thetraining. The techniques and processes arealso useful for the participants' own workrelated problems. In a manner of speak-ing, this approach to training creativitycould be called creativity engineering. It iscomprised of a teachable set of thinkingskills that one can systematically learn toapply individually or when interacting withothers. The more practice, the greater theskill. This should be particularly appealingto optimizers, for they value such system-atic, logical learning, and the convergentapplication of knowledge. This was testedby the hypotheses of the present investi-gation.

The primary hypotheses were as fol-lows: Compared to the other three styles,(generator, conceptualizer, and imple-mentor), participants with an optimizer stylewill show, after training in the 3-phaseprocess:

H la: Greater increases in preference for idea-tion (active divergence), and

H II,: Greater decreases in tendency to makepremature critical evaluations of ideas (pre-mature convergence).

Other factors may also play a role inshaping attitudes toward divergent think-ing. Fo~ example, because managers tendto face fore ambiguous and unstructuredproblenrs than nonmanagers (Mintzberg,1973; Sfmon, 1960), they may be more fa-miliar with divergent thinking and thusbetter understand the need for it thannonmanagers (e.g., engineers, supervisors,and technical specialists). Thus, managersshould rave more positive attitudes towarddivergent thinking than non managers. Thisstudy ekplored this possibility. It also ex-plored training effects on manager andnon manager subsamples.


Method

Research Setting and Participants

Over several years, training sessions havebeen conducted with a variety of NorthAmerican organizations under the super-vision of the senior author. A typical train-ing session involves 15 to 30 participants.Normally, participants spendthree 8-hourdays practicing the 3-phase process. Thisinvolves intensive practice in synchroniz-ing divergent and convergent thinking inall aspects of the three phases: (a) problemsensing and anticipation, fact finding, andproblem defining; (b) idea generating,evaluating, and selecting; and (c) planningfor implementation, gaining acceptance,and action taking. The sample for the pre-sent study consisted of 90 managers and66 non managers from a variety of func-tional specialties and a variety of organi-zations. They participated in training dur-ing 1986 or 1987. The manager sampleincluded middle managers, senior man-agers, and directors, and the non managersample consisted of engineers, first-line su-pervisors, and technical specialists from thesame training group. Because each train-ing session was conducted by the sametrainer, following the same instructionalsequence and methods, and using the sametraining material, differences between ses-sions in the way the training was given wereminimized.

Instrumentation and Procedure

A 6-item preference for ideation scale and an8-item premature critical evaluations of ideasscale were used to measure "active diver-gence" and "premature convergence" at-titudes. The items and scales are fully de-scribed in Basadur and Finkbeiner (1985).Items from the two scales were randomly


arranged on one 14-item questionnaire.Each item had a 5-point Likert agreementscale. The questionnaire was filled out twiceby each participant: before (the first day)and after (the third day) of the training.The participants were debriefed about therelevance and results of both the 14-itemscale and the Creatiue Problem-Solving in-ventory near the end of the training.

Each participant's creative problem-solving style was measured with the Crea-tive Problem-Solving Profile Invert/my (Bas-adur et £11., in press). As described above,this inventory is designed to meaSUIT aparticipant'S creative problem-solving st ylein terms of two dimensions, each definedas a continuum with two opposing tend-encies: (a) direct, concrete expericnciugversus detached, abstract thinking forgaining knowledge, and (b) ideation versusevaluation for using knowledge. Before thetraining sessions started, participants filledout the inventory from which each partic-ipant's dominant style was calculated. Thefour styles are: (a) an experience-ideationstyle (Generator), (b) a thinking-ideation style(Conceptualizer), (c) a thinking-evaluationstyle (Optimizer), and (d) an experience-evaluation style (Implementor).

Results

Analyses

The following data analyses were per-formed. First, means, standard deviations,and reliability coefficients for the two at-titude scales were calculated before andafter the training session. The reliabilitycoefficients were alphas (Cronbach, 1951).Correlations between the two scales werealso calculated.

Second, to test hypotheses H 1<1and H II"

subjects were classified into the four dif-



ferent dominant creative problem-solvingstyles using the methodology described byBasadur et al. (in press). The reliability ofthe Creative Problem-Solving Profile Inven-tory was calculated for this sample as fol-lows: The 12-item groups of the inventorywere randomly assigned to two parallel split-half inventories of 6-item groups each. Eachparticipant's four style scale scores werecomputed using each of the two parallel6-item split-half inventories so created, andcorrelations for each of the fou r style scaleswere computed. From this the Spearman-Brown corrected correlation coefficient wascalculated for each style scale' as the reli-ability estimate for the composite 12-iteminventory. (The composite inventory wasthe sum of the scores of the two parallelhalves.)

Hypotheses H1a and H1b were intendeddesigned to test differences among the cre-ative problem-solving styles in the degree

of effect of the training in the multiphaseprocess upon the two attitudes associatedwith divergent thinking. The third analysisperformed was to test H1a and H1b usinga Position (manager and non manager) xStyle (4 styles) ANOV A (with adjustmentsfor the unequal cell size). Finally, a meandifference analysis was conducted beforeand after the training on the two attitudes.Mean attitude levels were compared, andtheir changes over the training period wereassessed for managers and non managers.Similar analyses for the sample as a wholeand for the subgroups based on creativeproblem-solving style were performed.

Reliability

The results presented in Table 1 indicatethat attitude scales maintained satisfactoryinternal consistency coefficients before andafter the .training. The alpha reliabilitycoefficients displayed in Table I range from

Table 1Correlation Coeffzeienls Among Attitude ScaLes Before and After the TrainingExperience (N = 156)

II IIIM

Variable (S.D.) 2 2 2

I. BeforeI. Active 20.9 (.75)

divergence (5.7)2. Premature 24.4 -.26** (.80)

convergence (5.4)II. After

1. Active 22.9 .64** -.08 (.79)divergence (4.0)

2, Premature 18.0 -.24** .48** -.45** (.84)convergence (4.0)

Ill. ChangeI. Active 2.0 - .43** .21* .43** -.25**

divergence (3.4)2. Premature -6.4 .01 - .46** -.38** .55** - .45**

convergence (5.7)

Note. Chang.e = After minus Before data, with *P < .05, **P < .01. Numbers in parenthesesalong the diagonal denote reliability coefficients (Cronbach's alphas).


Table 2Distribution of Problem-solving Styles in Manager andNon-Manager Groups

Non-Manager Manager Total

Style % % %

I. Generator 10 20 30(Experience (11.1 ) (30.3) (19.1 )& Ideation)

2. Conceptualizer 32 15 47(Thinking (35.6) (22.7) (31.3)& Ideation)

3. Optimizer 20 II :{I(Thinking (22.2) ( Hi.7) (19.!1)& Evaluation)

4. Implementor 2H 20 4H(Experience (31.1) cm:{) (30.7)& Evaluation)

Total YO (iti 156( HW,:{,) (100%) (100'1<)

Noll'. Numbers in parentheses denote percentages.

a low of .75 for active divergence beforethe training to a high of .84 for prematureconvergence after the training. Also, thecoefficients in Table 1 suggest that attitudechanges for the sample as a whole oc-curred in such a way that subjects reducedpreference for premature convergence(thus, a negative sign in the change meas-ure) and increased preference for activedivergence (thus, a positive sign in thechange measure) after the training.

Table 1 also displays the correlationcoefficients among variables. Note that ac-tive divergence and premature conver-gence were negatively correlated before (r= - .26) and after (r = - .45) the training.Additionally, the two measures remainedrelatively stable before and after the train-ing (r = .64) and r = .48 for active diver-gence and premature convergence, re-spectively).

The Spearman-Brown corrected reli-ability estimates calculated for the four scalesof the Creative Problem-Solving Profile In-ventory ranged from .62 to .65. This indi-


cates a satisfactory level of internal con-sistency reliability for this sample, and issimilar to the values found in earlier re-search.

Table 2 indicates that conceptualizersand implementors each represented aboutone-third of the total sample, and gener-ators and optimizers each represented aboutone-fifth. There was a significant differ-ence in distribution of creative problem-solving styles between managers and 110n-managers (xi:l) = 10.5, P < .0 I). This dif-ference reflects the mix of generators andconceptualizers. Generators showed a 20%difference and conceptualizers showed a13% difference between managers andnon managers.

Group Differences

ANOV AS indicated that there was a sig-nificant main effect of Position for bothactive divergence, F( I, 140) = 5.33, I) <.01, and premature convergence, F( I, 140)= 7.15, I' < .01, on the before measures.(This is discussed below with respect toTable 3). There was also a significant maineffect of Style on the change of both activedivergence, F(3, 148) = 3.65, I) < .05, andpremature convergence, F(3, 14H) = 5.15,P < .01, and on the post-treatment meas-ure of premature convergence, F(3, 14H)= 3.58, P < .05. The interaction between'Position and Style was not significant.

Table 3 indicates that before training,managers had a small but significantly,1(154) = 2.5, P < .05, lower tendency forpremature convergence than non mana-gers (means of 23.7 vs. 25.4). Table 3 alsoindicates that, for both managers and non-managers, the decrease (improvement) inattitude towards premature convergenceafter training was significant, t(89) = 3.8and t(65) = 4.0, both respectively p < .01,



Table 3Means, Standard Deviations (in parentheses), and Comparisons of AltitudesBefore and Ajier Training for Manage1-s and Non-Managers

Active Divergence Premature Convergence

Before After I-value Before After- z-value

Managers 20.2 22.b 2.4* 2:).7 17.7 3.8**(1/ = 90) (3.9) (4.1) (!l.:{) (:>.5)

Non-Manager 21.9 23.4 ns 25.4 18.4 4.0**(II = !i6) (4.0) (4.1 ) (!l.(i) (5.8)

Group Differences(II = I!l4) 1.7 O.H I.H* 0.7/-valuc 2.0* O.H ~.5* 0.7

Null'. Change values were rourputcd as matched samples (Kcrlingcr. I97:{). with *P< .O!l and *f! < .0 I.

and the group differences narrowed afterthe training.

Table 3 also shows that non managersfavored active divergence slightly but sig-nificantly, t(154) = 2.0,p < .05, more thanthe manager group before the training(means of 20.2 vs. 21.9). The increase inattitude toward active divergence aftertraining was significant, t(89) = 2.4, P <.05, for the managers, with the differencesbetween the two groups reduced. Aftertraining, the manager and non managergroups were not significantly different oneither attitude.

Whether or not the effect of creativeproblem-solving style upon attitude changeindicated in Table 3 followed the patternhypothesized by Hj, and H l b was furtherexamined. Table 4 ,presents the mean dif-ferenc'es in the two attitudes for the fourcreative problem-solving styles fo~ thesample as a whole and for the managerand nonmanager subgroups separately. Forthe overall sample and the active diver-gence attitude, only the optimizer styleshowed a significant gain, t(30) = 2.1, P <.05, from before to after training. Tukey's

tests indicated that this gain by the opti-mizers was significantly greater than theaverage of the other three styles and sig-nificantly greater than either the concep-tualizers or implementors (p < .05). Forthe premature convergence attitude, allfour styles showed significant gains frombefore to after training.

For the non manager subsample and theactive divergence attitude, only the opti-mizer style showed a significant gain, t( 10)= 2.3, tJ < .05, from before to after train-ing. Tukey's tests indicated that this gainwas significantly greater than the averageof the other three styles and significantlygreater than the implementor style. Forthe premature convergence attitude, all fourstyles showed significant gains from beforeto after training. Here again, the Tukey'stests following the ANOV A indicated (p <.01) that the gain by the optimizer style wassignificantly greater than the average ofthe other three styles, and significantlygreater than the implementor style (p <.05).

For the manager subsample, the gainin the active divergence attitude from be-


fore to after training was not significantfor the optimizer, implementor, or con-ceptualizer styles. Only the gain for thegenerators was significant, t(9) = 2.6, P <.05. None of the comparisons of the gainsof the four styles was significant. For thepremature convergence attitude, all fourstyles showed a significant gains (p < .01)from before to after training. Tukey'scomparisons indicated that the gain by theoptimizers was significantly greater thanthe average of the other three styles, andalso significantly greater than the gain bythe implementors (p < .(5).

Possible regression toward the mean ef-fects (extreme values before training tend-ing to regresstoward the mean after train-ing) were then examined. Table 4 showsthat for the sample as a whole, the opti-mizer means before training for both at-titudes were both nestled within the ex-tremes provided by the means of the otherthree styles (20.7 within the range 20.4 to21.5 for active divergence and 24.8 withinthe range 23.4 to 25.3 for premature con-vergence). The same held true for the non-manager subsample. For active diver-gence, the mean (22.0) was nestled withinthe range (21.6 to 22.4). For prematureconvergence, the mean (24.4) before train-ing was actually lower than the other threemeans (24.5, 25.6, and 26.6). Regressioneffects are thus implausible because the ex-pected change after training would be togo ever lower. For the manager subsamplebefore training, the optimizer mean foractive divergence was nestled within theother three (20.1, with the range 19.6 to21.1) and the mean for premature con-vergence was only slightly higher than therange for the other three (25.1 vs. 22.8 to24.1). The latter reflects potential regres-sion effects. However, it seems reasonableto dismiss regression effects because of thesix possible comparisons for potential


regression effects, four were neutral, onehad potential for regression disfavoring thehypotheses, and one provided minor po-tential for regression favoring the hy-potheses .

Discussion

This study examined the effects of hands-on practice in concrete cognitive tech-niques and processes intended to improveskill in finding and solving real-world,nonprograunned. creative problems. Twoattitudes associated with creative problem-solving performance were measu red be-fore and after the training in the three-phase process. The premise was that mallYpeople do not solve problems as cre.u ivclyas they might because they do not haveadequate understanding or skill in lIsingappropriate techniques and processes. Thetraining particularly emphasized learninghow to think divergently when problem-finding, problem-solving, and implement-ing solutions.

The major finding of this study wasthat, as predicted by hypotheses H I" andH II>, practice with creative techniques andprocesses was particularly effective forpeople with an optimizer style of creativeproblem solving. For the sample as a whole,optimizers made greater gains than theother three styles on both attitudes. All fourcreative problem-solving styles improvedsignificantly on the premature conver-gence attitude, but the improvement onthe active divergence attitude was statisti-cally significant only for the optimizers. Forthe non manager subsample, the advan-tages for the optimizers were the same asfor the whole sample except the advantageover conceptualizers was not statisticallysignificant. For the manager subsample,the advantages for the optimizers were thesame as those found for the nonmanager

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Table 4Means, Standard Deviations (in parentheses), and DifJerences in Attitudes Toward DivergentThinking Jor the Four Problem-Solving Styles

Active PrematureDivergence Convergence

I IStyle Before After for change Before After for change

~ Total Sample (11 = 156)

Generator(n = 30/29) 21.0 23.1 25.3 18.4b 3.9**

(4.1 ) (4.5) (5.4) (5.4)

Conceptualizcr(n = 47/46) 21.5 2:{.1 23.4 17.4 3.7**

(3.6) (4.0) (5.2) (5.4)

Optimizer(11 = 31/30) 20.7 23.6 2.1* 24.8 15.9a,c 4.5**

(4.2) (4.3) (5.7) (5.7)

Implementor(n = 48/47) 20.4 22.2 24.7 19.7b 3.G**

(4.1) (4.2) (5.0) (5.2)

(ANOVA F) (ns) (ns) (3.8)* (ns) (5.2)**

Non-Managers (71 = 66)

Generator(n = 20119) 21.8 23.0 26.6 18.9 3.6**

(4.3) (4.4) (6'() (5.8)

Conceptualizer(n = 15/14) 22.4 23.7 24.5 17.8 3.6**

(3.9) (4.2) (5.4) (5.3)

Optimizer(n = 11110) 22.0 25.3a,c 2.3* 24.4 14.5a,c 4.7**

(4.1 ) (3.9) (5.6) (5.5)

Implementor(n) = 20/19) 21.6 22.3b 25.6 20.3b 3.5**

(4.3) (4.1 ) (5.2) (5.4)

(ANOVA F) (ns) (3.6)* (3.8)* (4.9)* (6.1)**

Managers (n = 90)Generator(71 = 10/9) 19.4 23.2 2.6* 22.8 17.4 3.4**

(4.5) (4.2) (6.2) (5.8)Conceptualizer(n = 32/31) 21.1 22.8 22.8 17.2 3.4**

(4.1 ) (4.0) (5.5) (5.7)

Optimizer i(n ;" 20119) 20.1 22.7 25.1 16.7a 4.0**

(4.1) (3.8) (5.2) (5.5)

Implementor(71 = 28/27) 19.6 22.1 24.1 19.2b 3.2**

(3.9) (4.0) (5.2) (4.9)

(ANOVA F) (ns) (ns) (ns) (ns) (5.7)** (5.5)**

Note. * and ** denote significant changes at p < .05 and p < .0 I respectively comparing After-Before means with z-tests for matched samples (Kerlinger, 1973).


subsample on premature convergence. Onactive divergence, there were no advan-tages for optimizers. Overall, then, hy-pothesis H Ib was strongly supported (pre-mature convergence), and hypothesis H 1<1

(active divergence) was strongly supportedfor the sample as a whole and for the non-manager subsample, but not for the man-ager subsample.

Thus, optimizer style parucipants onthe whole experienced the greatest atti-tude change toward divergent thinking asa result of exposure to the training. Recallthat optimizers are oriented toward eval-uative-thinking rather than ideation-ex-periencing in their creative problern-solv-ing styles. Also recall that the trainingfocused on practice rather than abstractdiscussion to encourage participants to valueand practice divergent thinking as an equalpartner with convergent thinking in cre-ative problem solving. Perhaps the train-ing in the 3-phase process impacted thoseparticipants with the least tendency towardideation and toward learning by practicalexperience. One might conclude that par-ticipants with the optimizing style of cre-ative problem-solving respond especiallywell to this training and improve attitudesassociated with divergent thinking the most.The underlying assumption for the hy-potheses was that optimizers, who have apreferential tendency to obtain knowledgeby detached and abstract thinking, and useknowledge for evaluation and judgment,should be affected most by training de-signed to let the participants learn just theopposite: to obtain knowledge throughpractice and to use it for ideation. Theresults of this investigation indicate that isa tenable view.

There are some additional findingsworth highlighting. First, managers' atti-tudes before training concerning prema-


ture convergence (criticizing new ideas)were somewhat better than non managers.However, their attitudes concerning activedivergence appeared somewhat worse. Thismay be understandable given the natureof managerial roles. Second, after trainingin the 3-phase process, the two attitudesassociated with divergent thinking wereimproved. For this sample of managers andnonmanagers from a variety of functionalspecialties, organizational hierarchical lev-els, and organizational types, preferencefor active divergence increased and pref-erence for premature convergence de-creased. Both managers and non managersimproved (lowered) their tendency forpremature convergence, and managersimproved (increased) their tendency foractive divergence. The improvement of thenonmanagers was not statistically signifi-cant.

One can speculate that one reason somepeople do not prefer divergent thinkingtechniques and processes is that they donot know how to use them. Perhaps theskill practice provided in this training wassufficiently intensive to break the habits ofoptimizers and get them interested in thedivergent thinking aspects of the 3-phaseprocess. As suggested by Hem (I Y(7),practice may have caused an increase inskill which then led to' improved attitudestoward the skill-in this case, divergentthinking. This needs to be tested in futurestudies. It would also be interesting to de-velop measures of attitudes associated withconvergent thinking (similar to those withdivergent thinking tested in the presentstudy) to investigate and compare how allfour styles move toward a good balanceafter training.

One implication of this research is thatorganizations which are substantially pop-ulated by optimizers may have the greatest

c·



opportunity to improve attitudes towarddivergent thinking. Also, organizations maybe able to target their training efforts moreeffectively by selecting optimizer stylemembers preferentially for training.

REFERENCES

Altemeyer, R. (1966). Education in the arts and sci-ences: Divergent paths. Doctoral dissertation, Car-negie Institute of Technology, Pittsburgh.

Basadur, M. S., Wakabayashi, M., & Graen, G. B.(in press). Identifying individual differences increative problem solving style. jou1"1wl of CiratiueBehavior, 24.

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Basadur, M. S., & Finkbeiner, C. T. (1985). Meas-uring preference for ideation in creative prob-lem-solving training. journal ofAp/Jlied BehauioralScience, 21, 37 -49.

Basadur, M. S., Graen, G. B., & Green, S. G. (1982).Training in creative problem solving: Effects onideation and problem finding and solving in anindustrial research organization. OrganizationalBehavior and Human Performance, 2(}, 41- 70.

Basadur, M. S., Graen, G. B., & Scandura, T. A.(1986). Training effects on attitudes toward di-vergent thinking among manufacturing engi-neers.journal of Applied Psychology, 71,612-617.

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Cronbach, L. J. (1951). Coefficient alpha and theinternal structure of tests. Psvclunnctrika, 16,297-334.

Doktor, R. (1970). The deurlopment and mapping ofcertain cognitive styles of prohlem solving. Doctoraldissertation. Graduate School of Business, Stan-ford University.

Kcrlingcr. F. N. (1973). Foundations 4 brluuuoralrcsrarrh, New York: Holt, Rinehart & Winston.

Kirton, M. .J. (1976). Adaptors and innovators: Adescription and measure . .I0U1"1W/ or ANJlin/ Psy-cholog», 61, 622-629.

MacKinnon, D. W. (1977). Foreword. In S. Parnes,B. Nolin, & A. Biondi (Eds.), Cuid« to creativeaction (pp. xiii). New York: Charles Scribner'sSons.

~ intzherg, H. (1973). Till' nature or lIul1lagnial work.New York: Harper & Row.

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