Creativity Enhancement in Problem Solving

8/6/2019 Creativity Enhancement in Problem Solving

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Creativity Enhancement in Problem Solving: Through Software or Process?

Author(s): George M. Marakas and Joyce J. ElamSource: Management Science, Vol. 43, No. 8 (Aug., 1997), pp. 1136-1146Published by: INFORMSStable URL: http://www.jstor.org/stable/2634575 .

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Creativity Enhancementn Problem SolvingThrough Software o r Process?

George M. Marakas * Joyce J. ElamDepartmentof InformationSystems, Collegeof Business Administration,University of Maryland, CollegePark,Maryland20742

Departmentof Decision SciencesandInformationSystems, Collegeof Business Administration,Florida nternationalUniversity,Miami, Florida33199

T his paper reports the results of a controlled laboratory experiment in which the work ofElam and Mead (1990) was both replicated and extended to explore how knowledge and

use of a creativity-enhancing process employed both manually and delivered via computersoftware affect the level of creativity in response to a problem-solving task. The results suggestthe enhancement of creativity in response to open-ended problems is significantly affected bythe process employed by the decision maker rather than the vehicle by which the process isdelivered. Further,the results indicate that the capability of a decision support system (DSS) toprovide directed guidance in the application of a process combined with user knowledge of theunderlying process model improves creativity enhancement over use of either the DSS or theprocess alone.(CreativityEnhancement; roblem-solving;Decision Support;DSS Design)

1. IntroductionThe rate of change in both what we know and are ex-pected to do in the course of our daily lives is rapidlyaccelerating. Many of today's seemingly common prob-lems require more unique approaches than those pro-vided in the past. Compounding the obstacles associ-ated with finding solutions to new problems is the factthat we can easily become blinded by our own thinkingand are often unable to break out of our customarysolution-seeking processes (Tversky and Kahneman1974, Simon 1969). In the last decade, the informationsystems (IS) research community has contributed sig-nificantly to the amount of reflection, research, and writ-ing associated with the stimulation and enhancement ofcreativity by focusing attention on increasing our un-derstanding of the relationship between creativity inproblem solving and the uses of computer technologyas a vehicle for its achievement (Young 1983; Proctor1988; Elam and Mead 1987, 1990; Couger 1990; Coll,Coll, and Rein 1991; Durand and VanHuss 1992;MacCrimmon and Wagner 1992, 1994).

A study conducted by Elam and Mead (1990) demon-strated the presence of a relationship between the use ofcomputer software intended to enhance the level of rela-tive creativity in responses to open-ended problems andthe measured level of relative creativity n these responses.The results of their study led to two primaryconclusions:(1) a computer-based decision support system (DSS) caninfluence the decision process adopted by its user, and (2)the use of software designed with the intention of im-proving its user's relative creativity can lead to greaterrelative creativity in the solutions than those producedwithout the aid of such software. While the Elam andMead study suggested that the software was the primaryfactor relating to the enhancement of relative creativity,it remains unclear whether the capabilities of the soft-ware served to affect the final solutions or whether thecreativity-enhancing process model embedded withinthe software was the primary contributor to enhancingthe level of relative creativity in the responses.

Through a controlled laboratory experiment, thisstudy replicated a portion of the work of Elam and

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MARAKAS AND ELAMCreativityEnhancementn ProblemSolving

Mead (1990) and additionally extended their work byexploring how knowledge and use of a creativity-enhancing process employed manually in a problem-solving task as well as delivered via computer softwareaffects the enhancement of relative creativity. Three hy-potheses, focusing on the effect of creativity-enhancingsoftware and the use of a manual creativity-enhancingprocess model for decision making, were tested.

2. Creativity Enhancement inProblem-SolvingCreativity has been referred to as one of the most vague,ambiguous, and confusing terms in education and psy-chology. Ancient views of creativity were closely relatedto the concept of geniuses, who were thought to be bornrather than made. The definition of creativity hasevolved, however, from such ancient views and has de-veloped itself into two main approaches (Table 1): (1)origin-oriented nd (2) process-oriented.

The origin-orientation pproach is generally more con-cerned with the origins of creativity than with what itactually is. The psychoanalytical perspective within thisapproach is best represented by Sigmund Freud whoheld that creativity arises from conflict within an indi-vidual. A stark contrast exists, however, betweenFreud's conceptualization and the process-orientationp-proach. While the former focuses on the origin of crea-tivity, the latter emphasizes the thought processes sur-rounding it. The various perspectives within theprocess-orientation view creativity as a property of athought process that can be acquired and improvedthrough instruction and practice (Ripple 1989). Thissuggests that the potential for creative thinking exists to

a greater or lesser degree in everyone (Fredricksen 1984,Hocevar 1981).

An important perspective within the process-orientation to this study is the InformationProcessinggroup. Proponents of this perspective see creativity inthe problem-solving process as present when one ormore of the following conditions are satisfied: (1) thesolution is novel and valuable, (2) the thinking is con-sidered unconventional, (3) the initial problem is vagueor ill-defined, and (4) the solution process requires highmotivation and intensity and generally takes place overa considerable span of time (Newell et al. 1962, Mum-ford and Gustafson 1988). Additional supporters of thisperspective see creativity as present when a solution isconsidered meaningful rather than random or idiosyn-cratic (Richards et al. 1988) or when the right combi-nation of particular problem-solving skills and individ-ual and social elements come together (Tardif andSternberg 1988).Although no widely agreed upon definition of crea-tivity exists (Ripple 1989), the definition provided byAmabile (1983), as adopted by Elam and Mead (1990),was employed in this study. Amabile states that a prod-uct or response will be judged as creative to the extentthat it is a novel and appropriate, useful, correct or valu-able response to the task at hand.

Amabile (1983) proposed a five-step model of the cre-ative process that closely parallels many of the stepwisedecision models present in the literature. Amabile'smodel contains the following steps: (1) Problemor TaskPresentation,where a specific problem statement is for-mulated, (2) Preparation,where recall of informationand solution approaches that appear to be relevant tothe problem at hand occurs, (3) Response Generation,

Table1 TheoreticalApproachesand Perspectives to CreativityORIGIN RIENTATION PROCESS RIENTATIONPsychoanalytic Associationist

Creativity rises from conflict withinan individual. Creativitys the act of associating remote responses to a problemHumanistic such that they create a new response.

Creativityrises from the removalof conflictand inhibition. GestaltPsychometrician Creativity esults from a conscious redirection f thoughtprocesses

Creativitys limitedby genetic endowmentbut can be measured. and can be learned.Information rocessing

Focuses on the developmentof information rocessing models ofproblem-solving nd creativity.

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where alternative solutions are produced, (4) ResponseValidation, where the alternatives solutions are evalu-ated, and (5) Outcome, where a successful solution isselected, no acceptable solution is found, or a conclu-sion is drawn that a loopback to a previous process stepis necessary. As with the Elam and Mead (1990) study,this research adopted the model of the creative processas proposed by Amabile (1983).'

The partnership between the computer and the prob-lem solver is celebrated as being one of desirability, ifnot necessity, in our fast-paced environment. Recently,a great deal of research has been conducted with regardto the integration of IS technology in the decision pro-cess (Coll et al. 1991, Forgionne 1991, Te'eni and Ginz-berg 1991) and in the use of decision support systemsas computer-based assistants (Jacobet al. 1989). In gen-eral, this stream of research represents the computer asbeing able to support and amplify the skills of decisionmakers faced with complex, unstructured problems. Sil-ver (1991) argues, however, that research in this areahas been somewhat fragmented and generally techno-logically oriented rather than focusing on the behavioraleffects of the technology use. As such, one finds nu-merous supporters for IS technology as a vehicle forenhancement of the decision process but little empiricalevidence to support their claims.

Recent efforts (cf. MacCrimmon and Wagner 1992,1994; Durand and VanHuss 1992) serve as evidence of amore concerted effort to obtain such empirical evidence,however. In addition to this increased focus on individ-ual creativity, recent research into the study of creativityenhancement in group problem-solving environments isappearing (Connolly et al. 1990).In summary, the recent research into enhancement ofidea generation and creativity through the computer gen-erally suggests the existence of a positive effect on the out-comes. To this end, a number of software programs havebeen developed with the intention of enhancing the levelof creativityin response to problem-solving tasks.2

' A more detailed description of the individual component steps of thecreativity model and its influencing factors can be found in Amabile(1983) and in Elam and Mead (1990).2Though not a comprehensive listing, examples include: Quad Micro-systems BRAIN,ESL's IdeaGenerator nd Thoughtline,Mindlink, Inc.'s,Mindlink, IdeaBank's, IdeaFisher,Process Consultant's ORACLE,andOrganizational Development's od/CONSULTANT.

3. HypothesesThe independent variables in this study are the use ofcreativity-enhancing software and knowledge of thecreativity-enhancing process delivered by the software.While a number of creativity-enhancing software pack-ages are currently available for a variety of platforms,Process version 1 in od/CONSULTANTwas the softwaretreatment of choice in the Elam and Mead (1990) studyand the desire to replicate and extend their work dic-tated its use in this study. A series of design specifica-tions for a DSS intended as a creativity-enhancing tool(see Elam and Mead 1987) was used in the selection ofod/CONSULTANT.Although the focus of the presentstudy was not concerned specifically with DSS per se,the rigorous selection process employed by Elam andMead (1990) in the selection of the software makes odlCONSULTANT an excellent example of a typicalcreativity-enhancing software package. Process 1 gen-erated the highest level of creative responses in the Elamand Mead (1990) study and, as can be seen in Figure 1,bears a comparable stepwise and functional resem-blance to both the general model of decision makingproposed by Coll et al. (1991) and to Amabile's (1983)model of the creative process.3

The dependent variable is the relative degree of cre-ativity generated by the decision maker in the solutionto an open-ended problem. One important researchquestion in the Elam and Mead (1990) study waswhether the use of software intended to enhance crea-tivity actually serves to provide such enhancement. Thisresearch effort extends that question by exploringwhether the software provides the primary creativityenhancement or simply serves as a structuring facilityfor the embedded creativity-enhancing process. Moti-vation for hypothesizing a positive relationship be-tween use of a specific process for enhancement of cre-ativity is rooted in the general beliefs of the process-oriented perspective of creativity (Mumford andGustafson 1988, Tardif and Sternberg 1988), which ar-gues that creativity can be learned and developed

3All further use of the word software,unless otherwise stated, will referspecifically to od/CONSULTANT.In addition, the term process,unlessotherwise stated, will refer to the specific creativity-enhancing process(Version 1) embedded within the software package.

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MARAKAS AND ELAMCreativityEnihancementn ProblemSolving

Figure1 Comparison f Software Process Modelwith GeneralModelof DecisionMakingand Amabile'sModelof Creativity(GeneralModel Amabile'sModelod/Consultant of ofDecidon-Making CreativityDescribeProblem PoblemG,ather Awareness PreTsenatoFactsPrsnair

Assess ~~~~~~~ProblemPrpatoRdevanc-y AnalysisPepato/01re,FactsDevedop Alternative RespnseatoExplanation 3eeration Gnrto

Test Afternative RsosExplanation Evaluation Validation

Identiy SokutlonSolutions Selection

ImpoementFdiow-tIpFine uning

through systematized approaches to problem solving.As such, the following hypothesis was advanced:

HYPOTHESIS 1. Subjectsusing either the softwareor theprocess will generatea relativelyhigher degreeof creativityin their solutionsthan subjectswho do not.

To answer the question of whether the software orthe embedded creativity-enhancing process providesthe primary enhancement to the level of creative solu-tions requires that the effect of the creativity-enhancingprocess embedded within the software be isolated fromthe software test. If the contention that the software isthe primary factor in the enhancement of creativity istenable, then it seems logical that no knowledge of theprocess contained within the software should be nec-essary for creativity enhancement to occur. If, however,the process is the primary contributor to creativity en-hancement, it seems equally logical that a thorough un-derstanding of the process should serve to heighten thecreativity-enhancing effects of the process. It followsthen that the manual application of the creativity-enhancing process using a distinct sequence of steps inarriving at a response to an open-ended problem solv-ing task should, therefore, result in higher levels of rel-ative creativity in the solutions generated.

HYPOTHESIS 2. Subjectswho receive nstruction in theprocess will generatea relativelyhigher degreeof creativityin theirsolutions thansubjectswhodo notreceive nstructionin theprocess.

Quite often discussions of computers and softwareare focused on the information technology's utility andcontribution to productivity. Despite the prevalence ofthis forum, however, questions still remain regardingthe computer's actual contribution to productivity andthe actual improvement to task performance and du-ration (Banker et al. 1989). What is less often noted isthat many people see the computer not just as a practicaltool but as a source of pleasure as well (Webster andMartocchio 1992, 1993).

Computers are undeniably liberating. Children canescape from their world of structure and obligation byimmersing themselves in a computer game that hope-lessly befuddles the adults around them. Writers canescape the tedium of typing draft after draft and caninstead sculpt and hew their sentences and words to ahigh level of refinement. Engineers can repeatedly buildand tear down intricate mechanisms in an attempt toachieve the exact end-product without ever setting footin, or waiting for, a machine shop. The essence ofcomputer-using pleasure appears to be the machine's



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responsiveness (Webster and Martocchio 1992, 1993).Elam and Mead (1987, 1990) extended this pleasurable"sense of liberation" to imply a sense of freedom on thepart of the problem solver to be involved with the taskat hand in whatever way he or she chooses. Further,they posit that such an environment of freedom willallow for deeper task involvement and will create asense of playfulness that is believed to be critical to thecreative process (Koestler 1964, Proctor 1991).

Support for this perspective is not limited to con-ceptual or anecdotal arguments but exists empiri-cally, as well. Numerous scholars have studied theeffects of process management on the cognitive effortassociated with the performance of a decision-makingtask (cf. Johnson and Payne 1985). More specifically,work by Gallupe et al. (1988), Jarvenpaa (1989), Toddand Benbasat (1991), and Webster et al. (1993) fo-cused on the role played by the computer in manag-ing all or part of the underlying process associatedwith solving a complex decision task. These collectiveworks provide strong evidence that the computerserves as more than a simple delivery mechanism orstructuring device in a decision-making environmentand that cognitive effort can be reduced, thus allow-ing the decision maker to focus more effectively onthe solution, when appropriate application of auto-mated resources and display formats are utilized.

HYPOTHESIS 3. Subjectswho receive nstruction in theprocessand who use thesoftwarewill generate olutions at arelativecreative evelhigherthan thosesubjectswho receiveinstruction n theprocessand applyit without the aid of thesoftware.

4. MethodSoftware treatment was manipulated over two levels:(1) a no software treatment, where the subjects weresupplied with pencil and paper for note taking, but nouse of software, and (2) a software treatment where sub-jects made use of the creativity-enhancing software.Subjects were randomly assigned to one of the fourgroups (see Figure 2) and were recruited from a widevariety of sources, including systems professionals froma local information center and senior undergraduateand graduate MIS students from a local university. Allsubjects using the software received a brief descriptionof the use of the help facilities contained within the soft-

ware and were allowed to freely explore the variousmenus and command structures prior to receiving thetask. No information regarding the intended applica-tion of the software other than that of a decision aid wasprovided, however.

Two levels of decision process treatment were alsomanipulated: (1) a no-instruction treatment, where thesubject received no instruction in the use of thecreativity-enhancing decision process under the study,and (2) an instruction treatment level, where the sub-jects received instruction in the creativity-enhancingprocess.4

All subjects were told that the experiment dealt withproblem-solving skills and methods and that theywould be solving problems that are open-ended situa-tions with no right or wrong answer. The task wasadapted from the business-related task used by Elamand Mead (1990).5 At no time before, during, or afterthe experimental session was creativity discussed withthe subjects. This was to prevent the subjects from tryingto be creative in their response generation, thus possiblyconfounding the results. Each treatment group wastested during a separate experimental session and thesubjects were individually isolated during the problem-solving activity.

For groups T3 (no software/process training) and T4(software/process training) a request was made thatthey attempt to follow the sequence of the problem-solving process presented to them during their training6and to make as much use of it as possible in performingthe task. While this procedure may raise some questionof method bias in the final results, it was neverthelessfelt to be an essential element in isolating the processfrom the delivery mechanism.

Since the relative level of creativity across each groupis the dependent measure, a method of ensuring that nogroup possessed a significantly higher level of creative

4 The creativity-process referred to is the sequence of steps containedin Version 1 of the old CONSULTANT oftware package (and listed inFigure 2). The experimental treatmentsubjectswere given a brief train-ing session regarding the application of each step in the sequence inreaching a solution to a problem-solving task.5 The complete text of the task and experimental materials used in thisstudy is available upon request from the first author.6 The process sequence referred to is that of Version 1 in old CON-SULTANTas shown in Figure 1.

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Figure FlowDiagramf ExperimentalroceduresNoSoftware Problem Adjecve Post-TestTi NoProcess OrientaUon Solving Checklist SurveyLFL. .J ~~~~~~~~~~~~~~~~~~~TaskH

T2 Software Software Problem Adjecve Post-TestT2 Only Orientation Introduction Solving Checklist SurveyT3 ~~~~~~~~~~~~~~~~~~~~~~~TsBothSoftwar Process Request o Software Problem Adjectve Post-TestT4 andProcess Orientation Training UseProcess introducton Solving Checklist Survey

potential than the others was needed. The AdjectiveCheck List was used as a measure of baseline creativepotential (Gough 1979). This widely used instrument(cf. Domino 1970) asks the subject to indicate which ad-jectives out of a total of 300 presented best describe hisor her personality characteristics. Within this list, 59 ad-jectives have been identified that serve to statisticallydifferentiate between known groups of high and lowcreatives with a remarkably high degree of discriminantability. Upon completion, the subject's raw score of thenumber of key adjectives checked is converted to anACL score using a simple table. ACL scores are linearin nature and can range from 1 to 126 representing alow to high potential for creativity. The results obtainedfrom the checklist indicated that no significant differ-ences across groups regarding baseline creative poten-tial were present.

Finally, a brief demographic questionnaire was com-pleted by all subjects. The instrument collected data re-garding level of education, number of years of profes-sional business experience, age, range, years of personalcomputer experience, and overall comfort level with apersonal computer. In addition, this instrument askedthe subjects to respond to a series of yes/no questionsregarding the process they used in solving the problemtask. The responses to these questions were used alongwith a review of any notes generated by the manualsubjects or computer logs generated by the softwaresubjects during the experiment as a manipulation checkto determine if the subjects were using the creativity-enhancing process where expected.

5. OperationalizationandMeasurement ProceduresMeasurement of the dependent variable employed atechnique of consensual assessment developed by Ama-bile (1982). Consensual assessment of creativity is a sub-jective technique that calls for a group of qualifiedjudges to independently assess the relative creativity ofa set of responses (Elam and Mead 1990). Amabile(1982) defines appropriately qualified judges as beingfamiliar with the domain in which the product is cre-ated or the response is articulated. Thus, creativity canbe regarded as the quality of products or responsesjudged to be creative by appropriate observers, and itcan also be regarded as the process by which somethingso judged is produced. Provided the interraterreliabil-ity of the judges is high, Amabile argues that we mustaccept the validity of their judgment (Amabile 1982).Variations of this assessment technique are commonlyused to judge competitors in athletic events that involvethe relative appraisal of multiple criteria to construct afinal score. Further, the ability of independent judges todistinguish creativity from other constructs such as in-telligence, achievement, and originality has been em-pirically demonstrated (Bachelor 1989).

Three judges were used in this experiment.7 Each ofthe judges received a packet containing all responsesgenerated by the subjects transcribed so as to appear

'All judges were members of the Business School faculty at a localuniversity.



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similar in format. The only identifying marking on thepage was the unique subject identification number. Thejudges were instructed to use their own subjective def-initions of creativity and were reminded that their ob-jective was to provide a relative creativity ranking thatreflected a comparison only among the responsesjudged and not against any absolute standard orreferent.

A single overall ranking was calculated from themean of the judges' ranks for each subject's response. Aset of interrater reliability indicated a significant levelof agreement among the three judges (X2 = 79.8585, df= 39; W = 0.6826, p = 0.0001;a = 0.7660). This level ofagreement is in keeping with the measure obtained byElam and Mead (1990) with a much smaller sample size(n = 12). In addition, no significant relationship ap-peared between the creativity ranking and word count(r = 0.0865; p = 0.596). These results suggest that nobias resulting from the length of the response enteredinto the judge's decisions regarding relative ranking.

6. ResultsNo significant differences were found among the treat-ment groups with regard to demographic characteristics

or baseline creativity scores (F = 0.690, p = 0.564). Inaddition, the answers to the binary response questionson the posttest questionnaire were used as manipula-tion checks to make inferences regarding the degree towhich the process was followed. Further, the computerlogs generated by the software groups were reviewedfor evidence of process utilization. Sufficient evidencewas collected to conclude that all subjects who wereexpected to use the specific creativity-enhancing processunder test did so.

Table 2 displays the mean creativity rankings foreach of the treatment groups. The results of a Kruskal-Wallis one-way ANOVA test showed that differences inthe relative creativity of responses between the fourcells were highly significant (X2 = 22.8160, df = 3, p< 0.0001). These results parallel those obtained by Elamand Mead (1990) using the same version of the softwareand suggest support for Hypothesis 1. Further, the re-sults of the post-hoc comparisons of the individualtreatment group means show all software and processtreatment group means to be significantly differentfrom the control group means, thus providing furthersupport for Hypothesis 1.

The mean creativity ranks for both T3 and T4 werealso found to be significantly different (p < 0.05) from

Table 2 CreativityRankingsHi: Subjects using either he softwareor the process will generatea relatively igherdegreeof creativityn their solutions thansubjectswho do not.H2: Subjects who receive nstruction n the process will generatea relatively igherdegree of creativityn their solutions than subjectswho do not receive

instruction n the process.H3: Subjects who receive nstruction n the process and who use the softwarewill generatesolutionsat a relative reative evelhigher han those subjectswho receivenstructionn the process ndapply t withouthe aidof the software.

Kruskal-Wallis-WayANOVA:orrectedorTiesTreatment roup n MeanRank x2 D.F. p-value

Ti: No Software/Norocess 10 31.75 22.8160 3


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those of Ti and T2, indicating support for Hypothesis2. The difference in relative creativity between the com-bination groups of Ti, 2 and T3, 4 was also found to besignificant (U = 37.0, p < 0.0001 [corrected for ties]).

Hypothesis 3 was tested by comparing the mean cre-ativity rank of T3 with that of T4. The mean creativityscore for T4 was lower than that of T3 (T4 = 8.70 versusT3 = 16.00). The results of a Mann-Whitney U test showthe difference between the two treatment group meansto be statistically significant (U = 26.0, p = 0.0693).Based on these results, support for Hypothesis 3 wasfound at the 0.10 level. It is possible that the lack ofsupport for this hypothesis at the 0.05 level may be theresult of a smaller than necessary sample size to detectan effect of moderate magnitude. Nevertheless, theseresults provide evidence suggesting that while the pro-cess employed enhances the level of creativity in thesolution response, the software is serving as more thansimply a delivery mechanism for the embedded processmodel. It appears that the capabilities of the DSS toguide the delivery and application of a multistage de-cision process serve to materially enhance the consciousapplication and use of the underlying principles of thedelivered creativity-enhancing process model.

7. DiscussionWhile the results obtained in this experiment appearencouraging, several important limitations need to betaken into consideration in their evaluation. As in mostcontrolled studies, generalizability is a concern. Thestudy used a relatively straightforward problem taskfrom the general business domain. Actual business en-vironment decisions might involve many more alter-natives and constraints than those present in the task.In addition, no manipulation was present to make thesubjects feel any sense of personal responsibility for thefinal decision product. In other words, issues such asjob stability or job security did not come into play in thesolution selection process. These emotional forces mayhave a material effect on how a decision maker ap-proaches a problem such as the one used in the exper-iment. In addition, the generalizability of the currentresults is tempered by the rest of a single process and asingle software application. We have much to learnabout the human mind and its creative potential. Wehave, however, seen evidence of a change in relative

creativity associated with the application of the specificprocess chosen in a controlled environment.

Second, the sample size, while significantly largerthan in Elam and Mead (1990), may nonetheless besmall with regards to the measurement of more modestenhancement effects to the relative creativity in a solu-tion. As discussed previously, the moderate sample sizemay have contributed to the lack of .05 level supportfor Hypothesis 3.

Finally, the study of creativity is, admittedly, a com-plex area and its measurement can be expected to besomewhat difficult. While many studies, including thisone, have found success in the use of surrogate mea-sures of baseline creative potential and relative creativ-ity enhancement, we have yet to fully understand theconstruct with regard to developing truly effective andgeneralizable measures of human creativity. Future re-search must strive toward improvement of this issue ofmeasurement while simultaneously being cognizant ofthe limitations imposed by current measurement tech-niques.

Taken within the context and limitations of the study,it appears that the initial research questions have beenanswered. The improvement in the relative level of cre-ativity between groups Ti and T2 was in keeping withthe findings of Elam and Mead (1990). The larger sam-ple size and the focus on a single task used in this studyboth respond to limitations identified by Elam andMead (1990) and, therefore, serve to strengthen supportfor their conclusions. It seems clear that the capabilitiesof a process-oriented DSS to deliver an explicit modelfor enhancing creativity and to create an environmentconductive to such enhancement are effectual. Further,the results lend support to the proponents of a process-oriented perspective of creativity by suggesting that cre-ativity can be learned, externally supported, and en-hanced through such learning and support.

More notable, however, are the results obtained bygroups T3 and T4. The knowledge derived from beingtrained in a specific creativity-enhancing process com-bined with conscious application of the process showedimprovement in the relative level of creativity over theunconscious use of the process in an automated envi-ronment (T3 and T4 versus T2). The problem fragmen-tation and reformulation inherent in the process al-lowed for a redirection of the problem solver's thoughtprocesses believed to be essential in the enhancement of

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creativity. Further, the knowledge gained from beingtrained in the creativity-enhancing process may haveserved to make the subjects more aware of certain self-imposed constraints to their own problem-solvingmechanisms, thus allowing them to exert more controlover the process.

The performance of group T4 (process knowledgeand software use) may be best explained by the concep-tual work of Silver (1991) and Elam and Mead (1987)with regard to guidelines for DSS design. Elam andMead (1987) proposed a reclassification of the compo-nents of Amabile's (1983) theoretical framework of cre-ativity into two segments (see Table 3): (1) internalfactors-those directly related to personality and cog-nitive characteristics of the DSS user and (2) externalfactors-those more directly influenced by the DSS en-vironment. The resultant guidelines for DSS design fo-cused on the inclusion of the external factors related tocreativity enhancement.

It is clear that the majority of the external and envi-ronmental factors shown in Table 3 were present invarying degrees in each of the experimental groups. Forexample, all subjects exposed to the application of thecreativity-enhancing process were beneficiaries in vary-ing degrees of all of the external factors thought to con-tribute to creative outcomes. Any differences within thegroups regarding the effect of such factors on their so-lutions can be argued to be the result of differences inboth the understanding of the process involved and theconscious application of that understanding in reachinga solution product.

A stronger explanation, however, comes from a re-view of the presence of the environmental conditionsacross the three treatment groups, the degree to whicheach was present being quite different. First, task mo-tivation and perceived task control may have been in-creased through the process training received by T3 and

T4. The T2 subjects were not presented with any train-ing other than a brief introduction to the menuing andhelp mechanisms of the software whereas the T3 andT4 subjects received training on the application of a spe-cific problem-solving process. Elam and Mead (1987)suggest that this perceived control nurtures a deeperinvolvement in the task, which serves to enhance crea-tivity. The T2 subjects would presumably not feel thesame level of control over the task given that their at-tention had to be divided between reaching a solutionand becoming comfortable with the software environ-ment.

A higher level of stress in T3 subjects over T4 subjectsmay also contribute to the performance of Ti (cf. Jar-venpaa 1989, Todd and Benbasat 1991). The manualmanipulation may have created a more "testlike" en-vironment in the subjects than would have been createdby the software. Thus, despite their understanding ofthe process to be applied to the task, the T3 subjectslikely faced increased stress through the conditions im-posed on them and possibly fatigue and boredom. TheT4 group was given an environment that fostered cre-ativity and increased competence while minimizing thestress-related conditions associated with subjects in T3.

One additional explanation for T4 can be found inSilver's (1991) typology of deliberate decisional guid-ance in DSS design. Silver's framework, shown in Table4, outlines the dimensions available to designers inwhich directed guidance can be built into a DSS envi-ronment.

Silver asserts that the objective of directed support isnot to point to a specific outcome, but rather to stronglyinfluence the way it is reached. A surprising number ofparallels can be drawn between Silver's framework andthe experimental manipulations. First, the targets ofguidance (both structuring and execution) were presentin escalating degrees for the three treatment groups.

Table 3 Reclassificationof Amabile's 1983) ComponentialModelof CreativityINTERNALACTORS EXTERNALACTORS ENVIRONMENTALONDITIONS

DomainRelevantKnowledge Creativity elevantSkills Task MotivationCreativePersonalityTraits BreakingPerceptual et Incubation

BreakingCognitiveSet RetainedControlOverDivergentThinking ProblemSituationDelayedJudgment A Sense of Competence

Stress

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Table4 Silver's (1991)Typologyof DeliberateDecisionalGuidanceTARGETS FORMS MODES

(WhichAspects of Decision-Makinghe (What he GuidanceOffers (How he GuidanceMechanismWorks)GuidanceAddresses) DecisionMakers) Predefined

Structuringhe Decision-Making rocess Suggestive Guidance Dynamic(choosing operators)Executing he Decision-Making rocess InformativeGuidance Participative

(using operators)

Each succeeding manipulation provided a greateramount of directed guidance toward enhancement ofcreativity in both structure and execution with the com-bination of process knowledge and software environ-ment (T4) providing the greatest degree.

Second, both formsof directed guidance were presentin proximate degree. Either through software, training,or a combination of both, all experimental subjects re-ceived both suggestiveand informativeguidance in boththe structuring of the process and its subsequent exe-cution. The primary difference in form of directed guid-ance among the three groups lies with the presence orabsence of training intended to heighten the subjects'understanding of the application of the process. Thisserved to increase the degree of suggestive guidanceduring both structuring and execution of the process byinforming the subjects where to go next and how to goabout organizing or reconciling the data in each step.

Finally, the modeof directed guidance was clearly pre-defined or all treatment groups. Again, it appears thatthe difference between the groups lies with their degreeof understanding of the application of the process com-bined with the capability of the DSS to both deliver theprocess and create an environment conducive to crea-tivity. Silver (1991) asks if directional guidance can beimplemented. The results of this study suggest thatit can.

8. ConclusionThe challenge raised by the work of Elam and Mead(1987, 1990) and many others is for research to pursuean experimental approach toward the extension of ourknowledge of how creativity is influenced by decisionsupport systems. In parallel, researchers such as Silver(1991) mandate that research investigate the effects ofspecific design criteria on all aspects of the decision-

making process and its outcomes. This study representsa singular response to those challenges. As with mostempirical efforts, the results presented here provide abasis for more extensive and targeted research efforts.

Future research in the enhancement of constructssuch as creativity, originality, and decision-making ef-ficiency and effectiveness must concentrate on the typeand degree of support needed within each step of aproblem-solving task rather than continuing to focus onthe more macro-level approach to supporting the entireprocess. An emphasis on increasing our understandingof decision making from an effort perspective ratherthan from a purely stepwise factor approach becomesimportant to furthering our understanding of the rolethat the computer can play in reducing the effort asso-ciated with solving a complex problem.

Though modest in scope, this study has providedsome additional insight into our efforts to understandthe link between creativity and problem solving. It ishoped that the results obtained here will encourage re-searchers to continue a vigorous and systematic agendain the area of creativity enhancement.88 The authors wish to thankJon Beard,Nicole Wishart,MartinSantana,the associate editor, and the four anonymous reviewers for their con-sistently insightful comments and suggestions bringing this manu-script to publication.ReferencesAmabile, T. M., "Social Psychology of Creativity: A Consensual As-sessment Technique," J. Personalityand Social Psychology, 43, 5

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Acceptedby JohnC. Henderson; eceivedDecember , 1994. Thispaperhas beenwith theauthors7 months or 2 revisions.

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