when feedback is ignored: disutility of outcome...

Journal or Applied Psychology1984. Vol 69 No 3, 531-545

Copyright 1984 by theAmerican Psychological Association, Inc

When Feedback is Ignored: Disutility of Outcome Feedback

Jacob JacobyDepartment of Marketing,

New York University

Tracy TroutmanDepartment of Marketing,

New York University

David MazurskyDepartment of Marketing,

Hebrew University, Jerusalem, Israel

Alfred KussInstitute for Marketing and Consumer Research,The Free University, West Berlin, West Germany

Following Hammond, McClelland, and Mumpower (1980), two types of feedbackare conceptualized—outcome feedback and cognitive feedback The latter is herehypothesized as having either predictive or explanatory value Using security analystsparticipating in a security analysis decision simulation, the hypothesis that, incontrast to poorer performing decision makers, better performing decision makersare more likely to ignore outcome-only feedback was confirmed (r = —.48, p =02). Implications for theory revision and future research are discussed

The concept of feedback has long occupieda hallowed place in the literature of appliedpsychology. Despite some evidence to the con-trary (e.g., Einhorn & Hogarth, 1978; Ham-mond & Summers, 1972; Steinman, 1976),both the human performance and organiza-tional behavior/management literature suggestthat feedback can exert a positive effect onperformance (e.g., Ammons, 1956; Annett,1969; Carver & Scheier, 1982; Greller, 1980;Ilgen, Fisher, & Taylor, 1979, Ivancevich &McMahon, 1982; Nadler, 1979)

The generally accepted feedback-perfor-mance relationship implies that, other things(e.g., task-related experience) being equal, bet-ter performing individuals would be morelikely to access and use feedback informationThis is consistent with the notion that "Feed-back is central to the learning of expertise"(Hogarth, 1981, p 202) There are, however,reasons for arguing just the opposite, especiallyin situations that involve complex, cogmtivelyrich decision making. Providing the rationalefor this argument requires a more detailedconsideration of the concept of feedback itself.

This work was supported, in part, by funding from NewYork University's Institute of Retail Management

The authors acknowledge with appreciation the assis-tance provided by their colleagues, Professors Fred Renwickand Arnold Sametz, in arriving at the set of 26 fundamentalfactors used in this investigation

Requests for reprints should be sent to Jacob Jacoby,Department of Marketing, New York University, Wash-ington Square, New York, New York 10003

The cybernetic theorist Wiener (1948, 1950,p. 35) is usually credited with introducing theconcept of feedback to the behavioral sciencesand popularizing its use (cf. Nadler, 1979)Although there might be minor disagreement,most would concur with the following descrip-tion provided by Ilgen et al. (1979, p. 351):"At its most basic level, feedback is infor-mation received by an individual about his orher past behavior (Annett, 1969). It providessome information about the correctness, ac-curacy or adequacy of the response (Bourne,1966)" In addition to the central notion of"information regarding the accuracy of re-sponse," Ilgen et al. conjectured that feedbackalso possesses other characteristics. Specifi-cally, they referred to the "information value"of feedback, which they said depends on "theincremental increase in knowledge about per-formance that the feedback provides the re-cipient" (p 351).

The notions of "accuracy of response" and"information value" find parallels m the SocialJudgment Theory concepts of "outcome feed-back" and "cognitive feedback," respectively(cf. Hammond, McClelland, & Mumpower,1980, p. 228). Whereas outcome feedback isinformation that describes the accuracy orcorrectness of the response, cognitive feedbackrepresents information regarding the how andwhy that underlies this accuracy

Diagnostic or "cognitive feedback" may alsobe considered in terms of the concepts of pre-dictive and explanatory value. Predictive value

531

532 JACOBY, MAZURSKY, TROUTMAN, AND K.USS

refers to the correlation between accurate in-formation regarding one's past performanceand the ability of this information to predictfuture levels of performance. In contrast, ex-planatory value represents information re-garding why certain relationships occurred.This may or may not be accompanied by anability to predict if (or when) these relation-ships will occur in the future, that is, predictiveand explanatory value are conceptually in-dependent. By way of example, a parent mightbe able to answer a child's question as to whyan eclipse of the sun has occurred but not beable to predict when it will occur again (i.e.,explanatory value). In contrast, the same par-ent might be able to demonstrate to his automechanic the existence of a predicted rela-tionship between stepping on the gas pedaland hearing a strange rattle yet be unable toexplain why this has occurred (i.e., predictivevalue).

Hammond and Summers (1972), Ham-mond, Summers, and Deane (1973) andSteinman (1976) found that when predictivevalue is low, providing feedback after each trialcan be both misleading and detrimental tolearning a functional relationship. This sug-gests that, given a situation involving experi-enced decision makers who have access to taskenvironment feedback that is neither predictivenor explanatory, the better performing decisionmakers are more likely to be the ones whohave learned to disregard this feedback. Morespecifically, a principal objective of the presentinvestigation is to test the hypothesis that taskenvironment feedback providing completelyaccurate response information (i.e., outcomefeedback) but containing neither predictive norexplanatory (i.e., cognitive or information)value is less likely to be used by better as com-pared to poorer performing decision-makers

Real-world decision makers operating in atask environment that was both cognitivelycomplex and dynamic were considered bestfor testing the hypothesis. This is because itis further anticipated (though not tested bythe present investigation) that the hypothesizedrelationship is more likely to be manifestedwith task environments that are relativelycomplex (i.e., ones in which the task environ-ment is rich in information) rather than sim-ple, and are dynamic (i.e., actively changingindependent of the actions of the decision

maker) rather than static. The securities mar-ket and security analyst decision making seemto satisfy these criteria. As Slovic (1969, p. 79)comments:

Security analysis, whether by expert or novice, might belabeled "the information game" In no other realm aresuch vast quantities of information from such diversesources brought to bear on so many important decisionsCareful accumulation and skilled interpretation of thisinformation is said to be the sine qua non of accurateevaluation of securities

For this reason, security analyst decision mak-ing has been found quite useful for testingvarious psychological propositions (e.g.,Clarkson, 1962; Ebert & Kruse, 1978; Koz-minsky, Kintsch, & Bourne, 1981; Slovic,1969, 1972; Slovic, Fleissner, & Bauman,1972).

The types of information believed to havean impact on stock prices are generally clas-sified into one of three categories: market fac-tors, industry-specific factors, and company-unique factors. Market factors refer to the gen-eral economic and political background andinclude such world and national events as wars,changes in governments, monetary policy, orshort-term interest rates. Industry-specificfactors refer to trends and developments af-fecting an industry as a whole (e.g., the impactof Japanese exports on the U.S electronicsand computer industry; deregulation of theairline and trucking industries, etc ), as wellas the activities of the various competing firmswithin that industry. Company-unique factorsmay be broken into two categories: those in-volving consideration of a specific company'sfinancial statement (often referred to as "fun-damental analysis") and those involving non-financial matters, such as the quality of itsmanagement or whether its labor force is aboutto negotiate a new contract.

There seems to be increasing agreement that"market factors have very little to do with in-vestment performance" (Hagaman & Jensen,1977, p 64). According to King (1966) andBlume (1971), general market factors explainonly about 30% of the total variation in theprice of a particular company's stock. Further,industry-specific factors seem to explain per-haps another 12% of this variance (King,1966). "The implication of these findings isthat 50% of the variance in stock prices is dueto factors unique to the specific firm" (Tersine

WHEN FEEDBACK IS IGNORED 533

&Celec, 1976, p. 32). Accordingly, it becomesimportant to learn just which types of com-pany-unique factors are most useful, sinceanalysis of these fundamental factors seems torepresent the key to rational investment de-cisions Regarding the two categories of com-pany-unique factors, Bernstein (1975) com-ments:

Even assuming that psychological and other (I e , marketand industry-specific) factors do account for as much as50% of price changes, those who abandon fundamentalanalysis on the basis of such an argument abandon controlover the other 50% of their investment decisions In fact,they surrender much more because the fundamental factorsaffecting a business entity lend themselves to a much moredetailed, disciplined and systematic analysis than do someof the extreme factors affecting the price-earnings ratioThus fundamental analysis is a necessary ingredient of arational and prudent investment decision (p 60)

Based on the above review, fundamentalfactors can thus be assumed to account for atleast 30% to 35% of the variance in securityanalyst decision making (see Figure 1). Ac-cordingly, this study used actual fundamentalfactor information for New York Stock Ex-

change stocks to test the hypothesis that com-pletely accurate outcome feedback from thetask environment that contains neither pre-dictive nor explanatory value is less likely tobe used by better as compared to poorer per-forming security analysts.

Method

Sample

Subjects were recruited through mailed invitations toprofessional security analysts working for different in-vestment firms in the Wall Street area These letters in-dicated that research was being undertaken on securityanalyst decision making and that this research would takethe form of a competition The core of the letter reads asfollows

The purpose of this letter is to invite you to participatein this competition The winner will receive a $500prize and a press release identifying the winner will bedistributed to the appropriate media

Briefly, each competitor will be confronted with thesame set of eight common stocks, representing eightconsumer oriented firms listed in the NYSE, and begiven access to 26 different types of fundamental factorsregarding each of these stocks The task will be to identify

30% CA.

10-12% CA.

58-60% CA.

NATIONAL ANDINTERNATIONALEVENTS

INDUSTRY-SPECIFICFACTORS

COMPANY-UNIQUEFACTORS

1. FUNDAMENTALFACTORS(30-35%)

2 OTHER FACTORS(E G , IMPRESSIONS OFMANAGEMENT QUALITY)(20-25%)

/ FUNDAMENTAL FACTORS/

' 1 P/E RATIO FOR LAST 12 MOS/ 2 %PR CHG LAST 3 MOS/ 3 PRICE LAST MONTH

/ 4 LAST EARN TREND (UP, DOWN, NO CHANGE)/ 5 EARN /COM SHR 12 MOS LAST QTR

/ 6 ADJTD ANNL EARN $/COM SHR , PAST 4 YRS/ 7 INT'MEARN LASTREPTDJ/COM SHR RECENT REPORT' 8 INT'M EARN PREV YR $/COM SHR

' 9 YLD ON IND1CTD 12 MOS OF DIV10 EARN/COM SHR 12 MOS ENDING NEXT TO LAST QTR

11 $ NET PR CHG (LAST 9 MOS)12 PR RANGE CURRT YR HI LO13 LONG TERM DEBT14 TOTAL CURRENT ASSETS15 TOTAL CURRENT LIABILITIES

16 PR RANGE LAST 10 YRS HI LO17 LAST DIV TRND (UP, DOWN, NO CHANGE)18 CASH AND EQUIVALENTS19 DIV ($ PER COM SHR , LAST YR ANN'L RATE)

v 20 NO OF COMMON SHARES\

\ 21 NO OFINST HOLDINGS\ 22 NO OF SHRS HELD BY INST

\ 23 CASH DIV /COM SHR LAST YR ANN'L RATE\ 24 NO OF PREFERRED SHARES

\ 25 CASH DIV/COM SHR THIS AND LAST CLNDR YRS

\ 2 6 DATE OF EX DIV

Figure 1 Elements incorporated into security analyst decision making

534 JACOBY, MAZURSKY, TROUTMAN, AND KUSS

the "best buy" from among the set of eight stocks, notonce, but for each of four different time periods spaced90 days apart . The analyst whose four decisionsproduce the highest cumulative percentage growth overthe four test periods will be declared the winner

The $500 prize and promised press coverage were bothused to instill a degree of realistic motivation The industryis accustomed to providing recognition in the form ofannually published and widely disseminated lists of topperforming analysts Furthermore, the $500 prize meantthat there was something of consequence riding on thedecision Hence, each participant could be expected totreat the task seriously, thereby satisfying the "conse-quentiahty" desideratum (cf Jams & Mann, 1977, p 69)

More than twice as many analysts returned responseforms volunteering to participate as the number that ac-tually participated The invitation letters were mailed nearthe end of an extended period of sluggish market activity(July, 1982) By the time most were phoned to schedulea test interview, the market experienced a dramatic re-surgence in activity and many analysts who had earlierreplied that they would participate then chose to declineSecond, because analysts had to be tested individually,scheduling problems made it inconvenient for others whohad expressed a willingness to participate to actually doso Third, the study was computer-administered and severalcomputer malfunctions, both between subjects and duringthe time a subject was actually participating, created furtherdecreases in sample size Notwithstanding these problems,the obtained sample of 17 is still considerably larger thanthat employed by either Clarkson (1962), Slovic (1969),or Ebert and Kruse (1978) and is only one less than thatemployed by Slovic et al (1972)

The sample consisted of 3 women and 14 men Sevenwere between 21 and 30 years old, another 7 were between31 and 40, and 3 were over 40 Their careers as professionalsecurity analysts ranged from 1 5 to 17 years (M = 6 9years, SD = 5 7), and the amount of time spent workingwith their present employer ranged from (L months to 6years (M = 2 3 years, SD = 1 8) Fifteen of the analystshad master's degrees (14 M B A s and I M S ) , two heldbachelor's degrees Two analysts declined to respond to anitem regarding the income they derived from their activitiesas professional security analysts Another 8 indicated thattheir income was below $75,000 a year, 7 indicated thattheir income was above this amount

Test Setting

Testing took place in a specially equipped test facilityat NYU's Graduate School of Business Administration,immediately adjacent to the American Stock Exchange,a site conveniently situated for most firms in the WallStreet area

Task and Instructions

Each analyst participated in a behavioral process (BP)simulation A general overview of the BP approach wasprovided in Jacoby, Chestnut, Weigl, and Fisher (1976)More recent papers (e g, Chestnut & Jacoby, 1982, Jacoby,1977, Jacoby, Chestnut, Hoyer, Sheluga, & Donahue, 1978,Major, 1980, Sheluga, Jaccard, & Jacoby, 1979) will guide

the reader to more than 20 investigations that have em-ployed this approach

The analysts were tested individually Upon arriving atthe test facility, analysts were asked to read the followingbrief descriptions of the simulation and the rules for thecompetition

The purpose of this task is to examine how profes-sional security analysts make decisions as to which com-mon stocks represent a good purchase

In a short while, you will be asked to decide whichone of eight stocks represents the "best buy" For pur-poses of this study, the best buy is denned as that stockmost likely to show the greatest percentage of growth inprice per share over the next 90 day period

The eight stocks represent major U.S retailing firmslisted on the New York Stock Exchange during the years1969-1970 This time frame coincides with the firstterm of the Nixon presidency, the Vietnam conflict, andwas generally a period characterized by economic ex-pansion and growth So that your knowledge regardingthe present condition of these firms does not influenceyour decision-making, we have given them fictitiousnames However, all the information to which you willhave access is authentic and is the actual informationthat was available for these stocks during the 1969-1970period

You will be able to acquire information on any of26 different fundamental factors for each stock A listof these factors is provided at the back page of theseinstructions While we recognize that professional an-alysts often integrate yet other kinds of information intotheir decision making, for purposes of this task, you areasked to arrive at a decision based only on the 26 fun-damental factors that are available

Thus, you will have access to 208 different items ofinformation—that is, 26 items of information for eachof 8 stocks (=208 total items of information) All thisinformation is stored in the computer Since it wouldbe unreasonable to expect you to keep all the informationin your head while you're arriving at your decision, aset of recording sheets has been provided so that, if youwish, you can make notes

You will actually be making four different "best buy"decisions The first will be for October 1969 Once youhave made this decision, the computer will update theavailable information by three months (so that theyreflect the data existing as of January 1970), and youwill be asked to arrive at a second "best buy" decisionregarding these same eight stocks After you have reachedthis second decision, the information will again be up-dated by three months to reflect April 1970 data Thiscycle is repeated for a total of four periods In all, youwill be asked to make four "best buy" decisions regardingthe same set of 8 stocks, beginning with October 1969and ending with July 1970

Throughout, there are five basic rules you need tobear in mind as you go about obtaining informationfrom the computer These are

1 You may obtain information only for the 3-monthperiod provided That is, you will not be able to gobackward to access information from an earlier period,or forward to access information for a future date

2 Within each period, you are free to examine upto 104 of the 208 items of information available There


is no "correct" or "right" amount of information tolook at You may choose to look at 2 items, 20 items,or 100 items, It's all up to you

3 You may examine the available information inany order you want

4 You may examine an item of information morethan once That is, you may return to look at informationyou have seen before Simply instruct the computer todisplay that item again

5. You are free to make as many or as few notes onthe recording sheets as you like

So that you can become familiar with how to operatethe computer, we will begin with a short practice taskinvolving three stocks taken from the airline industry

Although it was estimated that each session would re-quire I'h to 2'h hours, no time limit was imposed Theamount of time actually used ranged from V/i to 6V2 hoursThe 104-item limit noted in the second rule was neces-sitated by the storage limitations of the computer, as de-scribed below

Information Environment

For each test period, the information environment con-fronting the analyst consisted of 208 separate items ofinformation—26 fundamental factors for each of eightcompanies To eliminate the possibility of extraneous con-founds, all the stocks were drawn from the same industryTo reduce the possibility that the respondents' memorieswould exert an effect on their information accessing, thedata were taken from the period spanning October 1969through July 1970 The only information that was notauthentic was the names of the firms; the letters J, K, L,M, N, Q, R, and S were used instead of firm names Thenames of the firms actually involved were Lane Bryant,Gimbels, Hughes and Hatcher, R H Macy, J C Penney,Sears, Roebuck and Co, F W Woolworth, and Zayres

The fundamental factor information regarding each ofthese companies was taken from the "Monthly Stock Di-gest," which was published by Data Digests (1969-1970)and mailed monthly by Merrill Lynch to its clients Thisinformation was taken from the October 1969, January1970, April 1970, and July 1970 issues It should be notedthat test Periods 1, 2, and 4 reflected a relatively stableto "bullish" market, whereas Period 3 reflected a "bearish"market, that is, all eight stocks decreased in value duringthis period The 26 fundamental factors that were usedwere selected after both a review of the literature andconsultation with knowledgeable colleagues revealed thatthese 26 factors were rather comprehensive in terms ofcovering factors typically considered by security analysts(see Figure 1) Note, also, that one of the factors,"% pricechange over the past three months," was a precise statementof the performance criterion, that is, the analysts wereinstructed to select "that stock most likely to show thegreatest percentage of growth in price per share over thenext 90 day period "

All information was stored in a Cromemco Z-2H mi-crocomputer This enabled the order of presentation ofboth the stock and the fundamental factors to be ran-domized across respondents, that is, though each respon-dent received the same eight stocks and 26 fundamentalfactors, these were presented in different orders for each

respondent To avoid confusing the respondents, the orderremained constant for each respondent across the fourseparate test periods for that respondent

Procedure

Respondents commenced reaching their "best buy" de-cision for Period I by communicating with the computervia a light pen attached to a color video monitor Afteran initial series of person-machine interactions to reviewthe task instructions and rules, two "menu" lists weredisplayed—one containing the names of eight stocks, andthe other being a list of 26 fundamental factors—and theanalyst was asked to indicate which type of fundamentalfactor information he or she wanted to see for which se-curity The analyst was permitted to access only a singleitem of information at a time, for example, "price/earn-ings" information for Company J Accessing of this in-formation was accomplished by touching the screen twicewith the light pen—once to identify the stock for whichthe information was being requested and a second timeto identify the fundamental factor—and the desired in-formation appeared on the screen almost instantaneously.

After acquiring this first item of information, the com-puter inquired whether the analyst wanted to make a bestbuy recommendation at that point or wanted to acquireadditional information If the analyst replied that he orshe wanted to acquire information, the two lists were dis-played again, and the analyst used the light pen to indicatewhich item of information he or she next wished to con-sider Each analyst continued in this way until the pointat which he or she felt ready to make a "best buy" rec-ommendation This, too, was indicated via the light penAfter the analyst arrived at a "best buy" decision for Period1. the information for the eight securities was automaticallyupdated by three months and the analyst proceeded m asimilar manner to arrive at "best buy" decisions for Periods2, 3, and 4 As a result of having worked through the briefpractice task, which served as a warm-up exercise, noneof the analysts evidenced any difficulty with this procedure

Following completion of all four periods, the analystcompleted a brief questionnaire, which inquired about thetask, collected certain demographic and employment in-formation, and included the following manipulation checkas the very first question "Did you think you knew theidentity of any of the stocks''" In all there were (8 stocks X17 analysts) 136 opportunities for the analysts to correctlyguess the identities of the test stocks Only one of theanalysts responded "yes," and was directed to answer thefollowing question "If yes, please indicate the name ofthe firms that you think were involved by writing thenames of these firms next to the corresponding letter"This analyst wrote in the name of only one firm, whichwas correct This manipulation check thus revealed thatthe tactic of using letters to camouflage the identities ofthe stocks worked as intended

Results

The procedures described above generatevoluminous quantities of data, much of whichis relevant for answering other questions. Adetailed report of much of these data is being


Table 1Depth of Search (Total Sample)

Information accessed

Total no of items acquired (withredundancies)

RangeMMdn

Total no of different itemsMMdn

% of full S X F matrix accessedMMdn

No of different securities consideredMMdn

No of different factors consideredMMdn

% of submatnx accessedMMdn

Overall

4-10542.638 5

41.136 0

197173

798 0

7 97 5

65 960 0

Period 1

4-9762 560 0

59 359 5

28 528 6

7 880

11 410 5

66 770 8

Period 2

10-9635 628.5

34 526 0

16612 5

8 08 0

6 57 0

66.346 4

Period 3

14-10534.5315

33 830 5

16 3147

8 08 0

7 16 5

59 558 6

Period 4

6-9838 132 0

36 8315

17715 1

7 68 0

6 65 5

73 671.6

provided elsewhere (Jacoby, Kuss, Troutman,& Mazursky, 1984). Except for providing asuitable context by briefly summarizing someof the findings bearing on the depth of search,The present report focuses exclusively on thedata relating to feedback.

Background

Overall depth of search Table 1 summa-rizes the descriptive data regarding the amountof information acquired and provides per-spective across all four test periods. Severalfindings are noteworthy. First, on average andacross all four periods, analysts accessed 42.6items of information per test period. (Thismean decreases to 41.1 when accessing of apreviously accessed information is excluded.)In other words, the analysts accessed less than20% of all the available information.

Second, m most cases the respondents con-sidered information for each of the eight stocksat least once. However, on average, they con-sidered fewer than 8 of the 26 fundamentalfactors that were available {M = 7.9; Mdn =7.5). This latter finding compares quite fa-vorably to those reported by Slovic (1969).

Third, dividing the mean number of stocksconsidered (7.9) into the mean number ofitems considered (411) indicated that, on av-

erage, the analysts paid attention to only fiveitems of information for each of the stocksthat they considered.

Fourth, the 26 X 8 information environ-ment represents an investigator-oriented per-spective on depth of search. However, not allthe available information is necessarily useful,worthwhile, or even meaningful to all respon-dents Hence, it is also insightful to adopt arespondent-oriented perspective and ask: If at-tention were limited to only those stocks andfactors considered by the analyst at least once,then what percentage of the information fromthat submatrix was accessed? Using this per-spective, search seems to have been relativelyextensive. On average, 66% of each analyst'sown submatnx was accessed, indicating a ten-dency for the analyst to compare each of thestocks along a subset of similar factors.

Comparing depth of search across test pe-riods The breakdown for periods shows rel-atively extensive search in the first period(mean number of items accessed = 62.5), fol-lowed by a sharp and substantial decline forthe next three periods (means = 35.6, 34.5,and 38.1, respectively). This trend may be ex-plained as follows.

Several of the fundamental factors repre-sented annual information that did not changethroughout the four test periods (or at least,


for two successive periods). Thus, once ac-cessed, several analysts tended not to reaccessthis information in subsequent periods. More-over, although Periods 1, 2, and 4 reflectedeither a steady or increasing market, the thirdperiod represented a sharp decline in stockprices. Consequently, analysts may have en-gaged in more extensive information acqui-sition in the fourth period as a result of ob-taining negative feedback on their preceding(third period) performance.

The general trend (of a sharp decline indepth of search after the first period) is alsoconsistent with traditional learning theory. Al-though it could be assumed that the partici-pating analysts were unfamiliar with all thestocks during the first period, their increasedfamiliarity resulted in more efficient decisionprocesses in subsequent periods. The slightdeviation during the fourth period can be ex-plained by sharp decline in stock prices at theend of the third period

Operationahzing Performance

The performance criterion used to distin-guish between the better and poorer analystswas based directly on the increases in priceper share of each security, calculated separatelyfor each of the four test periods The analystwhose four choices produced the greatest netyield was declared the winner. The yields thatwere actually possible ranged from +33.1%to -76.3%.

Several interesting findings emerge from aconsideration of this performance index. First,had they invested money in a real-world sit-uation as they recommended in the task, onlytwo of the analysts would have managed tomake money over the 12-month period; theother 15 would have lost money—seven ofthem would have lost 40% to 55% of the orig-inal value of their stocks. Much of this losswas experienced during Period 3, which wascharacterized by a sharp market decline. Inall fairness to the participants, analysts op-erating in a real-world environment are ableto more closely monitor the day-to-day per-formance of their stocks and would not waitup to 3 months to divest themselves of a sink-ing security. If calculation of the performanceindex is based only on Periods 1, 2, and 4,then the numbers are reversed, with 15 out of

Table 2Comparing Analyst Performance to aBaseline Random Choice Strategy

No ofsuccesses

(r) Pr (r/p = 1/8)

Expectedno of

analysts(Pr X 17)

Obtainedno of

analysts

43210

Total

Now Pr =

.00020068071833505862

performance

003120

1225 689 97

17 00

001

106

17

17 analysts having positive indices (range =+35 4 to -7 .2 ; M = +14.25).

Second, performance was not related toseveral key characteristics Across the entiresample of 17 analysts, performance correlatednegatively and nonsigmficantly with age (r =—.22; p = .40) and tenure with present em-ployer (r = - .37; p= .14) and correlated neg-ligibly with either tenure as an analyst (r =.03; p = .92) or income (r = .04; p = .89).

Suggested by one anonymous reviewer, ad-ditional perspective on performance is pro-vided by comparing the actual performanceof the analysts against a random choice strategywhere the probability of picking the winningstock in each period is p = 1/8. Therefore, foreach analyst, the probability of getting r suc-cesses in four trials can be computed usingthe binomial expansion. As can be seen fromTable 2, though the obtained distribution re-flected slightly better than chance perfor-mance, this difference was not significant (chi-square = 5.03; ns).

Operationahzing Feedback

Several important points need to be maderegarding the manner in which feedback wasoperationalized. First, feedback informationwas made available as part of the external in-formation environment. More specifically, itwas one of the 26 fundamental factors (namely,"perceived price change over the last threemonths") that could be accessed by the ana-lysts.

Second, making feedback informationavailable is qualitatively different from insuring


that feedback information is actually providedto the decision maker. Although the real worldreflects both types of situations, virtually allprior research has involved providing infor-mation to the decision maker—either after theresponse has been made or, in some instances,before the decision maker even begins to in-teract with the task environment (see the dis-cussion of "feed forward" in Hammond et al.,1980, p. 229) Some might contend that simplymaking information available does not con-stitute feedback. Others, however, could con-tend that providing feedback information alsofails to satisfy the classic definition. For ex-ample, Nadler (1979, p. 310), in evaluatingWiener's (1950, p. 35) classic definition, ob-served: "Thus, feedback is information aboutthe actual performance or actions of a systemused to control the future actions of a system"(italics added). In other words, unless said in-formation is then used to control future ac-tions, simply providing feedback informationis insufficient for defining feedback in the clas-sic sense. For present purposes, we rely on alooser definition and justify this by observingthat many real-world situations may be char-acterized as ones that simply contain feedbackinformation as part of the external environ-ment. However, as is discussed below, the dis-tinction between "information availability"and "information provision" has substantialimplications for conceptualizing and modelingthe feedback process

Third, as is required for testing the hy-pothesis, the feedback information was com-pletely accurate m regard to the correctnessof the response. By accessing this information,the decision maker could tell exactly how welleach security had performed and, in turn, howwell he or she had performed

Fourth, also as is required for testing thehypothesis, the feedback information was notexplanatory; it provided no information as towhy the obtained outcome actually occurred.

Finally, the feedback information also failedto be predictive. That is, had a decision makeremployed the simple choice heuristic of ac-cessing only feedback information and thenbased a decision on just this information, thenet performance index (based on predictingperformance from Period 1 to Period 2, fromPeriod 2 to Period 3, and from Period 3 toPeriod 4) would have been —41.7%. This

would have tied such an hypothetical analystwith the 15th ranked analyst in our investi-gation. As Casey (1979, p. 91) pointed out:"It is an axiom in security analysis that pastperformance is not a guarantee of future per-formance."

Performance and the Accessing ofFeedback Information

Table 3 provides a detailed breakdown, foreach analyst and each security, of whether and,if so, just when feedback information was ac-cessed during each of the periods (2, 3 and 4)for which feedback is relevant. This table isexplained as follows. The 17 analysts are ar-ranged in order of their net performance acrossall four test periods, from the best performinganalyst on the left to the worst performinganalyst on the right. The block of rows underthe label "Period 2" refers to the informationthat the analysts accessed during Period 2 whilein the process of arriving at their stock selectiondecisions. How each security (labeled from Jto S) actually performed during the previous90-day period is indicated under the heading"Feedback." The row labeled "Total" indicatesthe total number of items of information ac-quired by each of the analysts during that pe-riod. Blank cells indicate that no feedback in-formation was accessed by that analyst duringthat period for that security. The numbers incells that contain a number indicates the orderin which that item of feedback informationwas accessed. For example, the fourth-best-performing security analyst accessed a totalof 56 items of information before making hischoice during Period 2. He did not access anyfeedback information until doing so for StockN on the 17th item. He then went on to con-sider feedback information for stocks Q, K,S, R, M, J, and L, in that order. From the25th through 56th item in his search, he lookedat other types of fundamental factor infor-mation. Note that the mne cells containingmultiple entries (e.g., the fifth-best-performinganalyst for stock S during Period 4) indicatethat the analyst accessed that particular feed-back information more than once.

Inspection of Table 3 reveals several inter-esting findings. First, not all the analysts choseto access feedback information. Five of theanalysts, including the very best and very worst

Table 3Extent and Sequence of Accessing Feedback Information Across All Securities Within Each Period

Analyst

Feedback Better performing(%) Stock 1 2 3 10 11 12 13 14 15

Poorer performing16 17

+9 60

- 5 8- 5 4

+ 12 5- 2 9

0- 5 3

Total

JKLMNQRS

96 26 52

2319242217*182120

56

7386125"4

2628232425292722

30

Period 2

5324a

671

25 37 25

1756

2°, y

21

181412164

1068"

48

17"

18

18

756"

102893

10 23

40433941"42383744

44

125

292133"178

25

34

1110"131289

29 31

+4 2+8 3

-125+8 6+6 2- 1 5- 2 4

0

Total

JKLMNQRS

105

596

1187*

1210

21 37 40 54 25 19

Period 3

54316"728

26

274"61835

31

6471523*

33

43"17625

14

436*81572

17

18

17"

19

24

39423840"41373643

43

135,363020"32168

25

36

1 _

6 —

723

30 32

-122- 9 0

-35 7-58 0-29 4-136-37 5-33 3

Total

JKLMNQRS

98 12 62

748"62315

55

3032272829*3331

25, 26

59 32 40

Period 4

53"246718

26

174"62835

23

6"471523

32

43"17625

16

532'

4, 116,8

91

10

31

5558"545657535259

64

144", 33a

2723.30

2918, 19

7, 8, 1225

33

2561"8734

24 35

" Denotes the stock selected by the analyst as the "best buy" for that period


performing analysts (Numbers 1 and 17), didnot consider any feedback information what-soever. Second, those that did access feedbackdid not do so consistently. More specifically,although five of the analysts accessed all theavailable feedback information for all eightstocks across all three periods, the remainingseven analysts accessed some feedback forsome of the securities some of the time butnot for other securities at other times.

In terms of the hypothesis, the patterns seemclear. Given three periods and eight securities,each analyst could have acquired up to 24different items of feedback. Yet, of the sevenbest performing analysts, four accessed nofeedback information whatsoever and only oneaccessed all the available feedback informa-tion In contrast, of the seven worst performingsecurity analysts, only one accessed no feed-back information at all and two accessed allthe available feedback information.

Moreover, within a period, when one of thetop seven performing analysts accessed feed-back for one security, he or she did so for alleight securities. In contrast, on five differentoccasions (out of 7 analysts X 3 periods = 21such occasions), when one of the seven poorestperforming analysts accessed feedback infor-mation for a security, he or she apparently didnot feel compelled to access feedback infor-mation for all the other securities. This ten-dency for better performing decision makersto engage m more systematic accessing is likelynot limited to the accessing of feedback in-formation but probably reflects a tendency to-ward predecision information accessing ingeneral (cf. Kahneman & Tversky, 1979; Koz-minskyetal., 198 l;Sternberg& Powell, 1983).

In all, the seven top performing analystsaccessed 48 items of feedback information outof a possible (3 periods X 7 analysts X 8 se-curities =) 168 such items, or 28.6%. In con-trast, the seven poorest analysts accessed a totalof 115 items of feedback information, or68.5%. Applying Ferguson's (1966, pp. 204-206) test for the significance of the differencebetween uncorrelated proportions yielded achi-square of 117 (p < 0.0001). However, thefact that the middle three analysts accessed allthe available feedback information suggeststhat a more appropriate test would be a cor-relation, based on the entire sample, betweenthe proportion (from 0 to 24) of feedback in-

formation accessed and the performance cri-terion. Across all 17 analysts, the correlationbetween the proportion of outcome-only feed-back accessed and decision quality is —.48 (p =.02). Based on these findings, the hypothesisthat task environment feedback that providesonly accurate outcome feedback but that con-tains neither predictive nor explanatory value(i.e., cognitive feedback) will be less used bybetter peformmg decision makers is consideredconfirmed.

Additional perspective is provided by Table4, which considers the order in which feedbackinformation was accessed on a secunty-by-se-cunty basis. The two numbers appearing undereach analyst should be read as follows. Thesecond number indicates the total number ofinformation items accessed by that analyst forthat security during that period; the first num-ber indicates the order in which feedback in-formation was first accessed from among allthe information accessed for that security dur-ing that period. For example, the fourth bestperforming analyst accessed a total of sevenitems for information regarding stock J duringPeriod 2, with feedback information being ac-cessed third out of these seven items.

Because different analysts engaged m dif-ferent amounts of search, the following pro-cedure was employed to enable aggregationand comparisons to be made across analysts,securities, and periods. For each analyst whoconsidered feedback information, the order inwhich that information was accessed relativeto the other items of information accessed forthat security was converted to a 100-pomtscale, where 1 indicated highest priority and100 indicated lowest priority. This was accom-plished by dividing the rank order by the totalnumber of items accessed. To illustrate, takethe example noted above m which the fourthbest performing analyst accessed seven itemsof information for stock J during Period 2,with feedback information being accessedthird out of this set. One seventh is equivalentto .14 and three sevenths equals .42; this istherefore the "accessing priority score" as-signed to the fourth analyst on Period 2 forStock J. Had the analyst accessed 12 items ofinformation for that security, with feedbackbeing the ninth fundamental factor considered,the accessing priority score would havebeen .75.

Table 4Extent and Sequence of Accessing Feedback Information for Each Security Within Each Period

Stock

Analyst

Better performing .1 2 3 10 11 12 13 14

Mean ofaccessing

Poorer performing priority15 16 17 indices (%)

JKLMNQRS

M

3,73 ,63,83,73,73,73,73 ,7

3,33 ,37, 73,33,43,33,34 ,4

1,41.31,31,31,21,31,31,4

Period 2

, 2, 2, 2, 3, 6, 2, 3, 1

2,62,51, 42,73,82,63, 62,6

3,3

3,3

, 1, I,2, 1, 1, 1, 1,2

5,55,55,55,57, 75,57, 75,5

3 ,45,54 ,44 ,44 ,54 ,43 ,44 ,4

2 ,32,91,22 ,32,32 ,3

667272606370677268

M

Period 3

JKLMNQRS

— 5,5 —— 1, 2 —— 1,2 —— 1,2 —— 1,2 —— 1,4 —— 1,2 —— 1,2 —

,5,5,5, 5,5,5,5,5

1,31,31,41,31,31,31,41,3

, 5, 3, 4, 4, 4, 3, 5, 3

, 4, 4, 3, 4,5, 4, 4,5

, 2, 2, 3, 2, 2, 1, 1, 1

1, 11, 11, 101, 11, 11, 11, 11, 1

—3,

7,

3,

4

11

3

5,55,55,55,55,55,56,67,7

4 ,45,64 ,42,52,43,53,44 ,4

, 3—, 3, 6, 4, 4, 4,3

585743484752585953

55

ia

JKLMNQRS

Period 4

1, 51,71, 101,51,91,61,71,6

4, 73,54,84,84,85,94 ,65,8

1,41, 41,21,31,31,41,31,3

,3,3,3,2,4,3,3,2

,4, 4, 4,4,4, 4,4,4

, 1, 2, 2,2, 4, !,2,2

1,6

1, 21,21, 111, 51,31,41, 21,2

7, 78,97, 7

10, 118,87,87,77, 7

4 ,43,72,24,52,24,53,62,2

, 2, 2, 4,6, 4, 2, 2, 2

M

564442434350445447


Table 5Changes in Search Content (i e,

Analyst

Five best performingFive worst performing

1—2*

27"32

Factors Accessed)

No

1—2" 1—2C

17" 15"36 3

Across Successive Periods

of properties accessed in periods

2—3

3528

2 - 3

79

2—3 1-4

11 378 32

3—4

94

3—4

514

* To be read as number of factors accessed by a given analyst in both Periods 1 and 2, summed across all five analystsbTo be read as number of factors accessed by a given analyst in Period 1, but not Period 2, summed across all fiveanalystsc To be read as number of factors not accessed by a given analyst in Period 1 which were accessed in Period 2, summedacross all five analysts" Cell entries are to be read as follows The five best performing analysts accessed information on a combined totalof 59 factors in Periods 1 and 2. Of these, 27 factors that had been accessed in Period 1 were also accessed in period2, 17 factors that had been accessed m Period 1 were not reaccessed in Period 2, and 15 factors not accessed in Period1 were accessed in Period 2

The right-hand column of Table 4 containsthe aggregated accessing priority scores forfeedback information for Periods 2, 3, and 4.To test whether the trend of increased prioritygiven to accessing feedback over the periodswas statistically significant, a one-way analysisof variance (ANOVA) was employed. The testyielded an F ratio of 31.8 (p = .0001), indi-cating a significant change in the accessingpriority given to feedback information acrossthe three periods. A Scheffe test was then ap-plied to determine whether the stages of ac-cessing information feedback differed betweenadjacent periods. The results show that thepriority that security analysts assigned to feed-back information in the second period wassignificantly lower than the priority that theyassigned to feedback in the third and fourthperiods (p = .05). However, despite the factthat the fourth period reflects earlier accessingof feedback information, the difference be-tween the third and fourth periods is not sig-nificant.

Learning From Experience

A related set of questions concerns infor-mation accessing of the other 25 fundamentalfactors across the four periods, particularly asthis relates to learning from experience (Ein-horn, 1980; Einhorn & Hogarth, 1981). Giventhat the analysts focused on only a limitednumber of the available factors (see Table 1),one might expect that, as the number of pe-riods increased, there would be greater con-

vergence or overlap in the factors consideredfrom one period to the next, and decreasedattention to factors not considered during theprevious period.

To examine this question, the data for thefive best and five worst performing analystswere sorted into three categories (see Table 5).The first category included all instances inwhich at least one item of information wasaccessed on the same factor in two successiveperiods. The second category included all casesin which information accessed on a given fac-tor m one period was not reaccessed on thatfactor in the subsequent period. The third cat-egory included all instances in which a factorthat was not considered in one period wasaccessed in the immediately succeeding period.

A one-way ANOVA applied to these data re-vealed that, though present, neither the trendtoward increasing convergence (27, 35, 37) northe trend of decreasing attention paid to dif-ferent factors (15, 11, 5) was significant forthe five better performing analysts. However,for the five poorer performing analysts, al-though the degree of overlap remained roughlythe same across periods, there was a significant,F(2, 13) = 5.3, p = .03, increase in the atten-tion paid to new factors (3, 8, 14). These datasuggest that the better performing analystsmight have been more effective learners. Al-ternatively, these data may also be interpretedas indicating that the poorer performing an-alysts were also learning—as evidenced by thefact that, m view of then- poor performance,they continued looking at other factors.


Discussion

Two basic findings are suggested by this in-vestigation. First, given an environment thatpermits decision makers to be selective inchoosing the information that they will andwill not consider, not everyone chooses to ac-cess feedback information—at least not whensuch information possesses only "outcomevalue" and fails to also possess either predictiveand/or explanatory value. Second, as pre-dicted, better performing decision makers arethe ones who are less likely to access such"outcome only" feedback information thanare poorer performing decision makers.

These findings need to be considered interms of several limitations. First, the sampleconsisted of 17 volunteer analysts. Muchgreater confidence would be obtained from alarger and more representative sample.

More importantly, the present investigationwas also limited by the fact that respondentswere able to access feedback information ononly three occasions (that is, during Periods2, 3, and 4). This was due to the amount oftime required for the task. As previously noted,the analysts devoted an average of 2'/2 hoursto the task, and one analyst actually spent morethan 6 hours. Real-world security analysts, aswell as other types of decision makers, generallyhave access to much greater numbers of feed-back occasions. Future research would do wellto examine the impact of a greater number offeedback trials.

Third, the task that was employed did notfaithfully model real-world security analystdecision making in at least two key respects.First, though it required that the analyst makea "buy" decision, it did not also permit a "sell"response; second, it did not adopt a "portfolio"approach, that is, the buying and selling oftwo or more securities at one time. Note, how-ever, that this limitation did not detract fromour ability to test the hypothesized relation-ship.

It should also be noted that the present in-vestigation provided what might be termed"absolute" rather than "relative" feedback.That is, the feedback that was available in-dicated to the respondents how well they hadperformed in selecting the best out of the eightavailable stocks. It did not, however, provideany indication as to how well they did relative

to the others with whom they were competing.Yet as Hogarth (1981, p. 210) pointed out:"An important ecological dimension missingfrom most research on judgment and choiceis that decisions are often made in competitiveand other social s i tuat ions . . . . In competitivesituations optimal responses are not necessaryfor survival. Instead, responses only have tobe better than those of competitors . . . (Ein-horn, 1980; Hammond, 1972)." Though thereal-world security analyst's task environmentdoes not reflect direct access to relative feed-back, it is clear that future research needs tobe devoted to this issue.

This study also has other implications. First,both in terms of developing theory and con-ducting research, it seems obvious that ad-ditional consideration needs to be devoted tothe diagnostic (that is predictive and explan-atory) nature of feedback information. To theextent possible, prior research needs to be re-viewed to determine the type of feedback thathad actually been provided to the respondent.It may be that outcome feedback has littleutility for cognitive (as opposed to motor) tasksand that only predictive and/or explanatoryfeedback would be useful. Hence, the generalfeedback-performance relationship wouldhave to be qualified.

Second, it is also clear that theorists needto revise the generally accepted view that theimpact of feedback on performance is virtuallyalways positive. Sufficient evidence is begin-ning to accumulate (Emhorn & Hogarth, 1978;Hammond & Summers, 1972; Steinman,1976; this study) to indicate that it dependson the type of feedback that is involved. Inparticular, outcome feedback may be partic-ularly disfunctional in a complex, dynamicenvironment

Another implication is that models of feed-back that implicitly assume that the decisionmaker will be given feedback information needto be revised in order to accommodate theactive search for and accessing of feedback.When the source of feedback is the task en-vironment itself, especially a complex task en-vironment, the individual must actively engagein the search for and accessing of this infor-mation. Without such prior accessing, the in-dividual never will have the opportunity to"perceive" (qua receive and interpret, cf. Ilgenet al., 1979) this feedback.


Finally, most theories of human judgmentand decision making actually fail to studyfeedback (cf. Hammond et al., 1980, p. 228).As but one example, after acknowledging thatfeedback/learning constituted one of three keysubprocesses of decision making, Payne's(1982, p. 386) integrative review continued."The feedback/learning processes, though as-sumed to interact with information acquisitionand evaluation, will not be stressed "

Yet the notion of feedback is not only centralto our understanding of such phenomena asinformation evaluation, learning, and decisionmaking, but to our understanding of all ofhuman behavior. Consider the following:

The human information processing system that hasevolved to cope with the environment is characterized byessentially sequential processing, limited memory, selectiveperception, and reliance on cognitive simplification mech-anisms (I e , heuristics) Central to these means is the roleplayed by feedback (Hogarth, 1981, p 199)

All behavior involves strong feedback effects, whetherone is considering spinal reflexes or self-actualizationFeedback is such an all-pervasive and fundamental aspectof behavior that it is as invisible as the air we breatheQuite literally it is behavior—we know nothing of our ownbehavior but the feedback effects of our own outputs (Pow-ers, 1973, p 351)

Given the apparently pivotal role of feedbackin all human behavior, greater conceptual andempirical attention to this phenomenon thanhas heretofore been the case appears war-ranted.

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Received November 29, 1983Revision received March 7, 1984

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