. . . .3 I . :1. o... .— ,— h. ~_ -—~*_ “A ffi" , ft‘ni-x“. cwn’..—._-‘v—. ‘ v7.33 nu ‘ , 1 an». .E Hum 0..» u»? .u; \4....“ ‘nrti. Han m...“- ,A an A. INC-Ln STATE IIIIUWIHHIIHII IIHIIIHIIIWIHJlllllllflllllllll 31293 00891 2374 This is to certify that the dissertation entitled Experience and Information Effects on Search Strategies in a Capital Budgeting Task presented by Monte Ray Swain has been accepted towards fulfillment of the requirements for Ph.D. Accounting degnmin SrfiJZUL ? /'/4‘/[(L~ Major professor February 6, 1992 [)ate MSU is an Affirmative Action/Equal Opportunity Imn'nm'on 0‘ 12771 PLACE IN RETURN BOX to remove this checkout from your record. TO AVOID FINES return on or before date due. F—-————_l————_———-_—_—_———— ATE DUE DATE DUE DATE DUE D ___J i=7 ____% MSU I. An Affirmative Action/Equal Opportunity lnetiMTon cmmhx EXPERIENCE AND INEORMATION EFFECTS ON SEARCH STRATEGIES IN A CAPITAL BUDGETING TASK BY Monte Ray Swain A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Department of Accounting 1992 Copyright by MONTE RAY SHIN 1992 ABSTRACT EXPERIENCE AND INFORMATION EFFECTS ON SEARCH STRATEGIES IN A CAPITAL BUDGETING TASK BY Monte Ray Swain The significance of capital budgeting to an organization's success makes the possibility of information impediments and individual biases in the decision process a particular concern. These potential confounds are exacerbated by the breadth of personnel experience typically found in the capital budgeting process. The research literature to date has not clearly determined potential effects of increasing information and past decision episodes on the capital investment decision task. Therefore, this study of experienced and inexperienced capital budgeters examines for information load, data fixation and functional fixation effects on information processing behavior. Results of this study are important to the design of capital budgeting decision support systems. Two groups of participants were involved in this study. Thirty-six experienced capital budgeting professionals served as the study's experienced group. Forty-eight undergraduate students majoring in accounting or finance served as the study's inexperienced group. Six capital investment choice scenarios were sequentially presented on a computer to each participant. The computer randomly varied the level of information attending each of the six choice task and traced the information search strategy employed by the participant for each task. Three hypotheses were examined in this study. The first hypothesis examines the effect of information load on the information search strategy. Results of an analysis of variance (ANOVA) indicated that the study's experienced capital budgeters, compared to inexperienced capital budgeters, were relatively more systematic and exhaustive in their information search as the information load increased. The second hypothesis tests the carry-over effect of previous search strategies on the current search strategy (functional fixation). Results of path analysis indicated that the inexperienced capital budgeters' search strategy was dependent on previous search strategies. The third hypothesis investigates the carry-over effect of previous information loads on the current search strategy (data fixation). ANOVA found that the experienced capital budgeters' search strategy for a particular decision was more systematic and exhaustive when preceded by an unrelated capital investment decision with a high level of attending information. This dissertation is dedicated to my loving wife and best friend, Shannon. ACKNOWLEDGMENTS No one writes a dissertation alone. The irony of it, though, is that only one name can be on the cover. However, without the important support of many people, the work could not have been accomplished. No dissertation committee could have proved themselves any more able and willing than has mine. Susan Haka, my chairperson, simply embodies every quality of a true scholar and mentor. She is a constant source of good advice when I am perplexed and encouragement when despondent. Always with an eye on the "big picture," if this study has anything important to say, much of the credit must go to her. The presence of my other committee members, Gary Cook and Frank Boster, were also very important to this work. Gary provided the important use of his ISLab instrument and kept the focus on design implications for decision support systems. Frank simply has an amazing grasp of research design and statistical analysis issues. I must also acknowledge the presence of an unofficial committee member. Harold Sollenberger's name is not on the official documents. However, without his encouragement and confidence and industry contacts, this work would have surely stalled. Additionally, the writer of a dissertation must have a colleague who will, at any time night or day, listen without judging, counsel without contempt, and laugh without restraint. My good friend, Bob Allen, proved himself to be such a colleague. I thank the students at Michigan State University and the professionals listed in this work who so willing gave of their time and served as participants. I am particularly indebted to the Department of Accounting at Michigan State University and the Institute of Management Accountants (formerly the National Association of Accountants) for their important financial assistance. Most importantly, I am grateful for the love and patience of a good wife and three terrific children. They make it meaningful and worthwhile. I am the product of good and loving parents who taught that all strength comes from a loving Heavenly Father. Therefore, I give the final credit to my God who provides the strength and inspiration for all good things done in this life. vi TABLE OF CONTENTS LIST OF TABLES. . . . . . LIST OF FIGURES . . . . . CHAPTER I e “Q“D’PO CHAPT ”nun flHPHPP eee ”Ho 2.3 2.4 INTRODUCTION . overview . . . Groundwork . . Statement of the Research A Decision-Making Model Contributions . Summary . . . . overview . . R II LITERATURE REVIEW Capital Budgeting . . Behavioral Decision 2.2.3 Search Strategies . . 2.3.1 Experience . . 2.4.1 2.4.3 Fixation . . 2H61Euncti_nal_fixati_o.n 2H62D_a.t__fixa_ti_on 2H63W. Summary . . . vii e e e "e e e e e e o e e e e e e n e e e Wiggles 2..32 Wigs xii NOO‘UHHH HP 13 14 15 16 17 18 22 24 25 27 27 29 31 32 33 34 35 38 42 43 44 44 48 CHAPTER III METHODOLOGY . . . 3.0 Overview . . . 3.1 Experimental Design 3. 2 3.4 3.6 CHAPTER IV DATA ANALYSIS . . . . . . . 4.0 4.1 4.2 Participants . . . 3-2-1 WW 3-2-2 W ‘ .3 Igsk incengivg “can: e e e .e e UIUIUIfl’Ib-h e e e as e d of Analysis . . . . . . . . gtthU Occura0 F Experience 0.11 1 0.11 0.02 0.888 Alternatives 21.04 2 10.52 2.17 0.119 Dimensions 5.50 2 2.75 0.62 0.544 Exper. * Altern. 0.24 2 0.12 0.02 0.968 Exper. * Dimen. 0.89 2 0.45 0.10 0.899 Altern. * Dimen. 25.47 4 6.37 1.44 0.222 Exp. * Alt. * Dim. 44.36 I 4 11.09 2.51 0.044 0.12 = 3.92% Inspection of the data reveals that at low levels of alternatives, experienced capital budgeters became more compensatory than inexperienced capital budgeters as time progressed. At high levels of alternatives, this trend reversed as experienced capital budgeters became relatively 105 more noncompensatory. However, since only a small proportion of variance in the ISS variable is accounted for by such effects (6:2 = 3.92%), participants’ training or fatigue is not expected to confound results of hypotheses testing. Since significant differences in the ISS variable between choice tasks of equal information load were generally not observed, further examination of ISS involved averaging the two ISS scores accompanying equal information loads for each participant. Effectively, out of the set of six decision tasks, each participant generated three observations of average ISS for statistical testing purposes. 4.2.3 Gender effepts. One final examination for potential confounds, gender effects, was undertaken before hypotheses testing. Of the 36 experienced participants in this study, 2 are female. This unrepresentative gender mix precludes an effective statistical evaluation of gender effects within the group of experienced participants. However, of the 48 inexperience participants (students) in this study, 20 are female. Consequently, ANOVA is used to examine for systematic variance of ISS across gender groups of the student sample. Results for a partial ANOVA table are presented in Table 4.7. 106 Table 4.7 ANOVA for Gender Effects SOURCE SS df MS F Pr > F a Gender 6.26 1 6.26 0.55 0.462 Gender * Altern. 38.65 2 19.33 1.70 0.195 Gender * Dimen. 4.83 2 2.42 1.58 0.210 Gender * Alt * Dim 7.54 4 1.89 1.23 0.304 w2 = 2.36% As displayed in Table 4.7, no significant gender effects on the dependent variable, ISS, are observed. 4.3 Hypothesis 1 -- Information Load 4.3.1 Initial ANOVA. Hypothesis 1 (H1), in null form, states that experienced and inexperienced capital budgeters do not display systematic differences in their information search strategy based on changes in information load attending the contemporary capital investment decision. To examine this hypothesis, the effect on ISS of increasing levels of alternatives and levels of dimensions attending the choice task is evaluated and contrasted across experienced and inexperienced participants in the study. Table 4.8 presents the results of this analysis in ANOVA form. 107 Table 4.8 ANOVA for Information Load Effects (H1) =2225CE SS df MS F Pr > F Experience 5.69 1. 5.69 p 0.53 0.469 Alternatives 100.88 2 50.44 4.68 0.012 Dimensions 124.07 2 62.03 38.77 0.000 Exper. * Altern. 5.60 2 2.80 0.26 0.774 Exper. * Dimen. 1.57 2 0.79 0.49 0.619 Altern. * Dimen. .8.71 4 2.18 1.36 0.246 Exp. * Alt. * Dim. 5.47 4 1.37 0.85 0.498 62 = 15.042% As seen in Table 4.8, there are substantial main effects on ISS as a result of changing the levels of both alternatives and dimensions. This supports past research results (Payne, 1976; Biggs et al., 1985; Anderson, 1988). However, experience does not display a main effect or an interaction effect on the decision maker’s information search strategy. Yet an inspection of the data indicate a potential trend. Experience seems to result in an ability to operate at more compensatory levels as compared to inexperience in the capital investment decision task. Though the effect is small, this pattern is more pronounced at lower levels of alternatives and dimensions. Such a trend, supported by the previous research of Biggs and Mock (1983) and Krogstad et al. (1984), is depicted in graph form 108 in Figure 4.1. As described in the following section, if this trend truly describes the data, traditional ANOVA inefficiently tests for interactive effects. Figure 4.1 Experience Remains Relatively Compensatory Information Search Strategy Comp - Experienced NonComp - Inexperienced Low Medium High Information Load 4.3.2 Qpptpa§p_gpgipg.' Traditional ANOVA strictly tests for cross-over effects as the only form of interaction among variables (Buckless and Ravenscroft, 1990). Therefore, the interactive effect of such a relationship depicted in Figure 4.1 is spread among the several sources of variance within the ANOVA presented in Table 4.8. To 109 test explicitly the pattern that experienced capital budgeters remain relatively compensatory in their search strategy (as portrayed in Figure 4.1), contrast coding is used. Contrast coding uses the traditional sum of squares formula to weight each cell mean in a manner appropriate to the a priori relationship (Buckless and Ravenscroft, 1990): Shlzlci)(EADi)]2 SSmodel = ---------------- 2(Ci) 2 where: SS = sum of squares sh = harmonic mean number of subjects per cell c = weights assigned to the cell means i = cell identifier, and EAD = cell means. Given the presence of unequal sample sizes within this experiment (see Table 3.2), Keppel (1982) suggests use of an average group size. The average group size (the harmonic mean) is found by dividing the total number of treatment means by the sum of the reciprocals of the various sample sizes. The harmonic mean is consistently applied throughout this study’s analysis. To ensure estimation of population parameters, ANOVA requires that the weights for each contrast sum to zero. This requirement is satisfied by the set of coefficients in Table 4.9 used to capture the a priori relationship. As the information load increases, the coefficients portray the 110 experienced capital budgeter generating a consistently high ISS score (indicative of a compensatory strategy) while the inexperienced capital budgeter progressively moves toward a noncompensatory information search strategy (as seen in Figure 4.1). Beneath each coefficient, in parentheses, is the observed cell mean. The harmonic cell size for this data run is 13.67. Table 4.9 Contrast Coding for H1 (Cell means are in parentheses) 3 Dimensions 5 Dimensions 7 Dimensions ‘ fl 3 Alternatives 1 o -1 Inexperience (1.38) (0.56) (0.20) Experience 5 Alternatives Inexperience (0.37) (-0.77) (-1.30) l 1 1 Experience (1.67) (-0.14) (-1.32) u=========================================£ 7 Alternatives -1 -2 -3 Inexperience (0.46) (-0.67) (-1.65) l 1 1 Experience (0.43) (-0.96) (-1.16) The result of this contrast coding amends the original ANOVA. A partial ANOVA, presented in Table 4.10, displays a significant F value for this particular relationship. This 111 amended ANOVA does not explain additional variance among the data. Rather, as stated above, use of contrast coding accurately unites variance appertaining to the hypothesized relationship that traditional ANOVA spreads among other main and interactive effects. Hence, the w2 for the entire ANOVA presented in Table 4.8 also relates to the partial ANOVA in Table 4.10. Therefore, tests for specific relationships between experience and information load results in a rejection of the null hypothesis. Table 4.10 Partial ANOVA for H1 Contrast Coding SOURCE SS df MS F Pr > F a H Model . . . . ! 5 34 1 5 34 3 34 0 086 Compared to inexperienced decision makers, experienced decision makers are consistently more systematic and thorough in their information search across various levels and types of information attending the capital investment choice task. 112 4.4 Hypothesis 3 -- Data Fixation 4.4.1 Initial ANOVA. Hypothesis 3 (H3), the data fixation hypothesis, examines the effect of information attending past decisions on experienced and inexperienced capital budgeters. The effect on ISS of increasing levels of alternatives and dimensions attending past choice tasks is evaluated and contrasted across experienced and inexperienced participants in the study. Table 4.11 presents the results of this analysis in ANOVA form. Table 4.11 ANOVA for Data Fixation Effects (H3) =ngCE SS df MS F Pr > F Experience 13.55 1 13.55 1.27 0.263 Alternatives 110.00 2 55.00 5.15 0.000 Dimensions 21.59 2 10.80 5.05 0.008 Exper. * Altern. 8.45 2 4422 0.40 0.677 Exper. * Dimen. 0.09 :2 0.05 0.02 0.968 Altern. * Dimen. 4.76 4 1.19 0.56 0.695 Exp. * Alt. * Dim. 15.77 4 3.94 1.84 0.123 62 = 8.60% As seen in Table 4.11, there are substantial main effects on ISS as a result of changing the level of dimensions attending past, unrelated choice tasks. Since this experimental design is nested on the level of 113 alternatives (independent groups) and crossed on the level of dimensions (repeated groups), effectively participants can only respond to number of dimensions attending previous decisions. The main effect seen for level of alternatives is actually the same information load response previously presented in Section 4.3. It is important to note this strong fixation on data of previous decisions. This result is useful for understanding past research that does not clearly distinguish data and functional fixation, e.g., Bloom et a1. (1984) versus Moon (1990). 4.4.2 Cgptrasp coding. The insignificant interaction between experience and past information loads in Table 4.11 does not justify rejection of the null. Yet further inspection of the data indicates a potential trend. Large levels of information load for previous decisions result in a proclivity for more compensatory search strategies for experienced participants. Inexperienced participants are not systematically affected by past levels of information. This trend is depicted in graph form in Figure 4.2. This trend, like Figure 4.1, is not the true cross-over interaction tested by traditional ANOVA. Therefore, contrast codes specific to this a priori relationship is employed to strengthen the ANOVA. 114 Figure 4.2 Experience Reacts to Previous Information Loads Information Search Strategy Experienced comp - / Inexperienced Low Medium ‘ High Previous Information Load This trend may be the result of a repercussion effect. For example, consider a decision maker struggling to utilize a large set of information. When subsequently presented ‘with a smaller information set attending a new decision, the decision maker feels the new decision set is easier to use as compared to the same decision set following an alternative information set equal or smaller in size. The capital budgeter is more systematic and exhaustive in the search strategy than would otherwise by displayed. This would be one type of a data fixation response, resulting in a rejection of the null hypothesis. 115 The a priori relationship is captured in the set of proposed contrast coefficients presented in Table 4.12. Experienced participants are assumed to move in a compensatory direction as the level of dimensions attending prior decision tasks increase. Inexperienced participants are not expected to shift their search strategy as a result of prior decision tasks. Although higher levels of alternatives cause experienced participants to operate at more noncompensatory levels, the same pattern of movement towards compensatory search strategies emerges as prior decision tasks increase in number of dimensions. Table 4.12 Contrast Coding for H3 (Cell means are in parentheses) 3 Dimensions 5 Dimensions 7 Dimensions 3 Alternatives 0 0 0 IneXperlence (0.50) (0.66) (1.12) 0 1 2 Experience (0.84) (1.21) (1.01) | 5 Alternatives 0 0 0 Inexperience (-0.99) (-0.44) (-0.41) -1 0 1 Experience (-0.08) (-0.67) (1.56) F—w—‘T—EL—g— 7 Alternatives 0 0 0 Inexperience (-0.95) (-0.93) (-0.38) -2 -1 0 Experience (-1.02) ('O'OBL. (-0.19) 116 The amended ANOVA, as a result of the contrast coding, appears in Table 4.13. The proposed relationship is significant with a P value equal to 0.0556. Similar to the evaluation of 31' the w2 value for the full ANOVA in Table 4.11 is also associated with the partial ANOVA in Table 4.13. Compared to inexperienced decision makers, experienced decision makers are consistently more systematic and thorough in their information search across various levels and types of information attending the capital investment choice task. Table 4.13 Partial ANOVA for H3 Contrast Coding SOURCE SS df MS F Pr > F H: Model 7.96 1;- 7.96 3.72 0.056 The testing for specific relationships between experience and information loads attending prior decisions results in evidence to reject the null hypothesis. Experienced capital budgeters exhibit a type of data fixation on past information loads. In response to large amounts of information, experienced capital budgets apparently utilized relatively more compensatory information search strategies when analyzing subsequent, unrelated 117 capital investment decisions. This tendency is not evident to the same degree in inexperienced capital investment decision makers. 4.5 Hypothesis 2 -- Functional Fixation 4.5.1 Simple causal chain mogel. Hypothesis 2 (H2), in null form, states that experienced and inexperienced capital budgeters are not differently affected in their current information search strategy by information search strategies utilized in past, unrelated capital investment decisions. This hypothesis questions the effect of functional fixation, as opposed to data fixation, on experienced and inexperienced capital budgeters. To examine this hypothesis, the effect of past ISS on the current ISS is analyzed and contrasted across experienced and inexperienced participants in the study. Table 4.14 presents the correlation matrix for the sequential set of ISS observed for both the experienced and inexperienced groups of participants. 118 Table 4.14 Correlations Among Sequential 188 for the Participants Panel A: Experienced Capital Budgeters n = 36 Observed Correlation Matrix 1881 1852 1583 1884 1585 1886 1881 1.00 1882 0.78 1.00 1883 0.54 0.53 1.00 ISS4 0.57 0.44 0.44 1.00 ISSS 0.45 0.41 0.40 0.43 1.00 1885 0.52 0.36 0.57 0.55 0.33 1.00 Panel B: Inexperienced Capital Budgeters n = 48 Observed Correlation Matrix , 1881 I882 Iss3 ISS, 1885 1886 1551 1.00 Iss2 0.48 1.00 ISS3 0.50 0.34 1.00 1854 0.36 0.56 0.65 1.00 1385 0.49 0.49 0.56 0.59 1.00 Path coefficients are generated from the correlation model (see Figure 3.9). matrices above to test the presence of a simple causal chain Evidence of such a chain would 119 support the rejection of the null hypothesis, indicating the presence of functional fixation. Figure 4.3 presents the path coefficients (equivalent to correlation coefficients) generated for both groups of participants based on a simple causal chain. Since each ISS observation is posited in Figure 4.3 to be impacted by a single antecedent ISS observation, the corresponding path coefficient is estimated to be the simple correlation between the two ISS observations seen in Table 4.14. Therefore, for the experienced participants, Prssz,1581 = r1582,1851 = 0°78 where: ISSn = the information search strategy observed in choice task n, p = the corresponding path coefficient between two tasks, and ”1552,1381 = the correlation obtained from Table 4.14, Panel A. 120 Figure 4.3 Path Model for 1 Antecedent Panel A: Experienced Participants Panel B: ISS TASK 8 \\1// Inexperienced Participants 121 Of the fifteen correlations presented for each participant group in Table 4.14, five of those correlations are defined (i.e., constrained) as equal to the proposed path coefficients (i.e., the model is over-identified). Therefore, ten of the correlations are testable using a product rule. The path coefficients, p, are used to predict the ten test correlations, r’. For example, for the experienced participants, r'Iss3,1551 = 0°41 = (P1552,1551) (Pissa,rssz) = (0°73) (0°53) where: ISSn = the information search strategy observed in choice task n and p = the corresponding path coefficient between two tasks as seen in Figure 4.3. The test correlations, as predicted by the product rule above, are compared to the observed correlations in Table 4.14 to obtain an error (residual) matrix. Residuals of significant size result in rejection of the validity of the simple causal chain model. The ten test correlations, as predicted by the product rule above, are displayed in the predicted matrices in Tables 4.15 and 4.16 for the experienced and inexperienced participants, respectively. The error matrices, also 122 presented in Tables 4.15 and 4.16, contain the difference between the predicted and actual test correlations. Table 4.15 Test of Path Model for 1 Antecedent (Experienced Participants) Predicted Correlation Matrix 1581 1332 1833 1854 1585 1586 1581 Iss2 1583 0.41 1854 0.18 0.23 1885 0.08 0.10 0.19 1835* 0.03 0.03 0.06 0.14 Error Matrix (Observed - Predicted) 1551 1882 1883 1834 1885 1886 1551 1552 1853 0.13 1854 0.39*** 0.21* 1885 0.37*** 0.31** 0.21* 1555, o.49*** 0.33** 0.51*** 0.41 *** * p < .10 ** p < .05 .eee p < .01 123 Table 4.16 Test of Path Model for 1 Antecedent (Inexperienced Participants) Predicted Correlation Matrix 1881 1882 1853 1884 1885 1586 1851 1882 1353 0.16 Iss4 0.11 0.22 1855 0.06 0.13 0.38 1856 0.04 0.08 0.25 0.38 Error Matrix (Observed fi Predicted) 1551 1582 1383 1334 1555 1556 1881 1552 1553 0.34*** 1334 0.25* 0.34*** 1585 0.43*** 0.36f** 0.21* 1556 0.35*** 0.364** 0.26** 0.23** * p < .10 ** p < .05 *** p < .01 The residuals in the error matrices are evaluated for size significance using a two-tailed t-test based on n = 36 for the experienced participants and n = 48 for the inexperienced participants. As can be seen in the Error Matrix of Table 4.15, most of the residuals are significant, 124 indicating the failure of the simple casual chain model of ISS for the experienced participants. In the Error Matrix of Table 4.16, again the majority of the ten residuals are significant, indicating similar failure of the simple casual chain model for the inexperienced participants. Initially, the above results indicate failure to reject the null Hypothesis 2. However, further inspection of the simple correlation matrices indicates the presence of systematic movement, possibly indicative of a more complex casual relationship among the ISS factors attending sequential capital budgeting choice tasks. Specifically, there may be a carry-over effect on the information search strategy resulting from the search strategies utilized in the past two choice tasks. 4.5.2 Complex causal path model. By positing that the ISS at time n (ISSn) has multiple causal antecedents (ISSn—1 and ISSn-2), then the path coefficients for ISSn-1 and ISSn- 2 are standardized beta weights in the multiple regression Aof ISSn onto ISSn-1 and ISSn-2 (Hunter and Gerbing, 1982). Therefore, ISSn = alISSn-l + aIISSn-l where: pISSn-l,ISSn = a1 and pISSn-2,ISSn " a2' 125 Path coefficients are generated from the correlation matrices in Table 4.14 to test the validity of this more complex path model involving multiple antecedents. Figure 4.4 presents the path coefficients generated for both groups of participants based on multiple antecedents. In Figure 4.4, each ISS observation is posited to be impacted by the two previous antecedent observations. Therefore, the corresponding path coefficient is estimated to be the multiple regression coefficient. Of the fifteen correlations presented for each participant group in Table 4.14, nine are constrained by the path model as equal to the sum of the direct, indirect and spurious effects (i.e., paths) of the antecedent variable on the dependent variable (Lewis-Beck, 1974; Hunter and Gerbing, 1982). Therefore, the remaining six correlations are testable using a product rule. 126 Figure 4.4 Path Model for 2 Antecedents Panel A: Experienced Participants Panel B: Inexperienced Participants 127 Each of the six test correlations are predicted to be the result of indirect and spurious effects of one variable on another variable. For example, according to the path model depicted in Figure 4.4, I882 is posited to have indirect effects on ISSS through two intermediate variables, 1853 and 1884. The indirect effect of ISS2 on ISSs is the sum of the indirect impact determined for each path from 1582 to I885. The impact of a path from 1882 to ISSS is the product of the path coefficients along that path. For example, for the experienced participants, the total indirect effect of ISSZ on ISSs is (pISS4,ISSZ) . (Prsss,rss4) + (pISS3,ISS2) . (pISS4,ISS3) r (P1555, Iss4) 1' (pISS3,ISSZ) r (Pisss,rss3) = (.29)(.31) + (.28)(.29)(.31) + (.28)(.26) = .19 where PM, = the corresponding path coefficient taken from Figure 4.4, Panel A. 1882 and ISSS have one common antecedent variable, ISSI. Each combination of paths from ISS1 to I832 and ISSS generates a contribution to the spurious effect, the product of the path coefficients on both paths. The net effect for the common antecedent, ISSl, is the sum of the products across all combinations of paths to ISS1 to ISSZ and 1885. Therefore, the total spurious effect for 1882 and ISS5 is 128 (Prssz,Iss1) v (P1553,1851) r (P1554,Iss3) I (Pisss,rss4) + (P1352,1381) r (P1553,Issfl r (Prsss,Iss3) = (.78)(.32)(.29)(.3l) + (.78)(.32)(.26) = .09 where F&, = the corresponding path coefficient taken from Figure 4.4, Panel A. The predicted correlation between ISS2 and ISS5 for the experienced participants is the sum of the indirect and spurious effects determined by the path model as follows, r'Issz,Isss = the Indirect Effect + the Spurious Effect = .19 + .09 = .28. The test correlations, as predicted by the product rule demonstrated above, are displayed in the predicted matrices in Table 4.17 and 4.18 for experienced and inexperienced participants, respectively. These test correlations are compared to the observed correlations in Table 4.14 to obtain the error matrices (also displayed in Tables 4.17 and 129 4.18). Residuals of significant size result in rejection of the path model depicted in Figure 4.4. Table 4.17 Test of Path Model for 2 Antecedents (Experienced Participants) Predicted Correlation Matrix Iss1 IS82 Iss3 Iss4 1885 1856 1851 I882 1583 1854 0.38 1855 0.26 0.28 1556 0.22 0.25 0.27 Error Matrix (Observed - Predicted) Iss1 1382 1583 1554 1585 1556 1581 1832 1833 1584 0.19* 1555 0.19 0.13 *‘k‘k I586 0.30*** 0.11 0.30 * p < .10 ** p < .05 *** p < .01 130 Table 4.18 Test of Path Model for 2 Antecedents (Inexperienced Participants) Predicted Correlation Matrix 1881 1552 1853 1854 Iss5 1586 1851 1882 1853 Iss4 0.44 1555 0.33 0.32 I886 0.30 0.34 0.48 Error Matrix (Observed - Predicted) 1851 1582 1853 I554 IssS Iss6 Iss1 1382 1883 1554 -0.08 1535 0.16 0.17 1886 0.09 0.10 0.03 * p < .10 ** p < .05 *** p < .01 As seen in the Error Matrix of Table 4.17, two of the six residuals for the experienced participants are significant in size and one residual is potentially significant. Therefore, the divergence of observed correlations from those predicted by the proposed path model 131 displayed in Figure 4.4 indicates failure of that model for the experienced participants. However, in Table 4.18, the size of each of the six residuals for the inexperienced participants are all insignificant. Hence, the proposed path model obtains for the inexperienced participants. This group displays carry-over effects on their information search strategy from unrelated search strategies attending the preceding two capital investment choice tasks. This finding results in a rejection of the null Hypothesis 2. Based on the path analysis above, the contemporary information search strategies of experienced and inexperienced capital budgeters are affected differently by past capital investment information search strategies. The correlation matrices in Table 4.14 were further inspected and similarly tested to determine the validity of an alternative path model other than the models examined above for the experienced participants. No such model was determinable. In performing the six capital budgeting choice tasks, the experienced participants did not display any systematic reliance on previous, unrelated information search strategies. Therefore, only the inexperienced participants displayed any tendency of functional fixation. 4.6 Summary The experimental results and data analysis were presented in this chapter. The first section presented the 132 development of a unidimensional measurement model of information search strategy (ISS). Confirmatory factor analysis revealed that the initial model based on the. indicators Proportion Searched (PS), Variability in Proportion Searched across Alternatives (VA), Variability in Proportion Searched across Dimensions (V0), and Search Direction (SD) is inappropriate. Subsequent analysis determined that PS, VA and VD satisfactorily combine to form a unidimensional model of ISS. This model was then used for hypothesis testing. Based on both theoretical and analytical problems, the SD indicator was not used in hypotheses testing involving shifts between compensatory and noncompensatory information search strategies. Before testing the hypothesis, manipulation checks were presented in the second section of this chapter. Analysis for potential training, fatigue and gender effects was also presented. Manipulation of information load for each capital investment choice task was satisfactory. No confounding effects of training, fatigue or gender were noted. Section 3 reported the results on Hypothesis 1 testing. Using contrast coding, it was revealed that experienced capital budgeters remain relatively compensatory in their ISS compared to inexperienced capital budgeters. The inexperienced group displayed effects of information load as demonstrated by their movement towards noncompensatory ISS 133 as the information load of capital investment choice tasks was increased. This evidence resulted in a rejection of the null Hypothesis 1. Section 4 reported the results on Hypothesis 3 testing. Using contrast coding, it was revealed that experienced capital budgeters react to high levels of information load accompanying past capital investment choice tasks. This reaction, a type of data fixation, is displayed as a movement towards more compensatory ISS in the current capital investment choice task. Inexperienced capital budgeters did not display any form of systematic data fixation, resulting in a rejection of null Hypothesis 3. Section 5 reported the results on Hypothesis 2 testing. Using path analysis, neither the experienced nor the inexperienced group of capital budgeters emulated a functional fixation on the ISS utilized in the most recent choice task. However, further investigation disclosed that inexperienced capital budgeters do display carry-over effects from the two most recent choice tasks -- a more complex version of functional fixation. Experienced capital budgeters display no systematic pattern of fixation on past ISS, resulting in a rejection of null Hypothesis 2. A discussion of these findings follows in the next chapter. CHAPTER V CONCLUSION 5.0 Overview In this chapter, a summary of the research results is presented, including a discussion of the implications, contributions and limitations of the current study. Also included are suggestions for future research. A summary of the research results is presented in the first section. In Section 2, implications and contributions based on the results of this study are offered. The third section describes limitations of this study. Suggestions for future research are contained in Section 4. 5.1 Summary of Results 5.1.1 Information load research guestiop [311. The initial research question of this study asks what effect increasing amounts of information has on the decision processes of inexperienced and experienced capital 'budgeters. A review of research literature to date does not allow the reader an unequivocal position on the interaction of the capital budgeter’s experience and his or her use of information in decision making. For example, research such as Biggs and Mock (1983), Krogstad et al. (1984) and Johnson (1985) suggests that as the level of information attending the capital investment 134 135 task increases, inexperienced capital budgeters are more systematic and thorough (i.e. compensatory) in their information search compared to experienced capital budgeters. However, research such as DeGroot (1965), Schroder et al. (1967), Newell and Simon (1972) and Hershey et a1. (1990) would indicate otherwise. The apparent conflict in the literature suggests the need to further examine effects of information load prior to designing decision support systems (DSS) for decision makers who differ in their capital budgeting experience. Therefore, in this study, the amount of information attending a series of capital budgeting tasks was manipulated for two groups of participants differentiated by their level of experience. In the current study, varying the level of alternatives or the level of dimensions creates a main effect on the capital budgeter’s information search strategy. Specifically, increasing levels of alternatives or dimensions results in a shift to more noncompensatory search strategies. This outcome supports past research, such as Biggs et al. (1985), relative to information load effects. More specific analysis using contrast coding suggests that experienced capital budgeters are not as susceptible to this information load effect. Experienced capital budgeters utilize more compensatory levels of information search. Thus, as information load increases, experience results in an ability to be more systematic and more exhaustive in the 136 search through capital investment data. These results support the rejection of 31° 5.1.2 Fixatlon research guestion (H2 and H31. The second research question explored by this study asks what effect capital budgeting experience has on the tendency for individuals to be fixated on factors attending previous capital budgeting tasks. Accounting research has not clearly resolved the issue of whether experience diminishes symptoms of decision making fixation (e.g. Barnes and Webb, 1986; and Paquette and Kida, 1988) or, in fact, engenders fixation (Haka et al., 1986; Davis and Solomon, 1989; and Frensch and Sternberg, 1989). Initially, this research question also must resolve apparent uncertainty in accounting literature on the very definition of fixation. Fixation has often been addressed in accounting literature without explicitly differentiating between fixation on accounting information (e.g. data fixation as seen in Ashton, 1976; Chang and Birnberg, 1977; Bloom et al., 1984; and Barnes and Webb, 1986) from fixation on how to use accounting information (e.g. functional fixation as seen in Wilner and Birnberg, 1986; and Moon, 1990). Similar to previous work, this study investigates the presence of fixation among decision makers in the capital budgeting arena. However, two important enhancements of past research are made. First, functional fixation and data fixation are clearly distinguished and 137 individually tested. Second, effects of these two types of fixation are examined across capital budgeters with different levels of experience. H2 tested for functional fixation effects among experienced and inexperienced capital budgeters. H3 tested for data fixation effects among experienced and inexperienced capital budgeters. Initially, functional fixation was tested by examining for presence of some type of causal model that describes a dependency on search strategies used in prior capital budgeting tasks. Path analysis uncovered inexperienced capital budgeters’ dependence on the previous two information search strategies, describable as a second-order autoregressive dependency. On the other hand, experienced capital budgeters did not display evidence of any carry-over effects from particular information search strategies used in previous decision tasks. Therefore, H2 is rejected. The results of this study also indicate carry-over effects of information attending previous, unrelated capital budgeting decisions. Overall, capital budgeters’ information search strategies display a data fixation effect. The use of contrast coding reveals that the trend relates to the level of experience attained by the capital budgeter. Essentially, experienced capital budgeters’s, though not affected by past use of a particular decision strategy, were affected by exposure to information attending past decisions. This propensity is described earlier 138 (Section 4.2.2) as a repercussion effect. In other words, large amounts of information attending previous capital investment choice tasks result in an increased use of compensatory strategies in the current capital budgeting task. An information search strategy preceded by an unrelated choice task with relatively lower levels of attending information is characteristically less systematic and exhaustive. Therefore, H3 is rejected. To summarize the fixation question addressed in this study, it is seen that both inexperienced and experienced capital budgeters display fixation tendencies. However, the nature of this fixation phenomenon differs among the two types of capital budgeters. The inexperienced participants are functionally fixated on previous information search strategies. Conversely, experienced participants are fixated on previous, unrelated data. These results, compared to previous studies, provide more comprehensive insight into the fixation issue as it relates to capital budgeting. This new insight specifically pertains to the data fixation versus functional fixation of experienced and inexperienced capital budgeters. 5.2 Implications and Contributions "The scarce resource today is not information, but the ability to process it" (Simon, 1977, p. 108). The results of the current study supports Simon’s position. Most 139 discussions of capital budgeting do not emphasize important relationships between the firm’s information system and the way capital budgeting decisions are made (Gordon and Pinches, 1984). The results of this study relative to information load suggests that capital budgeters are systematically affected in their decision process by information presented in the task. These results indicate that the capital budgeting DSS should be an information compressor -- designed to receive more information than it transmits. This bounded rationality mode of capital budgeting suggests that the DSS designer should consider the effect of increasing numbers of capital investment alternatives or information dimensions related to each alternative on the system user -- especially the inexperienced user. Two design approaches are suggested here. (1) The designer may develop a DSS to better filter, rather than proliferate, capital budgeting information (Simon, 1977). '(2) The DSS can be designed to structure the capital budgeting task in order to promote the decision process desired by the organization. For example, Stout, Liberatore and Monahan (1991) describe a capital budgeting DSS that promotes a compensatory information search strategy by automatically performing many of the compensatory functions for the decision maker. These support functions include using pairwise questions to derive the capital budgeter’s 140 inherent dimension weights, helping the individual derive dimension scores across investment alternatives, then combining weights and scores to determine a ranking of alternatives. One benefit of such a 088 design strategy is the flexibility to assign more administrative and strategic capital investment decisions to less-experienced individuals within the organization. Fixation research in the decision task is not as well- developed as information load research. This study increases understanding of the fixation phenomenon in the capital budgeting task. Fixation results are still exploratory and needs to be extended. However, the evidence that capital budgeters display fixation effects indicates that the DSS needs to be more than just an information compressor. In addition to information load effects, capital budgeting effectiveness may be impaired by factors not directly connected with the decision task at hand, as described below. For experienced capital budgeters, the DSS should monitor the amount of information received in previous decision tasks. A capital investment decision task preceded by other decision tasks containing undemanding information loads is not as exhaustively evaluated by experienced capital budgeters as the same decision preceded by decision tasks containing large information loads. By tracking the history of capital budgeting within organization, the DSS 141 can be designed to appropriately promote use of more compensatory or more noncompensatory search strategies by the system user. Procedures used to promote compensatory strategies are similar to those described above in the work of Stout et al. (1991). On the other hand, DSS protocol promoting noncompensatory search strategies could, for example, include use of a heuristic such as a cut-off criteria to initially pare down a large set of capital investment alternatives before allowing more systematic and exhaustive methods of information search. Based on the data analyzed in this study, the organization must also be cognizant of the performance of the inexperienced capital budgeters who are functionally fixated on strategies utilized in past decisions. Inadequate decision performance in one task, characterized by adherence to a undesirable information search strategy, is carried forward to affect at least two more capital investment decisions. Designers of capital budgeting DSS, who are aware of this fixation effect, can develop systems that track and evaluate the inexperienced decision maker’s past search strategies. Then, using similar techniques suggested above, inadequate decision performance is rectified through design of a capital budgeting DSS that promotes more desirable decision strategies. Haynes and Solomon (1962) argue that phases in the capital budgeting decision other than the actual selection 142 phase must be emphasized in research. Specifically, they note: Our case studies suggest that the highest priorities should be assigned to the search for alternatives, the search for information, and the correct processing of the available data before ranking formulas are applied (p. 46). Understanding potentials for bias in the pre-selection phases of the capital budgeting decision should provide the DSS designer with requisite insight and motivation to work for supervision of these potential biases. Design approaches similar to those suggested above for controlling information load effects may provide desired results. 5.3 Limitations Considerable research exists regarding effects of information load and decision maker fixation. However, little research exists describing implications of experience on information load and fixation. Additionally, accounting research has been inadequate in clearly distinguishing functional fixation versus data fixation. This study attempts to bridge gaps specific to capital budgeting in a computerized setting. However, several limitations of this study should be noted. First, developing an accurate map of the cognitive process is very difficult. There can be no direct observation of the capital budgeting decision process -- or, 143 for that matter, any decision process.‘ ISLab, while providing some advantages over other process-tracing techniques, still yields second-hand knowledge of the capital investment decision process. Therefore, some cognitive aspects of the capital budgeter’s information search strategy is not fully captured by ISLab. Second, participants were required to evaluate a large number of capital budgeting cases in a short time. To eliminate fatigue effects, a mixed research design was used. Manipulating the level of alternatives between, rather than within, subjects likely results in some differences in the analysis of alternatives versus dimensions. It is important to replicate this work, allowing the level of alternatives to be manipulated within subjects. This would provide further insight on the density effect described within this study. Decision tasks in this study were limited to a maximum of seven capital investment alternatives each defined over seven dimensions (a total of 49 cues). As noted in Section 4.2.1, it is important for later replications of this work to increase the diversity in levels of alternatives and dimensions. Actual capital budgeting involves much higher levels of information load than were presented in this study’s experiment. However, it is expected that larger differences in levels of alternatives and dimensions manipulated results in more extreme display of the 144 information load and fixation effects, further supporting the implications of this study. Similar to other research work, the results of this study cannot be generalized beyond the type of participants employed in this study. Student participants all came from a single midwestern university. Perhaps more importantly, experienced participants in this study tended to center around a particular industry, automotive manufacturing (see Table 4.1). Another group of participants might act differently from those in this study. Replications are needed to assess the pervasiveness and robustness of the findings of the current study. 5.4 Future Research The empirical findings of this study provide direction for future research endeavors. First, as indicated in Section 2, the nature of this study, especially as it relates to fixation, is somewhat exploratory. The idea that -experienced capital budgeters are data fixated and inexperienced capital budgeters are functionally fixated needs to be further pursued. Replicating these results with a different set of participants from a different industry and geographic region would strengthen the validity of the findings. Additionally, researchers should search for similar effects in decision environments other than capital budgeting. 145 The computerized process-tracing technology does not fully capture all aspects of the capital budgeter’s cognitive process. It is argued in this study that the process-tracing technology used provides significant advantages over other alternatives. However, it would be a useful extension of this work if the experiment were replicated using ISLab combined with another process-tracing methodology such as verbal protocol analysis. Consensus on the participant’s mental decision process between the two methods would enhanced the legitimacy of these results. Such a triangulation approach to validating the research instrument and better measuring the dependent variable, Information Search Strategy, is supported by other researchers such as Payne, Braunstein and Carroll (1978). Alternative independent variables affecting the dependent variable, Information Search Strategy, needs to be examined. For instance, this study carefully differentiated the effects of the independent variable information load from the issue of information overload. The information load emphasis contrasts with previous work by examining the ability of experience to diminish the effects of information load on the capital budgeting process. A useful extension of this study is to examine the interaction of experience and information overload in the capital budgeting task in a similar fashion. Such an approach would likely require the researcher to establish a measure of capital budgeting 146 quality as it relates to the information search strategy (not done in this study) and correlate this quality measure with increasing levels of information across different levels of capital budgeting experience. One new contribution to the decision process literature is the focus on the capital budgeters’ level of experience as an independent variable affecting information search strategy. Cognitive characteristics of those educated-as- to-capital budgeting is differentiated from those experienced-as-to-capital budgeting (Gibbins, 1988). This study did not attempt to define those experienced-as—to- capital budgeting from those expert-as-to-capital budgeting. Since it is reasonable that experienced capital budgeters will display different levels of expertise, future work should distinguish the effect of experience from the effect of expertise on the fixation and information load phenomenon. The work of others such as Bonner and Lewis (1990), which suggests methods for directly establishing the expertise of an experienced decision maker, is useful for such a replication. Finally,this study documents instances where capital budgeters, differentiated by level of experience, display systematic trends in their information search strategy relative to fixation and information load effects. However, little attempt is made to determine why these tendencies exist and how they may be influenced by the DSS designer. 147 Arguments advanced by others such as Wilner and Birnberg (1986) would suggest that these issues must be resolved before insight developed in this study can be incorporated into actual capital investment DSS. Some efforts in this direction have been attempted in a study to determine price setting for contract work (Barnes and Webb, 1986). More empirical effort, specific to the capital budgeting arena, must be invested in delineating the attributes of experience and inexperience that are associated with the information load and fixation effects observed in this study. Researchers and designers must comprehend exactly how the interaction of task, system, and decision-maker characteristics engender information load and fixation effects and how to influence those effects. As this comprehension is developed, large advances in the integration of DSS development with decision maker characteristics will be realized. 5.5 Summary In summary, this study examines information effects on capital budgeters with differing levels of experience. Section 1 reports the results of this study indicating that increased information causes inexperienced capital budgeters to be less systematic and thorough in their decision process compared to experienced capital budgeters. Additionally, inexperienced capital budgeters display fixation on decision 148 processes used in previous, unrelated capital budgeting tasks. On the other hand, experienced capital budgeters are affected by information attending previous, unrelated capital budgeting tasks. Section 2 describes the possible enhancements of capital budgeting decision support systems engendered by the insight gathered from this study. Cautions to be considered when adopting knowledge obtained from this empirical study are outlined in the third section of this chapter. Finally, in Section 4, suggestions for future research related to information effects on capital budgeters are offered. The most important suggestions include determining similar differenCes between experienced and expert capital budgeters. Also, future research should concentrate on discovering why these decision process differences occur and how the information system designer might build such knowledge into the decision support system for capital budgeting. APPENDIX A NOVICE PARTICIPANTS’ INSTRUCTIONS AND QUESTIONNAIRE 149 ISLab Experiment Package Cover Sheet Novice Participants 150 151 Thank you for your support of research at The Graduate School of Business Administration, Michigan State University. In this packet you should find: 1) ISLab Purpose of Study and Instructions 2) ISLab System Instruction Diagram 3) ISLab Questionnaire 4) ISLab 5 1/4" diskette 5) $1.00 initial payment (More to come!) Put the ISLab disk in the boot drive and reboot your machine (press [Ctrl]-[Alt]-[Del] simultaneously). Alternatively, you may switch to the appropriate disk drive, then type "GO" and press [Enter]. If requested, please enter Today’s Date. The machine will run for a moment before requesting some additional information. After entering the required data, read your ISLab Purpose of Study and Instructions. Proceed through the ISLab session, using the instructions and diagram whenever needed. Please try to use the computer to do all your decision- making work. If you feel you must make notes or calculations outside of the computer, please include your notes and calculations with the other ISLab materials. When the final task is completed, the computer will tabulate your final payment which you will receive after completing the questionnaire. If others are waiting to use your machine, have the experiment administrator note the final payment amount pefope you leave your computer. Please complete the questionnaire. Write your name on the disk, the questionnaire, and all other ISLab materials. Place everything back into the packet, write your name on the outside and give it to the experimenter. Be sure to sign for and receive any final payment. If you have any questions or problems, contact Monte Swain at MSU’s Department of Accounting, (517) 355-7486. 152 ISLab Pre-Test Instructions Novice Participants ISLab PRP TH TO The purpose of this study is to gain systematic knowledge of the capital investment decisions process. While you are performing the task, the computer will record your selections. Analysis of this data will provide a basis for the development of a detailed descriptive model of capital investment decision making. The model in turn will provide a basis for the development of computer-based decision aids to assist company management responsible for similar decisions. This study should take about one hour to complete. The aggregated information gathered from this study will be available for your inspection and possible use. All individual information gathered will be kept strictly confidential. At the end of the experiment, you indicate your voluntary agreement to participate by completing and returning the questionnaire attached to this instruction sheet. You will be paid a small monetary reward based your completion of the experiment and the questionnaire and on how closely your answers during the experiment are in agreement with a group of industry experts. IN R ION I. SITUATIONAL ASSUMPTIONS Assume you are a manager for a large company in need of modernizing a significant segment of the production process. In each of the following decision situations, there are various numbers of investment alternatives available, each requiring a significant expenditure. All investments have an expected life of 10 years. The cost of each investment is within the company’s budget constraints. Your task will be to select pp; and only one capital investment in each situatipn. Your company’s current need to update its production process and the significant cost to acquire any of the alternatives considered makes the tasks a rather significant investment decision. You should use as much time and information as you feel is necessary for making your decision. Use the computer for all information needs, i.e., please do not make notes to yourself on a separate piece of paper. 153 154 II. DECISION SITUATIONS There are six independent decision situations. In each decision situation you are to choose one capital investment from a set of possible alternatives. Each investment will be represented by various items of information. Additipnal explanation pf eggh ipfprpatiop item is found pp the end of thpse instructions. The information is both quantitative and qualitative in nature. The quantitative data will be given in its natural numerical form (dollars, percentages, etc.) Qualitative data will be represented on a five-point scale ranging from Very Low to Very High: (1) Very low (2) Low (3) Average (4) High (5) Very High Considerable care was taken to make sure that these capital budgeting situations use information typical of similar investment decisions in industry. III. THE SYSTEM The system you will interact with to gather information for the task has three parts: 1. An automatic demonstration 2. A hands-on practice decision situation 3. The six actual decision situations A. The Automatic Demonstration A series of steps will be displayed that show how the program operates. This is the same sequence of steps you will be using to make your selections. In the demonstration, the task is to choose a potential employee to hire based on information about GPA, experience, desired -salary, and self-motivation (referred to as dimensions). pp npp mgkg gptzips. In this demonsppgtipp, phe computer will m es 0 11 Use the attached System Instruction Diagram to follow the automatic operations of the computer. Upon completion of the automatic demonstration you will be prompted to either run another demonstration or to start the sample decision. You may repeat the demonstration if desired. 155 B. The Sample Decision In the sample decision, the task again is to select an employee to hire. This time ypp should respond to the prompts on the screen. All of the steps are the same as described above. Upon completion of a sample decision, you may either repeat the sample or start the actual decisions. C. THE ACTUAL DECISIONS There are six independent decision situations. As described earlier, your task will be to select one capital investment alternative in each decision situation. The sequence of entries is the same as in the samples and as described in the System Instruction Diagram. If you make a mistake when selecting an investment, please indicate on the questionnaire. You will soon see that ISLab does not present the entire set of information for the each decision task all at once. Please try to work with the computerized format given. If possible, please do not make separate notes and calculations as you work. A group of industry experts have recently completed these same six decision situations. At the end of the experiment, the computer will automatically tabulate additional compensation based on investment selections in agreement with industry experts. At the conclusion of the experiment, be sure to complete the attached questionnaire. 156 IV. EXPLANATION OF DIMENSIONS NPV - Net Present Value. A summation of the investment’s discounted cash flows and initial cost using the hurdle rate appropriate for this investment type. Initial Cost - The total cost of purchase, including transportation and set-up costs. Risk - A general assessment of the inherent risk of this investment, including potential problems with predicting and controlling its future cash flows. The investment’s risk may range from very high to very low. Payback - The number of years required to recover the initial investment. Estimation Uncertainty - The level of uncertainty attending the estimation of the investment’s future cash flows. Low uncertainty reflects confidence in forecasting the investment’s future. High uncertainty indicates that future cash flow are unreliable. Operating Leverage - The investment’s commitment to unavoidable fixed costs. Low leverage reflects an ability to reduce future operating costs in crisis times. High leverage denotes inflexibility. IRR - Internal Rate of Return. The investment’s discounted return on its initial cost. At this rate, the NPV = 0. Annual Net Cash Flow - The investment’s annual future incremental cash flow. Hurdle Rate - The minimal rate of return required by the company for this particular investment’s risk. A high hurdle rate indicates a risky investment. This rate is used to generate actual NPV. ISLab SYSTEM INSTRUCTION DIAGRAM ENTRY [S/Gl Retrieve a Single cell or a Group of cells [Group or G] ————Jh Retrieve a Row or a Column of cells? IR/CI [View or V] [$09le or R) [Column or C] INFORMATION PRESENTED View Information or Eliminate an Alternative? [No or N] «— Wish to decide [Y/N] [Yes or Y] [V/E] A Y [Eliminate or E] Y Select an alternative EX IT Select an alternative 1557 158 ISLab Post-Test Questionnaire Novice Participants ISLab QUESTIQNNAIRE Name Name of School Year in School (Sr, Jr, etc.) School Major Female Male Age Phone Number (Occasionally, clarification of some responses is required.) Finance 391 (Completed, Currently Enrolled, or Not Taken) Grade or Expected Grade in Finance 391 (voluntary, but appreciated) * On a scale of 1 to 10, please rate yourself as an experienced user of microcomputers. Assume 10 represents an individual with significant daily experience using a computer as a programming, designing, and/or decision-making tool and 1 represents an individual with no experience at all: (1 - 10) * On a scale of 1 to 5, please give your perception of the pealisp of the ISLab decision tasks. (Assume 1 = Very Low and 5 = Very High) (1 ‘ 5) 159 4. 160 Following is a list of the nine possible information items you were able to examine in order to make the investment decisions just completed. Assign a 1 to the dimension you believe was the most important or useful in selecting the appropriate capital investment, 2 to the next most important, and so on. Ties are not permitted. NPV Initial Cost Risk Payback Estimation Uncertainty Operating Leverage IRR Annual Net Cash Flow Hurdle Rate Did you have any major difficulties? Did you record any errors when entering an information request or investment choice? Please explain: Do you think anything should be changed in the way the information was presented (information order, format, etc)? Do you believe there was essential information missing in this decision task? Please explain. 161 5. Did the varying amount of information affect the way you evaluated each decision task? 6. Was there any attempt on your part to use a consistent approach in requesting and evaluating information (type, order, amount, etc.)? 162 Evaluate each of the following nine independent situations. Considering all else equal, please indicate which of the two capital investments you would prefer. If needed, refer back to the Instruction Sheet. 1. Investment A: Initial Cost is $210,000 Investment B: Initial Cost is $180,000 2. Investment A: NPV is $60,000 Investment B: NPV is $50,000 3. Investment A: Risk is Average Investment B: Risk is High 4. Investment A: IRR is 15% Investment B: IRR is 14% 5. Investment A: Annual Net Cash Flow is $100,000 Investment B: Annual Net Cash Flow is $110,000 6. Investment A: Hurdle Rate is 12% Investment B: Hurdle Rate is 13% 7. Investment A: Payback is 7 years Investment B: Payback is 8 years 8. Investment A: Estimation Uncertainty is Average Investment B: Estimation Uncertainty is Low 9. Investment A: Operating Leverage is Very High Investment B: Operating Leverage is High 163 Please answer the following questions regarding your capital budgeting experience. (Not all questions may be applicable for you.) 1. What type of education have you had relevant to capital investment decision making (finance, accounting, etc.) 2. Have you had experience assembling reports to justify capital investments? What types of capital investments? General cost range for these investments? How many years involved in this activity? 3. Have you had experience approving reports to justify capital investments or actually making capital investments? What types of capital investments? General cost range for these investments? How many years involved in this activity? 4. Do you feel you were influenced by any company policies regarding dimension categories or cut-offs? Please explain. *‘k 164 THANK YOU FOR YOUR PARTICIPATION! Please write your name on and place all materials (including all instructions, any separate notes you may have made, and the computer disk) into the packet provided. Be sure you have received and signed for any additional monetary compensation before leaving. APPENDIX B EXPERIENCED PARTICIPANTS’ INSTRUCTIONS AND QUESTIONNAIRE 165 166 ISLab Experiment Package Cover Sheet Experienced Participants 167 Thank you for your support of research at The Graduate School of Business Administration, Michigan State University. In this packet you should find: 1) ISLab Purpose of Study and set of Instructions 2) ISLab System Instruction Diagram 3) ISLab Questionnaire 4) ISLab 5 1/4" diskette with a floppy disk mailer 5) ISLab 3 1/2" microdisk This experiment must be run on an IBM-type computer. Use the appropriate ISLab disk for your particular disk drive (the other disk will not be used). If your machine is not set to run the disk drive containing the ISLab disk, please switch to the appropriate disk drive, then type "GO" and press [Enter]. Alternatively, you may put the ISLab disk in your boot drive and reboot your machine. Read your ISLab Purpose of Study and set of Instructions. Respond as requested by the computer. When your session is complete, be sure to complete the questionnaire. Write your name on the disk, the questionnaire, and all other ISLab materials. If you used the 5 1/4" diskette, place it back in the mailer. Place everything into the addressed mailing packet (including the floppy disk mailer) and either give or mail to the experimenter. Please try to use the computer to do all your decision- making work. If you feel you must make notes or calculations outside of the computer, please include your notes and calculations with the other ISLab materials. If you have any questions or problems, please contact Monte Swain at MSU’s Department of Accounting, (517) 355-7486. 168 ISLab Pre-Test Instructions Experienced Participants ISLab PURPOSE OF THE STUDY The purpose of this study is to gain systematic knowledge of the capital investment decisions process. While you are performing the task, the computer will record your selections. Analysis of this data will provide a basis for the development of a detailed descriptive model of capital investment decision making. The model in turn will provide a basis for the development of computer-based decision aids to assist company management responsible for similar decisions. This study should take about one hour to complete. The aggregated information gathered from this study will be available for your inspection and possible use. All individual information gathered will be kept strictly confidential. At the end of the experiment, you indicate your voluntary agreement to participate by completing and returning the questionnaire attached to this instruction sheet. INSTRUCTIONS I. SITUATIONAL ASSUMPTIONS Assume you are a manager for a large company in need of modernizing a significant segment of the production process. In each of the following decision situations, there are various numbers of investment alternatives available, each requiring a significant expenditure. All investments have an expected life of 10 years. The cost of each investment is within the company’s budget constraints. Your task will be to select ppg and only one capital investment in each situation. . Your company’s current need to update its production process and the significant cost to acquire any of the alternatives considered makes the tasks a rather significant investment decision. You should use as much time and information as you feel is necgssapy for making your decision. Use the computer for all information needs, i.e., please do not make notes to yourself on a separate piece of paper. II. DECISION SITUATIONS There are six independent decision situations. In each decision situation you are to choose one capital investment 169 170 from a set of possible alternatives. Each investment will be represented by various items of information. Additional explapation of each information item is found at the end of these instructions. The information is both quantitative and qualitative in nature. The quantitative data will be given in its natural numerical form (dollars, percentages, etc). Qualitative data will be represented on a five-point scale ranging from Very Low to Very High: (1) Very low (2) Low (3) Average (4) High (5) Very High Considerable care was taken to make sure that these capital budgeting situations use information typical of similar investment decisions in industry. III. THE SYSTEM The system you will interact with to gather information for the task has three parts: 1. An automatic demonstration 2. A hands-on practice decision situation 3. The six actual decision situations A. The Automatic Demonstration A series of steps will be displayed that show how the program operates. This is the same sequence of steps you will be using to make your selections. In the demonstration, the task is to choose a potential employee to hire based on information about GPA, experience, desired salary, and self-motivation (referred to as dimensions). pp not make entries. In this demonstration, the computer will make all entries fior you. Use the attached System Instruction Diagram to follow the ‘automatic operations of the computer. Upon completion of the automatic demonstration you will be prompted to either run another demonstration or to start the sample decision. You may repeat the demonstration if desired. B. The Sample Decision In the sample decision, the task again is to select an employee to hire. This time ypp should respond to the prompts on the screen. All of the steps are the same as described above. Upon completion of a sample decision, you may either repeat the sample or start the actual decisions. 171 C. The Actual Decisions There are six independent decision situations. As described earlier, your task will be to select one capital investment alternative in each decision situation. The sequence of entries is the same as in the samples and the summary screen (the El key) is available as described in the System Instruction Diagram. If you make a mistake when selecting an investment, please indicate on the questionnaire. At the conclusion of the task, be sure to complete the attached questionnaire. 172 IV. EXPLANATION OF DIMENSIONS NPV - Net Present Value. A summation of the investment’s discounted cash flows and initial cost using the hurdle rate appropriate for this investment type. Initial Cost - The total cost of purchase, including transportation and set-up costs. Risk - A general assessment of the inherent risk of this investment, including potential problems with predicting and controlling its future cash flows. The investment’s risk may range from very high to very low. Payback - The number of years required to recover the initial investment.‘ Estimation Uncertainty - The level of uncertainty attending the estimation of the investment’s future cash flows. Low uncertainty reflects confidence in forecasting the investment’s future. High uncertainty indicates that future cash flow are unreliable. Operating Leverage - The investment’s commitment to unavoidable fixed costs. Low leverage reflects an ability to reduce future operating costs in crisis times. High leverage denotes inflexibility. IRR - Internal Rate of Return. The investment’s discounted return on its initial cost. At this rate, the NPV = 0. Annual Net Cash Flow - The investment’s annual future incremental cash flow. Hurdle Rate - The minimal rate of return required by the company for this particular investment’s risk. A high hurdle rate indicates a risky investment. This rate is used to generate actual NPV. ISLab SYSTEM INSTRUCTIQN DIAGRAM ENTER! Retrieve a Single cell or a Group of cells [S/G] [View or V] A [Single or S] View Information or [No a, N] Eliminate an Alternative? [V/E] A [Eliminate or E] Y Select an alternative [Group or G] Retrieve a Row or a Column of cells? [R/C] [Row or R] IIHPORIUVTICRI PRIRIENHIND [Column or C] \ ‘\ ‘y/ Wish to decide [Y/N] [Yes or Y] 1373 L Select an alternative EXIT 174 ISLab Post-Test Questionnaire Experienced Participants ISLab QUESIIQNNAIRE Name Company Name Company Position Female Male Age Daytime Phone Number (Occasionally, clarification of some responses is required.) * On a scale of 1 to 10, please rate yourself as an experienced user of microcomputers. Assume 10 represents an individual with significant daily experience using a computer as a programming, designing, and/or decision-making tool and 1 represents an individual who has had no experience using a computer at all: (1 - 10) * On a scale of 1 to 5, please give your perception of the realism of the ISLab decision tasks. (Assume 1 = Very Low and 5 = Very High) (1 ’ 5) 175 4. 176 Following is a list of the nine possible information items you were able to examine in order to make the investment decisions just completed. Assign a 1 to the dimension you believe was the most important or useful in selecting the appropriate capital investment, 2 to the next most important, and so on. Ties are not permitted. NPV Initial Cost Risk Payback Estimation Uncertainty Operating Leverage IRR Annual Net Cash Flow Hurdle Rate Did you have any major difficulties? Did you record any errors when entering an information request or investment choice? Please explain: Do you think anything should be changed in the way the information was presented (information order, format, etc)? Do you believe there was essential information missing in this decision task? Please explain. 177 5. Did the varying amount of information affect the way you evaluated each decision task? 6. Was there any attempt on your part to use a consistent approach in requesting and evaluating information (type, order, amount, etc.)? 178 Evaluate each of the following nine independent situations. Considering all else equal, please indicate which of the two capital investments you would prefer. If needed, refer back to the Instruction Sheet. 1. Investment A: Initial Cost is $210,000 Investment B: Initial Cost is $180,000 2. Investment A: NPV is $60,000 Investment B: NPV is $50,000 3. Investment A: Risk is Average Investment B: Risk is High 4. Investment A: IRR is 15% Investment B: IRR is 14% 5. Investment A: Annual Net Cash Flow is $100,000 Investment B: Annual Net Cash Flow is $110,000 6. Investment A: Hurdle Rate is 12% Investment B: Hurdle Rate is 13% 7. Investment A: Payback is 7 years Investment B: Payback is 8 years 8. Investment A: Estimation Uncertainty is Average Investment B: Estimation Uncertainty is Low 9. Investment A: Operating Leverage is Very High Investment B: Operating Leverage is High 179 Please answer the following questions regarding your capital budgeting experience. (Not all questions may be applicable for you.) 1. What type of education have you had relevant to capital investment decision making (finance, accounting, etc.) 2. Have you had experience assembling reports to justify capital investments? What types of capital investments? General cost range for these investments? How many years involved in this activity? 3. Have you had experience gppppyimg reports to justify capital investments or actuglly making capital investments? What types of capital investments? General cost range for these investments? How many years involved in this activity? 4. Do you feel you were influenced by any company policies regarding dimension categories or cut-offs? Please explain. 180 THANK YOU FOR YOUR PARTICIPATION! ** Please write your name on and place all materials (including all instructions, any separate notes you may have made, and the computer disk) into the packet provided. 181 REFERENCES Abdolmohammadi, M. and A. Wright. 1987. 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