UlNIVLKSIIt OJT tuunols UBRARY STACKS

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2 UlNIVLKSIIt OJT tuunols UBRARY STACKS

3 Digitized by the Internet Archive in 2011 with funding frm University f Illinis Urbana-Champaign

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5 "^ "^ r'y 6%^- Q Ov> WW Faculty Wrking Papers Cllege f Cmmerce and Business Administratin University f lliinis at U rba n a - C ha m p a i g n

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7 Faculty Wrking Papers Cllege f Cmmerce and Business Administratin University f Illinis at U rba n a - C ha m pa ig n

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9 : FACULTY WORKING PAPERS Cllege f Cmmerce and Business Administratin University f Illinis at Urbana-Champaign Octber 10, 1979 Secnd Draft Nt Fr Qutatin HUMAN INFORMATION PROCESSING FOR DECISIONS INVESTIGATE ST VARIANCES TO Cliftn Brwn, Assistant Prfessr, Department f Accuntancy #614 Summary The principal fcus f this paper is manager cst variance investigatin decisins. A cnceptual framewrk is develped which predicts the effects f situatinal variables upn a manager's efficiency in cst variance infrmatin prcessing and in cst variance investigatin decisin making. This framewrk is based, in part, upn the psychlgical cncepts f signal detectin and heuristic decisin strategy. A simulatin is used t derive sme implicatins frm the cnceptual framewrk and a labratry experiment is cnducted t test these implicatins. Overall, the implicatins were supprted by the experimental results. An ex pst hypthesis is intrduced as a ptential explanatin fr the deviatins frm expectatins.

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11 HUMAN INFORMATION PROCESSING FOR DECISIONS TO INVESTIGATE ST VARIANCES An imprtant aspect f sme cntrl prcesses is the analysis and investigatin f standard cst variances prvided within accunting reprts. A substantial prtin f the accunting literature n variance investigatin has emplyed the nrmative mdel apprach researchers have created variance investigatin mdels which a manager shuld use. Rarely has attentin been given t hw the manager wuld interpret and integrate infrmatin required by the varius nrmative mdels. The principal fcus f this paper is n the effects f situatinal variables upn a manager's infrmatin prcessing fr purpses f making variance investigatin decisins. The specific bjectives are 1) t develp a cnceptual framewrk which will predict effects f specific situatinal variables n a manager's relative efficiency in infrmatin prcessing and in variance investigatin decisin making and 2) t empirically test sme implicatins f this cnceptual framewrk. The vari pas' investigatin literature cntains a paucity f research relating t the manager's ability and efficiency t interpret and integrate the infrmatin being prvided within accunting variance reprts r being prpsed fr inclusin within these reprts by the literature. The literature is cncerned mainly with mdeling the investigatin significance f variances. Sme mdeling appraches described within the literature include the Skewhart X chart prcedure (Prbst, 1971; Kehler, 1968; Luh, 1968; Jeurs, 1967; Zannets, 1964), classical statistics incrprating investigatin csts and benefits (Bierman et al., 1961), the cumulative sum and ecnmic cumulative sum prcedure (Kaplan, 1975;

12 -2- Jacbs, 1978), and decisin thery (Kaplan, 1969; Dyckman, 1969; Kaplan, 1975; Dittman and Prakash, 1978). As this literature has expanded the infrmatin requirements f the prpsed variance investigatin mdels have becme diverse and cmplex. Hwever, t the extent that nt all the parameters are peratinalizable (given either cst r utilizatin cnstraints), the manager must cntinue t make the variance investigatin decisins using the infrmatin prvided by the accuntant and by his wn experience, A manager's variance investigatin decisin may be viewed as the culminatin f a tw-stage prcess. The first stage cncerns the detec- 2 tin f the particular distributin (e,g., in-cntrl r ut-f-cntrl ) that generated the variance. The manager's perfrmance f this task is a functin f the sensitivity f his decisin prcess (mdel). The secnd stage cncerns the manager's investigatin decisin criteria. Having arrived at a cnclusin (albeit prbabilistic) abut the distributin that generated the variance, the manager must integrate and prcess varius bjective functin parameters in rder t arrive at his variance investigatin decisin. Althugh the variance investigatin decisin prcess has been described as tw stages, the manager may nt actually utilize such a sequential stage prcess, A manager's actual decisin prcess can be labeled a heuristic, a learned set f rules r principles. Hwever, the sequential stage prcess will facilitate the identificatin f variables which can affect bth the variance investigatin decisin prcess and the results f the prcess.

13 -3- General Cnceptual Develpment The manager's decisin cncerning the nature f a given variance is analgus t a decisin cncerning the presence f a signal in a backgrund f nise. The backgrund f nise is represented by the serial distributin f variances generated by a prductin system that is perating within an in-cntrl state. The signal is represented by an bserved variance generated by a system that has chemged and is currently perating within an ut-f-cntrl state. The general prblems cnfrnting the manager is that he must decide which state f cntrl is mst prbable based upn sme incmplete set f infrmatin and he must decide whether t investigate a variance based upn sme subjective decisin criteria. When a manager deals repetitively with a similar situatin the variables affecting his lng-run decisin efficiency can be identified utilizing the sequential stage prcess described earlier. The sensitivity f the manager's decisin prcess can be affected by the structure f the decisin situatin and by his knwledge f this structure. The manager's knwledge f the situatin is affected by the available infrmatin (bth prvided by the variance reprt and prvided frm ther surces) and by his ability t learn frm his experiences with the prductin system. The variables which can affect bth the manager's investigatin decisin prcess and the results f his prcess include: 1) the stinicture f the decisin situatin, 2) the cntents f the available infrmatin set, 3) the manager's infrmatin prcessing efficiency, and 4) the manager's learning efficiency. The structure f the particular

14 -4- declsin situatin primarily depends upn the number f pssible states f nature, the relative frequencies f the states, the varius statistical relatinships amng the states, and the relatinships between the varius decisin utcmes (the csts incurred given a specific decisin and the existence f a specific state). The cntents f the available infrmatin set refers t the infrmatin knwn by the individual prir t his decisin, either infrmatin that specifically relates t the current decisin r infrmatin that relates t the statistical relatinships amng the states f nature. The individual's efficiency in prcessing the available infrmatin relates t the particular heuristics r strategies emplyed in cmbining and weighting the varius items f infrmatin. The individual's decisin and infrmatin prcessing perfrmance can be evaluated by cmparing his perfrmance against that f an ptimal mdel under similar cnditins as the individual. Within this research the ptimal decisin rule is assumed t be the minimizatin (maximizatin) f the expected cst (value) f a series f investigatin decisins. The individual's learning efficiency refers t his ability t expand the available infrmatin set ver time. Such learning can ccur thrugh Imprved estimates f unknwn items f statistical infrmatin and thrugh mdificatins f infrmatin prcessing strategies t incrprate state relatinships which were vinknwn r undetected previusly. The bjective and methd utilized in this study require sane cmments n tw cncepts: signal detectin thery and human infrmatin prcessing. The relatinships between physical and psychlgical scales

15 -5- f measurement are part f the dmain f psychphysics. Mdern psychphysics adpts the view that subjects can make meaningful evaluatins f the magnitudes f their sensry experiences and therefre sensry magnitudes, as well as physical magnitudes, can be quanitifed. One apprach f mdern psychphysics is based upn the thery f signal detectin (TSD). TSD permits the separatin f the decisin maker's ability t discriminate between classes f stimuli (sensitivity) frm his mtivatinal respnse biases (decisin criteria). Traditinally, psychphysics has emplyed TSD t study perceptual prcesses; i.e., sensry prcesses such as auditin and visin. Over the last decade, 4 hwever, TSD has been applied t cnceptual prcesses. The basic TSD experiment utilizes the single- interval prcedure which cnsists f a series f trials, each trial cmprised f an bservatin interval and a respnse interval. The pssible stimulus events during the bservatin interval are: 1) the bservatin cntains a meaningful signal added t a backgrund f nise (sn trial) r 2) the bservatin cntains nly a backgrund f nise (n trial). It is assumed that trials are pairvise independent and that the prir prbabilities f n and sn are given and remain cnstant. The backgrund nise fluctuates at randm frm trial t trial; the stimulus, usually a fixed level, is added t the nise. The task f the subject is t detect which distributin (sn r n) generated the bservatin. On any trial in the basic experiment there exist fur pssible utcmes f the subject's decisin (either "Yes, a signal was presented" r "N, a signal was nt presented") in cnjunctin with the actual distributin (either sn was presented r n was presented). A 2 x 2 cnditinal

16 ; -6- prbability matrix fr a series f these events can be determined using bserved respnse frequencies. All parameters f the TSD mdel are derived frm this cnditinal prbability matrix. Given a single- interval task, and an bjective functin such as the maximizatin f expected value, a 2 x 2 payff matrix f these values can be specified where each value relates t ne f the event utcmes. Fr example, the subjective value related t the event utcme f saying "Yes" and sn actually being present wuld be V(Y,sn). The decisin rule fr maximizing the expected subjective value is t say "Yes" when: P(x P(x sn) P(n), VCN.n) - V(Y,n) p «^^r, i vt^ > vfj^\ ' T7 7v^«^ _ v^m «^ Equatin 1 n) P(sn) V(Y,sn) - V(N,sn)»' where x represents an bserved stimulus, Y is yes, N is n, and n and sn are defined as befre. An equality f terms in Equatin 1 means indifference. This pint can be cnsidered the critical value f the likelihd rati f the bservatins, LCx ), which, fr this decisin rule, has tw pssible values: 1) a theretical value which is a measure f the criteria f an ptimal (r ideal) subject and 2) a subjective value which is a measure f the criteria f an actual subject. The parameter which measures individual discriminatin sensitivity (d') is defined as fllws: y - ]i d' = -^ - z - z, Equatin 2 a n sn * ^ where p. = the mean f the ith distributin (either sn r n) a = the standard deviatin f bth distributins; z = the value f the nrmal distributin functin assciated with the ith distributin (either sn r n) and any decisin axis cutff value cmmn t bth distributins.

17 -7- The parameter which measures individual decisin criteria, g, is defined as: 6 = * (Zg^) / (j) (z^), Equatin 3 where <)>( ) dentes the nrmal density functin fr the value in parentheses* Graphic representatins f the TSD parameters are presented in Figure 1. Insert Figure 1 abut here Of particular interest here frm the literature n HIP is the anchring and adjustment heuristic. In many situatins, individuals first make decisins by starting with an Initial anchr (decisin pint) and then adjust this initial anchr as they learn frm their experiences. The initial anchr can be suggested by the structure f the decisin situatin, r can be the result f a partial cmputatin r estimate. Hwever, empirical tests invlving the anchring and adjustment heuristic indicate individuals d nt sufficiently adjust their initial decisin pint. That is, their adjustment is less than that which wuld allw ptimal prcessing f the available infrmatin (Slvic and Lichtenstein, 1971; Slvic, 1972; Alpert and Raiffa, 1968; Tversky and Kahneman, 1974). Research Methdlgy and Design Tw general methds are emplyed in this reserch simulatin and labratry experimentatin. The majr bjective f the simulatin is t prduce patterns which wuld assist in predicting the behavir f human decisin makers within the decisin situatin under study. The majr bjective f the labratry experiment is t test the cnceptual develpment thrugh the hyptheses derived by the simulatin. Bth parts f the study deal with the same task and variables.

18 . -8- Experimental Envirnment and Task The standard cst variance investigatin studied within this research was set in an envirnment a manufacturing cmpany. Mre specifically, the subjects were asked t assume the rle f the peratinal manager f an assanbly department which assembles a single prduct, a metal flding chair. The perating efficiency f the assembly department is determined cmpletely by the labr efficiency f the assembly wrkers. The labr efficiency standard (stated in terms f time per unit assembled) was based n engineering estimates that allwed fr unavidable labr inefficiencies and reasnable variatin in wrker perfrmance (i.e., the standards were currently attainable). The subjects were instructed t accept the labr efficiency standard as fair in terms f cntrl and perfrmance gals. The physical labr prcess f the department culd be in nly ne f tw mutually exclusive states f nature; either in-cntrl r ut-f-cntrl. The subjects were 86 senir year undergraduate and master's level graduate students enrlled in the business cllege at a large state university. The subjects participated in the experiment during a tw week perid; 47 participated in the first week and 39 participated in the secnd week. A ttal f 92 subjects initially vlunteered t participate but six subjects failed t cmplete the experiment. The 86 subjects wh cmpleted the experiment cnsisted f 63 males and 23 females Each subject received a sequential series f standard cst variance reprts and was asked fr each reprt t decide whether t investigate r nt t investigate the reprted labr efficiency variance. Tw

19 -9- assumptins were prvided t aid the subject's decisin making prcess. First, if they decided t investigate a variance and the labr prcess turned ut t be ut-f-cntrl, the prcess wuld be returned t the riginal in-cntrl state with certainty. Secnd, if they decided nt t investigate a variance and the labr prcess was ut-f-cntrl, the prcess wuld remain ut-f-cntrl with certainty. Each standard variance reprt was cncerned with the results f a single jb-rder t prduce a cnstant number f chairs and reprted nly aggregate (verall assembly department) results. Each reprt cntained the aggregate standard assembly time allwed per chair, the actual assembly time incurred per chair, the verall labr efficiency variance per chair, the ttal niimber f chairs prduced, and the estimated csts assciated with each pssible decisin in cmbinatin with each pssible state f cntrl. All time units were presented in minutes. The actual assembly time and the labr efficiency variance cntained in a variance reprt were cnditinally independent f thse cntained in previus variance reprts. The subjects were tld that their Immediate supervisr, the prduct sectin manager, wuld evaluate their cntrl perfrmance in teirms f minimizatin f bth investigatin and prductin csts abve the expected standard. A cash bnus was prmised t the subjects, the size f the bnus being cntingent upn the extent t which they minimized these csts. The measure f a subject's cntrl perfrmance was determined by summing his ttal investigatin decisin csts ver the series f variance reprts and dividing this sum by the sum f the ttal investigatin decisin csts incurred by an ptimal mdel ver the same series

20 -10- f variance reprts. The subject's cash bnus was inversely related t this measure. The minimum bnus was set at $2.00 and the maximum at $ The experiment was cnducted in tw phases a training phase and an experimental phase. The training phase cnsisted f three cntiguus sessins in which the subject learned the rle and presumably develped a decisin strategy. Perfrmance feedback was given at the cmpletin f each training sessin. The experimental phase cnsisted f a single sessin in which the subject received a series f variance reprts similar t thse presented in the training sessin. In this phase n perfrmance feedback was given until after the cmpletin f the entire experiment. Selectin and Operatinalizatin f Variables Three independent variables, each measured using a dichtmus classificatin, were emplyed: 1) the infrmatin variable, 2) the distributin variable, and 3) the cst variable. Individual prcess variables, measured n a cntinuus scale, included 1) individual decisin mdel sensitivity and 2) individual decisin criteria. The majr dependent variable was individual lng-run decisin efficiency (in terms f incurred csts). The general research design, presented in terms f dependent and independent variables, is depicted in Table 1. Insert Table 1 abut here The infrmatin variable. The effects f the cntents f the available infrmatin set are studied by manipulating the presence and absence f certain distributinal infrmatin. The first level, labeled II, is derived frm the set f infrmatin assumed t

21 -11- cme frm the individual's experience with the system. It includes: 1) the prtin f time in which the prcess had been fund t fall in each f the tw states, 2) the assumptin that the randm variable f interest (actual minutes incurred per chair r its assciated standard variance) is nrmally distributed in either state, 3) the lwest and highest past bserved values f the randm variable, and A) the minimum and maximum past csts assciated with each state. The secnd level f the infrmatin variable, labeled 12, additinally included the mean and the standard deviatin f the randm variable within each state. The distributin variable. The effects f the statistical structure f the decisin are studied by manipulating a distributinal infrmatin variable. Since the difficulty f the discriminatin task increases as the area f distributinal verlap between the tw states increases (see Figure 1), ne level f the distributin variable had a greater area f verlap than the ther. Manipulatin f the distributin variable invlved tw factrs: 1) the distributinal parameters f each state and 2) the statistical relatinship between the states. The tw levels f this variable are generated thrugh a change in the variance and in the standardized distance between the means f the tw states. A given set f parameters and relatinships were assumed fr the first level f the distributin variable, labeled ^. These are: 1) = y-i-i 36.0 actxial minutes incurred per chair; 2). ^ =, 2 = cr-i = 3.0 actual minutes incurred per chair; and

22 -12-3) ^12 ~ ^11 * l»5cf, = 40,5 actual minutes incurred per chair; where y., = the mean f the jth state f cntrl (in-cntrl = 1 and ut-f-cntrl = 2) given the ith distributin level (SI = 1 and S2 = 2) ; and.. - the standard deviatin f the jth state f cntrl given the ith distributin level. The secnd level f the distributin variable, labeled S2, had the fllwing parameters and relatinships: 1) Uji ~ 36.0 actual minutes incurred per chair; 2) c-, =» a_2 = a^ = 5.0 actual minutes incurred per chair; and 3) ^22 ~ ^^21 "*" ^* *^2 ~ ^5.0 actual minutes incurred per chair; where y.. and a,, are defined the same as in the SI level. The cst variable. Since the imprtance f the discriminatin task increases as the csts assciated with decisin utcmes that invlve different decisin errrs diverge, the different levels f the cst variable will be assciated with different decisin errr csts. One level f the cst variable is structured in favr f mre variance investigatins and the ther level f the cst variable is structured in favr f fewer variance investigatins. Given the tw pssible states f cntrl and tw pssible decisins, there fllws that tw types f errrs can be made in reaching a decisin: the decisin t investigate when the incntrl state exists and the decisin nt t investigate when the utf-cntrl state exists. Each level f the cst variable takes ne f the fllwing frms: 1) the margiixal cst f a decisin nt t investigate when the ut-f-cntrl state exists equals three times the

23 marginal cst f a decisin t investigate when the in-cntrl state exists (labeled level CI) and 2) the reverse f the Cl^ level decisin errr cst relatinships (labeled level C2) Individual decisin mdel sensitivity. The sensitivity f the individual's decisin mdel relative t the decisin situatin is measured using a functin f the TSD parameter d'. Fr an individual i, an empirical estimate f d' is btained using the individual's cnditinal prbabilities P(investigate ] ut-f-cntrl) and P(investigate incntrl) t calculate a subjective z^ and z-. If d/ is generated by using ptimal mdel k, then the empirical d' wuld fall belw d/ due t: 1) individual incnsistency in the use f the cutff value (emplying a variable respnse range) r the individual makes ne r mre temprary prcessing errrs and 2) the individual utilizes mre than ne cutff value. Based upn an analysis f each individual's decisins, d' can be adjusted fr the effects f using multiple cutff values. Defining d' t be the individual's decisin mdel sensitivity after eliminating the effects f multiple cutff values: DNA^ = dj^^ / d^. (Equatin 4) As the range arund the individual's cutff value (within which decisins are nt made using a strict relatin t this cutff value) appraches zer, the variable DNA, appraches a value f ne. Individual decisin criteria. The criteria the individual adpts in making decisins are measured using a fxinctin f the TSD parameter g.

24 -14- An empirical estimate fr the 3 f an individual, labeled 3.* is btained using the z. and z values assciated with the individual's cnditinal prbabilities emplyed in estimating d!. The measure 3. is relative t the decisin situatin. The 3. measure diverges frm the value f 3^ (generated using ptimal mdel k) as the result f several factrs: 1) the individual des nt prcess prperly the effects f the relative csts f the tw types f decisin errrs, 2) the individual des nt prcess prperly the effects f the relative frequencies f the tw states, and 3) the individual uses mre than ne cutff value. As befre, the 3. measure can be adjusted fr the effects f multiple a a cutff values (labeled 3^). A measure derived frm the 3. variable can be cnsidered a measure f individual anchring bias. In this study, anchring bias refers t the incmplete adjustment frm an initial decisin anchr twards the ptimal cutff value. Since the measure is dependent n the directin f adjustment it is cnditinal upn the level f the cst variable. Using BNC t dente the extent f an individual's anchring bias: BNCjCl = (3^ - 3,)/3,, and ^ IKK (Equatin 5) BNc^Ic2 = (3j^ - ei)/e^. BNC becmes larger as the extent f anchring bias increases, and appraches a value f zer as the extent f anchring bias decreases. Individual lng-run decisin efficiency. A majr dependent variable f interest in this research is the cst incurred as a result f the individual's variance investigatin decisins. The experimental bjective functin fr all decisin situatins is t minimize these csts.

25 . : -15- Since abslute investigatin decisin csts are nt cmparable between decisin situatins, a relative measure was used. Denting such a measure G.. it is cmputed as G^, = (IC^. - MC,.) / SMC, (Equatin 6) where IC.. = individual i's investigatin decisin cst fr decisin j ; MC,, = ptimal mdel k's investigatin decisin cst fr decisin j ; and SMC, = the sum f ptimal mdel k's investigatin decisin csts ver all decisins (m in number). The G. measure is the additinal cst f the decisins made by individual 1 abve the cst f the decisins made by ptimal mdel k as a percentage f the ttal cst f the ptimal mdel's decisins. This measure is cmputed as: m G, = Z G.., (Equatin 7) j=l ^ The lwer the G. value, the higher the decisin efficiency f the ith individual Simulatin and Hyptheses Frmatin Tw general types f simulatins are perfrmed simulatin f ptimal mdel perfrmances and simulatin f subjective investigatin decisin perfrmances. Simulatin f Optimal Mdel Perfrmances Optimal mdel perfrmance is simulated fr each treatment cnditin invlving the infrmatin, distributin, and cst variables. Fr each

26 -16- f the k treatment cnditins (k = 1,8), the utputs f the simulatin are the ptimal measures f decisin sensitivity, d\ decisin criteria, 3,, and iirvestigatin decisin cst fr each f j decisins, MC,., The simulatin is based upn varius assumptins and restrictins. First, the frm f the ptima] mdel is that represented by equatin 1. Secnd, the labr efficiency variance reprts used in the simulatin f ptimal mdel perfrmance are the same as thse presented t the subjects in the labratry experiment. Finally, the infrmatin available t the ptimal mdel is the same made available t an individual within the given treatment cnditin. Within ne level f the infrmatin variable the available infrmatin set des nt cntain all the parameters required t fit the ptimal mdel. Therefre, within this level the ptimal mdel must use the training sessin data t estimate the missing parameters. Simulatin f Subjective Mdel Perfrmances Subjective mdel perfrmance is simulated fr each treatment cnditin invlving the infrmatin, distributin, and cst variables. The simulatin f subjective mdel perfrmances is accmplished in tw stages. The first stage simulates the pst-training subjective decisin heuristic. The secnd stage simulates the main experiment subjective perfrmance measures. The first stage f simulatin is based upn varius assumptins and restrictins. First, the subjects will behave as if they use the anchring and adjustment heuristic during the training phase f the experiment. Secnd, the pre- training decisin anchr will be lcated 9 at a central pint between the means f the tw states f cntrl. Finally, the subjective adjustment prcesses will be apprximately equal

27 , ver the treatment cnditins. Due t the anchring and adjustment assumptin, the subjective adjustment prcess Is defined as a linear mvement alng the standard cst variance axis frm the initial decisin anchr twards the apprpriate ptimal decisin cutff. Fr each f the i treatment cnditins Ci=l,8) the utput f the first stage f simulatin is the subjective decisin heuristic (the variance cutff value used t make investigatin decisins) The secnd stage f the simulatin is based upn the assumptin that the subjective decisin cutff values btained in the first stage will be cnsistently used in making the variance investigatin decisins within the main experiment. Fr each f the 1 treatment cnditins, the utputs f the secnd stage f simulatin are the subjective mdel declsln criteria, 3., and the subjective mdel investigatin decisin cst fr each f j decisins, IC,. The perfrmance measures btained frm the ptimal mdel and subjective mdel simulatins will be cmbined (using Equatins 5, 6, and 7) t frm simulated BNC and G, measures fr each treatment cmbinatin. Hyptheses Frmatin The DNA, (Equatin 4) variable, which measures the relative deviatin f the individual's decisin mdel sensitivity frm that f the ptimal mdel's, is due generally t individual decisin incnsistencies (a variable respnse range) and temprary prcessing errrs, and shuld be unrelated t the independent variables. If individuals are randcmily assigned t the specific decisin situatins there is n a priri reasn t expect that significant differences in this measure are due t the variatins in the independent variables.

28 -18- The effects f the independent variables n the measure f individual decisin sensitivity can be hypthesized as fllws; Hl.l (MA. jll) = (DNA. I2). The infrmatin variable will have n significant effect n the subjects' relative decisin mdel sensitivity, HI. 2 (DNA^iSl) = (DNA^lS2). The distributin variable will have n significant effect n the subjects' relative decisin mdel sensitivity. HI. 3 (DNA^ICl) = (DNA^ C2). The cst variable will have n significant effect n the subjects' relative decisin mdel sensitivity. The variable f individual anchring bias, BNC., was simulated by cmbining the utput f the ptimal mdel simulatin, B.» and the utput f the subjective mdel simulatin, g., where i and k are the unique treatment cnditins (bth i and k = 1,8). The definitin f the BNC. variable is the same as described abve (Equatin 5). The results f this cmbinatin prcess were averaged ver all cnditinals except fr the independent variable f interest. Ptentially significant main effects f an independent variable were identified by cmparing the difference between the average BNC, given the levels f the independent variable t the standard errr f their estimates. This prcedure resulted in identifying ne independent variable with a ptentially 12 significant main effect, the cst variable. The main effect suggests that the individual anchring bias will be greater when given the C2 cst variable level than when given the CI level. Given the assumptins f the subjective mdel simulatin, the mre extreme the ptimal

29 II) -19- cutff relative t the assumed initial cutff (anchr) the greater shuld be the individual anchring bias. The treatment cnditins with the mst extreme ptimal cutffs are thse within the C2 cst level. The hyptheses cncerning the effects f the independent variables upn individual anchring bias can be summarized as fllws: H2.1 (Inc. I = (Inc. 1x2). The infrmatin variable will have n significant effect n the subjects' anchring bias. H2.2 (BNC^ISl) = (BNC^lS2). The distributin variable will have n significant effect n the subjects' anchring bias. H2.3 (BNC^ICI) < (BNC^ C2). The anchring bias f thse subjects within the CI cst level will be significantly smaller than that f thse subjects within the C2 level. The individual lng-run decisin efficiency variable, G,, was simulated by cmbining the utput f the ptimal mdel simulatin, MC,., and the utput f the subjective mdel simulatin, IC.. where k and i are the unique treatment cnditins. The definitin f the G. variable is the same as described abve (Equatin 7). The results f this cmbinatin prcess were averaged ver all cnditinals except fr the independent variable f interest. Ptentially significant main effects f an independent variable were identified by the same techniques used fr the individual anchring bias variable. This prcedure resulted in identifying ne independent variable with a ptentially significant main 13 effect, the cst variable. This main effect suggests that individual

30 lng-run decisin efficiency will be greater when given the CI cst level than when given the C2 level. The results f this cmbinatin prcess were extended by averaging ver all cnditinals except fr pairs f independent variables f interest. Ptentially significant interactin effects f a pair f independent variables were identified using the same techniques as described abve. This prcedure resulted in identifying ne interactin with a ptentially significant effect, the cst by distributin vari- 14 ables. This interactin suggests that the distributin variable is effective at ne level nly f the cst variable (the C2 level) Given the abve discussin the effects f the independent variables n the G. measure can be hypthesized as fllws: H3,l (G^ C1) < (G^ C2), Thse subjects within the Cl cst level will have significantly greater relative decisin efficiency than will thse subjects within the C2 level (recall there is an inverse relatinship between G. and decisin efficiency) H3,2 (G^ S2) < (G^lSl). Thse subjects within the SI distributin level will have smaller relative decisin efficiency than will thse subjects within the S2 level. Significance is nt predicted due t the interactin effect with the cst variable, H3.3 (G^ ll) = (G^ I2). The infrmatin variable will have n significant effect n the subjects' relative decisin efficiency.

31 -21- H3.4 [(G^1S1,C1) - (G^S2,C1)] < [(G^ S1,C2) - (G^ls2,C2)]. There will be a significant interactin f the distributin and cst variables in which the distributin variable will have n significant effect given the CI cst level but will have a significant effect given the C2 cst level. Experimental Materials The Experiment The experimental materials included a backgrund infrmatin bklet, variance investigatin decisin stimuli, and a mtivatin questinnaire. Backgrund infrmatin. A backgrund infrmatin bklet was designed t prvide the subjects with a cmmn experimental envirnment. The bklet prvided the subject with general cmpany infrmatin, general prduct infrmatin, general manufacturing prcess infrmatin, and specific assembly department infrmatin. The specific assembly department infrmatin included infrmatin cncerning the emplyees, the physical prcess, the accunting cntrl system, the subject's task as the peratinal manager, and the subject's perfrmance evaluatin as the peratinal manager. Variance investigatin decisin stimuli. Each variance investigatin decisin trial cnsisted f the presentatin f a labr efficiency variance reprt and a subject's respnse t tw questins. The questins were: 1) wuld yu investigate this reprted variance, and 2) hw strngly d yu feel abut yur decisin? During the training phase each decisin trial was fllwed by feedback cncerning the actual state

32 f the assembly line and the actual csts incurred fr each pssible decisin given the actual state. Decisin trials were presented in bklets f 33 trials (each trial included the reprt with questins fllwed by the feedback). Within the experimental phase decisin trials were presented in bklets f 50 trials (each trial included nly the reprt with questins) An example f a labr efficiency variance reprt with the set f questins is presented in Appendix A. The frmat f the reprt and questins was cnstant fr all treatment cnditins. Varius infrmatin cnstant ver all decisin trials within a treatment cnditin were presented n a separate page prir t the start f the decisin trials. Elicitatin f subject mtivatins. Subject mtivatins were elicited using a mtivatin questinnaire develped by Snwball and Brwn (1977). The questinnaire is a ten item Likert-type scale which has suit easures A fr bth intrinsic and extrinsic mtivatin. Experimental Prcedures Experimental prcedures included assignment f subjects t treatment cnditins, administratin f a training phase, administratin f an experimental phase, and final debriefing. Assignment f subjects t treatment cnditins. Since each f the 86 subjects was assigned t ne f eight grups, randmizatin per se culd nt be relied upn t cntrl fr individual attribute differences between grups. An alternative is t blck the randmizatin prcess n individual attribute dimensins assumed t significantly affect the subject's infrmatin prcessing within the task required by the experiment.

33 -23- In the present study, the randmizatin prcess f assigning subjects t treatment cnditins was blcked n individual intelligence. Thse subjects with a GPA abve the median fr all subjects were categrized as abve average intelligence and thse subjects with a GPA belw the median were categrized as average intelligence. Each subject within an intelligence categry then was assigned randmly t ne f the treatment cnditins with the restrictin that each intelligence grup cntributed an equal nvmiber f subjects t each cnditin. Upn assignment t a treatment cnditin each subject received the backgrund infrmatin bklet. Training phase. Each subject received training within the treatment cnditin t which he was assigned. Training was cnducted in grups f tw subjects within a 50 minute sessin administered by either the experimenter r by an experimental assistant. Training f all subjects (within each week) was cmpleted ver tw cntiguus days. The decisin trials with feedback were presented in three bklets f 33 trials, and additinal perfrmance feedback was given at the cmpletin f each bklet. This additinal feedback was the the perfrmance measure upn which the subject's payment wuld be based when in the main experiment. Experimental phase. The experimental sessin lasted ne hur and was administered by the experimenter. The experimental phase (within each week) was cmpleted ver tw cntiguus days immediately fllwing the training phase. The experimental sessin cnsisted f tw parts. The first part was the sequential presentatin f 100 decisin trials, the cmpletin

34 -24- f which twenty minutes were allwed. The secnd part f the experimental phase was the administratin f the mtivatin questinnaire. Final debriefing. Each subject's final perfrmance measure fr the variance investigatin decisins part f the experimental phase was presented individually at a later date. At this time cash payment was determined, the subject was debriefed as t the purpse f the experiment, and any questins were answered. Analyses and Results The methd f analysis emplyed is analysis f variance using the mdel cmparisn prcedure (Appelbuam and Cramer, 1974; Lewis and Keren, 1977). This methd f analysis was selected due t the nnrthgnality f the data structure. The prblem f nnrthgnality arises in this instance as a result f nn-equal cell frequencies. The mdel cmparisn prcedure invlves fitting a linear mdel allwing fr certain effects, and then cmparing the btained fit t that f a linear mdel which mits ne r mre f the effects. The bjective is t find the simplest mdel that adequately fits the data. The prcedure begins with the cmplete r full mdel (which allws fr all effects) and eliminates effects starting with the highest rder interactins. The F test as described by Lewis and Keren (1977) is used t test the fit f the varius mdels. Given a dependent variable, after the simplest (r reduced) mdel is fund the surces f this mdel are presented with their crrespnding F values. These surce F values are the same as mdel cmparisn F

35 -25- values where the cmparisn mdels are the reduced mdel and the reduced mdel withut the crrespnding surce. Unless therwise specified all 2-way interactins are tested against the withut 3-way interactin mdel, emplying the assumptin that the 3-way interactin effect is equal t zer within the subject ppulatin* All individual attributes are retained in each mdel, emplying the assumptin that these effects are nt necessarily equal t zer within the subject ppulatin. Relative Decisin Mdel Sensitivity Hyptheses 1.1, 1.2, and 1.3 relate t the adjusted relative deviatin f the individual's decisin mdel sensitivity frm ptimal sensitivity. The methd f analysis is the mdel cmparisn prcedure where the dependent variable is the DNA. measure. The independent variables are the three situatin variables (with their interactins), the three mtivatin factrs, and the GPA variable. The mdel cmparisn prcedure results and the F values assciated with the surces f the reduced mdel are presented in Table 2. The reduced mdel cntains the cst variable (significant at ^pife p<.01 Ifi^l) and the intrinsic mtivatin factr (significant at jife p<,10 fel), Means, variances, and sample sizes f the dependent variable given the levels f the significant situatin variable are included in Table 2. Using the F test f equal variances, the variances between the levels f the cst variable differ significantly (F=2.57, p<,01). The n difference predictins f bth hyptheses 1.1 and 1.2 are cnfirmed by the results. Hwever, hypthesis 1.3 which predicted n

36 difference between (DNA; C1) and (DNA; [C2) was nt cnfirmed. The CI cst level has a significantly larger mean (p<.01). Insert Table 2 abut here Relative Anchring Bias Hyptheses 2.1, 2.2 and 2.3 relate t the effect f anchring bias n relative individual decisin criteria. The methd f analysis is the mdel cmparisn prcedure where the dependent variable is the BNC. measure ind the independent variables are the three situatin variables (with their interactins), the three mtivatin factrs, and the GPA, variable The mdel cmparisn prcedure results and the F values assciated with the surces f the reduced BNC. mdel are presented in Table 3. The results indicate that the reduced BNC. mdel cntains the cst variable (significant at p<,05), the extrinsic (mnetary) mtivatin factr (significant at p<,01), and the GPA. variable (significant at p<.05). The means, variances, and sample sizes f the BNC. measure given the levels f the cst variable are included in Table 3. Using the F test f equal variances, the variances f the BNC. given the levels f the cst variable differ significantly (F=9.36, p<.01). The n difference predictins f bth hyptheses 2.1 and 2.2 are cnfirmed by the results. Hypthesis 2.3 predicted that (BNC, cl) wuld be significantly smaller than (BNC. C2) and the mdel cmparisn prcedure indicates that the difference is significant (p<.05). Hwever, the results are the ppsite f the predictin, with the C2 level having the smaller mean.

37 -27- Insert Table 3 abut here Individual Lng-Run Decisin Efficiency Hyptheses 3.1, 3.2, 3.3. and 3.4 relate t the decisin csts incurred by the individuals relative t the decisin csts incurred by the ptimal mdels. The methd f analysis was the mdel cmparisn prcedure where the dependent variable is the G. measure. The independent variables are the three situatin variables (with their interactins), the DNA. variable, the three mtivatin factrs, and the GPA. variable. Ideally, bth the DNA. and the BNC. variables shuld be inrcluded in the mdel; hwever, the BNC. variable had significantly greater assciatin with the ther independent variables than did the DNA. var- 2 2 iable (the R fr the full BNC. mdel was 0.647A, whereas the R fr the full DNA^ mdel was ). The mdel cmparisn prcedure results and the F values assciated with the surces f the reduced mdel are presented in Table 4. The results indicate that the reduced G. mdel cntains the distributin by cst interactin (significant at p<.01) and the DNA. variable Csignificant at p<.01). The means, variances, and sample sizes fr the distributin by cst interactin within the G. mdel are Included in S Table 4. Bartlett's test fr hmgeneity f variances indicate^ that the variances within the G, mdel distributin by cst interactin are 2 significantly hetergeneus (x =10.66, p<.05 with 3 d.f.). Hypthesis 3,4 predicted a significant distributin by cst interactin within the G. mdel in which (g" S1,C1)-(G S2,C1) wuld be

38 -28- smaller than (G. S1,C2)-(G S2,C2). The results f the mdel canparisn. prcedure cnfirm this hypthesis. Hypthesis 3.1 predicted that (G.lCl) wuld be significantly smaller than (G. IC2) and hypthesis 3,2 predicted that (g". S2) wuld be smaller than (g". S1). Bth hyptheses are cnfirmed by the results. Hypthesis 3,3, predicted that CG. ll) wuld nt differ significantly frm (G. 12), and the results indicate that the difference was nt significant. Insert Table 4 abut here Discussin f Results Overall Results First, variable respnse ranges were smaller and decisin anchring biases were larger within situatins where the adjustment prcess invlved cnvergence tward the standard. Althugh these effects were cntrary t thse predicted, the cncept f a subjective adjustment limit prvides a plausible explanatin. If this phenmenn exists it wuld affect bth the variable respnse range and the decisin anchring biases in the same manner as the btained results. Secnd, the btsiined effects f the situatin variables n the relative decisin csts were weaker than thse predicted. The predicted effects were based, in part, upn the assiimptin f equal learning efficiency between the levels f the varius situatin variables. Hwever, the btained learning efficiencies were nt equal between varius situatin variable levels. Incrpratin f unequal learning efficiencies within the predictin f the effects f the situatin variables n the relative decisin csts will prduce expected effects with strengths similar t the btained effects.

39 -29- Overall, the variable which had the largest impact n the relative decisin csts was the subjects' decisin anchring bias. The mean BNC. ver all subjects was This indicates that the average distance between the subjects' decisin criteria and the ptimal decisin criteria was 82,8 percent f the ptimal decisin criteria. Hwever, even given this level f verall decisin anchring bias the mean relative decisin efficiency (G.) ver all subjects was 0,06 (the average subject's ttal decisin csts were 6,0 percent greater than the ptimal mdel's ttal decisin csts). The mean DNA. measure ver all subjects was 0,926, Cnsidering that a value f ne wuld indicate variable respnse ranges were nt used, the subjects' verall decisin mdel sensitivity was apprximately that f the ptimal mdel. Discussin f Results Individual decisin mdel sensitivity. The variable respnse range (the DNA. measure) was affected primarily by the cst variable. That is, thse subjects within the C2 cst level demnstrated larger variable respnse ranges than did thse subjects within the CI cst level. The subjective adjustment limit cncept may explain the difference in effect f the cst variable n the DNA. measure. When the adjustment prcess invlved cnvergence tward the standard the subjects may have perceived the standard as a limit t their adjustment prcess, a limit which they culd have been reluctant t apprach. When the adjustment prcess invlved divergence frm the standard the subjects did nt have an bjective value t perceive as a limit t their adjustment prcess. Whether a subject's adjustment prcess invlved cnvergence tward r divergence frm the standard depended upn the lcatin f his initial decisin

40 -30- anchr relative t the ptimal decisin value. This factr was cnditinal upn the cst variable. Given the CI level the subject's initial decisin anchr was greater than the ptimal decisin value and the adjustment prcess invlved cnvergence tward the standard. Given the C2 level the ppsite held; i.e., the subject's initial decisin anchr was less than the ptimal decisin value and the adjustment prcess invlved divergence frm the standard. As subjects' adjustments within the CI level cnverged tward the standard, the subjective limit f the standard culd have acted as an intervening variable which reduced the relative magnitude f the variable respnse range. As subjects' adjustments within the C2 cst level diverged frm the standard n such subjective adjustment limit existed; therefre, the relative magnitude f 18 the variable respnse range culd have increased. Individual decisin anchring bias. The efficiency f infrmatin prcessing (BNC.) was affected primarily by the cst variable. Subjects within the CI cst level exhibited significantly greater anchring bias than did subjects within the C2 cst level. These results were the reverse f thse predicted by the hyptheses. The subjective limit cncept again may be intrduced as a pssible explanatin. As subjects' adjustments within the CI cst level cnverged tward the standard, the subjective adjustment limit f the standard culd have acted as an intervening variable which increased the level f anchring bias. As subjects' adjustments within the C2 cst level diverged frm the standard n such subjective adjustment limit existed, thus the level f anchring bias 19 culd have decreased.

41 -31- Individual lng-run decisin efficiency. The supprt btained fr the hyptheses cncerning the decisin efficiency variable wuld suggest that unequal learning efficiency did nt have a significant effect n the relative decisin csts. The majrity f the unequal learning efficiency ccurred between the levels f the cst variable Cthe mst significant effect > decisin anchring bias, was greater within the CI cst level than within the C2 level). A clser examinatin f bth the simulated and the btained cst variable effects n the G. variable indicated that the btained effects were nt as strng as the simulated effects. The simulated effects may be adjusted fr unequal learning efficiency by assuming that the effects f decisin csts within the CI cst level were increased by a factr f tw (relative t the C2 cst level). After this adjustment the simulated cst variable effects have similar strengths as 20 the btained effects. The simulated and btained distributin by cst variable interactins invlving the relative decisin csts had similar relatinships between their strengths; the effects f the btained interactin were nt as strng as the effects f the simulated interactin. Again, if the simulated effects are adjusted fr unequal learning efficiency, then the simulated distributin by cst variable interactin has a strength similar t that f 21 the btained effects. Limitatins There are several pssible limitatins invlving the experimental envirnment. First, the precisin f subject perfrmance feedback during the training phase culd be a limitatin. The results btained in this study might be mdified substantially if such accurate feedback was nt

42 -32- emplyed. The lack f decisin perfrmance difference between the levels f the available infrmatin variable culd be a direct result f this feedback; i.e., the accuracy f the perfrmance feedback and its relatinship with the ptimal mdel may have replaced the need fr such additinal statistical infrmatin. Secnd, the backgrund f the subjects in relatin t the experimental task is a pssible limitatin. Althugh the subjects received training in the experimental task, the primary surce f their knwledge cncerning standard cst variance investigatin may cme frm the cllege classrm. Cnsequently, if they were nt taught (within the classrm) that situatins exist in which investigatin decisin values are lcated relatively clse t the standard, then greater decisin anchring bias within these situatins culd be the result f the lack f such knwledge. This suggests the pssibility f an availability bias (Tversky and Kahneman, 1973). Hwever, t the extent a manager must learn frm his wn experiences, such a bias culd exist in the real wrld. Anther limitatin invlves the selectin f the levels f the situatin variables. Only specific cmbinatins f variable levels were studied within this research whereas an infinite nimber f cmbinatins are pssible. Different variable levels and different variable manipulatins wuld create a difference in the experimental envirnment which culd prduce results ther than thse btained in this study. Implicatins fr Accunting Value f additinal infrmatin. The manipulatin f the infrmatin variable invlved the quantity f infrmatin cntained in the available infrmatin set, specifically the presence r absence f varius distri-

43 -33- butin infrmatin items. The results indicated that the infrmatin variable did nt have a significant effect upn the relative individual decisin csts. An Implicatin f this result cncerns the net benefit (fr the cmpany) f prviding the additinal infrmatin within the expanded infrmatin level. The additinal infrmatin within an actual envirnment is nt cstless. Cnsequently, if such infrmatin is t be prvided, ther things being equal, the net benefit f such an actin shuld be psitive. Within this research the lack, f a significant infrmatin variable effect implies that the net benefit f prviding additinal infrmatin may nt be psitive. Hwever, it shuld be nted that the lack f an infrmatin variable effect culd be the result f either; 1) that the subjects within the reduced infrmatin level were able t estimate (with relative efficiency) the missing infrmatin as a result f their training experiences with the decisin task r 2) that the subjects within the expanded infrmatin level did nt utilize the additinal infrmatin efficiently. General standard setting prcess. Anther Implicatin f this research cncerns the general standard setting prcess. The standard used within this research may be cnceived f as a type f decisin behavir limit. Decisin anchring bias when the adjustment prcess diverges frm the standard culd be the result f the attractin f the standard that restrains the individual frm making a cmplete (divergent) adjustment t the ptimal decisin value. Decisin anchring bias when the adjustment prcess cnverges tward the standard culd be the result f the repelling frce f the standard, subjectively limiting a cmplete (cnvergent) adjustment t the ptimal decisin value.

44 -34- The questin f whether decisin makers culd learn Celther thrugh training r experience) t reduce the decisin biases implied by the decisin behavir limit cncept remains unanswered. An alternative apprach, hwever. Invlves the standard setting prcess Itself, Previus research invlving the nature f standards (e,g,, strict standards, currently attainable standards, laz standards) has primarily dealt with the standard's mtivatinal affects. Other things being equal, the nature f the standard may affect the decisin behavir limit biases. Within situatins requiring divergent adjustment, lax standards Cwhlch have values greater than the mean f the In-cntrl state) may reduce divergent decisin anchring bias. Within situatins requiring cnvergent adjustment, strict standards (which have values less than the mean f the in-cntrl state) may reduce cnvergent decisin anchring bias.

45 FOOTNOTES Sme research n this prblem has fllwed. Magee (1976) and Magee and Dickhaut (1978) Investigate pssible effects f manager perfrmance measures upn manager variance investigatin decisin heuris tics 2 An in-cntrl distributin cncerns statistical cngruence f prductin utput and planned utput in terms f cntrllable resurce utilizatin. 3 Tw theretical descriptins f TSD are presented by Green and Swets (1974) and Egan (1975); general surveys f the TSD thery are presented by Cmbs et al. (1970), Watsn (1973), and Pastre and Scheirer (1974). 4 These extensins t cnceptual prcesses have included numerical prcessing (Lieblich and Lieblich, 1969; Hammertn, 1970; Weissman et al,, 1975), medical diagnsis (Lusted, 1969; Lusted, 1971; Swets, 1972), cnceptual judgement (Ulehla et al., 1967a; Ulehla et al., 1967b), and memry (Bemabach, 1967; Banks, 1970). sn) /f (sn) where f(») dentes fre- Fr example, P(Yes sn) = f (Yes quency f ccurance fr the event in parentheses. Since the event utcmes are bth exhaustive and mutually exclusive, P(N sn) = 1 - P(Yes sn), A similar prcedure applies fr the remaining cnditinal prbabilities, P(Yes n) and P(N n). One set f TSD mdels assumes that bth cnditinal prbability distributins are Gaussian. The TSD parameters used in this study assume equal variance nrmal distributins. Such an assumptin is nt necessary t emply TSD. Egan (1975) demnstrates the use f TSD with expnential distributins, chi-square distributins, Bernulli distributins, and Pissn distributins. Grier (1971) develped nnparametric measures f discriminability and decisin criteria. Sme ther infrmatin prcessing and decisin rule biases identified thus far have been labeled as a representative heuristic (Tversky and Kahneman, 1971; Kahneman and Tversky, 1972; Swieringa et al., 1976) and an availability heuristic (Tversky and Kahneman, 1973). g Three subject selectins criteria were applied: 1) the subject must have cmpleted an intermediate-level managerial accunting curse, 2) the subject must have cmpleted an intrductry- level statistics curse, and 3) the subject must have earned an verall grade pint average (GPA) f at least 2.0 n a 4.0 scale. The gemetric intersectin pint f the tw states' distributin curves is arbitrarily emplyed as the initial decisin anchr (any

46 pint f central tendency wuld be equally valid), The lcatin f this initial decisin anchr is dependent upn the level f the distributin variable. The magnitude f the linear mvement used in the simulatin is is 50 percent f the distance between the initial decisin anchr and the apprpriate ptimal mdel decisin cutff. The 50 percent adjustment value is arbitrarily selected. The assumptin f an equal 50 percent adjustment ver the treatment cnditins is nt critical t the cnceptual develpment; the bjective is t facilitate a simple peratinalizatin f the anchring and adjustment heuristic. Given any dichtmus variable (e.g,, infrmatin, distributin, r cst) and eight treatment cnditins, fur cnditins will include the variable at ne level and fur cnditins will include the variable at the secnd level. 12 The prcedure fr the cst variable resulted in: ^ '^^ g,^-^l /.02a The prcedure fr the cst variable resulted in: G lc2 - G C1 -^.033/ = JsJ + S^ m: 14 The prcedure fr the cst by distributin variables resulted (G^ S1,C2 - G^ S2,C2) - (G^ S1,C1 - G^ S2,C1) / 'S^ ^ =.0291/ = Additinal infrmatin, nt analyzed in this paper, were cllected frm the subjects in the frm f a heuristics questinnaire. Fr mre detail see Brwn (1978) Ideally, individual intelligence shuld be measured using seme validated instrument (e.g., the Wesman Persnnel Classificatin Test r the Wechsler Adult Intelligence Scale), Due t resurce limitatins, hwever, subject grade pint average (GPA) was used as a surrgate fr such a measure.

47 CI) CI) -37- The assignment prcess invlved tvr prcedures. First, the assignment t the cst variable levels was by the week in which the subject participated in the experiment. Thse subjects wh participated during the first week were assigned t the CI cst level, and thse subjects wh participated during the secnd week were assigned t the C2 cst level. Secnd, the assignment t the infrmatin variable and the distributin variable levels was by randm selectin based upn a randm number table, 18 The higher (statistical) mments f the DNA, measure given the levels f the cst variable were cnsistent with tne cncept f a subjective adjustment limit. Such a limit shuld have had the effect f reducing the variance f this measure. The test f the variances indicated that (DNA. CI) had a significantly smaller variance than (DNA, C2), I The subjective limit als shuld have had the effect f skewing the measure away frm thse values which indicated larger variable respnse ranges. The third mment (as expressed by the cefficient f skewness) f the DNA. measure indicated that: 1) the skewness f the (DNA. C1) distributin was psitive (skated away frm values which indicated larger variable respnse ranges), and 2) the skewness f the (DNA, C2) distributin was negative (skewed tward values which indicated larger variable respnse ranges), 19 The higher (statistical) mments f the BNC. measure given the levels f the cst variable were cnsistent with the cncept f a subjective adjustment limit. The difference in variances f the BNC, measure was due t the difference in the ranges f the measure. The range f (BNC, was 4.67 and the range f (BNC. C2) was 1,57, The subjective adjustment limit shuld have had the effect f skewing the measure away frm thse value which indicated lwer levels f anchring bias. The third mment (as expressed by the cefficient f skewness) f the BNC. measure indicated that: 1) the skewness f the (BNC. distributin was psitive (skewed away frm values which indicated lwer levels f anchring biasj^ and 2) the skewness f the (BNC. C2) I distributin was negative (skewed tward values \rfiich indicated lwer levels f anchring bias). 20 Nte that fr the btained results: G. C2 / GJCI = 1,61, whereas fr the expected results: E(G^iC2) / E(G^1C1) = 3,27, The affect f the unequal learning efficiency adjustment wuld give expected results f:

48 -38- E(G^lC2) / 2E(G^ C1) = Nte that fr the btained results: (G. S1,C2) - (G. S2,C2) -f -^^ = 3.15, (G^ S1,C1) - (G^ S2,C1) whereas fr the expected results: E(G S1,C2) - E(G ls2,c2) J: -J: = E(G^ S1,C1) - E(G^ S2,C1) The affect f the unequal learning efficiency adjustment wuld give expected results f: E(G S1,C2) - E(G S2,C2) ^ ^ = [E(G S1,C1) - E(G ls2,cl)] M/B/128

49 BIBLIOGRAPHY Alpert, M., and Raiffa, H. "A Prgress Reprt n the Training f Prbability Assessrs." Unpublished manuscript. Harvard University, Appelbaum, M. I., and Cramer, E. M. "Sme Prblems in the Nnrthgnal Analysis f Variance." Psychlgical Bulletin, 81 (1974), Banks, W. P. "Signal Detectin Thery and Human Memry." Psychlgical Bulletin. 74 (1970), Bernbach, H. A. "Decisin Prcesses in Memry." Psychlgical Review, 74 (1967), Bierman, H. ; Furaker, L. E.; and Jaedicke, R. K. "A Use Prbability and Statistics in Perfrmance Evaluatin." The Accunting Review, 36 (1961), Brwn, C. E. Human Infrmatin Prcessing fr Decisins t Investigate Cst Variances. Dctral dissertatin. University f Flrida, 1978, Cmbs, C. H.; Dawes, R. M.; and Tversky, A. Mathematical Psychlgy; An Elementary Intrductin. Englewd Cliffs, N.J.: Prentice-Hall, Inc., Dittman, D. A., and Prakash, P. "Cst Variance Investigatin: Markvian Cntrl f Markv Prcesses." Jurnal f Accunting Research, 16 (1978), Dyckman, T. R. "The Investigatin f Cst Variances." Jurnal f Accunting Research, 7 (1969), Egan, J. P. Signal Detectin Thery and ROC Analysis. New Yrk: Academic Press, Inc., Green, D. M., and Swets, J. A. Signal Detectin Thery and Psychphysics. New Yrk: Rbert E. Krieger Publishing C., Grier, J. B. "Nbnparametric Indexes fr Sensitivity and Bias: Cmputing Frmulas." Psychlgical Bulletin, 75 (1971), Hammertn, M. "An Investigatin int Changes in Decisin Criteria and Other Details f a Decisin-Making Task." Psychnmic Science, 21 (1970), Jacbs, F. H. "An Evaluatin f the Effectiveness f Sme Cst Variance Investigatin Mdels." Jurnal f Accunting Research, 16 (1978),

50 . Juers, D. A. "Statistical Significance f Accunting Variances." Management Accunting, 49 (1967), Kahneman, D., and Tversky, A. "Subjective Prbability: A Judgement f Representativeness." Cgnitive Psychlgy, 5 (1972), Kaplan, R. S. "Optimal Investigatin Strategies with Imperfect Infrmatin." Jurnal f Accunting Research. 7 (1969), "The Significance and Investigatin f Cst Variances: Survey and Extensins." Jurnal f Accunting Research, 13 (1975), Kehler, R. W. "The Relevance f Prbability Statistics t Accunting Variance Cntrl." Management Accunting, 50 (1968), Lewis, C, and Keren, G. "Yu Can't Have Yur Cake and Eat It T: Sme Cnsideratins f the Errr Term." Psychlgical Bulletin, 84 (1977), Lieblich, A., and Lieblich, I. "Arithmetical Estimatin Under Cnditins f Different Payff Matrices." Psychnmic Science, 14 (1969), Luh, F. "Cntrlled Cst: An Operatinal Cncept and Statistical Apprach t Standard Csting." The Accunting Review, 43 (1968), Lusted, L. B. "Perceptin f the Rentgen Image: Applicatins f Signal Detectability Thery." Radilgical Clinics f Nrth America, 3 (1969), "Signal Detectability and Medical Decisin-Making." Science, 171 (1971), Magee, R. P. "A Simulatin Analysis f Alternative Cst Variance Investigatin Mdels." The Accunting Review, 51 (1976), , and Dickhaut, J. W. "Effects f Cmpensatin Plans n Heuristics in Cst Variance Investigatins." Jurnal f Accunting Research, 16 (1978), Pastre, R. E., and Scheirer, C. J. "Signal Detectin Thery: Cnsideratins fr General Applicatin." Psychlgical Bulletin, 81 (1974), Prbst, F. R. "Prbabilistic Cst Cntrls: A Behaviral Dimensin." The Accunting Review, 46 (1971), Scheffe, H. The Analysis f Variance. New Yrk: Jhn Wiley and Sns, Inc., 1959.

51 Slvic, P. "Frm Shakespeare t Simn: Speculatins and Sme Evidence Abut Man's Ability t Prcess Infrmatin." Oregn Research Institute Bulletin. 12 (1972)., and Lichtenstein, S. "Cmparisn f Bayesian and Regressin Appraches t the Study f Infrmatin Prcessing in Judgement." Organizatinal Behavir and Human Perfrmance, 6 (1971), Snwball, D., and Brwn, C. "Decisin-Making Invlving Sequential Events: Sme Effects f Disaggregated Data and Dispsitins Tward Risk." Decisin Sciences, frthcming. Swets, J. A. "Signal Detectin in Medical Diagnsis." In Cmputer Diagnsis and Diagnstic Methds, J. A. Jacquez (Ed.). Springfield, IL: C. C. Thmas, Swieringa, R. J.; Gibbins, M.; Larssn, L.; and Sweeney, J. L. "Experiments in the Heuristics f Human Infrmatin Prcessing." Jurnal f Accunting Research: Supplement n Studies n Human Infrmatin Prcessing in Accunting, 14 (1976), Tversky, A., and Kahneman, D. "Belief in the Law f Small Numbers." Psychlgical Bulletin. 76 (1971), "Availability: A Heuristic fr Judging Frequency and Prbability." Cgnitive Psychlgy. 5 (1973), "Judgement Under Uncertainty: Heuristics and Biases." Science, 185 (1974), Ulehla, Z. J.; Ganges, L.; Wackwitz, F. "Signal Detectability Thery Applied t Cnceptual Discriminatin." Psychnmlc Science, 8 (1967a), "Integratin f Cnceptual Infrmatin." Psychnmic Science, 8 (1967b), Watsn, G. S. "Psychphysics." In Handbk f General Psychlgy, B. B. Wlffian (Ed.). New Yrk: Prentice-Hall, Inc., Weissman, S. M.; Hllingwrth, S. R.; Baird, J. C. "Psychphysical Study f Numbers: III. Methdlgical Applicatins." Psychlgical Research, 38 (1975), Zannets, Z. A. "Standard Cst as a First Step t Prbabilistic Cntrl: A Theretical Justificatin, and Extensin and Implicatins." The Accunting Review, 39 (1964),

52 AMSE Metal Flding Chair Assembly Department Labr Efficiency Variance Reprt Fr Jb 5247 Standard Minutes Allwed Per Chair Actual Incurred Minutes Per Chair Labr Efficiency Variance Per Chair Ttal Chairs Prduced The Csts Assciated With Investigatin Are: If Yur And If The Then Yur Csts Are Investigatin Assembly Line Decisin Is State Is Investigatin Prductin Ttal ********** ********-* ** ***++*****+** ***********+ ************ Yes In-Cntrl $ $ 0.00 $ Yes Out-Of-Cntrl $ $ 0.00 $ N In-Cntrl $ 0.00 $ 0.00 $ 0.00 N Ou-Of-Cntrl $ 0.00 $ $ ########################################################?#################### Please answer the fllwing questins placing yur answers n the answer sheet: A. Wuld yu investigate this reprted variance /circle the apprpriate respnse n the answer sheet/ NO YES Hw strngly d yu feel abut yur decisin /select a number between and 100 which indicates the strength f yur feeling and place this number n the answer sheet/ *********** *************************************************** * Uncertain Reasnably Certain Almst Certain APPENDIX Ai VARIANCE INVESTIGATION DECISION TRIAL

53 Nise distributi Signal plus nise distributin stimulus values 'sn Nise distributi Signal plus nise distributin d)(z ^^ ) sn-^ Stimulus values n ^sn mean f the nise distributin mean f the signal plus nise distributin subjective decisin cutff FIGURE 1 GRAPHIC REPRESENTATION OF THE TSD PARAMETERS

54 TABLE 1 GENERAL RESEARCH DESIGN IN TEEMS OF EXPERIMENTAL VARIABLES Dependent Variables Independent Variables Individual Lng-Run Decisin Efficiency Individual Decisin Mdel Sensitivity Individual Decisin Criteria Cntents f the available infrmatin set X X Structure f the decisin situatin: Statistical relatinships X X X Decisin utcme relatinships X X X Individual decisin mdel sensitivity X Nte: An "X" within a cell indicates that the relatinship f the variables cncerned are included within the experimental design.

55 CD 4-1 (U CM cn O Vi H H U Q O S M O ss &4 fe I I I I 0) 3 O 'r < CVI 00 en u m m en 00 fn >> 00 en l-l c u ^ Q)?s H fl ^ 1^ O c 00 U t e 4J > 1 3> H a a a G 4J O C -H a -H v_^ S JJ s_/ JJ O > O > H W -H H -H 4-1 4J C c c H rt s H g 4-1 )^ u )^ 4J 4J 4J c»< X U M u Cd vo i-l ^ rh >3- a\ en CM «3- OO ^!hj en i-h vo \ CM CT. r^ v 0^ 00 r^ ^ u CTi en esi u 4-) c 3 * 0) H V4 0) «IH. B (U ih t 0) H V )H G > t 4-) x: c u 0) a c 3 t rh U l-( c 0) H TD 4J s t 4= ih 4.> ih 3 (0 X-l 0) H J= 4-1 u t «4^ h 4J 4.1 c t H V u (U ^ x: 4-) 4J H t4h O 0),U3 m -J- -a- m in CN ir> 00 t CTi eg m «3- VO CM r>. H CM u-i ih CM CM»a- rh t a> 4-1 a S CJ <u <4-l ih 0) rh ih 3 (U <4-l x: V 4.1 a <U J x: 4-> Ed H Q Z Cd Ed u a S Od [d &4 Ph O Cd a Q c M C H rt a H J-l X U X u X a C C (fl c 0) a M O s )-l c fi (U H H (U H H w 4J 4J H w H 4J H 4J c 3 O 4-1 c 4-) CJ 4-) 3 H H i H 2 t 2 H >> l-l <U ^ c ^ <u ^ M 0) 4-1 U a >4-l H u^ c <«t 1 H H c 4J c H c H m H 3 H H a a u 4J U H 4-1 4J 4J M 3 O : O O a O 4-1 a O O O r-l J= -C J= ^ x: J= j: ih u 4J U 4-1 4J 4J 4J 3 H H H H H H H ta S :s 3 3 x; 3 4J (0 H t 5 T3 rh lu m u-> 4J T3 t O H s V CJ <x 3 H O t.c 4J <u >*-l 3 rh 4-1 > <u rh fa 4J V a (U (U u g

56 en H J i,q ^ <r 0^ I* CM VO c 3 VI f^ 00 \ r^ (J^ " O fsi cr M PC C vo n t 0) U s CM r* sj- H VO H pes M.-H CJV ih u <u :3 ^ ^ w H O O. OT Q h4 > s c O w m 0) H h:: «4-l 00 e 00 H < ih S M M M «A < M ^ H G rh ih l-h ih rh M a vo r- M < c -a- ^ (U H U 0) 4J O 4J ih g^ > t O rh a U 0), VO M S.^^ W -. S B > :s >% < S rh O u >^ < C =3 CD OT U H M C c 4J ^*\ W M 4J.c H «0) >% O fe r>. a\ O 4J < ffj rl c U Z IH Z c- en B l-i U < z 0) U M CS g 4-> M O, a T3 c 3 W H H c c e c rh ih O SB <u > 1 0) ^K 1 W }-l 4J O > (U O < S^ 3 Q C -H e -H td > <H c O 6 -^ w \^ u c p= Hi 0) H C/2 Z H IJ - Cd a > O > <: O 4-1 S H H -H H -H 0) ih CM 6 08 ij X 4J 4J th U u ^ <; C C O C O z Xi r-l 4J > H H S H S t H 4J ^ U h pri H 3 (0 Pci 03 4J 4-1 4J < V4 «4-C 3 pq g O c»< ^ Pk O CJ M u u O > (U H 0= w «CJ W, u «4-l O (U 4J 4J c 0] H 0) Pu Xi ^ 4-1 <U u-> 1-1 CNJ CM rh <T t-i (D m U-1 CM 0^ \0 (U j: ^4J CM ih O O vo 4-1 ^-s H b sj- >* u-i m m ih r^ r- r^ r>» r^ r>. r<. r». 0) u 4-1 > A A A A AAA O a r-i a 14-1 pc< Ctf ^ ih rh ih ih <H ih ih O <u» ih H W 0^ O ih C". >a- in r^ 00 3 <u 1^ ^ lu ^ ih a^ sr CM C7N O r^ >4-l j: 3 flq CN4 m CM m ^O 00 VO 1^ u W < w 0) 00 0\ cr\ a^ CM 4= fo <r) <r) t^ en n -^ w lu < g w > ^ 3 4J S H, H a 3 G a 3 Cd» O H U Q H -O "H a ih O Z 4J a 4J lu (U as Ed U X CJ X a ir» 4J a 04 IX, S) a a t c a c O Q u V4 C )-i c a H e Z Q lu H T-t O 1) O -H O -H V O 4J U U H 4J T-I 4-1 H 4J. OD OT -H c 3 O 4J C 4-1 O Xi H r-l U rh H X> C3 -H t t XI A <U H!-i S U B -H t 4J < M T3 >, t-i <U g c M 0) Sj h -a &- O 4-1 u O O 4-1 4J 4J 0) (M s s ;» C K-l -H U-l C VH 3 T H -H C u fn 4-1 C -H C -H O i-h H 3 H 4J ^ (0 ij 4J 4J cn 4J -H 4J U 4J 4J 0) H -a a O 3 U 3 O rh >. 4J Q O CJ O 4-) O CJ ih 0= X! J= J2 x: j:: js a; (U S.i j j <H w 4.1 4J J 4-) V x: J= OJ 3 H H H H r-l -H -H a. H H N b t u a ^

57 n M CJ OS S3 Cd H ac Cd U Cd O PS Eh Ck Cd =3 < > Cd a a Cd O Q Cd cm i I 1 fc-l c H U3 H <0 00 \0 CM in \ CM 1^ (3 05 (0 rh c z CT\ O CM >> U CB a 4J / *\ 0) ;>% H c h -u a u > 1 ll> n c c c e u O C -H S -H s "^^ 4J ^-' 4J t CJ > > H H -H H T-l t (0 JJ 4J c c3 S c: H H a H S M n 1-1 JJ 4J U < c X >< PM H Cd Cd CM m M Cd O 1-4. Cd m, < M UO < ISi < c > Qi S ^' M (D, Cd CJ Cn ^ I-* z 2 S (U w; > 01 rh ^ H M > I \ ih r-. mi CM Pv \ t^i CM fo O f^» -a- ih O CN C^ U-> <T> ^ S' sj- CM O fo CJ\ CN «S ih t-l CM rh <N U O CJ Oi M ak M «J t-h rh CN CNI I O 4J (0 4J (U 4.) >-l c 3 cr (U -H h <u 10 u a. s (U rh rfl rh <i> th -a U a > H <J ^4J c (U c 3 rh rh U e 0) H -a 4J g 10 rh rh 3.C 4J (4H c (U H 4= 4.1 u O <u M 0) 4J 4J a m H (U -u 0) (U O A J= Cd O!n H t Cd (X, z M I Cd Q Cd -J M Cd < > H [d Z U Cd Q Q r4 ic0 en «N O 00 en >3-1^ c 1 1 C C H H i H U >< 4J Si O X X c c a 1 U c»-l a 0) H H a> O T-t 4J 4J 4J r-t u H.U c 3 U -U c 4J O ih H ^ fl 9 <U H ;4 s s O (0 w u c *^ T) >. h <u c: S 3 G <4-l ^ c 1 H H c u C -H en a U H S 4J u u t 4.1 H P M U 3 O A O O CJ O u O O rh J= x: JZ rh XJ u AJ u 3 H H H H Cd 3» 3 3 CN c u t4h c 3 c H 4J 3 X> n h 4^ H TS CJ U u U 4J 3 3 O ^ x: 4J jj H H 3 s V (U 4J a a a <u <4H UH <-i 0) f-i 3 (U U-l ^4J 0) J= 4.) OJ J3 4.1 H 3 a rh OJ 0) 4.1 a th O a ih J= ij (U <4H 3 rh 4.1 > (U Cb 4.1!U <U h s & s

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59 Faculty Wrking Papers Cllege f Cmmerce and Business Administratin Univariity f Illinis at U rba na - Cha m pa ig n

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