Journal of Education for Business
ISSN: 0883-2323 (Print) 1940-3356 (Online) Journal homepage: http://www.tandfonline.com/loi/vjeb20
Building Skills in Thinking: Toward a Pedagogy in
Metathinking
Victoria Crittenden & Arch G. Woodside
To cite this article: Victoria Crittenden & Arch G. Woodside (2007) Building Skills in Thinking: Toward a Pedagogy in Metathinking, Journal of Education for Business, 83:1, 37-44, DOI: 10.3200/JOEB.83.1.37-44
To link to this article: http://dx.doi.org/10.3200/JOEB.83.1.37-44
Published online: 07 Aug 2010.
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he way chief executive officers (CEOs) draw conclusions and makedecisionscanhavedisastrouscon-sequences. For example, government CEOs GeorgeW. Bush (United States), Tony Blair (United Kingdom), John Howard(Australia),andadditionalcoali-tion country CEOs concluded in early 2003 that Iraqi leaders had weapons of mass destruction and were refusing to disarm these weapons. Thus, these governmentCEOsmadethedecisionto declarewaronIraq.Inmid-2004(more thanayearafterthewarwasdeclaredas officiallywon),theCEOshadfoundno weapons of mass destruction, close-to-full-blowncivilwarwasraginginIraq, andworldterrorismhadincreased.Amid all of this, the U.S. CEO reported that therewereweaponsofmassdestruction.
At a more mundane level, consider whyhighlysuccessfulfirmssuchasPola-roid and Lucent became failures. The coverstoryoftheMay27,2002issueof Fortune magazinedescribed10bigmis-takes as the primary reasons why com-paniesfail,withacriticalmistakebeing that people presume that the future will begoodonthebasisofhistoricalsuccess-es rather than planning for unexpected changes(Charan,Useem,&Harrington, 2002).WeickandSutcliffe(2001)made thesameobservationintheiraptlytitled monograph,ManagingtheUnexpected.
These examples point out the obvi-ous:thatCEOs,middlelevelmanagers,
andtherestofusarepronetodrawing inaccurateconclusionsandmakingbad decisions. It is unfortunate that such thinking (a) occurs frequently, (b) can be very expensive, (c) often wrecks localeconomies,and(d)cancausemas-sive layoffs, terror, or even death (for reviewsonthispoint,seeBaron,2000; Bazerman,1998;Gilovich,1991).Rath-er than shaking our heads or pointing fingersatsomeoneelse’sbaddecisions, peopleshouldidentifytoolstoimprove the accuracy and quality of decisions (cf.Martz&Shepherd,2003).Inother words, what tools will help decision makersbecomemorecognizantoftheir decisionprocessesandoutcomes?With the high frequency and seriousness of baddecisionmaking,thecreation,test-ing, and teaching of tools to improve thinking (i.e., increasing sensemaking quality in becoming aware, acquiring knowledge,interpretingdataandinfor-mation,drawingconclusions,deciding, and evaluating) are important in the businessschoolclassroom(cf.Shadish, Cook,&Levitton,1991;Weick,1995). MarchandOlsen(1976)observed, “Individuals and organizations make sense of their experiences and mod-ify behavior in terms of their inter-pretations” (p. 56). Weick (1995) referred to this assensemaking, and he described sensemaking as “how peoplegeneratethatwhichtheyinter-pret”(p.13).
BuildingSkillsinThinking:Towarda
PedagogyinMetathinking
VICTORIACRITTENDEN ARCHG.WOODSIDE BOSTONCOLLEGE
CHESTNUTHILL,MASSACHUSETTS
T
ABSTRACT.Mostmanagersdonotreceiveformaltraininginmetathinking— thatis,theyarenottrainedformallyin thinkingaboutthinkingorinthinkingabout deciding.Inthisarticle,theauthorsreview thelackofeducationalfocusonmetathink-ingandsuggestseveraltoolsforimproving thedecision-makingprocessandforskill buildinginmetathinking.Thetoolsinclude twoexperientialexercisesthatfacilitate learninginmetathinking.
Keywords:decisionanalysis,learning theory,metathinking,pedagogy
Copyright©2007HeldrefPublications
Parry (2003) suggested that sense- makingisaprocessinwhichindividu-alsandgroupsinorganizationsorganize their experiences about reality. As an example,considerthequestion,“What’s reallyhappening?”Thisquestiongener-allycontainsfoursubissues:
1.What actions being done now help improvetheorganization’sperformance?
2.What actions are wasted motions (i.e.,whatactionsarewetakingthatdo notcontributebutdowasteourtime)?
3.What actions harm the organiza-tion’s performance (i.e., what actions are counterproductive in the organiza-tion’sachievementofwhatreallyneeds to be overlooked in this process is more important. How does one go abouttheprocessoffindingoutwhat isreallyhappening?An implicitmen-tal model that decision makers often useisoneinwhichthepersonbelieves thatwhatcomestomindfirstisaccu-rate(Senge,1990).
Itseemsthattheprocessofinterpretation is so reflexive and immediate that we often overlook it. This, combined with the widespread assumption that there is butoneobjectivereality,iswhatmaylead people to overlook the possibility that others may be responding to a very dif-ferentsituation.(Gilovich,1991,p.117; cf.Surowiecki,2004)
Thisfifthsubissuerequiresindividu-alstoengageinmetathinking.Leffand Nevin(1990)describedmetathinkingas thinkingandcreatingstrategiestoassist one’sthinking.
thinkinginrelationtodecisionquality. Then, we suggest a simple classroom example that engages students in the thinking-about-thinkingtopic.Wepro-videtworigorousexperientialexercise examples that are applicable in the businessschoolclassroom.Inthenext section, we discuss classroom use of the exercises and offer a brief list of recommended readings. We conclude withalookatthedifferencesbetween scientificandexecutivethinking.
ImprovingtheQualityof Decisions
Traditionally, educators have not included formal training in metathink-ing in the core (or even elective) busi-ness education curriculum. Few aca-demic programs, including those in executiveeducation,includecoursesin thinking about thinking or in thinking about deciding. Therefore, few busi-nessstudentsparticipateincoursesthat focusonacquiringprescriptivetoolsfor improving the quality of thinking and deciding(e.g.,Sterman,2001).
Two reasons may be responsible for thislackofeducationalfocusandtrain- ing.First,overconfidencebiasiswide-spread: Most executives tend to rely toooftenontheirunconsciouslydriven automatic thoughts (Bargh, Gollwitzer, Lee-Chai, Barndollar, & Troetschel, 2001; Wegner, 2002). There is a natu-ral tendency to assume that intuitive beliefsareaccurateandthatrelyingon externalheuristics(e.g.,writtencheck-lists,explicitprotocols)isunnecessary. People’s initial response is often one ofdisbeliefandresentment,evenwhen presented with hard evidence that for-
malexternalsearchingofrelevantinfor-ToolsforImprovingThinking Quality
Regarding the quality of decisions, Gilovich(1991)stated,
Afundamentaldifficultywitheffective policy evaluation is that we rarely get to observe what would have happened if the policy had not been put into effect.Policiesarenotimplementedas controlledexperiments,butasconcert-ed actions. Not knowing what would havehappenedunderadifferentpolicy makes it enormously difficult to dis-tinguish positive or negative outcomes fromgoodorbadstrategies.Ifthebase rate of success is high, even a dubious strategycanbeseenaswise;ifthebase rateislow,eventhewiseststrategycan seemfoolish.(pp.41–42)
Severalusefultoolsarenowavailable forimprovingsensemakingcapabilities (e.g., Baron, 2000; Gigerenzer, 2000; Green, 2002; Green & Armstrong, 2004).These tools include (a) estimat-ingwhatwouldhappenifapolicyhad notbeenputintoeffect(e.g.,Campbell, 1969) and (b) software programs that help structure problems and test the impactofalternativeproblemstructures (e.g.,Clemen&Reilly,2001).
Training in metathinking may help overcome fundamental attribution error. Fundamental attribution error is the tendency of a person to blame other peopleorenvironmentalforcesforabad decisionratherthanrecognizingthatthe processthatthepersonappliedtoreacha decisionreflectsshallowsystemsthink-ing (Plous, 1993). “When we attribute behavior to people rather than system structure, the focus of management becomes scapegoating rather than the design of organizations [and problem-solving procedures] in which ordinary peoplecanachieveextraordinaryresults”
ficialinthelearningprocess.Inan expe-rientialexercise,learnerscanconstruct a world by combining past informa-tionwithfuture-orienteddispositionsto activelyengageinthelearningprocess (cf.Kolb,1984).
Classroom-Oriented,Experiential ExercisesinMetathinking
Thinking about how one thinks is not an easy topic of discussion among students,regardlessofwhethertheyare undergraduates, graduates, or execu-tives.Bynature,thetopicof“thinking” is vague and hard to grasp. Davenport (2004)suggestedabasic,yetprovoking,
A natural response is, “Well, I just know that!” This drives home the pri-mary question, “How do you know that?”Althoughsimplistic,thisexercise engagesparticipantseasilyandactively inadiscussionabouthowbusinesspeo-ple address problems. In this scenario, participants may recall kindergarten classeswithfourofthesameitemson the board, envision flash cards, recall counting on fingers, or recall problem recitation. Regardless of the solution method,theexerciseforcesparticipants to become aware of their individual thinking processes. This awareness is thefirststepinmetacognition.
This simple exercise and subse-quent discussion engage students in the metathinking process.This may be the first time they—as business school students—have talked about thinking, althoughsomeoftheirsyllabi(particu-larly those in case-based courses) may havereferredtocriticalthinkingskills. Yet, becoming aware of one’s indi-vidual knowledge, assumptions, skills, and intellectual resources is a critical success factor in business (Davenport, 2004).
Afterstudentshavebecomeengaged in thinking about thinking,
profes-sors can implement the following two experiential exercises and the follow-ing decision-tree analytical tool in the classroom. They are good exercises in management classrooms and training programs that involve an overt effort to improve metathinking processes. Although in the classroom the discus-sion and analysis can become confus-ing,thedecision-treeframeworkallows theprofessortopresenttheprocessina straightforward manner. Thus, we rec-ommend that the decision-tree be used as a visual for framing the discussion. However,itisinterestingtoallowclass-roomparticipantstowanderthroughthe process before providing a process for structuringtheirthinking.
Example1:TheTaxicabAccident Acabwasinvolvedinahit-and-runacci-dent at night. Two cab companies, the Green and the Blue, operate in the city. You are given the following data: (a) 85%ofthecabsinthecityareGreenand 15% are Blue and (b) a witness identi-fiedthecabasBlue.Thecourttestedthe reliabilityofthewitnessunderthesame circumstances that existed on the night of the accident and concluded that the witness correctly identified each one of thetwocolors80%ofthetimeandfailed 20%ofthetime.
What is the probability that the cab involvedintheaccidentwasBluerather than Green? Please write your answer here:_____%
Most participants say that the prob-abilityisover50%thatthecabinvolved in the accident was blue, and many say that it is 80% (Tverksy & Kahne-man,1982).Thelaterdecisionfocuses mainly on the conditional probability that the witness accurately predicts a cab’s color, when the color is known, andignoresthebaseratemarginalprob-abilityofcabsbeingblueversusgreen. From a Bayesian analysis perspective, the correct answer is 41%. Structuring theprobleminadecisiontreeishelpful insolvingtheproblem(seeFigure1).
Additionaldefensiblesolutionstothe cab problem are found in the litera-ture(e.g.,Birnbaum,1983;Levi,1983). For example, according to Gigerenzer (2000),
IfNeyman-Pearsontheoryisappliedtothe cabproblem,solutionsrangebetween0.28 and0.82,dependingonthepsychological
theoryaboutthewitness’scriterionshift— the shift from witness testimony at the timeoftheaccidenttowitnesstestimony atthetimeofthecourt’stest.(p.16)
Thus,educatorscangobeyondTver-sky and Kahneman’s (1974) view of one correct answer that Bayes’ statis-ticssuppliedandgobeyondconsidering the deviation between the participant’s answer and the so-called normative answer as a bias of reasoning. Giger-enzer (2000, p. 17) quotes Neyman and Pearson (1928) on this point: “In many cases there is probably no sin-gle best solution” (p. 176). Because of the nuances (contingencies) in how the problem is framed, it is important for professors to advocate a particular theoretical model to follow in decid-ing on a final answer to the problem (cf.Koehler,1993;Woodside&Singer, 1994) rather than advocating exactly one correct solution. It is unfortunate that, except in a statistics classroom, extending the discussion beyond that offeredbyTverskyandKahnemanmay confuse students, causing them to lose sightoftheprimarymotivationforthe exercise: to think about their thinking. In many classrooms, keeping the deci-sion tree and subsequent discusdeci-sion as a Bayesian analysis may be the best approachforteachingandlearning. However, many business school situa-tions are not as straightforward as the taxicab example. There are generally various viewpoints and functional per-spectives in business decision making, andallopinionsmustbeincludedinthe decision-makingprocess.Thefollowing example,thepricingofanewproduct, presents a common business scenario and allows students to dig deeper into the use of a metathinking tool in the decisionprocess:
Plaswireisanewfencingwiremadefrom polyethylene terephthalate. Plaswire is
designedasareplacementforgalvanized steel wire in permanent fencing con-struction.ThepresidentofKiwiFencing requests that the assistant sales manager implementapricingstrategyforPlaswire thatwillhelpitachievenationaldistribu-tion. The president believes that pricing Plaswire substantially lower (30% less) than the competing steel wire price will help gain distributor acceptance of the product, as some farmers and livestock stationmanagersmaybepricesensitive. Thepresidentfeelscertainthatsteelwire
fourtimes.Thefinalresultwasfailureor verylowmarketsharefornewproductsin 92ofthe96casesthattheassistantsales managerhadreviewed.
However, the assistant sales manager alsoknewthatthepresidentoftenpredicted competitivereactioncorrectly.Thepresident hadbeencorrectinhispredictionsintwoof thethreerecentcasesconcerningcompeti-tors’ responses to new product prices.The assistantsalesmanagerfavorspricingPlas-wiretomatchthecurrentpriceofcompeting steel wire products. This pricing decision
thenewproductswerestillavailableeven when introduced at prices higher than competitors’prices.
What decision do you recommend? Whatisthelikelihoodofsuccessofyour strategy(i.e.,thenewproductisstillbeing marketed 5 years after market introduc-tion,anditisprofitable)?Pleaseprovide youranswershere.
Yourrecommendation,circleone: (a) Price Plaswire 30% below that of steelwire.
FIGURE1.Decisiontreefortaxicabcolorproblem.Theparticipantwillsay“blue”29%ofthetime(.17+.12=.29);the participantwillbeaccurate41%ofthetimewhensaying“blue”(.12/.29=.41).Thus,whentheparticipantsays“blue,” thechancesarestillgreaterthan50:50thatthecabwasgreen.
Baserate: Marginal
Probabilities ConditionalProbabilities
Joint Probabilities
Decision: Colorofcab
Blue
Green
Blue Blue Green
Green
.85
.80
.20
.20
.68
.17
.15
.80
.03
.12
matesleadstothesamerecommenda-tion: A price that is higher than that of steel wire results in the highest likelihood of success. Figure 2 and Figure 3 include the probabilities in the problem description and one set of reasonable probabilities for the other alternatives. Most students and executivesadvocateadoptingthepres-ident’srecommendationtopricelower than the competing steel wire. They do so without considering the base rate probability (0.75) that competi-torsusuallyreactwhenanewproduct isintroducedatapricelowerthanthat oftheirproduct.
As with the taxicab example, we recommend that the professor use the decisiontreesasvisualsinhelpingstu-dentsorganizetheirthinking.Inreality, the probability estimation process is notcomplicated,althoughitappearsto be so when thinking is not organized. Learningtoorganizeone’sthoughtsis critical to the success of teaching and learningmetathinking.
ClassroomUse
Professorshaveusedtheseexercises successfullyinvariousbusinessclass-rooms and seminars at several univer-sities around the world. Starting the discussion with the simple arithmetic problem engages participants in the topic of metathinking. Following the arithmetic problem with the taxicab example helps participants progress into the probabilistic components of decisionmaking.Themorecomplicat-edPlaswireexamplefocusesattention on the use of probabilities in decision makingandprovidesparticipantswith muchmoreinformationwithwhichto makeadecision.
People’s minds tend to limit cog-nitive effort (Payne, Bettman, & Johnson, 1993) and prefer to apply intuitive problem-solving routines evenwhengivenstrongevidencethat these routines are not very accurate. For example, in an executive MBA
programatTulaneUniversity(Wood-side, 1997), the instructors tested the Plaswire pricing case experimentally among24two-persongroupsofexec-utives.Theprofessorsinstructed12of thegroupsintheuseofdecisiontrees for framing problems and computing expected values of alternative solu-tions.Theother12groupsreceivedno such instruction. Of the 12 untrained groups,8recommendedthelow-price solution, and none recommended the high-pricesolution.Ofthe12trained groups, 7 decided on the high-price solution,andonly2selectedthelow-pricesolution.
Althoughtheprescriptivesolutionto eachoftheexamplesisinformativeand a necessary component of the class-room experience, the major objective of the exercises is to engage students intheartandscienceofunderstanding one’s metacognitive abilities. Accord- ingly,theprofessorshoulddebriefpar-ticipantsaftertheuseofeachexample. The following questions would facili-tatethedebriefing:
1.What were your thoughts when readingtheexample?
2.Howdidyouorganizeandusethe informationprovidedintheexample?
3.What (if any) personal knowledge didyouuseinarrivingatananswer?
Once participants dissect their own thought processes, they grow more awareoftheirownknowledge,assump-tions, skills, and intellectual resources andbecomecognizantofthewaythey use these resources in decision
mak-Summary
Executive thinking differs from sci-entific thinking in at least three funda-mental ways (cf. Kozak, 1996). First, scientists (e.g., academic researchers) gettochoosetheirproblem.Inorgani-zations, circumstances often thrust the problems (and symptoms of problems) on the executive (e.g., see Mintzberg, 1978). Second, scientists focus on a limited number of problems at a time. However, a vast number of potential
dence, (d) discounting disconfirming evidence if it does appear, (e) being hostile to the belief that using deci-sion tools such as computer software programs (see Gaither, 2002) results in more accurate problem framing than does trusting one’s own judg-ment, (f) not thinking outside the box and considering all theoretically possible—even if seemingly implau-sible—combinations of events and their outcomes, and (g) implement-ingadecisiononthebasisoflimited FIGURE3.CEOpredictions,accuracy,andoutcomes.Posteriorprobabilitycompetitorresponds=.0050+.0026+.4752 +.2524=.74.Posteriorprobabilitycompetitordoesnotrespond=.0016+.0008+.1584+.0841=.26.Conclusion: BecausetheCEOisnothighlyaccurateandthepriorprobabilityisveryhighthatthecompetitorwillrespond(.75),the posteriorlikelihoodofthecompetitorrespondingisclosetothesameasthepriorprobability.
Predictscompetitor responds
Predictscompetitor doesnotrespond CEO
predicts?
CEOaccurate? Priorprobability Revisedprobability
Yes
Yes
Yes Yes
Yes
Yes No
No
No
No
No
No .66
.34
.34 .66 .99
.01
.75
.75
.75
.75 .25
.25
.25
.25
1.=.0050
8.=.0841 7.=.2524 6.=.1584 5.=.4752 4.=.0008 3.=.0026 2.=.0016 CEO“certainty”
transformedinto .99probabilitythat competitorwillnot respond
NOTES
Dr. Victoria Crittenden’s research interests areformulationandimplementationofmarketing strategies,especiallyasrelatedtocross-functional decision making. She is widely recognized as a caseresearcher,casewriter,andcaseteacher.
Dr. Arch G. Woodside’s research interests are decisionmaking,marketingstrategies,andtourism.
Correspondence concerning this article should
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